From 9b2c221761254b1ebfbf2f0d739bb312b91c7462 Mon Sep 17 00:00:00 2001 From: Fabien Klein Date: Mon, 23 Mar 2026 18:32:21 +0100 Subject: [PATCH 1/2] Add experimental CMSIS-NN float16/float32 support - Introduce float16/float32 operator APIs aligned with quantized CMSIS-NN, using channel-last layouts only. - Make float support fully opt-in (`f16`, `f32`, or both) to avoid extra code size for integer-only builds. - Add optional optimized kernels for convolution, depthwise convolution, and pooling. - Expand float unit test coverage. - Add GitHub Actions for FVP tests, host tests, and code-size tracking. --- .github/workflows/float-fvp.yml | 161 + .github/workflows/float-host.yml | 121 + .github/workflows/integer-fvp.yml | 167 + .github/workflows/lib-variants.yml | 176 + ARM.CMSIS-NN.pdsc | 77 + CMakeLists.txt | 17 + Documentation/Doxygen/nn.dxy.in | 10 +- Documentation/Doxygen/src/mainpage.md | 65 + Include/Internal/arm_concatenation_common.h | 113 + Include/Internal/arm_conv1x1_opt_common.h | 53 + Include/Internal/arm_conv1x1_opt_f16.h | 69 + Include/Internal/arm_conv1x1_opt_f32.h | 69 + Include/Internal/arm_conv_opt_common.h | 72 + Include/Internal/arm_conv_opt_f16.h | 235 + Include/Internal/arm_conv_opt_f32.h | 235 + .../Internal/arm_depthwise_conv_opt_common.h | 69 + Include/Internal/arm_depthwise_conv_opt_f16.h | 269 + Include/Internal/arm_depthwise_conv_opt_f32.h | 204 + Include/Internal/arm_minmax_f16_common.h | 282 + Include/Internal/arm_minmax_f32_common.h | 281 + Include/Internal/arm_nn_activation_flt.h | 306 + Include/Internal/arm_nn_config.h | 93 + Include/Internal/arm_transpose_common.h | 244 + Include/arm_nn_math_types.h | 56 +- Include/arm_nn_math_types_flt.h | 105 + Include/arm_nn_types.h | 12 +- Include/arm_nn_types_flt.h | 432 + Include/arm_nnfunctions.h | 8 +- Include/arm_nnfunctions_flt.h | 1505 + Include/arm_nnsupportfunctions.h | 9 +- Include/arm_nnsupportfunctions_flt.h | 961 + README.md | 86 +- Source/ActivationFunctions/CMakeLists.txt | 11 +- .../arm_nn_activation_f16.c | 124 + .../arm_nn_activation_f32.c | 124 + Source/BasicMathFunctions/CMakeLists.txt | 19 +- .../arm_elementwise_add_f16.c | 78 + .../arm_elementwise_add_f32.c | 78 + .../arm_elementwise_mul_f16.c | 78 + .../arm_elementwise_mul_f32.c | 78 + Source/BasicMathFunctions/arm_maximum_f16.c | 54 + Source/BasicMathFunctions/arm_maximum_f32.c | 54 + Source/BasicMathFunctions/arm_minimum_f16.c | 54 + Source/BasicMathFunctions/arm_minimum_f32.c | 54 + Source/ConcatenationFunctions/CMakeLists.txt | 11 +- .../arm_concatenation_f16.c | 49 + .../arm_concatenation_f32.c | 49 + Source/ConvolutionFunctions/CMakeLists.txt | 19 +- .../arm_convolve_1_x_n_f16.c | 423 + .../arm_convolve_1_x_n_f32.c | 390 + .../arm_convolve_1x1_f16.c | 304 + .../arm_convolve_1x1_f32.c | 303 + .../ConvolutionFunctions/arm_convolve_f16.c | 463 + .../ConvolutionFunctions/arm_convolve_f32.c | 461 + .../arm_depthwise_conv_f16.c | 835 + 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176 + .../arm_nn_conv1d_k5_packed_f16.c | 196 + .../arm_nn_conv1d_k5_packed_f32.c | 144 + .../arm_nn_depthwise_conv1d_k3_f16.c | 63 + .../arm_nn_depthwise_conv1d_k3_f32.c | 63 + .../arm_nn_depthwise_conv2x5_f16.c | 111 + .../arm_nn_depthwise_conv3x3_f16.c | 321 + .../arm_nn_depthwise_conv3x3_f32.c | 321 + .../arm_nn_depthwise_conv_nt_t_f16.c | 243 + .../arm_nn_depthwise_conv_nt_t_f32.c | 253 + .../NNSupportFunctions/arm_nn_lstm_step_f16.c | 197 + .../NNSupportFunctions/arm_nn_lstm_step_f32.c | 196 + .../arm_nn_mat_mult_nt_n_packed_f16.c | 191 + .../arm_nn_mat_mult_nt_n_packed_f32.c | 191 + .../arm_nn_mat_mult_nt_t_f16.c | 214 + .../arm_nn_mat_mult_nt_t_f32.c | 172 + .../NNSupportFunctions/arm_nn_maxpool1d_f16.c | 100 + .../NNSupportFunctions/arm_nn_maxpool1d_f32.c | 95 + .../arm_nn_pack_conv_patch_f16.c | 86 + .../arm_nn_pack_conv_patch_f32.c | 86 + Source/NNSupportFunctions/arm_nntables_flt.c | 233 + Source/PadFunctions/CMakeLists.txt | 9 +- Source/PadFunctions/arm_pad_f16.c | 119 + 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Tests/UnitTest/cmsis/test_arm_svdf_flt/test_arm_svdf_flt.cproject.yml create mode 100644 Tests/UnitTest/cmsis/test_arm_transpose_conv_flt/test_arm_transpose_conv_flt.cproject.yml create mode 100644 Tests/UnitTest/cmsis/test_arm_transpose_flt/test_arm_transpose_flt.cproject.yml create mode 100644 Tests/UnitTest/concatenation_settings_flt.py create mode 100644 Tests/UnitTest/conv_settings_flt.py create mode 100644 Tests/UnitTest/depthwise_conv_settings_flt.py create mode 100644 Tests/UnitTest/ds_cnn_s_body_settings_flt.py create mode 100644 Tests/UnitTest/float_test_gen_utils.py create mode 100644 Tests/UnitTest/float_unit_test_coverage.md create mode 100644 Tests/UnitTest/float_unit_test_coverage.yml create mode 100644 Tests/UnitTest/fully_connected_settings_flt.py create mode 100644 Tests/UnitTest/lstm_settings_flt.py create mode 100644 Tests/UnitTest/minmax_settings_flt.py create mode 100644 Tests/UnitTest/pad_settings_flt.py create mode 100644 Tests/UnitTest/pooling_settings_flt.py create mode 100644 Tests/UnitTest/run_float_unit_tests.py create mode 100644 Tests/UnitTest/run_integer_unit_tests.py create mode 100644 Tests/UnitTest/softmax_settings_flt.py create mode 100644 Tests/UnitTest/svdf_settings_flt.py create mode 100644 Tests/UnitTest/transpose_conv_settings_flt.py create mode 100644 Tests/UnitTest/transpose_settings_flt.py create mode 100644 vcpkg-configuration.json diff --git a/.github/workflows/float-fvp.yml b/.github/workflows/float-fvp.yml new file mode 100644 index 00000000..c4352bc9 --- /dev/null +++ b/.github/workflows/float-fvp.yml @@ -0,0 +1,161 @@ +# SPDX-FileCopyrightText: Copyright 2024, 2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: CMSIS NN Float CM55 FVP Regression + +on: + workflow_dispatch: + inputs: + tests: + description: Comma-separated float test families to run + required: false + default: all + dtypes: + description: Comma-separated dtype list + required: false + default: f32,f16 + pull_request: + branches: [main] + push: + branches: [main, cmsis_nn_float] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +jobs: + fvp: + name: FVP ${{ matrix.toolchain_label }} + runs-on: ubuntu-24.04-arm + strategy: + fail-fast: false + matrix: + include: + - key: ac6 + toolchain: AC6 + toolchain_label: AC6@6.24.0 + - key: clang + toolchain: CLANG + toolchain_label: CLANG@20.1.0 + + env: + FLOAT_TESTS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.tests || 'all' }} + FLOAT_DTYPES: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.dtypes || 'f32,f16' }} + CMSIS_PACK_ROOT: /home/runner/.cache/arm/packs + + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + python -m pip install numpy termcolor pyserial + python -m pip install --index-url https://download.pytorch.org/whl/cpu torch + + - name: Install Arm tools + uses: ARM-software/cmsis-actions/vcpkg@v1 + + - name: Activate Arm tool license + if: matrix.toolchain == 'AC6' + uses: ARM-software/cmsis-actions/armlm@v1 + + - name: Cache CMSIS packs + uses: actions/cache@v5 + with: + key: cmsis-packs + path: /home/runner/.cache/arm/packs + + - name: Register local CMSIS-NN pack + run: | + rm -rf "${CMSIS_PACK_ROOT}/.Local" + cpackget add ARM.CMSIS-NN.pdsc -R "${CMSIS_PACK_ROOT}" -F + + - name: Bootstrap Unity test runners + working-directory: Tests/UnitTest + run: | + python3 unittest_targets.py --download-and-generate-test-runners + + - name: Export generic toolchain aliases + run: | + if [[ -n "${GCC_TOOLCHAIN_14_3_1:-}" ]]; then + echo "GCC_TOOLCHAIN=${GCC_TOOLCHAIN_14_3_1}" >> "${GITHUB_ENV}" + fi + if [[ -n "${AC6_TOOLCHAIN_6_24_0:-}" ]]; then + echo "AC6_TOOLCHAIN=${AC6_TOOLCHAIN_6_24_0}" >> "${GITHUB_ENV}" + fi + if [[ -n "${CLANG_TOOLCHAIN_20_1_0:-}" ]]; then + echo "CLANG_TOOLCHAIN=${CLANG_TOOLCHAIN_20_1_0}" >> "${GITHUB_ENV}" + fi + + - name: Run float CMSIS/FVP regression + working-directory: Tests/UnitTest + run: | + # GCC is intentionally omitted here until the arm-none-eabi-gcc + # internal compiler error on the float f16 min/max path is fixed. + # FVP runs are already executed sequentially by run_float_unit_tests.py. + set +e + set -o pipefail + + python3 -u run_float_unit_tests.py \ + --tests "${FLOAT_TESTS}" \ + --dtypes "${FLOAT_DTYPES}" \ + --build-cmsis \ + --run-fvp \ + --toolchains "${{ matrix.toolchain }}" \ + --jobs 4 \ + --fvp-timeout 120 \ + --cbuild-packs \ + --fvp-bin FVP_Corstone_SSE-300 \ + --fvp-image-arg=-a | tee float_fvp.log + + status=${PIPESTATUS[0]} + + { + echo "## ${{ matrix.toolchain_label }}" + echo "" + echo "- Tests: \`${FLOAT_TESTS}\`" + echo "- DTypes: \`${FLOAT_DTYPES}\`" + echo "" + echo '```text' + if grep -q '^Float Unit-Test Summary' float_fvp.log; then + sed -n '/^Float Unit-Test Summary/,$p' float_fvp.log + else + cat float_fvp.log + fi + echo '```' + } >> "${GITHUB_STEP_SUMMARY}" + + exit "${status}" + + - name: Upload float FVP logs + if: always() + uses: actions/upload-artifact@v4 + with: + name: float-fvp-${{ matrix.key }} + path: | + Tests/UnitTest/float_fvp.log + Tests/UnitTest/cmsis/**/*.log diff --git a/.github/workflows/float-host.yml b/.github/workflows/float-host.yml new file mode 100644 index 00000000..a62a59e4 --- /dev/null +++ b/.github/workflows/float-host.yml @@ -0,0 +1,121 @@ +# SPDX-FileCopyrightText: Copyright 2024, 2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +name: CMSIS NN Float Host Regression + +on: + workflow_dispatch: + inputs: + tests: + description: Comma-separated float test families to run + required: false + default: all + dtypes: + description: Comma-separated dtype list + required: false + default: f32,f16 + pull_request: + branches: [main] + push: + branches: [main, cmsis_nn_float] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +jobs: + host: + name: ${{ matrix.name }} + runs-on: ubuntu-24.04 + strategy: + fail-fast: false + matrix: + include: + - name: Host GCC + cc: gcc + cxx: g++ + - name: Host Clang + cc: clang + cxx: clang++ + + env: + FLOAT_TESTS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.tests || 'all' }} + FLOAT_DTYPES: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.dtypes || 'f32,f16' }} + + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + python -m pip install numpy termcolor pyserial + python -m pip install --index-url https://download.pytorch.org/whl/cpu torch + + - name: Run float host regression + working-directory: Tests/UnitTest + env: + CC: ${{ matrix.cc }} + CXX: ${{ matrix.cxx }} + run: | + set +e + set -o pipefail + + python3 -u run_float_unit_tests.py \ + --tests "${FLOAT_TESTS}" \ + --dtypes "${FLOAT_DTYPES}" \ + --generate \ + --build-host \ + --run-host \ + --clean-host \ + --cmsis-path "${GITHUB_WORKSPACE}" | tee float_host.log + + status=${PIPESTATUS[0]} + + { + echo "## ${{ matrix.name }}" + echo "" + echo "- Tests: \`${FLOAT_TESTS}\`" + echo "- DTypes: \`${FLOAT_DTYPES}\`" + echo "" + echo '```text' + if grep -q '^Float Unit-Test Summary' float_host.log; then + sed -n '/^Float Unit-Test Summary/,$p' float_host.log + else + cat float_host.log + fi + echo '```' + } >> "${GITHUB_STEP_SUMMARY}" + + exit "${status}" + + - name: Upload float host logs + if: always() + uses: actions/upload-artifact@v4 + with: + name: float-host-${{ matrix.cc }} + path: | + Tests/UnitTest/float_host.log diff --git a/.github/workflows/integer-fvp.yml b/.github/workflows/integer-fvp.yml new file mode 100644 index 00000000..90f84c7e --- /dev/null +++ b/.github/workflows/integer-fvp.yml @@ -0,0 +1,167 @@ +# SPDX-FileCopyrightText: Copyright 2024, 2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: CMSIS NN Integer CM55 FVP Regression + +on: + workflow_dispatch: + inputs: + tests: + description: Comma-separated integer test targets to run + required: false + default: all + toolchains: + description: Comma-separated toolchains to run (no spaces) + required: false + default: GCC@14.3.1,AC6@6.24.0 + pull_request: + branches: [main] + push: + branches: [main, cmsis_nn_float] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +jobs: + fvp: + name: FVP ${{ matrix.toolchain_label }} + runs-on: ubuntu-24.04-arm + strategy: + fail-fast: false + matrix: + include: + - key: gcc + toolchain: GCC + toolchain_label: GCC@14.3.1 + - key: ac6 + toolchain: AC6 + toolchain_label: AC6@6.24.0 + + env: + INTEGER_TESTS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.tests || 'all' }} + SELECTED_TOOLCHAINS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.toolchains || 'GCC@14.3.1,AC6@6.24.0' }} + CMSIS_PACK_ROOT: /home/runner/.cache/arm/packs + INTEGER_BUILD_ROOT: ${{ github.workspace }}/.ci-tmp/cmsis-nn-integer-fvp + + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + python -m pip install termcolor pyserial + + - name: Check toolchain selection + id: select + run: | + if [[ ",${SELECTED_TOOLCHAINS}," == *",${{ matrix.toolchain_label }},"* ]]; then + echo "enabled=true" >> "${GITHUB_OUTPUT}" + else + echo "enabled=false" >> "${GITHUB_OUTPUT}" + fi + + - name: Skip unselected toolchain + if: steps.select.outputs.enabled != 'true' + run: | + { + echo "## ${{ matrix.toolchain_label }}" + echo "" + echo "Skipped by workflow_dispatch filter." + } >> "${GITHUB_STEP_SUMMARY}" + + - name: Install Arm tools + if: steps.select.outputs.enabled == 'true' + uses: ARM-software/cmsis-actions/vcpkg@v1 + + - name: Activate Arm tool license + if: steps.select.outputs.enabled == 'true' && matrix.toolchain == 'AC6' + uses: ARM-software/cmsis-actions/armlm@v1 + + - name: Cache CMSIS packs + if: steps.select.outputs.enabled == 'true' + uses: actions/cache@v5 + with: + key: cmsis-packs + path: /home/runner/.cache/arm/packs + + - name: Install required CMSIS packs + if: steps.select.outputs.enabled == 'true' + run: | + cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" + cpackget add "ARM::Cortex_DFP@1.1.0" -R "${CMSIS_PACK_ROOT}" + + - name: Export generic toolchain aliases + if: steps.select.outputs.enabled == 'true' + run: | + if [[ -n "${GCC_TOOLCHAIN_14_3_1:-}" ]]; then + echo "GCC_TOOLCHAIN=${GCC_TOOLCHAIN_14_3_1}" >> "${GITHUB_ENV}" + fi + if [[ -n "${AC6_TOOLCHAIN_6_24_0:-}" ]]; then + echo "AC6_TOOLCHAIN=${AC6_TOOLCHAIN_6_24_0}" >> "${GITHUB_ENV}" + fi + + - name: Run integer CMSIS/FVP regression + if: steps.select.outputs.enabled == 'true' + working-directory: Tests/UnitTest + run: | + set +e + set -o pipefail + + python3 run_integer_unit_tests.py \ + --tests "${INTEGER_TESTS}" \ + --build-fvp \ + --run-fvp \ + --toolchains "${{ matrix.toolchain }}" \ + --jobs 4 \ + --fvp-timeout 120 \ + --cmsis-pack-root "${CMSIS_PACK_ROOT}" \ + --build-root "${INTEGER_BUILD_ROOT}" \ + --fvp-bin FVP_Corstone_SSE-300 \ + --fvp-image-arg=-a | tee integer_fvp.log + + status=${PIPESTATUS[0]} + + { + echo "## ${{ matrix.toolchain_label }}" + echo "" + echo "- Tests: \`${INTEGER_TESTS}\`" + echo "" + echo '```text' + sed -n '/^Integer Unit-Test Summary/,$p' integer_fvp.log + echo '```' + } >> "${GITHUB_STEP_SUMMARY}" + + exit "${status}" + + - name: Upload integer FVP logs + if: always() && steps.select.outputs.enabled == 'true' + uses: actions/upload-artifact@v4 + with: + name: integer-fvp-${{ matrix.key }} + path: | + Tests/UnitTest/integer_fvp.log + ${{ env.INTEGER_BUILD_ROOT }}/${{ matrix.key }}/**/*.log diff --git a/.github/workflows/lib-variants.yml b/.github/workflows/lib-variants.yml new file mode 100644 index 00000000..4bdc7481 --- /dev/null +++ b/.github/workflows/lib-variants.yml @@ -0,0 +1,176 @@ +# SPDX-FileCopyrightText: Copyright 2024, 2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: CMSIS NN Library Variant Matrix + +on: + workflow_dispatch: + inputs: + toolchains: + description: Comma-separated toolchains to run (no spaces) + required: false + default: GCC@14.3.1,AC6@6.24.0 + variants: + description: Comma-separated variant keys to build + required: false + default: all + cpu: + description: Target CPU passed to the toolchain + required: false + default: cortex-m55 + optimization: + description: Value forwarded to CMSIS_OPTIMIZATION_LEVEL + required: false + default: -Ofast + pull_request: + branches: [main] + push: + branches: [main, cmsis_nn_float] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +permissions: + contents: read + +jobs: + variants: + name: ${{ matrix.toolchain_label }} + runs-on: ubuntu-24.04 + strategy: + fail-fast: false + matrix: + include: + - key: gcc + toolchain: GCC + toolchain_label: GCC@14.3.1 + - key: ac6 + toolchain: AC6 + toolchain_label: AC6@6.24.0 + + env: + SELECTED_TOOLCHAINS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.toolchains || 'GCC@14.3.1,AC6@6.24.0' }} + LIB_VARIANTS: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.variants || 'all' }} + LIB_CPU: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.cpu || 'cortex-m55' }} + LIB_OPTIMIZATION: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.optimization || '' }} + LIB_BUILD_ROOT: ${{ github.workspace }}/.ci-tmp/cmsis-nn-lib-variants + CMSIS_PACK_ROOT: /home/runner/.cache/arm/packs + + steps: + - name: Checkout + uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Check toolchain selection + id: select + run: | + if [[ ",${SELECTED_TOOLCHAINS}," == *",${{ matrix.toolchain_label }},"* ]]; then + echo "enabled=true" >> "${GITHUB_OUTPUT}" + else + echo "enabled=false" >> "${GITHUB_OUTPUT}" + fi + + - name: Skip unselected toolchain + if: steps.select.outputs.enabled != 'true' + run: | + { + echo "## ${{ matrix.toolchain_label }}" + echo "" + echo "Skipped by workflow_dispatch filter." + } >> "${GITHUB_STEP_SUMMARY}" + + - name: Install Arm tools + if: steps.select.outputs.enabled == 'true' + uses: ARM-software/cmsis-actions/vcpkg@v1 + + - name: Cache CMSIS packs + if: steps.select.outputs.enabled == 'true' + uses: actions/cache@v5 + with: + key: cmsis-packs + path: /home/runner/.cache/arm/packs + + - name: Install required CMSIS packs + if: steps.select.outputs.enabled == 'true' + run: | + cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" + + - name: Activate Arm tool license + if: steps.select.outputs.enabled == 'true' && matrix.toolchain == 'AC6' + uses: ARM-software/cmsis-actions/armlm@v1 + + - name: Export generic toolchain aliases + if: steps.select.outputs.enabled == 'true' + run: | + if [[ -n "${GCC_TOOLCHAIN_14_3_1:-}" ]]; then + echo "GCC_TOOLCHAIN=${GCC_TOOLCHAIN_14_3_1}" >> "${GITHUB_ENV}" + fi + if [[ -n "${AC6_TOOLCHAIN_6_24_0:-}" ]]; then + echo "AC6_TOOLCHAIN=${AC6_TOOLCHAIN_6_24_0}" >> "${GITHUB_ENV}" + fi + + - name: Build library variants + if: steps.select.outputs.enabled == 'true' + run: | + # Keep this matrix on the toolchains that are already stable with the + # direct CMake library build path. Embedded CLANG is validated in the + # CMSIS/FVP workflow instead. + set +e + set -o pipefail + + optimization_args=() + if [[ -n "${LIB_OPTIMIZATION}" ]]; then + optimization_args=(--optimization "${LIB_OPTIMIZATION}") + fi + + python3 Tests/UnitTest/build_lib_variants.py \ + --toolchain "${{ matrix.toolchain }}" \ + --variants "${LIB_VARIANTS}" \ + --cpu "${LIB_CPU}" \ + --cmsis-pack-root "${CMSIS_PACK_ROOT}" \ + "${optimization_args[@]}" \ + --jobs 4 \ + --build-root "${LIB_BUILD_ROOT}" | tee build_lib_variants.log + + status=${PIPESTATUS[0]} + + { + echo "## ${{ matrix.toolchain_label }}" + echo "" + echo "- CPU: \`${LIB_CPU}\`" + echo "- Variants: \`${LIB_VARIANTS}\`" + echo "- Optimization: \`${LIB_OPTIMIZATION:-script default}\`" + echo "" + echo '```text' + sed -n '/^CMSIS-NN library variant summary/,$p' build_lib_variants.log + echo '```' + } >> "${GITHUB_STEP_SUMMARY}" + + exit "${status}" + + - name: Upload build logs + if: always() && steps.select.outputs.enabled == 'true' + uses: actions/upload-artifact@v4 + with: + name: lib-variants-${{ matrix.key }} + path: | + build_lib_variants.log + ${{ env.LIB_BUILD_ROOT }}/${{ matrix.key }}/**/*.log diff --git a/ARM.CMSIS-NN.pdsc b/ARM.CMSIS-NN.pdsc old mode 100644 new mode 100755 index dc02d43a..c5bc93d6 --- a/ARM.CMSIS-NN.pdsc +++ b/ARM.CMSIS-NN.pdsc @@ -35,10 +35,15 @@ + + + + + @@ -73,23 +78,41 @@ + + + + + + + + + + + + + + + + + + @@ -99,12 +122,26 @@ + + + + + + + + + + + + + + @@ -122,6 +159,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -129,6 +194,8 @@ + + @@ -136,23 +203,33 @@ + + + + + + + + + + diff --git a/CMakeLists.txt b/CMakeLists.txt index f4c9b2e7..6e3fb938 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -18,12 +18,29 @@ cmake_minimum_required(VERSION 3.15.6) set(CMSIS_OPTIMIZATION_LEVEL "-Ofast" CACHE STRING "Compiler optimization level.") +option(ARM_NN_ENABLE_F32 "Enable CMSIS-NN float32 extensions." OFF) +option(ARM_NN_ENABLE_F16 "Enable CMSIS-NN float16 extensions." OFF) project(CMSISNN C CXX) add_library(cmsis-nn STATIC) +if(ARM_NN_ENABLE_F32) + set(ARM_NN_ENABLE_F32_VALUE 1) +else() + set(ARM_NN_ENABLE_F32_VALUE 0) +endif() + +if(ARM_NN_ENABLE_F16) + set(ARM_NN_ENABLE_F16_VALUE 1) +else() + set(ARM_NN_ENABLE_F16_VALUE 0) +endif() + target_compile_options(cmsis-nn PRIVATE ${CMSIS_OPTIMIZATION_LEVEL}) +target_compile_definitions(cmsis-nn + PUBLIC ARM_NN_ENABLE_F32=${ARM_NN_ENABLE_F32_VALUE} + ARM_NN_ENABLE_F16=${ARM_NN_ENABLE_F16_VALUE}) target_include_directories(cmsis-nn PUBLIC "Include") diff --git a/Documentation/Doxygen/nn.dxy.in b/Documentation/Doxygen/nn.dxy.in index 391f80c6..82db0bde 100644 --- a/Documentation/Doxygen/nn.dxy.in +++ b/Documentation/Doxygen/nn.dxy.in @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2022-2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2022-2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -2410,7 +2410,13 @@ INCLUDE_FILE_PATTERNS = # recursively expanded use the := operator instead of the = operator. # This tag requires that the tag ENABLE_PREPROCESSING is set to YES. -PREDEFINED = ARM_MATH_NEON=1 ARM_FLOAT16_SUPPORTED=1 __STATIC_FORCEINLINE=static __STATIC_INLINE=static __ALIGNED(x)= +PREDEFINED = ARM_MATH_NEON=1 \ + ARM_FLOAT16_SUPPORTED=1 \ + ARM_NN_ENABLE_F32=1 \ + ARM_NN_ENABLE_F16=1 \ + __STATIC_FORCEINLINE=static \ + __STATIC_INLINE=static \ + __ALIGNED(x)= # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this # tag can be used to specify a list of macro names that should be expanded. The diff --git a/Documentation/Doxygen/src/mainpage.md b/Documentation/Doxygen/src/mainpage.md index 1637a9f8..49f16ed5 100644 --- a/Documentation/Doxygen/src/mainpage.md +++ b/Documentation/Doxygen/src/mainpage.md @@ -6,6 +6,33 @@ This user manual describes the CMSIS NN software library, a collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Arm Cortex-M processors. +### Experimental Floating-Point Extension {#FloatSupport} + +CMSIS-NN also includes an experimental floating-point extension covering `float32` +and `float16` APIs. + +The floating-point API surface is disabled by default and should only be enabled +for builds that explicitly require it. This keeps the default code size and API +surface aligned with the established integer CMSIS-NN usage model. + +The floating-point API style intentionally follows the existing integer CMSIS-NN +conventions, including the TensorFlow Lite Micro influenced parameter structure, +even though `float16` is not part of the TensorFlow Lite Micro API contract. + +These floating-point kernels are primarily intended for Cortex-M processors with +Helium (MVE) and hardware floating-point support. Scalar reference paths exist +for validation and fallback, but they are not the primary deployment target. +Floating-point inference should be reserved for specific use cases where integer +quantization is not possible or not acceptable, and for neural networks with +modest size and runtime requirements. + +The floating-point code can also be compiled for Arm A-class processors with +`float16` support and may benefit from NEON or SVE auto-vectorization. +However, this is not an intended deployment target for CMSIS-NN float support, +and the resulting performance is expected to be suboptimal compared to libraries +designed for that class of processor. For Arm A-class CPUs, prefer optimized +inference libraries such as Arm Compute Library or XNNPACK. + The library is divided into a number of functions each covering a specific category: - \ref NNConv @@ -31,6 +58,18 @@ CMSIS-NN targets Cortex-M processors with typically three different implementati The correct implementation is picked through feature flags and the user does not have to explicit set it. +Notes for floating-point kernels: + + - Floating-point support is mainly intended for processors with Helium and + floating-point hardware support. + - `float16` support is mainly intended for processors with native float16 + arithmetic support. + - Scalar floating-point implementations are available for reference and + fallback, but they are not the main optimization target. + - Arm A-class CPUs with `float16`, NEON, or SVE can compile generic kernels (non-Helium variants), but + this is not the intended deployment target. Prefer Arm Compute Library or + XNNPACK for better performance on those processors. + ## Access to CMSIS-NN {#pack} CMSIS-NN is actively maintained in the [**CMSIS-NN GitHub repository**](https://github.com/ARM-software/CMSIS-NN) and is released as a standalone [**CMSIS-NN pack**](https://www.keil.arm.com/packs/cmsis-nn-arm/versions/) in the [CMSIS-Pack format](https://open-cmsis-pack.github.io/Open-CMSIS-Pack-Spec/main/html/index.html). @@ -59,11 +98,37 @@ For a CMSIS-NN file compiled as `armclang -mcpu=cortex-m4 --target=arm-arm-none- - `ARM_MATH_MVEI` - Selects code for processors which supports MVE instructions. + - `ARM_MATH_MVEF` + - Selects code for processors which support MVE with floating-point instructions. + + - `ARM_MATH_MVE_FLOAT16` + - Selects code for processors which support MVE with float16 instructions. + **User set macros** - `ARM_MATH_AUTOVECTORIZE` - Applicable when ARM_MATH_MVEI is active to let the compiler auto vectorize functions, if available, that uses inline assembly. This has to be explicitly set at compile time. + - `ARM_NN_ENABLE_F32` + - Enables the experimental `float32` API and implementation set. + + - `ARM_NN_ENABLE_F16` + - Enables the experimental `float16` API and implementation set. + + - `NN_DISABLE_SPECIALIZATION` + - Disables optional shape/layout-specific fast paths and forces the corresponding generic implementation path. + - Useful when validating specialized kernels against the generic reference-style path. + + - `ARM_NN_USE_EXP_LUT` + - Selects the LUT-based scalar float softmax exp approximation. This is the default if no softmax exp macro is defined. + - Adds one 257-entry lookup table per enabled float precision: + - float16: 257 half-words, about 514 bytes + - float32: 257 words, about 1028 bytes + + - `ARM_NN_USE_EXP_TAYLOR` + - Selects the Taylor/Estrin scalar float softmax exp approximation to avoid the extra lookup-table storage. + - `ARM_NN_USE_EXP_LUT` and `ARM_NN_USE_EXP_TAYLOR` are mutually exclusive. + ## Inclusive Language {#Inclusive} This product confirms to Arm’s inclusive language policy and, to the best of our knowledge, does not contain any non-inclusive language. If you find something that concerns you, email terms@arm.com. diff --git a/Include/Internal/arm_concatenation_common.h b/Include/Internal/arm_concatenation_common.h new file mode 100644 index 00000000..bc457b0a --- /dev/null +++ b/Include/Internal/arm_concatenation_common.h @@ -0,0 +1,113 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_concatenation_common.h + * Description: Shared float/int concatenation helper templates + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONCATENATION_COMMON_H +#define ARM_CONCATENATION_COMMON_H + +#include "Internal/arm_nn_compiler.h" +#include "arm_nn_math_types.h" + +#define ARM_CONCATENATION_DEFINE(SUFFIX, TYPE) \ + void arm_concatenation_##SUFFIX##_x(const TYPE *input, \ + const int32_t input_x, \ + const int32_t input_y, \ + const int32_t input_z, \ + const int32_t input_w, \ + TYPE *output, \ + const int32_t output_x, \ + const uint32_t offset_x) \ + { \ + const uint32_t num_iterations = (uint32_t)input_y * (uint32_t)input_z * (uint32_t)input_w; \ + const TYPE *in = input; \ + TYPE *out = output + (size_t)offset_x; \ + const uint32_t input_copy_elems = (uint32_t)input_x; \ + const uint32_t output_stride_elems = (uint32_t)output_x; \ + for (uint32_t i = 0; i < num_iterations; ++i) \ + { \ + arm_memcpy_##SUFFIX(out, in, input_copy_elems); \ + in += input_copy_elems; \ + out += output_stride_elems; \ + } \ + } \ + void arm_concatenation_##SUFFIX##_y(const TYPE *input, \ + const int32_t input_x, \ + const int32_t input_y, \ + const int32_t input_z, \ + const int32_t input_w, \ + TYPE *output, \ + const int32_t output_y, \ + const uint32_t offset_y) \ + { \ + const uint32_t num_iterations = (uint32_t)input_z * (uint32_t)input_w; \ + const uint32_t input_copy_elems = (uint32_t)input_x * (uint32_t)input_y; \ + const uint32_t output_stride_elems = (uint32_t)input_x * (uint32_t)output_y; \ + const TYPE *in = input; \ + TYPE *out = output + (size_t)offset_y * (size_t)input_x; \ + for (uint32_t i = 0; i < num_iterations; ++i) \ + { \ + arm_memcpy_##SUFFIX(out, in, input_copy_elems); \ + in += input_copy_elems; \ + out += output_stride_elems; \ + } \ + } \ + void arm_concatenation_##SUFFIX##_z(const TYPE *input, \ + const int32_t input_x, \ + const int32_t input_y, \ + const int32_t input_z, \ + const int32_t input_w, \ + TYPE *output, \ + const int32_t output_z, \ + const uint32_t offset_z) \ + { \ + const uint32_t input_copy_elems = (uint32_t)input_x * (uint32_t)input_y * (uint32_t)input_z; \ + const uint32_t output_stride_elems = (uint32_t)input_x * (uint32_t)input_y * (uint32_t)output_z; \ + const TYPE *in = input; \ + TYPE *out = output + (size_t)offset_z * (size_t)input_x * (size_t)input_y; \ + for (uint32_t i = 0; i < (uint32_t)input_w; ++i) \ + { \ + arm_memcpy_##SUFFIX(out, in, input_copy_elems); \ + in += input_copy_elems; \ + out += output_stride_elems; \ + } \ + } \ + void arm_concatenation_##SUFFIX##_w(const TYPE *input, \ + const int32_t input_x, \ + const int32_t input_y, \ + const int32_t input_z, \ + const int32_t input_w, \ + TYPE *output, \ + const uint32_t offset_w) \ + { \ + const uint32_t input_copy_elems = \ + (uint32_t)input_x * (uint32_t)input_y * (uint32_t)input_z * (uint32_t)input_w; \ + TYPE *out = output + (size_t)offset_w * (size_t)input_x * (size_t)input_y * (size_t)input_z; \ + arm_memcpy_##SUFFIX(out, input, input_copy_elems); \ + } + +#endif /* ARM_CONCATENATION_COMMON_H */ diff --git a/Include/Internal/arm_conv1x1_opt_common.h b/Include/Internal/arm_conv1x1_opt_common.h new file mode 100644 index 00000000..a3b88e57 --- /dev/null +++ b/Include/Internal/arm_conv1x1_opt_common.h @@ -0,0 +1,53 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv1x1_opt_common.h + * Description: Shared float conv1x1 specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV1X1_OPT_COMMON_H +#define ARM_CONV1X1_OPT_COMMON_H + +#define ARM_CONV1X1_SPEC_ENTRY(MATCH_FN, CALL_FN) \ + { \ + (MATCH_FN), (CALL_FN) \ + } + +#define ARM_CONV1X1_ARRAY_SIZE(arr) (sizeof(arr) / sizeof((arr)[0])) + +#define ARM_CONV1X1_DISPATCH(TABLE, COUNT, ...) \ + do \ + { \ + for (size_t _i = 0; _i < (COUNT); ++_i) \ + { \ + if ((TABLE)[_i].match(__VA_ARGS__)) \ + { \ + return (TABLE)[_i].call(__VA_ARGS__); \ + } \ + } \ + } while (0) + +#endif /* ARM_CONV1X1_OPT_COMMON_H */ diff --git a/Include/Internal/arm_conv1x1_opt_f16.h b/Include/Internal/arm_conv1x1_opt_f16.h new file mode 100644 index 00000000..bd680160 --- /dev/null +++ b/Include/Internal/arm_conv1x1_opt_f16.h @@ -0,0 +1,69 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv1x1_opt_f16.h + * Description: Float16 conv1x1 specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV1X1_OPT_F16_H +#define ARM_CONV1X1_OPT_F16_H + +/* Internal specialization helpers (included from arm_convolve_1x1_f16.c). */ + +#include "Internal/arm_conv1x1_opt_common.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_conv1x1_match_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +typedef arm_cmsis_nn_status (*arm_conv1x1_call_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +typedef struct +{ + arm_conv1x1_match_f16 match; + arm_conv1x1_call_f16 call; +} arm_conv1x1_spec_f16; +#endif + +#endif /* ARM_CONV1X1_OPT_F16_H */ diff --git a/Include/Internal/arm_conv1x1_opt_f32.h b/Include/Internal/arm_conv1x1_opt_f32.h new file mode 100644 index 00000000..fade3a25 --- /dev/null +++ b/Include/Internal/arm_conv1x1_opt_f32.h @@ -0,0 +1,69 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv1x1_opt_f32.h + * Description: Float32 conv1x1 specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV1X1_OPT_F32_H +#define ARM_CONV1X1_OPT_F32_H + +/* Internal specialization helpers (included from arm_convolve_1x1_f32.c). */ + +#include "Internal/arm_conv1x1_opt_common.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_conv1x1_match_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +typedef arm_cmsis_nn_status (*arm_conv1x1_call_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +typedef struct +{ + arm_conv1x1_match_f32 match; + arm_conv1x1_call_f32 call; +} arm_conv1x1_spec_f32; +#endif + +#endif /* ARM_CONV1X1_OPT_F32_H */ diff --git a/Include/Internal/arm_conv_opt_common.h b/Include/Internal/arm_conv_opt_common.h new file mode 100644 index 00000000..dab35ba4 --- /dev/null +++ b/Include/Internal/arm_conv_opt_common.h @@ -0,0 +1,72 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv_opt_common.h + * Description: Shared float convolution specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV_OPT_COMMON_H +#define ARM_CONV_OPT_COMMON_H + +#define ARM_CONV_SPEC_ENTRY(MATCH_FN, CALL_FN) \ + { \ + (MATCH_FN), (CALL_FN) \ + } + +#define ARM_CONV_ARRAY_SIZE(arr) (sizeof(arr) / sizeof((arr)[0])) + +/* + * Heuristics for selecting the NHWC packed-patch-matrix + GEMM float32 path. + * Below these sizes the packing/setup overhead tends to outweigh the GEMM win. + * MAX_TILE_ROWS bounds scratch usage and keeps the packed panel cache-friendly. + */ +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MAX_TILE_ROWS (8) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_K (16) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_OC (8) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_POS (8) + +/* + * Heuristics for selecting the NHWC packed-patch-matrix + GEMM float16 path. + * These mirror the float32 thresholds and keep the packed tile large enough to amortize setup cost. + */ +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MAX_TILE_ROWS (8) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_K (16) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_OC (8) +#define ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_POS (8) + +#define ARM_CONV_DISPATCH(TABLE, COUNT, ...) \ + do \ + { \ + for (size_t _i = 0; _i < (COUNT); ++_i) \ + { \ + if ((TABLE)[_i].match(__VA_ARGS__)) \ + { \ + return (TABLE)[_i].call(__VA_ARGS__); \ + } \ + } \ + } while (0) + +#endif /* ARM_CONV_OPT_COMMON_H */ diff --git a/Include/Internal/arm_conv_opt_f16.h b/Include/Internal/arm_conv_opt_f16.h new file mode 100755 index 00000000..7cfe6c15 --- /dev/null +++ b/Include/Internal/arm_conv_opt_f16.h @@ -0,0 +1,235 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv_opt_f16.h + * Description: Float16 convolution specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV_OPT_F16_H +#define ARM_CONV_OPT_F16_H + +/* Internal specialization helpers (included from arm_convolve_f16.c). */ + +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_conv_match_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +typedef arm_cmsis_nn_status (*arm_conv_call_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +typedef struct +{ + arm_conv_match_f16 match; + arm_conv_call_f16 call; +} arm_conv_spec_f16; + +static bool arm_conv1d_spec_k5_nhwc_f16_match(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)input_data; + (void)filter_data; + (void)bias_dims; + (void)bias_data; + (void)output_data; + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t output_h = output_dims->h; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = params->stride.h; + const int32_t stride_w = params->stride.w; + const int32_t pad_h = params->padding.h; + const int32_t pad_w = params->padding.w; + const int32_t dil_h = params->dilation.h; + const int32_t dil_w = params->dilation.w; + + return (batch == 1 && input_h == 1 && output_h == 1 && kernel_h == 1 && kernel_w == 5 && stride_h == 1 && + stride_w == 1 && pad_h == 0 && pad_w == 0 && dil_h == 1 && dil_w == 1); +} + +static arm_cmsis_nn_status arm_conv1d_spec_k5_nhwc_f16_call(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + const int32_t input_c = input_dims->c; + const int32_t input_w = input_dims->w; + const int32_t output_c = output_dims->c; + const int32_t output_w = output_dims->w; + + if (params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + arm_nn_conv1d_k5_packed_f16( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + else + { + arm_nn_conv1d_k5_nhwc_f16( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + + const int32_t out_count = output_c * output_w * output_dims->h; + arm_nn_vector_clamp_f16(output_data, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static bool arm_conv1d_spec_k3_nhwc_f16_match(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)input_data; + (void)filter_data; + (void)bias_dims; + (void)bias_data; + (void)output_data; + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t output_h = output_dims->h; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = params->stride.h; + const int32_t stride_w = params->stride.w; + const int32_t pad_h = params->padding.h; + const int32_t pad_w = params->padding.w; + const int32_t dil_h = params->dilation.h; + const int32_t dil_w = params->dilation.w; + + return (batch == 1 && input_h == 1 && output_h == 1 && kernel_h == 1 && kernel_w == 3 && stride_h == 1 && + stride_w == 1 && pad_h == 0 && pad_w == 0 && dil_h == 1 && dil_w == 1); +} + +static arm_cmsis_nn_status arm_conv1d_spec_k3_nhwc_f16_call(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + const int32_t input_c = input_dims->c; + const int32_t input_w = input_dims->w; + const int32_t output_c = output_dims->c; + const int32_t output_w = output_dims->w; + + if (params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + arm_nn_conv1d_k3_packed_f16( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + else + { + arm_nn_conv1d_k3_nhwc_f16( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + + const int32_t out_count = output_c * output_w * output_dims->h; + arm_nn_vector_clamp_f16(output_data, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static const arm_conv_spec_f16 arm_conv_spec_nhwc_f16[] = { + ARM_CONV_SPEC_ENTRY(arm_conv1d_spec_k5_nhwc_f16_match, arm_conv1d_spec_k5_nhwc_f16_call), + ARM_CONV_SPEC_ENTRY(arm_conv1d_spec_k3_nhwc_f16_match, arm_conv1d_spec_k3_nhwc_f16_call), +}; + +__STATIC_INLINE bool arm_conv_spec_nhwc_f16_matches_any(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + for (size_t i = 0; i < ARM_CONV_ARRAY_SIZE(arm_conv_spec_nhwc_f16); ++i) + { + if (arm_conv_spec_nhwc_f16[i].match( + ctx, params, input_dims, NULL, filter_dims, NULL, NULL, NULL, output_dims, NULL)) + { + return true; + } + } + + return false; +} +#endif + +#endif /* ARM_CONV_OPT_F16_H */ diff --git a/Include/Internal/arm_conv_opt_f32.h b/Include/Internal/arm_conv_opt_f32.h new file mode 100755 index 00000000..6c47e06d --- /dev/null +++ b/Include/Internal/arm_conv_opt_f32.h @@ -0,0 +1,235 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_conv_opt_f32.h + * Description: Float32 convolution specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_CONV_OPT_F32_H +#define ARM_CONV_OPT_F32_H + +/* Internal specialization helpers (included from arm_convolve_f32.c). */ + +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_conv_match_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +typedef arm_cmsis_nn_status (*arm_conv_call_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +typedef struct +{ + arm_conv_match_f32 match; + arm_conv_call_f32 call; +} arm_conv_spec_f32; + +static bool arm_conv1d_spec_k5_nhwc_f32_match(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)input_data; + (void)filter_data; + (void)bias_dims; + (void)bias_data; + (void)output_data; + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t output_h = output_dims->h; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = params->stride.h; + const int32_t stride_w = params->stride.w; + const int32_t pad_h = params->padding.h; + const int32_t pad_w = params->padding.w; + const int32_t dil_h = params->dilation.h; + const int32_t dil_w = params->dilation.w; + + return (batch == 1 && input_h == 1 && output_h == 1 && kernel_h == 1 && kernel_w == 5 && stride_h == 1 && + stride_w == 1 && pad_h == 0 && pad_w == 0 && dil_h == 1 && dil_w == 1); +} + +static arm_cmsis_nn_status arm_conv1d_spec_k5_nhwc_f32_call(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + const int32_t input_c = input_dims->c; + const int32_t input_w = input_dims->w; + const int32_t output_c = output_dims->c; + const int32_t output_w = output_dims->w; + + if (params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + arm_nn_conv1d_k5_packed_f32( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + else + { + arm_nn_conv1d_k5_nhwc_f32( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + + const int32_t out_count = output_c * output_w * output_dims->h; + arm_nn_vector_clamp_f32(output_data, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static bool arm_conv1d_spec_k3_nhwc_f32_match(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)input_data; + (void)filter_data; + (void)bias_dims; + (void)bias_data; + (void)output_data; + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t output_h = output_dims->h; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = params->stride.h; + const int32_t stride_w = params->stride.w; + const int32_t pad_h = params->padding.h; + const int32_t pad_w = params->padding.w; + const int32_t dil_h = params->dilation.h; + const int32_t dil_w = params->dilation.w; + + return (batch == 1 && input_h == 1 && output_h == 1 && kernel_h == 1 && kernel_w == 3 && stride_h == 1 && + stride_w == 1 && pad_h == 0 && pad_w == 0 && dil_h == 1 && dil_w == 1); +} + +static arm_cmsis_nn_status arm_conv1d_spec_k3_nhwc_f32_call(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + const int32_t input_c = input_dims->c; + const int32_t input_w = input_dims->w; + const int32_t output_c = output_dims->c; + const int32_t output_w = output_dims->w; + + if (params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + arm_nn_conv1d_k3_packed_f32( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + else + { + arm_nn_conv1d_k3_nhwc_f32( + input_data, input_c, input_w, filter_data, bias_data, output_data, output_c, output_w); + } + + const int32_t out_count = output_c * output_w * output_dims->h; + arm_nn_vector_clamp_f32(output_data, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static const arm_conv_spec_f32 arm_conv_spec_nhwc_f32[] = { + ARM_CONV_SPEC_ENTRY(arm_conv1d_spec_k5_nhwc_f32_match, arm_conv1d_spec_k5_nhwc_f32_call), + ARM_CONV_SPEC_ENTRY(arm_conv1d_spec_k3_nhwc_f32_match, arm_conv1d_spec_k3_nhwc_f32_call), +}; + +__STATIC_INLINE bool arm_conv_spec_nhwc_f32_matches_any(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + for (size_t i = 0; i < ARM_CONV_ARRAY_SIZE(arm_conv_spec_nhwc_f32); ++i) + { + if (arm_conv_spec_nhwc_f32[i].match( + ctx, params, input_dims, NULL, filter_dims, NULL, NULL, NULL, output_dims, NULL)) + { + return true; + } + } + + return false; +} +#endif + +#endif /* ARM_CONV_OPT_F32_H */ diff --git a/Include/Internal/arm_depthwise_conv_opt_common.h b/Include/Internal/arm_depthwise_conv_opt_common.h new file mode 100644 index 00000000..2fa1a46e --- /dev/null +++ b/Include/Internal/arm_depthwise_conv_opt_common.h @@ -0,0 +1,69 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_depthwise_conv_opt_common.h + * Description: Shared float depthwise-convolution specialization helpers + * + * $Date: 30 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_DEPTHWISE_CONV_OPT_COMMON_H +#define ARM_DEPTHWISE_CONV_OPT_COMMON_H + +#include "Internal/arm_nn_compiler.h" + +#define ARM_DW_SPEC_ENTRY(MATCH_FN, CALL_FN) \ + { \ + (MATCH_FN), (CALL_FN) \ + } + +#define ARM_DW_ARRAY_SIZE(arr) (sizeof(arr) / sizeof((arr)[0])) + +/* NHWC input tensor linear index: ((y * width) + x) * channels + channel. */ +static inline int32_t +arm_depthwise_conv_input_index_nhwc(int32_t x, int32_t y, int32_t c, int32_t input_x, int32_t input_ch) +{ + return (y * input_x + x) * input_ch + c; +} + +/* NHWC output tensor linear index: ((y * width) + x) * channels + channel. */ +static inline int32_t +arm_depthwise_conv_output_index_nhwc(int32_t out_x, int32_t out_y, int32_t out_ch, int32_t output_x, int32_t output_ch) +{ + return (out_y * output_x + out_x) * output_ch + out_ch; +} + +#define ARM_DW_DISPATCH(TABLE, COUNT, ...) \ + do \ + { \ + for (size_t _i = 0; _i < (COUNT); ++_i) \ + { \ + if ((TABLE)[_i].match(__VA_ARGS__)) \ + { \ + return (TABLE)[_i].call(__VA_ARGS__); \ + } \ + } \ + } while (0) + +#endif /* ARM_DEPTHWISE_CONV_OPT_COMMON_H */ diff --git a/Include/Internal/arm_depthwise_conv_opt_f16.h b/Include/Internal/arm_depthwise_conv_opt_f16.h new file mode 100644 index 00000000..09e88bd0 --- /dev/null +++ b/Include/Internal/arm_depthwise_conv_opt_f16.h @@ -0,0 +1,269 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_depthwise_conv_opt_f16.h + * Description: Float16 depthwise-convolution specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_DEPTHWISE_CONV_OPT_F16_H +#define ARM_DEPTHWISE_CONV_OPT_F16_H + +/* Internal specialization helpers (included from arm_depthwise_conv_f16.c). */ + +#include "Internal/arm_depthwise_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_dw_match_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout); + +typedef arm_cmsis_nn_status (*arm_dw_call_f16)(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout); + +typedef struct +{ + arm_dw_match_f16 match; + arm_dw_call_f16 call; +} arm_dw_spec_f16; + +static bool arm_dw_spec_k3_1d_nhwc_f16_match(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)input; + (void)kernel; + (void)bias_dims; + (void)bias; + (void)output; + + const int32_t ch_mult = params->ch_mult; + const int32_t kernel_x = filter_dims->w; + const int32_t kernel_y = filter_dims->h; + const int32_t batch = input_dims->n; + const int32_t output_batch = output_dims->n; + const int32_t input_y = input_dims->h; + const int32_t output_y = output_dims->h; + + return (kernel_layout == ARM_NN_DW_KERNEL_KC && batch == 1 && output_batch == 1 && ch_mult == 1 && kernel_x == 3 && + kernel_y == 1 && input_y == 1 && output_y == 1 && params->dilation.h == 1 && params->dilation.w == 1 && + params->stride.h == 1 && params->stride.w == 1 && params->padding.h == 0 && params->padding.w == 0); +} + +static arm_cmsis_nn_status arm_dw_spec_k3_1d_nhwc_f16_call(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + if (kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_nn_depthwise_conv1d_k3_nhwc_f16(input, input_dims->c, input_dims->w, kernel, bias, output, output_dims->w); + + const int32_t out_count = output_dims->n * output_dims->c * output_dims->h * output_dims->w; + arm_nn_vector_clamp_f16(output, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static bool arm_dw_spec_2x5_nhwc_f16_match(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)input; + (void)kernel; + (void)bias_dims; + (void)bias; + (void)output; + + const int32_t kernel_x = filter_dims->w; + const int32_t kernel_y = filter_dims->h; + const int32_t input_y = input_dims->h; + const int32_t output_y = output_dims->h; + + return (kernel_layout == ARM_NN_DW_KERNEL_KC && kernel_x == 5 && kernel_y == 2 && input_y == 2 && output_y == 1 && + params->dilation.h == 1 && params->dilation.w == 1 && params->stride.h == 1 && params->stride.w == 1 && + params->padding.h == 0 && params->padding.w == 0); +} + +static arm_cmsis_nn_status arm_dw_spec_2x5_nhwc_f16_call(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + if (kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_nn_depthwise_conv2x5_nhwc_f16(input, + input_dims->n, + input_dims->c, + input_dims->w, + params->ch_mult, + kernel, + bias, + output, + output_dims->w, + params->activation.min, + params->activation.max); + return ARM_CMSIS_NN_SUCCESS; +} + +static bool arm_dw_spec_3x3_nhwc_f16_match(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)input; + (void)kernel; + (void)bias_dims; + (void)bias; + (void)output; + + const int32_t batch = input_dims->n; + const int32_t output_batch = output_dims->n; + + return (kernel_layout == ARM_NN_DW_KERNEL_KC && batch > 0 && batch == output_batch && params->ch_mult == 1 && + filter_dims->w == 3 && filter_dims->h == 3 && params->dilation.h == 1 && params->dilation.w == 1); +} + +static arm_cmsis_nn_status arm_dw_spec_3x3_nhwc_f16_call(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + if (kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_nn_depthwise_conv3x3_nhwc_f16(input, + input_dims->n, + input_dims->c, + input_dims->h, + input_dims->w, + kernel, + bias, + output, + params->stride.w, + params->stride.h, + params->padding.w, + params->padding.h, + output_dims->h, + output_dims->w, + params->activation.min, + params->activation.max); + return ARM_CMSIS_NN_SUCCESS; +} + +static const arm_dw_spec_f16 arm_dw_spec_nhwc_f16[] = { + ARM_DW_SPEC_ENTRY(arm_dw_spec_k3_1d_nhwc_f16_match, arm_dw_spec_k3_1d_nhwc_f16_call), + ARM_DW_SPEC_ENTRY(arm_dw_spec_2x5_nhwc_f16_match, arm_dw_spec_2x5_nhwc_f16_call), + ARM_DW_SPEC_ENTRY(arm_dw_spec_3x3_nhwc_f16_match, arm_dw_spec_3x3_nhwc_f16_call), +}; +#endif + +#endif /* ARM_DEPTHWISE_CONV_OPT_F16_H */ diff --git a/Include/Internal/arm_depthwise_conv_opt_f32.h b/Include/Internal/arm_depthwise_conv_opt_f32.h new file mode 100644 index 00000000..ed38f661 --- /dev/null +++ b/Include/Internal/arm_depthwise_conv_opt_f32.h @@ -0,0 +1,204 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_depthwise_conv_opt_f32.h + * Description: Float32 depthwise-convolution specialization helpers + * + * $Date: 27 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_DEPTHWISE_CONV_OPT_F32_H +#define ARM_DEPTHWISE_CONV_OPT_F32_H + +/* Internal specialization helpers (included from arm_depthwise_conv_f32.c). */ + +#include "Internal/arm_depthwise_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#ifndef NN_DISABLE_SPECIALIZATION +typedef bool (*arm_dw_match_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout); + +typedef arm_cmsis_nn_status (*arm_dw_call_f32)(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout); + +typedef struct +{ + arm_dw_match_f32 match; + arm_dw_call_f32 call; +} arm_dw_spec_f32; + +static bool arm_dw_spec_k3_1d_nhwc_f32_match(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ + (void)ctx; + (void)input; + (void)kernel; + (void)bias_dims; + (void)bias; + (void)output; + + const int32_t ch_mult = params->ch_mult; + const int32_t kernel_x = filter_dims->w; + const int32_t kernel_y = filter_dims->h; + const int32_t batch = input_dims->n; + const int32_t output_batch = output_dims->n; + const int32_t input_y = input_dims->h; + const int32_t output_y = output_dims->h; + + return (kernel_layout == ARM_NN_DW_KERNEL_KC && batch == 1 && output_batch == 1 && ch_mult == 1 && kernel_x == 3 && + kernel_y == 1 && input_y == 1 && output_y == 1 && params->dilation.h == 1 && params->dilation.w == 1 && + params->stride.h == 1 && params->stride.w == 1 && params->padding.h == 0 && params->padding.w == 0); +} + +static arm_cmsis_nn_status arm_dw_spec_k3_1d_nhwc_f32_call(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + if (kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_nn_depthwise_conv1d_k3_nhwc_f32(input, input_dims->c, input_dims->w, kernel, bias, output, output_dims->w); + + const int32_t out_count = output_dims->n * output_dims->c * output_dims->h * output_dims->w; + arm_nn_vector_clamp_f32(output, out_count, params->activation.min, params->activation.max); + + return ARM_CMSIS_NN_SUCCESS; +} + +static bool arm_dw_spec_3x3_nhwc_f32_match(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ + (void)ctx; + (void)input; + (void)kernel; + (void)bias_dims; + (void)bias; + (void)output; + + const int32_t batch = input_dims->n; + const int32_t output_batch = output_dims->n; + + return (kernel_layout == ARM_NN_DW_KERNEL_KC && batch > 0 && batch == output_batch && params->ch_mult == 1 && + filter_dims->w == 3 && filter_dims->h == 3 && params->dilation.h == 1 && params->dilation.w == 1); +} + +static arm_cmsis_nn_status arm_dw_spec_3x3_nhwc_f32_call(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ + (void)ctx; + (void)filter_dims; + (void)bias_dims; + + if (kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_nn_depthwise_conv3x3_nhwc_f32(input, + input_dims->n, + input_dims->c, + input_dims->h, + input_dims->w, + kernel, + bias, + output, + params->stride.w, + params->stride.h, + params->padding.w, + params->padding.h, + output_dims->h, + output_dims->w, + params->activation.min, + params->activation.max); + return ARM_CMSIS_NN_SUCCESS; +} + +static const arm_dw_spec_f32 arm_dw_spec_nhwc_f32[] = { + ARM_DW_SPEC_ENTRY(arm_dw_spec_k3_1d_nhwc_f32_match, arm_dw_spec_k3_1d_nhwc_f32_call), + ARM_DW_SPEC_ENTRY(arm_dw_spec_3x3_nhwc_f32_match, arm_dw_spec_3x3_nhwc_f32_call), +}; +#endif + +#endif /* ARM_DEPTHWISE_CONV_OPT_F32_H */ diff --git a/Include/Internal/arm_minmax_f16_common.h b/Include/Internal/arm_minmax_f16_common.h new file mode 100644 index 00000000..72a6e5a7 --- /dev/null +++ b/Include/Internal/arm_minmax_f16_common.h @@ -0,0 +1,282 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_minmax_f16_common.h + * Description: Shared float16 min/max helper templates + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_MINMAX_F16_COMMON_H +#define ARM_MINMAX_F16_COMMON_H + +#include "arm_nn_compiler.h" +#include "arm_nn_types_flt.h" + +__STATIC_INLINE int32_t arm_check_broadcast_required_f16(const cmsis_nn_dims *shape_1, const cmsis_nn_dims *shape_2) +{ + if ((shape_1->n != shape_2->n) || (shape_1->h != shape_2->h) || (shape_1->w != shape_2->w) || + (shape_1->c != shape_2->c)) + { + return 1; + } + + return 0; +} + +__STATIC_INLINE float16_t arm_minmax_select_f16(float16_t a, float16_t b, int32_t select_max) +{ + return ((_Float16)a >= (_Float16)b) ? (select_max ? (_Float16)a : (_Float16)b) + : (select_max ? (_Float16)b : (_Float16)a); +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_no_broadcast_f16(const float16_t *input_1, + const float16_t *input_2, + float16_t *output, + int32_t flat_size, + int32_t select_max) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (select_max) + { + for (int32_t i = 0; i < flat_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(flat_size - i)); + const float16x8_t v_in1 = vld1q_z(input_1 + i, p); + const float16x8_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vmaxnmq(v_in1, v_in2), p); + } + } + else + { + for (int32_t i = 0; i < flat_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(flat_size - i)); + const float16x8_t v_in1 = vld1q_z(input_1 + i, p); + const float16x8_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vminnmq(v_in1, v_in2), p); + } + } +#else + for (int32_t i = 0; i < flat_size; ++i) + { + output[i] = arm_minmax_select_f16(input_1[i], input_2[i], select_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_scalar_f16(const float16_t *input_1, + const float16_t *input_2, + float16_t *output, + int32_t flat_size, + int32_t select_max) +{ + const float16_t in1 = *input_1; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_in1 = vdupq_n_f16(in1); + if (select_max) + { + for (int32_t i = 0; i < flat_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(flat_size - i)); + const float16x8_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vmaxnmq(v_in1, v_in2), p); + } + } + else + { + for (int32_t i = 0; i < flat_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(flat_size - i)); + const float16x8_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vminnmq(v_in1, v_in2), p); + } + } +#else + for (int32_t i = 0; i < flat_size; ++i) + { + output[i] = arm_minmax_select_f16(in1, input_2[i], select_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_f16_impl(const cmsis_nn_context *ctx, + const float16_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float16_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float16_t *output_data, + const cmsis_nn_dims *output_dims, + int32_t select_max) +{ + (void)ctx; + if (!input_1_data || !input_2_data || !output_data || !input_1_dims || !input_2_dims || !output_dims) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t output_batch = output_dims->n; + const int32_t output_height = output_dims->h; + const int32_t output_width = output_dims->w; + + const int32_t input_1_batch = input_1_dims->n; + const int32_t input_1_height = input_1_dims->h; + const int32_t input_1_width = input_1_dims->w; + const int32_t input_1_channels = input_1_dims->c; + + const int32_t input_2_batch = input_2_dims->n; + const int32_t input_2_height = input_2_dims->h; + const int32_t input_2_width = input_2_dims->w; + const int32_t input_2_channels = input_2_dims->c; + + int32_t flat_size_1 = input_1_batch * input_1_height * input_1_width * input_1_channels; + int32_t flat_size_2 = input_2_batch * input_2_height * input_2_width * input_2_channels; + + if (arm_check_broadcast_required_f16(input_1_dims, input_2_dims)) + { + if (flat_size_1 == 1) + { + arm_minmax_scalar_f16(input_1_data, input_2_data, output_data, flat_size_2, select_max); + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f16(input_2_data, input_1_data, output_data, flat_size_1, select_max); + } + else + { + const int32_t width_1_diff = input_1_width >= input_2_width ? 0 : input_1_channels; + const int32_t width_2_diff = input_2_width >= input_1_width ? 0 : input_2_channels; + + const int32_t height_1_diff = + input_1_height >= input_2_height ? width_1_diff : -input_1_width * (input_1_channels - width_1_diff); + const int32_t height_2_diff = + input_2_height >= input_1_height ? width_2_diff : -input_2_width * (input_2_channels - width_2_diff); + + const int32_t batch_1_diff = + input_1_batch >= input_2_batch ? input_1_channels * input_1_width * input_1_height : 0; + const int32_t batch_2_diff = + input_2_batch >= input_1_batch ? input_2_channels * input_2_width * input_2_height : 0; + + for (int32_t i_out_batch = 0; i_out_batch < output_batch; i_out_batch++) + { + const float16_t *input_1_ptr = input_1_data; + const float16_t *input_2_ptr = input_2_data; + flat_size_1 = input_1_height * input_1_width * input_1_channels; + flat_size_2 = input_2_height * input_2_width * input_2_channels; + if (input_1_height == input_2_height && input_1_width == input_2_width && + input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f16(input_1_ptr, input_2_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + } + else if (flat_size_1 == 1) + { + arm_minmax_scalar_f16(input_1_ptr, input_2_ptr, output_data, flat_size_2, select_max); + output_data += flat_size_2; + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f16(input_2_ptr, input_1_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + } + else + { + flat_size_1 = input_1_width * input_1_channels; + flat_size_2 = input_2_width * input_2_channels; + for (int32_t i_out_height = 0; i_out_height < output_height; i_out_height++) + { + if (input_1_width == input_2_width && input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f16(input_1_ptr, input_2_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + input_1_ptr += flat_size_1; + input_2_ptr += flat_size_1; + } + else if (flat_size_1 == 1) + { + arm_minmax_scalar_f16(input_1_ptr, input_2_ptr, output_data, flat_size_2, select_max); + output_data += flat_size_2; + ++input_1_ptr; + input_2_ptr += flat_size_2; + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f16(input_2_ptr, input_1_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + ++input_2_ptr; + input_1_ptr += flat_size_1; + } + else + { + for (int32_t i_out_width = 0; i_out_width < output_width; i_out_width++) + { + if (input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f16( + input_1_ptr, input_2_ptr, output_data, input_1_channels, select_max); + output_data += input_1_channels; + input_1_ptr += input_1_channels; + input_2_ptr += input_1_channels; + } + else if (input_1_channels == 1) + { + arm_minmax_scalar_f16( + input_1_ptr, input_2_ptr, output_data, input_2_channels, select_max); + output_data += input_2_channels; + input_1_ptr++; + input_2_ptr += input_2_channels; + } + else if (input_2_channels == 1) + { + arm_minmax_scalar_f16( + input_2_ptr, input_1_ptr, output_data, input_1_channels, select_max); + output_data += input_1_channels; + input_1_ptr += input_1_channels; + input_2_ptr++; + } + input_1_ptr -= width_1_diff; + input_2_ptr -= width_2_diff; + } + } + input_1_ptr += height_1_diff; + input_2_ptr += height_2_diff; + } + } + input_1_data += batch_1_diff; + input_2_data += batch_2_diff; + } + } + } + else + { + arm_minmax_no_broadcast_f16(input_1_data, input_2_data, output_data, flat_size_1, select_max); + } + + return ARM_CMSIS_NN_SUCCESS; +} + +#endif /* ARM_MINMAX_F16_COMMON_H */ diff --git a/Include/Internal/arm_minmax_f32_common.h b/Include/Internal/arm_minmax_f32_common.h new file mode 100644 index 00000000..d630785c --- /dev/null +++ b/Include/Internal/arm_minmax_f32_common.h @@ -0,0 +1,281 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_minmax_f32_common.h + * Description: Shared float32 min/max helper templates + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_MINMAX_F32_COMMON_H +#define ARM_MINMAX_F32_COMMON_H + +#include "arm_nn_compiler.h" +#include "arm_nn_types_flt.h" + +__STATIC_INLINE int32_t arm_check_broadcast_required_f32(const cmsis_nn_dims *shape_1, const cmsis_nn_dims *shape_2) +{ + if ((shape_1->n != shape_2->n) || (shape_1->h != shape_2->h) || (shape_1->w != shape_2->w) || + (shape_1->c != shape_2->c)) + { + return 1; + } + + return 0; +} + +__STATIC_INLINE float32_t arm_minmax_select_f32(float32_t a, float32_t b, int32_t select_max) +{ + return (a >= b) ? (select_max ? a : b) : (select_max ? b : a); +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_no_broadcast_f32(const float32_t *input_1, + const float32_t *input_2, + float32_t *output, + int32_t flat_size, + int32_t select_max) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (select_max) + { + for (int32_t i = 0; i < flat_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(flat_size - i)); + const float32x4_t v_in1 = vld1q_z(input_1 + i, p); + const float32x4_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vmaxnmq(v_in1, v_in2), p); + } + } + else + { + for (int32_t i = 0; i < flat_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(flat_size - i)); + const float32x4_t v_in1 = vld1q_z(input_1 + i, p); + const float32x4_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vminnmq(v_in1, v_in2), p); + } + } +#else + for (int32_t i = 0; i < flat_size; ++i) + { + output[i] = arm_minmax_select_f32(input_1[i], input_2[i], select_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_scalar_f32(const float32_t *input_1, + const float32_t *input_2, + float32_t *output, + int32_t flat_size, + int32_t select_max) +{ + const float32_t in1 = *input_1; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_in1 = vdupq_n_f32(in1); + if (select_max) + { + for (int32_t i = 0; i < flat_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(flat_size - i)); + const float32x4_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vmaxnmq(v_in1, v_in2), p); + } + } + else + { + for (int32_t i = 0; i < flat_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(flat_size - i)); + const float32x4_t v_in2 = vld1q_z(input_2 + i, p); + vst1q_p(output + i, vminnmq(v_in1, v_in2), p); + } + } +#else + for (int32_t i = 0; i < flat_size; ++i) + { + output[i] = arm_minmax_select_f32(in1, input_2[i], select_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_minmax_f32_impl(const cmsis_nn_context *ctx, + const float32_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float32_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float32_t *output_data, + const cmsis_nn_dims *output_dims, + int32_t select_max) +{ + (void)ctx; + if (!input_1_data || !input_2_data || !output_data || !input_1_dims || !input_2_dims || !output_dims) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t output_batch = output_dims->n; + const int32_t output_height = output_dims->h; + const int32_t output_width = output_dims->w; + + const int32_t input_1_batch = input_1_dims->n; + const int32_t input_1_height = input_1_dims->h; + const int32_t input_1_width = input_1_dims->w; + const int32_t input_1_channels = input_1_dims->c; + + const int32_t input_2_batch = input_2_dims->n; + const int32_t input_2_height = input_2_dims->h; + const int32_t input_2_width = input_2_dims->w; + const int32_t input_2_channels = input_2_dims->c; + + int32_t flat_size_1 = input_1_batch * input_1_height * input_1_width * input_1_channels; + int32_t flat_size_2 = input_2_batch * input_2_height * input_2_width * input_2_channels; + + if (arm_check_broadcast_required_f32(input_1_dims, input_2_dims)) + { + if (flat_size_1 == 1) + { + arm_minmax_scalar_f32(input_1_data, input_2_data, output_data, flat_size_2, select_max); + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f32(input_2_data, input_1_data, output_data, flat_size_1, select_max); + } + else + { + const int32_t width_1_diff = input_1_width >= input_2_width ? 0 : input_1_channels; + const int32_t width_2_diff = input_2_width >= input_1_width ? 0 : input_2_channels; + + const int32_t height_1_diff = + input_1_height >= input_2_height ? width_1_diff : -input_1_width * (input_1_channels - width_1_diff); + const int32_t height_2_diff = + input_2_height >= input_1_height ? width_2_diff : -input_2_width * (input_2_channels - width_2_diff); + + const int32_t batch_1_diff = + input_1_batch >= input_2_batch ? input_1_channels * input_1_width * input_1_height : 0; + const int32_t batch_2_diff = + input_2_batch >= input_1_batch ? input_2_channels * input_2_width * input_2_height : 0; + + for (int32_t i_out_batch = 0; i_out_batch < output_batch; i_out_batch++) + { + const float32_t *input_1_ptr = input_1_data; + const float32_t *input_2_ptr = input_2_data; + flat_size_1 = input_1_height * input_1_width * input_1_channels; + flat_size_2 = input_2_height * input_2_width * input_2_channels; + if (input_1_height == input_2_height && input_1_width == input_2_width && + input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f32(input_1_ptr, input_2_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + } + else if (flat_size_1 == 1) + { + arm_minmax_scalar_f32(input_1_ptr, input_2_ptr, output_data, flat_size_2, select_max); + output_data += flat_size_2; + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f32(input_2_ptr, input_1_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + } + else + { + flat_size_1 = input_1_width * input_1_channels; + flat_size_2 = input_2_width * input_2_channels; + for (int32_t i_out_height = 0; i_out_height < output_height; i_out_height++) + { + if (input_1_width == input_2_width && input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f32(input_1_ptr, input_2_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + input_1_ptr += flat_size_1; + input_2_ptr += flat_size_1; + } + else if (flat_size_1 == 1) + { + arm_minmax_scalar_f32(input_1_ptr, input_2_ptr, output_data, flat_size_2, select_max); + output_data += flat_size_2; + ++input_1_ptr; + input_2_ptr += flat_size_2; + } + else if (flat_size_2 == 1) + { + arm_minmax_scalar_f32(input_2_ptr, input_1_ptr, output_data, flat_size_1, select_max); + output_data += flat_size_1; + ++input_2_ptr; + input_1_ptr += flat_size_1; + } + else + { + for (int32_t i_out_width = 0; i_out_width < output_width; i_out_width++) + { + if (input_1_channels == input_2_channels) + { + arm_minmax_no_broadcast_f32( + input_1_ptr, input_2_ptr, output_data, input_1_channels, select_max); + output_data += input_1_channels; + input_1_ptr += input_1_channels; + input_2_ptr += input_1_channels; + } + else if (input_1_channels == 1) + { + arm_minmax_scalar_f32( + input_1_ptr, input_2_ptr, output_data, input_2_channels, select_max); + output_data += input_2_channels; + input_1_ptr++; + input_2_ptr += input_2_channels; + } + else if (input_2_channels == 1) + { + arm_minmax_scalar_f32( + input_2_ptr, input_1_ptr, output_data, input_1_channels, select_max); + output_data += input_1_channels; + input_1_ptr += input_1_channels; + input_2_ptr++; + } + input_1_ptr -= width_1_diff; + input_2_ptr -= width_2_diff; + } + } + input_1_ptr += height_1_diff; + input_2_ptr += height_2_diff; + } + } + input_1_data += batch_1_diff; + input_2_data += batch_2_diff; + } + } + } + else + { + arm_minmax_no_broadcast_f32(input_1_data, input_2_data, output_data, flat_size_1, select_max); + } + + return ARM_CMSIS_NN_SUCCESS; +} + +#endif /* ARM_MINMAX_F32_COMMON_H */ diff --git a/Include/Internal/arm_nn_activation_flt.h b/Include/Internal/arm_nn_activation_flt.h new file mode 100644 index 00000000..95ddaeff --- /dev/null +++ b/Include/Internal/arm_nn_activation_flt.h @@ -0,0 +1,306 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_activation_flt.h + * Description: Internal floating-point activation helper utilities + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_NN_ACTIVATION_FLT_H +#define ARM_NN_ACTIVATION_FLT_H + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +__STATIC_INLINE float32_t arm_nn_hardswish_scalar_f32(float32_t x) +{ + float32_t t = x * (1.0f / 6.0f) + 0.5f; + t = CLAMP(t, 1.0f, 0.0f); + return x * t; +} + +/* + * Tanh deliberately uses different scalar approximations by dtype. + * Float32 keeps a LUT + linear interpolation path to preserve tighter + * accuracy over a wider input range, while float16 uses a compact rational + * approximation because half precision does not benefit as much from a + * larger table and the lower-order form is sufficient for its target error. + */ +__STATIC_INLINE float32_t arm_nn_tanh_scalar_ref_f32(float32_t x) +{ + float32_t ax = (x < 0.0f) ? -x : x; + const float32_t xmax = 4.0f; + + if (ax > xmax) + { + return (x < 0.0f) ? -1.0f : 1.0f; + } + + const float32_t t = ax * (256.0f / xmax); + int32_t idx = (int32_t)t; + idx = CLAMP(idx, 255, 0); + const float32_t frac = t - (float32_t)idx; + const float32_t y0 = arm_nn_tanh_lut256_f32[idx]; + const float32_t y1 = arm_nn_tanh_lut256_f32[idx + 1]; + const float32_t y = y0 + (y1 - y0) * frac; + return (x < 0.0f) ? -y : y; +} + +/* + * Keep the float32 sigmoid on the non-positive exp domain used by softmax. + * This mirrors the float16 helper and avoids needlessly evaluating exp() + * on large positive arguments. + */ +__STATIC_INLINE float32_t arm_nn_sigmoid_scalar_f32(float32_t x) +{ + if (x >= 0.0f) + { + const float32_t e = arm_nn_softmax_exp_scalar_f32(-x); + return 1.0f / (1.0f + e); + } + + const float32_t e = arm_nn_softmax_exp_scalar_f32(x); + return e / (1.0f + e); +} + +__STATIC_INLINE float32_t arm_nn_apply_activation_type_f32(float32_t x, + arm_nn_activation_type_flt type, + float32_t act_param) +{ + switch (type) + { + case ARM_NN_FLT_ACT_NONE: + return x; + case ARM_NN_FLT_ACT_SIGMOID: + return arm_nn_sigmoid_scalar_f32(x); + case ARM_NN_FLT_ACT_RELU: + return (x < 0.0f) ? 0.0f : x; + case ARM_NN_FLT_ACT_RELU6: { + const float32_t y = (x < 0.0f) ? 0.0f : x; + return (y > 6.0f) ? 6.0f : y; + } + case ARM_NN_FLT_ACT_TANH: + return arm_nn_tanh_scalar_ref_f32(x); + case ARM_NN_FLT_ACT_HARDSWISH: + return arm_nn_hardswish_scalar_f32(x); + case ARM_NN_FLT_ACT_LEAKY_RELU: + return (x >= 0.0f) ? x : (act_param * x); + default: + return x; + } +} + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float32x4_t arm_nn_clamp_mve_f32(float32x4_t x, float32x4_t min_v, float32x4_t max_v) +{ + x = vmaxnmq(x, min_v); + x = vminnmq(x, max_v); + return x; +} + +__STATIC_INLINE float32x4_t arm_nn_vtanh_lut_direct_mve_f32(float32x4_t x) +{ + float32x4_t ax = vabsq(x); + const mve_pred16_t sat_p = vcmpgtq(ax, 4.0f); + ax = vminnmq(ax, vdupq_n_f32(4.0f)); + const uint32_t xmax = 4U; + const uint32_t lut_tbl_max_idx = 256U; + const float32x4_t t = vmulq(ax, (float32_t)(lut_tbl_max_idx / xmax)); + uint32x4_t idx = vcvtmq_u32_f32(t); + idx = vminq(idx, vdupq_n_u32(255U)); + const float32x4_t frac = vsubq(t, vcvtq_f32_u32(idx)); + const float32x4_t y0 = vldrwq_gather_shifted_offset((const float32_t *)arm_nn_tanh_lut256_f32, idx); + const float32x4_t y1 = vldrwq_gather_shifted_offset((const float32_t *)arm_nn_tanh_lut256_f32, vaddq(idx, 1U)); + float32x4_t y = vfmaq(y0, vsubq(y1, y0), frac); + y = vpselq(vdupq_n_f32(1.0f), y, sat_p); + return vnegq_m(y, y, vcmpltq(x, 0.0f)); +} + +__STATIC_INLINE float32x4_t arm_nn_vhardswish_mve_f32(float32x4_t x) +{ + float32x4_t t = vfmaq(vdupq_n_f32(0.5f), x, (1.0f / 6.0f)); + t = vmaxnmq(t, vdupq_n_f32(0.0f)); + t = vminnmq(t, vdupq_n_f32(1.0f)); + return vmulq(x, t); +} + #endif + +__STATIC_INLINE void +arm_nn_vector_clamp_f32(float32_t *data, int32_t block_size, float32_t activation_min, float32_t activation_max) +{ + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t vmin = vdupq_n_f32(activation_min); + const float32x4_t vmax = vdupq_n_f32(activation_max); + for (int32_t i = 0; i < block_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(block_size - i)); + float32x4_t v = vld1q_z(data + i, p); + v = arm_nn_clamp_mve_f32(v, vmin, vmax); + vst1q_p(data + i, v, p); + } + #else + for (int32_t i = 0; i < block_size; ++i) + { + data[i] = CLAMP(data[i], activation_max, activation_min); + } + #endif +} + +#endif /* ARM_NN_ENABLE_F32 */ + +#if ARM_NN_ENABLE_F16 + +__STATIC_INLINE float16_t arm_nn_clamp_scalar_f16(float16_t x, float16_t min_v, float16_t max_v) +{ + const _Float16 xh = (_Float16)x; + const _Float16 min_h = (_Float16)min_v; + const _Float16 max_h = (_Float16)max_v; + const _Float16 y = (xh < min_h) ? min_h : ((xh > max_h) ? max_h : xh); + return (float16_t)y; +} + +__STATIC_INLINE float16_t arm_nn_hardswish_scalar_f16(float16_t x) +{ + _Float16 t = (_Float16)x * (_Float16)(1.0f / 6.0f) + (_Float16)0.5f; + t = CLAMP(t, (_Float16)1.0f, (_Float16)0.0f); + return (float16_t)((_Float16)x * t); +} + +__STATIC_INLINE float16_t arm_nn_tanh_scalar_ref_f16(float16_t x) +{ + const _Float16 ax = ((_Float16)x < (_Float16)0.0f) ? -(_Float16)x : (_Float16)x; + if (ax > (_Float16)arm_nn_tanh_approx_coeffs_f16[0]) + { + return ((_Float16)x < (_Float16)0.0f) ? (_Float16)-1.0f : (_Float16)1.0f; + } + + const _Float16 x2 = (_Float16)x * (_Float16)x; + const _Float16 num = (_Float16)x * ((_Float16)arm_nn_tanh_approx_coeffs_f16[1] + x2); + const _Float16 den = (_Float16)arm_nn_tanh_approx_coeffs_f16[1] + (_Float16)arm_nn_tanh_approx_coeffs_f16[2] * x2; + return (float16_t)(num / den); +} + +/* + * Keep the float16 sigmoid on the non-positive exp domain used by softmax. + * This avoids relying on the positive-side exp + */ +__STATIC_INLINE float16_t arm_nn_sigmoid_scalar_f16(float16_t x) +{ + if ((_Float16)x >= (_Float16)0.0f) + { + const float32_t e = (float32_t)arm_nn_softmax_exp_scalar_f16((float16_t)(-(_Float16)x)); + return (float16_t)(1.0f / (1.0f + e)); + } + + const float32_t e = (float32_t)arm_nn_softmax_exp_scalar_f16((float16_t)(_Float16)x); + return (float16_t)(e / (1.0f + e)); +} + +__STATIC_INLINE float16_t arm_nn_apply_activation_type_f16(float16_t x, + arm_nn_activation_type_flt type, + float16_t act_param) +{ + switch (type) + { + case ARM_NN_FLT_ACT_NONE: + return x; + case ARM_NN_FLT_ACT_SIGMOID: + return arm_nn_sigmoid_scalar_f16(x); + case ARM_NN_FLT_ACT_RELU: + return (float16_t)((_Float16)x < (_Float16)0.0f ? (_Float16)0.0f : (_Float16)x); + case ARM_NN_FLT_ACT_RELU6: { + const float16_t y = ((_Float16)x < (_Float16)0.0f) ? (_Float16)0.0f : (_Float16)x; + return (float16_t)((_Float16)y > (_Float16)6.0f ? (_Float16)6.0f : (_Float16)y); + } + case ARM_NN_FLT_ACT_TANH: + return arm_nn_tanh_scalar_ref_f16(x); + case ARM_NN_FLT_ACT_HARDSWISH: + return arm_nn_hardswish_scalar_f16(x); + case ARM_NN_FLT_ACT_LEAKY_RELU: + return (float16_t)((_Float16)x >= (_Float16)0.0f ? (_Float16)x : (_Float16)((_Float16)act_param * (_Float16)x)); + default: + return x; + } +} + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float16x8_t arm_nn_clamp_mve_f16(float16x8_t x, float16x8_t min_v, float16x8_t max_v) +{ + x = vmaxnmq(x, min_v); + x = vminnmq(x, max_v); + return x; +} + +__STATIC_INLINE float16x8_t arm_nn_vtanh_lut_direct_mve_f16(float16x8_t x) +{ + float16x8_t ax = vabsq(x); + const mve_pred16_t sat_p = vcmpgtq(ax, (float16_t)4.0f); + ax = vminnmq(ax, vdupq_n_f16((float16_t)4.0f)); + const uint16_t xmax = 4U; + const uint16_t lut_tbl_max_idx = 256U; + const float16x8_t t = vmulq(ax, (float16_t)(lut_tbl_max_idx / xmax)); + uint16x8_t idx = vcvtmq_u16_f16(t); + idx = vminq(idx, vdupq_n_u16(255U)); + const float16x8_t frac = vsubq(t, vcvtq_f16_u16(idx)); + const float16x8_t y0 = vldrhq_gather_shifted_offset((const float16_t *)arm_nn_tanh_lut256_f16, idx); + const float16x8_t y1 = vldrhq_gather_shifted_offset((const float16_t *)arm_nn_tanh_lut256_f16, vaddq(idx, 1U)); + float16x8_t y = vfmaq(y0, vsubq(y1, y0), frac); + y = vpselq(vdupq_n_f16((float16_t)1.0f), y, sat_p); + return vnegq_m(y, y, vcmpltq(x, (float16_t)0.0f)); +} + +__STATIC_INLINE float16x8_t arm_nn_vhardswish_mve_f16(float16x8_t x) +{ + float16x8_t t = vfmaq(vdupq_n_f16((float16_t)0.5f), x, (float16_t)(1.0f / 6.0f)); + t = vmaxnmq(t, vdupq_n_f16((float16_t)0.0f)); + t = vminnmq(t, vdupq_n_f16((float16_t)1.0f)); + return vmulq(x, t); +} + #endif + +__STATIC_INLINE void +arm_nn_vector_clamp_f16(float16_t *data, int32_t block_size, float16_t activation_min, float16_t activation_max) +{ + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t vmin = vdupq_n_f16(activation_min); + const float16x8_t vmax = vdupq_n_f16(activation_max); + for (int32_t i = 0; i < block_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(block_size - i)); + float16x8_t v = vld1q_z(data + i, p); + v = arm_nn_clamp_mve_f16(v, vmin, vmax); + vst1q_p(data + i, v, p); + } + #else + for (int32_t i = 0; i < block_size; ++i) + { + data[i] = arm_nn_clamp_scalar_f16(data[i], activation_min, activation_max); + } + #endif +} + +#endif /* ARM_NN_ENABLE_F16 */ + +#endif /* ARM_NN_ACTIVATION_FLT_H */ diff --git a/Include/Internal/arm_nn_config.h b/Include/Internal/arm_nn_config.h new file mode 100644 index 00000000..1582d73c --- /dev/null +++ b/Include/Internal/arm_nn_config.h @@ -0,0 +1,93 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_config.h + * Description: Internal optional feature configuration for CMSIS-NN + * + * $Date: 24 April 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +/* + * This header is part of the internal CMSIS-NN build configuration surface. + * Applications are expected to include the public arm_nnfunctions*.h headers, + * which pull the needed configuration through the public type/math headers. + * + * The current options focus on floating-point support, but the file name is + * kept generic so wider CMSIS-NN configuration can be added here over time, + * including settings shared with quantized paths. + */ + +#ifndef ARM_NN_CONFIG_H +#define ARM_NN_CONFIG_H + +/** + * + * @brief Optional feature gates for floating-point extensions + * + * These are disabled by default so integer-only builds keep their current + * code size and API surface. The float16 feature gate still depends on + * toolchain and target support such as ARM_FLOAT16_SUPPORTED where + * applicable. + * + */ + +#ifndef ARM_NN_ENABLE_F32 + #define ARM_NN_ENABLE_F32 0 +#endif + +#ifndef ARM_NN_ENABLE_F16 + #define ARM_NN_ENABLE_F16 0 +#endif + +#define ARM_NN_FLOAT_API_ENABLED (ARM_NN_ENABLE_F32 || ARM_NN_ENABLE_F16) + +/* + * NN_DISABLE_SPECIALIZATION disables optional shape/layout-specific fast paths + * and forces the corresponding generic implementations. This is useful for + * debugging or validating specialized kernels against the generic path. + */ + +/** + * + * @brief Optional floating-point softmax exp approximation selection + * + * Define one of these macros at build time to select the scalar float softmax + * exp path: + * - ARM_NN_USE_EXP_LUT + * - ARM_NN_USE_EXP_TAYLOR + * + * If neither macro is defined, the scalar softmax path defaults to the LUT + * implementation for performance. This adds one 257-entry lookup table per + * enabled float precision: + * - float16: 257 half-words, about 514 bytes + * - float32: 257 words, about 1028 bytes + * + * Define ARM_NN_USE_EXP_TAYLOR to avoid the extra lookup-table storage. + * + */ + +#if defined(ARM_NN_USE_EXP_LUT) && defined(ARM_NN_USE_EXP_TAYLOR) + #error "ARM_NN_USE_EXP_LUT and ARM_NN_USE_EXP_TAYLOR are mutually exclusive" +#endif + +#endif /* ARM_NN_CONFIG_H */ diff --git a/Include/Internal/arm_transpose_common.h b/Include/Internal/arm_transpose_common.h new file mode 100644 index 00000000..17d3cace --- /dev/null +++ b/Include/Internal/arm_transpose_common.h @@ -0,0 +1,244 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_transpose_common.h + * Description: Shared transpose helper templates for CMSIS-NN + * + * $Date: 2 April 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_TRANSPOSE_COMMON_H +#define ARM_TRANSPOSE_COMMON_H + +#include "arm_nn_types_flt.h" + +#define ARM_TRANSPOSE_DEFINE(FUNC_NAME, SCALAR_T, PARAMS_T, LAYOUT_T, TRANSPOSE_2D_FUNC) \ + static void FUNC_NAME##_copy_elems(const SCALAR_T *input, SCALAR_T *output, size_t elems) \ + { \ + for (size_t i = 0; i < elems; ++i) \ + { \ + output[i] = input[i]; \ + } \ + } \ + \ + static arm_cmsis_nn_status FUNC_NAME##_transpose_2d( \ + const SCALAR_T *input, SCALAR_T *output, int32_t rows, int32_t cols) \ + { \ + return TRANSPOSE_2D_FUNC(input, output, rows, cols); \ + } \ + \ + static arm_cmsis_nn_status FUNC_NAME##_transpose_swap_last2_3d( \ + const SCALAR_T *input, SCALAR_T *output, int32_t d0, int32_t d1, int32_t d2) \ + { \ + const size_t in_stride0 = (size_t)d1 * (size_t)d2; \ + const size_t out_stride0 = (size_t)d2 * (size_t)d1; \ + for (int32_t i0 = 0; i0 < d0; ++i0) \ + { \ + const SCALAR_T *in_base = input + (size_t)i0 * in_stride0; \ + SCALAR_T *out_base = output + (size_t)i0 * out_stride0; \ + FUNC_NAME##_transpose_2d(in_base, out_base, d1, d2); \ + } \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + \ + static arm_cmsis_nn_status FUNC_NAME##_transpose_swap_last2_4d( \ + const SCALAR_T *input, SCALAR_T *output, int32_t d0, int32_t d1, int32_t d2, int32_t d3) \ + { \ + const size_t in_stride0 = (size_t)d1 * (size_t)d2 * (size_t)d3; \ + const size_t in_stride1 = (size_t)d2 * (size_t)d3; \ + const size_t in_stride2 = (size_t)d3; \ + const size_t out_stride0 = (size_t)d1 * (size_t)d3 * (size_t)d2; \ + const size_t out_stride1 = (size_t)d3 * (size_t)d2; \ + const size_t out_stride2 = (size_t)d2; \ + for (int32_t i0 = 0; i0 < d0; ++i0) \ + { \ + for (int32_t i1 = 0; i1 < d1; ++i1) \ + { \ + const SCALAR_T *in_base = input + (size_t)i0 * in_stride0 + (size_t)i1 * in_stride1; \ + SCALAR_T *out_base = output + (size_t)i0 * out_stride0 + (size_t)i1 * out_stride1; \ + (void)in_stride2; \ + (void)out_stride2; \ + FUNC_NAME##_transpose_2d(in_base, out_base, d2, d3); \ + } \ + } \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + \ + arm_cmsis_nn_status FUNC_NAME(const cmsis_nn_context *ctx, \ + const PARAMS_T *params, \ + const cmsis_nn_dims *input_dims, \ + const SCALAR_T *input, \ + const cmsis_nn_dims *output_dims, \ + SCALAR_T *output) \ + { \ + (void)ctx; \ + if (!params || !input_dims || !input || !output_dims || !output) \ + { \ + return ARM_CMSIS_NN_ARG_ERROR; \ + } \ + \ + const int32_t num_dims = params->num_dims; \ + if (num_dims < 1 || num_dims > 4) \ + { \ + return ARM_CMSIS_NN_ARG_ERROR; \ + } \ + \ + const LAYOUT_T layout = params->layout; \ + if (layout != ARM_NN_LAYOUT_NHWC) \ + { \ + return ARM_CMSIS_NN_ARG_ERROR; \ + } \ + \ + int32_t in_all[4]; \ + int32_t out_all[4]; \ + in_all[0] = input_dims->n; \ + in_all[1] = input_dims->h; \ + in_all[2] = input_dims->w; \ + in_all[3] = input_dims->c; \ + out_all[0] = output_dims->n; \ + out_all[1] = output_dims->h; \ + out_all[2] = output_dims->w; \ + out_all[3] = output_dims->c; \ + \ + int32_t in_dims[4] = {1, 1, 1, 1}; \ + int32_t out_dims[4] = {1, 1, 1, 1}; \ + int32_t perm[4] = {0, 1, 2, 3}; \ + for (int32_t i = 0; i < num_dims; ++i) \ + { \ + in_dims[i] = in_all[i]; \ + out_dims[i] = out_all[i]; \ + perm[i] = params->perm[i]; \ + if (perm[i] < 0 || perm[i] >= num_dims) \ + { \ + return ARM_CMSIS_NN_ARG_ERROR; \ + } \ + } \ + \ + if (num_dims == 1) \ + { \ + size_t elems = (size_t)in_dims[0]; \ + FUNC_NAME##_copy_elems(input, output, elems); \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + \ + if (num_dims == 2) \ + { \ + if (perm[0] == 0 && perm[1] == 1) \ + { \ + size_t elems = (size_t)in_dims[0] * (size_t)in_dims[1]; \ + FUNC_NAME##_copy_elems(input, output, elems); \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + if (perm[0] == 1 && perm[1] == 0) \ + { \ + return FUNC_NAME##_transpose_2d(input, output, in_dims[0], in_dims[1]); \ + } \ + } \ + \ + if (num_dims == 3 && perm[0] == 0 && perm[1] == 2 && perm[2] == 1) \ + { \ + return FUNC_NAME##_transpose_swap_last2_3d(input, output, in_dims[0], in_dims[1], in_dims[2]); \ + } \ + \ + if (num_dims == 4 && perm[0] == 0 && perm[1] == 1 && perm[2] == 3 && perm[3] == 2) \ + { \ + return FUNC_NAME##_transpose_swap_last2_4d(input, output, in_dims[0], in_dims[1], in_dims[2], in_dims[3]); \ + } \ + \ + size_t in_strides[4] = {0, 0, 0, 0}; \ + size_t out_strides[4] = {0, 0, 0, 0}; \ + in_strides[num_dims - 1] = 1; \ + out_strides[num_dims - 1] = 1; \ + for (int32_t i = num_dims - 2; i >= 0; --i) \ + { \ + in_strides[i] = in_strides[i + 1] * (size_t)in_dims[i + 1]; \ + out_strides[i] = out_strides[i + 1] * (size_t)out_dims[i + 1]; \ + } \ + \ + int32_t identity = 1; \ + int32_t inv_perm[4] = {0, 1, 2, 3}; \ + for (int32_t i = 0; i < num_dims; ++i) \ + { \ + identity &= (perm[i] == i); \ + inv_perm[perm[i]] = i; \ + } \ + if (identity) \ + { \ + size_t elems = 1; \ + for (int32_t i = 0; i < num_dims; ++i) \ + { \ + elems *= (size_t)in_dims[i]; \ + } \ + FUNC_NAME##_copy_elems(input, output, elems); \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + \ + if (num_dims == 3) \ + { \ + const size_t out_stride0 = out_strides[inv_perm[0]]; \ + const size_t out_stride1 = out_strides[inv_perm[1]]; \ + const size_t out_stride2 = out_strides[inv_perm[2]]; \ + for (int32_t i0 = 0; i0 < in_dims[0]; ++i0) \ + { \ + const SCALAR_T *in0 = input + (size_t)i0 * in_strides[0]; \ + SCALAR_T *out0 = output + (size_t)i0 * out_stride0; \ + for (int32_t i1 = 0; i1 < in_dims[1]; ++i1) \ + { \ + const SCALAR_T *in1 = in0 + (size_t)i1 * in_strides[1]; \ + SCALAR_T *out1 = out0 + (size_t)i1 * out_stride1; \ + for (int32_t i2 = 0; i2 < in_dims[2]; ++i2) \ + { \ + out1[(size_t)i2 * out_stride2] = in1[i2]; \ + } \ + } \ + } \ + return ARM_CMSIS_NN_SUCCESS; \ + } \ + \ + const size_t out_stride0 = out_strides[inv_perm[0]]; \ + const size_t out_stride1 = out_strides[inv_perm[1]]; \ + const size_t out_stride2 = out_strides[inv_perm[2]]; \ + const size_t out_stride3 = out_strides[inv_perm[3]]; \ + for (int32_t i0 = 0; i0 < in_dims[0]; ++i0) \ + { \ + const SCALAR_T *in0 = input + (size_t)i0 * in_strides[0]; \ + SCALAR_T *out0 = output + (size_t)i0 * out_stride0; \ + for (int32_t i1 = 0; i1 < in_dims[1]; ++i1) \ + { \ + const SCALAR_T *in1 = in0 + (size_t)i1 * in_strides[1]; \ + SCALAR_T *out1 = out0 + (size_t)i1 * out_stride1; \ + for (int32_t i2 = 0; i2 < in_dims[2]; ++i2) \ + { \ + const SCALAR_T *in2 = in1 + (size_t)i2 * in_strides[2]; \ + SCALAR_T *out2 = out1 + (size_t)i2 * out_stride2; \ + for (int32_t i3 = 0; i3 < in_dims[3]; ++i3) \ + { \ + out2[(size_t)i3 * out_stride3] = in2[i3]; \ + } \ + } \ + } \ + } \ + return ARM_CMSIS_NN_SUCCESS; \ + } + +#endif /* ARM_TRANSPOSE_COMMON_H */ diff --git a/Include/arm_nn_math_types.h b/Include/arm_nn_math_types.h index 5134fe1a..227a11a3 100644 --- a/Include/arm_nn_math_types.h +++ b/Include/arm_nn_math_types.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates + * SPDX-FileCopyrightText: Copyright 2010-2023, 2026 Arm Limited and/or its affiliates * * SPDX-License-Identifier: Apache-2.0 * @@ -21,8 +21,8 @@ * Title: arm_nn_math_types.h * Description: Compiler include and basic types * - * $Date: 15 August 2023 - * $Revision: V.1.3.3 + * $Date: 2 April 2026 + * $Revision: V.1.4.0 * * Target : Arm(R) M-Profile Architecture * -------------------------------------------------------------------- */ @@ -32,9 +32,16 @@ #define ARM_NN_MATH_TYPES_H #include +#include #include #include +#include "Internal/arm_nn_config.h" + +#if ARM_NN_FLOAT_API_ENABLED + #include "arm_nn_math_types_flt.h" +#endif + #ifdef __cplusplus extern "C" { #endif @@ -58,6 +65,49 @@ extern "C" { #endif #endif +/* + * Align floating-point Helium feature defines with CMSIS-DSP style while + * preserving the current CMSIS-NN integer behavior where ARM_MATH_MVEI + * remains available even when ARM_MATH_AUTOVECTORIZE is defined. + */ +#if !defined(ARM_MATH_AUTOVECTORIZE) + #if defined(__ARM_FEATURE_MVE) + #if (__ARM_FEATURE_MVE & 2) + #ifndef ARM_MATH_MVEF + #define ARM_MATH_MVEF 1 + #endif + #ifndef ARM_MATH_MVE_FLOAT16 + #define ARM_MATH_MVE_FLOAT16 1 + #endif + #endif + #endif +#endif + +#if defined(ARM_MATH_HELIUM) + #ifndef ARM_MATH_MVEF + #define ARM_MATH_MVEF 1 + #endif + #ifndef ARM_MATH_MVEI + #define ARM_MATH_MVEI 1 + #endif + #ifndef ARM_MATH_MVE_FLOAT16 + #define ARM_MATH_MVE_FLOAT16 1 + #endif +#endif + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + /* + * Float16 MVE gather intrinsics use 16-bit per-lane offsets. For a regular + * strided gather, the furthest lane touches (lanes - 1) * stride, so the + * stride must fit within UINT16_MAX / (lanes - 1). + */ + #define ARM_NN_MVE_F16_GATHER_OFFSET_MAX ((size_t)UINT16_MAX) + #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE(_lanes) (ARM_NN_MVE_F16_GATHER_OFFSET_MAX / ((size_t)(_lanes)-1U)) + #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_4 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(4U) + #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(8U) + #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_16 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(16U) +#endif + /** * * @brief Limits macros diff --git a/Include/arm_nn_math_types_flt.h b/Include/arm_nn_math_types_flt.h new file mode 100644 index 00000000..53223e2b --- /dev/null +++ b/Include/arm_nn_math_types_flt.h @@ -0,0 +1,105 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_math_types_flt.h + * Description: Public math type extensions for CMSIS-NN float APIs + * + * $Date: 17 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_NN_MATH_TYPES_FLT_H +#define ARM_NN_MATH_TYPES_FLT_H + +#ifndef ARM_NN_MATH_TYPES_H + #include "arm_nn_math_types.h" +#endif + +#if ARM_NN_FLOAT_API_ENABLED && defined(__ARM_FEATURE_MVE) && (__ARM_FEATURE_MVE & 2) + #include +#endif + +#if ARM_NN_FLOAT_API_ENABLED +/** + * @brief 32-bit floating-point type definition for CMSIS-NN float extensions. + */ +typedef float float32_t; +#endif + +#if ARM_NN_FLOAT_API_ENABLED + /** + * @brief Largest finite float32 value representable by the toolchain. + */ + #define ARM_NN_F32_FINITE_MAX ((float32_t)__FLT_MAX__) + + /** + * @brief Lowest finite float32 value representable by the toolchain. + */ + #define ARM_NN_F32_FINITE_LOWEST (-ARM_NN_F32_FINITE_MAX) +#endif + +#if ARM_NN_ENABLE_F16 + + /* + * Align float16 availability with CMSIS float16 usage. + * When MVE float16 is enabled the type is typically provided by arm_mve.h. + * Otherwise fall back to the compiler scalar float16 types when available. + */ + #if !defined(ARM_FLOAT16_SUPPORTED) + #if defined(__ARM_FEATURE_MVE) && (__ARM_FEATURE_MVE & 2) + #define ARM_FLOAT16_SUPPORTED + #elif defined(__ARM_FP16_FORMAT_IEEE) || defined(__ARM_FP16_FORMAT_ALTERNATIVE) || defined(__FLT16_MAX__) + #define ARM_FLOAT16_SUPPORTED + #endif + #endif + + #if !defined(ARM_FLOAT16_SUPPORTED) + #error "ARM_NN_ENABLE_F16 requires toolchain support for float16_t" + #endif + + #if !(defined(__ARM_FEATURE_MVE) && (__ARM_FEATURE_MVE & 2)) + #if defined(__ARM_FP16_FORMAT_IEEE) || defined(__ARM_FP16_FORMAT_ALTERNATIVE) +typedef __fp16 float16_t; + #elif defined(__FLT16_MAX__) +typedef _Float16 float16_t; + #else + #error "ARM_NN_ENABLE_F16 requires toolchain support for float16_t" + #endif + #endif + + /** + * @brief Largest finite float16 value representable by the toolchain. + */ + #if defined(__FLT16_MAX__) + #define ARM_NN_F16_FINITE_MAX ((float16_t)__FLT16_MAX__) + #else + #define ARM_NN_F16_FINITE_MAX ((float16_t)65504.0f) + #endif + + /** + * @brief Lowest finite float16 value representable by the toolchain. + */ + #define ARM_NN_F16_FINITE_LOWEST ((float16_t)-ARM_NN_F16_FINITE_MAX) + +#endif + +#endif /* ARM_NN_MATH_TYPES_FLT_H */ diff --git a/Include/arm_nn_types.h b/Include/arm_nn_types.h index 2846e621..1ca62d4d 100644 --- a/Include/arm_nn_types.h +++ b/Include/arm_nn_types.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates + * SPDX-FileCopyrightText: Copyright 2020-2024, 2026 Arm Limited and/or its affiliates * * SPDX-License-Identifier: Apache-2.0 * @@ -22,8 +22,8 @@ * Description: Public header file to contain the CMSIS-NN structs for the * TensorFlowLite micro compliant functions * - * $Date: 21 Oct 2024 - * $Revision: V.3.5.0 + * $Date: 27 March 2026 + * $Revision: V.3.6.0 * * Target : Arm(R) M-Profile Architecture * -------------------------------------------------------------------- */ @@ -31,6 +31,8 @@ #ifndef ARM_NN_TYPES_H #define ARM_NN_TYPES_H +#include "Internal/arm_nn_config.h" + #include #include @@ -278,4 +280,8 @@ typedef struct * @} // end group genPubTypes */ +#if ARM_NN_FLOAT_API_ENABLED + #include "arm_nn_types_flt.h" +#endif + #endif /* ARM_NN_TYPES_H */ diff --git a/Include/arm_nn_types_flt.h b/Include/arm_nn_types_flt.h new file mode 100644 index 00000000..636292be --- /dev/null +++ b/Include/arm_nn_types_flt.h @@ -0,0 +1,432 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_types_flt.h + * Description: Public type extensions for CMSIS-NN float APIs + * + * $Date: 17 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_NN_TYPES_FLT_H +#define ARM_NN_TYPES_FLT_H + +#ifndef ARM_NN_TYPES_H + #include "arm_nn_types.h" +#endif + +#if !ARM_NN_ENABLE_F32 && !ARM_NN_ENABLE_F16 + #ifndef __DOXYGEN__ + #error \ + "CMSIS-NN float headers require ARM_NN_ENABLE_F32 or ARM_NN_ENABLE_F16. Prefer including the umbrella CMSIS-NN headers and enabling float support in the build configuration." + #endif +#endif + +#include "arm_nn_math_types_flt.h" + +#ifndef ARM_NN_STATIC_ASSERT + #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) + #define ARM_NN_STATIC_ASSERT(cond, msg) _Static_assert(cond, #msg) + #else + #define ARM_NN_STATIC_ASSERT_GLUE(a, b) a##b + #define ARM_NN_STATIC_ASSERT_XGLUE(a, b) ARM_NN_STATIC_ASSERT_GLUE(a, b) + #define ARM_NN_STATIC_ASSERT(cond, msg) \ + typedef char ARM_NN_STATIC_ASSERT_XGLUE(arm_nn_static_assert_, __LINE__)[(cond) ? 1 : -1] + #endif +#endif + +/** + * @addtogroup genPubTypes + * @{ + */ + +/** + * @brief Tensor layout selector for floating-point APIs. + * + * Float public APIs currently accept NHWC layout only. + */ +typedef enum +{ + ARM_NN_LAYOUT_NHWC = 0, /**< Tensor dimensions are ordered as [N, H, W, C]. */ +} arm_nn_tensor_layout; + +/** + * @brief Activation selector for floating-point operator APIs. + * + * Numeric values intentionally live in a dedicated floating-point range to + * avoid overlap with the legacy integer public activation enum. + */ +typedef enum +{ + ARM_NN_FLT_ACT_NONE = 32, /**< Identity activation function. */ + ARM_NN_FLT_ACT_SIGMOID = 33, /**< Sigmoid activation function. */ + ARM_NN_FLT_ACT_TANH = 34, /**< Hyperbolic tangent activation function. */ + ARM_NN_FLT_ACT_RELU = 35, /**< ReLU activation function. */ + ARM_NN_FLT_ACT_RELU6 = 36, /**< ReLU6 activation function. */ + ARM_NN_FLT_ACT_HARDSWISH = 37, /**< Hard-swish activation function. */ + ARM_NN_FLT_ACT_LEAKY_RELU = 38 /**< Leaky ReLU activation function. */ +} arm_nn_activation_type_flt; + +ARM_NN_STATIC_ASSERT(((int)ARM_TANH) < ((int)ARM_NN_FLT_ACT_NONE), + integer_activation_enum_must_not_overlap_float_activation_enum); + +/** + * @brief Depthwise kernel storage layout selector for floating-point kernels. + * + * Public float depthwise entry points currently use KC storage (`[k][c]`). + * + */ +typedef enum +{ + ARM_NN_DW_KERNEL_KC = 0, /**< Depthwise kernel stored as `[kernel][channel]`. */ + ARM_NN_DW_KERNEL_CK = 1 /**< Depthwise kernel stored as `[channel][kernel]`. */ +} arm_nn_dw_kernel_layout_f32; + +/** + * @brief Weight storage format selector for floating-point operators. + * + * This enum allows frameworks to describe whether weights are provided in the + * standard public operator layout or in a backend-specific packed layout. + * + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED` matches the packed RHS layout consumed by + * `arm_nn_mat_mult_nt_n_packed_f16/f32`. + * + * The packed NTxN layout exists because MVE kernels typically perform best + * when output-channel blocks can be loaded contiguously. With the standard + * `NT x T` formulation, vectorizing over output channels tends to require + * gather-load accesses to the RHS, which is less efficient than a packed non-transposed RHS layout. + * + * For operators that support `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED`, supplying + * offline-repacked constant weights in this layout is therefore generally the + * preferred way to achieve the best MVE performance. + */ +typedef enum +{ + ARM_NN_WEIGHT_FORMAT_STANDARD = 0, /**< Standard public operator layout. */ + ARM_NN_WEIGHT_FORMAT_NT_N_PACKED = 1, /**< Packed `[K][N-block]` layout for NTxN matmul helpers. */ +} arm_nn_weight_format_flt; + +#if ARM_NN_ENABLE_F32 + +/** + * @brief Activation clamp range for floating-point operators. + */ +typedef struct +{ + float32_t min; /**< Minimum value used to clamp the result. */ + float32_t max; /**< Maximum value used to clamp the result. */ +} cmsis_nn_activation_f32; + +/** + * @brief Convolution parameters for float32 operators. + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt weight_format; /**< Filter storage format. */ +} cmsis_nn_conv_params_f32; + +/** + * @brief Transpose convolution parameters for float32 operators. + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile padding_offsets; /**< Output padding adjustment for transpose convolution. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ +} cmsis_nn_transpose_conv_params_f32; + +/** + * @brief Depthwise convolution parameters for float32 operators. + */ +typedef struct +{ + int32_t ch_mult; /**< Channel multiplier. `ch_mult * in_ch = out_ch`. */ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ +} cmsis_nn_dw_conv_params_f32; + +/** + * @brief Pooling parameters for float32 operators. + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ +} cmsis_nn_pool_params_f32; + +/** + * @brief Fully connected layer parameters for float32 operators. + */ +typedef struct +{ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt weight_format; /**< Weight storage format. */ +} cmsis_nn_fc_params_f32; + +/** + * @brief Batched matrix multiplication parameters for float32 operators. + */ +typedef struct +{ + const bool adj_x; /**< True when the left-hand-side operand is stored transposed. */ + const bool adj_y; /**< True when the right-hand-side operand is stored transposed. */ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt rhs_format; /**< Right-hand-side operand storage format. + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED` is currently supported only when + * `adj_x == false` and `adj_y == false`. + */ +} cmsis_nn_bmm_params_f32; + +/** + * @brief Elementwise operator parameters for float32 operators. + */ +typedef struct +{ + cmsis_nn_activation_f32 activation; /**< Output activation clamp range. */ +} cmsis_nn_ew_params_f32; + +/** + * @brief Transpose parameters for float32 operators. + */ +typedef struct +{ + int32_t num_dims; /**< Number of active dimensions in the permutation. */ + int32_t perm[4]; /**< Permutation indices. */ + arm_nn_tensor_layout layout; /**< Layout convention used to interpret tensor dimensions. */ +} cmsis_nn_transpose_params_f32; + +/** + * @brief Singular value decomposition filter parameters for float32 operators. + */ +typedef struct +{ + int32_t rank; /**< SVDF rank. */ + cmsis_nn_activation_f32 input_activation; /**< Clamp range applied after the input projection. */ + cmsis_nn_activation_f32 output_activation; /**< Clamp range applied to the final output. */ +} cmsis_nn_svdf_params_f32; + +/** + * @brief Read-only weights and bias metadata for one float32 LSTM gate. + */ +typedef struct +{ + const float32_t *input_weights; /**< Input-to-gate weight matrix. */ + const float32_t *hidden_weights; /**< Hidden-state-to-gate weight matrix. */ + const float32_t *bias; /**< Optional gate bias vector. */ + arm_nn_activation_type_flt activation_type; /**< Gate activation selector. */ +} cmsis_nn_lstm_gate_f32; + +/** + * @brief Parameters for a unidirectional float32 LSTM layer. + */ +typedef struct +{ + int32_t time_major; /**< Non-zero when input/output tensors are time-major. */ + int32_t batch_size; /**< Batch size processed per invocation. */ + int32_t time_steps; /**< Number of time steps processed per invocation. */ + int32_t input_size; /**< Input feature size per time step. */ + int32_t hidden_size; /**< Hidden-state size. */ + float32_t cell_clip; /**< Optional cell-state clip value. */ + + cmsis_nn_lstm_gate_f32 forget_gate; /**< Forget gate weights and activation. */ + cmsis_nn_lstm_gate_f32 input_gate; /**< Input gate weights and activation. */ + cmsis_nn_lstm_gate_f32 cell_gate; /**< Cell-update gate weights and activation. */ + cmsis_nn_lstm_gate_f32 output_gate; /**< Output gate weights and activation. */ +} cmsis_nn_lstm_params_f32; + +/** + * @brief Scratch and mutable state buffers for a float32 LSTM invocation. + */ +typedef struct +{ + float32_t *temp1; /**< Temporary buffer used by matrix and gate computations. */ + float32_t *temp2; /**< Temporary buffer used by matrix and gate computations. */ + float32_t *cell_state; /**< Mutable cell-state buffer. */ +} cmsis_nn_lstm_context_f32; + +#endif + +#if ARM_NN_ENABLE_F16 + +typedef arm_nn_dw_kernel_layout_f32 arm_nn_dw_kernel_layout_f16; + +/** + * @copydoc cmsis_nn_activation_f32 + */ +typedef struct +{ + float16_t min; /**< Minimum value used to clamp the result. */ + float16_t max; /**< Maximum value used to clamp the result. */ +} cmsis_nn_activation_f16; + +/** + * @copydoc cmsis_nn_conv_params_f32 + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt weight_format; /**< Filter storage format. */ +} cmsis_nn_conv_params_f16; + +/** + * @copydoc cmsis_nn_transpose_conv_params_f32 + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile padding_offsets; /**< Output padding adjustment for transpose convolution. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ +} cmsis_nn_transpose_conv_params_f16; + +/** + * @copydoc cmsis_nn_dw_conv_params_f32 + */ +typedef struct +{ + int32_t ch_mult; /**< Channel multiplier. `ch_mult * in_ch = out_ch`. */ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_tile dilation; /**< Spatial dilation. */ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ +} cmsis_nn_dw_conv_params_f16; + +/** + * @copydoc cmsis_nn_pool_params_f32 + */ +typedef struct +{ + cmsis_nn_tile stride; /**< Spatial stride. */ + cmsis_nn_tile padding; /**< Spatial zero-padding. */ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ +} cmsis_nn_pool_params_f16; + +/** + * @copydoc cmsis_nn_fc_params_f32 + */ +typedef struct +{ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt weight_format; /**< Weight storage format. */ +} cmsis_nn_fc_params_f16; + +/** + * @copydoc cmsis_nn_bmm_params_f32 + */ +typedef struct +{ + const bool adj_x; /**< True when the left-hand-side operand is stored transposed. */ + const bool adj_y; /**< True when the right-hand-side operand is stored transposed. */ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ + arm_nn_weight_format_flt rhs_format; /**< Right-hand-side operand storage format. + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED` is currently supported only when + * `adj_x == false` and `adj_y == false`. + */ +} cmsis_nn_bmm_params_f16; + +/** + * @copydoc cmsis_nn_ew_params_f32 + */ +typedef struct +{ + cmsis_nn_activation_f16 activation; /**< Output activation clamp range. */ +} cmsis_nn_ew_params_f16; + +/** + * @copydoc cmsis_nn_transpose_params_f32 + */ +typedef struct +{ + int32_t num_dims; /**< Number of active dimensions in the permutation. */ + int32_t perm[4]; /**< Permutation indices. */ + arm_nn_tensor_layout layout; /**< Layout convention used to interpret tensor dimensions. */ +} cmsis_nn_transpose_params_f16; + +/** + * @copydoc cmsis_nn_svdf_params_f32 + */ +typedef struct +{ + int32_t rank; /**< SVDF rank. */ + cmsis_nn_activation_f16 input_activation; /**< Clamp range applied after the input projection. */ + cmsis_nn_activation_f16 output_activation; /**< Clamp range applied to the final output. */ +} cmsis_nn_svdf_params_f16; + +/** + * @copydoc cmsis_nn_lstm_gate_f32 + */ +typedef struct +{ + const float16_t *input_weights; /**< Input-to-gate weight matrix. */ + const float16_t *hidden_weights; /**< Hidden-state-to-gate weight matrix. */ + const float16_t *bias; /**< Optional gate bias vector. */ + arm_nn_activation_type_flt activation_type; /**< Gate activation selector. */ +} cmsis_nn_lstm_gate_f16; + +/** + * @copydoc cmsis_nn_lstm_params_f32 + */ +typedef struct +{ + int32_t time_major; /**< Non-zero when input/output tensors are time-major. */ + int32_t batch_size; /**< Batch size processed per invocation. */ + int32_t time_steps; /**< Number of time steps processed per invocation. */ + int32_t input_size; /**< Input feature size per time step. */ + int32_t hidden_size; /**< Hidden-state size. */ + float16_t cell_clip; /**< Optional cell-state clip value. */ + + cmsis_nn_lstm_gate_f16 forget_gate; /**< Forget gate weights and activation. */ + cmsis_nn_lstm_gate_f16 input_gate; /**< Input gate weights and activation. */ + cmsis_nn_lstm_gate_f16 cell_gate; /**< Cell-update gate weights and activation. */ + cmsis_nn_lstm_gate_f16 output_gate; /**< Output gate weights and activation. */ +} cmsis_nn_lstm_params_f16; + +/** + * @copydoc cmsis_nn_lstm_context_f32 + */ +typedef struct +{ + float16_t *temp1; /**< Temporary buffer used by matrix and gate computations. */ + float16_t *temp2; /**< Temporary buffer used by matrix and gate computations. */ + float16_t *cell_state; /**< Mutable cell-state buffer. */ +} cmsis_nn_lstm_context_f16; + +#endif + +/** + * @} + */ + +#endif /* ARM_NN_TYPES_FLT_H */ diff --git a/Include/arm_nnfunctions.h b/Include/arm_nnfunctions.h index e48c333c..9a7ad47b 100644 --- a/Include/arm_nnfunctions.h +++ b/Include/arm_nnfunctions.h @@ -21,8 +21,8 @@ * Title: arm_nnfunctions.h * Description: Public header file for CMSIS NN Library * - * $Date: 9 Mars 2026 - * $Revision: V.19.0.0 + * $Date: 27 March 2026 + * $Revision: V.19.1.0 * * Target : Arm(R) M-Profile Architecture * -------------------------------------------------------------------- */ @@ -39,6 +39,10 @@ #include "arm_nn_math_types.h" #include "arm_nn_types.h" +#if ARM_NN_FLOAT_API_ENABLED + #include "arm_nnfunctions_flt.h" +#endif + #define USE_INTRINSIC #ifdef __cplusplus diff --git a/Include/arm_nnfunctions_flt.h b/Include/arm_nnfunctions_flt.h new file mode 100644 index 00000000..85dbc961 --- /dev/null +++ b/Include/arm_nnfunctions_flt.h @@ -0,0 +1,1505 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nnfunctions_flt.h + * Description: Public floating-point API extensions for CMSIS-NN + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_NNFUNCTIONS_FLT_H +#define ARM_NNFUNCTIONS_FLT_H + +#include "arm_nn_types_flt.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * Floating-point public APIs. + * Float operators currently use NHWC layout. + */ + +#if ARM_NN_ENABLE_F32 + +/** + * @addtogroup NNConv + * @{ + */ + +/** + * @brief Depthwise convolution, NHWC layout. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] dw_conv_params Depthwise convolution parameters (stride, padding, dilation, channel multiplier and + * activation clamp). + * @param[in] input_dims Input tensor dimensions in NHWC format. + * @param[in] input Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions in NHWC-compatible depthwise format. + * @param[in] kernel Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]. + * @param[in] bias Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions in NHWC format. + * @param[out] output Pointer to the output tensor data. + * + * @note When @p ctx->buf is used for internal kernel repacking, it must be aligned to the element type stored in + * scratch: at least 4-byte aligned for float32_t and, via @copydoc, at least 2-byte aligned for float16_t. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_depthwise_nhwc_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output); + +/** + * @brief Depthwise convolution, dispatch by layout. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] dw_conv_params Depthwise convolution parameters (stride, padding, dilation, channel multiplier and + * activation clamp). + * @param[in] input_dims Input tensor dimensions. Format depends on @p layout. + * @param[in] input Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions. Format depends on @p layout. + * @param[in] kernel Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]. + * @param[in] bias Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. Format depends on @p layout. + * @param[out] output Pointer to the output tensor data. + * @param[in] layout Tensor layout selector. Current float APIs require `ARM_NN_LAYOUT_NHWC`. + * + * @note When @p ctx->buf is used for internal kernel repacking, it must be aligned to the element type stored in + * scratch: at least 4-byte aligned for float32_t and, via @copydoc, at least 2-byte aligned for float16_t. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_depthwise_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_tensor_layout layout); + +/** + * @brief Depthwise convolution wrapper using the CMSIS-NN baseline path. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] dw_conv_params Depthwise convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] input Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] kernel Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]. + * @param[in] bias Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output Pointer to the output tensor data. + * + * @note When @p ctx->buf is used for internal kernel repacking, it must be aligned to the element type stored in + * scratch: at least 4-byte aligned for float32_t and, via @copydoc, at least 2-byte aligned for float16_t. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_depthwise_conv_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output); + +/** + * @brief Get the temporary buffer size required by depthwise convolution. + * + * @param[in] dw_conv_params Depthwise convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] output_dims Output tensor dimensions. + * @param[in] layout Tensor layout selector. + * + * @return Required buffer size in bytes, or 0 when no scratch buffer is needed. + */ +int32_t arm_depthwise_conv_f32_get_buffer_size(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @brief Get the buffer size required by the depthwise convolution wrapper. + */ +int32_t arm_depthwise_conv_wrapper_f32_get_buffer_size(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims); + +/** + * @brief Convolution, NHWC layout. + */ +arm_cmsis_nn_status arm_convolve_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** + * @brief Convolution, dispatch by layout. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] conv_params Convolution parameters (stride, padding, dilation and activation clamp). + * @param[in] input_dims Input tensor dimensions. Format depends on @p layout. + * @param[in] input_data Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions. Format depends on @p layout. + * @param[in] filter_data Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. Format: [C_OUT]. + * @param[in] bias_data Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. Format depends on @p layout. + * @param[out] output_data Pointer to the output tensor data. + * @param[in] layout Tensor layout selector. Current float APIs require `ARM_NN_LAYOUT_NHWC`. + * + * @note When `conv_params->weight_format` is set to + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED`, the matmul-backed convolution + * paths interpret @p filter_data as an already prepacked `NTxN` RHS + * buffer instead of the standard public filter layout. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_convolve_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @brief Convolution wrapper using the CMSIS-NN baseline path. + */ +arm_cmsis_nn_status arm_convolve_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** + * @brief 1x1 convolution, NHWC layout. + */ +arm_cmsis_nn_status arm_convolve_1x1_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** + * @brief 1x1 convolution, dispatch by layout. + * + * @note When `conv_params->weight_format` is set to + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED`, the matmul-backed 1x1 + * convolution paths interpret @p filter_data as an already prepacked + * `NTxN` RHS buffer instead of the standard public filter layout. + */ +arm_cmsis_nn_status arm_convolve_1x1_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @brief 1xN convolution, NHWC layout. + */ +arm_cmsis_nn_status arm_convolve_1_x_n_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** + * @brief 1xN convolution, dispatch by layout. + */ +arm_cmsis_nn_status arm_convolve_1_x_n_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @brief Get the temporary buffer size required by convolution. + * + * @param[in] conv_params Convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] output_dims Output tensor dimensions. + * @param[in] layout Tensor layout selector. + * + * @note When `conv_params->weight_format` is + * `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED`, this still reports only the + * temporary input/im2col scratch requirement. Any offline-packed + * filter storage is expected to be provided by the caller. + * + * @return Required buffer size in bytes, or 0 when no scratch buffer is needed. + */ +int32_t arm_convolve_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @brief Get the buffer size required by the convolution wrapper. + */ +int32_t arm_convolve_wrapper_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims); + +/** + * @brief Get the buffer size required by 1x1 convolution. + * + * @note Returns `0` for the unity-stride no-pack path. For non-unity-stride NHWC + * 1x1 convolution, the returned scratch size enables the packed-tile + GEMM path. + * When `conv_params->weight_format` is `ARM_NN_WEIGHT_FORMAT_NT_N_PACKED`, + * this still excludes the offline-packed filter storage itself. + */ +int32_t arm_convolve_1x1_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @brief Get the buffer size required by 1xN convolution. + */ +int32_t arm_convolve_1_x_n_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** @} */ + +/** + * @addtogroup Pooling + * @{ + */ + +/** + * @brief Max pooling. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] pool_params Pooling parameters (stride, padding and activation clamp). + * @param[in] input_dims Input tensor dimensions. + * @param[in] src Pointer to the input tensor data. + * @param[in] filter_dims Pooling kernel dimensions. + * @param[in] output_dims Output tensor dimensions. + * @param[out] dst Pointer to the output tensor data. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_max_pool_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst); + +/** + * @brief Average pooling. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] pool_params Pooling parameters (stride, padding and activation clamp). + * @param[in] input_dims Input tensor dimensions. + * @param[in] src Pointer to the input tensor data. + * @param[in] filter_dims Pooling kernel dimensions. + * @param[in] output_dims Output tensor dimensions. + * @param[out] dst Pointer to the output tensor data. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_avg_pool_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst); + +/** @} */ + +/** + * @addtogroup Acti + * @{ + */ + +/** + * @brief Elementwise activation. + * + * @param[in] input Pointer to the input samples. + * @param[out] output Pointer to the output samples. + * @param[in] size Number of elements to process. + * @param[in] type Activation selector. + * @param[in] act_param Extra activation parameter. Used for parameterized activations such as leaky ReLU. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_nn_activation_f32(const float32_t *input, + float32_t *output, + int32_t size, + arm_nn_activation_type_flt type, + float32_t act_param); + +/** @} */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +/** + * @brief Elementwise add with optional output clamp. + * + * @param[in] input_1_vect Pointer to the first input vector. + * @param[in] input_2_vect Pointer to the second input vector. + * @param[out] output Pointer to the output vector. + * @param[in] out_activation_min Minimum output clamp value. + * @param[in] out_activation_max Maximum output clamp value. + * @param[in] block_size Number of elements to process. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_elementwise_add_f32(const float32_t *input_1_vect, + const float32_t *input_2_vect, + float32_t *output, + float32_t out_activation_min, + float32_t out_activation_max, + int32_t block_size); + +/** + * @brief Elementwise multiply with optional output clamp. + * + * @param[in] input_1_vect Pointer to the first input vector. + * @param[in] input_2_vect Pointer to the second input vector. + * @param[out] output Pointer to the output vector. + * @param[in] out_activation_min Minimum output clamp value. + * @param[in] out_activation_max Maximum output clamp value. + * @param[in] block_size Number of elements to process. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_elementwise_mul_f32(const float32_t *input_1_vect, + const float32_t *input_2_vect, + float32_t *output, + float32_t out_activation_min, + float32_t out_activation_max, + int32_t block_size); + +/** + * @brief Elementwise minimum. + */ +arm_cmsis_nn_status arm_minimum_f32(const cmsis_nn_context *ctx, + const float32_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float32_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float32_t *output_data, + const cmsis_nn_dims *output_dims); + +/** + * @brief Elementwise maximum. + */ +arm_cmsis_nn_status arm_maximum_f32(const cmsis_nn_context *ctx, + const float32_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float32_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float32_t *output_data, + const cmsis_nn_dims *output_dims); + +/** @} */ + +/** + * @addtogroup FC + * @{ + */ + +/** + * @brief Fully connected layer, NHWC layout. + */ +arm_cmsis_nn_status arm_fully_connected_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output); + +/** + * @brief Fully connected layer, dispatch by layout. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] fc_params Fully connected parameters and activation clamp. + * @param[in] input_dims Input tensor dimensions. + * @param[in] input Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] kernel Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. + * @param[in] bias Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output Pointer to the output tensor data. + * @param[in] layout Tensor layout selector. Current float APIs require `ARM_NN_LAYOUT_NHWC`. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_fully_connected_f32(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_tensor_layout layout); + +/** + * @brief Get the temporary buffer size required by the fully connected layer. + * + * @param[in] fc_params Fully connected parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] output_dims Output tensor dimensions. + * @param[in] layout Tensor layout selector. + * + * @return Required buffer size in bytes, or 0 when no scratch buffer is needed. + */ +int32_t arm_fully_connected_f32_get_buffer_size(const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** @} */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/** + * @brief Transpose a floating-point tensor. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] params Transpose parameters, including permutation and layout information. + * @param[in] input_dims Input tensor dimensions. + * @param[in] input Pointer to the input tensor data. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output Pointer to the output tensor data. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_transpose_f32(const cmsis_nn_context *ctx, + const cmsis_nn_transpose_params_f32 *params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *output_dims, + float32_t *output); + +/** + * @brief Concatenate tensors along the X axis. + */ +void arm_concatenation_f32_x(const float32_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float32_t *output, + int32_t output_x, + uint32_t offset_x); + +/** + * @brief Concatenate tensors along the Y axis. + */ +void arm_concatenation_f32_y(const float32_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float32_t *output, + int32_t output_y, + uint32_t offset_y); + +/** + * @brief Concatenate tensors along the Z axis. + */ +void arm_concatenation_f32_z(const float32_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float32_t *output, + int32_t output_z, + uint32_t offset_z); + +/** + * @brief Concatenate tensors along the W axis. + */ +void arm_concatenation_f32_w(const float32_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float32_t *output, + uint32_t offset_w); + +/** @} */ + +/** + * @addtogroup Pad + * @{ + */ + +/** + * @brief Pad a tensor with a constant value. + */ +arm_cmsis_nn_status arm_pad_f32(const float32_t *input, + float32_t *output, + float32_t pad_value, + const cmsis_nn_dims *input_size, + const cmsis_nn_dims *pre_pad, + const cmsis_nn_dims *post_pad); + +/** @} */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/** + * @brief Apply batch normalization. + */ +arm_cmsis_nn_status arm_batch_norm_f32(const float32_t *input, + float32_t *output, + const float32_t *scale, + const float32_t *bias, + const cmsis_nn_dims *input_dims, + arm_nn_tensor_layout layout); + +/** + * @brief Reshape by copying data without changing element order. + */ +void arm_reshape_f32(const float32_t *input, float32_t *output, uint32_t total_size); + +/** @} */ + +/** + * @addtogroup FC + * @{ + */ + +/** + * @brief Batched matrix multiplication. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in] bmm_params Batch matmul parameters and activation clamp. + * @param[in] input_lhs_dims Left-hand-side input tensor dimensions. + * @param[in] input_lhs Pointer to the left-hand-side input tensor. + * @param[in] input_rhs_dims Right-hand-side input tensor dimensions. + * @param[in] input_rhs Pointer to the right-hand-side input tensor. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output Pointer to the output tensor. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_batch_matmul_f32(const cmsis_nn_context *ctx, + const cmsis_nn_bmm_params_f32 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const float32_t *input_lhs, + const cmsis_nn_dims *input_rhs_dims, + const float32_t *input_rhs, + const cmsis_nn_dims *output_dims, + float32_t *output); + +/** + * @brief Get the temporary buffer size required by batched matrix multiplication. + * + * @param[in] bmm_params Batch matmul parameters. + * @param[in] input_lhs_dims Left-hand-side input tensor dimensions. + * @param[in] input_rhs_dims Right-hand-side input tensor dimensions. + * @param[in] output_dims Output tensor dimensions. + * + * @return Required buffer size in bytes, or 0 when no scratch buffer is needed. + */ +int32_t arm_batch_matmul_f32_get_buffer_size(const cmsis_nn_bmm_params_f32 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const cmsis_nn_dims *input_rhs_dims, + const cmsis_nn_dims *output_dims); + +/** @} */ + +/** + * @addtogroup NNConv + * @{ + */ + +/** + * @brief Transpose convolution wrapper using the CMSIS-NN baseline path. + */ +arm_cmsis_nn_status arm_transpose_conv_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @brief Transpose convolution, NHWC layout. + */ +arm_cmsis_nn_status arm_transpose_conv_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** + * @brief Transpose convolution, dispatch by layout. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in,out] output_ctx Output accumulation context for helper implementations. + * @param[in] transpose_conv_params Transpose convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] input_data Pointer to the input tensor data. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] filter_data Pointer to the filter tensor data. + * @param[in] bias_dims Bias tensor dimensions. + * @param[in] bias_data Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output_data Pointer to the output tensor data. + * @param[in] layout Tensor layout selector. Current float APIs require `ARM_NN_LAYOUT_NHWC`. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_transpose_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @brief Get the temporary buffer size required by transpose convolution. + * + * @param[in] transpose_conv_params Transpose convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] filter_dims Filter tensor dimensions. + * @param[in] out_dims Output tensor dimensions. + * + * @return Required buffer size in bytes, or 0 when no scratch buffer is needed. + */ +int32_t arm_transpose_conv_f32_get_buffer_size(const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *out_dims); + +/** + * @brief Get the reverse-convolution workspace size used by transpose convolution helpers. + * + * @param[in] transpose_conv_params Transpose convolution parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] filter_dims Filter tensor dimensions. + * + * @return Required buffer size in bytes, or 0 when no reverse-convolution buffer is needed. + */ +int32_t +arm_transpose_conv_f32_get_reverse_conv_buffer_size(const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims); + +/** @} */ + +/** + * @addtogroup SVDF + * @{ + */ + +/** + * @brief Stateful singular value decomposition filter. + * + * @param[in,out] ctx Function context that may hold a temporary scratch buffer. + * @param[in,out] input_ctx Optional input staging context. + * @param[in,out] output_ctx Optional output staging context. + * @param[in] svdf_params SVDF operator parameters. + * @param[in] input_dims Input tensor dimensions. + * @param[in] input_data Pointer to the input tensor data. + * @param[in] state_dims State tensor dimensions. + * @param[in,out] state_data Pointer to the mutable state tensor. + * @param[in] weights_feature_dims Feature-weight tensor dimensions. + * @param[in] weights_feature_data Pointer to the feature-weight tensor. + * @param[in] weights_time_dims Time-weight tensor dimensions. + * @param[in] weights_time_data Pointer to the time-weight tensor. + * @param[in] bias_dims Bias tensor dimensions. + * @param[in] bias_data Optional bias tensor data. + * @param[in] output_dims Output tensor dimensions. + * @param[out] output_data Pointer to the output tensor data. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_svdf_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *input_ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_svdf_params_f32 *svdf_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *state_dims, + float32_t *state_data, + const cmsis_nn_dims *weights_feature_dims, + const float32_t *weights_feature_data, + const cmsis_nn_dims *weights_time_dims, + const float32_t *weights_time_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data); + +/** @} */ + +/** + * @addtogroup LSTM + * @{ + */ + +/** + * @brief Unidirectional LSTM inference. + * + * @param[in] input Pointer to the input sequence tensor. + * @param[out] output Pointer to the output sequence tensor. + * @param[in] params LSTM parameters and weights. + * @param[in,out] buffers Mutable LSTM scratch and state buffers. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_lstm_unidirectional_f32(const float32_t *input, + float32_t *output, + const cmsis_nn_lstm_params_f32 *params, + cmsis_nn_lstm_context_f32 *buffers); + +/** @} */ + +/** + * @addtogroup Softmax + * @{ + */ + +/** + * @brief Softmax using the float-native API signature. + * + * @param[in] input Pointer to the input matrix stored as @p num_rows rows of @p row_size values. + * @param[in] num_rows Number of rows in the input matrix. + * @param[in] row_size Number of columns per row. + * @param[out] output Pointer to the output matrix. + * + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_softmax_f32(const float32_t *input, int32_t num_rows, int32_t row_size, float32_t *output); + + /** @} */ + +#endif /* ARM_NN_ENABLE_F32 */ + +#if ARM_NN_ENABLE_F16 + +/** + * @addtogroup NNConv + * @{ + */ + +/** + * @copydoc arm_depthwise_nhwc_conv_f32 + */ +arm_cmsis_nn_status arm_depthwise_nhwc_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output); + +/** + * @copydoc arm_depthwise_conv_f32 + */ +arm_cmsis_nn_status arm_depthwise_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_depthwise_conv_wrapper_f32 + */ +arm_cmsis_nn_status arm_depthwise_conv_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output); + +/** + * @copydoc arm_depthwise_conv_f32_get_buffer_size + */ +int32_t arm_depthwise_conv_f16_get_buffer_size(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_depthwise_conv_wrapper_f32_get_buffer_size + */ +int32_t arm_depthwise_conv_wrapper_f16_get_buffer_size(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims); + +/** + * @copydoc arm_convolve_nhwc_f32 + */ +arm_cmsis_nn_status arm_convolve_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** + * @copydoc arm_convolve_f32 + */ +arm_cmsis_nn_status arm_convolve_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_convolve_wrapper_f32 + */ +arm_cmsis_nn_status arm_convolve_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** + * @copydoc arm_convolve_1x1_nhwc_f32 + */ +arm_cmsis_nn_status arm_convolve_1x1_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** + * @copydoc arm_convolve_1x1_f32 + */ +arm_cmsis_nn_status arm_convolve_1x1_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_convolve_1_x_n_nhwc_f32 + */ +arm_cmsis_nn_status arm_convolve_1_x_n_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** + * @copydoc arm_convolve_1_x_n_f32 + */ +arm_cmsis_nn_status arm_convolve_1_x_n_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_convolve_f32_get_buffer_size + */ +int32_t arm_convolve_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_convolve_wrapper_f32_get_buffer_size + */ +int32_t arm_convolve_wrapper_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims); + +/** + * @copydoc arm_convolve_1x1_f32_get_buffer_size + */ +int32_t arm_convolve_1x1_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_convolve_1_x_n_f32_get_buffer_size + */ +int32_t arm_convolve_1_x_n_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** @} */ + +/** + * @addtogroup Pooling + * @{ + */ + +/** + * @copydoc arm_max_pool_f32 + */ +arm_cmsis_nn_status arm_max_pool_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst); + +/** + * @copydoc arm_avg_pool_f32 + */ +arm_cmsis_nn_status arm_avg_pool_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst); + +/** @} */ + +/** + * @addtogroup Acti + * @{ + */ + +/** + * @copydoc arm_nn_activation_f32 + */ +arm_cmsis_nn_status arm_nn_activation_f16(const float16_t *input, + float16_t *output, + int32_t size, + arm_nn_activation_type_flt type, + float16_t act_param); + +/** @} */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +/** + * @copydoc arm_elementwise_add_f32 + */ +arm_cmsis_nn_status arm_elementwise_add_f16(const float16_t *input_1_vect, + const float16_t *input_2_vect, + float16_t *output, + float16_t out_activation_min, + float16_t out_activation_max, + int32_t block_size); + +/** + * @copydoc arm_elementwise_mul_f32 + */ +arm_cmsis_nn_status arm_elementwise_mul_f16(const float16_t *input_1_vect, + const float16_t *input_2_vect, + float16_t *output, + float16_t out_activation_min, + float16_t out_activation_max, + int32_t block_size); + +/** + * @copydoc arm_minimum_f32 + */ +arm_cmsis_nn_status arm_minimum_f16(const cmsis_nn_context *ctx, + const float16_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float16_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float16_t *output_data, + const cmsis_nn_dims *output_dims); + +/** + * @copydoc arm_maximum_f32 + */ +arm_cmsis_nn_status arm_maximum_f16(const cmsis_nn_context *ctx, + const float16_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float16_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float16_t *output_data, + const cmsis_nn_dims *output_dims); + +/** @} */ + +/** + * @addtogroup FC + * @{ + */ + +/** + * @copydoc arm_fully_connected_nhwc_f32 + */ +arm_cmsis_nn_status arm_fully_connected_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output); + +/** + * @copydoc arm_fully_connected_f32 + */ +arm_cmsis_nn_status arm_fully_connected_f16(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_fully_connected_f32_get_buffer_size + */ +int32_t arm_fully_connected_f16_get_buffer_size(const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout); + +/** @} */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/** + * @copydoc arm_transpose_f32 + */ +arm_cmsis_nn_status arm_transpose_f16(const cmsis_nn_context *ctx, + const cmsis_nn_transpose_params_f16 *params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *output_dims, + float16_t *output); + +/** + * @brief arm concatenation f16 x + */ +void arm_concatenation_f16_x(const float16_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float16_t *output, + int32_t output_x, + uint32_t offset_x); + +/** + * @brief arm concatenation f16 y + */ +void arm_concatenation_f16_y(const float16_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float16_t *output, + int32_t output_y, + uint32_t offset_y); + +/** + * @brief arm concatenation f16 z + */ +void arm_concatenation_f16_z(const float16_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float16_t *output, + int32_t output_z, + uint32_t offset_z); + +/** + * @brief arm concatenation f16 w + */ +void arm_concatenation_f16_w(const float16_t *input, + int32_t input_x, + int32_t input_y, + int32_t input_z, + int32_t input_w, + float16_t *output, + uint32_t offset_w); + +/** @} */ + +/** + * @addtogroup Pad + * @{ + */ + +/** + * @copydoc arm_pad_f32 + */ +arm_cmsis_nn_status arm_pad_f16(const float16_t *input, + float16_t *output, + float16_t pad_value, + const cmsis_nn_dims *input_size, + const cmsis_nn_dims *pre_pad, + const cmsis_nn_dims *post_pad); + +/** @} */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/** + * @copydoc arm_batch_norm_f32 + */ +arm_cmsis_nn_status arm_batch_norm_f16(const float16_t *input, + float16_t *output, + const float16_t *scale, + const float16_t *bias, + const cmsis_nn_dims *input_dims, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_reshape_f32 + */ +void arm_reshape_f16(const float16_t *input, float16_t *output, uint32_t total_size); + +/** @} */ + +/** + * @addtogroup FC + * @{ + */ + +/** + * @copydoc arm_batch_matmul_f32 + */ +arm_cmsis_nn_status arm_batch_matmul_f16(const cmsis_nn_context *ctx, + const cmsis_nn_bmm_params_f16 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const float16_t *input_lhs, + const cmsis_nn_dims *input_rhs_dims, + const float16_t *input_rhs, + const cmsis_nn_dims *output_dims, + float16_t *output); + +/** + * @copydoc arm_batch_matmul_f32_get_buffer_size + */ +int32_t arm_batch_matmul_f16_get_buffer_size(const cmsis_nn_bmm_params_f16 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const cmsis_nn_dims *input_rhs_dims, + const cmsis_nn_dims *output_dims); + +/** @} */ + +/** + * @addtogroup NNConv + * @{ + */ + +/** + * @copydoc arm_transpose_conv_wrapper_f32 + */ +arm_cmsis_nn_status arm_transpose_conv_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_transpose_conv_nhwc_f32 + */ +arm_cmsis_nn_status arm_transpose_conv_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** + * @copydoc arm_transpose_conv_f32 + */ +arm_cmsis_nn_status arm_transpose_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout); + +/** + * @copydoc arm_transpose_conv_f32_get_buffer_size + */ +int32_t arm_transpose_conv_f16_get_buffer_size(const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *out_dims); + +/** + * @copydoc arm_transpose_conv_f32_get_reverse_conv_buffer_size + */ +int32_t +arm_transpose_conv_f16_get_reverse_conv_buffer_size(const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims); + +/** @} */ + +/** + * @addtogroup SVDF + * @{ + */ + +/** + * @copydoc arm_svdf_f32 + */ +arm_cmsis_nn_status arm_svdf_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *input_ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_svdf_params_f16 *svdf_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *state_dims, + float16_t *state_data, + const cmsis_nn_dims *weights_feature_dims, + const float16_t *weights_feature_data, + const cmsis_nn_dims *weights_time_dims, + const float16_t *weights_time_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data); + +/** @} */ + +/** + * @addtogroup LSTM + * @{ + */ + +/** + * @copydoc arm_lstm_unidirectional_f32 + */ +arm_cmsis_nn_status arm_lstm_unidirectional_f16(const float16_t *input, + float16_t *output, + const cmsis_nn_lstm_params_f16 *params, + cmsis_nn_lstm_context_f16 *buffers); + +/** @} */ + +/** + * @addtogroup Softmax + * @{ + */ + +/** + * @copydoc arm_softmax_f32 + */ +arm_cmsis_nn_status arm_softmax_f16(const float16_t *input, int32_t num_rows, int32_t row_size, float16_t *output); + + /** @} */ + +#endif /* ARM_NN_ENABLE_F16 */ + +#ifdef __cplusplus +} +#endif + +#endif /* ARM_NNFUNCTIONS_FLT_H */ diff --git a/Include/arm_nnsupportfunctions.h b/Include/arm_nnsupportfunctions.h index 23d818cb..0b9a0e8d 100644 --- a/Include/arm_nnsupportfunctions.h +++ b/Include/arm_nnsupportfunctions.h @@ -21,8 +21,8 @@ * Title: arm_nnsupportfunctions.h * Description: Public header file of support functions for CMSIS NN Library * - * $Date: 21 May 2026 - * $Revision: V.22.9.1 + * $Date: 15 June 2026 + * $Revision: V.22.11.0 * * Target : Arm(R) M-Profile Architecture * -------------------------------------------------------------------- */ @@ -53,6 +53,7 @@ extern "C" { #define MAX(A, B) ((A) > (B) ? (A) : (B)) #define MIN(A, B) ((A) < (B) ? (A) : (B)) #define CLAMP(x, h, l) MAX(MIN((x), (h)), (l)) +#define ARM_NN_ROUND_UP(x, multiple) ((((x) + (multiple)-1) / (multiple)) * (multiple)) #define REDUCE_MULTIPLIER(_mult) ((_mult < 0x7FFF0000) ? ((_mult + (1 << 15)) >> 16) : 0x7FFF) // Number of channels processed in a block for DW Conv with Int8 weights(MVE) @@ -91,6 +92,10 @@ extern "C" { #define OPTIONAL_RESTRICT_KEYWORD #endif +#if ARM_NN_FLOAT_API_ENABLED + #include "arm_nnsupportfunctions_flt.h" +#endif + /** * @brief definition to pack four 8 bit values. */ diff --git a/Include/arm_nnsupportfunctions_flt.h b/Include/arm_nnsupportfunctions_flt.h new file mode 100755 index 00000000..1e0b40c2 --- /dev/null +++ b/Include/arm_nnsupportfunctions_flt.h @@ -0,0 +1,961 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nnsupportfunctions_flt.h + * Description: Floating-point support API extensions for CMSIS-NN + * + * $Date: 17 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_NNSUPPORTFUNCTIONS_FLT_H +#define ARM_NNSUPPORTFUNCTIONS_FLT_H + +#include "Internal/arm_nn_compiler.h" +#include "arm_nn_types_flt.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* Floating-point internal support APIs. */ + +/** + * @addtogroup groupSupport + * @{ + */ + +#if ARM_NN_ENABLE_F32 + +/** + * @brief Polynomial coefficients used by the float32 MVE exp approximation. + */ +extern const float32_t arm_nn_exp_poly_coeffs_f32[8]; + +/** + * @brief LUT for `2^(i/256)` used by the float32 LUT softmax approximation. + * + * Stores 257 samples for `i = 0..256` so interpolation can safely read + * `lut[idx + 1]` while indexing the 256 fractional segments. + */ +extern const float32_t arm_nn_exp2_lut256_f32[257]; + +__STATIC_INLINE int32_t arm_nn_softmax_floor_to_int_f32(float32_t x) +{ + const int32_t n = (int32_t)x; + return (x < (float32_t)n) ? (n - 1) : n; +} + +__STATIC_INLINE float32_t arm_nn_softmax_fp32_from_bits(uint32_t bits) +{ + union + { + uint32_t u; + float32_t f; + } cvt; + cvt.u = bits; + return cvt.f; +} + +__STATIC_INLINE float32_t arm_nn_softmax_exp2i_f32(int32_t n) +{ + const int32_t float32_min_normal_exponent = -126; + const int32_t float32_max_finite_exponent = 127; + const int32_t float32_exponent_bias = 127; + const int32_t float32_mantissa_bits = 23; + + n = CLAMP(n, float32_max_finite_exponent, float32_min_normal_exponent); + return arm_nn_softmax_fp32_from_bits((uint32_t)(n + float32_exponent_bias) << float32_mantissa_bits); +} + +/* + * Taylor/Estrin exp approximation on r in [-ln2/2, ln2/2]. + * Coefficients come from the Maclaurin series of exp(r): + * exp(r) ~= 1 + r + r^2/2! + r^3/3! + r^4/4! + r^5/5! + r^6/6! + * Grouped via Estrin to reduce dependency depth: + * p = (1 + r) + r^2*(1/2 + r/6) + r^4*(1/24 + r/120) + r^6*(1/720) + * + * Range reduction follows: + * x = n * ln(2) + r, exp(x) = exp(r) * 2^n + */ +__STATIC_INLINE float32_t arm_nn_softmax_exp_taylor_f32(float32_t x) +{ + const float32_t max_value = 80.0f; + const float32_t min_value = -80.0f; + const float32_t log2e = 1.44269504088896341f; + const float32_t ln2 = 0.69314718055994531f; + + x = CLAMP(x, max_value, min_value); + + const float32_t t = x * log2e; + const int32_t n = (t >= 0.0f) ? (int32_t)(t + 0.5f) : (int32_t)(t - 0.5f); + const float32_t r = x - (float32_t)n * ln2; + + const float32_t r2 = r * r; + const float32_t r4 = r2 * r2; + const float32_t r6 = r4 * r2; + + const float32_t t0 = 1.0f + r; + const float32_t t1 = 0.5f + (1.0f / 6.0f) * r; + const float32_t t2 = (1.0f / 24.0f) + (1.0f / 120.0f) * r; + const float32_t t3 = (1.0f / 720.0f); + + return (t0 + t1 * r2 + t2 * r4 + t3 * r6) * arm_nn_softmax_exp2i_f32(n); +} + +__STATIC_INLINE float32_t arm_nn_softmax_exp_lut_f32(float32_t x) +{ + const float32_t max_value = 80.0f; + const float32_t min_value = -80.0f; + const float32_t log2e = 1.44269504088896341f; + const float32_t exp2_lut_segments = 256.0f; + const int32_t exp2_lut_max_index = 255; + + x = CLAMP(x, max_value, min_value); + + const float32_t t = x * log2e; + const int32_t n = arm_nn_softmax_floor_to_int_f32(t); + const float32_t f = t - (float32_t)n; + const float32_t idx_f = f * exp2_lut_segments; + int32_t idx = (int32_t)idx_f; + + if (idx < 0) + { + idx = 0; + } + else if (idx > exp2_lut_max_index) + { + idx = exp2_lut_max_index; + } + + const float32_t frac = idx_f - (float32_t)idx; + const float32_t y0 = arm_nn_exp2_lut256_f32[idx]; + const float32_t y1 = arm_nn_exp2_lut256_f32[idx + 1]; + return (y0 + (y1 - y0) * frac) * arm_nn_softmax_exp2i_f32(n); +} + +__STATIC_INLINE float32_t arm_nn_softmax_exp_scalar_f32(float32_t x) +{ + #if defined(ARM_NN_USE_EXP_TAYLOR) + return arm_nn_softmax_exp_taylor_f32(x); + #else + return arm_nn_softmax_exp_lut_f32(x); + #endif +} + +#endif + +#if ARM_NN_ENABLE_F32 + +/** + * @brief LUT for tanh(x) sampled over `x in [0, 4]` for float32 helpers. + * + * Stores 257 samples so interpolation can safely read `lut[idx + 1]` while + * indexing the 256 fractional segments across the interval. + */ +extern const float32_t arm_nn_tanh_lut256_f32[257]; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) +/** + * @brief Reduce a float32 MVE vector with addition. + */ +__STATIC_INLINE float32_t arm_nn_vec_reduce_add_f32(float32x4_t v) +{ + return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); +} + +/** + * @brief MVE float32 exp approximation used by float softmax paths. + */ +__STATIC_INLINE float32x4_t arm_nn_vexpq_poly_mve_f32(float32x4_t x) +{ + const int32x4_t m = vcvtq_s32_f32(vmulq(x, 1.4426950408f)); + const float32x4_t val = vfmsq(x, vcvtq_f32_s32(m), vdupq_n_f32(0.6931471805f)); + + const float32x4_t a = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f32[4]), val, arm_nn_exp_poly_coeffs_f32[0]); + const float32x4_t b = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f32[6]), val, arm_nn_exp_poly_coeffs_f32[2]); + const float32x4_t c = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f32[5]), val, arm_nn_exp_poly_coeffs_f32[1]); + const float32x4_t d = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f32[7]), val, arm_nn_exp_poly_coeffs_f32[3]); + const float32x4_t x2 = vmulq(val, val); + const float32x4_t x4 = vmulq(x2, x2); + float32x4_t poly = vfmaq(vfmaq(a, b, x2), vfmaq(c, d, x2), x4); + + poly = vreinterpretq_f32_s32(vqaddq_s32(vreinterpretq_s32_f32(poly), vqshlq_n_s32(m, 23))); + poly = vdupq_m(poly, 0.0f, vcmpltq(m, -126)); + return poly; +} + #endif + +/** + * @brief Copy a float32 vector. + * @param[out] dst Destination buffer. + * @param[in] src Source buffer. + * @param[in] block_size Number of elements to copy. + */ +__STATIC_FORCEINLINE void +arm_memcpy_f32(float32_t *__RESTRICT dst, const float32_t *__RESTRICT src, uint32_t block_size) +{ + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + __asm volatile(" wlstp.32 lr, %[cnt], 1f \n" + "2: \n" + " vldrw.32 q0, [%[in]], #16 \n" + " vstrw.32 q0, [%[out]], #16 \n" + " letp lr, 2b \n" + "1: \n" + : [in] "+r"(src), [out] "+r"(dst) + : [cnt] "r"(block_size) + : "q0", "memory", "r14"); + #else + __builtin_memcpy(dst, src, (size_t)block_size * sizeof(float32_t)); + #endif +} + +/** + * @brief Set a float32 vector to a constant value. + * @param[out] dst Destination buffer. + * @param[in] val Fill value. + * @param[in] block_size Number of elements to write. + */ +__STATIC_FORCEINLINE void arm_memset_f32(float32_t *__RESTRICT dst, const float32_t val, uint32_t block_size) +{ + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t vec = vdupq_n_f32(val); + uint32_t i = 0; + for (; i + 4U <= block_size; i += 4U) + { + vst1q(dst + i, vec); + } + if (i < block_size) + { + const mve_pred16_t p = vctp32q(block_size - i); + vst1q_p(dst + i, vec, p); + } + #else + for (uint32_t i = 0; i < block_size; ++i) + { + dst[i] = val; + } + #endif +} + +/** + * @brief Specialized NHWC depthwise 1D kernel for `k=3`, `ch_mult=1` (float32). + */ +void arm_nn_depthwise_conv1d_k3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_w); + +/** + * @brief Specialized NHWC depthwise `3x3` kernel (float32, `ch_mult=1`). + */ +void arm_nn_depthwise_conv3x3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float32_t act_min, + float32_t act_max); + +/** + * @brief Generic depthwise helper with packed lhs tiles and transposed rhs layout (float32). + */ +arm_cmsis_nn_status arm_nn_depthwise_conv_nt_t_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT out, + int32_t lhs_rows, + int32_t total_ch, + int32_t row_x_col, + int32_t out_row_stride, + float32_t activation_min, + float32_t activation_max); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=5` (float32). + */ +void arm_nn_conv1d_k5_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=5` (float32, packed weights). + * + * The packed kernel uses the same `NTxN` RHS layout as + * `arm_nn_mat_mult_nt_n_packed_f32`, i.e. `[(5 * in_c)][out_c_block_of_4]`. + */ +void arm_nn_conv1d_k5_packed_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel_packed, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=3` (float32). + */ +void arm_nn_conv1d_k3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=3` (float32, packed weights). + * + * The packed kernel uses the same `NTxN` RHS layout as + * `arm_nn_mat_mult_nt_n_packed_f32`, i.e. `[(3 * in_c)][out_c_block_of_4]`. + */ +void arm_nn_conv1d_k3_packed_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel_packed, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=3`, `s=3` (float32). + */ +void arm_nn_maxpool1d_k3s3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=2`, `s=2` without output clamp (float32). + */ +void arm_nn_maxpool1d_k2s2_nhwc_noclip_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=2`, `s=2` with clamp (float32). + */ +void arm_nn_maxpool1d_k2s2_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w, + float32_t act_min, + float32_t act_max); + +/** + * @brief Matrix multiply with non-transposed lhs and transposed rhs rows (float32). + * + * @param[in] lhs Left-hand matrix stored row-major. + * @param[in] rhs Right-hand matrix stored row-major, one row per output channel. + * @param[in] bias Optional bias vector. + * @param[out] dst Output matrix. + * @param[in] lhs_rows Number of rows in @p lhs. + * @param[in] rhs_rows Number of rows in @p rhs. + * @param[in] rhs_cols Number of columns in @p rhs. + * @param[in] row_address_offset Output row stride, expressed in elements. + * @param[in] activation_min Lower clamp bound. + * @param[in] activation_max Upper clamp bound. + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_t_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float32_t activation_min, + float32_t activation_max); + +/** + * @brief Matrix multiply with non-transposed lhs and packed non-transposed rhs (float32). + * + * @param[in] lhs Left-hand matrix stored row-major with logical shape `[lhs_rows, rhs_cols]`. + * @param[in] rhs_packed Right-hand matrix with logical shape `[rhs_cols, rhs_rows]`, packed in column blocks + * of 4. The final block uses the same packed stride and inactive tail lanes are ignored. + * @param[in] bias Optional bias vector. + * @param[out] dst Output matrix. + * @param[in] lhs_rows Number of rows in @p lhs. + * @param[in] rhs_rows Number of logical output columns in the unpacked rhs matrix. + * @param[in] rhs_cols Shared reduction dimension `K`. + * @param[in] row_address_offset Output row stride, expressed in elements. + * @param[in] activation_min Lower clamp bound. + * @param[in] activation_max Upper clamp bound. + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_n_packed_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs_packed, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float32_t activation_min, + float32_t activation_max); + +/** + * @brief Pack a single convolution patch into one row of a contiguous float32 patch matrix. + * + * Developers familiar with im2row/im2col terminology can think of this as packing one output patch into one row. + */ +void arm_nn_pack_conv_patch_f32(const float32_t *__RESTRICT input, + int32_t in_h, + int32_t in_w, + int32_t in_c, + int32_t kernel_h, + int32_t kernel_w, + int32_t stride_h, + int32_t stride_w, + int32_t pad_h, + int32_t pad_w, + int32_t dilation_h, + int32_t dilation_w, + int32_t out_y, + int32_t out_x, + float32_t pad_value, + float32_t *__RESTRICT patch_row); + +/** + * @brief Specialized softmax helper for a single float32 row of length 2. + */ +void arm_nn_softmax_1x2_f32(const float32_t in[2], float32_t out[2]); + +#endif /* ARM_NN_ENABLE_F32 */ + +#if ARM_NN_ENABLE_F16 + +/** + * @brief Polynomial coefficients used by the float16 MVE exp approximation. + * + * The float16 MVE helper evaluates the polynomial in widened float32 lanes, + * but it uses a dedicated coefficient table to keep the float16 path isolated + * from the float32 feature gate and softmax support stack. + */ +extern const float32_t arm_nn_exp_poly_coeffs_f16[8]; + +/** + * @brief Coefficients used by the float16 tanh rational approximation. + */ +extern const float32_t arm_nn_tanh_approx_coeffs_f16[3]; + +/** + * @brief Quantized binary16 LUT for `2^(i/256)` used by float16 helpers. + * + * Stores 257 samples for `i = 0..256` so interpolation can safely read + * `lut[idx + 1]` while indexing the 256 fractional segments. + */ +extern const uint16_t arm_nn_exp2_lut256_f16[257]; + +/** + * @brief Quantized binary16 LUT for tanh(x) with `x in [0, 4]`. + * + * Stores 257 samples so interpolation can safely read `lut[idx + 1]` while + * indexing the 256 fractional segments across the interval. + */ +extern const uint16_t arm_nn_tanh_lut256_f16[257]; + +__STATIC_INLINE float16_t arm_nn_softmax_fp16_from_bits(uint16_t bits) +{ + union + { + uint16_t u; + float16_t f; + } cvt; + cvt.u = bits; + return cvt.f; +} + +__STATIC_INLINE int32_t arm_nn_softmax_floor_to_int_f16(float16_t x) +{ + const float32_t x_f32 = (float32_t)x; + const int32_t n = (int32_t)x_f32; + return (x_f32 < (float32_t)n) ? (n - 1) : n; +} + +__STATIC_INLINE float16_t arm_nn_softmax_exp2i_f16(int32_t n) +{ + const int32_t float16_min_normal_exponent = -14; + const int32_t float16_max_finite_exponent = 15; + const int32_t float16_exponent_bias = 15; + const int32_t float16_mantissa_bits = 10; + + n = CLAMP(n, float16_max_finite_exponent, float16_min_normal_exponent); + return arm_nn_softmax_fp16_from_bits((uint16_t)((n + float16_exponent_bias) << float16_mantissa_bits)); +} + +/* + * Taylor/Estrin exp approximation for float16 softmax helpers. + * The evaluation uses float32 intermediates to keep the approximation stable, + * but it is fully independent from the float32 softmax support tables. + */ +__STATIC_INLINE float16_t arm_nn_softmax_exp_taylor_f16(float16_t x) +{ + const float32_t max_value = 80.0f; + const float32_t min_value = -80.0f; + const float32_t log2e = 1.44269504088896341f; + const float32_t ln2 = 0.69314718055994531f; + + float32_t x_f32 = (float32_t)x; + x_f32 = CLAMP(x_f32, max_value, min_value); + + const float32_t t = x_f32 * log2e; + const int32_t n = (t >= 0.0f) ? (int32_t)(t + 0.5f) : (int32_t)(t - 0.5f); + const float32_t r = x_f32 - (float32_t)n * ln2; + + const float32_t r2 = r * r; + const float32_t r4 = r2 * r2; + const float32_t r6 = r4 * r2; + + const float32_t t0 = 1.0f + r; + const float32_t t1 = 0.5f + (1.0f / 6.0f) * r; + const float32_t t2 = (1.0f / 24.0f) + (1.0f / 120.0f) * r; + const float32_t t3 = (1.0f / 720.0f); + + const float32_t poly = t0 + t1 * r2 + t2 * r4 + t3 * r6; + return (float16_t)(poly * (float32_t)arm_nn_softmax_exp2i_f16(n)); +} + +__STATIC_INLINE float16_t arm_nn_softmax_exp_lut_f16(float16_t x) +{ + const float32_t max_value = 80.0f; + const float32_t min_value = -80.0f; + const float32_t log2e = 1.44269504088896341f; + const float32_t exp2_lut_segments = 256.0f; + const int32_t exp2_lut_max_index = 255; + + float32_t x_f32 = (float32_t)x; + x_f32 = CLAMP(x_f32, max_value, min_value); + + const float32_t t = x_f32 * log2e; + const int32_t n = arm_nn_softmax_floor_to_int_f16((float16_t)t); + const float32_t f = t - (float32_t)n; + const float32_t idx_f = f * exp2_lut_segments; + int32_t idx = (int32_t)idx_f; + + if (idx < 0) + { + idx = 0; + } + else if (idx > exp2_lut_max_index) + { + idx = exp2_lut_max_index; + } + + const float32_t frac = idx_f - (float32_t)idx; + const float32_t y0 = (float32_t)arm_nn_softmax_fp16_from_bits(arm_nn_exp2_lut256_f16[idx]); + const float32_t y1 = (float32_t)arm_nn_softmax_fp16_from_bits(arm_nn_exp2_lut256_f16[idx + 1]); + return (float16_t)((y0 + (y1 - y0) * frac) * (float32_t)arm_nn_softmax_exp2i_f16(n)); +} + +__STATIC_INLINE float16_t arm_nn_softmax_exp_scalar_f16(float16_t x) +{ + #if defined(ARM_NN_USE_EXP_TAYLOR) + return arm_nn_softmax_exp_taylor_f16(x); + #else + return arm_nn_softmax_exp_lut_f16(x); + #endif +} + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) +/** + * @brief Reduce a float16 MVE vector with addition. + */ +__STATIC_INLINE float16_t arm_nn_vec_reduce_add_f16(float16x8_t in) +{ + float16x8_t tmp_vec = (float16x8_t)vrev32q_s16((int16x8_t)in); + in = vaddq(tmp_vec, in); + tmp_vec = (float16x8_t)vrev64q_s32((int32x4_t)in); + in = vaddq(tmp_vec, in); + return (float16_t)((_Float16)vgetq_lane_f16(in, 0) + (_Float16)vgetq_lane_f16(in, 4)); +} + +/** + * @brief MVE float16 exp approximation used by float softmax paths. + */ +__STATIC_INLINE float16x8_t arm_nn_vexpq_poly_mve_f16(float16x8_t x) +{ + const float32x4_t x_lo = vcvtbq_f32_f16(x); + const float32x4_t x_hi = vcvttq_f32_f16(x); + const int32x4_t m_lo = vcvtq_s32_f32(vmulq(x_lo, 1.4426950408f)); + const int32x4_t m_hi = vcvtq_s32_f32(vmulq(x_hi, 1.4426950408f)); + const float32x4_t val_lo = vfmsq(x_lo, vcvtq_f32_s32(m_lo), vdupq_n_f32(0.6931471805f)); + const float32x4_t val_hi = vfmsq(x_hi, vcvtq_f32_s32(m_hi), vdupq_n_f32(0.6931471805f)); + + const float32x4_t a_lo = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[4]), val_lo, arm_nn_exp_poly_coeffs_f16[0]); + const float32x4_t b_lo = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[6]), val_lo, arm_nn_exp_poly_coeffs_f16[2]); + const float32x4_t c_lo = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[5]), val_lo, arm_nn_exp_poly_coeffs_f16[1]); + const float32x4_t d_lo = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[7]), val_lo, arm_nn_exp_poly_coeffs_f16[3]); + const float32x4_t a_hi = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[4]), val_hi, arm_nn_exp_poly_coeffs_f16[0]); + const float32x4_t b_hi = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[6]), val_hi, arm_nn_exp_poly_coeffs_f16[2]); + const float32x4_t c_hi = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[5]), val_hi, arm_nn_exp_poly_coeffs_f16[1]); + const float32x4_t d_hi = vfmasq(vdupq_n_f32(arm_nn_exp_poly_coeffs_f16[7]), val_hi, arm_nn_exp_poly_coeffs_f16[3]); + const float32x4_t x2_lo = vmulq(val_lo, val_lo); + const float32x4_t x2_hi = vmulq(val_hi, val_hi); + const float32x4_t x4_lo = vmulq(x2_lo, x2_lo); + const float32x4_t x4_hi = vmulq(x2_hi, x2_hi); + float32x4_t y_lo = vfmaq(vfmaq(a_lo, b_lo, x2_lo), vfmaq(c_lo, d_lo, x2_lo), x4_lo); + float32x4_t y_hi = vfmaq(vfmaq(a_hi, b_hi, x2_hi), vfmaq(c_hi, d_hi, x2_hi), x4_hi); + + y_lo = vreinterpretq_f32_s32(vqaddq_s32(vreinterpretq_s32_f32(y_lo), vqshlq_n_s32(m_lo, 23))); + y_hi = vreinterpretq_f32_s32(vqaddq_s32(vreinterpretq_s32_f32(y_hi), vqshlq_n_s32(m_hi, 23))); + y_lo = vdupq_m(y_lo, 0.0f, vcmpltq(m_lo, -126)); + y_hi = vdupq_m(y_hi, 0.0f, vcmpltq(m_hi, -126)); + + float16x8_t y = vdupq_n_f16((float16_t)0.0f); + y = vcvtbq_f16_f32(y, y_lo); + y = vcvttq_f16_f32(y, y_hi); + return y; +} + #endif + +/** + * @copydoc arm_memcpy_f32 + */ +__STATIC_FORCEINLINE void +arm_memcpy_f16(float16_t *__RESTRICT dst, const float16_t *__RESTRICT src, uint32_t block_size) +{ + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + __asm volatile(" wlstp.16 lr, %[cnt], 1f \n" + "2: \n" + " vldrh.16 q0, [%[in]], #16 \n" + " vstrh.16 q0, [%[out]], #16 \n" + " letp lr, 2b \n" + "1: \n" + : [in] "+r"(src), [out] "+r"(dst) + : [cnt] "r"(block_size) + : "q0", "memory", "r14"); + #else + __builtin_memcpy(dst, src, (size_t)block_size * sizeof(float16_t)); + #endif +} + +/** + * @copydoc arm_memset_f32 + */ +__STATIC_FORCEINLINE void arm_memset_f16(float16_t *__RESTRICT dst, const float16_t val, uint32_t block_size) +{ + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t vec = vdupq_n_f16(val); + uint32_t i = 0; + for (; i + 8U <= block_size; i += 8U) + { + vst1q(dst + i, vec); + } + if (i < block_size) + { + const mve_pred16_t p = vctp16q(block_size - i); + vst1q_p(dst + i, vec, p); + } + #else + for (uint32_t i = 0; i < block_size; ++i) + { + dst[i] = val; + } + #endif +} + +/** + * @brief Specialized NHWC depthwise `2x5` kernel (float16). + */ +void arm_nn_depthwise_conv2x5_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_w, + int32_t ch_mult, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_w, + float16_t act_min, + float16_t act_max); + +/** + * @copydoc arm_nn_depthwise_conv1d_k3_nhwc_f32 + */ +void arm_nn_depthwise_conv1d_k3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_w); + +/** + * @copydoc arm_nn_depthwise_conv3x3_nhwc_f32 + */ +void arm_nn_depthwise_conv3x3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float16_t act_min, + float16_t act_max); + +/** + * @copydoc arm_nn_depthwise_conv_nt_t_f32 + */ +arm_cmsis_nn_status arm_nn_depthwise_conv_nt_t_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT out, + int32_t lhs_rows, + int32_t total_ch, + int32_t row_x_col, + int32_t out_row_stride, + float16_t activation_min, + float16_t activation_max); + +/** + * @copydoc arm_nn_conv1d_k5_nhwc_f32 + */ +void arm_nn_conv1d_k5_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=5` (float16, packed weights). + * + * The packed kernel uses the same `NTxN` RHS layout as + * `arm_nn_mat_mult_nt_n_packed_f16`, i.e. `[(5 * in_c)][out_c_block_of_8]`. + */ +void arm_nn_conv1d_k5_packed_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel_packed, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @copydoc arm_nn_conv1d_k3_nhwc_f32 + */ +void arm_nn_conv1d_k3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC 1D convolution kernel for `k=3` (float16, packed weights). + * + * The packed kernel uses the same `NTxN` RHS layout as + * `arm_nn_mat_mult_nt_n_packed_f16`, i.e. `[(3 * in_c)][out_c_block_of_8]`. + */ +void arm_nn_conv1d_k3_packed_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel_packed, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=3`, `s=3` (float16). + */ +void arm_nn_maxpool1d_k3s3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=2`, `s=2` without output clamp (float16). + */ +void arm_nn_maxpool1d_k2s2_nhwc_noclip_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w); + +/** + * @brief Specialized NHWC max-pool 1D kernel for `k=2`, `s=2` with clamp (float16). + */ +void arm_nn_maxpool1d_k2s2_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w, + float16_t act_min, + float16_t act_max); + +/** + * @copydoc arm_nn_mat_mult_nt_t_f32 + */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_t_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float16_t activation_min, + float16_t activation_max); + +/** + * @brief Matrix multiply with non-transposed lhs and packed non-transposed rhs (float16). + * + * @param[in] lhs Left-hand matrix stored row-major with logical shape `[lhs_rows, rhs_cols]`. + * @param[in] rhs_packed Right-hand matrix with logical shape `[rhs_cols, rhs_rows]`, packed in column blocks + * of 8. The final block uses the same packed stride and inactive tail lanes are ignored. + * @param[in] bias Optional bias vector. + * @param[out] dst Output matrix. + * @param[in] lhs_rows Number of rows in @p lhs. + * @param[in] rhs_rows Number of logical output columns in the unpacked rhs matrix. + * @param[in] rhs_cols Shared reduction dimension `K`. + * @param[in] row_address_offset Output row stride, expressed in elements. + * @param[in] activation_min Lower clamp bound. + * @param[in] activation_max Upper clamp bound. + * @return `ARM_CMSIS_NN_SUCCESS` on success or `ARM_CMSIS_NN_ARG_ERROR` on invalid arguments. + */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_n_packed_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs_packed, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float16_t activation_min, + float16_t activation_max); + +/** + * @brief Update LSTM function for an iteration step using float16 input, output and state. + * + * @param[in] data_in Data input pointer. + * @param[in] hidden_in Hidden state / recurrent input pointer. May be NULL for the first step. + * @param[out] hidden_out Hidden state / recurrent output pointer. + * @param[in] params Struct containing all information about the LSTM operator. + * @param[in] buffers Struct containing pointers to mutable cell-state storage. + * @param[in] batch_offset Number of timesteps between consecutive batches. + * @return The function returns ARM_CMSIS_NN_SUCCESS. + */ +arm_cmsis_nn_status arm_nn_lstm_step_f16(const float16_t *data_in, + const float16_t *hidden_in, + float16_t *hidden_out, + const cmsis_nn_lstm_params_f16 *params, + cmsis_nn_lstm_context_f16 *buffers, + const int32_t batch_offset); + +/** + * @copydoc arm_nn_pack_conv_patch_f32 + */ +void arm_nn_pack_conv_patch_f16(const float16_t *__RESTRICT input, + int32_t in_h, + int32_t in_w, + int32_t in_c, + int32_t kernel_h, + int32_t kernel_w, + int32_t stride_h, + int32_t stride_w, + int32_t pad_h, + int32_t pad_w, + int32_t dilation_h, + int32_t dilation_w, + int32_t out_y, + int32_t out_x, + float16_t pad_value, + float16_t *__RESTRICT patch_row); + +/** + * @copydoc arm_nn_softmax_1x2_f32 + */ +void arm_nn_softmax_1x2_f16(const float16_t in[2], float16_t out[2]); + +#endif /* ARM_NN_ENABLE_F16 */ + +#if ARM_NN_ENABLE_F32 + +/** + * @brief Update LSTM function for an iteration step using float32 input, output and state. + * + * @param[in] data_in Data input pointer. + * @param[in] hidden_in Hidden state / recurrent input pointer. May be NULL for the first step. + * @param[out] hidden_out Hidden state / recurrent output pointer. + * @param[in] params Struct containing all information about the LSTM operator. + * @param[in] buffers Struct containing pointers to mutable cell-state storage. + * @param[in] batch_offset Number of timesteps between consecutive batches. + * @return The function returns ARM_CMSIS_NN_SUCCESS. + */ +arm_cmsis_nn_status arm_nn_lstm_step_f32(const float32_t *data_in, + const float32_t *hidden_in, + float32_t *hidden_out, + const cmsis_nn_lstm_params_f32 *params, + cmsis_nn_lstm_context_f32 *buffers, + const int32_t batch_offset); + +#endif /* ARM_NN_ENABLE_F32 */ + +/** + * @} + */ + +#ifdef __cplusplus +} +#endif + +#endif /* ARM_NNSUPPORTFUNCTIONS_FLT_H */ diff --git a/README.md b/README.md index fc2fd3a2..0e0ab34c 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,24 @@ performance and minimize the memory footprint of neural networks on Arm Cortex-M The library follows the [int8](https://www.tensorflow.org/lite/performance/quantization_spec) and int16 quantization specification of TensorFlow Lite for Microcontrollers. This means CMSIS-NN is bit-exact with Tensorflow Lite reference kernels. In some cases TFL and TFLM reference kernels may not be bit-exact. In that case CMSIS-NN follows TFLM reference kernels. The unit test readme provides an [overview](https://github.com/ARM-software/CMSIS-NN/blob/main/Tests/UnitTest/README.md#tests-depending-on-tflm-interpreter). +### Experimental Float API +CMSIS-NN also provides experimental float32 and float16 APIs. The float API intentionally follows the same CMSIS-NN integer style, which is itself shaped by TFLM integration patterns, to keep the public surface consistent across data types. This includes float16 even though TFLM does not define a float16 operator contract. + +The float API is primarily intended for Cortex-M CPUs with Arm Helium Technology (MVE). Pure C scalar reference implementations are provided for correctness, bring-up, and fallback, but practical deployment is expected to target MVE-enabled CPUs. In general, float kernels should be reserved for specific use cases where integer quantization is not possible or not acceptable, and where the neural network remains modest enough for Cortex-M class devices. CMSIS-NN float support is intended to integrate with frameworks that can carry float16 operator flows, such as [ExecuTorch](https://executorch.ai/). + +For the float operators that support `arm_nn_weight_format_flt`, MVE +performance is generally better when constant weights are provided in the +packed `NTxN` layout instead of the standard `NT x T` layout. This avoids the +gather-heavy RHS access pattern of the standard formulation and is therefore +the preferred deployment format when offline repacking is available. + +The floating-point scalar code can also be compiled for Arm A-class CPUs with `float16` +support and may benefit from NEON or SVE auto-vectorization. However, this is +not an intended deployment target for CMSIS-NN float support, and the resulting +performance is expected to be suboptimal compared to libraries designed for that +class of processor. For Arm A-class CPUs, prefer optimized inference libraries +such as Arm Compute Library or XNNPACK. + ## Branches and Tags There is a single branch called 'main'. Tags are created during a release. Two releases are planned to be done in a year. The releases can be found @@ -27,24 +45,26 @@ processors here are Cortex-M4 or a Cortex-M33 configured with optional DSP exten Processors with Arm Helium Technology use the Arm M-profile Vector Extension(MVE) instructions for optimization. Examples are Cortex-M55 or Cortex-M85 configured with MVE. -| Operator | C
int8 | C
int16 | C
int4* | DSP
int8 | DSP
int16 | DSP
int4* | MVE
int8 | MVE
int16 | MVE
int4* | -| --------------- | ----------- | ---------- |------------|-------------| -------------|--------------|-------------| -------------|--------------| -| Conv2D | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | -| DepthwiseConv2D | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | -| TransposeConv2D | Yes | No | No | Yes | No | No | Yes | No | No | -| Fully Connected | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | -| Batch Matmul | Yes | Yes | No | Yes | Yes | No | Yes | Yes | No | -| Add | Yes | Yes | N/A | Yes | Yes | N/A | Yes | Yes | N/A | -| Minimum | Yes | No | N/A | No | No | N/A | Yes | No | N/A | -| Maximum | Yes | No | N/A | No | No | N/A | Yes | No | N/A | -| Mul | Yes | Yes | N/A | Yes | Yes | N/A | Yes | Yes | N/A | -| MaxPooling | Yes | Yes | N/A | Yes | Yes | N/A | Yes | Yes | N/A | -| AvgPooling | Yes | Yes | N/A | Yes | Yes | N/A | Yes | Yes | N/A | -| Softmax | Yes | Yes | N/A | Yes | Yes | N/A | Yes | No | N/A | -| LSTM | Yes | Yes | No | Yes | Yes | No | Yes | Yes | No | -| SVDF | Yes | No | No | Yes | No | No | Yes | No | No | -| Pad | Yes | No | N/A | No | No | N/A | Yes | No | N/A | -| Transpose | Yes | No | N/A | No | No | N/A | Yes | No | N/A | +The float columns below summarize the current experimental float coverage. Float kernels are available in pure C reference form and, for the operators listed below, in Helium-optimized form where available. Float support is primarily intended for cores with Helium and hardware floating-point support; on cores that only provide the classic DSP extension, float kernels may still compile through the scalar C path but are not a performance target. + +| Operator | C
int8 | C
int16 | C
int4* | C
float16/float32 | DSP
int8 | DSP
int16 | DSP
int4* | MVE
int8 | MVE
int16 | MVE
int4* | MVE
float16/float32 | +| --------------- | ----------- | ---------- |------------|----------------------|-------------| -------------|--------------|-------------| -------------|--------------|------------------------| +| Conv2D | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | +| DepthwiseConv2D | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | +| TransposeConv2D | Yes | No | No | Yes | Yes | No | No | Yes | No | No | Yes | +| Fully Connected | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | +| Batch Matmul | Yes | Yes | No | Yes | Yes | Yes | No | Yes | Yes | No | Yes | +| Add | Yes | Yes | N/A | Yes | Yes | Yes | N/A | Yes | Yes | N/A | Yes | +| Minimum | Yes | No | N/A | Yes | No | No | N/A | Yes | No | N/A | Yes | +| Maximum | Yes | No | N/A | Yes | No | No | N/A | Yes | No | N/A | Yes | +| Mul | Yes | Yes | N/A | Yes | Yes | Yes | N/A | Yes | Yes | N/A | Yes | +| MaxPooling | Yes | Yes | N/A | Yes | Yes | Yes | N/A | Yes | Yes | N/A | Yes | +| AvgPooling | Yes | Yes | N/A | Yes | Yes | Yes | N/A | Yes | Yes | N/A | Yes | +| Softmax | Yes | Yes | N/A | Yes | Yes | Yes | N/A | Yes | No | N/A | Yes | +| LSTM | Yes | Yes | No | Yes | Yes | Yes | No | Yes | Yes | No | Yes | +| SVDF | Yes | No | No | Yes | Yes | No | No | Yes | No | No | Yes | +| Pad | Yes | No | N/A | Yes | No | No | N/A | Yes | No | N/A | Yes | +| Transpose | Yes | No | N/A | Yes | No | No | N/A | Yes | No | N/A | Yes | * int4 weights + int8 activations @@ -70,6 +90,13 @@ For any new features and bug fixes, new unit tests are needed. Improvements have not have the means to execute the tests, you can still make the PR and comment that you need help in completing/executing the unit tests. +The repository also provides a `vcpkg-configuration.json` used to provision the +tool environment required by the unit-test and CI flows. This is consumed by +the `vcpkg` package manager, locally or in the cloud, to avoid manual setup of +CMSIS-Toolbox, compilers, and FVP dependencies. The same setup is used by the +GitHub Actions CI workflows that run the unit-test regressions on pushes and +pull requests. + ### Version & Date Each File has a version number and a date field that must be updated when making any change to that file. The versioning follows Semantic Versioning 2.0.0 format. For details check: https://semver.org/ @@ -148,10 +175,33 @@ The compiler option *'-fno-builtin'* does not utilize optimized implementations For processors with DSP extension, int4 and int8 convolutions make use of the restrict keyword for the output pointer. This can allow the compiler to make optimizations but the actual performance result depends on the Arm(R) Cortex(R)-M processor, the compiler and the model. This optimization can be enabled by providing the compiler with a defition of OPTIONAL_RESTRICT_KEYWORD=__restrict . In general Arm Cortex-M7 will benefit from this. Similar Arm Cortex-M4 and Cortex-M33, will generally not benefit from it, but it may still bring an uplift depending on the model and compiler. It is recommended to enable this for Cortex-M7. +Experimental float support is disabled by default. This is intentional to keep the library code size and public API surface small for integer-only builds. Enable the float options only when the application strictly needs them. + +For performance reasons, the current floating-point kernels do not +specifically target IEEE edge cases such as `NaN`, `Inf`, +denormals/subnormals, or signed zero. The intended use is that the neural +network pre-processing stage provides finite, numerically safe input data and +that the model weights and biases do not contain aberrant values. + +For the scalar floating-point softmax path, the LUT-based exp approximation is +enabled by default for performance. This improves speed on supported targets, +but it adds one 257-entry lookup table per enabled float precision: + +- float16: 257 half-words, about 514 bytes +- float32: 257 words, about 1028 bytes + +Define `ARM_NN_USE_EXP_TAYLOR` to avoid the extra lookup-table storage. Do not +define both `ARM_NN_USE_EXP_LUT` and `ARM_NN_USE_EXP_TAYLOR` at the same time. + Further compile-time options: | Name | Explanation | Affects headers(*) | |------|-----|-----| +| ARM_NN_ENABLE_F32 | Enable experimental float32 operator support. Leave disabled unless the application needs float32 kernels. | Yes | +| ARM_NN_ENABLE_F16 | Enable experimental float16 operator support. Leave disabled unless the application needs float16 kernels and the toolchain/target support them. | Yes | +| NN_DISABLE_SPECIALIZATION | Disable optional shape/layout-specific fast paths and force the corresponding generic implementations. Useful for debugging or validating specialized kernels against the generic path. | No | +| ARM_NN_USE_EXP_LUT | Select the LUT-based scalar float softmax exp approximation. This is the default if no softmax exp macro is defined. | No | +| ARM_NN_USE_EXP_TAYLOR | Select the Taylor/Estrin scalar float softmax exp approximation to avoid the extra LUT storage. | No | | CMSIS_NN_USE_SINGLE_ROUNDING | Use a single instead of double rounding in requantizazion. This may affect the output. | Yes | | CMSIS_NN_USE_REQUANTIZE_INLINE_ASSEMBLY | Use inline assembly for `arm_nn_requantize`. This code branch is faster on Cortex-M4, but slower on others. Results should be bit-identical, but was observed to cause differences with Arm Compiler and Cortex-M7. | Yes | diff --git a/Source/ActivationFunctions/CMakeLists.txt b/Source/ActivationFunctions/CMakeLists.txt index 90d16e36..1204eeb8 100644 --- a/Source/ActivationFunctions/CMakeLists.txt +++ b/Source/ActivationFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2023 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2023, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -17,4 +17,11 @@ # file(GLOB SRC "./*_s8*.c") file(GLOB SRC_S16 "./*_s16*.c") -target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_S16} arm_relu_q7.c arm_relu_q15.c) \ No newline at end of file +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_nn_activation_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_nn_activation_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_S16} ${SRC_FLT} arm_relu_q7.c arm_relu_q15.c) diff --git a/Source/ActivationFunctions/arm_nn_activation_f16.c b/Source/ActivationFunctions/arm_nn_activation_f16.c new file mode 100644 index 00000000..0712ba12 --- /dev/null +++ b/Source/ActivationFunctions/arm_nn_activation_f16.c @@ -0,0 +1,124 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_activation_f16.c + * Description: Activation functions (float16) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Acti + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_activation_f16(const float16_t *input, + float16_t *output, + int32_t size, + arm_nn_activation_type_flt type, + float16_t act_param) +{ + if (input == NULL || output == NULL || size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t n = (size_t)size; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (type == ARM_NN_FLT_ACT_TANH) + { + for (size_t i = 0; i < n; i += 8U) + { + const mve_pred16_t p = vctp16q((uint32_t)(n - i)); + float16x8_t v = vld1q_z(&input[i], p); + v = arm_nn_vtanh_lut_direct_mve_f16(v); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_HARDSWISH) + { + for (size_t i = 0; i < n; i += 8U) + { + const mve_pred16_t p = vctp16q((uint32_t)(n - i)); + float16x8_t v = vld1q_z(&input[i], p); + v = arm_nn_vhardswish_mve_f16(v); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_RELU) + { + const float16x8_t v_zero = vdupq_n_f16((float16_t)0.0f); + for (size_t i = 0; i < n; i += 8U) + { + const mve_pred16_t p = vctp16q((uint32_t)(n - i)); + float16x8_t v = vld1q_z(&input[i], p); + v = vmaxnmq(v, v_zero); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_RELU6) + { + const float16x8_t v_zero = vdupq_n_f16((float16_t)0.0f); + const float16x8_t v_six = vdupq_n_f16((float16_t)6.0f); + for (size_t i = 0; i < n; i += 8U) + { + const mve_pred16_t p = vctp16q((uint32_t)(n - i)); + float16x8_t v = vld1q_z(&input[i], p); + v = arm_nn_clamp_mve_f16(v, v_zero, v_six); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (size_t i = 0; i < n; ++i) + { + output[i] = arm_nn_apply_activation_type_f16(input[i], type, act_param); + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of Acti group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/ActivationFunctions/arm_nn_activation_f32.c b/Source/ActivationFunctions/arm_nn_activation_f32.c new file mode 100644 index 00000000..66bf909a --- /dev/null +++ b/Source/ActivationFunctions/arm_nn_activation_f32.c @@ -0,0 +1,124 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_activation_f32.c + * Description: Activation functions (float32) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Acti + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_activation_f32(const float32_t *input, + float32_t *output, + int32_t size, + arm_nn_activation_type_flt type, + float32_t act_param) +{ + if (input == NULL || output == NULL || size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t n = (size_t)size; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (type == ARM_NN_FLT_ACT_TANH) + { + for (size_t i = 0; i < n; i += 4U) + { + const mve_pred16_t p = vctp32q((uint32_t)(n - i)); + float32x4_t v = vld1q_z(&input[i], p); + v = arm_nn_vtanh_lut_direct_mve_f32(v); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_HARDSWISH) + { + for (size_t i = 0; i < n; i += 4U) + { + const mve_pred16_t p = vctp32q((uint32_t)(n - i)); + float32x4_t v = vld1q_z(&input[i], p); + v = arm_nn_vhardswish_mve_f32(v); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_RELU) + { + const float32x4_t v_zero = vdupq_n_f32(0.0f); + for (size_t i = 0; i < n; i += 4U) + { + const mve_pred16_t p = vctp32q((uint32_t)(n - i)); + float32x4_t v = vld1q_z(&input[i], p); + v = vmaxnmq(v, v_zero); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (type == ARM_NN_FLT_ACT_RELU6) + { + const float32x4_t v_zero = vdupq_n_f32(0.0f); + const float32x4_t v_six = vdupq_n_f32(6.0f); + for (size_t i = 0; i < n; i += 4U) + { + const mve_pred16_t p = vctp32q((uint32_t)(n - i)); + float32x4_t v = vld1q_z(&input[i], p); + v = arm_nn_clamp_mve_f32(v, v_zero, v_six); + vst1q_p(&output[i], v, p); + } + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (size_t i = 0; i < n; ++i) + { + output[i] = arm_nn_apply_activation_type_f32(input[i], type, act_param); + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of Acti group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/BasicMathFunctions/CMakeLists.txt b/Source/BasicMathFunctions/CMakeLists.txt index 9d3f543a..5c840ca0 100755 --- a/Source/BasicMathFunctions/CMakeLists.txt +++ b/Source/BasicMathFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2021 Arm Limited. +# Copyright (c) 2019-2021, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -16,5 +16,18 @@ # limitations under the License. # -file(GLOB SRC "./*_*.c") -target_sources(cmsis-nn PRIVATE ${SRC}) +file(GLOB SRC "./*_s8*.c" "./*_s16*.c") +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_elementwise_add_f32.c + arm_elementwise_mul_f32.c + arm_minimum_f32.c + arm_maximum_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_elementwise_add_f16.c + arm_elementwise_mul_f16.c + arm_minimum_f16.c + arm_maximum_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_FLT}) diff --git a/Source/BasicMathFunctions/arm_elementwise_add_f16.c b/Source/BasicMathFunctions/arm_elementwise_add_f16.c new file mode 100644 index 00000000..6658e721 --- /dev/null +++ b/Source/BasicMathFunctions/arm_elementwise_add_f16.c @@ -0,0 +1,78 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_elementwise_add_f16.c + * Description: Elementwise add for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_elementwise_add_f16(const float16_t *input_1_vect, + const float16_t *input_2_vect, + float16_t *output, + float16_t out_activation_min, + float16_t out_activation_max, + int32_t block_size) +{ + if (!input_1_vect || !input_2_vect || !output || block_size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t i = 0; + const float16x8_t vmin = vdupq_n_f16(out_activation_min); + const float16x8_t vmax = vdupq_n_f16(out_activation_max); + for (; i < block_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(block_size - i)); + const float16x8_t va = vld1q_z(&input_1_vect[i], p); + const float16x8_t vb = vld1q_z(&input_2_vect[i], p); + float16x8_t vr = vaddq(va, vb); + vr = arm_nn_clamp_mve_f16(vr, vmin, vmax); + vstrhq_p(&output[i], vr, p); + } +#else + for (int32_t i = 0; i < block_size; ++i) + { + const _Float16 v = (_Float16)input_1_vect[i] + (_Float16)input_2_vect[i]; + output[i] = arm_nn_clamp_scalar_f16((float16_t)v, out_activation_min, out_activation_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_elementwise_add_f32.c b/Source/BasicMathFunctions/arm_elementwise_add_f32.c new file mode 100644 index 00000000..6a480c27 --- /dev/null +++ b/Source/BasicMathFunctions/arm_elementwise_add_f32.c @@ -0,0 +1,78 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_elementwise_add_f32.c + * Description: Elementwise add for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_elementwise_add_f32(const float32_t *input_1_vect, + const float32_t *input_2_vect, + float32_t *output, + float32_t out_activation_min, + float32_t out_activation_max, + int32_t block_size) +{ + if (!input_1_vect || !input_2_vect || !output || block_size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t i = 0; + const float32x4_t vmin = vdupq_n_f32(out_activation_min); + const float32x4_t vmax = vdupq_n_f32(out_activation_max); + for (; i < block_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(block_size - i)); + const float32x4_t va = vld1q_z(&input_1_vect[i], p); + const float32x4_t vb = vld1q_z(&input_2_vect[i], p); + float32x4_t vr = vaddq(va, vb); + vr = arm_nn_clamp_mve_f32(vr, vmin, vmax); + vstrwq_p(&output[i], vr, p); + } +#else + for (int32_t i = 0; i < block_size; ++i) + { + const float32_t v = input_1_vect[i] + input_2_vect[i]; + output[i] = CLAMP(v, out_activation_max, out_activation_min); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_elementwise_mul_f16.c b/Source/BasicMathFunctions/arm_elementwise_mul_f16.c new file mode 100644 index 00000000..345828c3 --- /dev/null +++ b/Source/BasicMathFunctions/arm_elementwise_mul_f16.c @@ -0,0 +1,78 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_elementwise_mul_f16.c + * Description: Elementwise multiply for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_elementwise_mul_f16(const float16_t *input_1_vect, + const float16_t *input_2_vect, + float16_t *output, + float16_t out_activation_min, + float16_t out_activation_max, + int32_t block_size) +{ + if (!input_1_vect || !input_2_vect || !output || block_size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t i = 0; + const float16x8_t vmin = vdupq_n_f16(out_activation_min); + const float16x8_t vmax = vdupq_n_f16(out_activation_max); + for (; i < block_size; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(block_size - i)); + const float16x8_t va = vld1q_z(&input_1_vect[i], p); + const float16x8_t vb = vld1q_z(&input_2_vect[i], p); + float16x8_t vr = vmulq(va, vb); + vr = arm_nn_clamp_mve_f16(vr, vmin, vmax); + vstrhq_p(&output[i], vr, p); + } +#else + for (int32_t i = 0; i < block_size; ++i) + { + const _Float16 v = (_Float16)input_1_vect[i] * (_Float16)input_2_vect[i]; + output[i] = arm_nn_clamp_scalar_f16((float16_t)v, out_activation_min, out_activation_max); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_elementwise_mul_f32.c b/Source/BasicMathFunctions/arm_elementwise_mul_f32.c new file mode 100644 index 00000000..9b6379b7 --- /dev/null +++ b/Source/BasicMathFunctions/arm_elementwise_mul_f32.c @@ -0,0 +1,78 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_elementwise_mul_f32.c + * Description: Elementwise multiply for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_elementwise_mul_f32(const float32_t *input_1_vect, + const float32_t *input_2_vect, + float32_t *output, + float32_t out_activation_min, + float32_t out_activation_max, + int32_t block_size) +{ + if (!input_1_vect || !input_2_vect || !output || block_size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t i = 0; + const float32x4_t vmin = vdupq_n_f32(out_activation_min); + const float32x4_t vmax = vdupq_n_f32(out_activation_max); + for (; i < block_size; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(block_size - i)); + const float32x4_t va = vld1q_z(&input_1_vect[i], p); + const float32x4_t vb = vld1q_z(&input_2_vect[i], p); + float32x4_t vr = vmulq(va, vb); + vr = arm_nn_clamp_mve_f32(vr, vmin, vmax); + vstrwq_p(&output[i], vr, p); + } +#else + for (int32_t i = 0; i < block_size; ++i) + { + const float32_t v = input_1_vect[i] * input_2_vect[i]; + output[i] = CLAMP(v, out_activation_max, out_activation_min); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_maximum_f16.c b/Source/BasicMathFunctions/arm_maximum_f16.c new file mode 100644 index 00000000..84de4329 --- /dev/null +++ b/Source/BasicMathFunctions/arm_maximum_f16.c @@ -0,0 +1,54 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_maximum_f16.c + * Description: Elementwise maximum for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_minmax_f16_common.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_maximum_f16(const cmsis_nn_context *ctx, + const float16_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float16_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float16_t *output_data, + const cmsis_nn_dims *output_dims) +{ + return arm_minmax_f16_impl( + ctx, input_1_data, input_1_dims, input_2_data, input_2_dims, output_data, output_dims, 1); +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_maximum_f32.c b/Source/BasicMathFunctions/arm_maximum_f32.c new file mode 100644 index 00000000..16b7eb08 --- /dev/null +++ b/Source/BasicMathFunctions/arm_maximum_f32.c @@ -0,0 +1,54 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_maximum_f32.c + * Description: Elementwise maximum for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_minmax_f32_common.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_maximum_f32(const cmsis_nn_context *ctx, + const float32_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float32_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float32_t *output_data, + const cmsis_nn_dims *output_dims) +{ + return arm_minmax_f32_impl( + ctx, input_1_data, input_1_dims, input_2_data, input_2_dims, output_data, output_dims, 1); +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_minimum_f16.c b/Source/BasicMathFunctions/arm_minimum_f16.c new file mode 100644 index 00000000..2d4d22eb --- /dev/null +++ b/Source/BasicMathFunctions/arm_minimum_f16.c @@ -0,0 +1,54 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_minimum_f16.c + * Description: Elementwise minimum for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_minmax_f16_common.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_minimum_f16(const cmsis_nn_context *ctx, + const float16_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float16_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float16_t *output_data, + const cmsis_nn_dims *output_dims) +{ + return arm_minmax_f16_impl( + ctx, input_1_data, input_1_dims, input_2_data, input_2_dims, output_data, output_dims, 0); +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/BasicMathFunctions/arm_minimum_f32.c b/Source/BasicMathFunctions/arm_minimum_f32.c new file mode 100644 index 00000000..0eebed40 --- /dev/null +++ b/Source/BasicMathFunctions/arm_minimum_f32.c @@ -0,0 +1,54 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_minimum_f32.c + * Description: Elementwise minimum for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_minmax_f32_common.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup groupElementwise + * @{ + */ + +arm_cmsis_nn_status arm_minimum_f32(const cmsis_nn_context *ctx, + const float32_t *input_1_data, + const cmsis_nn_dims *input_1_dims, + const float32_t *input_2_data, + const cmsis_nn_dims *input_2_dims, + float32_t *output_data, + const cmsis_nn_dims *output_dims) +{ + return arm_minmax_f32_impl( + ctx, input_1_data, input_1_dims, input_2_data, input_2_dims, output_data, output_dims, 0); +} +/** + * @} end of groupElementwise group + */ diff --git a/Source/ConcatenationFunctions/CMakeLists.txt b/Source/ConcatenationFunctions/CMakeLists.txt index 9d3f543a..ad5d36c3 100644 --- a/Source/ConcatenationFunctions/CMakeLists.txt +++ b/Source/ConcatenationFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2021 Arm Limited. +# Copyright (c) 2019-2021, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -16,5 +16,12 @@ # limitations under the License. # -file(GLOB SRC "./*_*.c") +file(GLOB SRC "./*_s8*.c") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC arm_concatenation_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC arm_concatenation_f16.c) +endif() + target_sources(cmsis-nn PRIVATE ${SRC}) diff --git a/Source/ConcatenationFunctions/arm_concatenation_f16.c b/Source/ConcatenationFunctions/arm_concatenation_f16.c new file mode 100644 index 00000000..2fa9eab6 --- /dev/null +++ b/Source/ConcatenationFunctions/arm_concatenation_f16.c @@ -0,0 +1,49 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_concatenation_f16.c + * Description: Concatenation operators for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_concatenation_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNSupport + * @{ + */ + +ARM_CONCATENATION_DEFINE(f16, float16_t) + +/** @} */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/ConcatenationFunctions/arm_concatenation_f32.c b/Source/ConcatenationFunctions/arm_concatenation_f32.c new file mode 100644 index 00000000..db158a1e --- /dev/null +++ b/Source/ConcatenationFunctions/arm_concatenation_f32.c @@ -0,0 +1,49 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_concatenation_f32.c + * Description: Concatenation operators for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_concatenation_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNSupport + * @{ + */ + +ARM_CONCATENATION_DEFINE(f32, float32_t) + +/** @} */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/ConvolutionFunctions/CMakeLists.txt b/Source/ConvolutionFunctions/CMakeLists.txt index 21350644..24367a03 100644 --- a/Source/ConvolutionFunctions/CMakeLists.txt +++ b/Source/ConvolutionFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2023 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2023, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -19,4 +19,19 @@ file(GLOB SRC_S4 "./*_s4*.c") file(GLOB SRC_S8 "./*_s8*.c") file(GLOB SRC_S16 "./*_s16*.c") -target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16}) +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_convolve_f32.c + arm_convolve_1_x_n_f32.c + arm_convolve_1x1_f32.c + arm_depthwise_conv_f32.c + arm_transpose_conv_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_convolve_f16.c + arm_convolve_1_x_n_f16.c + arm_convolve_1x1_f16.c + arm_depthwise_conv_f16.c + arm_transpose_conv_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16} ${SRC_FLT}) diff --git a/Source/ConvolutionFunctions/arm_convolve_1_x_n_f16.c b/Source/ConvolutionFunctions/arm_convolve_1_x_n_f16.c new file mode 100644 index 00000000..63ab8833 --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_1_x_n_f16.c @@ -0,0 +1,423 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_1_x_n_f16.c + * Description: Dedicated float16 1xN convolution + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS (8) + #define ARM_NN_CONV_1XN_F16_MVE_DUAL_BLOCK_ROWS (16) + #define ARM_NN_CONV_1XN_F16_MVE_SUB_BLOCK_ROWS (4) + #define ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8) + #define ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS_DUAL ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_16) + #define ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS_SUB ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_4) +#endif + +__STATIC_INLINE float16_t arm_convolve_1_x_n_dot_f16(const float16_t *lhs, const float16_t *rhs, int32_t len) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + for (int32_t i = 0; i < len; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(len - i)); + vacc = vfmaq_m(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p), p); + } + return arm_nn_vec_reduce_add_f16(vacc); +#else + _Float16 acc = (_Float16)0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += (_Float16)lhs[i] * (_Float16)rhs[i]; + } + return (float16_t)acc; +#endif +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_1_x_n_mat_mult_nt_t_strided_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t lhs_cols_offset, + int32_t row_address_offset, + float16_t activation_min, + float16_t activation_max) +{ + if (!lhs || !rhs || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || lhs_cols_offset <= 0 || + row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t r = 0; r < lhs_rows; ++r) + { + const float16_t *lhs_row = lhs + (size_t)r * lhs_cols_offset; + float16_t *dst_row = dst + (size_t)r * row_address_offset; + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t c = 0; + if (rhs_cols <= ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS) + { + const uint16_t rhs_cols_u16 = (uint16_t)rhs_cols; + const uint16x8_t offsets = vmulq(vidupq_u16((uint32_t)0, 1), rhs_cols_u16); + const float16x8_t vmin = vdupq_n_f16(activation_min); + const float16x8_t vmax = vdupq_n_f16(activation_max); + + if (rhs_cols <= ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS_DUAL) + { + const uint16x8_t offsets_hi = + vaddq(offsets, (uint16_t)(ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS * rhs_cols_u16)); + + for (; c + ARM_NN_CONV_1XN_F16_MVE_DUAL_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_CONV_1XN_F16_MVE_DUAL_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc_lo = bias ? vld1q(bias + c) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc_hi = + bias ? vld1q(bias + c + ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16_t lhs_v = lhs_row[k]; + const float16x8_t vrhs_lo = vldrhq_gather_shifted_offset(rhs_block + k, offsets); + const float16x8_t vrhs_hi = vldrhq_gather_shifted_offset(rhs_block + k, offsets_hi); + vacc_lo = vfmaq(vacc_lo, vrhs_lo, lhs_v); + vacc_hi = vfmaq(vacc_hi, vrhs_hi, lhs_v); + } + + vacc_lo = arm_nn_clamp_mve_f16(vacc_lo, vmin, vmax); + vacc_hi = arm_nn_clamp_mve_f16(vacc_hi, vmin, vmax); + vst1q(dst_row + c, vacc_lo); + vst1q(dst_row + c + ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS, vacc_hi); + } + } + + for (; c + ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS <= rhs_rows; c += ARM_NN_CONV_1XN_F16_MVE_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc = bias ? vld1q(bias + c) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16x8_t vrhs = vldrhq_gather_shifted_offset(rhs_block + k, offsets); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f16(vacc, vmin, vmax); + vst1q(dst_row + c, vacc); + } + + if (rhs_cols <= ARM_NN_CONV_1XN_F16_MVE_MAX_RHS_COLS_SUB) + { + const mve_pred16_t p = vctp16q((uint32_t)ARM_NN_CONV_1XN_F16_MVE_SUB_BLOCK_ROWS); + + for (; c + ARM_NN_CONV_1XN_F16_MVE_SUB_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_CONV_1XN_F16_MVE_SUB_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16x8_t vrhs = vldrhq_gather_shifted_offset_z(rhs_block + k, offsets, p); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f16(vacc, vmin, vmax); + vst1q_p(dst_row + c, vacc, p); + } + } + } +#else + int32_t c = 0; +#endif + + for (; c < rhs_rows; ++c) + { + const float16_t *rhs_row = rhs + (size_t)c * rhs_cols; + _Float16 acc = bias ? (_Float16)bias[c] : (_Float16)0.0f; + acc += (_Float16)arm_convolve_1_x_n_dot_f16(lhs_row, rhs_row, rhs_cols); + dst_row[c] = (float16_t)(_Float16)CLAMP(acc, (_Float16)activation_max, (_Float16)activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE void arm_convolve_1_x_n_find_regions(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + int32_t *left_pad_num, + int32_t *no_pad_num, + int32_t *right_pad_num) +{ + int32_t first_valid = 0; + while (first_valid < output_dims->w) + { + const int32_t base_x = first_valid * conv_params->stride.w - conv_params->padding.w; + if (base_x >= 0 && (base_x + filter_dims->w) <= input_dims->w) + { + break; + } + ++first_valid; + } + + int32_t last_valid = output_dims->w - 1; + while (last_valid >= first_valid) + { + const int32_t base_x = last_valid * conv_params->stride.w - conv_params->padding.w; + if (base_x >= 0 && (base_x + filter_dims->w) <= input_dims->w) + { + break; + } + --last_valid; + } + + *left_pad_num = first_valid; + *no_pad_num = MAX(last_valid - first_valid + 1, 0); + *right_pad_num = output_dims->w - *left_pad_num - *no_pad_num; +} + +__STATIC_INLINE void arm_convolve_1_x_n_pack_rows_f16(float16_t *scratch, + const float16_t *input_b, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + int32_t start_out_x, + int32_t rows) +{ + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t kernel_w = filter_dims->w; + const int32_t rhs_cols = kernel_w * input_c; + + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_x = start_out_x + r; + const int32_t base_x = out_x * conv_params->stride.w - conv_params->padding.w; + const int32_t left_pad_cols = MAX(0, -base_x); + const int32_t valid_x0 = MAX(base_x, 0); + const int32_t valid_x1 = MIN(base_x + kernel_w, input_w); + const int32_t valid_cols = MAX(valid_x1 - valid_x0, 0); + const int32_t right_pad_cols = kernel_w - left_pad_cols - valid_cols; + float16_t *patch_row = scratch + (size_t)r * rhs_cols; + + if (left_pad_cols > 0) + { + arm_memset_f16(patch_row, (float16_t)0.0f, (uint32_t)((size_t)left_pad_cols * input_c)); + } + if (valid_cols > 0) + { + arm_memcpy_f16(patch_row + (size_t)left_pad_cols * input_c, + input_b + (size_t)valid_x0 * input_c, + (uint32_t)((size_t)valid_cols * input_c)); + } + if (right_pad_cols > 0) + { + arm_memset_f16(patch_row + (size_t)(left_pad_cols + valid_cols) * input_c, + (float16_t)0.0f, + (uint32_t)((size_t)right_pad_cols * input_c)); + } + } +} + +arm_cmsis_nn_status arm_convolve_1_x_n_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)bias_dims; + + if (!ctx || !ctx->buf || !conv_params || !input_dims || !input_data || !filter_dims || !filter_data || + !output_dims || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || filter_dims->w <= 1 || + conv_params->stride.h != 1 || conv_params->stride.w <= 0 || conv_params->padding.h != 0 || + conv_params->dilation.h != 1 || conv_params->dilation.w != 1 || input_dims->c != filter_dims->c) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t buf_size = + arm_convolve_1_x_n_f16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); + if (buf_size <= 0 || ctx->size < buf_size) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_w = filter_dims->w; + const int32_t rhs_cols = kernel_w * input_c; + const int32_t lhs_cols_offset = input_c * conv_params->stride.w; + const int32_t tile_rows = (int32_t)((size_t)ctx->size / ((size_t)rhs_cols * sizeof(float16_t))); + float16_t *scratch = (float16_t *)ctx->buf; + int32_t left_pad_num = 0; + int32_t no_pad_num = 0; + int32_t right_pad_num = 0; + + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_convolve_1_x_n_find_regions( + conv_params, input_dims, filter_dims, output_dims, &left_pad_num, &no_pad_num, &right_pad_num); + + for (int32_t b = 0; b < batch; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_w * input_c; + float16_t *output_b = output_data + (size_t)b * output_w * output_c; + + for (int32_t row = 0; row < left_pad_num; row += tile_rows) + { + const int32_t rows = MIN(tile_rows, left_pad_num - row); + arm_convolve_1_x_n_pack_rows_f16(scratch, input_b, conv_params, input_dims, filter_dims, row, rows); + + arm_cmsis_nn_status st = arm_nn_mat_mult_nt_t_f16(scratch, + filter_data, + bias_data, + output_b, + rows, + output_c, + rhs_cols, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)rows * output_c; + } + + if (no_pad_num > 0) + { + const int32_t input_start = (conv_params->stride.w * left_pad_num - conv_params->padding.w) * input_c; + arm_cmsis_nn_status st = arm_convolve_1_x_n_mat_mult_nt_t_strided_f16(input_b + input_start, + filter_data, + bias_data, + output_b, + no_pad_num, + output_c, + rhs_cols, + lhs_cols_offset, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)no_pad_num * output_c; + } + + for (int32_t row = 0; row < right_pad_num; row += tile_rows) + { + const int32_t rows = MIN(tile_rows, right_pad_num - row); + const int32_t start_out_x = left_pad_num + no_pad_num + row; + arm_convolve_1_x_n_pack_rows_f16(scratch, input_b, conv_params, input_dims, filter_dims, start_out_x, rows); + + arm_cmsis_nn_status st = arm_nn_mat_mult_nt_t_f16(scratch, + filter_data, + bias_data, + output_b, + rows, + output_c, + rhs_cols, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)rows * output_c; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_1_x_n_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_1_x_n_nhwc_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +/** @} end of NNConv group */ diff --git a/Source/ConvolutionFunctions/arm_convolve_1_x_n_f32.c b/Source/ConvolutionFunctions/arm_convolve_1_x_n_f32.c new file mode 100644 index 00000000..0ec04def --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_1_x_n_f32.c @@ -0,0 +1,390 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_1_x_n_f32.c + * Description: Dedicated float32 1xN convolution + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS (4) + #define ARM_NN_CONV_1XN_F32_MVE_DUAL_BLOCK_ROWS (8) +#endif + +__STATIC_INLINE float32_t arm_convolve_1_x_n_dot_f32(const float32_t *lhs, const float32_t *rhs, int32_t len) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + float32x4_t vacc = vdupq_n_f32(0.0f); + for (int32_t i = 0; i < len; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(len - i)); + vacc = vfmaq_m(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p), p); + } + return arm_nn_vec_reduce_add_f32(vacc); +#else + float32_t acc = 0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += lhs[i] * rhs[i]; + } + return acc; +#endif +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_1_x_n_mat_mult_nt_t_strided_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t lhs_cols_offset, + int32_t row_address_offset, + float32_t activation_min, + float32_t activation_max) +{ + if (!lhs || !rhs || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || lhs_cols_offset <= 0 || + row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const uint32_t rhs_cols_u32 = (uint32_t)rhs_cols; + const uint32x4_t offsets = vmulq(vidupq_u32((uint32_t)0, 1), rhs_cols_u32); + const uint32x4_t offsets_hi = vaddq(offsets, (uint32_t)(ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS * rhs_cols_u32)); + const float32x4_t vmin = vdupq_n_f32(activation_min); + const float32x4_t vmax = vdupq_n_f32(activation_max); +#endif + + for (int32_t r = 0; r < lhs_rows; ++r) + { + const float32_t *lhs_row = lhs + (size_t)r * lhs_cols_offset; + float32_t *dst_row = dst + (size_t)r * row_address_offset; + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t c = 0; + for (; c + ARM_NN_CONV_1XN_F32_MVE_DUAL_BLOCK_ROWS <= rhs_rows; c += ARM_NN_CONV_1XN_F32_MVE_DUAL_BLOCK_ROWS) + { + const float32_t *rhs_block = rhs + (size_t)c * rhs_cols; + float32x4_t vacc_lo = bias ? vld1q(bias + c) : vdupq_n_f32(0.0f); + float32x4_t vacc_hi = bias ? vld1q(bias + c + ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float32_t lhs_v = lhs_row[k]; + const float32x4_t vrhs_lo = vldrwq_gather_shifted_offset(rhs_block + k, offsets); + const float32x4_t vrhs_hi = vldrwq_gather_shifted_offset(rhs_block + k, offsets_hi); + vacc_lo = vfmaq(vacc_lo, vrhs_lo, lhs_v); + vacc_hi = vfmaq(vacc_hi, vrhs_hi, lhs_v); + } + + vacc_lo = arm_nn_clamp_mve_f32(vacc_lo, vmin, vmax); + vacc_hi = arm_nn_clamp_mve_f32(vacc_hi, vmin, vmax); + vst1q(dst_row + c, vacc_lo); + vst1q(dst_row + c + ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS, vacc_hi); + } + + for (; c + ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS <= rhs_rows; c += ARM_NN_CONV_1XN_F32_MVE_BLOCK_ROWS) + { + const float32_t *rhs_block = rhs + (size_t)c * rhs_cols; + float32x4_t vacc = bias ? vld1q(bias + c) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float32x4_t vrhs = vldrwq_gather_shifted_offset(rhs_block + k, offsets); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f32(vacc, vmin, vmax); + vst1q(dst_row + c, vacc); + } +#else + int32_t c = 0; +#endif + + for (; c < rhs_rows; ++c) + { + const float32_t *rhs_row = rhs + (size_t)c * rhs_cols; + float32_t acc = bias ? bias[c] : 0.0f; + acc += arm_convolve_1_x_n_dot_f32(lhs_row, rhs_row, rhs_cols); + dst_row[c] = CLAMP(acc, activation_max, activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +__STATIC_INLINE void arm_convolve_1_x_n_find_regions(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + int32_t *left_pad_num, + int32_t *no_pad_num, + int32_t *right_pad_num) +{ + int32_t first_valid = 0; + while (first_valid < output_dims->w) + { + const int32_t base_x = first_valid * conv_params->stride.w - conv_params->padding.w; + if (base_x >= 0 && (base_x + filter_dims->w) <= input_dims->w) + { + break; + } + ++first_valid; + } + + int32_t last_valid = output_dims->w - 1; + while (last_valid >= first_valid) + { + const int32_t base_x = last_valid * conv_params->stride.w - conv_params->padding.w; + if (base_x >= 0 && (base_x + filter_dims->w) <= input_dims->w) + { + break; + } + --last_valid; + } + + *left_pad_num = first_valid; + *no_pad_num = MAX(last_valid - first_valid + 1, 0); + *right_pad_num = output_dims->w - *left_pad_num - *no_pad_num; +} + +__STATIC_INLINE void arm_convolve_1_x_n_pack_rows_f32(float32_t *scratch, + const float32_t *input_b, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + int32_t start_out_x, + int32_t rows) +{ + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t kernel_w = filter_dims->w; + const int32_t rhs_cols = kernel_w * input_c; + + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_x = start_out_x + r; + const int32_t base_x = out_x * conv_params->stride.w - conv_params->padding.w; + const int32_t left_pad_cols = MAX(0, -base_x); + const int32_t valid_x0 = MAX(base_x, 0); + const int32_t valid_x1 = MIN(base_x + kernel_w, input_w); + const int32_t valid_cols = MAX(valid_x1 - valid_x0, 0); + const int32_t right_pad_cols = kernel_w - left_pad_cols - valid_cols; + float32_t *patch_row = scratch + (size_t)r * rhs_cols; + + if (left_pad_cols > 0) + { + arm_memset_f32(patch_row, 0.0f, (uint32_t)((size_t)left_pad_cols * input_c)); + } + if (valid_cols > 0) + { + arm_memcpy_f32(patch_row + (size_t)left_pad_cols * input_c, + input_b + (size_t)valid_x0 * input_c, + (uint32_t)((size_t)valid_cols * input_c)); + } + if (right_pad_cols > 0) + { + arm_memset_f32(patch_row + (size_t)(left_pad_cols + valid_cols) * input_c, + 0.0f, + (uint32_t)((size_t)right_pad_cols * input_c)); + } + } +} + +arm_cmsis_nn_status arm_convolve_1_x_n_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)bias_dims; + + if (!ctx || !ctx->buf || !conv_params || !input_dims || !input_data || !filter_dims || !filter_data || + !output_dims || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || filter_dims->w <= 1 || + conv_params->stride.h != 1 || conv_params->stride.w <= 0 || conv_params->padding.h != 0 || + conv_params->dilation.h != 1 || conv_params->dilation.w != 1 || input_dims->c != filter_dims->c) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t buf_size = + arm_convolve_1_x_n_f32_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); + if (buf_size <= 0 || ctx->size < buf_size) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_w = filter_dims->w; + const int32_t rhs_cols = kernel_w * input_c; + const int32_t lhs_cols_offset = input_c * conv_params->stride.w; + const int32_t tile_rows = (int32_t)((size_t)ctx->size / ((size_t)rhs_cols * sizeof(float32_t))); + float32_t *scratch = (float32_t *)ctx->buf; + int32_t left_pad_num = 0; + int32_t no_pad_num = 0; + int32_t right_pad_num = 0; + + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + arm_convolve_1_x_n_find_regions( + conv_params, input_dims, filter_dims, output_dims, &left_pad_num, &no_pad_num, &right_pad_num); + + for (int32_t b = 0; b < batch; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_w * input_c; + float32_t *output_b = output_data + (size_t)b * output_w * output_c; + + for (int32_t row = 0; row < left_pad_num; row += tile_rows) + { + const int32_t rows = MIN(tile_rows, left_pad_num - row); + arm_convolve_1_x_n_pack_rows_f32(scratch, input_b, conv_params, input_dims, filter_dims, row, rows); + + arm_cmsis_nn_status st = arm_nn_mat_mult_nt_t_f32(scratch, + filter_data, + bias_data, + output_b, + rows, + output_c, + rhs_cols, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)rows * output_c; + } + + if (no_pad_num > 0) + { + const int32_t input_start = (conv_params->stride.w * left_pad_num - conv_params->padding.w) * input_c; + arm_cmsis_nn_status st = arm_convolve_1_x_n_mat_mult_nt_t_strided_f32(input_b + input_start, + filter_data, + bias_data, + output_b, + no_pad_num, + output_c, + rhs_cols, + lhs_cols_offset, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)no_pad_num * output_c; + } + + for (int32_t row = 0; row < right_pad_num; row += tile_rows) + { + const int32_t rows = MIN(tile_rows, right_pad_num - row); + const int32_t start_out_x = left_pad_num + no_pad_num + row; + arm_convolve_1_x_n_pack_rows_f32(scratch, input_b, conv_params, input_dims, filter_dims, start_out_x, rows); + + arm_cmsis_nn_status st = arm_nn_mat_mult_nt_t_f32(scratch, + filter_data, + bias_data, + output_b, + rows, + output_c, + rhs_cols, + output_c, + conv_params->activation.min, + conv_params->activation.max); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + output_b += (size_t)rows * output_c; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_1_x_n_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_1_x_n_nhwc_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +/** @} end of NNConv group */ diff --git a/Source/ConvolutionFunctions/arm_convolve_1x1_f16.c b/Source/ConvolutionFunctions/arm_convolve_1x1_f16.c new file mode 100644 index 00000000..6eb39d7a --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_1x1_f16.c @@ -0,0 +1,304 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_1x1_f16.c + * Description: Generic float16 1x1 convolution + * + * $Date: 24 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +/* Generic float16 1x1 convolution. */ + +#include "Internal/arm_conv1x1_opt_common.h" +#include "Internal/arm_conv1x1_opt_f16.h" +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +__STATIC_INLINE bool arm_conv_1x1_nhwc_use_patch_gemm_f16(const cmsis_nn_context *ctx, + int32_t input_c, + int32_t output_c, + int32_t output_positions) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || input_c <= 0) + { + return false; + } + + if (input_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_K || output_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_OC || + output_positions < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_POS) + { + return false; + } + + const size_t row_bytes = (size_t)input_c * sizeof(float16_t); + return row_bytes > 0U && (size_t)ctx->size >= row_bytes; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_1x1_mat_mul_f16(const float16_t *lhs, + const float16_t *rhs, + const float16_t *bias, + float16_t *dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + const cmsis_nn_conv_params_f16 *conv_params) +{ + if (conv_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f16(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f16(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); +} + +static arm_cmsis_nn_status arm_convolve_1x1_nhwc_patch_gemm_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const float16_t *filter_data, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t output_positions = output_h * output_w; + + const size_t row_bytes = (size_t)input_c * sizeof(float16_t); + if (row_bytes == 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t tile_rows = ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MAX_TILE_ROWS; + const int32_t max_rows_by_ctx = (int32_t)((size_t)ctx->size / row_bytes); + if (max_rows_by_ctx < tile_rows) + { + tile_rows = max_rows_by_ctx; + } + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float16_t *patch_matrix = (float16_t *)ctx->buf; + for (int32_t b = 0; b < batch; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t pos = 0; pos < output_positions; pos += tile_rows) + { + const int32_t rows = ((output_positions - pos) < tile_rows) ? (output_positions - pos) : tile_rows; + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + const int32_t in_y = out_y * stride_h; + const int32_t in_x = out_x * stride_w; + const int32_t src = ((in_y * input_w) + in_x) * input_c; + arm_memcpy_f16(patch_matrix + (size_t)r * input_c, input_b + src, (uint32_t)input_c); + } + + arm_cmsis_nn_status st = arm_convolve_1x1_mat_mul_f16(patch_matrix, + filter_data, + bias_data, + output_b + (size_t)pos * output_c, + rows, + output_c, + input_c, + output_c, + conv_params); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_1x1_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)bias_dims; + + if (!conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (conv_params->padding.w != 0 || conv_params->padding.h != 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + if (filter_dims->w != 1 || filter_dims->h != 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_c = input_dims->c; + const int32_t output_c = output_dims->c; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t input_hw = input_h * input_w; + const int32_t output_positions = output_h * output_w; + + if (stride_h == 1 && stride_w == 1 && output_h == input_h && output_w == input_w) + { + for (int32_t b = 0; b < input_dims->n; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_hw * input_c; + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + arm_cmsis_nn_status status = arm_convolve_1x1_mat_mul_f16( + input_b, filter_data, bias_data, output_b, input_hw, output_c, input_c, output_c, conv_params); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (arm_conv_1x1_nhwc_use_patch_gemm_f16(ctx, input_c, output_c, output_positions)) + { + return arm_convolve_1x1_nhwc_patch_gemm_f16( + ctx, conv_params, input_dims, input_data, filter_data, bias_data, output_dims, output_data); + } + + for (int32_t b = 0; b < input_dims->n; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + const int32_t in_y = out_y * stride_h; + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + const int32_t in_x = out_x * stride_w; + const int32_t in_base = (in_y * input_w + in_x) * input_c; + float16_t *output_pixel = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + + arm_cmsis_nn_status status = arm_convolve_1x1_mat_mul_f16(input_b + in_base, + filter_data, + bias_data, + output_pixel, + 1, + output_c, + input_c, + output_c, + conv_params); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} +arm_cmsis_nn_status arm_convolve_1x1_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_1x1_nhwc_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_convolve_1x1_f32.c b/Source/ConvolutionFunctions/arm_convolve_1x1_f32.c new file mode 100644 index 00000000..66558b86 --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_1x1_f32.c @@ -0,0 +1,303 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_1x1_f32.c + * Description: Generic float32 1x1 convolution + * + * $Date: 2 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +/* Generic float32 1x1 convolution. */ + +#include "Internal/arm_conv1x1_opt_common.h" +#include "Internal/arm_conv1x1_opt_f32.h" +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +__STATIC_INLINE bool arm_conv_1x1_nhwc_use_patch_gemm_f32(const cmsis_nn_context *ctx, + int32_t input_c, + int32_t output_c, + int32_t output_positions) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || input_c <= 0) + { + return false; + } + + if (input_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_K || output_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_OC || + output_positions < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_POS) + { + return false; + } + + const size_t row_bytes = (size_t)input_c * sizeof(float32_t); + return row_bytes > 0U && (size_t)ctx->size >= row_bytes; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_1x1_mat_mul_f32(const float32_t *lhs, + const float32_t *rhs, + const float32_t *bias, + float32_t *dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + const cmsis_nn_conv_params_f32 *conv_params) +{ + if (conv_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f32(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f32(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); +} + +static arm_cmsis_nn_status arm_convolve_1x1_nhwc_patch_gemm_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const float32_t *filter_data, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t output_positions = output_h * output_w; + + const size_t row_bytes = (size_t)input_c * sizeof(float32_t); + if (row_bytes == 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t tile_rows = ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MAX_TILE_ROWS; + const int32_t max_rows_by_ctx = (int32_t)((size_t)ctx->size / row_bytes); + if (max_rows_by_ctx < tile_rows) + { + tile_rows = max_rows_by_ctx; + } + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float32_t *patch_matrix = (float32_t *)ctx->buf; + for (int32_t b = 0; b < batch; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t pos = 0; pos < output_positions; pos += tile_rows) + { + const int32_t rows = ((output_positions - pos) < tile_rows) ? (output_positions - pos) : tile_rows; + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + const int32_t in_y = out_y * stride_h; + const int32_t in_x = out_x * stride_w; + const int32_t src = ((in_y * input_w) + in_x) * input_c; + arm_memcpy_f32(patch_matrix + (size_t)r * input_c, input_b + src, (uint32_t)input_c); + } + + arm_cmsis_nn_status st = arm_convolve_1x1_mat_mul_f32(patch_matrix, + filter_data, + bias_data, + output_b + (size_t)pos * output_c, + rows, + output_c, + input_c, + output_c, + conv_params); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_1x1_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)bias_dims; + + if (!conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (conv_params->padding.w != 0 || conv_params->padding.h != 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + if (filter_dims->w != 1 || filter_dims->h != 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_c = input_dims->c; + const int32_t output_c = output_dims->c; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t input_hw = input_h * input_w; + const int32_t output_positions = output_h * output_w; + + if (stride_h == 1 && stride_w == 1 && output_h == input_h && output_w == input_w) + { + for (int32_t b = 0; b < input_dims->n; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_hw * input_c; + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + arm_cmsis_nn_status status = arm_convolve_1x1_mat_mul_f32( + input_b, filter_data, bias_data, output_b, input_hw, output_c, input_c, output_c, conv_params); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + return ARM_CMSIS_NN_SUCCESS; + } + + if (arm_conv_1x1_nhwc_use_patch_gemm_f32(ctx, input_c, output_c, output_positions)) + { + return arm_convolve_1x1_nhwc_patch_gemm_f32( + ctx, conv_params, input_dims, input_data, filter_data, bias_data, output_dims, output_data); + } + + for (int32_t b = 0; b < input_dims->n; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + const int32_t in_y = out_y * stride_h; + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + const int32_t in_x = out_x * stride_w; + const int32_t in_base = (in_y * input_w + in_x) * input_c; + float32_t *output_pixel = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + arm_cmsis_nn_status status = arm_convolve_1x1_mat_mul_f32(input_b + in_base, + filter_data, + bias_data, + output_pixel, + 1, + output_c, + input_c, + output_c, + conv_params); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} +arm_cmsis_nn_status arm_convolve_1x1_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_1x1_nhwc_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_convolve_f16.c b/Source/ConvolutionFunctions/arm_convolve_f16.c new file mode 100755 index 00000000..c66ae8a3 --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_f16.c @@ -0,0 +1,463 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_f16.c + * Description: Generic float16 convolution + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +/* Generic float16 convolution. */ + +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_conv_opt_f16.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +__STATIC_INLINE float16_t arm_conv_dot_f16(const float16_t *lhs, const float16_t *rhs, int32_t len) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + for (int32_t i = 0; i < len; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(len - i)); + vacc = vfmaq_m(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p), p); + } + return arm_nn_vec_reduce_add_f16(vacc); +#else + _Float16 acc = (_Float16)0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += (_Float16)lhs[i] * (_Float16)rhs[i]; + } + return (float16_t)acc; +#endif +} + +__STATIC_INLINE bool arm_conv_nhwc_use_patch_gemm_f16(const cmsis_nn_context *ctx, + int32_t patch_len, + int32_t output_c, + int32_t output_positions) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || patch_len <= 0) + { + return false; + } + + if (patch_len < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_K || output_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_OC || + output_positions < ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MIN_POS) + { + return false; + } + + const size_t row_bytes = (size_t)patch_len * sizeof(float16_t); + return row_bytes > 0U && (size_t)ctx->size >= row_bytes; +} + +__STATIC_INLINE bool arm_conv_nhwc_use_1x1_f16(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *filter_dims) +{ + return conv_params && filter_dims && filter_dims->h == 1 && filter_dims->w == 1 && conv_params->padding.h == 0 && + conv_params->padding.w == 0; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_patch_mat_mul_f16(const float16_t *lhs, + const float16_t *rhs, + const float16_t *bias, + float16_t *dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + const cmsis_nn_conv_params_f16 *conv_params) +{ + if (conv_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f16(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f16(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); +} + +__STATIC_INLINE bool arm_conv_nhwc_use_1xn_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || !conv_params || !input_dims || !filter_dims || !output_dims) + { + return false; + } + + /* This helper only selects the generic 1xN NHWC kernel family. */ + if (input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || filter_dims->w <= 1 || + conv_params->stride.h != 1 || conv_params->stride.w <= 0 || conv_params->padding.h != 0 || + conv_params->dilation.h != 1 || conv_params->dilation.w != 1) + { + return false; + } + +#ifndef NN_DISABLE_SPECIALIZATION + /* + * If a direct specialization already claims the shape, let the normal + * specialization dispatcher handle it instead of forcing the generic 1xN + * implementation to know kernel-specific details. + */ + if (arm_conv_spec_nhwc_f16_matches_any(ctx, conv_params, input_dims, filter_dims, output_dims)) + { + return false; + } +#endif + + /* Remaining 1xN shapes use the generic packed-input helper when workspace is available. */ + const int32_t buf_size = + arm_convolve_1_x_n_f16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); + return buf_size > 0 && ctx->size >= buf_size; +} + +static arm_cmsis_nn_status arm_convolve_nhwc_patch_gemm_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t pad_h = conv_params->padding.h; + const int32_t pad_w = conv_params->padding.w; + const int32_t dil_h = conv_params->dilation.h; + const int32_t dil_w = conv_params->dilation.w; + const int32_t patch_len = kernel_h * kernel_w * input_c; + const int32_t output_positions = output_h * output_w; + + const size_t row_bytes = (size_t)patch_len * sizeof(float16_t); + if (row_bytes == 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t tile_rows = ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MAX_TILE_ROWS; + const int32_t max_rows_by_ctx = (int32_t)((size_t)ctx->size / row_bytes); + if (max_rows_by_ctx < tile_rows) + { + tile_rows = max_rows_by_ctx; + } + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + /* Developers familiar with im2row terminology: each output position becomes one packed row here. */ + float16_t *patch_matrix = (float16_t *)ctx->buf; + for (int32_t b = 0; b < batch; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t pos = 0; pos < output_positions; pos += tile_rows) + { + const int32_t rows = ((output_positions - pos) < tile_rows) ? (output_positions - pos) : tile_rows; + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + float16_t *patch_row = patch_matrix + (size_t)r * patch_len; + arm_nn_pack_conv_patch_f16(input_b, + input_h, + input_w, + input_c, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dil_h, + dil_w, + out_y, + out_x, + (float16_t)0.0f, + patch_row); + } + + arm_cmsis_nn_status st = arm_convolve_patch_mat_mul_f16(patch_matrix, + filter_data, + bias_data, + output_b + (size_t)pos * output_c, + rows, + output_c, + patch_len, + output_c, + conv_params); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +#include + +arm_cmsis_nn_status arm_convolve_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)bias_dims; + + if (!conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t pad_h = conv_params->padding.h; + const int32_t pad_w = conv_params->padding.w; + const int32_t dil_h = conv_params->dilation.h; + const int32_t dil_w = conv_params->dilation.w; + const int32_t patch_len = kernel_h * kernel_w * input_c; + const int32_t output_positions = output_h * output_w; + + if (arm_conv_nhwc_use_1x1_f16(conv_params, filter_dims)) + { + return arm_convolve_1x1_nhwc_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); + } + + if (arm_conv_nhwc_use_1xn_f16(ctx, conv_params, input_dims, filter_dims, output_dims)) + { + return arm_convolve_1_x_n_nhwc_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); + } + +#ifndef NN_DISABLE_SPECIALIZATION + /* + * Let direct specializations claim their shapes first. Packed-patch GEMM + * remains the generic fallback for shapes that are not handled by a tuned + * direct kernel. + */ + ARM_CONV_DISPATCH(arm_conv_spec_nhwc_f16, + ARM_CONV_ARRAY_SIZE(arm_conv_spec_nhwc_f16), + ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +#endif + + const bool use_patch_gemm = arm_conv_nhwc_use_patch_gemm_f16(ctx, patch_len, output_c, output_positions); + + if (use_patch_gemm) + { + arm_cmsis_nn_status st = arm_convolve_nhwc_patch_gemm_f16( + ctx, conv_params, input_dims, input_data, filter_dims, filter_data, bias_data, output_dims, output_data); + if (st == ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + + for (int32_t b = 0; b < batch; ++b) + { + const float16_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + const int32_t in_y0 = out_y * stride_h - pad_h; + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + const int32_t in_x0 = out_x * stride_w - pad_w; + for (int32_t oc = 0; oc < output_c; ++oc) + { + _Float16 acc = bias_data ? (_Float16)bias_data[oc] : (_Float16)0; + const float16_t *w_oc = filter_data + (size_t)oc * kernel_h * kernel_w * input_c; + + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t in_y = in_y0 + ky * dil_h; + if (in_y < 0 || in_y >= input_h) + { + continue; + } + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t in_x = in_x0 + kx * dil_w; + if (in_x < 0 || in_x >= input_w) + { + continue; + } + const float16_t *w_k = w_oc + ((size_t)ky * kernel_w + (size_t)kx) * input_c; + const float16_t *x = input_b + ((size_t)in_y * input_w + (size_t)in_x) * input_c; + acc += (_Float16)arm_conv_dot_f16(x, w_k, input_c); + } + } + + acc = CLAMP(acc, (_Float16)conv_params->activation.max, (_Float16)conv_params->activation.min); + output_b[((size_t)out_y * output_w + (size_t)out_x) * output_c + (size_t)oc] = (float16_t)acc; + } + } + } + } + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_nhwc_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +arm_cmsis_nn_status arm_convolve_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + return arm_convolve_f16(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data, + ARM_NN_LAYOUT_NHWC); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_convolve_f32.c b/Source/ConvolutionFunctions/arm_convolve_f32.c new file mode 100755 index 00000000..8e2d8e66 --- /dev/null +++ b/Source/ConvolutionFunctions/arm_convolve_f32.c @@ -0,0 +1,461 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_f32.c + * Description: Generic float32 convolution + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +/* Generic float32 convolution. */ + +#include "Internal/arm_conv_opt_common.h" +#include "Internal/arm_conv_opt_f32.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +__STATIC_INLINE float32_t arm_conv_dot_f32(const float32_t *lhs, const float32_t *rhs, int32_t len) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + float32x4_t vacc = vdupq_n_f32(0.0f); + for (int32_t i = 0; i < len; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(len - i)); + vacc = vfmaq_m(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p), p); + } + return arm_nn_vec_reduce_add_f32(vacc); +#else + float32_t acc = 0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += lhs[i] * rhs[i]; + } + return acc; +#endif +} + +__STATIC_INLINE bool arm_conv_nhwc_use_patch_gemm_f32(const cmsis_nn_context *ctx, + int32_t patch_len, + int32_t output_c, + int32_t output_positions) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || patch_len <= 0) + { + return false; + } + + if (patch_len < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_K || output_c < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_OC || + output_positions < ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MIN_POS) + { + return false; + } + + const size_t row_bytes = (size_t)patch_len * sizeof(float32_t); + return row_bytes > 0U && (size_t)ctx->size >= row_bytes; +} + +__STATIC_INLINE bool arm_conv_nhwc_use_1x1_f32(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *filter_dims) +{ + return conv_params && filter_dims && filter_dims->h == 1 && filter_dims->w == 1 && conv_params->padding.h == 0 && + conv_params->padding.w == 0; +} + +__STATIC_INLINE arm_cmsis_nn_status arm_convolve_patch_mat_mul_f32(const float32_t *lhs, + const float32_t *rhs, + const float32_t *bias, + float32_t *dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + const cmsis_nn_conv_params_f32 *conv_params) +{ + if (conv_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f32(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f32(lhs, + rhs, + bias, + dst, + lhs_rows, + rhs_rows, + rhs_cols, + row_address_offset, + conv_params->activation.min, + conv_params->activation.max); +} + +__STATIC_INLINE bool arm_conv_nhwc_use_1xn_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || !conv_params || !input_dims || !filter_dims || !output_dims) + { + return false; + } + + /* This helper only selects the generic 1xN NHWC kernel family. */ + if (input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || filter_dims->w <= 1 || + conv_params->stride.h != 1 || conv_params->stride.w <= 0 || conv_params->padding.h != 0 || + conv_params->dilation.h != 1 || conv_params->dilation.w != 1) + { + return false; + } + +#ifndef NN_DISABLE_SPECIALIZATION + /* + * If a direct specialization already claims the shape, let the normal + * specialization dispatcher handle it instead of forcing the generic 1xN + * implementation to know kernel-specific details. + */ + if (arm_conv_spec_nhwc_f32_matches_any(ctx, conv_params, input_dims, filter_dims, output_dims)) + { + return false; + } +#endif + + /* Remaining 1xN shapes use the generic packed-input helper when workspace is available. */ + const int32_t buf_size = + arm_convolve_1_x_n_f32_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); + return buf_size > 0 && ctx->size >= buf_size; +} + +static arm_cmsis_nn_status arm_convolve_nhwc_patch_gemm_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t pad_h = conv_params->padding.h; + const int32_t pad_w = conv_params->padding.w; + const int32_t dil_h = conv_params->dilation.h; + const int32_t dil_w = conv_params->dilation.w; + const int32_t patch_len = kernel_h * kernel_w * input_c; + const int32_t output_positions = output_h * output_w; + + const size_t row_bytes = (size_t)patch_len * sizeof(float32_t); + if (row_bytes == 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t tile_rows = ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MAX_TILE_ROWS; + const int32_t max_rows_by_ctx = (int32_t)((size_t)ctx->size / row_bytes); + if (max_rows_by_ctx < tile_rows) + { + tile_rows = max_rows_by_ctx; + } + if (tile_rows <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + /* Developers familiar with im2row terminology: each output position becomes one packed row here. */ + float32_t *patch_matrix = (float32_t *)ctx->buf; + for (int32_t b = 0; b < batch; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t pos = 0; pos < output_positions; pos += tile_rows) + { + const int32_t rows = ((output_positions - pos) < tile_rows) ? (output_positions - pos) : tile_rows; + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + float32_t *patch_row = patch_matrix + (size_t)r * patch_len; + arm_nn_pack_conv_patch_f32(input_b, + input_h, + input_w, + input_c, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dil_h, + dil_w, + out_y, + out_x, + 0.0f, + patch_row); + } + + arm_cmsis_nn_status st = arm_convolve_patch_mat_mul_f32(patch_matrix, + filter_data, + bias_data, + output_b + (size_t)pos * output_c, + rows, + output_c, + patch_len, + output_c, + conv_params); + if (st != ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)bias_dims; + + if (!conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t stride_h = conv_params->stride.h; + const int32_t stride_w = conv_params->stride.w; + const int32_t pad_h = conv_params->padding.h; + const int32_t pad_w = conv_params->padding.w; + const int32_t dil_h = conv_params->dilation.h; + const int32_t dil_w = conv_params->dilation.w; + const int32_t patch_len = kernel_h * kernel_w * input_c; + const int32_t output_positions = output_h * output_w; + + if (arm_conv_nhwc_use_1x1_f32(conv_params, filter_dims)) + { + return arm_convolve_1x1_nhwc_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); + } + + if (arm_conv_nhwc_use_1xn_f32(ctx, conv_params, input_dims, filter_dims, output_dims)) + { + return arm_convolve_1_x_n_nhwc_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); + } + +#ifndef NN_DISABLE_SPECIALIZATION + /* + * Let direct specializations claim their shapes first. Packed-patch GEMM + * remains the generic fallback for shapes that are not handled by a tuned + * direct kernel. + */ + ARM_CONV_DISPATCH(arm_conv_spec_nhwc_f32, + ARM_CONV_ARRAY_SIZE(arm_conv_spec_nhwc_f32), + ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +#endif + + const bool use_patch_gemm = arm_conv_nhwc_use_patch_gemm_f32(ctx, patch_len, output_c, output_positions); + + if (use_patch_gemm) + { + arm_cmsis_nn_status st = arm_convolve_nhwc_patch_gemm_f32( + ctx, conv_params, input_dims, input_data, filter_dims, filter_data, bias_data, output_dims, output_data); + if (st == ARM_CMSIS_NN_SUCCESS) + { + return st; + } + } + + for (int32_t b = 0; b < batch; ++b) + { + const float32_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + const int32_t in_y0 = out_y * stride_h - pad_h; + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + const int32_t in_x0 = out_x * stride_w - pad_w; + for (int32_t oc = 0; oc < output_c; ++oc) + { + float32_t acc = bias_data ? bias_data[oc] : 0.0f; + const float32_t *w_oc = filter_data + (size_t)oc * kernel_h * kernel_w * input_c; + + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t in_y = in_y0 + ky * dil_h; + if (in_y < 0 || in_y >= input_h) + { + continue; + } + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t in_x = in_x0 + kx * dil_w; + if (in_x < 0 || in_x >= input_w) + { + continue; + } + const float32_t *w_k = w_oc + ((size_t)ky * kernel_w + (size_t)kx) * input_c; + const float32_t *x = input_b + ((size_t)in_y * input_w + (size_t)in_x) * input_c; + acc += arm_conv_dot_f32(x, w_k, input_c); + } + } + + acc = CLAMP(acc, conv_params->activation.max, conv_params->activation.min); + output_b[((size_t)out_y * output_w + (size_t)out_x) * output_c + (size_t)oc] = acc; + } + } + } + } + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_convolve_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_convolve_nhwc_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +arm_cmsis_nn_status arm_convolve_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + return arm_convolve_f32(ctx, + conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data, + ARM_NN_LAYOUT_NHWC); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_depthwise_conv_f16.c b/Source/ConvolutionFunctions/arm_depthwise_conv_f16.c new file mode 100644 index 00000000..2fd1bbbd --- /dev/null +++ b/Source/ConvolutionFunctions/arm_depthwise_conv_f16.c @@ -0,0 +1,835 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_depthwise_conv_f16.c + * Description: Convolution: depthwise (float16) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_depthwise_conv_opt_common.h" +#include "Internal/arm_depthwise_conv_opt_f16.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +/* Number of packed output rows processed per depthwise NT-T tile. */ +#define ARM_NN_DW_NT_T_F16_TILE_ROWS (4) + +__STATIC_INLINE void +arm_depthwise_accumulate_vec_f16(float16_t *acc, const float16_t *input, const float16_t *kernel, int32_t channels) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < channels; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channels - c)); + float16x8_t vacc = vld1q_z(acc + c, p); + vacc = vfmaq(vacc, vld1q_z(input + c, p), vld1q_z(kernel + c, p)); + vst1q_p(acc + c, vacc, p); + } +#else + for (int32_t c = 0; c < channels; ++c) + { + const _Float16 sum = (_Float16)acc[c] + (_Float16)input[c] * (_Float16)kernel[c]; + acc[c] = (float16_t)sum; + } +#endif +} + +static void arm_depthwise_conv_nhwc_fast_chmult1_kc_f16(const float16_t *input, + int32_t input_batches, + int32_t input_x, + int32_t input_y, + int32_t input_ch, + const float16_t *kernel, + int32_t kernel_x, + int32_t kernel_y, + int32_t pad_x, + int32_t pad_y, + int32_t stride_x, + int32_t stride_y, + const float16_t *bias, + float16_t *output, + int32_t output_x, + int32_t output_y, + float16_t output_activation_min, + float16_t output_activation_max) +{ + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * input_ch; + + for (int32_t i_batch = 0; i_batch < input_batches; ++i_batch) + { + const float16_t *input_b = input + i_batch * in_batch_stride; + float16_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; ++i_out_y) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + const int32_t ker_y_start = (base_idx_y < 0) ? -base_idx_y : 0; + const int32_t ker_y_end = (kernel_y < (input_y - base_idx_y)) ? kernel_y : (input_y - base_idx_y); + + for (int32_t i_out_x = 0; i_out_x < output_x; ++i_out_x) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + const int32_t ker_x_start = (base_idx_x < 0) ? -base_idx_x : 0; + const int32_t ker_x_end = (kernel_x < (input_x - base_idx_x)) ? kernel_x : (input_x - base_idx_x); + + float16_t *out_px = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + for (int32_t c = 0; c < input_ch; ++c) + { + out_px[c] = bias ? bias[c] : (float16_t)0; + } + + for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; ++i_ker_y) + { + const int32_t idx_y = base_idx_y + i_ker_y; + for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; ++i_ker_x) + { + const int32_t idx_x = base_idx_x + i_ker_x; + const float16_t *in_px = input_b + ((size_t)idx_y * input_x + idx_x) * input_ch; + const float16_t *w_px = kernel + ((size_t)i_ker_y * kernel_x + i_ker_x) * input_ch; + arm_depthwise_accumulate_vec_f16(out_px, in_px, w_px, input_ch); + } + } + + arm_nn_vector_clamp_f16(out_px, input_ch, output_activation_min, output_activation_max); + } + } + } +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_fast_chmult1_kc_nt_t_f16(const cmsis_nn_context *ctx, + const float16_t *input, + int32_t input_batches, + int32_t input_x, + int32_t input_y, + int32_t input_ch, + const float16_t *kernel, + int32_t kernel_x, + int32_t kernel_y, + int32_t pad_x, + int32_t pad_y, + int32_t stride_x, + int32_t stride_y, + const float16_t *bias, + float16_t *output, + int32_t output_x, + int32_t output_y, + float16_t output_activation_min, + float16_t output_activation_max) +{ + if (!ctx || !ctx->buf || ctx->size <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + const int32_t kernel_size = kernel_x * kernel_y; + if (kernel_size <= 0 || input_ch <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t lhs_row_elems = (size_t)kernel_size * (size_t)input_ch; + const size_t lhs_row_bytes = lhs_row_elems * sizeof(float16_t); + if (lhs_row_bytes == 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t max_rows_from_buf = (size_t)ctx->size / lhs_row_bytes; + int32_t tile_row_limit = (int32_t)max_rows_from_buf; + if (tile_row_limit > ARM_NN_DW_NT_T_F16_TILE_ROWS) + { + tile_row_limit = ARM_NN_DW_NT_T_F16_TILE_ROWS; + } + if (tile_row_limit < 1) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + float16_t *lhs_buffer = (float16_t *)ctx->buf; + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * input_ch; + + for (int32_t i_batch = 0; i_batch < input_batches; ++i_batch) + { + const float16_t *input_b = input + i_batch * in_batch_stride; + float16_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; ++i_out_y) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + + for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x += tile_row_limit) + { + int32_t tile_rows = output_x - i_out_x; + if (tile_rows > tile_row_limit) + { + tile_rows = tile_row_limit; + } + + float16_t *lhs_ptr = lhs_buffer; + for (int32_t row = 0; row < tile_rows; ++row) + { + const int32_t base_idx_x = ((i_out_x + row) * stride_x) - pad_x; + + for (int32_t i_ker_y = 0; i_ker_y < kernel_y; ++i_ker_y) + { + const int32_t idx_y = base_idx_y + i_ker_y; + for (int32_t i_ker_x = 0; i_ker_x < kernel_x; ++i_ker_x) + { + const int32_t idx_x = base_idx_x + i_ker_x; + if ((uint32_t)idx_y >= (uint32_t)input_y || (uint32_t)idx_x >= (uint32_t)input_x) + { + arm_memset_f16(lhs_ptr, (float16_t)0.0f, (uint32_t)input_ch); + } + else + { + const float16_t *in_px = input_b + ((size_t)idx_y * input_x + idx_x) * input_ch; + arm_memcpy_f16(lhs_ptr, in_px, (uint32_t)input_ch); + } + lhs_ptr += input_ch; + } + } + } + + float16_t *out_ptr = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + arm_cmsis_nn_status status = arm_nn_depthwise_conv_nt_t_f16(lhs_buffer, + kernel, + bias, + out_ptr, + tile_rows, + input_ch, + kernel_size, + input_ch, + output_activation_min, + output_activation_max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE bool arm_depthwise_conv_nhwc_convert_to_conv_f16(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *output_dims) +{ + return dw_conv_params && input_dims && output_dims && input_dims->c == 1 && + output_dims->c >= CONVERT_DW_CONV_WITH_ONE_INPUT_CH_AND_OUTPUT_CH_ABOVE_THRESHOLD; +} + +__STATIC_INLINE void arm_depthwise_pack_conv_kernel_nt_n_f16(const float16_t *kernel, + int32_t output_c, + int32_t kernel_h, + int32_t kernel_w, + arm_nn_dw_kernel_layout_f16 kernel_layout, + float16_t *packed_kernel) +{ + const int32_t kernel_elems = kernel_h * kernel_w; + const int32_t block_cols = 8; + const int32_t packed_output_c = ARM_NN_ROUND_UP(output_c, block_cols); + + for (int32_t n_base = 0; n_base < packed_output_c; n_base += block_cols) + { + for (int32_t k = 0; k < kernel_elems; ++k) + { + float16_t *dst = packed_kernel + (size_t)n_base * kernel_elems + (size_t)k * block_cols; + for (int32_t lane = 0; lane < block_cols; ++lane) + { + const int32_t oc = n_base + lane; + if (oc < output_c) + { + dst[lane] = (kernel_layout == ARM_NN_DW_KERNEL_CK) ? kernel[(size_t)oc * kernel_elems + k] + : kernel[(size_t)k * output_c + oc]; + } + else + { + dst[lane] = (float16_t)0.0f; + } + } + } + } +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_to_conv_packed_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *packed_kernel, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || !dw_conv_params || !input_dims || !input || !packed_kernel || + !output_dims || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t patch_len = kernel_h * kernel_w; + const int32_t output_positions = output_h * output_w; + + if (input_dims->c != 1 || output_c <= 0 || kernel_h <= 0 || kernel_w <= 0 || patch_len <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t stride_h = dw_conv_params->stride.h; + const int32_t stride_w = dw_conv_params->stride.w; + const int32_t pad_h = dw_conv_params->padding.h; + const int32_t pad_w = dw_conv_params->padding.w; + const int32_t dil_h = dw_conv_params->dilation.h; + const int32_t dil_w = dw_conv_params->dilation.w; + const size_t row_bytes = (size_t)patch_len * sizeof(float16_t); + const int32_t max_rows = (int32_t)((size_t)ctx->size / row_bytes); + + if (max_rows <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + /* + * The converted depthwise path always has input_c == 1, so each patch row + * is simply the kernel footprint scalars. Packing that directly here avoids + * the general NHWC patch helper overhead. + */ + float16_t *lhs_buffer = (float16_t *)ctx->buf; + const int32_t in_batch_stride = input_h * input_w; + const int32_t out_batch_stride = output_h * output_w * output_c; + + for (int32_t b = 0; b < batch; ++b) + { + const float16_t *input_b = input + (size_t)b * in_batch_stride; + float16_t *output_b = output + (size_t)b * out_batch_stride; + + for (int32_t pos = 0; pos < output_positions; pos += max_rows) + { + const int32_t rows = ((output_positions - pos) < max_rows) ? (output_positions - pos) : max_rows; + + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + const int32_t base_idx_y = (out_y * stride_h) - pad_h; + const int32_t base_idx_x = (out_x * stride_w) - pad_w; + float16_t *patch_row = lhs_buffer + (size_t)r * patch_len; + + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t idx_y = base_idx_y + dil_h * ky; + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t idx_x = base_idx_x + dil_w * kx; + *patch_row++ = ((uint32_t)idx_y < (uint32_t)input_h && (uint32_t)idx_x < (uint32_t)input_w) + ? input_b[idx_y * input_w + idx_x] + : (float16_t)0.0f; + } + } + } + + arm_cmsis_nn_status status = arm_nn_mat_mult_nt_n_packed_f16(lhs_buffer, + packed_kernel, + bias, + output_b + (size_t)pos * output_c, + rows, + output_c, + patch_len, + output_c, + dw_conv_params->activation.min, + dw_conv_params->activation.max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_to_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + if (!ctx || !ctx->buf || ctx->size <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const size_t kernel_row_elems = (size_t)kernel_h * (size_t)kernel_w; + const size_t packed_output_c = (size_t)ARM_NN_ROUND_UP(output_c, 8); + const size_t kernel_bytes = packed_output_c * kernel_row_elems * sizeof(float16_t); + uint8_t *ctx_bytes = (uint8_t *)ctx->buf; + const float16_t *conv_kernel; + size_t scratch_offset = 0U; + + if ((size_t)ctx->size < kernel_bytes) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + /* + * The converted conv path stores the filter in packed NTxN scratch memory + * and then consumes it as float16_t data. Require float16_t-aligned scratch + * here so the typed view is explicit and safe. + */ + if (((uintptr_t)ctx->buf & (sizeof(float16_t) - 1U)) != 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (kernel_layout != ARM_NN_DW_KERNEL_CK && kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float16_t *packed_kernel = (float16_t *)ctx->buf; + arm_depthwise_pack_conv_kernel_nt_n_f16(kernel, output_c, kernel_h, kernel_w, kernel_layout, packed_kernel); + conv_kernel = packed_kernel; + scratch_offset = kernel_bytes; + + cmsis_nn_context conv_ctx = {0}; + if ((size_t)ctx->size > scratch_offset) + { + conv_ctx.buf = ctx_bytes + scratch_offset; + conv_ctx.size = ctx->size - (int32_t)scratch_offset; + } + + const cmsis_nn_conv_params_f16 conv_params = {.stride = dw_conv_params->stride, + .padding = dw_conv_params->padding, + .dilation = dw_conv_params->dilation, + .activation = dw_conv_params->activation, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims conv_filter_dims = {.n = output_c, .h = kernel_h, .w = kernel_w, .c = input_dims->c}; + (void)bias_dims; + (void)conv_filter_dims; + (void)conv_params; + + return arm_depthwise_conv_nhwc_to_conv_packed_f16( + &conv_ctx, dw_conv_params, input_dims, input, filter_dims, conv_kernel, bias, output_dims, output); +} +#endif + +static void arm_depthwise_conv_f16_generic(const float16_t *input, + const int32_t input_batches, + const int32_t input_x, + const int32_t input_y, + const int32_t input_ch, + const float16_t *kernel, + const int32_t ch_mult, + const int32_t kernel_x, + const int32_t kernel_y, + const int32_t pad_x, + const int32_t pad_y, + const int32_t stride_x, + const int32_t stride_y, + const float16_t *bias, + float16_t *output, + const int32_t output_x, + const int32_t output_y, + const float16_t output_activation_min, + const float16_t output_activation_max, + const int32_t dilation_x, + const int32_t dilation_y, + const cmsis_nn_dw_conv_params_f16 *params, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ + (void)params; + const int32_t output_ch = input_ch * ch_mult; + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * output_ch; + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + const float16_t *input_b = input + i_batch * in_batch_stride; + float16_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + for (int32_t i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) + { + for (int32_t i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) + { + const int32_t idx_out_ch = i_ch_mult + i_input_ch * ch_mult; + _Float16 acc_0 = (_Float16)0; + + int32_t ker_y_start; + int32_t ker_x_start; + int32_t ker_y_end; + int32_t ker_x_end; + + if (dilation_x > 1) + { + const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x; + ker_x_start = (start_x_max > 0) ? start_x_max : 0; + const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x; + ker_x_end = (kernel_x < end_min_x) ? kernel_x : end_min_x; + } + else + { + ker_x_start = (base_idx_x < 0) ? -base_idx_x : 0; + const int32_t end_min_x = input_x - base_idx_x; + ker_x_end = (kernel_x < end_min_x) ? kernel_x : end_min_x; + } + + if (dilation_y > 1) + { + const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y; + ker_y_start = (start_y_max > 0) ? start_y_max : 0; + const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y; + ker_y_end = (kernel_y < end_min_y) ? kernel_y : end_min_y; + } + else + { + ker_y_start = (base_idx_y < 0) ? -base_idx_y : 0; + const int32_t end_min_y = input_y - base_idx_y; + ker_y_end = (kernel_y < end_min_y) ? kernel_y : end_min_y; + } + + if (bias) + { + acc_0 = (_Float16)bias[idx_out_ch]; + } + + for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) + { + const int32_t idx_y = base_idx_y + dilation_y * i_ker_y; + for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) + { + const int32_t idx_x = base_idx_x + dilation_x * i_ker_x; + const int32_t idx_0 = + arm_depthwise_conv_input_index_nhwc(idx_x, idx_y, i_input_ch, input_x, input_ch); + int32_t ker_idx_0; + if (kernel_layout == ARM_NN_DW_KERNEL_CK) + { + ker_idx_0 = idx_out_ch * (kernel_x * kernel_y) + i_ker_y * kernel_x + i_ker_x; + } + else + { + ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (output_ch) + idx_out_ch; + } + + acc_0 += (_Float16)input_b[idx_0] * (_Float16)kernel[ker_idx_0]; + } + } + + acc_0 = CLAMP(acc_0, (_Float16)output_activation_max, (_Float16)output_activation_min); + const int32_t out_idx = + arm_depthwise_conv_output_index_nhwc(i_out_x, i_out_y, idx_out_ch, output_x, output_ch); + output_b[out_idx] = (float16_t)acc_0; + } + } + } + } + } +} + +static arm_cmsis_nn_status arm_depthwise_conv_f16_validate(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + const float16_t *input, + const float16_t *kernel, + const float16_t *output, + arm_nn_dw_kernel_layout_f16 forced_kernel_layout, + arm_nn_dw_kernel_layout_f16 *kernel_layout) +{ + if (!dw_conv_params || !input_dims || !filter_dims || !output_dims || !input || !kernel || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + *kernel_layout = forced_kernel_layout; + + return ARM_CMSIS_NN_SUCCESS; +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_dispatch_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_dw_kernel_layout_f16 kernel_layout) +{ +#ifndef NN_DISABLE_SPECIALIZATION + /* First try exact-shape NHWC specializations such as 1D-k3, 2x5, and 3x3 kernels. */ + ARM_DW_DISPATCH(arm_dw_spec_nhwc_f16, + ARM_DW_ARRAY_SIZE(arm_dw_spec_nhwc_f16), + ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +#endif + + /* Then try the broader ch_mult=1 fast NHWC routes before falling back to the generic kernel. */ + if (dw_conv_params->ch_mult == 1 && dw_conv_params->dilation.w == 1 && dw_conv_params->dilation.h == 1) + { + if (arm_depthwise_conv_nhwc_fast_chmult1_kc_nt_t_f16(ctx, + input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max) == ARM_CMSIS_NN_SUCCESS) + { + return ARM_CMSIS_NN_SUCCESS; + } + + arm_depthwise_conv_nhwc_fast_chmult1_kc_f16(input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max); + return ARM_CMSIS_NN_SUCCESS; + } + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + /* For one-input-channel cases on MVE, reusing the float convolution wrapper can be faster. */ + if (arm_depthwise_conv_nhwc_convert_to_conv_f16(dw_conv_params, input_dims, output_dims)) + { + arm_cmsis_nn_status conv_status = arm_depthwise_conv_nhwc_to_conv_f16(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); + if (conv_status == ARM_CMSIS_NN_SUCCESS) + { + return conv_status; + } + } +#endif + + /* Generic NHWC fallback for remaining channel-multiplier, dilation, or kernel-layout cases. */ + arm_depthwise_conv_f16_generic(input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + dw_conv_params->ch_mult, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max, + dw_conv_params->dilation.w, + dw_conv_params->dilation.h, + dw_conv_params, + kernel_layout); + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_depthwise_nhwc_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + arm_nn_dw_kernel_layout_f16 kernel_layout; + arm_cmsis_nn_status status = arm_depthwise_conv_f16_validate(dw_conv_params, + input_dims, + filter_dims, + output_dims, + input, + kernel, + output, + ARM_NN_DW_KERNEL_KC, + &kernel_layout); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + + return arm_depthwise_conv_nhwc_dispatch_f16(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +} + +arm_cmsis_nn_status arm_depthwise_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_tensor_layout layout) +{ + if (!dw_conv_params || layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_depthwise_nhwc_conv_f16( + ctx, dw_conv_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +arm_cmsis_nn_status arm_depthwise_conv_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + arm_nn_dw_kernel_layout_f16 kernel_layout; + arm_cmsis_nn_status status = arm_depthwise_conv_f16_validate(dw_conv_params, + input_dims, + filter_dims, + output_dims, + input, + kernel, + output, + ARM_NN_DW_KERNEL_KC, + &kernel_layout); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + + return arm_depthwise_conv_nhwc_dispatch_f16(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_depthwise_conv_f32.c b/Source/ConvolutionFunctions/arm_depthwise_conv_f32.c new file mode 100644 index 00000000..42c820ae --- /dev/null +++ b/Source/ConvolutionFunctions/arm_depthwise_conv_f32.c @@ -0,0 +1,968 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_depthwise_conv_f32.c + * Description: Float32 depthwise convolution + * + * $Date: 2 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +/* + * Float32 depthwise convolution (generic reference). + * CMSIS-NN style API, without quantization. + */ + +#include "Internal/arm_depthwise_conv_opt_common.h" +#include "Internal/arm_depthwise_conv_opt_f32.h" +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +/* Number of packed output rows processed per depthwise NT-T tile. */ +#define ARM_NN_DW_NT_T_F32_TILE_ROWS (4) + +__STATIC_INLINE void +arm_depthwise_accumulate_vec_f32(float32_t *acc, const float32_t *input, const float32_t *kernel, int32_t channels) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + float32x4_t vacc = vld1q_z(acc + c, p); + vacc = vfmaq(vacc, vld1q_z(input + c, p), vld1q_z(kernel + c, p)); + vst1q_p(acc + c, vacc, p); + } +#else + for (int32_t c = 0; c < channels; ++c) + { + acc[c] += input[c] * kernel[c]; + } +#endif +} + +__STATIC_INLINE void arm_depthwise_init_vec_f32(float32_t *dst, const float32_t *bias, int32_t channels) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (bias) + { + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + vst1q_p(dst + c, vld1q_z(bias + c, p), p); + } + } + else + { + const float32x4_t vzero = vdupq_n_f32(0.0f); + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + vst1q_p(dst + c, vzero, p); + } + } +#else + if (bias) + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = bias[c]; + } + } + else + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = 0.0f; + } + } +#endif +} + +__STATIC_INLINE void arm_depthwise_accumulate_pixel_nhwc_kc_f32(const float32_t *input_b, + int32_t input_x, + int32_t input_y, + int32_t input_ch, + const float32_t *kernel, + int32_t kernel_x, + int32_t kernel_y, + int32_t base_idx_x, + int32_t base_idx_y, + float32_t *out_px, + int32_t interior_only) +{ + if (interior_only) + { + for (int32_t i_ker_y = 0; i_ker_y < kernel_y; ++i_ker_y) + { + const int32_t idx_y = base_idx_y + i_ker_y; + const float32_t *in_row = input_b + ((size_t)idx_y * input_x + base_idx_x) * input_ch; + const float32_t *w_row = kernel + ((size_t)i_ker_y * kernel_x) * input_ch; + for (int32_t i_ker_x = 0; i_ker_x < kernel_x; ++i_ker_x) + { + arm_depthwise_accumulate_vec_f32( + out_px, in_row + (size_t)i_ker_x * input_ch, w_row + (size_t)i_ker_x * input_ch, input_ch); + } + } + return; + } + + const int32_t ker_y_start = MAX(0, -base_idx_y); + const int32_t ker_y_end = MIN(kernel_y, input_y - base_idx_y); + const int32_t ker_x_start = MAX(0, -base_idx_x); + const int32_t ker_x_end = MIN(kernel_x, input_x - base_idx_x); + + for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; ++i_ker_y) + { + const int32_t idx_y = base_idx_y + i_ker_y; + for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; ++i_ker_x) + { + const int32_t idx_x = base_idx_x + i_ker_x; + const float32_t *in_px = input_b + ((size_t)idx_y * input_x + idx_x) * input_ch; + const float32_t *w_px = kernel + ((size_t)i_ker_y * kernel_x + i_ker_x) * input_ch; + arm_depthwise_accumulate_vec_f32(out_px, in_px, w_px, input_ch); + } + } +} + +__STATIC_INLINE void arm_depthwise_compute_interior_range_1d(int32_t input_size, + int32_t kernel_size, + int32_t stride, + int32_t pad, + int32_t output_size, + int32_t *first_valid, + int32_t *last_valid) +{ + int32_t first = 0; + while (first < output_size && ((int64_t)first * stride - pad) < 0) + { + ++first; + } + + int32_t last = output_size - 1; + while (last >= first && ((int64_t)last * stride - pad + kernel_size) > input_size) + { + --last; + } + + *first_valid = first; + *last_valid = last; +} + +static void arm_depthwise_conv_nhwc_fast_chmult1_kc_f32(const float32_t *input, + int32_t input_batches, + int32_t input_x, + int32_t input_y, + int32_t input_ch, + const float32_t *kernel, + int32_t kernel_x, + int32_t kernel_y, + int32_t pad_x, + int32_t pad_y, + int32_t stride_x, + int32_t stride_y, + const float32_t *bias, + float32_t *output, + int32_t output_x, + int32_t output_y, + float32_t output_activation_min, + float32_t output_activation_max) +{ + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * input_ch; + int32_t interior_x_first; + int32_t interior_x_last; + int32_t interior_y_first; + int32_t interior_y_last; + + arm_depthwise_compute_interior_range_1d( + input_x, kernel_x, stride_x, pad_x, output_x, &interior_x_first, &interior_x_last); + arm_depthwise_compute_interior_range_1d( + input_y, kernel_y, stride_y, pad_y, output_y, &interior_y_first, &interior_y_last); + + for (int32_t i_batch = 0; i_batch < input_batches; ++i_batch) + { + const float32_t *input_b = input + i_batch * in_batch_stride; + float32_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; ++i_out_y) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + const int32_t y_interior = (i_out_y >= interior_y_first && i_out_y <= interior_y_last); + + int32_t i_out_x = 0; + if (y_interior && interior_x_first <= interior_x_last) + { + for (; i_out_x < interior_x_first && i_out_x < output_x; ++i_out_x) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + float32_t *out_px = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + arm_depthwise_init_vec_f32(out_px, bias, input_ch); + arm_depthwise_accumulate_pixel_nhwc_kc_f32(input_b, + input_x, + input_y, + input_ch, + kernel, + kernel_x, + kernel_y, + base_idx_x, + base_idx_y, + out_px, + 0); + arm_nn_vector_clamp_f32(out_px, input_ch, output_activation_min, output_activation_max); + } + + for (; i_out_x <= interior_x_last && i_out_x < output_x; ++i_out_x) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + float32_t *out_px = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + arm_depthwise_init_vec_f32(out_px, bias, input_ch); + arm_depthwise_accumulate_pixel_nhwc_kc_f32(input_b, + input_x, + input_y, + input_ch, + kernel, + kernel_x, + kernel_y, + base_idx_x, + base_idx_y, + out_px, + 1); + arm_nn_vector_clamp_f32(out_px, input_ch, output_activation_min, output_activation_max); + } + } + + for (; i_out_x < output_x; ++i_out_x) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + float32_t *out_px = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + arm_depthwise_init_vec_f32(out_px, bias, input_ch); + arm_depthwise_accumulate_pixel_nhwc_kc_f32( + input_b, input_x, input_y, input_ch, kernel, kernel_x, kernel_y, base_idx_x, base_idx_y, out_px, 0); + arm_nn_vector_clamp_f32(out_px, input_ch, output_activation_min, output_activation_max); + } + } + } +} + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE bool arm_depthwise_conv_nhwc_convert_to_conv_f32(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *output_dims) +{ + return dw_conv_params && input_dims && output_dims && input_dims->c == 1 && + output_dims->c >= CONVERT_DW_CONV_WITH_ONE_INPUT_CH_AND_OUTPUT_CH_ABOVE_THRESHOLD; +} + +__STATIC_INLINE void arm_depthwise_pack_conv_kernel_nt_n_f32(const float32_t *kernel, + int32_t output_c, + int32_t kernel_h, + int32_t kernel_w, + arm_nn_dw_kernel_layout_f32 kernel_layout, + float32_t *packed_kernel) +{ + const int32_t kernel_elems = kernel_h * kernel_w; + const int32_t block_cols = 4; + const int32_t packed_output_c = ARM_NN_ROUND_UP(output_c, block_cols); + + for (int32_t n_base = 0; n_base < packed_output_c; n_base += block_cols) + { + for (int32_t k = 0; k < kernel_elems; ++k) + { + float32_t *dst = packed_kernel + (size_t)n_base * kernel_elems + (size_t)k * block_cols; + for (int32_t lane = 0; lane < block_cols; ++lane) + { + const int32_t oc = n_base + lane; + if (oc < output_c) + { + dst[lane] = (kernel_layout == ARM_NN_DW_KERNEL_CK) ? kernel[(size_t)oc * kernel_elems + k] + : kernel[(size_t)k * output_c + oc]; + } + else + { + dst[lane] = 0.0f; + } + } + } + } +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_to_conv_packed_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *packed_kernel, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + if (!ctx || !ctx->buf || ctx->size <= 0 || !dw_conv_params || !input_dims || !input || !packed_kernel || + !output_dims || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const int32_t patch_len = kernel_h * kernel_w; + const int32_t output_positions = output_h * output_w; + + if (input_dims->c != 1 || output_c <= 0 || kernel_h <= 0 || kernel_w <= 0 || patch_len <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t stride_h = dw_conv_params->stride.h; + const int32_t stride_w = dw_conv_params->stride.w; + const int32_t pad_h = dw_conv_params->padding.h; + const int32_t pad_w = dw_conv_params->padding.w; + const int32_t dil_h = dw_conv_params->dilation.h; + const int32_t dil_w = dw_conv_params->dilation.w; + const size_t row_bytes = (size_t)patch_len * sizeof(float32_t); + const int32_t max_rows = (int32_t)((size_t)ctx->size / row_bytes); + + if (max_rows <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + /* + * The converted depthwise path always has input_c == 1, so each patch row + * is simply the kernel footprint scalars. Packing that directly here avoids + * the general NHWC patch helper overhead. + */ + float32_t *lhs_buffer = (float32_t *)ctx->buf; + const int32_t in_batch_stride = input_h * input_w; + const int32_t out_batch_stride = output_h * output_w * output_c; + + for (int32_t b = 0; b < batch; ++b) + { + const float32_t *input_b = input + (size_t)b * in_batch_stride; + float32_t *output_b = output + (size_t)b * out_batch_stride; + + for (int32_t pos = 0; pos < output_positions; pos += max_rows) + { + const int32_t rows = ((output_positions - pos) < max_rows) ? (output_positions - pos) : max_rows; + + for (int32_t r = 0; r < rows; ++r) + { + const int32_t out_pos = pos + r; + const int32_t out_y = out_pos / output_w; + const int32_t out_x = out_pos - out_y * output_w; + const int32_t base_idx_y = (out_y * stride_h) - pad_h; + const int32_t base_idx_x = (out_x * stride_w) - pad_w; + float32_t *patch_row = lhs_buffer + (size_t)r * patch_len; + + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t idx_y = base_idx_y + dil_h * ky; + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t idx_x = base_idx_x + dil_w * kx; + *patch_row++ = ((uint32_t)idx_y < (uint32_t)input_h && (uint32_t)idx_x < (uint32_t)input_w) + ? input_b[idx_y * input_w + idx_x] + : 0.0f; + } + } + } + + arm_cmsis_nn_status status = arm_nn_mat_mult_nt_n_packed_f32(lhs_buffer, + packed_kernel, + bias, + output_b + (size_t)pos * output_c, + rows, + output_c, + patch_len, + output_c, + dw_conv_params->activation.min, + dw_conv_params->activation.max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_to_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ + if (!ctx || !ctx->buf || ctx->size <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + const int32_t output_c = output_dims->c; + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + const size_t kernel_row_elems = (size_t)kernel_h * (size_t)kernel_w; + const size_t packed_output_c = (size_t)ARM_NN_ROUND_UP(output_c, 4); + const size_t kernel_bytes = packed_output_c * kernel_row_elems * sizeof(float32_t); + uint8_t *ctx_bytes = (uint8_t *)ctx->buf; + const float32_t *conv_kernel = kernel; + size_t scratch_offset = 0U; + + if ((size_t)ctx->size < kernel_bytes) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + /* + * The converted conv path stores the filter in packed NTxN scratch memory + * and then consumes it as float32_t data. Require float32_t-aligned scratch + * here so the typed view is explicit and safe. + */ + if (((uintptr_t)ctx->buf & (sizeof(float32_t) - 1U)) != 0U) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (kernel_layout != ARM_NN_DW_KERNEL_CK && kernel_layout != ARM_NN_DW_KERNEL_KC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float32_t *packed_kernel = (float32_t *)ctx->buf; + arm_depthwise_pack_conv_kernel_nt_n_f32(kernel, output_c, kernel_h, kernel_w, kernel_layout, packed_kernel); + conv_kernel = packed_kernel; + scratch_offset = kernel_bytes; + + cmsis_nn_context conv_ctx = {0}; + if ((size_t)ctx->size > scratch_offset) + { + conv_ctx.buf = ctx_bytes + scratch_offset; + conv_ctx.size = ctx->size - (int32_t)scratch_offset; + } + + const cmsis_nn_conv_params_f32 conv_params = {.stride = dw_conv_params->stride, + .padding = dw_conv_params->padding, + .dilation = dw_conv_params->dilation, + .activation = dw_conv_params->activation, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims conv_filter_dims = {.n = output_c, .h = kernel_h, .w = kernel_w, .c = input_dims->c}; + (void)bias_dims; + (void)conv_filter_dims; + (void)conv_params; + + return arm_depthwise_conv_nhwc_to_conv_packed_f32( + &conv_ctx, dw_conv_params, input_dims, input, filter_dims, conv_kernel, bias, output_dims, output); +} +#endif + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_fast_chmult1_kc_nt_t_f32(const cmsis_nn_context *ctx, + const float32_t *input, + int32_t input_batches, + int32_t input_x, + int32_t input_y, + int32_t input_ch, + const float32_t *kernel, + int32_t kernel_x, + int32_t kernel_y, + int32_t pad_x, + int32_t pad_y, + int32_t stride_x, + int32_t stride_y, + const float32_t *bias, + float32_t *output, + int32_t output_x, + int32_t output_y, + float32_t output_activation_min, + float32_t output_activation_max) +{ + if (!ctx || !ctx->buf || ctx->size <= 0) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + const int32_t kernel_size = kernel_x * kernel_y; + if (kernel_size <= 0 || input_ch <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t lhs_row_elems = (size_t)kernel_size * (size_t)input_ch; + const size_t lhs_row_bytes = lhs_row_elems * sizeof(float32_t); + if (lhs_row_bytes == 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const size_t max_rows_from_buf = (size_t)ctx->size / lhs_row_bytes; + int32_t tile_row_limit = (int32_t)max_rows_from_buf; + if (tile_row_limit > ARM_NN_DW_NT_T_F32_TILE_ROWS) + { + tile_row_limit = ARM_NN_DW_NT_T_F32_TILE_ROWS; + } + if (tile_row_limit < 1) + { + return ARM_CMSIS_NN_NO_IMPL_ERROR; + } + + float32_t *lhs_buffer = (float32_t *)ctx->buf; + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * input_ch; + + for (int32_t i_batch = 0; i_batch < input_batches; ++i_batch) + { + const float32_t *input_b = input + i_batch * in_batch_stride; + float32_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; ++i_out_y) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + + for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x += tile_row_limit) + { + int32_t tile_rows = output_x - i_out_x; + if (tile_rows > tile_row_limit) + { + tile_rows = tile_row_limit; + } + + float32_t *lhs_ptr = lhs_buffer; + for (int32_t row = 0; row < tile_rows; ++row) + { + const int32_t base_idx_x = ((i_out_x + row) * stride_x) - pad_x; + + for (int32_t i_ker_y = 0; i_ker_y < kernel_y; ++i_ker_y) + { + const int32_t idx_y = base_idx_y + i_ker_y; + for (int32_t i_ker_x = 0; i_ker_x < kernel_x; ++i_ker_x) + { + const int32_t idx_x = base_idx_x + i_ker_x; + if ((uint32_t)idx_y >= (uint32_t)input_y || (uint32_t)idx_x >= (uint32_t)input_x) + { + arm_memset_f32(lhs_ptr, 0.0f, (uint32_t)input_ch); + } + else + { + const float32_t *in_px = input_b + ((size_t)idx_y * input_x + idx_x) * input_ch; + arm_memcpy_f32(lhs_ptr, in_px, (uint32_t)input_ch); + } + lhs_ptr += input_ch; + } + } + } + + float32_t *out_ptr = output_b + ((size_t)i_out_y * output_x + i_out_x) * input_ch; + arm_cmsis_nn_status status = arm_nn_depthwise_conv_nt_t_f32(lhs_buffer, + kernel, + bias, + out_ptr, + tile_rows, + input_ch, + kernel_size, + input_ch, + output_activation_min, + output_activation_max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +static void arm_depthwise_conv_f32_generic(const float32_t *input, + const int32_t input_batches, + const int32_t input_x, + const int32_t input_y, + const int32_t input_ch, + const float32_t *kernel, + const int32_t ch_mult, + const int32_t kernel_x, + const int32_t kernel_y, + const int32_t pad_x, + const int32_t pad_y, + const int32_t stride_x, + const int32_t stride_y, + const float32_t *bias, + float32_t *output, + const int32_t output_x, + const int32_t output_y, + const float32_t output_activation_min, + const float32_t output_activation_max, + const int32_t dilation_x, + const int32_t dilation_y, + const cmsis_nn_dw_conv_params_f32 *params) +{ + (void)params; + const int32_t output_ch = input_ch * ch_mult; + const int32_t in_batch_stride = input_x * input_y * input_ch; + const int32_t out_batch_stride = output_x * output_y * output_ch; + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + const float32_t *input_b = input + i_batch * in_batch_stride; + float32_t *output_b = output + i_batch * out_batch_stride; + + for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++) + { + const int32_t base_idx_y = (i_out_y * stride_y) - pad_y; + for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++) + { + const int32_t base_idx_x = (i_out_x * stride_x) - pad_x; + for (int32_t i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) + { + for (int32_t i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) + { + const int32_t idx_out_ch = i_ch_mult + i_input_ch * ch_mult; + float32_t acc_0 = 0.0f; + + int32_t ker_y_start; + int32_t ker_x_start; + int32_t ker_y_end; + int32_t ker_x_end; + + if (dilation_x > 1) + { + const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x; + ker_x_start = MAX(0, start_x_max); + const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x; + ker_x_end = MIN(kernel_x, end_min_x); + } + else + { + ker_x_start = MAX(0, -base_idx_x); + ker_x_end = MIN(kernel_x, input_x - base_idx_x); + } + + if (dilation_y > 1) + { + const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y; + ker_y_start = MAX(0, start_y_max); + const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y; + ker_y_end = MIN(kernel_y, end_min_y); + } + else + { + ker_y_start = MAX(0, -base_idx_y); + ker_y_end = MIN(kernel_y, input_y - base_idx_y); + } + + if (bias) + { + acc_0 = bias[idx_out_ch]; + } + + for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) + { + const int32_t idx_y = base_idx_y + dilation_y * i_ker_y; + for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) + { + const int32_t idx_x = base_idx_x + dilation_x * i_ker_x; + const int32_t idx_0 = + arm_depthwise_conv_input_index_nhwc(idx_x, idx_y, i_input_ch, input_x, input_ch); + const int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * output_ch + idx_out_ch; + + acc_0 += input_b[idx_0] * kernel[ker_idx_0]; + } + } + + acc_0 = CLAMP(acc_0, output_activation_max, output_activation_min); + const int32_t out_idx = + arm_depthwise_conv_output_index_nhwc(i_out_x, i_out_y, idx_out_ch, output_x, output_ch); + output_b[out_idx] = acc_0; + } + } + } + } + } +} + +static arm_cmsis_nn_status arm_depthwise_conv_f32_validate(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + const float32_t *input, + const float32_t *kernel, + const float32_t *output, + arm_nn_dw_kernel_layout_f32 forced_kernel_layout, + arm_nn_dw_kernel_layout_f32 *kernel_layout) +{ + if (!dw_conv_params || !input_dims || !filter_dims || !output_dims || !input || !kernel || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + *kernel_layout = forced_kernel_layout; + + return ARM_CMSIS_NN_SUCCESS; +} + +static arm_cmsis_nn_status arm_depthwise_conv_nhwc_dispatch_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_dw_kernel_layout_f32 kernel_layout) +{ +#ifndef NN_DISABLE_SPECIALIZATION + /* First try exact-shape NHWC specializations such as 1D-k3 and 3x3 kernels. */ + ARM_DW_DISPATCH(arm_dw_spec_nhwc_f32, + ARM_DW_ARRAY_SIZE(arm_dw_spec_nhwc_f32), + ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +#endif + + /* Then try the broader ch_mult=1 fast NHWC routes before falling back to the generic kernel. */ + if (dw_conv_params->ch_mult == 1 && dw_conv_params->dilation.w == 1 && dw_conv_params->dilation.h == 1) + { + if (arm_depthwise_conv_nhwc_fast_chmult1_kc_nt_t_f32(ctx, + input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max) == ARM_CMSIS_NN_SUCCESS) + { + return ARM_CMSIS_NN_SUCCESS; + } + + arm_depthwise_conv_nhwc_fast_chmult1_kc_f32(input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max); + return ARM_CMSIS_NN_SUCCESS; + } + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + /* For one-input-channel cases on MVE, reusing the float convolution wrapper can be faster. */ + if (arm_depthwise_conv_nhwc_convert_to_conv_f32(dw_conv_params, input_dims, output_dims)) + { + arm_cmsis_nn_status conv_status = arm_depthwise_conv_nhwc_to_conv_f32(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + ARM_NN_DW_KERNEL_KC); + if (conv_status == ARM_CMSIS_NN_SUCCESS) + { + return conv_status; + } + } +#endif + + /* Generic NHWC fallback for remaining channel-multiplier, dilation, or kernel-layout cases. */ + arm_depthwise_conv_f32_generic(input, + input_dims->n, + input_dims->w, + input_dims->h, + input_dims->c, + kernel, + dw_conv_params->ch_mult, + filter_dims->w, + filter_dims->h, + dw_conv_params->padding.w, + dw_conv_params->padding.h, + dw_conv_params->stride.w, + dw_conv_params->stride.h, + bias, + output, + output_dims->w, + output_dims->h, + dw_conv_params->activation.min, + dw_conv_params->activation.max, + dw_conv_params->dilation.w, + dw_conv_params->dilation.h, + dw_conv_params); + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_depthwise_nhwc_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + arm_nn_dw_kernel_layout_f32 kernel_layout; + arm_cmsis_nn_status status = arm_depthwise_conv_f32_validate(dw_conv_params, + input_dims, + filter_dims, + output_dims, + input, + kernel, + output, + ARM_NN_DW_KERNEL_KC, + &kernel_layout); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + + return arm_depthwise_conv_nhwc_dispatch_f32(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +} + +arm_cmsis_nn_status arm_depthwise_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_tensor_layout layout) +{ + if (!dw_conv_params || layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_depthwise_nhwc_conv_f32( + ctx, dw_conv_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +arm_cmsis_nn_status arm_depthwise_conv_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + arm_nn_dw_kernel_layout_f32 kernel_layout; + arm_cmsis_nn_status status = arm_depthwise_conv_f32_validate(dw_conv_params, + input_dims, + filter_dims, + output_dims, + input, + kernel, + output, + ARM_NN_DW_KERNEL_KC, + &kernel_layout); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + + return arm_depthwise_conv_nhwc_dispatch_f32(ctx, + dw_conv_params, + input_dims, + input, + filter_dims, + kernel, + bias_dims, + bias, + output_dims, + output, + kernel_layout); +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_transpose_conv_f16.c b/Source/ConvolutionFunctions/arm_transpose_conv_f16.c new file mode 100644 index 00000000..b5648aed --- /dev/null +++ b/Source/ConvolutionFunctions/arm_transpose_conv_f16.c @@ -0,0 +1,296 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_transpose_conv_f16.c + * Description: Transpose convolution (float16, NHWC) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +arm_cmsis_nn_status arm_transpose_conv_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)output_ctx; + (void)bias_dims; + + if (!transpose_conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || + !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + + const int32_t stride_h = transpose_conv_params->stride.h; + const int32_t stride_w = transpose_conv_params->stride.w; + const int32_t pad_h = transpose_conv_params->padding.h; + const int32_t pad_w = transpose_conv_params->padding.w; + const int32_t pad_off_h = transpose_conv_params->padding_offsets.h; + const int32_t pad_off_w = transpose_conv_params->padding_offsets.w; + const int32_t dil_h = transpose_conv_params->dilation.h; + const int32_t dil_w = transpose_conv_params->dilation.w; + const size_t kernel_oc_stride = (size_t)kernel_h * (size_t)kernel_w * (size_t)input_c; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const int32_t oc_gather_mve_ok = (kernel_oc_stride <= ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8); + uint16x8_t oc_offsets = vdupq_n_u16(0); + if (oc_gather_mve_ok) + { + const uint16_t kernel_oc_stride_u16 = (uint16_t)kernel_oc_stride; + oc_offsets = vmulq(vidupq_u16((uint32_t)0, 1), kernel_oc_stride_u16); + } +#endif + + for (int32_t b = 0; b < batch; ++b) + { + float16_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + const float16_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + float16_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + if (bias_data) + { +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t oc = 0; oc < output_c; oc += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(output_c - oc)); + vst1q_p(out_px + oc, vld1q_z(bias_data + oc, p), p); + } +#else + for (int32_t oc = 0; oc < output_c; ++oc) + { + out_px[oc] = bias_data[oc]; + } +#endif + } + else + { +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t vzero = vdupq_n_f16((float16_t)0); + for (int32_t oc = 0; oc < output_c; oc += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(output_c - oc)); + vst1q_p(out_px + oc, vzero, p); + } +#else + for (int32_t oc = 0; oc < output_c; ++oc) + { + out_px[oc] = (float16_t)0; + } +#endif + } + } + } + + for (int32_t in_y = 0; in_y < input_h; ++in_y) + { + for (int32_t in_x = 0; in_x < input_w; ++in_x) + { + const float16_t *input_px = input_b + ((size_t)in_y * input_w + (size_t)in_x) * input_c; + for (int32_t ic = 0; ic < input_c; ++ic) + { + const float16_t val = input_px[ic]; + if ((_Float16)val == (_Float16)0) + { + continue; + } + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t out_y = in_y * stride_h - pad_h + ky * dil_h + pad_off_h; + if ((uint32_t)out_y >= (uint32_t)output_h) + { + continue; + } + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t out_x = in_x * stride_w - pad_w + kx * dil_w + pad_off_w; + if ((uint32_t)out_x >= (uint32_t)output_w) + { + continue; + } + float16_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + const float16_t *w_k_ic = + filter_data + ((size_t)ky * kernel_w + (size_t)kx) * input_c + (size_t)ic; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + if (oc_gather_mve_ok) + { + for (; oc < output_c; oc += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(output_c - oc)); + const float16_t *w_oc = w_k_ic + (size_t)oc * kernel_oc_stride; + const float16x8_t vw = vldrhq_gather_shifted_offset_z(w_oc, oc_offsets, p); + float16x8_t vout = vld1q_z(out_px + oc, p); + vout = vfmaq_m(vout, vw, val, p); + vst1q_p(out_px + oc, vout, p); + } + } +#else + int32_t oc = 0; +#endif + for (; oc < output_c; ++oc) + { + out_px[oc] += (_Float16)val * (_Float16)w_k_ic[(size_t)oc * kernel_oc_stride]; + } + } + } + } + } + } + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + float16_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + arm_nn_vector_clamp_f16( + out_px, output_c, transpose_conv_params->activation.min, transpose_conv_params->activation.max); + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_transpose_conv_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_transpose_conv_nhwc_f16(ctx, + output_ctx, + transpose_conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +arm_cmsis_nn_status arm_transpose_conv_wrapper_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *filter_dims, + const float16_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data, + arm_nn_tensor_layout layout) +{ + return arm_transpose_conv_f16(ctx, + output_ctx, + transpose_conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data, + layout); +} + +int32_t arm_transpose_conv_f16_get_buffer_size(const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *out_dims) +{ + /* Placeholder: current float16 transpose conv is bufferless. */ + /* Future optimized kernels may require scratch buffers; keep API for compatibility. */ + (void)transpose_conv_params; + (void)input_dims; + (void)filter_dims; + (void)out_dims; + return 0; +} + +int32_t +arm_transpose_conv_f16_get_reverse_conv_buffer_size(const cmsis_nn_transpose_conv_params_f16 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims) +{ + /* Placeholder: no reverse-convolution workspace needed today. */ + /* Reserved for future implementations that may require temporary buffers. */ + (void)transpose_conv_params; + (void)input_dims; + (void)filter_dims; + return 0; +} +/** + * @} end of NNConv group + */ diff --git a/Source/ConvolutionFunctions/arm_transpose_conv_f32.c b/Source/ConvolutionFunctions/arm_transpose_conv_f32.c new file mode 100644 index 00000000..3fa0489f --- /dev/null +++ b/Source/ConvolutionFunctions/arm_transpose_conv_f32.c @@ -0,0 +1,289 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_transpose_conv_f32.c + * Description: Transpose convolution (float32, NHWC) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNConv + * @{ + */ + +arm_cmsis_nn_status arm_transpose_conv_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)output_ctx; + (void)bias_dims; + + if (!transpose_conv_params || !input_dims || !filter_dims || !output_dims || !input_data || !filter_data || + !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = input_dims->n; + const int32_t input_h = input_dims->h; + const int32_t input_w = input_dims->w; + const int32_t input_c = input_dims->c; + + const int32_t output_h = output_dims->h; + const int32_t output_w = output_dims->w; + const int32_t output_c = output_dims->c; + + const int32_t kernel_h = filter_dims->h; + const int32_t kernel_w = filter_dims->w; + + const int32_t stride_h = transpose_conv_params->stride.h; + const int32_t stride_w = transpose_conv_params->stride.w; + const int32_t pad_h = transpose_conv_params->padding.h; + const int32_t pad_w = transpose_conv_params->padding.w; + const int32_t pad_off_h = transpose_conv_params->padding_offsets.h; + const int32_t pad_off_w = transpose_conv_params->padding_offsets.w; + const int32_t dil_h = transpose_conv_params->dilation.h; + const int32_t dil_w = transpose_conv_params->dilation.w; + const size_t kernel_oc_stride = (size_t)kernel_h * (size_t)kernel_w * (size_t)input_c; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const uint32_t kernel_oc_stride_u32 = (uint32_t)kernel_oc_stride; + const uint32x4_t oc_offsets = vmulq(vidupq_u32((uint32_t)0, 1), kernel_oc_stride_u32); +#endif + + for (int32_t b = 0; b < batch; ++b) + { + float32_t *output_b = output_data + (size_t)b * output_h * output_w * output_c; + const float32_t *input_b = input_data + (size_t)b * input_h * input_w * input_c; + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + float32_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + if (bias_data) + { +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t oc = 0; oc < output_c; oc += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(output_c - oc)); + vst1q_p(out_px + oc, vld1q_z(bias_data + oc, p), p); + } +#else + for (int32_t oc = 0; oc < output_c; ++oc) + { + out_px[oc] = bias_data[oc]; + } +#endif + } + else + { +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t vzero = vdupq_n_f32(0.0f); + for (int32_t oc = 0; oc < output_c; oc += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(output_c - oc)); + vst1q_p(out_px + oc, vzero, p); + } +#else + for (int32_t oc = 0; oc < output_c; ++oc) + { + out_px[oc] = 0.0f; + } +#endif + } + } + } + + for (int32_t in_y = 0; in_y < input_h; ++in_y) + { + for (int32_t in_x = 0; in_x < input_w; ++in_x) + { + const float32_t *input_px = input_b + ((size_t)in_y * input_w + (size_t)in_x) * input_c; + for (int32_t ic = 0; ic < input_c; ++ic) + { + const float32_t val = input_px[ic]; + if (val == 0.0f) + { + continue; + } + for (int32_t ky = 0; ky < kernel_h; ++ky) + { + const int32_t out_y = in_y * stride_h - pad_h + ky * dil_h + pad_off_h; + if ((uint32_t)out_y >= (uint32_t)output_h) + { + continue; + } + for (int32_t kx = 0; kx < kernel_w; ++kx) + { + const int32_t out_x = in_x * stride_w - pad_w + kx * dil_w + pad_off_w; + if ((uint32_t)out_x >= (uint32_t)output_w) + { + continue; + } + float32_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + const float32_t *w_k_ic = + filter_data + ((size_t)ky * kernel_w + (size_t)kx) * input_c + (size_t)ic; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + for (; oc < output_c; oc += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(output_c - oc)); + const float32_t *w_oc = w_k_ic + (size_t)oc * kernel_oc_stride; + const float32x4_t vw = + vldrwq_gather_shifted_offset_z((const float32_t *)w_oc, oc_offsets, p); + float32x4_t vout = vld1q_z(out_px + oc, p); + vout = vfmaq_m(vout, vw, val, p); + vst1q_p(out_px + oc, vout, p); + } +#else + int32_t oc = 0; +#endif + for (; oc < output_c; ++oc) + { + out_px[oc] += val * w_k_ic[(size_t)oc * kernel_oc_stride]; + } + } + } + } + } + } + + for (int32_t out_y = 0; out_y < output_h; ++out_y) + { + for (int32_t out_x = 0; out_x < output_w; ++out_x) + { + float32_t *out_px = output_b + ((size_t)out_y * output_w + (size_t)out_x) * output_c; + arm_nn_vector_clamp_f32( + out_px, output_c, transpose_conv_params->activation.min, transpose_conv_params->activation.max); + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_transpose_conv_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_transpose_conv_nhwc_f32(ctx, + output_ctx, + transpose_conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data); +} + +arm_cmsis_nn_status arm_transpose_conv_wrapper_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *filter_dims, + const float32_t *filter_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data, + arm_nn_tensor_layout layout) +{ + return arm_transpose_conv_f32(ctx, + output_ctx, + transpose_conv_params, + input_dims, + input_data, + filter_dims, + filter_data, + bias_dims, + bias_data, + output_dims, + output_data, + layout); +} + +int32_t arm_transpose_conv_f32_get_buffer_size(const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *out_dims) +{ + /* Placeholder: current float32 transpose conv is bufferless. */ + /* Future optimized kernels may require scratch buffers; keep API for compatibility. */ + (void)transpose_conv_params; + (void)input_dims; + (void)filter_dims; + (void)out_dims; + return 0; +} + +int32_t +arm_transpose_conv_f32_get_reverse_conv_buffer_size(const cmsis_nn_transpose_conv_params_f32 *transpose_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims) +{ + /* Placeholder: no reverse-convolution workspace needed today. */ + /* Reserved for future implementations that may require temporary buffers. */ + (void)transpose_conv_params; + (void)input_dims; + (void)filter_dims; + return 0; +} +/** + * @} end of NNConv group + */ diff --git a/Source/FullyConnectedFunctions/CMakeLists.txt b/Source/FullyConnectedFunctions/CMakeLists.txt index 1d254c87..ffc0de7c 100644 --- a/Source/FullyConnectedFunctions/CMakeLists.txt +++ b/Source/FullyConnectedFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2021, 2023-2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2021, 2023-2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -20,5 +20,14 @@ file(GLOB SRC_S4 "./*_s4.c") file(GLOB SRC_S8 "./*_s8.c") file(GLOB SRC_S16 "./*_s16*.c") file(GLOB SRC_S64 "./*_s64.c") +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_batch_matmul_f32.c + arm_fully_connected_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_batch_matmul_f16.c + arm_fully_connected_f16.c) +endif() -target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16} ${SRC_S64}) +target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16} ${SRC_S64} ${SRC_FLT}) diff --git a/Source/FullyConnectedFunctions/arm_batch_matmul_f16.c b/Source/FullyConnectedFunctions/arm_batch_matmul_f16.c new file mode 100644 index 00000000..d135990e --- /dev/null +++ b/Source/FullyConnectedFunctions/arm_batch_matmul_f16.c @@ -0,0 +1,300 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_batch_matmul_f16.c + * Description: Batch matrix multiplication (float16) + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_BATCH_MATMUL_F16_MVE_BLOCK (8) + #define ARM_BATCH_MATMUL_F16_MVE_MAX_STRIDE ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8) + #endif + +/** + * @ingroup Public + */ + +/** + * @addtogroup FC + * @{ + */ + +__STATIC_INLINE float16_t dot_contig_strided_f16_scalar(const float16_t *__RESTRICT contig_row, + const float16_t *__RESTRICT strided_base, + int32_t len, + int32_t stride) +{ + _Float16 acc = (_Float16)0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += (_Float16)contig_row[i] * (_Float16)strided_base[(size_t)i * stride]; + } + return (float16_t)acc; +} + +__STATIC_INLINE float16_t dot_strided_strided_f16_scalar(const float16_t *__RESTRICT lhs_base, + int32_t lhs_stride, + const float16_t *__RESTRICT rhs_base, + int32_t rhs_stride, + int32_t len) +{ + _Float16 acc = (_Float16)0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += (_Float16)lhs_base[(size_t)i * lhs_stride] * (_Float16)rhs_base[(size_t)i * rhs_stride]; + } + return (float16_t)acc; +} + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float16_t dot_contig_strided_f16_mve(const float16_t *__RESTRICT contig_row, + const float16_t *__RESTRICT strided_base, + int32_t len, + int32_t stride, + uint16x8_t offsets) +{ + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + + for (int32_t i = 0; i < len; i += ARM_BATCH_MATMUL_F16_MVE_BLOCK) + { + const mve_pred16_t p = vctp16q((uint32_t)(len - i)); + const float16x8_t vcontig = vld1q_z(contig_row + i, p); + const float16x8_t vstrided = vldrhq_gather_shifted_offset_z(strided_base + (size_t)i * stride, offsets, p); + vacc = vfmaq(vacc, vcontig, vstrided); + } + + return arm_nn_vec_reduce_add_f16(vacc); +} + +__STATIC_INLINE float16_t dot_strided_strided_f16_mve(const float16_t *__RESTRICT lhs_base, + int32_t lhs_stride, + uint16x8_t lhs_offsets, + const float16_t *__RESTRICT rhs_base, + int32_t rhs_stride, + uint16x8_t rhs_offsets, + int32_t len) +{ + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + + for (int32_t i = 0; i < len; i += ARM_BATCH_MATMUL_F16_MVE_BLOCK) + { + const mve_pred16_t p = vctp16q((uint32_t)(len - i)); + const float16x8_t vlhs = vldrhq_gather_shifted_offset_z(lhs_base + (size_t)i * lhs_stride, lhs_offsets, p); + const float16x8_t vrhs = vldrhq_gather_shifted_offset_z(rhs_base + (size_t)i * rhs_stride, rhs_offsets, p); + vacc = vfmaq(vacc, vlhs, vrhs); + } + + return arm_nn_vec_reduce_add_f16(vacc); +} + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_batch_matmul_f16(const cmsis_nn_context *ctx, + const cmsis_nn_bmm_params_f16 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const float16_t *input_lhs, + const cmsis_nn_dims *input_rhs_dims, + const float16_t *input_rhs, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + (void)ctx; + if (!bmm_params || !input_lhs_dims || !input_rhs_dims || !output_dims || !input_lhs || !input_rhs || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t output_batch = output_dims->n; + const int32_t output_height = output_dims->h; + const int32_t lhs_rows = input_lhs_dims->c; + const int32_t rhs_rows = input_rhs_dims->c; + const int32_t rhs_cols = input_rhs_dims->w; + const bool adj_x = bmm_params->adj_x; + const bool adj_y = bmm_params->adj_y; + + const int32_t inner_lhs_diff = input_lhs_dims->h >= input_rhs_dims->h ? 0 : lhs_rows * rhs_cols; + const int32_t inner_rhs_diff = input_rhs_dims->h >= input_lhs_dims->h ? rhs_rows * rhs_cols : 0; + const int32_t outer_lhs_diff = input_lhs_dims->n >= input_rhs_dims->n + ? inner_lhs_diff + : -((lhs_rows * rhs_cols) - inner_lhs_diff) * input_lhs_dims->h; + const int32_t outer_rhs_diff = input_rhs_dims->n >= input_lhs_dims->n ? (rhs_rows * rhs_cols) - inner_rhs_diff + : -inner_rhs_diff * input_rhs_dims->h; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const bool lhs_stride_mve = lhs_rows <= ARM_BATCH_MATMUL_F16_MVE_MAX_STRIDE; + const bool rhs_stride_mve = rhs_rows <= ARM_BATCH_MATMUL_F16_MVE_MAX_STRIDE; + uint16x8_t lhs_offsets = vdupq_n_u16(0); + uint16x8_t rhs_offsets = vdupq_n_u16(0); + if (adj_x && lhs_stride_mve) + { + lhs_offsets = vmulq(vidupq_u16((uint32_t)0, 1), (uint16_t)lhs_rows); + } + if (adj_y && rhs_stride_mve) + { + rhs_offsets = vmulq(vidupq_u16((uint32_t)0, 1), (uint16_t)rhs_rows); + } + #endif + + for (int32_t i_out_batch = 0; i_out_batch < output_batch; i_out_batch++) + { + for (int32_t i_out_height = 0; i_out_height < output_height; i_out_height++) + { + const float16_t *lhs_mat = input_lhs; + const float16_t *rhs_mat = input_rhs; + if (!adj_x && !adj_y) + { + arm_cmsis_nn_status status = (bmm_params->rhs_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + ? arm_nn_mat_mult_nt_n_packed_f16(lhs_mat, + rhs_mat, + NULL, + output, + lhs_rows, + rhs_rows, + rhs_cols, + rhs_rows, + bmm_params->activation.min, + bmm_params->activation.max) + : arm_nn_mat_mult_nt_t_f16(lhs_mat, + rhs_mat, + NULL, + output, + lhs_rows, + rhs_rows, + rhs_cols, + rhs_rows, + bmm_params->activation.min, + bmm_params->activation.max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + output += lhs_rows * rhs_rows; + } + else if (bmm_params->rhs_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + else if (!adj_x && adj_y) + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + const float16_t *lhs_ptr = lhs_mat + (size_t)i_lhs_rows * rhs_cols; + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + float16_t acc; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (rhs_stride_mve) + { + acc = dot_contig_strided_f16_mve( + lhs_ptr, rhs_mat + i_rhs_rows, rhs_cols, rhs_rows, rhs_offsets); + } + else + #endif + { + acc = dot_contig_strided_f16_scalar(lhs_ptr, rhs_mat + i_rhs_rows, rhs_cols, rhs_rows); + } + output[i_rhs_rows] = (float16_t)(_Float16)CLAMP( + (_Float16)acc, (_Float16)bmm_params->activation.max, (_Float16)bmm_params->activation.min); + } + output += rhs_rows; + } + } + else if (adj_x && !adj_y) + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + const float16_t *rhs_ptr = rhs_mat + (size_t)i_rhs_rows * rhs_cols; + float16_t acc; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (lhs_stride_mve) + { + acc = dot_contig_strided_f16_mve( + rhs_ptr, lhs_mat + i_lhs_rows, rhs_cols, lhs_rows, lhs_offsets); + } + else + #endif + { + acc = dot_contig_strided_f16_scalar(rhs_ptr, lhs_mat + i_lhs_rows, rhs_cols, lhs_rows); + } + output[i_rhs_rows] = (float16_t)(_Float16)CLAMP( + (_Float16)acc, (_Float16)bmm_params->activation.max, (_Float16)bmm_params->activation.min); + } + output += rhs_rows; + } + } + else + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + float16_t acc; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (lhs_stride_mve && rhs_stride_mve) + { + acc = dot_strided_strided_f16_mve(lhs_mat + i_lhs_rows, + lhs_rows, + lhs_offsets, + rhs_mat + i_rhs_rows, + rhs_rows, + rhs_offsets, + rhs_cols); + } + else + #endif + { + acc = dot_strided_strided_f16_scalar( + lhs_mat + i_lhs_rows, lhs_rows, rhs_mat + i_rhs_rows, rhs_rows, rhs_cols); + } + output[i_rhs_rows] = (float16_t)(_Float16)CLAMP( + (_Float16)acc, (_Float16)bmm_params->activation.max, (_Float16)bmm_params->activation.min); + } + output += rhs_rows; + } + } + input_lhs += lhs_rows * rhs_cols; + input_lhs -= inner_lhs_diff; + input_rhs += inner_rhs_diff; + } + input_lhs += outer_lhs_diff; + input_rhs += outer_rhs_diff; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of FC group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/FullyConnectedFunctions/arm_batch_matmul_f32.c b/Source/FullyConnectedFunctions/arm_batch_matmul_f32.c new file mode 100644 index 00000000..33d8ecbc --- /dev/null +++ b/Source/FullyConnectedFunctions/arm_batch_matmul_f32.c @@ -0,0 +1,281 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_batch_matmul_f32.c + * Description: Batch matrix multiplication (float32) + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_BATCH_MATMUL_F32_MVE_BLOCK (4) + #endif + +/** + * @ingroup Public + */ + +/** + * @addtogroup FC + * @{ + */ + + #if !defined(ARM_MATH_MVEF) || defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float32_t dot_contig_strided_f32_scalar(const float32_t *__RESTRICT contig_row, + const float32_t *__RESTRICT strided_base, + int32_t len, + int32_t stride) +{ + float32_t acc = 0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += contig_row[i] * strided_base[(size_t)i * stride]; + } + return acc; +} + +__STATIC_INLINE float32_t dot_strided_strided_f32_scalar(const float32_t *__RESTRICT lhs_base, + int32_t lhs_stride, + const float32_t *__RESTRICT rhs_base, + int32_t rhs_stride, + int32_t len) +{ + float32_t acc = 0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += lhs_base[(size_t)i * lhs_stride] * rhs_base[(size_t)i * rhs_stride]; + } + return acc; +} + #endif + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float32_t dot_contig_strided_f32_mve(const float32_t *__RESTRICT contig_row, + const float32_t *__RESTRICT strided_base, + int32_t len, + int32_t stride, + uint32x4_t offsets) +{ + float32x4_t vacc = vdupq_n_f32(0.0f); + + for (int32_t i = 0; i < len; i += ARM_BATCH_MATMUL_F32_MVE_BLOCK) + { + const mve_pred16_t p = vctp32q((uint32_t)(len - i)); + const float32x4_t vcontig = vld1q_z(contig_row + i, p); + const float32x4_t vstrided = vldrwq_gather_shifted_offset_z(strided_base + (size_t)i * stride, offsets, p); + vacc = vfmaq(vacc, vcontig, vstrided); + } + + return arm_nn_vec_reduce_add_f32(vacc); +} + +__STATIC_INLINE float32_t dot_strided_strided_f32_mve(const float32_t *__RESTRICT lhs_base, + int32_t lhs_stride, + uint32x4_t lhs_offsets, + const float32_t *__RESTRICT rhs_base, + int32_t rhs_stride, + uint32x4_t rhs_offsets, + int32_t len) +{ + float32x4_t vacc = vdupq_n_f32(0.0f); + + for (int32_t i = 0; i < len; i += ARM_BATCH_MATMUL_F32_MVE_BLOCK) + { + const mve_pred16_t p = vctp32q((uint32_t)(len - i)); + const float32x4_t vlhs = vldrwq_gather_shifted_offset_z(lhs_base + (size_t)i * lhs_stride, lhs_offsets, p); + const float32x4_t vrhs = vldrwq_gather_shifted_offset_z(rhs_base + (size_t)i * rhs_stride, rhs_offsets, p); + vacc = vfmaq(vacc, vlhs, vrhs); + } + + return arm_nn_vec_reduce_add_f32(vacc); +} + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_batch_matmul_f32(const cmsis_nn_context *ctx, + const cmsis_nn_bmm_params_f32 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const float32_t *input_lhs, + const cmsis_nn_dims *input_rhs_dims, + const float32_t *input_rhs, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + (void)ctx; + if (!bmm_params || !input_lhs_dims || !input_rhs_dims || !output_dims || !input_lhs || !input_rhs || !output) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t output_batch = output_dims->n; + const int32_t output_height = output_dims->h; + const int32_t lhs_rows = input_lhs_dims->c; + const int32_t rhs_rows = input_rhs_dims->c; + const int32_t rhs_cols = input_rhs_dims->w; + const bool adj_x = bmm_params->adj_x; + const bool adj_y = bmm_params->adj_y; + + const int32_t inner_lhs_diff = input_lhs_dims->h >= input_rhs_dims->h ? 0 : lhs_rows * rhs_cols; + const int32_t inner_rhs_diff = input_rhs_dims->h >= input_lhs_dims->h ? rhs_rows * rhs_cols : 0; + const int32_t outer_lhs_diff = input_lhs_dims->n >= input_rhs_dims->n + ? inner_lhs_diff + : -((lhs_rows * rhs_cols) - inner_lhs_diff) * input_lhs_dims->h; + const int32_t outer_rhs_diff = input_rhs_dims->n >= input_lhs_dims->n ? (rhs_rows * rhs_cols) - inner_rhs_diff + : -inner_rhs_diff * input_rhs_dims->h; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + uint32x4_t lhs_offsets = vdupq_n_u32(0); + uint32x4_t rhs_offsets = vdupq_n_u32(0); + if (adj_x) + { + lhs_offsets = vmulq(vidupq_u32((uint32_t)0, 1), (uint32_t)lhs_rows); + } + if (adj_y) + { + rhs_offsets = vmulq(vidupq_u32((uint32_t)0, 1), (uint32_t)rhs_rows); + } + #endif + + for (int32_t i_out_batch = 0; i_out_batch < output_batch; i_out_batch++) + { + for (int32_t i_out_height = 0; i_out_height < output_height; i_out_height++) + { + const float32_t *lhs_mat = input_lhs; + const float32_t *rhs_mat = input_rhs; + if (!adj_x && !adj_y) + { + arm_cmsis_nn_status status = (bmm_params->rhs_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + ? arm_nn_mat_mult_nt_n_packed_f32(lhs_mat, + rhs_mat, + NULL, + output, + lhs_rows, + rhs_rows, + rhs_cols, + rhs_rows, + bmm_params->activation.min, + bmm_params->activation.max) + : arm_nn_mat_mult_nt_t_f32(lhs_mat, + rhs_mat, + NULL, + output, + lhs_rows, + rhs_rows, + rhs_cols, + rhs_rows, + bmm_params->activation.min, + bmm_params->activation.max); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + output += lhs_rows * rhs_rows; + } + else if (bmm_params->rhs_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + else if (!adj_x && adj_y) + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + const float32_t *lhs_ptr = lhs_mat + (size_t)i_lhs_rows * rhs_cols; + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + float32_t acc; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + acc = + dot_contig_strided_f32_mve(lhs_ptr, rhs_mat + i_rhs_rows, rhs_cols, rhs_rows, rhs_offsets); + #else + acc = dot_contig_strided_f32_scalar(lhs_ptr, rhs_mat + i_rhs_rows, rhs_cols, rhs_rows); + #endif + output[i_rhs_rows] = CLAMP(acc, bmm_params->activation.max, bmm_params->activation.min); + } + output += rhs_rows; + } + } + else if (adj_x && !adj_y) + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + const float32_t *rhs_ptr = rhs_mat + (size_t)i_rhs_rows * rhs_cols; + float32_t acc; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + acc = + dot_contig_strided_f32_mve(rhs_ptr, lhs_mat + i_lhs_rows, rhs_cols, lhs_rows, lhs_offsets); + #else + acc = dot_contig_strided_f32_scalar(rhs_ptr, lhs_mat + i_lhs_rows, rhs_cols, lhs_rows); + #endif + output[i_rhs_rows] = CLAMP(acc, bmm_params->activation.max, bmm_params->activation.min); + } + output += rhs_rows; + } + } + else + { + for (int32_t i_lhs_rows = 0; i_lhs_rows < lhs_rows; i_lhs_rows++) + { + for (int32_t i_rhs_rows = 0; i_rhs_rows < rhs_rows; i_rhs_rows++) + { + float32_t acc; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + acc = dot_strided_strided_f32_mve(lhs_mat + i_lhs_rows, + lhs_rows, + lhs_offsets, + rhs_mat + i_rhs_rows, + rhs_rows, + rhs_offsets, + rhs_cols); + #else + acc = dot_strided_strided_f32_scalar( + lhs_mat + i_lhs_rows, lhs_rows, rhs_mat + i_rhs_rows, rhs_rows, rhs_cols); + #endif + output[i_rhs_rows] = CLAMP(acc, bmm_params->activation.max, bmm_params->activation.min); + } + output += rhs_rows; + } + } + input_lhs += lhs_rows * rhs_cols; + input_lhs -= inner_lhs_diff; + input_rhs += inner_rhs_diff; + } + input_lhs += outer_lhs_diff; + input_rhs += outer_rhs_diff; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of FC group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/FullyConnectedFunctions/arm_fully_connected_f16.c b/Source/FullyConnectedFunctions/arm_fully_connected_f16.c new file mode 100644 index 00000000..ffc030e9 --- /dev/null +++ b/Source/FullyConnectedFunctions/arm_fully_connected_f16.c @@ -0,0 +1,137 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_fully_connected_f16.c + * Description: Fully connected function (float16) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup FC + * @{ + */ + +static arm_cmsis_nn_status arm_fully_connected_core_f16(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + (void)ctx; + (void)bias_dims; + + const int32_t batch_cnt = input_dims->n; + const int32_t input_len = filter_dims->n; + const int32_t output_len = output_dims->c; + + if (fc_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f16(input, + kernel, + bias, + output, + batch_cnt, + output_len, + input_len, + output_len, + fc_params->activation.min, + fc_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f16(input, + kernel, + bias, + output, + batch_cnt, + output_len, + input_len, + output_len, + fc_params->activation.min, + fc_params->activation.max); +} + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_fully_connected_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output) +{ + const int32_t input_len = input_dims->h * input_dims->w * input_dims->c; + if (filter_dims->n != input_len) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_fully_connected_core_f16( + ctx, fc_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_fully_connected_f16(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const float16_t *input, + const cmsis_nn_dims *filter_dims, + const float16_t *kernel, + const cmsis_nn_dims *bias_dims, + const float16_t *bias, + const cmsis_nn_dims *output_dims, + float16_t *output, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_fully_connected_nhwc_f16( + ctx, fc_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +/** + * @} end of FC group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/FullyConnectedFunctions/arm_fully_connected_f32.c b/Source/FullyConnectedFunctions/arm_fully_connected_f32.c new file mode 100644 index 00000000..bde8411e --- /dev/null +++ b/Source/FullyConnectedFunctions/arm_fully_connected_f32.c @@ -0,0 +1,137 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_fully_connected_f32.c + * Description: Fully connected function (float32) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup FC + * @{ + */ + +static arm_cmsis_nn_status arm_fully_connected_core_f32(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + (void)ctx; + (void)bias_dims; + + const int32_t batch_cnt = input_dims->n; + const int32_t input_len = filter_dims->n; + const int32_t output_len = output_dims->c; + + if (fc_params->weight_format == ARM_NN_WEIGHT_FORMAT_NT_N_PACKED) + { + return arm_nn_mat_mult_nt_n_packed_f32(input, + kernel, + bias, + output, + batch_cnt, + output_len, + input_len, + output_len, + fc_params->activation.min, + fc_params->activation.max); + } + + return arm_nn_mat_mult_nt_t_f32(input, + kernel, + bias, + output, + batch_cnt, + output_len, + input_len, + output_len, + fc_params->activation.min, + fc_params->activation.max); +} + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_fully_connected_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output) +{ + const int32_t input_len = input_dims->h * input_dims->w * input_dims->c; + if (filter_dims->n != input_len) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_fully_connected_core_f32( + ctx, fc_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_fully_connected_f32(const cmsis_nn_context *ctx, + const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const float32_t *input, + const cmsis_nn_dims *filter_dims, + const float32_t *kernel, + const cmsis_nn_dims *bias_dims, + const float32_t *bias, + const cmsis_nn_dims *output_dims, + float32_t *output, + arm_nn_tensor_layout layout) +{ + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + return arm_fully_connected_nhwc_f32( + ctx, fc_params, input_dims, input, filter_dims, kernel, bias_dims, bias, output_dims, output); +} + +/** + * @} end of FC group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/LSTMFunctions/CMakeLists.txt b/Source/LSTMFunctions/CMakeLists.txt index e201e3a0..ae0aa4f1 100644 --- a/Source/LSTMFunctions/CMakeLists.txt +++ b/Source/LSTMFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2022, 2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2022, 2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -18,4 +18,11 @@ file(GLOB SRC_S8 "./*_s8.c") file(GLOB SRC_S16 "./*_s16.c") -target_sources(cmsis-nn PRIVATE ${SRC_S8} ${SRC_S16}) +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_lstm_unidirectional_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_lstm_unidirectional_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC_S8} ${SRC_S16} ${SRC_FLT}) diff --git a/Source/LSTMFunctions/arm_lstm_unidirectional_f16.c b/Source/LSTMFunctions/arm_lstm_unidirectional_f16.c new file mode 100644 index 00000000..0befb5fc --- /dev/null +++ b/Source/LSTMFunctions/arm_lstm_unidirectional_f16.c @@ -0,0 +1,97 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_lstm_unidirectional_f16.c + * Description: Unidirectional LSTM (float16) + * + * $Date: 26 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture with FP16 support + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup LSTM + * @{ + */ + +/* + * Unidirectional LSTM (float16) + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_lstm_unidirectional_f16(const float16_t *input, + float16_t *output, + const cmsis_nn_lstm_params_f16 *params, + cmsis_nn_lstm_context_f16 *buffers) +{ + if (!input || !output || !params || !buffers || !buffers->cell_state) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float16_t *hidden_in = NULL; + arm_memset_f16( + buffers->cell_state, (float16_t)0.0f, (uint32_t)((size_t)params->batch_size * (size_t)params->hidden_size)); + + if (params->time_major) + { + for (int32_t t = 0; t < params->time_steps; t++) + { + const float16_t *data_in = input + (size_t)t * (size_t)params->batch_size * (size_t)params->input_size; + float16_t *hidden_out = output + (size_t)t * (size_t)params->batch_size * (size_t)params->hidden_size; + arm_cmsis_nn_status status = arm_nn_lstm_step_f16(data_in, hidden_in, hidden_out, params, buffers, 1); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + hidden_in = hidden_out; + } + } + else + { + for (int32_t t = 0; t < params->time_steps; t++) + { + const float16_t *data_in = input + (size_t)t * (size_t)params->input_size; + float16_t *hidden_out = output + (size_t)t * (size_t)params->hidden_size; + arm_cmsis_nn_status status = + arm_nn_lstm_step_f16(data_in, hidden_in, hidden_out, params, buffers, params->time_steps); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + hidden_in = hidden_out; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} */ diff --git a/Source/LSTMFunctions/arm_lstm_unidirectional_f32.c b/Source/LSTMFunctions/arm_lstm_unidirectional_f32.c new file mode 100644 index 00000000..28576fd2 --- /dev/null +++ b/Source/LSTMFunctions/arm_lstm_unidirectional_f32.c @@ -0,0 +1,96 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_lstm_unidirectional_f32.c + * Description: Unidirectional LSTM (float32) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup LSTM + * @{ + */ + +/* + * Unidirectional LSTM (float32) + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_lstm_unidirectional_f32(const float32_t *input, + float32_t *output, + const cmsis_nn_lstm_params_f32 *params, + cmsis_nn_lstm_context_f32 *buffers) +{ + if (!input || !output || !params || !buffers || !buffers->cell_state) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + float32_t *hidden_in = NULL; + arm_memset_f32(buffers->cell_state, 0.0f, (uint32_t)((size_t)params->batch_size * (size_t)params->hidden_size)); + + if (params->time_major) + { + for (int32_t t = 0; t < params->time_steps; t++) + { + const float32_t *data_in = input + (size_t)t * (size_t)params->batch_size * (size_t)params->input_size; + float32_t *hidden_out = output + (size_t)t * (size_t)params->batch_size * (size_t)params->hidden_size; + arm_cmsis_nn_status status = arm_nn_lstm_step_f32(data_in, hidden_in, hidden_out, params, buffers, 1); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + hidden_in = hidden_out; + } + } + else + { + for (int32_t t = 0; t < params->time_steps; t++) + { + const float32_t *data_in = input + (size_t)t * (size_t)params->input_size; + float32_t *hidden_out = output + (size_t)t * (size_t)params->hidden_size; + arm_cmsis_nn_status status = + arm_nn_lstm_step_f32(data_in, hidden_in, hidden_out, params, buffers, params->time_steps); + if (status != ARM_CMSIS_NN_SUCCESS) + { + return status; + } + hidden_in = hidden_out; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} */ diff --git a/Source/NNSupportFunctions/CMakeLists.txt b/Source/NNSupportFunctions/CMakeLists.txt old mode 100644 new mode 100755 index 1ee562de..c5d9237c --- a/Source/NNSupportFunctions/CMakeLists.txt +++ b/Source/NNSupportFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -20,6 +20,48 @@ file(GLOB SRC_S4 "./*_s4*.c") file(GLOB SRC_S8 "./*_s8*.c") file(GLOB SRC_S16 "./*_s16*.c") file(GLOB SRC_S32 "./*_s32*.c") -target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16} ${SRC_S32} arm_nntables.c +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32 OR ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_nntables_flt.c) +endif() +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_batch_norm_f32.c + arm_get_buffer_size_f32.c + arm_nn_conv1d_k3_f32.c + arm_nn_conv1d_k3_packed_f32.c + arm_nn_conv1d_k5_f32.c + arm_nn_conv1d_k5_packed_f32.c + arm_nn_depthwise_conv1d_k3_f32.c + arm_nn_depthwise_conv3x3_f32.c + arm_nn_depthwise_conv_nt_t_f32.c + arm_nn_maxpool1d_f32.c + arm_nn_pack_conv_patch_f32.c + arm_nn_lstm_step_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_batch_norm_f16.c + arm_get_buffer_size_f16.c + arm_nn_conv1d_k3_f16.c + arm_nn_conv1d_k3_packed_f16.c + arm_nn_conv1d_k5_f16.c + arm_nn_conv1d_k5_packed_f16.c + arm_nn_depthwise_conv1d_k3_f16.c + arm_nn_depthwise_conv2x5_f16.c + arm_nn_depthwise_conv3x3_f16.c + arm_nn_depthwise_conv_nt_t_f16.c + arm_nn_pack_conv_patch_f16.c + arm_nn_lstm_step_f16.c + arm_nn_maxpool1d_f16.c) +endif() +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_nn_mat_mult_nt_t_f32.c + arm_nn_mat_mult_nt_n_packed_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_nn_mat_mult_nt_t_f16.c + arm_nn_mat_mult_nt_n_packed_f16.c) +endif() + +target_sources(cmsis-nn PRIVATE ${SRC_S4} ${SRC_S8} ${SRC_S16} ${SRC_S32} ${SRC_FLT} arm_nntables.c arm_q7_to_q15_with_offset.c arm_s8_to_s16_unordered_with_offset.c) diff --git a/Source/NNSupportFunctions/arm_batch_norm_f16.c b/Source/NNSupportFunctions/arm_batch_norm_f16.c new file mode 100644 index 00000000..730ec6ac --- /dev/null +++ b/Source/NNSupportFunctions/arm_batch_norm_f16.c @@ -0,0 +1,106 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_batch_norm_f16.c + * Description: Batch normalization for float16 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_batch_norm_f16(const float16_t *input, + float16_t *output, + const float16_t *scale, + const float16_t *bias, + const cmsis_nn_dims *input_dims, + arm_nn_tensor_layout layout) +{ + if (!input || !output || !scale || !bias || !input_dims) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t n = input_dims->n; + const int32_t h = input_dims->h; + const int32_t w = input_dims->w; + const int32_t c = input_dims->c; + + if (n <= 0 || h <= 0 || w <= 0 || c <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t bn = 0; bn < n; ++bn) + { + for (int32_t y = 0; y < h; ++y) + { + for (int32_t x = 0; x < w; ++x) + { + const size_t base = (((size_t)bn * (size_t)h + (size_t)y) * (size_t)w + (size_t)x) * (size_t)c; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t ch = 0; ch < c; ch += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(c - ch)); + const float16x8_t v_in = vld1q_z(input + base + (size_t)ch, p); + const float16x8_t v_scale = vld1q_z(scale + ch, p); + const float16x8_t v_bias = vld1q_z(bias + ch, p); + const float16x8_t v_out = vfmaq(v_bias, v_in, v_scale); + vst1q_p(output + base + (size_t)ch, v_out, p); + } + #else + for (int32_t ch = 0; ch < c; ++ch) + { + const _Float16 in_val = (_Float16)input[base + (size_t)ch]; + const _Float16 scale_val = (_Float16)scale[ch]; + const _Float16 bias_val = (_Float16)bias[ch]; + output[base + (size_t)ch] = (float16_t)(in_val * scale_val + bias_val); + } + #endif + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_batch_norm_f32.c b/Source/NNSupportFunctions/arm_batch_norm_f32.c new file mode 100644 index 00000000..162b021a --- /dev/null +++ b/Source/NNSupportFunctions/arm_batch_norm_f32.c @@ -0,0 +1,103 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_batch_norm_f32.c + * Description: Batch normalization for float32 tensors + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup NNSupport + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_batch_norm_f32(const float32_t *input, + float32_t *output, + const float32_t *scale, + const float32_t *bias, + const cmsis_nn_dims *input_dims, + arm_nn_tensor_layout layout) +{ + if (!input || !output || !scale || !bias || !input_dims) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t n = input_dims->n; + const int32_t h = input_dims->h; + const int32_t w = input_dims->w; + const int32_t c = input_dims->c; + + if (n <= 0 || h <= 0 || w <= 0 || c <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + if (layout != ARM_NN_LAYOUT_NHWC) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t bn = 0; bn < n; ++bn) + { + for (int32_t y = 0; y < h; ++y) + { + for (int32_t x = 0; x < w; ++x) + { + const size_t base = (((size_t)bn * (size_t)h + (size_t)y) * (size_t)w + (size_t)x) * (size_t)c; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t ch = 0; ch < c; ch += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(c - ch)); + const float32x4_t v_in = vld1q_z(input + base + (size_t)ch, p); + const float32x4_t v_scale = vld1q_z(scale + ch, p); + const float32x4_t v_bias = vld1q_z(bias + ch, p); + const float32x4_t v_out = vfmaq(v_bias, v_in, v_scale); + vst1q_p(output + base + (size_t)ch, v_out, p); + } + #else + for (int32_t ch = 0; ch < c; ++ch) + { + output[base + (size_t)ch] = input[base + (size_t)ch] * scale[ch] + bias[ch]; + } + #endif + } + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_get_buffer_size_common.h b/Source/NNSupportFunctions/arm_get_buffer_size_common.h new file mode 100644 index 00000000..389d152e --- /dev/null +++ b/Source/NNSupportFunctions/arm_get_buffer_size_common.h @@ -0,0 +1,49 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_get_buffer_size_common.h + * Description: Shared float buffer-size helper utilities + * + * $Date: 30 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#ifndef ARM_GET_BUFFER_SIZE_COMMON_H +#define ARM_GET_BUFFER_SIZE_COMMON_H + +#include "arm_nn_math_types.h" + +static inline int arm_nn_checked_size_mul(size_t lhs, size_t rhs, size_t *out) +{ + if (lhs != 0U && rhs > (((size_t)-1) / lhs)) + { + return 0; + } + + *out = lhs * rhs; + return 1; +} + +static inline int32_t arm_nn_size_to_i32_or_zero(size_t size) { return (size > (size_t)INT32_MAX) ? 0 : (int32_t)size; } + +#endif /* ARM_GET_BUFFER_SIZE_COMMON_H */ diff --git a/Source/NNSupportFunctions/arm_get_buffer_size_f16.c b/Source/NNSupportFunctions/arm_get_buffer_size_f16.c new file mode 100644 index 00000000..450fcbd2 --- /dev/null +++ b/Source/NNSupportFunctions/arm_get_buffer_size_f16.c @@ -0,0 +1,275 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_get_buffer_size_f16.c + * Description: Buffer size helpers (float16) + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_conv_opt_common.h" +#include "arm_get_buffer_size_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup NNConv + */ + +/** + * @addtogroup GetBufferSizeNNConv + * @{ + */ + +/* Keep in sync with arm_depthwise_conv_f16.c fast NT_T packing tile. */ +#define ARM_NN_DW_NT_T_F16_TILE_ROWS (4) + +int32_t arm_depthwise_conv_f16_get_buffer_size(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!dw_conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (layout == ARM_NN_LAYOUT_NHWC && input_dims->c == 1 && + output_dims->c >= CONVERT_DW_CONV_WITH_ONE_INPUT_CH_AND_OUTPUT_CH_ABOVE_THRESHOLD) + { + const cmsis_nn_conv_params_f16 conv_params = {.stride = dw_conv_params->stride, + .padding = dw_conv_params->padding, + .dilation = dw_conv_params->dilation, + .activation = dw_conv_params->activation}; + const cmsis_nn_dims conv_filter_dims = { + .n = output_dims->c, .h = filter_dims->h, .w = filter_dims->w, .c = input_dims->c}; + const int32_t conv_buf_size = + arm_convolve_wrapper_f16_get_buffer_size(&conv_params, input_dims, &conv_filter_dims, output_dims); + size_t filter_bytes = (size_t)ARM_NN_ROUND_UP(output_dims->c, 8); + + if (!arm_nn_checked_size_mul(filter_bytes, (size_t)filter_dims->h, &filter_bytes) || + !arm_nn_checked_size_mul(filter_bytes, (size_t)filter_dims->w, &filter_bytes) || + !arm_nn_checked_size_mul(filter_bytes, sizeof(float16_t), &filter_bytes)) + { + return 0; + } + + if (conv_buf_size > 0) + { + if (filter_bytes > (((size_t)-1) - (size_t)conv_buf_size)) + { + return 0; + } + filter_bytes += (size_t)conv_buf_size; + } + + return arm_nn_size_to_i32_or_zero(filter_bytes); + } +#endif + + /* Scratch is used only by the NHWC ch_mult=1, dilation=1 fast NT_T kernel. */ + if (layout != ARM_NN_LAYOUT_NHWC || dw_conv_params->ch_mult != 1 || dw_conv_params->dilation.w != 1 || + dw_conv_params->dilation.h != 1) + { + return 0; + } + + if (input_dims->c <= 0 || filter_dims->w <= 0 || filter_dims->h <= 0) + { + return 0; + } + + const size_t kernel_size = (size_t)filter_dims->w * (size_t)filter_dims->h; + const size_t channels = (size_t)input_dims->c; + size_t lhs_bytes = (size_t)ARM_NN_DW_NT_T_F16_TILE_ROWS; + + if (!arm_nn_checked_size_mul(lhs_bytes, kernel_size, &lhs_bytes) || + !arm_nn_checked_size_mul(lhs_bytes, channels, &lhs_bytes) || + !arm_nn_checked_size_mul(lhs_bytes, sizeof(float16_t), &lhs_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(lhs_bytes); +} + +int32_t arm_depthwise_conv_wrapper_f16_get_buffer_size(const cmsis_nn_dw_conv_params_f16 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + return arm_depthwise_conv_f16_get_buffer_size( + dw_conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); +} + +int32_t arm_convolve_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (input_dims->c <= 0 || filter_dims->h <= 0 || filter_dims->w <= 0 || output_dims->c <= 0) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC) + { + return 0; + } + + if (filter_dims->h == 1 && filter_dims->w == 1 && conv_params->padding.h == 0 && conv_params->padding.w == 0) + { + return arm_convolve_1x1_f16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, layout); + } + + if (input_dims->h == 1 && output_dims->h == 1 && filter_dims->h == 1 && filter_dims->w > 1 && + conv_params->stride.h == 1 && conv_params->stride.w > 0 && conv_params->padding.h == 0 && + conv_params->dilation.h == 1 && conv_params->dilation.w == 1 && + !((input_dims->n == 1 && filter_dims->w == 3 && conv_params->stride.w == 1 && conv_params->padding.w == 0) || + (input_dims->n == 1 && filter_dims->w == 5 && conv_params->stride.w == 1 && conv_params->padding.w == 0))) + { + return arm_convolve_1_x_n_f16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, layout); + } + + size_t tile_bytes = (size_t)ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MAX_TILE_ROWS; + + if (!arm_nn_checked_size_mul(tile_bytes, (size_t)filter_dims->h, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, (size_t)filter_dims->w, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, (size_t)input_dims->c, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, sizeof(float16_t), &tile_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(tile_bytes); +} + +int32_t arm_convolve_wrapper_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + return arm_convolve_f16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); +} + +int32_t arm_convolve_1x1_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC || filter_dims->h != 1 || filter_dims->w != 1 || conv_params->padding.h != 0 || + conv_params->padding.w != 0 || input_dims->c <= 0) + { + return 0; + } + + if (conv_params->stride.h == 1 && conv_params->stride.w == 1 && output_dims->h == input_dims->h && + output_dims->w == input_dims->w) + { + return 0; + } + + size_t tile_bytes = (size_t)ARM_NN_CONV_NHWC_PATCH_GEMM_F16_MAX_TILE_ROWS; + if (!arm_nn_checked_size_mul(tile_bytes, (size_t)input_dims->c, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, sizeof(float16_t), &tile_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(tile_bytes); +} + +int32_t arm_convolve_1_x_n_f16_get_buffer_size(const cmsis_nn_conv_params_f16 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC || input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || + filter_dims->w <= 1 || conv_params->stride.h != 1 || conv_params->stride.w <= 0 || + conv_params->padding.h != 0 || conv_params->dilation.h != 1 || conv_params->dilation.w != 1 || + input_dims->c <= 0 || input_dims->c != filter_dims->c) + { + return 0; + } + + size_t row_bytes = (size_t)filter_dims->w; + if (!arm_nn_checked_size_mul(row_bytes, (size_t)input_dims->c, &row_bytes) || + !arm_nn_checked_size_mul(row_bytes, sizeof(float16_t), &row_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(row_bytes); +} + +int32_t arm_fully_connected_f16_get_buffer_size(const cmsis_nn_fc_params_f16 *fc_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + /* Current float16 fully-connected implementations are bufferless. */ + (void)fc_params; + (void)input_dims; + (void)filter_dims; + (void)output_dims; + (void)layout; + return 0; +} + +int32_t arm_batch_matmul_f16_get_buffer_size(const cmsis_nn_bmm_params_f16 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const cmsis_nn_dims *input_rhs_dims, + const cmsis_nn_dims *output_dims) +{ + /* Current float16 batch-matmul implementations are bufferless. */ + (void)bmm_params; + (void)input_lhs_dims; + (void)input_rhs_dims; + (void)output_dims; + return 0; +} +/** + * @} end of GetBufferSizeNNConv group + */ diff --git a/Source/NNSupportFunctions/arm_get_buffer_size_f32.c b/Source/NNSupportFunctions/arm_get_buffer_size_f32.c new file mode 100644 index 00000000..465dc41a --- /dev/null +++ b/Source/NNSupportFunctions/arm_get_buffer_size_f32.c @@ -0,0 +1,275 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_get_buffer_size_f32.c + * Description: Buffer size helpers (float32) + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_conv_opt_common.h" +#include "arm_get_buffer_size_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup NNConv + */ + +/** + * @addtogroup GetBufferSizeNNConv + * @{ + */ + +/* Keep in sync with arm_depthwise_conv_f32.c fast NT_T packing tile. */ +#define ARM_NN_DW_NT_T_F32_TILE_ROWS (4) + +int32_t arm_depthwise_conv_f32_get_buffer_size(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!dw_conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (layout == ARM_NN_LAYOUT_NHWC && input_dims->c == 1 && + output_dims->c >= CONVERT_DW_CONV_WITH_ONE_INPUT_CH_AND_OUTPUT_CH_ABOVE_THRESHOLD) + { + const cmsis_nn_conv_params_f32 conv_params = {.stride = dw_conv_params->stride, + .padding = dw_conv_params->padding, + .dilation = dw_conv_params->dilation, + .activation = dw_conv_params->activation}; + const cmsis_nn_dims conv_filter_dims = { + .n = output_dims->c, .h = filter_dims->h, .w = filter_dims->w, .c = input_dims->c}; + const int32_t conv_buf_size = + arm_convolve_wrapper_f32_get_buffer_size(&conv_params, input_dims, &conv_filter_dims, output_dims); + size_t filter_bytes = (size_t)ARM_NN_ROUND_UP(output_dims->c, 4); + + if (!arm_nn_checked_size_mul(filter_bytes, (size_t)filter_dims->h, &filter_bytes) || + !arm_nn_checked_size_mul(filter_bytes, (size_t)filter_dims->w, &filter_bytes) || + !arm_nn_checked_size_mul(filter_bytes, sizeof(float32_t), &filter_bytes)) + { + return 0; + } + + if (conv_buf_size > 0) + { + if (filter_bytes > (((size_t)-1) - (size_t)conv_buf_size)) + { + return 0; + } + filter_bytes += (size_t)conv_buf_size; + } + + return arm_nn_size_to_i32_or_zero(filter_bytes); + } +#endif + + /* Scratch is used only by the NHWC ch_mult=1, dilation=1 fast NT_T kernel. */ + if (layout != ARM_NN_LAYOUT_NHWC || dw_conv_params->ch_mult != 1 || dw_conv_params->dilation.w != 1 || + dw_conv_params->dilation.h != 1) + { + return 0; + } + + if (input_dims->c <= 0 || filter_dims->w <= 0 || filter_dims->h <= 0) + { + return 0; + } + + const size_t kernel_size = (size_t)filter_dims->w * (size_t)filter_dims->h; + const size_t channels = (size_t)input_dims->c; + size_t lhs_bytes = (size_t)ARM_NN_DW_NT_T_F32_TILE_ROWS; + + if (!arm_nn_checked_size_mul(lhs_bytes, kernel_size, &lhs_bytes) || + !arm_nn_checked_size_mul(lhs_bytes, channels, &lhs_bytes) || + !arm_nn_checked_size_mul(lhs_bytes, sizeof(float32_t), &lhs_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(lhs_bytes); +} + +int32_t arm_depthwise_conv_wrapper_f32_get_buffer_size(const cmsis_nn_dw_conv_params_f32 *dw_conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + return arm_depthwise_conv_f32_get_buffer_size( + dw_conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); +} + +int32_t arm_convolve_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (input_dims->c <= 0 || filter_dims->h <= 0 || filter_dims->w <= 0 || output_dims->c <= 0) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC) + { + return 0; + } + + if (filter_dims->h == 1 && filter_dims->w == 1 && conv_params->padding.h == 0 && conv_params->padding.w == 0) + { + return arm_convolve_1x1_f32_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, layout); + } + + if (input_dims->h == 1 && output_dims->h == 1 && filter_dims->h == 1 && filter_dims->w > 1 && + conv_params->stride.h == 1 && conv_params->stride.w > 0 && conv_params->padding.h == 0 && + conv_params->dilation.h == 1 && conv_params->dilation.w == 1 && + !((input_dims->n == 1 && filter_dims->w == 3 && conv_params->stride.w == 1 && conv_params->padding.w == 0) || + (input_dims->n == 1 && filter_dims->w == 5 && conv_params->stride.w == 1 && conv_params->padding.w == 0))) + { + return arm_convolve_1_x_n_f32_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, layout); + } + + size_t tile_bytes = (size_t)ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MAX_TILE_ROWS; + + if (!arm_nn_checked_size_mul(tile_bytes, (size_t)filter_dims->h, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, (size_t)filter_dims->w, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, (size_t)input_dims->c, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, sizeof(float32_t), &tile_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(tile_bytes); +} + +int32_t arm_convolve_wrapper_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims) +{ + return arm_convolve_f32_get_buffer_size(conv_params, input_dims, filter_dims, output_dims, ARM_NN_LAYOUT_NHWC); +} + +int32_t arm_convolve_1x1_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC || filter_dims->h != 1 || filter_dims->w != 1 || conv_params->padding.h != 0 || + conv_params->padding.w != 0 || input_dims->c <= 0) + { + return 0; + } + + if (conv_params->stride.h == 1 && conv_params->stride.w == 1 && output_dims->h == input_dims->h && + output_dims->w == input_dims->w) + { + return 0; + } + + size_t tile_bytes = (size_t)ARM_NN_CONV_NHWC_PATCH_GEMM_F32_MAX_TILE_ROWS; + if (!arm_nn_checked_size_mul(tile_bytes, (size_t)input_dims->c, &tile_bytes) || + !arm_nn_checked_size_mul(tile_bytes, sizeof(float32_t), &tile_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(tile_bytes); +} + +int32_t arm_convolve_1_x_n_f32_get_buffer_size(const cmsis_nn_conv_params_f32 *conv_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + if (!conv_params || !input_dims || !filter_dims || !output_dims) + { + return 0; + } + + if (layout != ARM_NN_LAYOUT_NHWC || input_dims->h != 1 || output_dims->h != 1 || filter_dims->h != 1 || + filter_dims->w <= 1 || conv_params->stride.h != 1 || conv_params->stride.w <= 0 || + conv_params->padding.h != 0 || conv_params->dilation.h != 1 || conv_params->dilation.w != 1 || + input_dims->c <= 0 || input_dims->c != filter_dims->c) + { + return 0; + } + + size_t row_bytes = (size_t)filter_dims->w; + if (!arm_nn_checked_size_mul(row_bytes, (size_t)input_dims->c, &row_bytes) || + !arm_nn_checked_size_mul(row_bytes, sizeof(float32_t), &row_bytes)) + { + return 0; + } + + return arm_nn_size_to_i32_or_zero(row_bytes); +} + +int32_t arm_fully_connected_f32_get_buffer_size(const cmsis_nn_fc_params_f32 *fc_params, + const cmsis_nn_dims *input_dims, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + arm_nn_tensor_layout layout) +{ + /* Current float32 fully-connected implementations are bufferless. */ + (void)fc_params; + (void)input_dims; + (void)filter_dims; + (void)output_dims; + (void)layout; + return 0; +} + +int32_t arm_batch_matmul_f32_get_buffer_size(const cmsis_nn_bmm_params_f32 *bmm_params, + const cmsis_nn_dims *input_lhs_dims, + const cmsis_nn_dims *input_rhs_dims, + const cmsis_nn_dims *output_dims) +{ + /* Current float32 batch-matmul implementations are bufferless. */ + (void)bmm_params; + (void)input_lhs_dims; + (void)input_rhs_dims; + (void)output_dims; + return 0; +} +/** + * @} end of GetBufferSizeNNConv group + */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k3_f16.c b/Source/NNSupportFunctions/arm_nn_conv1d_k3_f16.c new file mode 100644 index 00000000..90f8efe2 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k3_f16.c @@ -0,0 +1,134 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k3_f16.c + * Description: Support: NHWC 1D convolution kernel size 3 for f16 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV1D_K3_NHWC_F16_OC4_BLOCK (4) + #endif + +void arm_nn_conv1d_k3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + + for (; oc + ARM_NN_CONV1D_K3_NHWC_F16_OC4_BLOCK <= out_c; oc += ARM_NN_CONV1D_K3_NHWC_F16_OC4_BLOCK) + { + const float16_t *w_base0 = kernel + (size_t)(oc + 0) * 3U * (size_t)in_c; + const float16_t *w_base1 = kernel + (size_t)(oc + 1) * 3U * (size_t)in_c; + const float16_t *w_base2 = kernel + (size_t)(oc + 2) * 3U * (size_t)in_c; + const float16_t *w_base3 = kernel + (size_t)(oc + 3) * 3U * (size_t)in_c; + float16x8_t vacc0 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc2 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc3 = vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - ic)); + const float16x8_t vx0 = vld1q_z(x0 + ic, p); + const float16x8_t vx1 = vld1q_z(x1 + ic, p); + const float16x8_t vx2 = vld1q_z(x2 + ic, p); + + vacc0 = vfmaq(vacc0, vx0, vld1q_z(w_base0 + 0 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx1, vld1q_z(w_base0 + 1 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx2, vld1q_z(w_base0 + 2 * in_c + ic, p)); + + vacc1 = vfmaq(vacc1, vx0, vld1q_z(w_base1 + 0 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx1, vld1q_z(w_base1 + 1 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx2, vld1q_z(w_base1 + 2 * in_c + ic, p)); + + vacc2 = vfmaq(vacc2, vx0, vld1q_z(w_base2 + 0 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx1, vld1q_z(w_base2 + 1 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx2, vld1q_z(w_base2 + 2 * in_c + ic, p)); + + vacc3 = vfmaq(vacc3, vx0, vld1q_z(w_base3 + 0 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx1, vld1q_z(w_base3 + 1 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx2, vld1q_z(w_base3 + 2 * in_c + ic, p)); + } + + const _Float16 acc0 = + (b ? (_Float16)b[oc + 0] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc0); + const _Float16 acc1 = + (b ? (_Float16)b[oc + 1] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc1); + const _Float16 acc2 = + (b ? (_Float16)b[oc + 2] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc2); + const _Float16 acc3 = + (b ? (_Float16)b[oc + 3] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc3); + + y[oc + 0] = (float16_t)acc0; + y[oc + 1] = (float16_t)acc1; + y[oc + 2] = (float16_t)acc2; + y[oc + 3] = (float16_t)acc3; + } + + for (; oc < out_c; ++oc) + { + const float16_t *w0 = kernel + (size_t)oc * 3U * (size_t)in_c; + const float16_t *w1 = w0 + in_c; + const float16_t *w2 = w1 + in_c; + _Float16 acc = b ? (_Float16)b[oc] : (_Float16)0.0f; + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - ic)); + vacc = vfmaq(vacc, vld1q_z(x0 + ic, p), vld1q_z(w0 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x1 + ic, p), vld1q_z(w1 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x2 + ic, p), vld1q_z(w2 + ic, p)); + } + + acc += (_Float16)arm_nn_vec_reduce_add_f16(vacc); + y[oc] = (float16_t)acc; + } + #else + for (int32_t oc = 0; oc < out_c; ++oc) + { + const float16_t *w = kernel + (size_t)oc * 3U * (size_t)in_c; + float32_t acc = b ? (float32_t)b[oc] : 0.0f; + for (int32_t ic = 0; ic < in_c; ++ic) + { + acc += (float32_t)x0[ic] * (float32_t)w[ic]; + acc += (float32_t)x1[ic] * (float32_t)w[in_c + ic]; + acc += (float32_t)x2[ic] * (float32_t)w[2 * in_c + ic]; + } + y[oc] = (float16_t)acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k3_f32.c b/Source/NNSupportFunctions/arm_nn_conv1d_k3_f32.c new file mode 100644 index 00000000..a32cc50d --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k3_f32.c @@ -0,0 +1,109 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k3_f32.c + * Description: Support: NHWC 1D convolution kernel size 3 for f32 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV1D_K3_NHWC_F32_OC2_BLOCK (2) + #endif + +void arm_nn_conv1d_k3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float32_t *y = out + (size_t)ow * (size_t)out_c; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + + for (; oc + ARM_NN_CONV1D_K3_NHWC_F32_OC2_BLOCK <= out_c; oc += ARM_NN_CONV1D_K3_NHWC_F32_OC2_BLOCK) + { + const float32_t *w_base0 = kernel + (size_t)(oc + 0) * 3U * (size_t)in_c; + const float32_t *w_base1 = kernel + (size_t)(oc + 1) * 3U * (size_t)in_c; + float32x4_t vacc0 = vdupq_n_f32(0.0f); + float32x4_t vacc1 = vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - ic)); + float32x4_t vx = vld1q_z(x0 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 0 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 0 * in_c + ic, p), p); + + vx = vld1q_z(x1 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 1 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 1 * in_c + ic, p), p); + + vx = vld1q_z(x2 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 2 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 2 * in_c + ic, p), p); + } + + y[oc + 0] = (b ? b[oc + 0] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc0); + y[oc + 1] = (b ? b[oc + 1] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc1); + } + + for (; oc < out_c; ++oc) + { + const float32_t *w0 = kernel + (size_t)oc * 3U * (size_t)in_c; + const float32_t *w1 = w0 + in_c; + const float32_t *w2 = w1 + in_c; + float32x4_t vacc = vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - ic)); + vacc = vfmaq_m(vacc, vld1q_z(x0 + ic, p), vld1q_z(w0 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x1 + ic, p), vld1q_z(w1 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x2 + ic, p), vld1q_z(w2 + ic, p), p); + } + + y[oc] = (b ? b[oc] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc); + } + #else + for (int32_t oc = 0; oc < out_c; ++oc) + { + const float32_t *w = kernel + (size_t)oc * 3U * (size_t)in_c; + float32_t acc = b ? b[oc] : 0.0f; + for (int32_t ic = 0; ic < in_c; ++ic) + { + acc += x0[ic] * w[ic]; + acc += x1[ic] * w[in_c + ic]; + acc += x2[ic] * w[2 * in_c + ic]; + } + y[oc] = acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f16.c b/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f16.c new file mode 100755 index 00000000..d22e4e5e --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f16.c @@ -0,0 +1,171 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k3_packed_f16.c + * Description: Support: NHWC 1D convolution kernel size 3 for packed f16 weights + * + * $Date: 29 Apr 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +void arm_nn_conv1d_k3_packed_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel_packed, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + const int32_t block_cols = 8; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t ow = 0; + for (; ow + 1 < out_w; ow += 2) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float16_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + float16_t *y0 = out + (size_t)(ow + 0) * (size_t)out_c; + float16_t *y1 = out + (size_t)(ow + 1) * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float16x8_t vacc0 = b ? vld1q(b + oc) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + const float16x8_t vw0 = vld1q(w_ic + 0U * in_c * block_cols); + const float16x8_t vw1 = vld1q(w_ic + 1U * in_c * block_cols); + const float16x8_t vw2 = vld1q(w_ic + 2U * in_c * block_cols); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + } + + vst1q(y0 + oc, vacc0); + vst1q(y1 + oc, vacc1); + } + + if (oc < out_c) + { + const int32_t valid_cols = out_c - oc; + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float16x8_t vacc0 = b ? vld1q_z(b + oc, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + const float16x8_t vw0 = vld1q_z(w_ic + 0U * in_c * block_cols, p); + const float16x8_t vw1 = vld1q_z(w_ic + 1U * in_c * block_cols, p); + const float16x8_t vw2 = vld1q_z(w_ic + 2U * in_c * block_cols, p); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + } + + vst1q_p(y0 + oc, vacc0, p); + vst1q_p(y1 + oc, vacc1, p); + } + } + + if (ow < out_w) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float16x8_t vacc = b ? vld1q(b + oc) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q(w_ic + 0U * in_c * block_cols), x0[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 1U * in_c * block_cols), x1[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 2U * in_c * block_cols), x2[ic]); + } + + vst1q(y + oc, vacc); + } + + if (oc < out_c) + { + const int32_t valid_cols = out_c - oc; + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float16x8_t vacc = b ? vld1q_z(b + oc, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q_z(w_ic + 0U * in_c * block_cols, p), x0[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 1U * in_c * block_cols, p), x1[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 2U * in_c * block_cols, p), x2[ic]); + } + + vst1q_p(y + oc, vacc, p); + } + } + #else + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; ++oc) + { + const int32_t lane = oc % block_cols; + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + _Float16 acc = b ? (_Float16)b[oc] : (_Float16)0.0f; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + acc += (_Float16)x0[ic] * (_Float16)w_ic[(size_t)0 * in_c * block_cols + lane]; + acc += (_Float16)x1[ic] * (_Float16)w_ic[(size_t)1 * in_c * block_cols + lane]; + acc += (_Float16)x2[ic] * (_Float16)w_ic[(size_t)2 * in_c * block_cols + lane]; + } + + y[oc] = (float16_t)acc; + } + } + #endif +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f32.c b/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f32.c new file mode 100755 index 00000000..9de58c7c --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k3_packed_f32.c @@ -0,0 +1,128 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k3_packed_f32.c + * Description: Support: NHWC 1D convolution kernel size 3 for packed f32 weights + * + * $Date: 30 Apr 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +void arm_nn_conv1d_k3_packed_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel_packed, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + const int32_t block_cols = 4; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t ow = 0; + for (; ow + 1 < out_w; ow += 2) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float32_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + float32_t *y0 = out + (size_t)(ow + 0) * (size_t)out_c; + float32_t *y1 = out + (size_t)(ow + 1) * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float32x4_t vacc0 = b ? vld1q(b + oc) : vdupq_n_f32(0.0f); + float32x4_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + const float32x4_t vw0 = vld1q(w_ic + 0U * in_c * block_cols); + const float32x4_t vw1 = vld1q(w_ic + 1U * in_c * block_cols); + const float32x4_t vw2 = vld1q(w_ic + 2U * in_c * block_cols); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + } + + vst1q(y0 + oc, vacc0); + vst1q(y1 + oc, vacc1); + } + } + + if ((out_w & 1) != 0) + { + const int32_t ow_tail = out_w - 1; + const float32_t *x0 = x_nhwc + (size_t)(ow_tail + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow_tail + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow_tail + 2) * (size_t)in_c; + float32_t *y = out + (size_t)ow_tail * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; oc += block_cols) + { + const int32_t valid_cols = out_c - oc < block_cols ? out_c - oc : block_cols; + const mve_pred16_t p = vctp32q((uint32_t)valid_cols); + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float32x4_t vacc = b ? vld1q_z(b + oc, p) : vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q_z(w_ic + 0U * in_c * block_cols, p), x0[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 1U * in_c * block_cols, p), x1[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 2U * in_c * block_cols, p), x2[ic]); + } + + vst1q_p(y + oc, vacc, p); + } + } + #else + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float32_t *y = out + (size_t)ow * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; ++oc) + { + const int32_t lane = oc % block_cols; + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 3U * (size_t)in_c * block_cols; + float32_t acc = b ? b[oc] : 0.0f; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + acc += x0[ic] * w_ic[(size_t)0 * in_c * block_cols + lane]; + acc += x1[ic] * w_ic[(size_t)1 * in_c * block_cols + lane]; + acc += x2[ic] * w_ic[(size_t)2 * in_c * block_cols + lane]; + } + + y[oc] = acc; + } + } + #endif +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k5_f16.c b/Source/NNSupportFunctions/arm_nn_conv1d_k5_f16.c new file mode 100644 index 00000000..ad43784a --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k5_f16.c @@ -0,0 +1,196 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k5_f16.c + * Description: Support: NHWC 1D convolution kernel size 5 for f16 + * + * $Date: 31 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV1D_K5_NHWC_F16_OC4_BLOCK (4) + #define ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK (16) + #endif + +void arm_nn_conv1d_k5_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float16_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float16_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + + if (in_c == 16 && b != NULL) + { + const float16x8_t vx00 = vld1q(x0 + 0); + const float16x8_t vx01 = vld1q(x1 + 0); + const float16x8_t vx02 = vld1q(x2 + 0); + const float16x8_t vx03 = vld1q(x3 + 0); + const float16x8_t vx04 = vld1q(x4 + 0); + const float16x8_t vx10 = vld1q(x0 + 8); + const float16x8_t vx11 = vld1q(x1 + 8); + const float16x8_t vx12 = vld1q(x2 + 8); + const float16x8_t vx13 = vld1q(x3 + 8); + const float16x8_t vx14 = vld1q(x4 + 8); + + for (; oc + ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK <= out_c; oc += ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK) + { + float16x8_t partials[ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK]; + + for (int32_t of = 0; of < ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK; ++of) + { + const float16_t *w_base = kernel + (size_t)(oc + of) * 5U * (size_t)in_c; + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + + vacc = vfmaq(vacc, vx00, vld1q(w_base + 0 * in_c + 0)); + vacc = vfmaq(vacc, vx01, vld1q(w_base + 1 * in_c + 0)); + vacc = vfmaq(vacc, vx02, vld1q(w_base + 2 * in_c + 0)); + vacc = vfmaq(vacc, vx03, vld1q(w_base + 3 * in_c + 0)); + vacc = vfmaq(vacc, vx04, vld1q(w_base + 4 * in_c + 0)); + partials[of] = vacc; + } + + for (int32_t of = 0; of < ARM_NN_CONV1D_K5_NHWC_F16_OC16_BLOCK; ++of) + { + const float16_t *w_base = kernel + (size_t)(oc + of) * 5U * (size_t)in_c + 8; + float16x8_t vacc = partials[of]; + + vacc = vfmaq(vacc, vx10, vld1q(w_base + 0 * in_c)); + vacc = vfmaq(vacc, vx11, vld1q(w_base + 1 * in_c)); + vacc = vfmaq(vacc, vx12, vld1q(w_base + 2 * in_c)); + vacc = vfmaq(vacc, vx13, vld1q(w_base + 3 * in_c)); + vacc = vfmaq(vacc, vx14, vld1q(w_base + 4 * in_c)); + + y[oc + of] = (float16_t)((_Float16)b[oc + of] + (_Float16)arm_nn_vec_reduce_add_f16(vacc)); + } + } + } + + for (; oc + ARM_NN_CONV1D_K5_NHWC_F16_OC4_BLOCK <= out_c; oc += ARM_NN_CONV1D_K5_NHWC_F16_OC4_BLOCK) + { + const float16_t *w_base0 = kernel + (size_t)(oc + 0) * 5U * (size_t)in_c; + const float16_t *w_base1 = kernel + (size_t)(oc + 1) * 5U * (size_t)in_c; + const float16_t *w_base2 = kernel + (size_t)(oc + 2) * 5U * (size_t)in_c; + const float16_t *w_base3 = kernel + (size_t)(oc + 3) * 5U * (size_t)in_c; + float16x8_t vacc0 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc2 = vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc3 = vdupq_n_f16((float16_t)0.0f); + for (int32_t ic = 0; ic < in_c; ic += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - ic)); + const float16x8_t vx0 = vld1q_z(x0 + ic, p); + const float16x8_t vx1 = vld1q_z(x1 + ic, p); + const float16x8_t vx2 = vld1q_z(x2 + ic, p); + const float16x8_t vx3 = vld1q_z(x3 + ic, p); + const float16x8_t vx4 = vld1q_z(x4 + ic, p); + + vacc0 = vfmaq(vacc0, vx0, vld1q_z(w_base0 + 0 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx1, vld1q_z(w_base0 + 1 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx2, vld1q_z(w_base0 + 2 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx3, vld1q_z(w_base0 + 3 * in_c + ic, p)); + vacc0 = vfmaq(vacc0, vx4, vld1q_z(w_base0 + 4 * in_c + ic, p)); + + vacc1 = vfmaq(vacc1, vx0, vld1q_z(w_base1 + 0 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx1, vld1q_z(w_base1 + 1 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx2, vld1q_z(w_base1 + 2 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx3, vld1q_z(w_base1 + 3 * in_c + ic, p)); + vacc1 = vfmaq(vacc1, vx4, vld1q_z(w_base1 + 4 * in_c + ic, p)); + + vacc2 = vfmaq(vacc2, vx0, vld1q_z(w_base2 + 0 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx1, vld1q_z(w_base2 + 1 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx2, vld1q_z(w_base2 + 2 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx3, vld1q_z(w_base2 + 3 * in_c + ic, p)); + vacc2 = vfmaq(vacc2, vx4, vld1q_z(w_base2 + 4 * in_c + ic, p)); + + vacc3 = vfmaq(vacc3, vx0, vld1q_z(w_base3 + 0 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx1, vld1q_z(w_base3 + 1 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx2, vld1q_z(w_base3 + 2 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx3, vld1q_z(w_base3 + 3 * in_c + ic, p)); + vacc3 = vfmaq(vacc3, vx4, vld1q_z(w_base3 + 4 * in_c + ic, p)); + } + + const _Float16 acc0 = + (b ? (_Float16)b[oc + 0] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc0); + const _Float16 acc1 = + (b ? (_Float16)b[oc + 1] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc1); + const _Float16 acc2 = + (b ? (_Float16)b[oc + 2] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc2); + const _Float16 acc3 = + (b ? (_Float16)b[oc + 3] : (_Float16)0.0f) + (_Float16)arm_nn_vec_reduce_add_f16(vacc3); + + y[oc + 0] = (float16_t)acc0; + y[oc + 1] = (float16_t)acc1; + y[oc + 2] = (float16_t)acc2; + y[oc + 3] = (float16_t)acc3; + } + + for (; oc < out_c; ++oc) + { + const float16_t *w0 = kernel + (size_t)oc * 5U * (size_t)in_c; + const float16_t *w1 = w0 + in_c; + const float16_t *w2 = w1 + in_c; + const float16_t *w3 = w2 + in_c; + const float16_t *w4 = w3 + in_c; + _Float16 acc = b ? (_Float16)b[oc] : (_Float16)0.0f; + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + for (int32_t ic = 0; ic < in_c; ic += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - ic)); + vacc = vfmaq(vacc, vld1q_z(x0 + ic, p), vld1q_z(w0 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x1 + ic, p), vld1q_z(w1 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x2 + ic, p), vld1q_z(w2 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x3 + ic, p), vld1q_z(w3 + ic, p)); + vacc = vfmaq(vacc, vld1q_z(x4 + ic, p), vld1q_z(w4 + ic, p)); + } + acc += (_Float16)arm_nn_vec_reduce_add_f16(vacc); + y[oc] = (float16_t)acc; + } + #else + for (int32_t oc = 0; oc < out_c; ++oc) + { + const float16_t *w = kernel + (size_t)oc * 5U * (size_t)in_c; + float32_t acc = b ? (float32_t)b[oc] : 0.0f; + for (int32_t ic = 0; ic < in_c; ++ic) + { + acc += (float32_t)x0[ic] * (float32_t)w[ic]; + acc += (float32_t)x1[ic] * (float32_t)w[in_c + ic]; + acc += (float32_t)x2[ic] * (float32_t)w[2 * in_c + ic]; + acc += (float32_t)x3[ic] * (float32_t)w[3 * in_c + ic]; + acc += (float32_t)x4[ic] * (float32_t)w[4 * in_c + ic]; + } + y[oc] = (float16_t)acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k5_f32.c b/Source/NNSupportFunctions/arm_nn_conv1d_k5_f32.c new file mode 100644 index 00000000..1db7466a --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k5_f32.c @@ -0,0 +1,176 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k5_f32.c + * Description: Support: NHWC 1D convolution kernel size 5 for f32 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_CONV1D_K5_NHWC_F32_OC2_BLOCK (2) + #define ARM_NN_CONV1D_K5_NHWC_F32_OC4_BLOCK (4) + #endif + +void arm_nn_conv1d_k5_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float32_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float32_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + float32_t *y = out + (size_t)ow * (size_t)out_c; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t oc = 0; + + if (in_c == 4) + { + const float32x4_t vx0 = vld1q(x0); + const float32x4_t vx1 = vld1q(x1); + const float32x4_t vx2 = vld1q(x2); + const float32x4_t vx3 = vld1q(x3); + const float32x4_t vx4 = vld1q(x4); + + for (; oc + ARM_NN_CONV1D_K5_NHWC_F32_OC4_BLOCK <= out_c; oc += ARM_NN_CONV1D_K5_NHWC_F32_OC4_BLOCK) + { + const float32_t *w_base0 = kernel + (size_t)(oc + 0) * 5U * (size_t)in_c; + const float32_t *w_base1 = kernel + (size_t)(oc + 1) * 5U * (size_t)in_c; + const float32_t *w_base2 = kernel + (size_t)(oc + 2) * 5U * (size_t)in_c; + const float32_t *w_base3 = kernel + (size_t)(oc + 3) * 5U * (size_t)in_c; + float32x4_t vacc0 = vdupq_n_f32(0.0f); + float32x4_t vacc1 = vdupq_n_f32(0.0f); + float32x4_t vacc2 = vdupq_n_f32(0.0f); + float32x4_t vacc3 = vdupq_n_f32(0.0f); + + vacc0 = vfmaq(vacc0, vx0, vld1q(w_base0 + 0 * in_c)); + vacc0 = vfmaq(vacc0, vx1, vld1q(w_base0 + 1 * in_c)); + vacc0 = vfmaq(vacc0, vx2, vld1q(w_base0 + 2 * in_c)); + vacc0 = vfmaq(vacc0, vx3, vld1q(w_base0 + 3 * in_c)); + vacc0 = vfmaq(vacc0, vx4, vld1q(w_base0 + 4 * in_c)); + + vacc1 = vfmaq(vacc1, vx0, vld1q(w_base1 + 0 * in_c)); + vacc1 = vfmaq(vacc1, vx1, vld1q(w_base1 + 1 * in_c)); + vacc1 = vfmaq(vacc1, vx2, vld1q(w_base1 + 2 * in_c)); + vacc1 = vfmaq(vacc1, vx3, vld1q(w_base1 + 3 * in_c)); + vacc1 = vfmaq(vacc1, vx4, vld1q(w_base1 + 4 * in_c)); + + vacc2 = vfmaq(vacc2, vx0, vld1q(w_base2 + 0 * in_c)); + vacc2 = vfmaq(vacc2, vx1, vld1q(w_base2 + 1 * in_c)); + vacc2 = vfmaq(vacc2, vx2, vld1q(w_base2 + 2 * in_c)); + vacc2 = vfmaq(vacc2, vx3, vld1q(w_base2 + 3 * in_c)); + vacc2 = vfmaq(vacc2, vx4, vld1q(w_base2 + 4 * in_c)); + + vacc3 = vfmaq(vacc3, vx0, vld1q(w_base3 + 0 * in_c)); + vacc3 = vfmaq(vacc3, vx1, vld1q(w_base3 + 1 * in_c)); + vacc3 = vfmaq(vacc3, vx2, vld1q(w_base3 + 2 * in_c)); + vacc3 = vfmaq(vacc3, vx3, vld1q(w_base3 + 3 * in_c)); + vacc3 = vfmaq(vacc3, vx4, vld1q(w_base3 + 4 * in_c)); + + y[oc + 0] = (b ? b[oc + 0] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc0); + y[oc + 1] = (b ? b[oc + 1] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc1); + y[oc + 2] = (b ? b[oc + 2] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc2); + y[oc + 3] = (b ? b[oc + 3] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc3); + } + } + + for (; oc + ARM_NN_CONV1D_K5_NHWC_F32_OC2_BLOCK <= out_c; oc += ARM_NN_CONV1D_K5_NHWC_F32_OC2_BLOCK) + { + const float32_t *w_base0 = kernel + (size_t)(oc + 0) * 5U * (size_t)in_c; + const float32_t *w_base1 = kernel + (size_t)(oc + 1) * 5U * (size_t)in_c; + float32x4_t vacc0 = vdupq_n_f32(0.0f); + float32x4_t vacc1 = vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - ic)); + float32x4_t vx = vld1q_z(x0 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 0 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 0 * in_c + ic, p), p); + + vx = vld1q_z(x1 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 1 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 1 * in_c + ic, p), p); + + vx = vld1q_z(x2 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 2 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 2 * in_c + ic, p), p); + + vx = vld1q_z(x3 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 3 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 3 * in_c + ic, p), p); + + vx = vld1q_z(x4 + ic, p); + vacc0 = vfmaq_m(vacc0, vx, vld1q_z(w_base0 + 4 * in_c + ic, p), p); + vacc1 = vfmaq_m(vacc1, vx, vld1q_z(w_base1 + 4 * in_c + ic, p), p); + } + + y[oc + 0] = (b ? b[oc + 0] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc0); + y[oc + 1] = (b ? b[oc + 1] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc1); + } + + for (; oc < out_c; ++oc) + { + const float32_t *w0 = kernel + (size_t)oc * 5U * (size_t)in_c; + const float32_t *w1 = w0 + in_c; + const float32_t *w2 = w1 + in_c; + const float32_t *w3 = w2 + in_c; + const float32_t *w4 = w3 + in_c; + float32x4_t vacc = vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ic += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - ic)); + vacc = vfmaq_m(vacc, vld1q_z(x0 + ic, p), vld1q_z(w0 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x1 + ic, p), vld1q_z(w1 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x2 + ic, p), vld1q_z(w2 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x3 + ic, p), vld1q_z(w3 + ic, p), p); + vacc = vfmaq_m(vacc, vld1q_z(x4 + ic, p), vld1q_z(w4 + ic, p), p); + } + + y[oc] = (b ? b[oc] : 0.0f) + arm_nn_vec_reduce_add_f32(vacc); + } + #else + for (int32_t oc = 0; oc < out_c; ++oc) + { + const float32_t *w = kernel + (size_t)oc * 5U * (size_t)in_c; + float32_t acc = b ? b[oc] : 0.0f; + for (int32_t ic = 0; ic < in_c; ++ic) + { + acc += x0[ic] * w[ic]; + acc += x1[ic] * w[in_c + ic]; + acc += x2[ic] * w[2 * in_c + ic]; + acc += x3[ic] * w[3 * in_c + ic]; + acc += x4[ic] * w[4 * in_c + ic]; + } + y[oc] = acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f16.c b/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f16.c new file mode 100755 index 00000000..3634b3a7 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f16.c @@ -0,0 +1,196 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k5_packed_f16.c + * Description: Support: NHWC 1D convolution kernel size 5 for packed f16 weights + * + * $Date: 29 Apr 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +void arm_nn_conv1d_k5_packed_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel_packed, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + const int32_t block_cols = 8; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t ow = 0; + for (; ow + 1 < out_w; ow += 2) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float16_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float16_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + const float16_t *x5 = x_nhwc + (size_t)(ow + 5) * (size_t)in_c; + float16_t *y0 = out + (size_t)(ow + 0) * (size_t)out_c; + float16_t *y1 = out + (size_t)(ow + 1) * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float16x8_t vacc0 = b ? vld1q(b + oc) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + const float16x8_t vw0 = vld1q(w_ic + 0U * in_c * block_cols); + const float16x8_t vw1 = vld1q(w_ic + 1U * in_c * block_cols); + const float16x8_t vw2 = vld1q(w_ic + 2U * in_c * block_cols); + const float16x8_t vw3 = vld1q(w_ic + 3U * in_c * block_cols); + const float16x8_t vw4 = vld1q(w_ic + 4U * in_c * block_cols); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + vacc0 = vfmaq(vacc0, vw3, x3[ic]); + vacc1 = vfmaq(vacc1, vw3, x4[ic]); + vacc0 = vfmaq(vacc0, vw4, x4[ic]); + vacc1 = vfmaq(vacc1, vw4, x5[ic]); + } + + vst1q(y0 + oc, vacc0); + vst1q(y1 + oc, vacc1); + } + + if (oc < out_c) + { + const int32_t valid_cols = out_c - oc; + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float16x8_t vacc0 = b ? vld1q_z(b + oc, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + const float16x8_t vw0 = vld1q_z(w_ic + 0U * in_c * block_cols, p); + const float16x8_t vw1 = vld1q_z(w_ic + 1U * in_c * block_cols, p); + const float16x8_t vw2 = vld1q_z(w_ic + 2U * in_c * block_cols, p); + const float16x8_t vw3 = vld1q_z(w_ic + 3U * in_c * block_cols, p); + const float16x8_t vw4 = vld1q_z(w_ic + 4U * in_c * block_cols, p); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + vacc0 = vfmaq(vacc0, vw3, x3[ic]); + vacc1 = vfmaq(vacc1, vw3, x4[ic]); + vacc0 = vfmaq(vacc0, vw4, x4[ic]); + vacc1 = vfmaq(vacc1, vw4, x5[ic]); + } + + vst1q_p(y0 + oc, vacc0, p); + vst1q_p(y1 + oc, vacc1, p); + } + } + + if (ow < out_w) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float16_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float16_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float16x8_t vacc = b ? vld1q(b + oc) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q(w_ic + 0U * in_c * block_cols), x0[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 1U * in_c * block_cols), x1[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 2U * in_c * block_cols), x2[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 3U * in_c * block_cols), x3[ic]); + vacc = vfmaq(vacc, vld1q(w_ic + 4U * in_c * block_cols), x4[ic]); + } + + vst1q(y + oc, vacc); + } + + if (oc < out_c) + { + const int32_t valid_cols = out_c - oc; + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float16x8_t vacc = b ? vld1q_z(b + oc, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q_z(w_ic + 0U * in_c * block_cols, p), x0[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 1U * in_c * block_cols, p), x1[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 2U * in_c * block_cols, p), x2[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 3U * in_c * block_cols, p), x3[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 4U * in_c * block_cols, p), x4[ic]); + } + + vst1q_p(y + oc, vacc, p); + } + } + + #else + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float16_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float16_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + float16_t *y = out + (size_t)ow * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; ++oc) + { + const int32_t lane = oc % block_cols; + const float16_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + _Float16 acc = b ? (_Float16)b[oc] : (_Float16)0.0f; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float16_t *w_ic = w_base + (size_t)ic * block_cols; + acc += (_Float16)x0[ic] * (_Float16)w_ic[(size_t)0 * in_c * block_cols + lane]; + acc += (_Float16)x1[ic] * (_Float16)w_ic[(size_t)1 * in_c * block_cols + lane]; + acc += (_Float16)x2[ic] * (_Float16)w_ic[(size_t)2 * in_c * block_cols + lane]; + acc += (_Float16)x3[ic] * (_Float16)w_ic[(size_t)3 * in_c * block_cols + lane]; + acc += (_Float16)x4[ic] * (_Float16)w_ic[(size_t)4 * in_c * block_cols + lane]; + } + + y[oc] = (float16_t)acc; + } + } + #endif +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f32.c b/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f32.c new file mode 100755 index 00000000..893ae036 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_conv1d_k5_packed_f32.c @@ -0,0 +1,144 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_conv1d_k5_packed_f32.c + * Description: Support: NHWC 1D convolution kernel size 5 for packed f32 weights + * + * $Date: 30 Apr 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +void arm_nn_conv1d_k5_packed_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel_packed, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_c, + int32_t out_w) +{ + (void)in_w; + + const int32_t block_cols = 4; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t ow = 0; + for (; ow + 1 < out_w; ow += 2) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float32_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float32_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + const float32_t *x5 = x_nhwc + (size_t)(ow + 5) * (size_t)in_c; + float32_t *y0 = out + (size_t)(ow + 0) * (size_t)out_c; + float32_t *y1 = out + (size_t)(ow + 1) * (size_t)out_c; + + int32_t oc = 0; + for (; oc + block_cols <= out_c; oc += block_cols) + { + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float32x4_t vacc0 = b ? vld1q(b + oc) : vdupq_n_f32(0.0f); + float32x4_t vacc1 = vacc0; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + const float32x4_t vw0 = vld1q(w_ic + 0U * in_c * block_cols); + const float32x4_t vw1 = vld1q(w_ic + 1U * in_c * block_cols); + const float32x4_t vw2 = vld1q(w_ic + 2U * in_c * block_cols); + const float32x4_t vw3 = vld1q(w_ic + 3U * in_c * block_cols); + const float32x4_t vw4 = vld1q(w_ic + 4U * in_c * block_cols); + + vacc0 = vfmaq(vacc0, vw0, x0[ic]); + vacc1 = vfmaq(vacc1, vw0, x1[ic]); + vacc0 = vfmaq(vacc0, vw1, x1[ic]); + vacc1 = vfmaq(vacc1, vw1, x2[ic]); + vacc0 = vfmaq(vacc0, vw2, x2[ic]); + vacc1 = vfmaq(vacc1, vw2, x3[ic]); + vacc0 = vfmaq(vacc0, vw3, x3[ic]); + vacc1 = vfmaq(vacc1, vw3, x4[ic]); + vacc0 = vfmaq(vacc0, vw4, x4[ic]); + vacc1 = vfmaq(vacc1, vw4, x5[ic]); + } + + vst1q(y0 + oc, vacc0); + vst1q(y1 + oc, vacc1); + } + } + + if ((out_w & 1) != 0) + { + const int32_t ow_tail = out_w - 1; + const float32_t *x0 = x_nhwc + (size_t)(ow_tail + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow_tail + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow_tail + 2) * (size_t)in_c; + const float32_t *x3 = x_nhwc + (size_t)(ow_tail + 3) * (size_t)in_c; + const float32_t *x4 = x_nhwc + (size_t)(ow_tail + 4) * (size_t)in_c; + float32_t *y = out + (size_t)ow_tail * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; oc += block_cols) + { + const int32_t valid_cols = out_c - oc < block_cols ? out_c - oc : block_cols; + const mve_pred16_t p = vctp32q((uint32_t)valid_cols); + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float32x4_t vacc = b ? vld1q_z(b + oc, p) : vdupq_n_f32(0.0f); + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + vacc = vfmaq(vacc, vld1q_z(w_ic + 0U * in_c * block_cols, p), x0[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 1U * in_c * block_cols, p), x1[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 2U * in_c * block_cols, p), x2[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 3U * in_c * block_cols, p), x3[ic]); + vacc = vfmaq(vacc, vld1q_z(w_ic + 4U * in_c * block_cols, p), x4[ic]); + } + + vst1q_p(y + oc, vacc, p); + } + } + #else + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + const float32_t *x3 = x_nhwc + (size_t)(ow + 3) * (size_t)in_c; + const float32_t *x4 = x_nhwc + (size_t)(ow + 4) * (size_t)in_c; + float32_t *y = out + (size_t)ow * (size_t)out_c; + + for (int32_t oc = 0; oc < out_c; ++oc) + { + const int32_t lane = oc % block_cols; + const float32_t *w_base = kernel_packed + ((size_t)oc / block_cols) * 5U * (size_t)in_c * block_cols; + float32_t acc = b ? b[oc] : 0.0f; + + for (int32_t ic = 0; ic < in_c; ++ic) + { + const float32_t *w_ic = w_base + (size_t)ic * block_cols; + acc += x0[ic] * w_ic[(size_t)0 * in_c * block_cols + lane]; + acc += x1[ic] * w_ic[(size_t)1 * in_c * block_cols + lane]; + acc += x2[ic] * w_ic[(size_t)2 * in_c * block_cols + lane]; + acc += x3[ic] * w_ic[(size_t)3 * in_c * block_cols + lane]; + acc += x4[ic] * w_ic[(size_t)4 * in_c * block_cols + lane]; + } + + y[oc] = acc; + } + } + #endif +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f16.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f16.c new file mode 100644 index 00000000..706d5359 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f16.c @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv1d_k3_f16.c + * Description: Support: NHWC depthwise 1D convolution kernel size 3 for f16 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +void arm_nn_depthwise_conv1d_k3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float16_t *dst = out + (size_t)ow * (size_t)in_c; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - c)); + float16x8_t acc = b ? vld1q_z(b + c, p) : vdupq_n_f16((float16_t)0.0f); + acc = vfmaq_m(acc, vld1q_z(x0 + c, p), vld1q_z(kernel + c, p), p); + acc = vfmaq_m(acc, vld1q_z(x1 + c, p), vld1q_z(kernel + in_c + c, p), p); + acc = vfmaq_m(acc, vld1q_z(x2 + c, p), vld1q_z(kernel + (2 * in_c) + c, p), p); + vst1q_p(dst + c, acc, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + float32_t acc = b ? (float32_t)b[c] : 0.0f; + acc += (float32_t)x0[c] * (float32_t)kernel[c]; + acc += (float32_t)x1[c] * (float32_t)kernel[in_c + c]; + acc += (float32_t)x2[c] * (float32_t)kernel[2 * in_c + c]; + dst[c] = (float16_t)acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f32.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f32.c new file mode 100644 index 00000000..4ade6a7b --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv1d_k3_f32.c @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv1d_k3_f32.c + * Description: Support: NHWC depthwise 1D convolution kernel size 3 for f32 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +void arm_nn_depthwise_conv1d_k3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow + 2) * (size_t)in_c; + float32_t *dst = out + (size_t)ow * (size_t)in_c; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - c)); + float32x4_t acc = b ? vld1q_z(b + c, p) : vdupq_n_f32(0.0f); + acc = vfmaq_m(acc, vld1q_z(x0 + c, p), vld1q_z(kernel + c, p), p); + acc = vfmaq_m(acc, vld1q_z(x1 + c, p), vld1q_z(kernel + in_c + c, p), p); + acc = vfmaq_m(acc, vld1q_z(x2 + c, p), vld1q_z(kernel + (2 * in_c) + c, p), p); + vst1q_p(dst + c, acc, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + float32_t acc = b ? b[c] : 0.0f; + acc += x0[c] * kernel[c]; + acc += x1[c] * kernel[in_c + c]; + acc += x2[c] * kernel[2 * in_c + c]; + dst[c] = acc; + } + #endif + } +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv2x5_f16.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv2x5_f16.c new file mode 100644 index 00000000..1a0aa5b6 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv2x5_f16.c @@ -0,0 +1,111 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv2x5_f16.c + * Description: Support: NHWC depthwise 2x5 convolution kernel for f16 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +void arm_nn_depthwise_conv2x5_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_w, + int32_t ch_mult, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t out_w, + float16_t act_min, + float16_t act_max) +{ + const int32_t out_c = in_c * ch_mult; + const size_t in_batch_stride = (size_t)2U * (size_t)in_w * (size_t)in_c; + const size_t out_batch_stride = (size_t)out_w * (size_t)out_c; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_act_min = vdupq_n_f16(act_min); + const float16x8_t v_act_max = vdupq_n_f16(act_max); + #endif + + for (int32_t batch = 0; batch < batches; ++batch) + { + const float16_t *in_batch = x_nhwc + (size_t)batch * in_batch_stride; + float16_t *out_batch = out + (size_t)batch * out_batch_stride; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *row0 = in_batch + (size_t)ow * (size_t)in_c; + const float16_t *row1 = in_batch + ((size_t)in_w + (size_t)ow) * (size_t)in_c; + + for (int32_t c = 0; c < in_c; ++c) + { + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16_t x00 = row0[(size_t)0 * (size_t)in_c + (size_t)c]; + const float16_t x01 = row0[(size_t)1 * (size_t)in_c + (size_t)c]; + const float16_t x02 = row0[(size_t)2 * (size_t)in_c + (size_t)c]; + const float16_t x03 = row0[(size_t)3 * (size_t)in_c + (size_t)c]; + const float16_t x04 = row0[(size_t)4 * (size_t)in_c + (size_t)c]; + const float16_t x10 = row1[(size_t)0 * (size_t)in_c + (size_t)c]; + const float16_t x11 = row1[(size_t)1 * (size_t)in_c + (size_t)c]; + const float16_t x12 = row1[(size_t)2 * (size_t)in_c + (size_t)c]; + const float16_t x13 = row1[(size_t)3 * (size_t)in_c + (size_t)c]; + const float16_t x14 = row1[(size_t)4 * (size_t)in_c + (size_t)c]; + + for (int32_t m = 0; m < ch_mult; m += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(ch_mult - m)); + const int32_t oc = c * ch_mult + m; + float16x8_t vacc = b ? vld1q_z(b + oc, p) : vdupq_n_f16((float16_t)0.0f); + + vacc = vfmaq(vacc, vdupq_n_f16(x00), vld1q_z(kernel + (size_t)0 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x01), vld1q_z(kernel + (size_t)1 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x02), vld1q_z(kernel + (size_t)2 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x03), vld1q_z(kernel + (size_t)3 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x04), vld1q_z(kernel + (size_t)4 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x10), vld1q_z(kernel + (size_t)5 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x11), vld1q_z(kernel + (size_t)6 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x12), vld1q_z(kernel + (size_t)7 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x13), vld1q_z(kernel + (size_t)8 * (size_t)out_c + (size_t)oc, p)); + vacc = vfmaq(vacc, vdupq_n_f16(x14), vld1q_z(kernel + (size_t)9 * (size_t)out_c + (size_t)oc, p)); + + vacc = vmaxnmq(vacc, v_act_min); + vacc = vminnmq(vacc, v_act_max); + vst1q_p(out_batch + (size_t)ow * (size_t)out_c + (size_t)oc, vacc, p); + } + #else + for (int32_t m = 0; m < ch_mult; ++m) + { + const int32_t oc = c * ch_mult + m; + float32_t acc = b ? (float32_t)b[oc] : 0.0f; + for (int32_t kx = 0; kx < 5; ++kx) + { + const size_t base0 = (size_t)kx * (size_t)out_c + (size_t)oc; + const size_t base1 = ((size_t)5U + (size_t)kx) * (size_t)out_c + (size_t)oc; + acc += (float32_t)row0[(size_t)kx * (size_t)in_c + (size_t)c] * (float32_t)kernel[base0]; + acc += (float32_t)row1[(size_t)kx * (size_t)in_c + (size_t)c] * (float32_t)kernel[base1]; + } + acc = MAX(acc, (float32_t)act_min); + acc = MIN(acc, (float32_t)act_max); + out_batch[(size_t)ow * (size_t)out_c + (size_t)oc] = (float16_t)acc; + } + #endif + } + } + } +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f16.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f16.c new file mode 100644 index 00000000..827090b5 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f16.c @@ -0,0 +1,321 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv3x3_f16.c + * Description: Support: depthwise conv 3x3 kernels for f16 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +__STATIC_INLINE void depthwise_init_vec_f16(float16_t *dst, const float16_t *bias, int32_t channels) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (bias) + { + for (int32_t c = 0; c < channels; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channels - c)); + vst1q_p(dst + c, vld1q_z(bias + c, p), p); + } + } + else + { + const float16x8_t vzero = vdupq_n_f16((float16_t)0); + for (int32_t c = 0; c < channels; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channels - c)); + vst1q_p(dst + c, vzero, p); + } + } +#else + if (bias) + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = bias[c]; + } + } + else + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = (float16_t)0; + } + } +#endif +} + +__STATIC_INLINE void +depthwise_accumulate_vec_f16(float16_t *acc, const float16_t *input, const float16_t *kernel, int32_t channels) +{ +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < channels; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channels - c)); + float16x8_t vacc = vld1q_z(acc + c, p); + vacc = vfmaq(vacc, vld1q_z(input + c, p), vld1q_z(kernel + c, p)); + vst1q_p(acc + c, vacc, p); + } +#else + for (int32_t c = 0; c < channels; ++c) + { + acc[c] = (float16_t)((_Float16)acc[c] + (_Float16)input[c] * (_Float16)kernel[c]); + } +#endif +} + +__STATIC_INLINE void depthwise_clamp_vec_f16(float16_t *data, int32_t channels, float16_t act_min, float16_t act_max) +{ + arm_nn_vector_clamp_f16(data, channels, act_min, act_max); +} + +static void depthwise_conv3x3_nhwc_valid_f16(const float16_t *x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float16_t *kernel, + const float16_t *b, + float16_t *out, + int32_t out_h, + int32_t out_w, + float16_t act_min, + float16_t act_max) +{ + const size_t in_row_stride = (size_t)in_w * (size_t)in_c; + const size_t in_batch_stride = (size_t)in_h * in_row_stride; + const size_t out_row_stride = (size_t)out_w * (size_t)in_c; + const size_t out_batch_stride = (size_t)out_h * out_row_stride; + const float16_t *w0 = kernel; + const float16_t *w1 = w0 + in_c; + const float16_t *w2 = w1 + in_c; + const float16_t *w3 = w2 + in_c; + const float16_t *w4 = w3 + in_c; + const float16_t *w5 = w4 + in_c; + const float16_t *w6 = w5 + in_c; + const float16_t *w7 = w6 + in_c; + const float16_t *w8 = w7 + in_c; + + for (int32_t batch = 0; batch < batches; ++batch) + { + const float16_t *input_batch = x_nhwc + (size_t)batch * in_batch_stride; + float16_t *output_batch = out + (size_t)batch * out_batch_stride; + + for (int32_t oy = 0; oy < out_h; ++oy) + { + const float16_t *x0 = input_batch + (size_t)oy * in_row_stride; + const float16_t *x1 = x0 + in_row_stride; + const float16_t *x2 = x1 + in_row_stride; + float16_t *y = output_batch + (size_t)oy * out_row_stride; + + for (int32_t ox = 0; ox < out_w; ++ox) + { + float16_t *out_px = y + (size_t)ox * (size_t)in_c; + depthwise_init_vec_f16(out_px, b, in_c); + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)(ox + 0) * (size_t)in_c, w0, in_c); + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)(ox + 1) * (size_t)in_c, w1, in_c); + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)(ox + 2) * (size_t)in_c, w2, in_c); + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)(ox + 0) * (size_t)in_c, w3, in_c); + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)(ox + 1) * (size_t)in_c, w4, in_c); + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)(ox + 2) * (size_t)in_c, w5, in_c); + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)(ox + 0) * (size_t)in_c, w6, in_c); + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)(ox + 1) * (size_t)in_c, w7, in_c); + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)(ox + 2) * (size_t)in_c, w8, in_c); + depthwise_clamp_vec_f16(out_px, in_c, act_min, act_max); + } + } + } +} + +static void depthwise_conv3x3_nhwc_generic_f16(const float16_t *x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float16_t *kernel, + const float16_t *b, + float16_t *out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float16_t act_min, + float16_t act_max) +{ + const size_t in_row_stride = (size_t)in_w * (size_t)in_c; + const size_t in_batch_stride = (size_t)in_h * in_row_stride; + const size_t out_row_stride = (size_t)out_w * (size_t)in_c; + const size_t out_batch_stride = (size_t)out_h * out_row_stride; + const float16_t *w0 = kernel; + const float16_t *w1 = w0 + in_c; + const float16_t *w2 = w1 + in_c; + const float16_t *w3 = w2 + in_c; + const float16_t *w4 = w3 + in_c; + const float16_t *w5 = w4 + in_c; + const float16_t *w6 = w5 + in_c; + const float16_t *w7 = w6 + in_c; + const float16_t *w8 = w7 + in_c; + + for (int32_t batch = 0; batch < batches; ++batch) + { + const float16_t *input_batch = x_nhwc + (size_t)batch * in_batch_stride; + float16_t *output_batch = out + (size_t)batch * out_batch_stride; + + for (int32_t oy = 0; oy < out_h; ++oy) + { + const int32_t base_y = oy * stride_y - pad_y; + const int32_t y0_idx = base_y; + const int32_t y1_idx = base_y + 1; + const int32_t y2_idx = base_y + 2; + const bool y0_valid = (uint32_t)y0_idx < (uint32_t)in_h; + const bool y1_valid = (uint32_t)y1_idx < (uint32_t)in_h; + const bool y2_valid = (uint32_t)y2_idx < (uint32_t)in_h; + const float16_t *x0 = y0_valid ? (input_batch + (size_t)y0_idx * in_row_stride) : NULL; + const float16_t *x1 = y1_valid ? (input_batch + (size_t)y1_idx * in_row_stride) : NULL; + const float16_t *x2 = y2_valid ? (input_batch + (size_t)y2_idx * in_row_stride) : NULL; + float16_t *y = output_batch + (size_t)oy * out_row_stride; + + for (int32_t ox = 0; ox < out_w; ++ox) + { + const int32_t base_x = ox * stride_x - pad_x; + const int32_t x0_idx = base_x; + const int32_t x1_idx = base_x + 1; + const int32_t x2_idx = base_x + 2; + float16_t *out_px = y + (size_t)ox * (size_t)in_c; + + depthwise_init_vec_f16(out_px, b, in_c); + + if (y0_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)x0_idx * (size_t)in_c, w0, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)x1_idx * (size_t)in_c, w1, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x0 + (size_t)x2_idx * (size_t)in_c, w2, in_c); + } + } + + if (y1_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)x0_idx * (size_t)in_c, w3, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)x1_idx * (size_t)in_c, w4, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x1 + (size_t)x2_idx * (size_t)in_c, w5, in_c); + } + } + + if (y2_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)x0_idx * (size_t)in_c, w6, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)x1_idx * (size_t)in_c, w7, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f16(out_px, x2 + (size_t)x2_idx * (size_t)in_c, w8, in_c); + } + } + + depthwise_clamp_vec_f16(out_px, in_c, act_min, act_max); + } + } + } +} + +void arm_nn_depthwise_conv3x3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float16_t *__RESTRICT kernel, + const float16_t *__RESTRICT b, + float16_t *__RESTRICT out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float16_t act_min, + float16_t act_max) +{ + if (stride_x == 1 && stride_y == 1 && pad_x == 0 && pad_y == 0) + { + depthwise_conv3x3_nhwc_valid_f16( + x_nhwc, batches, in_c, in_h, in_w, kernel, b, out, out_h, out_w, act_min, act_max); + return; + } + + depthwise_conv3x3_nhwc_generic_f16(x_nhwc, + batches, + in_c, + in_h, + in_w, + kernel, + b, + out, + stride_x, + stride_y, + pad_x, + pad_y, + out_h, + out_w, + act_min, + act_max); +} + +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f32.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f32.c new file mode 100644 index 00000000..572be5e2 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv3x3_f32.c @@ -0,0 +1,321 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv3x3_f32.c + * Description: Support: depthwise conv 3x3 kernels for f32 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +__STATIC_INLINE void depthwise_init_vec_f32(float32_t *dst, const float32_t *bias, int32_t channels) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (bias) + { + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + vst1q_p(dst + c, vld1q_z(bias + c, p), p); + } + } + else + { + const float32x4_t vzero = vdupq_n_f32(0.0f); + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + vst1q_p(dst + c, vzero, p); + } + } +#else + if (bias) + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = bias[c]; + } + } + else + { + for (int32_t c = 0; c < channels; ++c) + { + dst[c] = 0.0f; + } + } +#endif +} + +__STATIC_INLINE void +depthwise_accumulate_vec_f32(float32_t *acc, const float32_t *input, const float32_t *kernel, int32_t channels) +{ +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < channels; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channels - c)); + float32x4_t vacc = vld1q_z(acc + c, p); + vacc = vfmaq(vacc, vld1q_z(input + c, p), vld1q_z(kernel + c, p)); + vst1q_p(acc + c, vacc, p); + } +#else + for (int32_t c = 0; c < channels; ++c) + { + acc[c] += input[c] * kernel[c]; + } +#endif +} + +__STATIC_INLINE void depthwise_clamp_vec_f32(float32_t *data, int32_t channels, float32_t act_min, float32_t act_max) +{ + arm_nn_vector_clamp_f32(data, channels, act_min, act_max); +} + +static void depthwise_conv3x3_nhwc_valid_f32(const float32_t *x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float32_t *kernel, + const float32_t *b, + float32_t *out, + int32_t out_h, + int32_t out_w, + float32_t act_min, + float32_t act_max) +{ + const size_t in_row_stride = (size_t)in_w * (size_t)in_c; + const size_t in_batch_stride = (size_t)in_h * in_row_stride; + const size_t out_row_stride = (size_t)out_w * (size_t)in_c; + const size_t out_batch_stride = (size_t)out_h * out_row_stride; + const float32_t *w0 = kernel; + const float32_t *w1 = w0 + in_c; + const float32_t *w2 = w1 + in_c; + const float32_t *w3 = w2 + in_c; + const float32_t *w4 = w3 + in_c; + const float32_t *w5 = w4 + in_c; + const float32_t *w6 = w5 + in_c; + const float32_t *w7 = w6 + in_c; + const float32_t *w8 = w7 + in_c; + + for (int32_t batch = 0; batch < batches; ++batch) + { + const float32_t *input_batch = x_nhwc + (size_t)batch * in_batch_stride; + float32_t *output_batch = out + (size_t)batch * out_batch_stride; + + for (int32_t oy = 0; oy < out_h; ++oy) + { + const float32_t *x0 = input_batch + (size_t)oy * in_row_stride; + const float32_t *x1 = x0 + in_row_stride; + const float32_t *x2 = x1 + in_row_stride; + float32_t *y = output_batch + (size_t)oy * out_row_stride; + + for (int32_t ox = 0; ox < out_w; ++ox) + { + float32_t *out_px = y + (size_t)ox * (size_t)in_c; + depthwise_init_vec_f32(out_px, b, in_c); + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)(ox + 0) * (size_t)in_c, w0, in_c); + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)(ox + 1) * (size_t)in_c, w1, in_c); + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)(ox + 2) * (size_t)in_c, w2, in_c); + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)(ox + 0) * (size_t)in_c, w3, in_c); + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)(ox + 1) * (size_t)in_c, w4, in_c); + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)(ox + 2) * (size_t)in_c, w5, in_c); + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)(ox + 0) * (size_t)in_c, w6, in_c); + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)(ox + 1) * (size_t)in_c, w7, in_c); + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)(ox + 2) * (size_t)in_c, w8, in_c); + depthwise_clamp_vec_f32(out_px, in_c, act_min, act_max); + } + } + } +} + +static void depthwise_conv3x3_nhwc_generic_f32(const float32_t *x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float32_t *kernel, + const float32_t *b, + float32_t *out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float32_t act_min, + float32_t act_max) +{ + const size_t in_row_stride = (size_t)in_w * (size_t)in_c; + const size_t in_batch_stride = (size_t)in_h * in_row_stride; + const size_t out_row_stride = (size_t)out_w * (size_t)in_c; + const size_t out_batch_stride = (size_t)out_h * out_row_stride; + const float32_t *w0 = kernel; + const float32_t *w1 = w0 + in_c; + const float32_t *w2 = w1 + in_c; + const float32_t *w3 = w2 + in_c; + const float32_t *w4 = w3 + in_c; + const float32_t *w5 = w4 + in_c; + const float32_t *w6 = w5 + in_c; + const float32_t *w7 = w6 + in_c; + const float32_t *w8 = w7 + in_c; + + for (int32_t batch = 0; batch < batches; ++batch) + { + const float32_t *input_batch = x_nhwc + (size_t)batch * in_batch_stride; + float32_t *output_batch = out + (size_t)batch * out_batch_stride; + + for (int32_t oy = 0; oy < out_h; ++oy) + { + const int32_t base_y = oy * stride_y - pad_y; + const int32_t y0_idx = base_y; + const int32_t y1_idx = base_y + 1; + const int32_t y2_idx = base_y + 2; + const bool y0_valid = (uint32_t)y0_idx < (uint32_t)in_h; + const bool y1_valid = (uint32_t)y1_idx < (uint32_t)in_h; + const bool y2_valid = (uint32_t)y2_idx < (uint32_t)in_h; + const float32_t *x0 = y0_valid ? (input_batch + (size_t)y0_idx * in_row_stride) : NULL; + const float32_t *x1 = y1_valid ? (input_batch + (size_t)y1_idx * in_row_stride) : NULL; + const float32_t *x2 = y2_valid ? (input_batch + (size_t)y2_idx * in_row_stride) : NULL; + float32_t *y = output_batch + (size_t)oy * out_row_stride; + + for (int32_t ox = 0; ox < out_w; ++ox) + { + const int32_t base_x = ox * stride_x - pad_x; + const int32_t x0_idx = base_x; + const int32_t x1_idx = base_x + 1; + const int32_t x2_idx = base_x + 2; + float32_t *out_px = y + (size_t)ox * (size_t)in_c; + + depthwise_init_vec_f32(out_px, b, in_c); + + if (y0_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)x0_idx * (size_t)in_c, w0, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)x1_idx * (size_t)in_c, w1, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x0 + (size_t)x2_idx * (size_t)in_c, w2, in_c); + } + } + + if (y1_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)x0_idx * (size_t)in_c, w3, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)x1_idx * (size_t)in_c, w4, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x1 + (size_t)x2_idx * (size_t)in_c, w5, in_c); + } + } + + if (y2_valid) + { + if ((uint32_t)x0_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)x0_idx * (size_t)in_c, w6, in_c); + } + if ((uint32_t)x1_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)x1_idx * (size_t)in_c, w7, in_c); + } + if ((uint32_t)x2_idx < (uint32_t)in_w) + { + depthwise_accumulate_vec_f32(out_px, x2 + (size_t)x2_idx * (size_t)in_c, w8, in_c); + } + } + + depthwise_clamp_vec_f32(out_px, in_c, act_min, act_max); + } + } + } +} + +void arm_nn_depthwise_conv3x3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t batches, + int32_t in_c, + int32_t in_h, + int32_t in_w, + const float32_t *__RESTRICT kernel, + const float32_t *__RESTRICT b, + float32_t *__RESTRICT out, + int32_t stride_x, + int32_t stride_y, + int32_t pad_x, + int32_t pad_y, + int32_t out_h, + int32_t out_w, + float32_t act_min, + float32_t act_max) +{ + if (stride_x == 1 && stride_y == 1 && pad_x == 0 && pad_y == 0) + { + depthwise_conv3x3_nhwc_valid_f32( + x_nhwc, batches, in_c, in_h, in_w, kernel, b, out, out_h, out_w, act_min, act_max); + return; + } + + depthwise_conv3x3_nhwc_generic_f32(x_nhwc, + batches, + in_c, + in_h, + in_w, + kernel, + b, + out, + stride_x, + stride_y, + pad_x, + pad_y, + out_h, + out_w, + act_min, + act_max); +} + +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f16.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f16.c new file mode 100644 index 00000000..af09f611 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f16.c @@ -0,0 +1,243 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv_nt_t_f16.c + * Description: Support: depthwise NT_T kernel for f16 + * + * $Date: 09 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +arm_cmsis_nn_status arm_nn_depthwise_conv_nt_t_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT out, + int32_t lhs_rows, + int32_t total_ch, + int32_t row_x_col, + int32_t out_row_stride, + float16_t activation_min, + float16_t activation_max) +{ + if (!lhs || !rhs || !out || lhs_rows < 1 || lhs_rows > 4 || total_ch <= 0 || row_x_col <= 0 || + out_row_stride < total_ch) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t c = 0; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_act_min = vdupq_n_f16(activation_min); + const float16x8_t v_act_max = vdupq_n_f16(activation_max); + for (; c + 8 <= total_ch; c += 8) + { + float16x8_t acc0 = bias ? vld1q(bias + c) : vdupq_n_f16((float16_t)0.0f); + float16x8_t acc1 = acc0; + float16x8_t acc2 = acc0; + float16x8_t acc3 = acc0; + + const float16_t *lhs_row0_base = lhs + c; + const float16_t *lhs_row1_base = lhs + (size_t)row_x_col * total_ch + c; + const float16_t *lhs_row2_base = lhs + (size_t)2 * row_x_col * total_ch + c; + const float16_t *lhs_row3_base = lhs + (size_t)3 * row_x_col * total_ch + c; + switch (lhs_rows) + { + case 1: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q(out + c, acc0); + break; + } + case 2: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q(out + c, acc0); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q(out + out_row_stride + c, acc1); + break; + } + case 3: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + acc2 = vfmaq(acc2, vld1q(lhs_row2_base + offset), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q(out + c, acc0); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q(out + out_row_stride + c, acc1); + acc2 = arm_nn_clamp_mve_f16(acc2, v_act_min, v_act_max); + vst1q(out + (size_t)2 * out_row_stride + c, acc2); + break; + } + default: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + acc2 = vfmaq(acc2, vld1q(lhs_row2_base + offset), w); + acc3 = vfmaq(acc3, vld1q(lhs_row3_base + offset), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q(out + c, acc0); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q(out + out_row_stride + c, acc1); + acc2 = arm_nn_clamp_mve_f16(acc2, v_act_min, v_act_max); + vst1q(out + (size_t)2 * out_row_stride + c, acc2); + acc3 = arm_nn_clamp_mve_f16(acc3, v_act_min, v_act_max); + vst1q(out + (size_t)3 * out_row_stride + c, acc3); + break; + } + } + } + + if (c < total_ch) + { + const mve_pred16_t p = vctp16q((uint32_t)(total_ch - c)); + float16x8_t acc0 = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t acc1 = acc0; + float16x8_t acc2 = acc0; + float16x8_t acc3 = acc0; + + const float16_t *lhs_row0_base = lhs + c; + const float16_t *lhs_row1_base = lhs + (size_t)row_x_col * total_ch + c; + const float16_t *lhs_row2_base = lhs + (size_t)2 * row_x_col * total_ch + c; + const float16_t *lhs_row3_base = lhs + (size_t)3 * row_x_col * total_ch + c; + switch (lhs_rows) + { + case 1: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + break; + } + case 2: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + break; + } + case 3: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + acc2 = vfmaq(acc2, vld1q_z(lhs_row2_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + acc2 = arm_nn_clamp_mve_f16(acc2, v_act_min, v_act_max); + vst1q_p(out + (size_t)2 * out_row_stride + c, acc2, p); + break; + } + default: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float16x8_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + acc2 = vfmaq(acc2, vld1q_z(lhs_row2_base + offset, p), w); + acc3 = vfmaq(acc3, vld1q_z(lhs_row3_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f16(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f16(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + acc2 = arm_nn_clamp_mve_f16(acc2, v_act_min, v_act_max); + vst1q_p(out + (size_t)2 * out_row_stride + c, acc2, p); + acc3 = arm_nn_clamp_mve_f16(acc3, v_act_min, v_act_max); + vst1q_p(out + (size_t)3 * out_row_stride + c, acc3, p); + break; + } + } + c = total_ch; + } +#endif + + for (int32_t row = 0; row < lhs_rows; ++row) + { + float16_t *out_row = out + (size_t)row * out_row_stride; + for (int32_t cc = c; cc < total_ch; ++cc) + { + _Float16 acc = bias ? (_Float16)bias[cc] : (_Float16)0.0f; + for (int32_t k = 0; k < row_x_col; ++k) + { + const float16_t *lhs_val = lhs + ((size_t)row * row_x_col + k) * total_ch + cc; + const float16_t *rhs_val = rhs + (size_t)k * total_ch + cc; + acc += (_Float16)(*lhs_val) * (_Float16)(*rhs_val); + } + out_row[cc] = (float16_t)CLAMP(acc, (_Float16)activation_max, (_Float16)activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f32.c b/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f32.c new file mode 100644 index 00000000..c09ce377 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_f32.c @@ -0,0 +1,253 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_depthwise_conv_nt_t_f32.c + * Description: Support: depthwise NT_T kernel for f32 + * + * $Date: 09 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +arm_cmsis_nn_status arm_nn_depthwise_conv_nt_t_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT out, + int32_t lhs_rows, + int32_t total_ch, + int32_t row_x_col, + int32_t out_row_stride, + float32_t activation_min, + float32_t activation_max) +{ + if (!lhs || !rhs || !out || lhs_rows < 1 || lhs_rows > 4 || total_ch <= 0 || row_x_col <= 0 || + out_row_stride < total_ch) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + int32_t c = 0; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_act_min = vdupq_n_f32(activation_min); + const float32x4_t v_act_max = vdupq_n_f32(activation_max); + for (; c + 4 <= total_ch; c += 4) + { + float32x4_t acc0 = bias ? vld1q(bias + c) : vdupq_n_f32(0.0f); + float32x4_t acc1 = acc0; + float32x4_t acc2 = acc0; + float32x4_t acc3 = acc0; + + const float32_t *lhs_row0_base = lhs + c; + const float32_t *lhs_row1_base = lhs + (size_t)row_x_col * total_ch + c; + const float32_t *lhs_row2_base = lhs + (size_t)2 * row_x_col * total_ch + c; + const float32_t *lhs_row3_base = lhs + (size_t)3 * row_x_col * total_ch + c; + switch (lhs_rows) + { + case 1: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + } + acc0 = vmaxnmq(acc0, v_act_min); + acc0 = vminnmq(acc0, v_act_max); + vst1q(out + c, acc0); + break; + } + case 2: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + } + acc0 = vmaxnmq(acc0, v_act_min); + acc0 = vminnmq(acc0, v_act_max); + vst1q(out + c, acc0); + acc1 = vmaxnmq(acc1, v_act_min); + acc1 = vminnmq(acc1, v_act_max); + vst1q(out + out_row_stride + c, acc1); + break; + } + case 3: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + acc2 = vfmaq(acc2, vld1q(lhs_row2_base + offset), w); + } + acc0 = vmaxnmq(acc0, v_act_min); + acc0 = vminnmq(acc0, v_act_max); + vst1q(out + c, acc0); + acc1 = vmaxnmq(acc1, v_act_min); + acc1 = vminnmq(acc1, v_act_max); + vst1q(out + out_row_stride + c, acc1); + acc2 = vmaxnmq(acc2, v_act_min); + acc2 = vminnmq(acc2, v_act_max); + vst1q(out + (size_t)2 * out_row_stride + c, acc2); + break; + } + default: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q(rhs + offset + c); + acc0 = vfmaq(acc0, vld1q(lhs_row0_base + offset), w); + acc1 = vfmaq(acc1, vld1q(lhs_row1_base + offset), w); + acc2 = vfmaq(acc2, vld1q(lhs_row2_base + offset), w); + acc3 = vfmaq(acc3, vld1q(lhs_row3_base + offset), w); + } + acc0 = vmaxnmq(acc0, v_act_min); + acc0 = vminnmq(acc0, v_act_max); + vst1q(out + c, acc0); + acc1 = vmaxnmq(acc1, v_act_min); + acc1 = vminnmq(acc1, v_act_max); + vst1q(out + out_row_stride + c, acc1); + acc2 = vmaxnmq(acc2, v_act_min); + acc2 = vminnmq(acc2, v_act_max); + vst1q(out + (size_t)2 * out_row_stride + c, acc2); + acc3 = vmaxnmq(acc3, v_act_min); + acc3 = vminnmq(acc3, v_act_max); + vst1q(out + (size_t)3 * out_row_stride + c, acc3); + break; + } + } + } + + if (c < total_ch) + { + const mve_pred16_t p = vctp32q((uint32_t)(total_ch - c)); + float32x4_t acc0 = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + float32x4_t acc1 = acc0; + float32x4_t acc2 = acc0; + float32x4_t acc3 = acc0; + + const float32_t *lhs_row0_base = lhs + c; + const float32_t *lhs_row1_base = lhs + (size_t)row_x_col * total_ch + c; + const float32_t *lhs_row2_base = lhs + (size_t)2 * row_x_col * total_ch + c; + const float32_t *lhs_row3_base = lhs + (size_t)3 * row_x_col * total_ch + c; + switch (lhs_rows) + { + case 1: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f32(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + break; + } + case 2: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f32(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f32(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + break; + } + case 3: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + acc2 = vfmaq(acc2, vld1q_z(lhs_row2_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f32(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f32(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + acc2 = arm_nn_clamp_mve_f32(acc2, v_act_min, v_act_max); + vst1q_p(out + (size_t)2 * out_row_stride + c, acc2, p); + break; + } + default: { + for (int32_t k = 0; k < row_x_col; ++k) + { + const size_t offset = (size_t)k * total_ch; + const float32x4_t w = vld1q_z(rhs + offset + c, p); + acc0 = vfmaq(acc0, vld1q_z(lhs_row0_base + offset, p), w); + acc1 = vfmaq(acc1, vld1q_z(lhs_row1_base + offset, p), w); + acc2 = vfmaq(acc2, vld1q_z(lhs_row2_base + offset, p), w); + acc3 = vfmaq(acc3, vld1q_z(lhs_row3_base + offset, p), w); + } + acc0 = arm_nn_clamp_mve_f32(acc0, v_act_min, v_act_max); + vst1q_p(out + c, acc0, p); + acc1 = arm_nn_clamp_mve_f32(acc1, v_act_min, v_act_max); + vst1q_p(out + out_row_stride + c, acc1, p); + acc2 = arm_nn_clamp_mve_f32(acc2, v_act_min, v_act_max); + vst1q_p(out + (size_t)2 * out_row_stride + c, acc2, p); + acc3 = arm_nn_clamp_mve_f32(acc3, v_act_min, v_act_max); + vst1q_p(out + (size_t)3 * out_row_stride + c, acc3, p); + break; + } + } + c = total_ch; + } +#endif + + for (int32_t row = 0; row < lhs_rows; ++row) + { + float32_t *out_row = out + (size_t)row * out_row_stride; + for (int32_t cc = c; cc < total_ch; ++cc) + { + float32_t acc = bias ? bias[cc] : 0.0f; + for (int32_t k = 0; k < row_x_col; ++k) + { + const float32_t *lhs_val = lhs + ((size_t)row * row_x_col + k) * total_ch + cc; + const float32_t *rhs_val = rhs + (size_t)k * total_ch + cc; + acc += (*lhs_val) * (*rhs_val); + } + out_row[cc] = CLAMP(acc, activation_max, activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nn_lstm_step_f16.c b/Source/NNSupportFunctions/arm_nn_lstm_step_f16.c new file mode 100644 index 00000000..0b72c1f5 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_lstm_step_f16.c @@ -0,0 +1,197 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_lstm_step_f16.c + * Description: Update LSTM function for a single iteration step (float16) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture with FP16 support + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportLSTM + * @{ + */ + +/* + * Calculate the output state tensor of an LSTM step, float16 version. + * Refer to header file for details. + */ + +__STATIC_INLINE float16_t arm_nn_lstm_activate_gate_f16(float16_t x, arm_nn_activation_type_flt type) +{ + if (type == ARM_NN_FLT_ACT_TANH) + { + return arm_nn_tanh_scalar_ref_f16(x); + } + return arm_nn_sigmoid_scalar_f16(x); +} + +__STATIC_INLINE float16_t arm_nn_lstm_dot_f16(const float16_t *lhs, const float16_t *rhs, int32_t count) +{ + _Float16 acc = (_Float16)0.0f; + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + + for (int32_t i = 0; i < count; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(count - i)); + vacc = vfmaq(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p)); + } + + acc += (_Float16)arm_nn_vec_reduce_add_f16(vacc); +#else + for (int32_t i = 0; i < count; ++i) + { + acc += (_Float16)lhs[i] * (_Float16)rhs[i]; + } +#endif + + return (float16_t)acc; +} + +static float16_t arm_nn_lstm_gate_compute_f16(const cmsis_nn_lstm_gate_f16 *gate, + const float16_t *input, + const float16_t *hidden, + int32_t input_size, + int32_t hidden_size, + int32_t h_idx) +{ + _Float16 acc = (_Float16)0.0f; + if (gate->bias) + { + acc = (_Float16)gate->bias[h_idx]; + } + if (gate->input_weights) + { + const float16_t *w_in = gate->input_weights + (size_t)h_idx * (size_t)input_size; + acc += (_Float16)arm_nn_lstm_dot_f16(input, w_in, input_size); + } + if (hidden && gate->hidden_weights) + { + const float16_t *w_h = gate->hidden_weights + (size_t)h_idx * (size_t)hidden_size; + acc += (_Float16)arm_nn_lstm_dot_f16(hidden, w_h, hidden_size); + } + return arm_nn_lstm_activate_gate_f16((float16_t)acc, gate->activation_type); +} + +arm_cmsis_nn_status arm_nn_lstm_step_f16(const float16_t *data_in, + const float16_t *hidden_in, + float16_t *hidden_out, + const cmsis_nn_lstm_params_f16 *params, + cmsis_nn_lstm_context_f16 *buffers, + const int32_t batch_offset) +{ + if (!data_in || !hidden_out || !params || !buffers || !buffers->cell_state || batch_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = params->batch_size; + const int32_t input_size = params->input_size; + const int32_t hidden_size = params->hidden_size; + const _Float16 cell_clip = (_Float16)params->cell_clip; + + float16_t *cell_state = buffers->cell_state; + + for (int32_t b = 0; b < batch; b++) + { + const float16_t *x = data_in + (size_t)b * (size_t)batch_offset * (size_t)input_size; + const float16_t *h_prev = + hidden_in ? (hidden_in + (size_t)b * (size_t)batch_offset * (size_t)hidden_size) : NULL; + float16_t *c_prev = cell_state + (size_t)b * (size_t)hidden_size; + float16_t *h_out = hidden_out + (size_t)b * (size_t)batch_offset * (size_t)hidden_size; + + int32_t h = 0; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (; h + 8 <= hidden_size; h += 8) + { + float16_t f_gate[8]; + float16_t i_gate[8]; + float16_t g_gate[8]; + float16_t o_gate[8]; + + for (int32_t lane = 0; lane < 8; ++lane) + { + const int32_t idx = h + lane; + f_gate[lane] = + arm_nn_lstm_gate_compute_f16(¶ms->forget_gate, x, h_prev, input_size, hidden_size, idx); + i_gate[lane] = + arm_nn_lstm_gate_compute_f16(¶ms->input_gate, x, h_prev, input_size, hidden_size, idx); + g_gate[lane] = + arm_nn_lstm_gate_compute_f16(¶ms->cell_gate, x, h_prev, input_size, hidden_size, idx); + o_gate[lane] = + arm_nn_lstm_gate_compute_f16(¶ms->output_gate, x, h_prev, input_size, hidden_size, idx); + } + + const float16x8_t vf = vld1q(f_gate); + const float16x8_t vi = vld1q(i_gate); + const float16x8_t vg = vld1q(g_gate); + const float16x8_t vo = vld1q(o_gate); + const float16x8_t vc_prev = vld1q(c_prev + h); + + float16x8_t vc = vfmaq(vmulq(vf, vc_prev), vi, vg); + if (cell_clip > (_Float16)0.0f) + { + const _Float16 clip = (_Float16)cell_clip; + vc = vmaxnmq(vc, vdupq_n_f16((float16_t)-clip)); + vc = vminnmq(vc, vdupq_n_f16(clip)); + } + vst1q(c_prev + h, vc); + vst1q(h_out + h, vmulq(vo, arm_nn_vtanh_lut_direct_mve_f16(vc))); + } +#endif + for (; h < hidden_size; h++) + { + const float16_t f = + arm_nn_lstm_gate_compute_f16(¶ms->forget_gate, x, h_prev, input_size, hidden_size, h); + const float16_t i = + arm_nn_lstm_gate_compute_f16(¶ms->input_gate, x, h_prev, input_size, hidden_size, h); + const float16_t g = arm_nn_lstm_gate_compute_f16(¶ms->cell_gate, x, h_prev, input_size, hidden_size, h); + const float16_t o = + arm_nn_lstm_gate_compute_f16(¶ms->output_gate, x, h_prev, input_size, hidden_size, h); + + _Float16 c = (_Float16)((_Float16)f * (_Float16)c_prev[h] + (_Float16)i * (_Float16)g); + if (cell_clip > (_Float16)0.0f) + { + const _Float16 clip = (_Float16)cell_clip; + c = CLAMP(c, clip, -clip); + } + c_prev[h] = (float16_t)c; + h_out[h] = (float16_t)((_Float16)o * (_Float16)arm_nn_tanh_scalar_ref_f16((float16_t)c)); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} end of supportLSTM group */ diff --git a/Source/NNSupportFunctions/arm_nn_lstm_step_f32.c b/Source/NNSupportFunctions/arm_nn_lstm_step_f32.c new file mode 100644 index 00000000..a3a061c3 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_lstm_step_f32.c @@ -0,0 +1,196 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_lstm_step_f32.c + * Description: Update LSTM function for a single iteration step (float32) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportLSTM + * @{ + */ + +/* + * Calculate the output state tensor of an LSTM step, float32 version. + * Refer to header file for details. + */ + +__STATIC_INLINE float32_t arm_nn_lstm_activate_gate_f32(float32_t x, arm_nn_activation_type_flt type) +{ + if (type == ARM_NN_FLT_ACT_TANH) + { + return arm_nn_tanh_scalar_ref_f32(x); + } + return arm_nn_sigmoid_scalar_f32(x); +} + +__STATIC_INLINE float32_t arm_nn_lstm_dot_f32(const float32_t *lhs, const float32_t *rhs, int32_t count) +{ + float32_t acc = 0.0f; + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + float32x4_t vacc = vdupq_n_f32(0.0f); + + for (int32_t i = 0; i < count; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(count - i)); + vacc = vfmaq(vacc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p)); + } + + acc += arm_nn_vec_reduce_add_f32(vacc); +#else + for (int32_t i = 0; i < count; ++i) + { + acc += lhs[i] * rhs[i]; + } +#endif + + return acc; +} + +static float32_t arm_nn_lstm_gate_compute_f32(const cmsis_nn_lstm_gate_f32 *gate, + const float32_t *input, + const float32_t *hidden, + int32_t input_size, + int32_t hidden_size, + int32_t h_idx) +{ + float32_t acc = 0.0f; + if (gate->bias) + { + acc = gate->bias[h_idx]; + } + if (gate->input_weights) + { + const float32_t *w_in = gate->input_weights + (size_t)h_idx * (size_t)input_size; + acc += arm_nn_lstm_dot_f32(input, w_in, input_size); + } + if (hidden && gate->hidden_weights) + { + const float32_t *w_h = gate->hidden_weights + (size_t)h_idx * (size_t)hidden_size; + acc += arm_nn_lstm_dot_f32(hidden, w_h, hidden_size); + } + return arm_nn_lstm_activate_gate_f32(acc, gate->activation_type); +} + +arm_cmsis_nn_status arm_nn_lstm_step_f32(const float32_t *data_in, + const float32_t *hidden_in, + float32_t *hidden_out, + const cmsis_nn_lstm_params_f32 *params, + cmsis_nn_lstm_context_f32 *buffers, + const int32_t batch_offset) +{ + if (!data_in || !hidden_out || !params || !buffers || !buffers->cell_state || batch_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t batch = params->batch_size; + const int32_t input_size = params->input_size; + const int32_t hidden_size = params->hidden_size; + const float32_t cell_clip = params->cell_clip; + + float32_t *cell_state = buffers->cell_state; + + for (int32_t b = 0; b < batch; b++) + { + const float32_t *x = data_in + (size_t)b * (size_t)batch_offset * (size_t)input_size; + const float32_t *h_prev = + hidden_in ? (hidden_in + (size_t)b * (size_t)batch_offset * (size_t)hidden_size) : NULL; + float32_t *c_prev = cell_state + (size_t)b * (size_t)hidden_size; + float32_t *h_out = hidden_out + (size_t)b * (size_t)batch_offset * (size_t)hidden_size; + + int32_t h = 0; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (; h + 4 <= hidden_size; h += 4) + { + float32_t f_gate[4]; + float32_t i_gate[4]; + float32_t g_gate[4]; + float32_t o_gate[4]; + + for (int32_t lane = 0; lane < 4; ++lane) + { + const int32_t idx = h + lane; + f_gate[lane] = + arm_nn_lstm_gate_compute_f32(¶ms->forget_gate, x, h_prev, input_size, hidden_size, idx); + i_gate[lane] = + arm_nn_lstm_gate_compute_f32(¶ms->input_gate, x, h_prev, input_size, hidden_size, idx); + g_gate[lane] = + arm_nn_lstm_gate_compute_f32(¶ms->cell_gate, x, h_prev, input_size, hidden_size, idx); + o_gate[lane] = + arm_nn_lstm_gate_compute_f32(¶ms->output_gate, x, h_prev, input_size, hidden_size, idx); + } + + const float32x4_t vf = vld1q(f_gate); + const float32x4_t vi = vld1q(i_gate); + const float32x4_t vg = vld1q(g_gate); + const float32x4_t vo = vld1q(o_gate); + const float32x4_t vc_prev = vld1q(c_prev + h); + + float32x4_t vc = vfmaq(vmulq(vf, vc_prev), vi, vg); + if (cell_clip > 0.0f) + { + const float32x4_t v_clip = vdupq_n_f32(cell_clip); + vc = vmaxnmq(vc, vnegq(v_clip)); + vc = vminnmq(vc, v_clip); + } + vst1q(c_prev + h, vc); + vst1q(h_out + h, vmulq(vo, arm_nn_vtanh_lut_direct_mve_f32(vc))); + } +#endif + for (; h < hidden_size; h++) + { + const float32_t f = + arm_nn_lstm_gate_compute_f32(¶ms->forget_gate, x, h_prev, input_size, hidden_size, h); + const float32_t i = + arm_nn_lstm_gate_compute_f32(¶ms->input_gate, x, h_prev, input_size, hidden_size, h); + const float32_t g = arm_nn_lstm_gate_compute_f32(¶ms->cell_gate, x, h_prev, input_size, hidden_size, h); + const float32_t o = + arm_nn_lstm_gate_compute_f32(¶ms->output_gate, x, h_prev, input_size, hidden_size, h); + + float32_t c = f * c_prev[h] + i * g; + if (cell_clip > 0.0f) + { + c = CLAMP(c, cell_clip, -cell_clip); + } + c_prev[h] = c; + h_out[h] = o * arm_nn_tanh_scalar_ref_f32(c); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** @} end of supportLSTM group */ diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f16.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f16.c new file mode 100644 index 00000000..e22a184e --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f16.c @@ -0,0 +1,191 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_mat_mult_nt_n_packed_f16.c + * Description: Support: matrix multiply (lhs non-transposed, rhs packed non-transposed) + * + * $Date: 16 April 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Private + */ + +/** + * @addtogroup groupSupport + * @{ + */ + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_MAT_MULT_NT_N_PACKED_F16_BLOCK_COLS (8) + #define ARM_NN_MAT_MULT_NT_N_PACKED_F16_BLOCK_ROWS (4) + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_n_packed_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs_packed, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float16_t activation_min, + float16_t activation_max) +{ + if (!lhs || !rhs_packed || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t block_cols = 8; + const int32_t rhs_blocks = (rhs_rows + block_cols - 1) / block_cols; + int32_t r = 0; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t vmin = vdupq_n_f16(activation_min); + const float16x8_t vmax = vdupq_n_f16(activation_max); + + for (; r + ARM_NN_MAT_MULT_NT_N_PACKED_F16_BLOCK_ROWS <= lhs_rows; r += ARM_NN_MAT_MULT_NT_N_PACKED_F16_BLOCK_ROWS) + { + const float16_t *lhs_row0 = lhs + (size_t)(r + 0) * rhs_cols; + const float16_t *lhs_row1 = lhs + (size_t)(r + 1) * rhs_cols; + const float16_t *lhs_row2 = lhs + (size_t)(r + 2) * rhs_cols; + const float16_t *lhs_row3 = lhs + (size_t)(r + 3) * rhs_cols; + float16_t *dst_row0 = dst + (size_t)(r + 0) * row_address_offset; + float16_t *dst_row1 = dst + (size_t)(r + 1) * row_address_offset; + float16_t *dst_row2 = dst + (size_t)(r + 2) * row_address_offset; + float16_t *dst_row3 = dst + (size_t)(r + 3) * row_address_offset; + + for (int32_t b = 0; b < rhs_blocks; ++b) + { + const int32_t c = b * block_cols; + const int32_t remaining = rhs_rows - c; + const int32_t valid_cols = remaining < block_cols ? remaining : block_cols; + const float16_t *rhs_block = rhs_packed + (size_t)b * rhs_cols * block_cols; + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + + float16x8_t vacc0 = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc1 = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc2 = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc3 = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + float16x8_t vrhs; + if (valid_cols == block_cols) + { + vrhs = vld1q(rhs_block + (size_t)k * block_cols); + } + else + { + vrhs = vld1q_z(rhs_block + (size_t)k * block_cols, p); + } + vacc0 = vfmaq(vacc0, vrhs, lhs_row0[k]); + vacc1 = vfmaq(vacc1, vrhs, lhs_row1[k]); + vacc2 = vfmaq(vacc2, vrhs, lhs_row2[k]); + vacc3 = vfmaq(vacc3, vrhs, lhs_row3[k]); + } + + vacc0 = arm_nn_clamp_mve_f16(vacc0, vmin, vmax); + vacc1 = arm_nn_clamp_mve_f16(vacc1, vmin, vmax); + vacc2 = arm_nn_clamp_mve_f16(vacc2, vmin, vmax); + vacc3 = arm_nn_clamp_mve_f16(vacc3, vmin, vmax); + + if (valid_cols == block_cols) + { + vst1q(dst_row0 + c, vacc0); + vst1q(dst_row1 + c, vacc1); + vst1q(dst_row2 + c, vacc2); + vst1q(dst_row3 + c, vacc3); + } + else + { + vst1q_p(dst_row0 + c, vacc0, p); + vst1q_p(dst_row1 + c, vacc1, p); + vst1q_p(dst_row2 + c, vacc2, p); + vst1q_p(dst_row3 + c, vacc3, p); + } + } + } + #endif + + for (; r < lhs_rows; ++r) + { + const float16_t *lhs_row = lhs + (size_t)r * rhs_cols; + float16_t *dst_row = dst + (size_t)r * row_address_offset; + + for (int32_t b = 0; b < rhs_blocks; ++b) + { + const int32_t c = b * block_cols; + const int32_t remaining = rhs_rows - c; + const int32_t valid_cols = remaining < block_cols ? remaining : block_cols; + const float16_t *rhs_block = rhs_packed + (size_t)b * rhs_cols * block_cols; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const mve_pred16_t p = vctp16q((uint32_t)valid_cols); + float16x8_t vacc = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16x8_t vrhs = (valid_cols == block_cols) ? vld1q(rhs_block + (size_t)k * block_cols) + : vld1q_z(rhs_block + (size_t)k * block_cols, p); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f16(vacc, vmin, vmax); + if (valid_cols == block_cols) + { + vst1q(dst_row + c, vacc); + } + else + { + vst1q_p(dst_row + c, vacc, p); + } + #else + for (int32_t lane = 0; lane < valid_cols; ++lane) + { + _Float16 acc = bias ? (_Float16)bias[c + lane] : (_Float16)0.0f; + for (int32_t k = 0; k < rhs_cols; ++k) + { + acc += (_Float16)lhs_row[k] * (_Float16)rhs_block[(size_t)k * block_cols + lane]; + } + dst_row[c + lane] = arm_nn_clamp_scalar_f16((float16_t)acc, activation_min, activation_max); + } + #endif + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of groupSupport group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f32.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f32.c new file mode 100644 index 00000000..df8dfbd0 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_n_packed_f32.c @@ -0,0 +1,191 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_mat_mult_nt_n_packed_f32.c + * Description: Support: matrix multiply (lhs non-transposed, rhs packed non-transposed) + * + * $Date: 16 April 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Private + */ + +/** + * @addtogroup groupSupport + * @{ + */ + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_MAT_MULT_NT_N_PACKED_F32_BLOCK_COLS (4) + #define ARM_NN_MAT_MULT_NT_N_PACKED_F32_BLOCK_ROWS (4) + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_n_packed_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs_packed, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float32_t activation_min, + float32_t activation_max) +{ + if (!lhs || !rhs_packed || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t block_cols = 4; + const int32_t rhs_blocks = (rhs_rows + block_cols - 1) / block_cols; + int32_t r = 0; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t vmin = vdupq_n_f32(activation_min); + const float32x4_t vmax = vdupq_n_f32(activation_max); + + for (; r + ARM_NN_MAT_MULT_NT_N_PACKED_F32_BLOCK_ROWS <= lhs_rows; r += ARM_NN_MAT_MULT_NT_N_PACKED_F32_BLOCK_ROWS) + { + const float32_t *lhs_row0 = lhs + (size_t)(r + 0) * rhs_cols; + const float32_t *lhs_row1 = lhs + (size_t)(r + 1) * rhs_cols; + const float32_t *lhs_row2 = lhs + (size_t)(r + 2) * rhs_cols; + const float32_t *lhs_row3 = lhs + (size_t)(r + 3) * rhs_cols; + float32_t *dst_row0 = dst + (size_t)(r + 0) * row_address_offset; + float32_t *dst_row1 = dst + (size_t)(r + 1) * row_address_offset; + float32_t *dst_row2 = dst + (size_t)(r + 2) * row_address_offset; + float32_t *dst_row3 = dst + (size_t)(r + 3) * row_address_offset; + + for (int32_t b = 0; b < rhs_blocks; ++b) + { + const int32_t c = b * block_cols; + const int32_t remaining = rhs_rows - c; + const int32_t valid_cols = remaining < block_cols ? remaining : block_cols; + const float32_t *rhs_block = rhs_packed + (size_t)b * rhs_cols * block_cols; + const mve_pred16_t p = vctp32q((uint32_t)valid_cols); + + float32x4_t vacc0 = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + float32x4_t vacc1 = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + float32x4_t vacc2 = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + float32x4_t vacc3 = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + float32x4_t vrhs; + if (valid_cols == block_cols) + { + vrhs = vld1q(rhs_block + (size_t)k * block_cols); + } + else + { + vrhs = vld1q_z(rhs_block + (size_t)k * block_cols, p); + } + vacc0 = vfmaq(vacc0, vrhs, lhs_row0[k]); + vacc1 = vfmaq(vacc1, vrhs, lhs_row1[k]); + vacc2 = vfmaq(vacc2, vrhs, lhs_row2[k]); + vacc3 = vfmaq(vacc3, vrhs, lhs_row3[k]); + } + + vacc0 = arm_nn_clamp_mve_f32(vacc0, vmin, vmax); + vacc1 = arm_nn_clamp_mve_f32(vacc1, vmin, vmax); + vacc2 = arm_nn_clamp_mve_f32(vacc2, vmin, vmax); + vacc3 = arm_nn_clamp_mve_f32(vacc3, vmin, vmax); + + if (valid_cols == block_cols) + { + vst1q(dst_row0 + c, vacc0); + vst1q(dst_row1 + c, vacc1); + vst1q(dst_row2 + c, vacc2); + vst1q(dst_row3 + c, vacc3); + } + else + { + vst1q_p(dst_row0 + c, vacc0, p); + vst1q_p(dst_row1 + c, vacc1, p); + vst1q_p(dst_row2 + c, vacc2, p); + vst1q_p(dst_row3 + c, vacc3, p); + } + } + } + #endif + + for (; r < lhs_rows; ++r) + { + const float32_t *lhs_row = lhs + (size_t)r * rhs_cols; + float32_t *dst_row = dst + (size_t)r * row_address_offset; + + for (int32_t b = 0; b < rhs_blocks; ++b) + { + const int32_t c = b * block_cols; + const int32_t remaining = rhs_rows - c; + const int32_t valid_cols = remaining < block_cols ? remaining : block_cols; + const float32_t *rhs_block = rhs_packed + (size_t)b * rhs_cols * block_cols; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const mve_pred16_t p = vctp32q((uint32_t)valid_cols); + float32x4_t vacc = bias ? vld1q_z(bias + c, p) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float32x4_t vrhs = (valid_cols == block_cols) ? vld1q(rhs_block + (size_t)k * block_cols) + : vld1q_z(rhs_block + (size_t)k * block_cols, p); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f32(vacc, vmin, vmax); + if (valid_cols == block_cols) + { + vst1q(dst_row + c, vacc); + } + else + { + vst1q_p(dst_row + c, vacc, p); + } + #else + for (int32_t lane = 0; lane < valid_cols; ++lane) + { + float32_t acc = bias ? bias[c + lane] : 0.0f; + for (int32_t k = 0; k < rhs_cols; ++k) + { + acc += lhs_row[k] * rhs_block[(size_t)k * block_cols + lane]; + } + dst_row[c + lane] = CLAMP(acc, activation_max, activation_min); + } + #endif + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of groupSupport group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f16.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f16.c new file mode 100644 index 00000000..8f23bd20 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f16.c @@ -0,0 +1,214 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_mat_mult_nt_t_f16.c + * Description: Support: matrix multiply (lhs non-transposed, rhs transposed) + * + * $Date: 31 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Private + */ + +/** + * @addtogroup groupSupport + * @{ + */ + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS (8) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_DUAL_BLOCK_ROWS (16) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_SUB_BLOCK_ROWS (4) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS_DUAL ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_16) + #define ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS_SUB ((int32_t)ARM_NN_MVE_F16_MAX_GATHER_STRIDE_4) + #endif + + #if !defined(ARM_MATH_MVE_FLOAT16) || defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float16_t dot_nt_t_f16_scalar(const float16_t *__RESTRICT lhs_row, + const float16_t *__RESTRICT rhs_row, + int32_t len) +{ + _Float16 acc = (_Float16)0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += (_Float16)lhs_row[i] * (_Float16)rhs_row[i]; + } + return (float16_t)acc; +} + #endif + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float16_t dot_nt_t_f16_mve(const float16_t *__RESTRICT lhs_row, + const float16_t *__RESTRICT rhs_row, + int32_t len) +{ + float16x8_t vacc = vdupq_n_f16((float16_t)0.0f); + int32_t i = 0; + + for (; i + ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS <= len; i += ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS) + { + vacc = vfmaq(vacc, vld1q(lhs_row + i), vld1q(rhs_row + i)); + } + + _Float16 acc = (_Float16)arm_nn_vec_reduce_add_f16(vacc); + + for (; i < len; ++i) + { + acc += (_Float16)lhs_row[i] * (_Float16)rhs_row[i]; + } + + return (float16_t)acc; +} + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_t_f16(const float16_t *__RESTRICT lhs, + const float16_t *__RESTRICT rhs, + const float16_t *__RESTRICT bias, + float16_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float16_t activation_min, + float16_t activation_max) +{ + if (!lhs || !rhs || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t r = 0; r < lhs_rows; ++r) + { + const float16_t *lhs_row = lhs + (size_t)r * rhs_cols; + float16_t *dst_row = dst + (size_t)r * row_address_offset; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t c = 0; + if (rhs_cols <= ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS) + { + const uint16_t rhs_cols_u16 = (uint16_t)rhs_cols; + const uint16x8_t offsets = vmulq(vidupq_u16((uint32_t)0, 1), rhs_cols_u16); + const float16x8_t vmin = vdupq_n_f16(activation_min); + const float16x8_t vmax = vdupq_n_f16(activation_max); + + if (rhs_cols <= ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS_DUAL) + { + const uint16x8_t offsets_hi = + vaddq(offsets, (uint16_t)(ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS * rhs_cols_u16)); + + for (; c + ARM_NN_MAT_MULT_NT_T_F16_MVE_DUAL_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_MAT_MULT_NT_T_F16_MVE_DUAL_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc_lo = bias ? vld1q(bias + c) : vdupq_n_f16((float16_t)0.0f); + float16x8_t vacc_hi = + bias ? vld1q(bias + c + ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16_t lhs_v = lhs_row[k]; + const float16x8_t vrhs_lo = vldrhq_gather_shifted_offset(rhs_block + k, offsets); + const float16x8_t vrhs_hi = vldrhq_gather_shifted_offset(rhs_block + k, offsets_hi); + vacc_lo = vfmaq(vacc_lo, vrhs_lo, lhs_v); + vacc_hi = vfmaq(vacc_hi, vrhs_hi, lhs_v); + } + + vacc_lo = arm_nn_clamp_mve_f16(vacc_lo, vmin, vmax); + vacc_hi = arm_nn_clamp_mve_f16(vacc_hi, vmin, vmax); + vst1q(dst_row + c, vacc_lo); + vst1q(dst_row + c + ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS, vacc_hi); + } + } + + for (; c + ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_MAT_MULT_NT_T_F16_MVE_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc = bias ? vld1q(bias + c) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16x8_t vrhs = vldrhq_gather_shifted_offset(rhs_block + k, offsets); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f16(vacc, vmin, vmax); + vst1q(dst_row + c, vacc); + } + + if (rhs_cols <= ARM_NN_MAT_MULT_NT_T_F16_MVE_MAX_RHS_COLS_SUB) + { + const mve_pred16_t p = vctp16q((uint32_t)ARM_NN_MAT_MULT_NT_T_F16_MVE_SUB_BLOCK_ROWS); + + for (; c + ARM_NN_MAT_MULT_NT_T_F16_MVE_SUB_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_MAT_MULT_NT_T_F16_MVE_SUB_BLOCK_ROWS) + { + const float16_t *rhs_block = rhs + (size_t)c * rhs_cols; + float16x8_t vacc = bias ? vld1q_z(bias + c, p) : vdupq_n_f16((float16_t)0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float16x8_t vrhs = vldrhq_gather_shifted_offset_z(rhs_block + k, offsets, p); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f16(vacc, vmin, vmax); + vst1q_p(dst_row + c, vacc, p); + } + } + } + #else + int32_t c = 0; + #endif + + for (; c < rhs_rows; ++c) + { + const float16_t *rhs_row = rhs + (size_t)c * rhs_cols; + _Float16 acc = bias ? (_Float16)bias[c] : (_Float16)0.0f; + + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + acc += (_Float16)dot_nt_t_f16_mve(lhs_row, rhs_row, rhs_cols); + #else + acc += (_Float16)dot_nt_t_f16_scalar(lhs_row, rhs_row, rhs_cols); + #endif + + dst_row[c] = (float16_t)CLAMP(acc, (_Float16)activation_max, (_Float16)activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of groupSupport group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f32.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f32.c new file mode 100644 index 00000000..ad6649d0 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_f32.c @@ -0,0 +1,172 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_mat_mult_nt_t_f32.c + * Description: Support: matrix multiply (lhs non-transposed, rhs transposed) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Private + */ + +/** + * @addtogroup groupSupport + * @{ + */ + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + #define ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS (4) + #define ARM_NN_MAT_MULT_NT_T_F32_MVE_DUAL_BLOCK_ROWS (8) + #endif + + #if !defined(ARM_MATH_MVEF) || defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float32_t dot_nt_t_f32_scalar(const float32_t *__RESTRICT lhs_row, + const float32_t *__RESTRICT rhs_row, + int32_t len) +{ + float32_t acc = 0.0f; + for (int32_t i = 0; i < len; ++i) + { + acc += lhs_row[i] * rhs_row[i]; + } + return acc; +} + #endif + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) +__STATIC_INLINE float32_t dot_nt_t_f32_mve(const float32_t *__RESTRICT lhs_row, + const float32_t *__RESTRICT rhs_row, + int32_t len) +{ + float32x4_t vacc = vdupq_n_f32(0.0f); + for (int32_t i = 0; i < len; i += ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS) + { + const mve_pred16_t p = vctp32q((uint32_t)(len - i)); + vacc = vfmaq_m(vacc, vld1q_z(lhs_row + i, p), vld1q_z(rhs_row + i, p), p); + } + + return arm_nn_vec_reduce_add_f32(vacc); +} + #endif + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_nn_mat_mult_nt_t_f32(const float32_t *__RESTRICT lhs, + const float32_t *__RESTRICT rhs, + const float32_t *__RESTRICT bias, + float32_t *__RESTRICT dst, + int32_t lhs_rows, + int32_t rhs_rows, + int32_t rhs_cols, + int32_t row_address_offset, + float32_t activation_min, + float32_t activation_max) +{ + if (!lhs || !rhs || !dst || lhs_rows <= 0 || rhs_rows <= 0 || rhs_cols <= 0 || row_address_offset <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const uint32_t rhs_cols_u32 = (uint32_t)rhs_cols; + const uint32x4_t offsets = vmulq(vidupq_u32((uint32_t)0, 1), rhs_cols_u32); + const uint32x4_t offsets_hi = vaddq(offsets, (uint32_t)(ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS * rhs_cols_u32)); + const float32x4_t vmin = vdupq_n_f32(activation_min); + const float32x4_t vmax = vdupq_n_f32(activation_max); + #endif + + for (int32_t r = 0; r < lhs_rows; ++r) + { + const float32_t *lhs_row = lhs + (size_t)r * rhs_cols; + float32_t *dst_row = dst + (size_t)r * row_address_offset; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t c = 0; + for (; c + ARM_NN_MAT_MULT_NT_T_F32_MVE_DUAL_BLOCK_ROWS <= rhs_rows; + c += ARM_NN_MAT_MULT_NT_T_F32_MVE_DUAL_BLOCK_ROWS) + { + const float32_t *rhs_block = rhs + (size_t)c * rhs_cols; + float32x4_t vacc_lo = bias ? vld1q(bias + c) : vdupq_n_f32(0.0f); + float32x4_t vacc_hi = bias ? vld1q(bias + c + ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float32_t lhs_v = lhs_row[k]; + const float32x4_t vrhs_lo = vldrwq_gather_shifted_offset(rhs_block + k, offsets); + const float32x4_t vrhs_hi = vldrwq_gather_shifted_offset(rhs_block + k, offsets_hi); + vacc_lo = vfmaq(vacc_lo, vrhs_lo, lhs_v); + vacc_hi = vfmaq(vacc_hi, vrhs_hi, lhs_v); + } + + vacc_lo = arm_nn_clamp_mve_f32(vacc_lo, vmin, vmax); + vacc_hi = arm_nn_clamp_mve_f32(vacc_hi, vmin, vmax); + vst1q(dst_row + c, vacc_lo); + vst1q(dst_row + c + ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS, vacc_hi); + } + + for (; c + ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS <= rhs_rows; c += ARM_NN_MAT_MULT_NT_T_F32_MVE_BLOCK_ROWS) + { + const float32_t *rhs_block = rhs + (size_t)c * rhs_cols; + float32x4_t vacc = bias ? vld1q(bias + c) : vdupq_n_f32(0.0f); + + for (int32_t k = 0; k < rhs_cols; ++k) + { + const float32x4_t vrhs = vldrwq_gather_shifted_offset(rhs_block + k, offsets); + vacc = vfmaq(vacc, vrhs, lhs_row[k]); + } + + vacc = arm_nn_clamp_mve_f32(vacc, vmin, vmax); + vst1q(dst_row + c, vacc); + } + #else + int32_t c = 0; + #endif + + for (; c < rhs_rows; ++c) + { + const float32_t *rhs_row = rhs + (size_t)c * rhs_cols; + float32_t acc = bias ? bias[c] : 0.0f; + + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + acc += dot_nt_t_f32_mve(lhs_row, rhs_row, rhs_cols); + #else + acc += dot_nt_t_f32_scalar(lhs_row, rhs_row, rhs_cols); + #endif + dst_row[c] = CLAMP(acc, activation_max, activation_min); + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of groupSupport group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_maxpool1d_f16.c b/Source/NNSupportFunctions/arm_nn_maxpool1d_f16.c new file mode 100644 index 00000000..e0aea687 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_maxpool1d_f16.c @@ -0,0 +1,100 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_maxpool1d_f16.c + * Description: Support: NHWC 1D max pooling kernels for f16 + * + * $Date: 30 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +void arm_nn_maxpool1d_k3s3_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow * 3 + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow * 3 + 1) * (size_t)in_c; + const float16_t *x2 = x_nhwc + (size_t)(ow * 3 + 2) * (size_t)in_c; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - c)); + float16x8_t v = vmaxnmq(vld1q_z(x0 + c, p), vld1q_z(x1 + c, p)); + v = vmaxnmq(v, vld1q_z(x2 + c, p)); + vst1q_p(out + (size_t)ow * (size_t)in_c + (size_t)c, v, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + const _Float16 x0h = (_Float16)x0[c]; + const _Float16 x1h = (_Float16)x1[c]; + const _Float16 x2h = (_Float16)x2[c]; + const _Float16 max01 = (x0h > x1h) ? x0h : x1h; + out[(size_t)ow * (size_t)in_c + (size_t)c] = (float16_t)((max01 > x2h) ? max01 : x2h); + } + #endif + } +} + +void arm_nn_maxpool1d_k2s2_nhwc_noclip_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float16_t *x0 = x_nhwc + (size_t)(ow * 2 + 0) * (size_t)in_c; + const float16_t *x1 = x_nhwc + (size_t)(ow * 2 + 1) * (size_t)in_c; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(in_c - c)); + const float16x8_t v = vmaxnmq(vld1q_z(x0 + c, p), vld1q_z(x1 + c, p)); + vst1q_p(out + (size_t)ow * (size_t)in_c + (size_t)c, v, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + const _Float16 x0h = (_Float16)x0[c]; + const _Float16 x1h = (_Float16)x1[c]; + out[(size_t)ow * (size_t)in_c + (size_t)c] = (float16_t)((x0h > x1h) ? x0h : x1h); + } + #endif + } +} + +void arm_nn_maxpool1d_k2s2_nhwc_f16(const float16_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float16_t *__RESTRICT out, + int32_t out_w, + float16_t act_min, + float16_t act_max) +{ + arm_nn_maxpool1d_k2s2_nhwc_noclip_f16(x_nhwc, in_c, in_w, out, out_w); + arm_nn_vector_clamp_f16(out, out_w * in_c, act_min, act_max); +} + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/NNSupportFunctions/arm_nn_maxpool1d_f32.c b/Source/NNSupportFunctions/arm_nn_maxpool1d_f32.c new file mode 100644 index 00000000..748d4e84 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_maxpool1d_f32.c @@ -0,0 +1,95 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_maxpool1d_f32.c + * Description: Support: NHWC 1D max pooling kernels for f32 + * + * $Date: 2 Apr 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +void arm_nn_maxpool1d_k3s3_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow * 3 + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow * 3 + 1) * (size_t)in_c; + const float32_t *x2 = x_nhwc + (size_t)(ow * 3 + 2) * (size_t)in_c; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - c)); + float32x4_t v = vmaxnmq(vld1q_z(x0 + c, p), vld1q_z(x1 + c, p)); + v = vmaxnmq(v, vld1q_z(x2 + c, p)); + vst1q_p(out + (size_t)ow * (size_t)in_c + (size_t)c, v, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + float32_t max_val = MAX(x0[c], x1[c]); + out[(size_t)ow * (size_t)in_c + (size_t)c] = MAX(max_val, x2[c]); + } + #endif + } +} + +void arm_nn_maxpool1d_k2s2_nhwc_noclip_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w) +{ + (void)in_w; + + for (int32_t ow = 0; ow < out_w; ++ow) + { + const float32_t *x0 = x_nhwc + (size_t)(ow * 2 + 0) * (size_t)in_c; + const float32_t *x1 = x_nhwc + (size_t)(ow * 2 + 1) * (size_t)in_c; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t c = 0; c < in_c; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(in_c - c)); + const float32x4_t v = vmaxnmq(vld1q_z(x0 + c, p), vld1q_z(x1 + c, p)); + vst1q_p(out + (size_t)ow * (size_t)in_c + (size_t)c, v, p); + } + #else + for (int32_t c = 0; c < in_c; ++c) + { + out[(size_t)ow * (size_t)in_c + (size_t)c] = MAX(x0[c], x1[c]); + } + #endif + } +} + +void arm_nn_maxpool1d_k2s2_nhwc_f32(const float32_t *__RESTRICT x_nhwc, + int32_t in_c, + int32_t in_w, + float32_t *__RESTRICT out, + int32_t out_w, + float32_t act_min, + float32_t act_max) +{ + arm_nn_maxpool1d_k2s2_nhwc_noclip_f32(x_nhwc, in_c, in_w, out, out_w); + arm_nn_vector_clamp_f32(out, out_w * in_c, act_min, act_max); +} + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f16.c b/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f16.c new file mode 100644 index 00000000..16a3ef0b --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f16.c @@ -0,0 +1,86 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_pack_conv_patch_f16.c + * Description: Support: pack one convolution patch row (float16) + * + * $Date: 2 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +void arm_nn_pack_conv_patch_f16(const float16_t *__RESTRICT input, + int32_t in_h, + int32_t in_w, + int32_t in_c, + int32_t kernel_h, + int32_t kernel_w, + int32_t stride_h, + int32_t stride_w, + int32_t pad_h, + int32_t pad_w, + int32_t dilation_h, + int32_t dilation_w, + int32_t out_y, + int32_t out_x, + float16_t pad_value, + float16_t *__RESTRICT patch_row) +{ + const int32_t base_y = out_y * stride_h - pad_h; + const int32_t base_x = out_x * stride_w - pad_w; + + /* This is effectively one im2row-style packed patch. */ + int32_t dst = 0; + for (int32_t kh = 0; kh < kernel_h; ++kh) + { + const int32_t in_y = base_y + kh * dilation_h; + for (int32_t kw = 0; kw < kernel_w; ++kw) + { + const int32_t in_x = base_x + kw * dilation_w; + if ((uint32_t)in_y < (uint32_t)in_h && (uint32_t)in_x < (uint32_t)in_w) + { + const int32_t src = ((in_y * in_w) + in_x) * in_c; + arm_memcpy_f16(patch_row + dst, input + src, (uint32_t)in_c); + } + else + { + arm_memset_f16(patch_row + dst, pad_value, (uint32_t)in_c); + } + dst += in_c; + } + } +} + +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f32.c b/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f32.c new file mode 100644 index 00000000..430debe7 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nn_pack_conv_patch_f32.c @@ -0,0 +1,86 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nn_pack_conv_patch_f32.c + * Description: Support: pack one convolution patch row (float32) + * + * $Date: 2 Mar 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup supportConvolution + * @{ + */ + +void arm_nn_pack_conv_patch_f32(const float32_t *__RESTRICT input, + int32_t in_h, + int32_t in_w, + int32_t in_c, + int32_t kernel_h, + int32_t kernel_w, + int32_t stride_h, + int32_t stride_w, + int32_t pad_h, + int32_t pad_w, + int32_t dilation_h, + int32_t dilation_w, + int32_t out_y, + int32_t out_x, + float32_t pad_value, + float32_t *__RESTRICT patch_row) +{ + const int32_t base_y = out_y * stride_h - pad_h; + const int32_t base_x = out_x * stride_w - pad_w; + + /* This is effectively one im2row-style packed patch. */ + int32_t dst = 0; + for (int32_t kh = 0; kh < kernel_h; ++kh) + { + const int32_t in_y = base_y + kh * dilation_h; + for (int32_t kw = 0; kw < kernel_w; ++kw) + { + const int32_t in_x = base_x + kw * dilation_w; + if ((uint32_t)in_y < (uint32_t)in_h && (uint32_t)in_x < (uint32_t)in_w) + { + const int32_t src = ((in_y * in_w) + in_x) * in_c; + arm_memcpy_f32(patch_row + dst, input + src, (uint32_t)in_c); + } + else + { + arm_memset_f32(patch_row + dst, pad_value, (uint32_t)in_c); + } + dst += in_c; + } + } +} + +/** + * @} end of supportConvolution group + */ diff --git a/Source/NNSupportFunctions/arm_nntables_flt.c b/Source/NNSupportFunctions/arm_nntables_flt.c new file mode 100644 index 00000000..16749f29 --- /dev/null +++ b/Source/NNSupportFunctions/arm_nntables_flt.c @@ -0,0 +1,233 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_nntables_flt.c + * Description: Common floating-point lookup tables used by CMSIS-NN + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Private + */ + +/** + * @addtogroup groupSupport + * @{ + */ + +#if ARM_NN_ENABLE_F32 + +/* + * Polynomial coefficients used by the float32 MVE exp approximation. + * + * Provenance: + * - matches CMSIS-DSP `exp_tab` used by `vexpq_f32()` in `arm_vec_math.h` + * - exposed here as `arm_nn_exp_poly_coeffs_f32` for the floating-point MVE + * exp helper used by CMSIS-NN + * + * The ordering follows the grouped evaluation scheme used by the MVE exp helper, + * rather than a simple ascending polynomial coefficient order. + */ +const float32_t arm_nn_exp_poly_coeffs_f32[8] = { + 1.0f, + 0.0416598916054f, + 0.500000596046f, + 0.0014122662833f, + 1.00000011921f, + 0.00833693705499f, + 0.166665703058f, + 0.000195780929062f, +}; + +/* + * LUT for 2^(i/256), i in [0, 256]. + * Generation formula: + * arm_nn_exp2_lut256_f32[i] = powf(2.0f, (float)i / 256.0f) + */ +const float32_t arm_nn_exp2_lut256_f32[257] = { + 1.000000000f, 1.002711275f, 1.005429901f, 1.008155898f, 1.010889286f, 1.013630085f, 1.016378315f, 1.019133996f, + 1.021897149f, 1.024667793f, 1.027445949f, 1.030231638f, 1.033024879f, 1.035825694f, 1.038634102f, 1.041450125f, + 1.044273782f, 1.047105096f, 1.049944086f, 1.052790773f, 1.055645178f, 1.058507323f, 1.061377227f, 1.064254913f, + 1.067140401f, 1.070033712f, 1.072934868f, 1.075843889f, 1.078760798f, 1.081685615f, 1.084618362f, 1.087559061f, + 1.090507733f, 1.093464399f, 1.096429082f, 1.099401803f, 1.102382583f, 1.105371446f, 1.108368412f, 1.111373503f, + 1.114386743f, 1.117408152f, 1.120437752f, 1.123475567f, 1.126521619f, 1.129575929f, 1.132638520f, 1.135709414f, + 1.138788635f, 1.141876204f, 1.144972144f, 1.148076479f, 1.151189230f, 1.154310421f, 1.157440074f, 1.160578212f, + 1.163724859f, 1.166880037f, 1.170043770f, 1.173216080f, 1.176396992f, 1.179586527f, 1.182784711f, 1.185991566f, + 1.189207115f, 1.192431383f, 1.195664392f, 1.198906167f, 1.202156731f, 1.205416109f, 1.208684324f, 1.211961399f, + 1.215247360f, 1.218542230f, 1.221846033f, 1.225158794f, 1.228480536f, 1.231811285f, 1.235151064f, 1.238499898f, + 1.241857812f, 1.245224830f, 1.248600977f, 1.251986278f, 1.255380757f, 1.258784440f, 1.262197350f, 1.265619515f, + 1.269050957f, 1.272491703f, 1.275941778f, 1.279401208f, 1.282870016f, 1.286348230f, 1.289835873f, 1.293332973f, + 1.296839555f, 1.300355643f, 1.303881265f, 1.307416446f, 1.310961212f, 1.314515588f, 1.318079601f, 1.321653278f, + 1.325236643f, 1.328829724f, 1.332432547f, 1.336045138f, 1.339667524f, 1.343299731f, 1.346941786f, 1.350593716f, + 1.354255547f, 1.357927306f, 1.361609021f, 1.365300717f, 1.369002423f, 1.372714165f, 1.376435971f, 1.380167867f, + 1.383909882f, 1.387662042f, 1.391424376f, 1.395196910f, 1.398979673f, 1.402772691f, 1.406575994f, 1.410389608f, + 1.414213562f, 1.418047884f, 1.421892602f, 1.425747744f, 1.429613338f, 1.433489413f, 1.437375997f, 1.441273119f, + 1.445180807f, 1.449099090f, 1.453027996f, 1.456967554f, 1.460917794f, 1.464878744f, 1.468850433f, 1.472832891f, + 1.476826146f, 1.480830228f, 1.484845166f, 1.488870990f, 1.492907728f, 1.496955412f, 1.501014070f, 1.505083732f, + 1.509164428f, 1.513256187f, 1.517359041f, 1.521473019f, 1.525598151f, 1.529734467f, 1.533881998f, 1.538040774f, + 1.542210825f, 1.546392183f, 1.550584878f, 1.554788940f, 1.559004400f, 1.563231290f, 1.567469640f, 1.571719481f, + 1.575980845f, 1.580253763f, 1.584538265f, 1.588834384f, 1.593142151f, 1.597461598f, 1.601792756f, 1.606135656f, + 1.610490332f, 1.614856814f, 1.619235135f, 1.623625327f, 1.628027422f, 1.632441452f, 1.636867450f, 1.641305448f, + 1.645755478f, 1.650217574f, 1.654691768f, 1.659178092f, 1.663676580f, 1.668187265f, 1.672710180f, 1.677245357f, + 1.681792831f, 1.686352633f, 1.690924799f, 1.695509361f, 1.700106354f, 1.704715810f, 1.709337763f, 1.713972248f, + 1.718619298f, 1.723278948f, 1.727951231f, 1.732636182f, 1.737333835f, 1.742044225f, 1.746767386f, 1.751503353f, + 1.756252160f, 1.761013843f, 1.765788436f, 1.770575974f, 1.775376493f, 1.780190027f, 1.785016611f, 1.789856282f, + 1.794709075f, 1.799575025f, 1.804454168f, 1.809346539f, 1.814252176f, 1.819171112f, 1.824103385f, 1.829049031f, + 1.834008086f, 1.838980587f, 1.843966569f, 1.848966070f, 1.853979125f, 1.859005772f, 1.864046048f, 1.869099990f, + 1.874167634f, 1.879249018f, 1.884344179f, 1.889453154f, 1.894575982f, 1.899712698f, 1.904863342f, 1.910027950f, + 1.915206561f, 1.920399213f, 1.925605944f, 1.930826791f, 1.936061793f, 1.941310990f, 1.946574418f, 1.951852116f, + 1.957144124f, 1.962450480f, 1.967771223f, 1.973106392f, 1.978456026f, 1.983820165f, 1.989198847f, 1.994592112f, + 2.000000000f, +}; + +/* + * LUT for tanh(x) sampled over x in [0, 4]. + * Generation formula: + * arm_nn_tanh_lut256_f32[i] = tanhf(4.0f * (float)i / 256.0f) + */ +const float32_t arm_nn_tanh_lut256_f32[257] = { + 0.000000000f, 0.015623729f, 0.031239831f, 0.046840698f, 0.062418747f, 0.077966441f, 0.093476304f, 0.108940930f, + 0.124353002f, 0.139705303f, 0.154990730f, 0.170202308f, 0.185333200f, 0.200376719f, 0.215326340f, 0.230175711f, + 0.244918662f, 0.259549215f, 0.274061589f, 0.288450213f, 0.302709729f, 0.316835001f, 0.330821117f, 0.344663398f, + 0.358357398f, 0.371898910f, 0.385283966f, 0.398508842f, 0.411570056f, 0.424464368f, 0.437188785f, 0.449740552f, + 0.462117157f, 0.474316325f, 0.486336017f, 0.498174426f, 0.509829974f, 0.521301305f, 0.532587286f, 0.543686996f, + 0.554599722f, 0.565324958f, 0.575862391f, 0.586211902f, 0.596373555f, 0.606347593f, 0.616134427f, 0.625734636f, + 0.635148952f, 0.644378261f, 0.653423588f, 0.662286096f, 0.670967074f, 0.679467935f, 0.687790205f, 0.695935517f, + 0.703905604f, 0.711702294f, 0.719327501f, 0.726783220f, 0.734071520f, 0.741194537f, 0.748154470f, 0.754953575f, + 0.761594156f, 0.768078563f, 0.774409187f, 0.780588452f, 0.786618812f, 0.792502746f, 0.798242755f, 0.803841353f, + 0.809301070f, 0.814624443f, 0.819814012f, 0.824872321f, 0.829801910f, 0.834605315f, 0.839285062f, 0.843843670f, + 0.848283640f, 0.852607461f, 0.856817601f, 0.860916511f, 0.864906618f, 0.868790325f, 0.872570011f, 0.876248029f, + 0.879826700f, 0.883308319f, 0.886695149f, 0.889989423f, 0.893193340f, 0.896309067f, 0.899338735f, 0.902284443f, + 0.905148254f, 0.907932195f, 0.910638259f, 0.913268402f, 0.915824544f, 0.918308568f, 0.920722322f, 0.923067616f, + 0.925346225f, 0.927559888f, 0.929710307f, 0.931799149f, 0.933828043f, 0.935798587f, 0.937712339f, 0.939570826f, + 0.941375538f, 0.943127934f, 0.944829436f, 0.946481434f, 0.948085286f, 0.949642317f, 0.951153820f, 0.952621057f, + 0.954045260f, 0.955427629f, 0.956769334f, 0.958071518f, 0.959335293f, 0.960561744f, 0.961751926f, 0.962906871f, + 0.964027580f, 0.965115031f, 0.966170173f, 0.967193935f, 0.968187217f, 0.969150896f, 0.970085827f, 0.970992841f, + 0.971872746f, 0.972726329f, 0.973554356f, 0.974357571f, 0.975136698f, 0.975892441f, 0.976625484f, 0.977336493f, + 0.978026115f, 0.978694978f, 0.979343695f, 0.979972859f, 0.980583047f, 0.981174821f, 0.981748725f, 0.982305290f, + 0.982845029f, 0.983368443f, 0.983876017f, 0.984368222f, 0.984845517f, 0.985308347f, 0.985757143f, 0.986192324f, + 0.986614298f, 0.987023461f, 0.987420196f, 0.987804876f, 0.988177862f, 0.988539507f, 0.988890151f, 0.989230124f, + 0.989559749f, 0.989879336f, 0.990189189f, 0.990489600f, 0.990780856f, 0.991063231f, 0.991336996f, 0.991602409f, + 0.991859725f, 0.992109186f, 0.992351033f, 0.992585494f, 0.992812795f, 0.993033152f, 0.993246775f, 0.993453870f, + 0.993654634f, 0.993849260f, 0.994037935f, 0.994220838f, 0.994398146f, 0.994570029f, 0.994736652f, 0.994898175f, + 0.995054754f, 0.995206538f, 0.995353675f, 0.995496305f, 0.995634567f, 0.995768593f, 0.995898513f, 0.996024452f, + 0.996146531f, 0.996264868f, 0.996379578f, 0.996490771f, 0.996598555f, 0.996703034f, 0.996804309f, 0.996902478f, + 0.996997635f, 0.997089874f, 0.997179283f, 0.997265949f, 0.997349955f, 0.997431384f, 0.997510313f, 0.997586821f, + 0.997660979f, 0.997732862f, 0.997802538f, 0.997870075f, 0.997935538f, 0.997998991f, 0.998060496f, 0.998120112f, + 0.998177898f, 0.998233908f, 0.998288199f, 0.998340822f, 0.998391828f, 0.998441268f, 0.998489189f, 0.998535637f, + 0.998580659f, 0.998624298f, 0.998666595f, 0.998707593f, 0.998747332f, 0.998785849f, 0.998823182f, 0.998859369f, + 0.998894443f, 0.998928439f, 0.998961390f, 0.998993329f, 0.999024286f, 0.999054291f, 0.999083374f, 0.999111563f, + 0.999138886f, 0.999165368f, 0.999191037f, 0.999215916f, 0.999240031f, 0.999263404f, 0.999286059f, 0.999308017f, + 0.999329300f, +}; + +#endif /* ARM_NN_ENABLE_F32 */ + +#if ARM_NN_ENABLE_F16 + +/* + * Polynomial coefficients used by the float16 MVE exp approximation. + * + * The float16 MVE helper evaluates the polynomial in widened float32 lanes, + * but the coefficient table is kept distinct from the float32 one so the + * float16 feature gate stays isolated. + */ +const float32_t arm_nn_exp_poly_coeffs_f16[8] = { + 1.0f, + 0.0416598916054f, + 0.500000596046f, + 0.0014122662833f, + 1.00000011921f, + 0.00833693705499f, + 0.166665703058f, + 0.000195780929062f, +}; + +/* + * Rational tanh approximation used by float16 helpers. + */ +const float32_t arm_nn_tanh_approx_coeffs_f16[3] = { + 3.0f, + 27.0f, + 9.0f, +}; + +/* + * Quantized binary16 LUT for 2^(i/256), i in [0, 256]. + */ +const uint16_t arm_nn_exp2_lut256_f16[257] = { + 0x3C00u, 0x3C03u, 0x3C06u, 0x3C08u, 0x3C0Bu, 0x3C0Eu, 0x3C11u, 0x3C14u, 0x3C16u, 0x3C19u, 0x3C1Cu, 0x3C1Fu, 0x3C22u, + 0x3C25u, 0x3C28u, 0x3C2Au, 0x3C2Du, 0x3C30u, 0x3C33u, 0x3C36u, 0x3C39u, 0x3C3Cu, 0x3C3Fu, 0x3C42u, 0x3C45u, 0x3C48u, + 0x3C4Bu, 0x3C4Eu, 0x3C51u, 0x3C54u, 0x3C57u, 0x3C5Au, 0x3C5Du, 0x3C60u, 0x3C63u, 0x3C66u, 0x3C69u, 0x3C6Cu, 0x3C6Fu, + 0x3C72u, 0x3C75u, 0x3C78u, 0x3C7Bu, 0x3C7Eu, 0x3C82u, 0x3C85u, 0x3C88u, 0x3C8Bu, 0x3C8Eu, 0x3C91u, 0x3C94u, 0x3C98u, + 0x3C9Bu, 0x3C9Eu, 0x3CA1u, 0x3CA4u, 0x3CA8u, 0x3CABu, 0x3CAEu, 0x3CB1u, 0x3CB5u, 0x3CB8u, 0x3CBBu, 0x3CBEu, 0x3CC2u, + 0x3CC5u, 0x3CC8u, 0x3CCCu, 0x3CCFu, 0x3CD2u, 0x3CD6u, 0x3CD9u, 0x3CDCu, 0x3CE0u, 0x3CE3u, 0x3CE7u, 0x3CEAu, 0x3CEDu, + 0x3CF1u, 0x3CF4u, 0x3CF8u, 0x3CFBu, 0x3CFFu, 0x3D02u, 0x3D06u, 0x3D09u, 0x3D0Cu, 0x3D10u, 0x3D14u, 0x3D17u, 0x3D1Bu, + 0x3D1Eu, 0x3D22u, 0x3D25u, 0x3D29u, 0x3D2Cu, 0x3D30u, 0x3D34u, 0x3D37u, 0x3D3Bu, 0x3D3Eu, 0x3D42u, 0x3D46u, 0x3D49u, + 0x3D4Du, 0x3D51u, 0x3D54u, 0x3D58u, 0x3D5Cu, 0x3D60u, 0x3D63u, 0x3D67u, 0x3D6Bu, 0x3D6Fu, 0x3D72u, 0x3D76u, 0x3D7Au, + 0x3D7Eu, 0x3D81u, 0x3D85u, 0x3D89u, 0x3D8Du, 0x3D91u, 0x3D95u, 0x3D99u, 0x3D9Cu, 0x3DA0u, 0x3DA4u, 0x3DA8u, 0x3DACu, + 0x3DB0u, 0x3DB4u, 0x3DB8u, 0x3DBCu, 0x3DC0u, 0x3DC4u, 0x3DC8u, 0x3DCCu, 0x3DD0u, 0x3DD4u, 0x3DD8u, 0x3DDCu, 0x3DE0u, + 0x3DE4u, 0x3DE8u, 0x3DECu, 0x3DF0u, 0x3DF5u, 0x3DF9u, 0x3DFDu, 0x3E01u, 0x3E05u, 0x3E09u, 0x3E0Eu, 0x3E12u, 0x3E16u, + 0x3E1Au, 0x3E1Eu, 0x3E23u, 0x3E27u, 0x3E2Bu, 0x3E30u, 0x3E34u, 0x3E38u, 0x3E3Cu, 0x3E41u, 0x3E45u, 0x3E49u, 0x3E4Eu, + 0x3E52u, 0x3E57u, 0x3E5Bu, 0x3E5Fu, 0x3E64u, 0x3E68u, 0x3E6Du, 0x3E71u, 0x3E76u, 0x3E7Au, 0x3E7Fu, 0x3E83u, 0x3E88u, + 0x3E8Cu, 0x3E91u, 0x3E95u, 0x3E9Au, 0x3E9Eu, 0x3EA3u, 0x3EA8u, 0x3EACu, 0x3EB1u, 0x3EB5u, 0x3EBAu, 0x3EBFu, 0x3EC4u, + 0x3EC8u, 0x3ECDu, 0x3ED2u, 0x3ED6u, 0x3EDBu, 0x3EE0u, 0x3EE5u, 0x3EE9u, 0x3EEEu, 0x3EF3u, 0x3EF8u, 0x3EFDu, 0x3F02u, + 0x3F06u, 0x3F0Bu, 0x3F10u, 0x3F15u, 0x3F1Au, 0x3F1Fu, 0x3F24u, 0x3F29u, 0x3F2Eu, 0x3F33u, 0x3F38u, 0x3F3Du, 0x3F42u, + 0x3F47u, 0x3F4Cu, 0x3F51u, 0x3F56u, 0x3F5Bu, 0x3F60u, 0x3F65u, 0x3F6Au, 0x3F70u, 0x3F75u, 0x3F7Au, 0x3F7Fu, 0x3F84u, + 0x3F8Au, 0x3F8Fu, 0x3F94u, 0x3F99u, 0x3F9Fu, 0x3FA4u, 0x3FA9u, 0x3FAEu, 0x3FB4u, 0x3FB9u, 0x3FBFu, 0x3FC4u, 0x3FC9u, + 0x3FCFu, 0x3FD4u, 0x3FDAu, 0x3FDFu, 0x3FE4u, 0x3FEAu, 0x3FEFu, 0x3FF5u, 0x3FFAu, 0x4000u, +}; + +/* + * Quantized binary16 LUT for tanh(x) with x in [0, 4]. + */ +const uint16_t arm_nn_tanh_lut256_f16[257] = { + 0x0000u, 0x2400u, 0x27FFu, 0x29FFu, 0x2BFDu, 0x2CFDu, 0x2DFCu, 0x2EF9u, 0x2FF5u, 0x3078u, 0x30F6u, 0x3172u, 0x31EEu, + 0x3269u, 0x32E4u, 0x335Eu, 0x33D6u, 0x3427u, 0x3463u, 0x349Du, 0x34D8u, 0x3512u, 0x354Bu, 0x3584u, 0x35BCu, 0x35F3u, + 0x362Au, 0x3660u, 0x3696u, 0x36CBu, 0x36FFu, 0x3732u, 0x3765u, 0x3797u, 0x37C8u, 0x37F9u, 0x3814u, 0x382Cu, 0x3843u, + 0x3859u, 0x3870u, 0x3886u, 0x389Bu, 0x38B1u, 0x38C5u, 0x38DAu, 0x38EEu, 0x3902u, 0x3915u, 0x3928u, 0x393Au, 0x394Cu, + 0x395Eu, 0x3970u, 0x3981u, 0x3991u, 0x39A2u, 0x39B2u, 0x39C1u, 0x39D0u, 0x39DFu, 0x39EEu, 0x39FCu, 0x3A0Au, 0x3A18u, + 0x3A25u, 0x3A32u, 0x3A3Fu, 0x3A4Bu, 0x3A57u, 0x3A63u, 0x3A6Eu, 0x3A79u, 0x3A84u, 0x3A8Fu, 0x3A99u, 0x3AA3u, 0x3AADu, + 0x3AB7u, 0x3AC0u, 0x3AC9u, 0x3AD2u, 0x3ADBu, 0x3AE3u, 0x3AEBu, 0x3AF3u, 0x3AFBu, 0x3B03u, 0x3B0Au, 0x3B11u, 0x3B18u, + 0x3B1Fu, 0x3B25u, 0x3B2Cu, 0x3B32u, 0x3B38u, 0x3B3Eu, 0x3B43u, 0x3B49u, 0x3B4Eu, 0x3B54u, 0x3B59u, 0x3B5Eu, 0x3B62u, + 0x3B67u, 0x3B6Cu, 0x3B70u, 0x3B74u, 0x3B78u, 0x3B7Du, 0x3B80u, 0x3B84u, 0x3B88u, 0x3B8Cu, 0x3B8Fu, 0x3B92u, 0x3B96u, + 0x3B99u, 0x3B9Cu, 0x3B9Fu, 0x3BA2u, 0x3BA5u, 0x3BA7u, 0x3BAAu, 0x3BADu, 0x3BAFu, 0x3BB2u, 0x3BB4u, 0x3BB6u, 0x3BB9u, + 0x3BBBu, 0x3BBDu, 0x3BBFu, 0x3BC1u, 0x3BC3u, 0x3BC5u, 0x3BC6u, 0x3BC8u, 0x3BCAu, 0x3BCBu, 0x3BCDu, 0x3BCFu, 0x3BD0u, + 0x3BD2u, 0x3BD3u, 0x3BD4u, 0x3BD6u, 0x3BD7u, 0x3BD8u, 0x3BD9u, 0x3BDBu, 0x3BDCu, 0x3BDDu, 0x3BDEu, 0x3BDFu, 0x3BE0u, + 0x3BE1u, 0x3BE2u, 0x3BE3u, 0x3BE4u, 0x3BE5u, 0x3BE5u, 0x3BE6u, 0x3BE7u, 0x3BE8u, 0x3BE9u, 0x3BE9u, 0x3BEAu, 0x3BEBu, + 0x3BEBu, 0x3BECu, 0x3BEDu, 0x3BEDu, 0x3BEEu, 0x3BEEu, 0x3BEFu, 0x3BEFu, 0x3BF0u, 0x3BF0u, 0x3BF1u, 0x3BF1u, 0x3BF2u, + 0x3BF2u, 0x3BF3u, 0x3BF3u, 0x3BF3u, 0x3BF4u, 0x3BF4u, 0x3BF5u, 0x3BF5u, 0x3BF5u, 0x3BF6u, 0x3BF6u, 0x3BF6u, 0x3BF6u, + 0x3BF7u, 0x3BF7u, 0x3BF7u, 0x3BF8u, 0x3BF8u, 0x3BF8u, 0x3BF8u, 0x3BF9u, 0x3BF9u, 0x3BF9u, 0x3BF9u, 0x3BF9u, 0x3BFAu, + 0x3BFAu, 0x3BFAu, 0x3BFAu, 0x3BFAu, 0x3BFBu, 0x3BFBu, 0x3BFBu, 0x3BFBu, 0x3BFBu, 0x3BFBu, 0x3BFBu, 0x3BFCu, 0x3BFCu, + 0x3BFCu, 0x3BFCu, 0x3BFCu, 0x3BFCu, 0x3BFCu, 0x3BFCu, 0x3BFDu, 0x3BFDu, 0x3BFDu, 0x3BFDu, 0x3BFDu, 0x3BFDu, 0x3BFDu, + 0x3BFDu, 0x3BFDu, 0x3BFDu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, + 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFEu, 0x3BFFu, 0x3BFFu, 0x3BFFu, +}; + +#endif /* ARM_NN_ENABLE_F16 */ + +/** @} */ diff --git a/Source/PadFunctions/CMakeLists.txt b/Source/PadFunctions/CMakeLists.txt index 5da8af95..46707685 100644 --- a/Source/PadFunctions/CMakeLists.txt +++ b/Source/PadFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2010-2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -17,4 +17,11 @@ # file(GLOB SRC "./*_s8.c") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC arm_pad_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC arm_pad_f16.c) +endif() + target_sources(cmsis-nn PRIVATE ${SRC}) diff --git a/Source/PadFunctions/arm_pad_f16.c b/Source/PadFunctions/arm_pad_f16.c new file mode 100644 index 00000000..a05722f7 --- /dev/null +++ b/Source/PadFunctions/arm_pad_f16.c @@ -0,0 +1,119 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_pad_f16.c + * Description: Pad a float16 tensor + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pad + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_pad_f16(const float16_t *input, + float16_t *output, + float16_t pad_value, + const cmsis_nn_dims *input_size, + const cmsis_nn_dims *pre_pad, + const cmsis_nn_dims *post_pad) +{ + if (!input || !output || !input_size || !pre_pad || !post_pad) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const cmsis_nn_dims output_size = {pre_pad->n + input_size->n + post_pad->n, + pre_pad->h + input_size->h + post_pad->h, + pre_pad->w + input_size->w + post_pad->w, + pre_pad->c + input_size->c + post_pad->c}; + + if (output_size.n <= 0 || output_size.h <= 0 || output_size.w <= 0 || output_size.c <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const uint32_t batch_block_size = (uint32_t)((size_t)output_size.h * (size_t)output_size.w * (size_t)output_size.c); + const uint32_t row_block_size = (uint32_t)((size_t)output_size.w * (size_t)output_size.c); + const uint32_t col_block_size = (uint32_t)output_size.c; + + arm_memset_f16(output, pad_value, batch_block_size * (uint32_t)pre_pad->n); + output += batch_block_size * (uint32_t)pre_pad->n; + for (int32_t b = 0; b < input_size->n; ++b) + { + arm_memset_f16(output, pad_value, row_block_size * (uint32_t)pre_pad->h); + output += row_block_size * (uint32_t)pre_pad->h; + for (int32_t y = 0; y < input_size->h; ++y) + { + arm_memset_f16(output, pad_value, col_block_size * (uint32_t)pre_pad->w); + output += col_block_size * (uint32_t)pre_pad->w; + if (input_size->c == output_size.c) + { + arm_memcpy_f16(output, input, (uint32_t)input_size->w * (uint32_t)input_size->c); + output += (uint32_t)input_size->w * (uint32_t)input_size->c; + input += (uint32_t)input_size->w * (uint32_t)input_size->c; + } + else + { + for (int32_t x = 0; x < input_size->w; ++x) + { + arm_memset_f16(output, pad_value, (uint32_t)pre_pad->c); + output += pre_pad->c; + + arm_memcpy_f16(output, input, (uint32_t)input_size->c); + output += input_size->c; + input += input_size->c; + + arm_memset_f16(output, pad_value, (uint32_t)post_pad->c); + output += post_pad->c; + } + } + + arm_memset_f16(output, pad_value, col_block_size * (uint32_t)post_pad->w); + output += col_block_size * (uint32_t)post_pad->w; + } + + arm_memset_f16(output, pad_value, row_block_size * (uint32_t)post_pad->h); + output += row_block_size * (uint32_t)post_pad->h; + } + arm_memset_f16(output, pad_value, batch_block_size * (uint32_t)post_pad->n); + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of Pad group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/PadFunctions/arm_pad_f32.c b/Source/PadFunctions/arm_pad_f32.c new file mode 100644 index 00000000..f0e23b49 --- /dev/null +++ b/Source/PadFunctions/arm_pad_f32.c @@ -0,0 +1,119 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_pad_f32.c + * Description: Pad a float32 tensor + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pad + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_pad_f32(const float32_t *input, + float32_t *output, + float32_t pad_value, + const cmsis_nn_dims *input_size, + const cmsis_nn_dims *pre_pad, + const cmsis_nn_dims *post_pad) +{ + if (!input || !output || !input_size || !pre_pad || !post_pad) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const cmsis_nn_dims output_size = {pre_pad->n + input_size->n + post_pad->n, + pre_pad->h + input_size->h + post_pad->h, + pre_pad->w + input_size->w + post_pad->w, + pre_pad->c + input_size->c + post_pad->c}; + + if (output_size.n <= 0 || output_size.h <= 0 || output_size.w <= 0 || output_size.c <= 0) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const uint32_t batch_block_size = (uint32_t)((size_t)output_size.h * (size_t)output_size.w * (size_t)output_size.c); + const uint32_t row_block_size = (uint32_t)((size_t)output_size.w * (size_t)output_size.c); + const uint32_t col_block_size = (uint32_t)output_size.c; + + arm_memset_f32(output, pad_value, batch_block_size * (uint32_t)pre_pad->n); + output += batch_block_size * (uint32_t)pre_pad->n; + for (int32_t b = 0; b < input_size->n; ++b) + { + arm_memset_f32(output, pad_value, row_block_size * (uint32_t)pre_pad->h); + output += row_block_size * (uint32_t)pre_pad->h; + for (int32_t y = 0; y < input_size->h; ++y) + { + arm_memset_f32(output, pad_value, col_block_size * (uint32_t)pre_pad->w); + output += col_block_size * (uint32_t)pre_pad->w; + if (input_size->c == output_size.c) + { + arm_memcpy_f32(output, input, (uint32_t)input_size->w * (uint32_t)input_size->c); + output += (uint32_t)input_size->w * (uint32_t)input_size->c; + input += (uint32_t)input_size->w * (uint32_t)input_size->c; + } + else + { + for (int32_t x = 0; x < input_size->w; ++x) + { + arm_memset_f32(output, pad_value, (uint32_t)pre_pad->c); + output += pre_pad->c; + + arm_memcpy_f32(output, input, (uint32_t)input_size->c); + output += input_size->c; + input += input_size->c; + + arm_memset_f32(output, pad_value, (uint32_t)post_pad->c); + output += post_pad->c; + } + } + + arm_memset_f32(output, pad_value, col_block_size * (uint32_t)post_pad->w); + output += col_block_size * (uint32_t)post_pad->w; + } + + arm_memset_f32(output, pad_value, row_block_size * (uint32_t)post_pad->h); + output += row_block_size * (uint32_t)post_pad->h; + } + arm_memset_f32(output, pad_value, batch_block_size * (uint32_t)post_pad->n); + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of Pad group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/PoolingFunctions/CMakeLists.txt b/Source/PoolingFunctions/CMakeLists.txt index a37503b6..6353044c 100644 --- a/Source/PoolingFunctions/CMakeLists.txt +++ b/Source/PoolingFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2022 Arm Limited. +# Copyright (c) 2019-2022, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -18,7 +18,14 @@ file(GLOB SRC "./*_s8.c") file(GLOB SRC_S16 "./*_s16.c") -target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_S16}) - - +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_avg_pool_f32.c + arm_max_pool_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_avg_pool_f16.c + arm_max_pool_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_S16} ${SRC_FLT}) diff --git a/Source/PoolingFunctions/arm_avg_pool_f16.c b/Source/PoolingFunctions/arm_avg_pool_f16.c new file mode 100644 index 00000000..92f0d604 --- /dev/null +++ b/Source/PoolingFunctions/arm_avg_pool_f16.c @@ -0,0 +1,161 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_avg_pool_f16.c + * Description: Avg pooling function implementations (float16) + * + * $Date: 26 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pooling + * @{ + */ + +/* + * Avg pooling function for f16 + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_avg_pool_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst) +{ + (void)ctx; + if (!pool_params || !input_dims || !filter_dims || !output_dims || !src || !dst) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_y = input_dims->h; + const int32_t input_x = input_dims->w; + const int32_t output_y = output_dims->h; + const int32_t output_x = output_dims->w; + const int32_t stride_y = pool_params->stride.h; + const int32_t stride_x = pool_params->stride.w; + const int32_t kernel_y = filter_dims->h; + const int32_t kernel_x = filter_dims->w; + const int32_t pad_y = pool_params->padding.h; + const int32_t pad_x = pool_params->padding.w; + const float16_t act_min = pool_params->activation.min; + const float16_t act_max = pool_params->activation.max; + const int32_t channel_in = input_dims->c; + const int32_t batch_size = input_x * input_y * channel_in; + int32_t batch_cnt = input_dims->n; + + if (batch_cnt < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + while (batch_cnt) + { + for (int32_t i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) + { + for (int32_t i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) + { + const int32_t ker_y_start = MAX(0, -base_idx_y); + const int32_t ker_x_start = MAX(0, -base_idx_x); + const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); + const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); + const int32_t count = (kernel_y_end - ker_y_start) * (kernel_x_end - ker_x_start); + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_act_min = vdupq_n_f16(act_min); + const float16x8_t v_act_max = vdupq_n_f16(act_max); + const float16_t inv_count = (count > 0) ? (float16_t)(1.0f / (float32_t)count) : (float16_t)0.0f; + + for (int32_t c = 0; c < channel_in; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channel_in - c)); + float16x8_t v_sum = vdupq_n_f16((float16_t)0.0f); + + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + v_sum = vaddq(v_sum, vld1q_z(src + idx, p)); + } + } + + float16x8_t v_avg = vmulq(v_sum, inv_count); + v_avg = arm_nn_clamp_mve_f16(v_avg, v_act_min, v_act_max); + vst1q_p(dst + (i_y * output_x + i_x) * channel_in + c, v_avg, p); + } +#else + for (int32_t c = 0; c < channel_in; ++c) + { + float32_t sum = 0.0f; + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + sum += (float32_t)src[idx]; + } + } + const float16_t v = (count > 0) ? (float16_t)(sum / (float32_t)count) : (float16_t)0.0f; + dst[(i_y * output_x + i_x) * channel_in + c] = arm_nn_clamp_scalar_f16(v, act_min, act_max); + } +#endif + } + } + + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_avg_pool_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst) +{ + return arm_avg_pool_f16(ctx, pool_params, input_dims, src, filter_dims, output_dims, dst); +} + +/** + * @} end of Pooling group + */ diff --git a/Source/PoolingFunctions/arm_avg_pool_f32.c b/Source/PoolingFunctions/arm_avg_pool_f32.c new file mode 100644 index 00000000..3e887746 --- /dev/null +++ b/Source/PoolingFunctions/arm_avg_pool_f32.c @@ -0,0 +1,158 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_avg_pool_f32.c + * Description: Avg pooling function implementations (float32) + * + * $Date: 2 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pooling + * @{ + */ + +/* + * Avg pooling function for f32 + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_avg_pool_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst) +{ + (void)ctx; + if (!pool_params || !input_dims || !filter_dims || !output_dims || !src || !dst) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_y = input_dims->h; + const int32_t input_x = input_dims->w; + const int32_t output_y = output_dims->h; + const int32_t output_x = output_dims->w; + const int32_t stride_y = pool_params->stride.h; + const int32_t stride_x = pool_params->stride.w; + const int32_t kernel_y = filter_dims->h; + const int32_t kernel_x = filter_dims->w; + const int32_t pad_y = pool_params->padding.h; + const int32_t pad_x = pool_params->padding.w; + const float32_t act_min = pool_params->activation.min; + const float32_t act_max = pool_params->activation.max; + const int32_t channel_in = input_dims->c; + const int32_t batch_size = input_x * input_y * channel_in; + int32_t batch_cnt = input_dims->n; + + if (batch_cnt < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + while (batch_cnt) + { + for (int32_t i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) + { + for (int32_t i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) + { + const int32_t ker_y_start = MAX(0, -base_idx_y); + const int32_t ker_x_start = MAX(0, -base_idx_x); + const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); + const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); + const int32_t count = (kernel_y_end - ker_y_start) * (kernel_x_end - ker_x_start); + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_act_min = vdupq_n_f32(act_min); + const float32x4_t v_act_max = vdupq_n_f32(act_max); + const float32_t inv_count = (count > 0) ? (1.0f / (float32_t)count) : 0.0f; + for (int32_t c = 0; c < channel_in; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channel_in - c)); + float32x4_t v_sum = vdupq_n_f32(0.0f); + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + v_sum = vaddq(v_sum, vld1q_z(src + idx, p)); + } + } + float32x4_t v = vmulq(v_sum, inv_count); + v = arm_nn_clamp_mve_f32(v, v_act_min, v_act_max); + vst1q_p(dst + (i_y * output_x + i_x) * channel_in + c, v, p); + } +#else + for (int32_t c = 0; c < channel_in; ++c) + { + float32_t sum = 0.0f; + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + sum += src[idx]; + } + } + float32_t v = (count > 0) ? (sum / (float32_t)count) : 0.0f; + dst[(i_y * output_x + i_x) * channel_in + c] = CLAMP(v, act_max, act_min); + } +#endif + } + } + + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_avg_pool_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst) +{ + return arm_avg_pool_f32(ctx, pool_params, input_dims, src, filter_dims, output_dims, dst); +} + +/** + * @} end of Pooling group + */ diff --git a/Source/PoolingFunctions/arm_max_pool_f16.c b/Source/PoolingFunctions/arm_max_pool_f16.c new file mode 100644 index 00000000..d3664be1 --- /dev/null +++ b/Source/PoolingFunctions/arm_max_pool_f16.c @@ -0,0 +1,197 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_max_pool_f16.c + * Description: Max pooling function implementations (float16) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pooling + * @{ + */ + +/* + * Max pooling function for f16 + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_max_pool_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst) +{ + (void)ctx; + if (!pool_params || !input_dims || !filter_dims || !output_dims || !src || !dst) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_y = input_dims->h; + const int32_t input_x = input_dims->w; + const int32_t output_y = output_dims->h; + const int32_t output_x = output_dims->w; + const int32_t stride_y = pool_params->stride.h; + const int32_t stride_x = pool_params->stride.w; + const int32_t kernel_y = filter_dims->h; + const int32_t kernel_x = filter_dims->w; + const int32_t pad_y = pool_params->padding.h; + const int32_t pad_x = pool_params->padding.w; + const _Float16 f16_finite_max = (_Float16)ARM_NN_F16_FINITE_MAX; + const _Float16 f16_finite_lowest = -f16_finite_max; + const _Float16 act_min = (_Float16)pool_params->activation.min; + const _Float16 act_max = (_Float16)pool_params->activation.max; + const int32_t channel_in = input_dims->c; + const int32_t batch_size = input_x * input_y * channel_in; + int32_t batch_cnt = input_dims->n; + + if (batch_cnt < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + while (batch_cnt) + { + bool use_specialized = (input_y == 1 && output_y == 1 && kernel_y == 1 && stride_y == 1 && pad_y == 0 && + kernel_x == 2 && stride_x == 2 && pad_x == 0); +#ifdef NN_DISABLE_SPECIALIZATION + use_specialized = false; +#endif + if (use_specialized) + { + if (act_min <= f16_finite_lowest && act_max >= f16_finite_max) + { + arm_nn_maxpool1d_k2s2_nhwc_noclip_f16(src, channel_in, input_x, dst, output_x); + } + else + { + arm_nn_maxpool1d_k2s2_nhwc_f16( + src, channel_in, input_x, dst, output_x, (float16_t)act_min, (float16_t)act_max); + } + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + continue; + } + + use_specialized = + (input_y == 1 && output_y == 1 && kernel_y == 1 && stride_y == 1 && pad_y == 0 && kernel_x == 3 && + stride_x == 3 && pad_x == 0 && act_min <= f16_finite_lowest && act_max >= f16_finite_max); +#ifdef NN_DISABLE_SPECIALIZATION + use_specialized = false; +#endif + if (use_specialized) + { + arm_nn_maxpool1d_k3s3_nhwc_f16(src, channel_in, input_x, dst, output_x); + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + continue; + } + + for (int32_t i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) + { + for (int32_t i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) + { + const int32_t ker_y_start = MAX(0, -base_idx_y); + const int32_t ker_x_start = MAX(0, -base_idx_x); + const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); + const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_act_min = vdupq_n_f16(act_min); + const float16x8_t v_act_max = vdupq_n_f16(act_max); + for (int32_t c = 0; c < channel_in; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(channel_in - c)); + float16x8_t v_max = vdupq_n_f16((float16_t)f16_finite_lowest); + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + float16x8_t v = vld1q_z(src + idx, p); + v = vpselq(v, v_max, p); + v_max = vmaxnmq(v_max, v); + } + } + v_max = arm_nn_clamp_mve_f16(v_max, v_act_min, v_act_max); + vst1q_p(dst + (i_y * output_x + i_x) * channel_in + c, v_max, p); + } +#else + for (int32_t c = 0; c < channel_in; ++c) + { + _Float16 max_val = f16_finite_lowest; + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + const _Float16 src_val = (_Float16)src[idx]; + max_val = (src_val > max_val) ? src_val : max_val; + } + } + dst[(i_y * output_x + i_x) * channel_in + c] = + arm_nn_clamp_scalar_f16((float16_t)max_val, (float16_t)act_min, (float16_t)act_max); + } +#endif + } + } + + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +arm_cmsis_nn_status arm_max_pool_nhwc_f16(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f16 *pool_params, + const cmsis_nn_dims *input_dims, + const float16_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float16_t *dst) +{ + return arm_max_pool_f16(ctx, pool_params, input_dims, src, filter_dims, output_dims, dst); +} + +/** + * @} end of Pooling group + */ diff --git a/Source/PoolingFunctions/arm_max_pool_f32.c b/Source/PoolingFunctions/arm_max_pool_f32.c new file mode 100644 index 00000000..435027c5 --- /dev/null +++ b/Source/PoolingFunctions/arm_max_pool_f32.c @@ -0,0 +1,192 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_max_pool_f32.c + * Description: Max pooling function implementations (float32) + * + * $Date: 2 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup Pooling + * @{ + */ + +/* + * Max pooling function for f32 + * + * Refer to header file for details. + * + */ + +arm_cmsis_nn_status arm_max_pool_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst) +{ + (void)ctx; + if (!pool_params || !input_dims || !filter_dims || !output_dims || !src || !dst) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t input_y = input_dims->h; + const int32_t input_x = input_dims->w; + const int32_t output_y = output_dims->h; + const int32_t output_x = output_dims->w; + const int32_t stride_y = pool_params->stride.h; + const int32_t stride_x = pool_params->stride.w; + const int32_t kernel_y = filter_dims->h; + const int32_t kernel_x = filter_dims->w; + const int32_t pad_y = pool_params->padding.h; + const int32_t pad_x = pool_params->padding.w; + const float32_t act_min = pool_params->activation.min; + const float32_t act_max = pool_params->activation.max; + const int32_t channel_in = input_dims->c; + const int32_t batch_size = input_x * input_y * channel_in; + int32_t batch_cnt = input_dims->n; + + if (batch_cnt < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + while (batch_cnt) + { + bool use_specialized = (input_y == 1 && output_y == 1 && kernel_y == 1 && stride_y == 1 && pad_y == 0 && + kernel_x == 2 && stride_x == 2 && pad_x == 0); +#ifdef NN_DISABLE_SPECIALIZATION + use_specialized = false; +#endif + if (use_specialized) + { + if (act_min <= ARM_NN_F32_FINITE_LOWEST && act_max >= ARM_NN_F32_FINITE_MAX) + { + arm_nn_maxpool1d_k2s2_nhwc_noclip_f32(src, channel_in, input_x, dst, output_x); + } + else + { + arm_nn_maxpool1d_k2s2_nhwc_f32(src, channel_in, input_x, dst, output_x, act_min, act_max); + } + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + continue; + } + + use_specialized = + (input_y == 1 && output_y == 1 && kernel_y == 1 && stride_y == 1 && pad_y == 0 && kernel_x == 3 && + stride_x == 3 && pad_x == 0 && act_min <= ARM_NN_F32_FINITE_LOWEST && act_max >= ARM_NN_F32_FINITE_MAX); +#ifdef NN_DISABLE_SPECIALIZATION + use_specialized = false; +#endif + if (use_specialized) + { + arm_nn_maxpool1d_k3s3_nhwc_f32(src, channel_in, input_x, dst, output_x); + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + continue; + } + + for (int32_t i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) + { + for (int32_t i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) + { + const int32_t ker_y_start = MAX(0, -base_idx_y); + const int32_t ker_x_start = MAX(0, -base_idx_x); + const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); + const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_act_min = vdupq_n_f32(act_min); + const float32x4_t v_act_max = vdupq_n_f32(act_max); + for (int32_t c = 0; c < channel_in; c += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(channel_in - c)); + float32x4_t v_max = vdupq_n_f32(ARM_NN_F32_FINITE_LOWEST); + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + float32x4_t v = vld1q_z(src + idx, p); + v = vpselq(v, v_max, p); + v_max = vmaxnmq(v_max, v); + } + } + v_max = arm_nn_clamp_mve_f32(v_max, v_act_min, v_act_max); + vst1q_p(dst + (i_y * output_x + i_x) * channel_in + c, v_max, p); + } +#else + for (int32_t c = 0; c < channel_in; ++c) + { + float32_t max_val = ARM_NN_F32_FINITE_LOWEST; + for (int32_t k_y = ker_y_start; k_y < kernel_y_end; k_y++) + { + for (int32_t k_x = ker_x_start; k_x < kernel_x_end; k_x++) + { + const int32_t idx = (k_x + base_idx_x + (k_y + base_idx_y) * input_x) * channel_in + c; + max_val = (src[idx] > max_val) ? src[idx] : max_val; + } + } + dst[(i_y * output_x + i_x) * channel_in + c] = CLAMP(max_val, act_max, act_min); + } +#endif + } + } + + src += batch_size; + dst += output_x * output_y * channel_in; + batch_cnt--; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of Pooling group + */ + +arm_cmsis_nn_status arm_max_pool_nhwc_f32(const cmsis_nn_context *ctx, + const cmsis_nn_pool_params_f32 *pool_params, + const cmsis_nn_dims *input_dims, + const float32_t *src, + const cmsis_nn_dims *filter_dims, + const cmsis_nn_dims *output_dims, + float32_t *dst) +{ + return arm_max_pool_f32(ctx, pool_params, input_dims, src, filter_dims, output_dims, dst); +} diff --git a/Source/ReshapeFunctions/CMakeLists.txt b/Source/ReshapeFunctions/CMakeLists.txt index 9d3f543a..9ffb9b26 100644 --- a/Source/ReshapeFunctions/CMakeLists.txt +++ b/Source/ReshapeFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2021 Arm Limited. +# Copyright (c) 2019-2021, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -16,5 +16,11 @@ # limitations under the License. # -file(GLOB SRC "./*_*.c") +set(SRC arm_reshape_s8.c) +if (ARM_NN_ENABLE_F32) + list(APPEND SRC arm_reshape_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC arm_reshape_f16.c) +endif() target_sources(cmsis-nn PRIVATE ${SRC}) diff --git a/Source/ReshapeFunctions/arm_reshape_f16.c b/Source/ReshapeFunctions/arm_reshape_f16.c new file mode 100644 index 00000000..ea452a26 --- /dev/null +++ b/Source/ReshapeFunctions/arm_reshape_f16.c @@ -0,0 +1,60 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_reshape_f16.c + * Description: Reshape a float16 vector + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Reshape + * @{ + */ + +/* Refer header file for details. */ +void arm_reshape_f16(const float16_t *input, float16_t *output, const uint32_t total_size) +{ + if (total_size == 0U || input == output) + { + return; + } + + arm_memcpy_f16(output, input, total_size); +} + +/** + * @} end of Reshape group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/ReshapeFunctions/arm_reshape_f32.c b/Source/ReshapeFunctions/arm_reshape_f32.c new file mode 100644 index 00000000..bbbf744a --- /dev/null +++ b/Source/ReshapeFunctions/arm_reshape_f32.c @@ -0,0 +1,60 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_reshape_f32.c + * Description: Reshape a float32 vector + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Reshape + * @{ + */ + +/* Refer header file for details. */ +void arm_reshape_f32(const float32_t *input, float32_t *output, const uint32_t total_size) +{ + if (total_size == 0U || input == output) + { + return; + } + + arm_memcpy_f32(output, input, total_size); +} + +/** + * @} end of Reshape group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/SVDFunctions/CMakeLists.txt b/Source/SVDFunctions/CMakeLists.txt index 67c3f799..79a37e40 100644 --- a/Source/SVDFunctions/CMakeLists.txt +++ b/Source/SVDFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2021 Arm Limited. +# Copyright (c) 2019-2021, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -17,4 +17,11 @@ # file(GLOB SRC "./*_s8.c") -target_sources(cmsis-nn PRIVATE ${SRC}) +set(SRC_FLT "") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC_FLT arm_svdf_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC_FLT arm_svdf_f16.c) +endif() +target_sources(cmsis-nn PRIVATE ${SRC} ${SRC_FLT}) diff --git a/Source/SVDFunctions/arm_svdf_f16.c b/Source/SVDFunctions/arm_svdf_f16.c new file mode 100644 index 00000000..a8c605b9 --- /dev/null +++ b/Source/SVDFunctions/arm_svdf_f16.c @@ -0,0 +1,228 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_svdf_f16.c + * Description: SVDF layer (float16) + * + * $Date: 26 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture with FP16 support + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup SVDF + * @{ + */ + +__STATIC_INLINE float16_t clamp_and_activate_f16(float16_t x, const cmsis_nn_activation_f16 *act) +{ + return arm_nn_clamp_scalar_f16(x, act->min, act->max); +} + +__STATIC_INLINE float16_t arm_nn_svdf_dot_f16(const float16_t *lhs, const float16_t *rhs, int32_t count) +{ + _Float16 sum = (_Float16)0.0f; + +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + float16x8_t v_acc = vdupq_n_f16((float16_t)0.0f); + for (int32_t i = 0; i < count; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(count - i)); + v_acc = vfmaq(v_acc, vld1q_z(lhs + i, p), vld1q_z(rhs + i, p)); + } + sum = arm_nn_vec_reduce_add_f16(v_acc); +#else + for (int32_t i = 0; i < count; ++i) + { + sum += (_Float16)lhs[i] * (_Float16)rhs[i]; + } +#endif + + return (float16_t)sum; +} + +arm_cmsis_nn_status arm_svdf_f16(const cmsis_nn_context *ctx, + const cmsis_nn_context *input_ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_svdf_params_f16 *svdf_params, + const cmsis_nn_dims *input_dims, + const float16_t *input_data, + const cmsis_nn_dims *state_dims, + float16_t *state_data, + const cmsis_nn_dims *weights_feature_dims, + const float16_t *weights_feature_data, + const cmsis_nn_dims *weights_time_dims, + const float16_t *weights_time_data, + const cmsis_nn_dims *bias_dims, + const float16_t *bias_data, + const cmsis_nn_dims *output_dims, + float16_t *output_data) +{ + (void)ctx; + (void)state_dims; + (void)bias_dims; + (void)output_dims; + + if (!svdf_params || !input_dims || !weights_feature_dims || !weights_time_dims || !input_data || !state_data || + !weights_feature_data || !weights_time_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t rank = svdf_params->rank; + const int32_t input_batches = input_dims->n; + const int32_t input_height = input_dims->h; + const int32_t feature_batches = weights_feature_dims->n; + const int32_t time_batches = weights_time_dims->h; + const int32_t unit_count = feature_batches / rank; + + float16_t *buffer_a = (float16_t *)(input_ctx ? input_ctx->buf : NULL); + float16_t *buffer_b = (float16_t *)(output_ctx ? output_ctx->buf : NULL); + if (!buffer_a || !buffer_b) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float16_t *state_b = state_data + (i_batch * feature_batches * time_batches); + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + float16_t *state_f = state_b + i_feat * time_batches; + if (time_batches > 1) + { + for (int32_t t = 0; t < time_batches - 1; ++t) + { + state_f[t] = state_f[t + 1]; + } + } + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + const float16_t *input = input_data + i_batch * input_height; + float16_t *state_b = state_data + (i_batch * feature_batches * time_batches); + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + const float16_t *w_feat = weights_feature_data + i_feat * input_height; + float16_t acc_h = arm_nn_svdf_dot_f16(input, w_feat, input_height); + acc_h = clamp_and_activate_f16(acc_h, &svdf_params->input_activation); + state_b[i_feat * time_batches + (time_batches - 1)] = acc_h; + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float16_t *state_b = state_data + (i_batch * feature_batches * time_batches); + float16_t *out_a = buffer_a + i_batch * feature_batches; + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + const float16_t *w_time = weights_time_data + i_feat * time_batches; + const float16_t *s_time = state_b + i_feat * time_batches; + out_a[i_feat] = arm_nn_svdf_dot_f16(w_time, s_time, time_batches); + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float16_t *out_b = buffer_b + i_batch * unit_count; + float16_t *ptr_a = buffer_a + i_batch * feature_batches; + if (bias_data) + { + if (unit_count == feature_batches) + { +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + int32_t j = 0; + for (; j < feature_batches; j += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(feature_batches - j)); + const float16x8_t v_a = vld1q_z(ptr_a + j, p); + const float16x8_t v_b = vld1q_z(bias_data + j, p); + vst1q_p(out_b + j, vaddq(v_a, v_b), p); + } +#else + for (int32_t j = 0; j < feature_batches; j++) + { + out_b[j] = (float16_t)((float32_t)ptr_a[j] + (float32_t)bias_data[j]); + } +#endif + } + else + { + for (int32_t i = 0; i < unit_count; i++) + { + float32_t sum = (float32_t)bias_data[i]; + for (int32_t j = 0; j < rank; j++) + { + sum += (float32_t)(*ptr_a++); + } + out_b[i] = (float16_t)sum; + } + } + } + else + { + for (int32_t i = 0; i < unit_count; i++) + { + float32_t sum = 0.0f; + for (int32_t j = 0; j < rank; j++) + { + sum += (float32_t)(*ptr_a++); + } + out_b[i] = (float16_t)sum; + } + } + } + + const int32_t output_count = input_batches * unit_count; +#if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_act_min = vdupq_n_f16(svdf_params->output_activation.min); + const float16x8_t v_act_max = vdupq_n_f16(svdf_params->output_activation.max); + for (int32_t i = 0; i < output_count; i += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(output_count - i)); + float16x8_t v = vld1q_z(buffer_b + i, p); + v = arm_nn_clamp_mve_f16(v, v_act_min, v_act_max); + vst1q_p(output_data + i, v, p); + } +#else + for (int32_t i = 0; i < output_count; i++) + { + output_data[i] = clamp_and_activate_f16(buffer_b[i], &svdf_params->output_activation); + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of SVDF group + */ diff --git a/Source/SVDFunctions/arm_svdf_f32.c b/Source/SVDFunctions/arm_svdf_f32.c new file mode 100644 index 00000000..812f7e09 --- /dev/null +++ b/Source/SVDFunctions/arm_svdf_f32.c @@ -0,0 +1,234 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_svdf_f32.c + * Description: SVDF layer (float32) + * + * $Date: 23 Feb 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "Internal/arm_nn_activation_flt.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup Public + */ + +/** + * @addtogroup SVDF + * @{ + */ + +__STATIC_INLINE float32_t clamp_and_activate(float32_t x, const cmsis_nn_activation_f32 *act) +{ + return CLAMP(x, act->max, act->min); +} + +__STATIC_INLINE float32_t arm_nn_svdf_dot_f32(const float32_t *lhs, const float32_t *rhs, int32_t count) +{ + float32_t sum = 0.0f; + +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + float32x4_t v_acc = vdupq_n_f32(0.0f); + int32_t i = 0; + for (; i + 4 <= count; i += 4) + { + v_acc = vfmaq(v_acc, vld1q(lhs + i), vld1q(rhs + i)); + } + sum = arm_nn_vec_reduce_add_f32(v_acc); + for (; i < count; ++i) + { + sum += lhs[i] * rhs[i]; + } +#else + for (int32_t i = 0; i < count; ++i) + { + sum += lhs[i] * rhs[i]; + } +#endif + + return sum; +} + +arm_cmsis_nn_status arm_svdf_f32(const cmsis_nn_context *ctx, + const cmsis_nn_context *input_ctx, + const cmsis_nn_context *output_ctx, + const cmsis_nn_svdf_params_f32 *svdf_params, + const cmsis_nn_dims *input_dims, + const float32_t *input_data, + const cmsis_nn_dims *state_dims, + float32_t *state_data, + const cmsis_nn_dims *weights_feature_dims, + const float32_t *weights_feature_data, + const cmsis_nn_dims *weights_time_dims, + const float32_t *weights_time_data, + const cmsis_nn_dims *bias_dims, + const float32_t *bias_data, + const cmsis_nn_dims *output_dims, + float32_t *output_data) +{ + (void)ctx; + (void)state_dims; + (void)bias_dims; + (void)output_dims; + + if (!svdf_params || !input_dims || !weights_feature_dims || !weights_time_dims || !input_data || !state_data || + !weights_feature_data || !weights_time_data || !output_data) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const int32_t rank = svdf_params->rank; + const int32_t input_batches = input_dims->n; + const int32_t input_height = input_dims->h; + const int32_t feature_batches = weights_feature_dims->n; + const int32_t time_batches = weights_time_dims->h; + const int32_t unit_count = feature_batches / rank; + + float32_t *buffer_a = (float32_t *)(input_ctx ? input_ctx->buf : NULL); + float32_t *buffer_b = (float32_t *)(output_ctx ? output_ctx->buf : NULL); + if (!buffer_a || !buffer_b) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float32_t *state_b = state_data + (i_batch * feature_batches * time_batches); + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + float32_t *state_f = state_b + i_feat * time_batches; + if (time_batches > 1) + { + for (int32_t t = 0; t < time_batches - 1; ++t) + { + state_f[t] = state_f[t + 1]; + } + } + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + const float32_t *input = input_data + i_batch * input_height; + float32_t *state_b = state_data + (i_batch * feature_batches * time_batches); + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + const float32_t *w_feat = weights_feature_data + i_feat * input_height; + float32_t acc = arm_nn_svdf_dot_f32(input, w_feat, input_height); + acc = clamp_and_activate(acc, &svdf_params->input_activation); + state_b[i_feat * time_batches + (time_batches - 1)] = acc; + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float32_t *state_b = state_data + (i_batch * feature_batches * time_batches); + float32_t *out_a = buffer_a + i_batch * feature_batches; + for (int32_t i_feat = 0; i_feat < feature_batches; i_feat++) + { + const float32_t *w_time = weights_time_data + i_feat * time_batches; + const float32_t *s_time = state_b + i_feat * time_batches; + out_a[i_feat] = arm_nn_svdf_dot_f32(w_time, s_time, time_batches); + } + } + + for (int32_t i_batch = 0; i_batch < input_batches; i_batch++) + { + float32_t *out_b = buffer_b + i_batch * unit_count; + float32_t *ptr_a = buffer_a + i_batch * feature_batches; + if (bias_data) + { + if (unit_count == feature_batches) + { +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + for (int32_t j = 0; j < feature_batches; j += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(feature_batches - j)); + const float32x4_t v_a = vld1q_z(ptr_a + j, p); + const float32x4_t v_b = vld1q_z(bias_data + j, p); + vst1q_p(out_b + j, vaddq(v_a, v_b), p); + } +#else + for (int32_t j = 0; j < feature_batches; j++) + { + out_b[j] = ptr_a[j] + bias_data[j]; + } +#endif + } + else + { + for (int32_t i = 0; i < unit_count; i++) + { + float32_t sum = bias_data[i]; + for (int32_t j = 0; j < rank; j++) + { + sum += *ptr_a++; + } + out_b[i] = sum; + } + } + } + else + { + for (int32_t i = 0; i < unit_count; i++) + { + float32_t sum = 0.0f; + for (int32_t j = 0; j < rank; j++) + { + sum += *ptr_a++; + } + out_b[i] = sum; + } + } + } + + const int32_t output_count = input_batches * unit_count; +#if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_act_min = vdupq_n_f32(svdf_params->output_activation.min); + const float32x4_t v_act_max = vdupq_n_f32(svdf_params->output_activation.max); + int32_t i = 0; + for (; i < output_count; i += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(output_count - i)); + float32x4_t v = vld1q_z(buffer_b + i, p); + v = arm_nn_clamp_mve_f32(v, v_act_min, v_act_max); + vst1q_p(output_data + i, v, p); + } +#else + for (int32_t i = 0; i < output_count; i++) + { + float32_t v = buffer_b[i]; + v = clamp_and_activate(v, &svdf_params->output_activation); + output_data[i] = v; + } +#endif + + return ARM_CMSIS_NN_SUCCESS; +} + +/** + * @} end of SVDF group + */ diff --git a/Source/SoftmaxFunctions/CMakeLists.txt b/Source/SoftmaxFunctions/CMakeLists.txt index 622b9909..776c589b 100644 --- a/Source/SoftmaxFunctions/CMakeLists.txt +++ b/Source/SoftmaxFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# Copyright (c) 2019-2022 Arm Limited. +# Copyright (c) 2019-2022, 2026 Arm Limited. # # SPDX-License-Identifier: Apache-2.0 # @@ -17,6 +17,12 @@ # file(GLOB SRC "./*_s8.c") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC arm_softmax_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC arm_softmax_f16.c) +endif() target_sources(cmsis-nn PRIVATE ${SRC} arm_softmax_s8_s16.c arm_softmax_s16.c arm_nn_softmax_common_s8.c) diff --git a/Source/SoftmaxFunctions/arm_softmax_f16.c b/Source/SoftmaxFunctions/arm_softmax_f16.c new file mode 100644 index 00000000..f862e034 --- /dev/null +++ b/Source/SoftmaxFunctions/arm_softmax_f16.c @@ -0,0 +1,177 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_softmax_f16.c + * Description: Softmax function (float16) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture with FP16 support + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Softmax + * @{ + */ + +/* Refer header file for details. */ +arm_cmsis_nn_status arm_softmax_f16(const float16_t *input, int32_t rows, int32_t cols, float16_t *output) +{ + if (input == NULL || output == NULL || rows < 1 || cols < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + #ifndef NN_DISABLE_SPECIALIZATION + if (rows == 1 && cols == 2) + { + arm_nn_softmax_1x2_f16(input, output); + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (int32_t r = 0; r < rows; ++r) + { + const float16_t *in_row = input + r * cols; + float16_t *out_row = output + r * cols; + + /* Subtracting row max avoids overflow in exp and preserves relative probabilities. */ + float16_t max_val; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + { + const _Float16 f16_finite_max = (_Float16)ARM_NN_F16_FINITE_MAX; + const _Float16 f16_finite_lowest = -f16_finite_max; + const float16x8_t v_low = vdupq_n_f16((float16_t)f16_finite_lowest); + float16x8_t v_max = v_low; + int32_t c = 0; + for (; c < cols; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(cols - c)); + float16x8_t vin = vld1q_z(in_row + c, p); + vin = vpselq(vin, v_low, p); + v_max = vmaxnmq(v_max, vin); + } + max_val = vmaxnmvq_f16((float16_t)f16_finite_lowest, v_max); + } + #else + _Float16 max_val_scalar = (_Float16)in_row[0]; + for (int32_t c = 1; c < cols; ++c) + { + const _Float16 in_val = (_Float16)in_row[c]; + if (in_val > max_val_scalar) + { + max_val_scalar = in_val; + } + } + max_val = (float16_t)max_val_scalar; + #endif + + /* Compute exp(x - max) and sum */ + float32_t sum = 0.0f; + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_max = vdupq_n_f16(max_val); + const float16x8_t v_zero = vdupq_n_f16((float16_t)0.0f); + int32_t c = 0; + for (; c < cols; c += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(cols - c)); + const float16x8_t x = vsubq(vld1q_z(in_row + c, p), v_max); + float16x8_t e = arm_nn_vexpq_poly_mve_f16(x); + e = vpselq(e, v_zero, p); + vst1q_p(out_row + c, e, p); + sum += (float32_t)arm_nn_vec_reduce_add_f16(e); + } + #else + for (int32_t c = 0; c < cols; ++c) + { + const _Float16 x = (_Float16)in_row[c]; + const float16_t e = arm_nn_softmax_exp_scalar_f16((float16_t)(x - (_Float16)max_val)); + out_row[c] = e; + sum += (float32_t)e; + } + #endif + + /* + * For any finite row, subtracting the row max guarantees at least one + * exponent term is exp(0) = 1, so the accumulated sum should stay >= 1. + * Treat non-positive sums as invalid input rather than silently + * normalizing a degenerate row. + */ + if (sum <= 0.0f) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + /* Normalize */ + const _Float16 inv_sum = (_Float16)(1.0f / sum); + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + const float16x8_t v_inv = vdupq_n_f16((float16_t)inv_sum); + int32_t c_norm = 0; + for (; c_norm < cols; c_norm += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(cols - c_norm)); + const float16x8_t out = vmulq(vld1q_z(out_row + c_norm, p), v_inv); + vst1q_p(out_row + c_norm, out, p); + } + #else + for (int32_t c = 0; c < cols; ++c) + { + out_row[c] = (float16_t)((_Float16)out_row[c] * inv_sum); + } + #endif + } + + return ARM_CMSIS_NN_SUCCESS; +} + +void arm_nn_softmax_1x2_f16(const float16_t in[2], float16_t out[2]) +{ + _Float16 e0; + _Float16 e1; + if ((_Float16)in[0] >= (_Float16)in[1]) + { + e0 = (_Float16)1; + e1 = arm_nn_softmax_exp_scalar_f16((float16_t)((_Float16)in[1] - (_Float16)in[0])); + } + else + { + e1 = (_Float16)1; + e0 = arm_nn_softmax_exp_scalar_f16((float16_t)((_Float16)in[0] - (_Float16)in[1])); + } + const _Float16 sum = e0 + e1; + out[0] = (float16_t)(e0 / sum); + out[1] = (float16_t)(e1 / sum); +} + +/** + * @} end of Softmax group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/SoftmaxFunctions/arm_softmax_f32.c b/Source/SoftmaxFunctions/arm_softmax_f32.c new file mode 100644 index 00000000..5a3f3b43 --- /dev/null +++ b/Source/SoftmaxFunctions/arm_softmax_f32.c @@ -0,0 +1,169 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_softmax_f32.c + * Description: Softmax function (float32) + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Softmax + * @{ + */ +/* Refer header file for details. */ +arm_cmsis_nn_status +arm_softmax_f32(const float32_t *input, const int32_t num_rows, const int32_t row_size, float32_t *output) +{ + if (input == NULL || output == NULL || num_rows < 1 || row_size < 1) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + #ifndef NN_DISABLE_SPECIALIZATION + if (num_rows == 1 && row_size == 2) + { + arm_nn_softmax_1x2_f32(input, output); + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (int32_t row = 0; row < num_rows; ++row) + { + float32_t max_val = input[0]; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + { + float32x4_t v_max = vdupq_n_f32(ARM_NN_F32_FINITE_LOWEST); + const float32x4_t v_low = vdupq_n_f32(ARM_NN_F32_FINITE_LOWEST); + int32_t col_max = 0; + for (; col_max < row_size; col_max += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(row_size - col_max)); + float32x4_t vin = vld1q_z(input + col_max, p); + vin = vpselq(vin, v_low, p); + v_max = vmaxnmq(v_max, vin); + } + max_val = vmaxnmvq_f32(ARM_NN_F32_FINITE_LOWEST, v_max); + } + #else + for (int32_t col = 1; col < row_size; ++col) + { + if (input[col] > max_val) + { + max_val = input[col]; + } + } + #endif + + float32_t sum = 0.0f; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_max = vdupq_n_f32(max_val); + const float32x4_t v_zero = vdupq_n_f32(0.0f); + int32_t col_exp = 0; + for (; col_exp < row_size; col_exp += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(row_size - col_exp)); + const float32x4_t x = vsubq(vld1q_z(input + col_exp, p), v_max); + float32x4_t e = arm_nn_vexpq_poly_mve_f32(x); + e = vpselq(e, v_zero, p); + vst1q_p(output + col_exp, e, p); + sum += arm_nn_vec_reduce_add_f32(e); + } + #else + for (int32_t col = 0; col < row_size; ++col) + { + float32_t e = arm_nn_softmax_exp_scalar_f32(input[col] - max_val); + output[col] = e; + sum += e; + } + #endif + + /* + * For any finite row, subtracting the row max guarantees at least one + * exponent term is exp(0) = 1, so the accumulated sum should stay >= 1. + * Treat non-positive sums as invalid input rather than silently + * normalizing a degenerate row. + */ + if (sum <= 0.0f) + { + return ARM_CMSIS_NN_ARG_ERROR; + } + + const float32_t inv_sum = 1.0f / sum; + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + const float32x4_t v_inv = vdupq_n_f32(inv_sum); + int32_t col_norm = 0; + for (; col_norm < row_size; col_norm += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(row_size - col_norm)); + const float32x4_t out = vmulq(vld1q_z(output + col_norm, p), v_inv); + vst1q_p(output + col_norm, out, p); + } + #else + for (int32_t col = 0; col < row_size; ++col) + { + output[col] *= inv_sum; + } + #endif + + input += row_size; + output += row_size; + } + + return ARM_CMSIS_NN_SUCCESS; +} + +void arm_nn_softmax_1x2_f32(const float32_t in[2], float32_t out[2]) +{ + float32_t e0; + float32_t e1; + if (in[0] >= in[1]) + { + e0 = 1.0f; + e1 = arm_nn_softmax_exp_scalar_f32(in[1] - in[0]); + } + else + { + e1 = 1.0f; + e0 = arm_nn_softmax_exp_scalar_f32(in[0] - in[1]); + } + const float32_t sum = e0 + e1; + out[0] = e0 / sum; + out[1] = e1 / sum; +} + +/** + * @} end of Softmax group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Source/TransposeFunctions/CMakeLists.txt b/Source/TransposeFunctions/CMakeLists.txt index d66d66dc..8c5e11f0 100644 --- a/Source/TransposeFunctions/CMakeLists.txt +++ b/Source/TransposeFunctions/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright 2019-2024 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2019-2024, 2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -17,4 +17,11 @@ # file(GLOB SRC "./*_s8*.c") +if (ARM_NN_ENABLE_F32) + list(APPEND SRC arm_transpose_f32.c) +endif() +if (ARM_NN_ENABLE_F16) + list(APPEND SRC arm_transpose_f16.c) +endif() + target_sources(cmsis-nn PRIVATE ${SRC}) diff --git a/Source/TransposeFunctions/arm_transpose_f16.c b/Source/TransposeFunctions/arm_transpose_f16.c new file mode 100644 index 00000000..d77cbff5 --- /dev/null +++ b/Source/TransposeFunctions/arm_transpose_f16.c @@ -0,0 +1,95 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_transpose_f16.c + * Description: Transpose a float16 tensor + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_transpose_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F16 +/** + * @ingroup Public + */ + +/** + * @addtogroup Transpose + * @{ + */ + +static arm_cmsis_nn_status +arm_transpose_2d_kernel_f16(const float16_t *input, float16_t *output, int32_t rows, int32_t cols) +{ + #if defined(ARM_MATH_MVE_FLOAT16) && !defined(ARM_MATH_AUTOVECTORIZE) + if (rows > 0 && cols > 0 && (size_t)rows * (size_t)cols >= 128U && + (size_t)cols <= ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8) + { + const uint16_t cols_u16 = (uint16_t)cols; + const uint16x8_t row_offsets = vmulq(vidupq_u16((uint32_t)0, 1), cols_u16); + + for (int32_t c = 0; c < cols; ++c) + { + const float16_t *input_col = input + c; + float16_t *output_col = output + (size_t)c * (size_t)rows; + + for (int32_t r = 0; r < rows; r += 8) + { + const mve_pred16_t p = vctp16q((uint32_t)(rows - r)); + const float16x8_t vin = vldrhq_gather_shifted_offset_z(input_col, row_offsets, p); + vst1q_p(output_col, vin, p); + input_col += (size_t)8U * (size_t)cols; + output_col += 8; + } + } + + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (int32_t r = 0; r < rows; ++r) + { + const float16_t *in_row = input + (size_t)r * (size_t)cols; + for (int32_t c = 0; c < cols; ++c) + { + output[(size_t)c * (size_t)rows + (size_t)r] = in_row[c]; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +ARM_TRANSPOSE_DEFINE(arm_transpose_f16, + float16_t, + cmsis_nn_transpose_params_f16, + arm_nn_tensor_layout, + arm_transpose_2d_kernel_f16) + +/** + * @} end of Transpose group + */ + +#endif /* ARM_NN_ENABLE_F16 */ diff --git a/Source/TransposeFunctions/arm_transpose_f32.c b/Source/TransposeFunctions/arm_transpose_f32.c new file mode 100644 index 00000000..cfb60e51 --- /dev/null +++ b/Source/TransposeFunctions/arm_transpose_f32.c @@ -0,0 +1,95 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_transpose_f32.c + * Description: Transpose a float32 tensor + * + * $Date: 19 March 2026 + * $Revision: V.1.0.0 + * + * Target : Arm(R) M-Profile Architecture + * -------------------------------------------------------------------- */ + +#include "Internal/arm_transpose_common.h" +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +#if ARM_NN_ENABLE_F32 + +/** + * @ingroup Public + */ + +/** + * @addtogroup Transpose + * @{ + */ + +static arm_cmsis_nn_status +arm_transpose_2d_kernel_f32(const float32_t *input, float32_t *output, int32_t rows, int32_t cols) +{ + #if defined(ARM_MATH_MVEF) && !defined(ARM_MATH_AUTOVECTORIZE) + if (rows > 0 && cols > 0 && (size_t)rows * (size_t)cols >= 128U && (size_t)cols <= ((size_t)UINT32_MAX / 3U)) + { + const uint32_t cols_u32 = (uint32_t)cols; + const uint32x4_t row_offsets = vmulq(vidupq_u32((uint32_t)0, 1), cols_u32); + + for (int32_t c = 0; c < cols; ++c) + { + const float32_t *input_col = input + c; + float32_t *output_col = output + (size_t)c * (size_t)rows; + + for (int32_t r = 0; r < rows; r += 4) + { + const mve_pred16_t p = vctp32q((uint32_t)(rows - r)); + const float32x4_t vin = vldrwq_gather_shifted_offset_z(input_col, row_offsets, p); + vst1q_p(output_col, vin, p); + input_col += (size_t)4U * (size_t)cols; + output_col += 4; + } + } + + return ARM_CMSIS_NN_SUCCESS; + } + #endif + + for (int32_t r = 0; r < rows; ++r) + { + const float32_t *in_row = input + (size_t)r * (size_t)cols; + for (int32_t c = 0; c < cols; ++c) + { + output[(size_t)c * (size_t)rows + (size_t)r] = in_row[c]; + } + } + + return ARM_CMSIS_NN_SUCCESS; +} + +ARM_TRANSPOSE_DEFINE(arm_transpose_f32, + float32_t, + cmsis_nn_transpose_params_f32, + arm_nn_tensor_layout, + arm_transpose_2d_kernel_f32) + +/** + * @} end of Transpose group + */ + +#endif /* ARM_NN_ENABLE_F32 */ diff --git a/Tests/UnitTest/CMakeLists.txt b/Tests/UnitTest/CMakeLists.txt index 42b68340..53b8bdfb 100644 --- a/Tests/UnitTest/CMakeLists.txt +++ b/Tests/UnitTest/CMakeLists.txt @@ -51,11 +51,28 @@ if(BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300) set(FVP_CORSTONE_300_PATH "${CMAKE_CURRENT_SOURCE_DIR}/Corstone-300" CACHE PATH "Dependencies for using FVP based on Arm Corstone-300 software.") set(CMAKE_EXECUTABLE_SUFFIX ".elf") +elseif(CMAKE_C_COMPILER_ID STREQUAL "Clang") + # The host Clang regression build treats -Ofast as deprecated and Unity + # relies on isnan/isinf checks, so use a conforming optimization level. + set(CMSIS_OPTIMIZATION_LEVEL "-O3" CACHE STRING "Compiler optimization level." FORCE) endif() # Build the functions to be tested. add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../.. cmsis-nn) -add_compile_options(${CMSIS_OPTIMIZATION_LEVEL}) +if(NOT BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300 AND CMAKE_C_COMPILER_ID STREQUAL "Clang") + target_compile_options(cmsis-nn PRIVATE -Wno-error=unknown-attributes) +endif() +if(BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300) + add_compile_options(${CMSIS_OPTIMIZATION_LEVEL}) +elseif(CMAKE_C_COMPILER_ID STREQUAL "Clang") + # Unity checks NaN/Inf explicitly in host regressions, so keep finite-math + # assumptions disabled for Clang instead of inheriting the library's -Ofast. + add_compile_options(-O3 + -fno-finite-math-only + -Wno-error=unknown-attributes) +else() + add_compile_options(${CMSIS_OPTIMIZATION_LEVEL}) +endif() # Target for all unit tests. add_custom_target(cmsis_nn_unit_tests) @@ -67,7 +84,7 @@ function(add_cmsis_nn_unit_test_executable) foreach(target ${ARGV}) set(tmp "${tmp} ${target}") add_executable(${target}) - if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + if (BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300 AND CMAKE_CXX_COMPILER_ID STREQUAL "GNU") target_link_options(${target} PRIVATE "--specs=nosys.specs") endif() add_dependencies(cmsis_nn_unit_tests ${target}) @@ -117,7 +134,54 @@ add_subdirectory(TestCases/test_arm_svdf_state_s16_s8) add_subdirectory(TestCases/test_arm_transpose_conv_s8) add_subdirectory(TestCases/test_arm_transpose_s8) -set(MAKE_CMD "python3") +if(ARM_NN_ENABLE_F32) + add_subdirectory(TestCases/test_arm_activation_f32) + add_subdirectory(TestCases/test_arm_avg_pool_f32) + add_subdirectory(TestCases/test_arm_batch_matmul_f32) + add_subdirectory(TestCases/test_arm_batch_norm_f32) + add_subdirectory(TestCases/test_arm_concatenation_f32) + add_subdirectory(TestCases/test_arm_ds_cnn_s_body_f32) + add_subdirectory(TestCases/test_arm_convolve_f32) + add_subdirectory(TestCases/test_arm_depthwise_conv_f32) + add_subdirectory(TestCases/test_arm_elementwise_add_f32) + add_subdirectory(TestCases/test_arm_elementwise_mul_f32) + add_subdirectory(TestCases/test_arm_fully_connected_f32) + add_subdirectory(TestCases/test_arm_lstm_unidirectional_f32) + add_subdirectory(TestCases/test_arm_max_pool_f32) + add_subdirectory(TestCases/test_arm_maximum_minimum_f32) + add_subdirectory(TestCases/test_arm_pad_f32) + add_subdirectory(TestCases/test_arm_reshape_f32) + add_subdirectory(TestCases/test_arm_softmax_f32) + add_subdirectory(TestCases/test_arm_svdf_f32) + add_subdirectory(TestCases/test_arm_transpose_f32) + add_subdirectory(TestCases/test_arm_transpose_conv_f32) +endif() + +if(ARM_NN_ENABLE_F16) + add_subdirectory(TestCases/test_arm_activation_f16) + add_subdirectory(TestCases/test_arm_avg_pool_f16) + add_subdirectory(TestCases/test_arm_batch_matmul_f16) + add_subdirectory(TestCases/test_arm_batch_norm_f16) + add_subdirectory(TestCases/test_arm_concatenation_f16) + add_subdirectory(TestCases/test_arm_ds_cnn_s_body_f16) + add_subdirectory(TestCases/test_arm_convolve_f16) + add_subdirectory(TestCases/test_arm_depthwise_conv_f16) + add_subdirectory(TestCases/test_arm_elementwise_add_f16) + add_subdirectory(TestCases/test_arm_elementwise_mul_f16) + add_subdirectory(TestCases/test_arm_fully_connected_f16) + add_subdirectory(TestCases/test_arm_lstm_unidirectional_f16) + add_subdirectory(TestCases/test_arm_max_pool_f16) + add_subdirectory(TestCases/test_arm_maximum_minimum_f16) + add_subdirectory(TestCases/test_arm_pad_f16) + add_subdirectory(TestCases/test_arm_reshape_f16) + add_subdirectory(TestCases/test_arm_softmax_f16) + add_subdirectory(TestCases/test_arm_svdf_f16) + add_subdirectory(TestCases/test_arm_transpose_f16) + add_subdirectory(TestCases/test_arm_transpose_conv_f16) +endif() + +find_package(Python3 COMPONENTS Interpreter REQUIRED) +set(MAKE_CMD "${Python3_EXECUTABLE}") set(MAKE_CMD_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/unittest_targets.py") set(MAKE_CMD_SCRIPT_OPTION "--download-and-generate-test-runners") MESSAGE(STATUS "Downloading Unity and generating test runners for CMSIS-NN unit tests if needed..") @@ -131,6 +195,9 @@ string(REPLACE " " ";" cmsis_nn_unit_test_list_of_executables ${executables}) foreach(target ${cmsis_nn_unit_test_list_of_executables}) target_link_libraries(${target} LINK_PUBLIC unity) target_link_libraries(${target} LINK_PUBLIC cmsis-nn) + if(NOT BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300) + target_link_libraries(${target} PRIVATE m) + endif() endforeach() if(BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300) @@ -179,7 +246,6 @@ if(BUILD_CMSIS_NN_UNIT_TESTS_FOR_FVP_BASED_CORSTONE_300) foreach(target ${cmsis_nn_unit_test_list_of_executables}) target_link_libraries(${target} PRIVATE retarget) target_link_libraries(${target} PRIVATE $ cmsis_startup) - add_dependencies(${target} retarget cmsis_startup) target_compile_definitions(${target} PUBLIC USING_FVP_CORSTONE_300) diff --git a/Tests/UnitTest/Corstone-300/linker_clang.sct b/Tests/UnitTest/Corstone-300/linker_clang.sct new file mode 100644 index 00000000..58426e43 --- /dev/null +++ b/Tests/UnitTest/Corstone-300/linker_clang.sct @@ -0,0 +1,260 @@ +/* + * SPDX-FileCopyrightText: Copyright 2019 Keith Packard + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: BSD-3-Clause + */ + +#if defined (__ARM_FEATURE_CMSE) && (__ARM_FEATURE_CMSE == 3U) +#define __STACKSEAL_SIZE ( 8 ) +#else +#define __STACKSEAL_SIZE ( 0 ) +#endif + +#define ITCM_BASE S_CODE_START +#define ITCM_SIZE S_CODE_SIZE +#define DTCM_BASE S_DATA_START +#define DTCM_SIZE S_DATA_SIZE +#define ISRAM_BASE S_ISRAM_START +#define ISRAM_SIZE S_ISRAM_SIZE +#define DDR_BASE S_DDR4_START +#define DDR_SIZE S_DDR4_SIZE + +MEMORY +{ + ITCM (rx!w) : ORIGIN = ITCM_BASE, LENGTH = ITCM_SIZE + DTCM (w!rx) : ORIGIN = DTCM_BASE, LENGTH = DTCM_SIZE + ISRAM (w!rx) : ORIGIN = ISRAM_BASE, LENGTH = ISRAM_SIZE + DDR (w!rx) : ORIGIN = DDR_BASE, LENGTH = DDR_SIZE +} + +ENTRY(Reset_Handler) + +PHDRS +{ + text PT_LOAD; + ram PT_LOAD; + ram_init PT_LOAD; + isram PT_LOAD; + ddr PT_LOAD; + tls PT_TLS; +} + +SECTIONS +{ + .init : { + KEEP (*(.vectors)) + KEEP (*(.text.init.enter)) + KEEP (*(.data.init.enter)) + KEEP (*(SORT_BY_NAME(.init) SORT_BY_NAME(.init.*))) + } >ITCM AT>ITCM :text + + .text : { + *(.text.unlikely .text.unlikely.*) + *(.text.startup .text.startup.*) + *(.text .text.* .opd .opd.*) + *(.gnu.linkonce.t.*) + KEEP (*(.fini .fini.*)) + __text_end = .; + + PROVIDE (__etext = __text_end); + PROVIDE (_etext = __text_end); + PROVIDE (etext = __text_end); + + *(.rdata) + *(.gnu.linkonce.r.*) + *(.srodata.cst16) + *(.srodata.cst8) + *(.srodata.cst4) + *(.srodata.cst2) + *(.srodata .srodata.*) + *(.data.rel.ro .data.rel.ro.*) + *(.got .got.*) + + . = ALIGN(8); + + PROVIDE_HIDDEN ( __preinit_array_start = . ); + KEEP (*(.preinit_array)) + PROVIDE_HIDDEN ( __preinit_array_end = . ); + + PROVIDE_HIDDEN ( __init_array_start = . ); + KEEP (*(SORT_BY_INIT_PRIORITY(.init_array.*) SORT_BY_INIT_PRIORITY(.ctors.*))) + KEEP (*(.init_array .ctors)) + PROVIDE_HIDDEN ( __init_array_end = . ); + + PROVIDE_HIDDEN ( __fini_array_start = . ); + KEEP (*(SORT_BY_INIT_PRIORITY(.fini_array.*) SORT_BY_INIT_PRIORITY(.dtors.*))) + KEEP (*(.fini_array .dtors)) + PROVIDE_HIDDEN ( __fini_array_end = . ); + } >ITCM AT>ITCM :text + +#if defined (__ARM_FEATURE_CMSE) && (__ARM_FEATURE_CMSE == 3U) + .veneers : + { + . = ALIGN(32); + KEEP(*(.gnu.sgstubs)) + } > ITCM AT>ITCM :text +#endif + + .toc : { + *(.toc .toc.*) + } >ITCM AT>ITCM :text + + .except_ordered : { + *(.gcc_except_table *.gcc_except_table.*) + KEEP (*(.eh_frame .eh_frame.*)) + *(.ARM.extab* .gnu.linkonce.armextab.*) + } >ITCM AT>ITCM :text + + .except_unordered : { + . = ALIGN(8); + PROVIDE(__exidx_start = .); + *(.ARM.exidx*) + PROVIDE(__exidx_end = .); + } >ITCM AT>ITCM :text + + .preserve (NOLOAD) : { + PROVIDE(__preserve_start__ = .); + KEEP(*(SORT_BY_NAME(.preserve.*))) + KEEP(*(.preserve)) + PROVIDE(__preserve_end__ = .); + } >DTCM AT>DTCM :ram + + .isram (NOLOAD): { + *(.bss.NoInit.ethos_u_cache) + *(.bss.NoInit.activation_buf_sram) + } > ISRAM : isram + + .data : { + *(.data .data.*) + *(.gnu.linkonce.d.*) + *(.rodata .rodata.*) + + . = ALIGN(8); + PROVIDE(__global_pointer$ = . + 0x800); + *(.sdata .sdata.* .sdata2.*) + *(.gnu.linkonce.s.*) + } >DTCM AT>ITCM :ram_init + PROVIDE(__data_start = ADDR(.data)); + PROVIDE(__data_source = LOADADDR(.data)); + + .tdata : { + *(.tdata .tdata.* .gnu.linkonce.td.*) + PROVIDE(__data_end = .); + PROVIDE(__tdata_end = .); + } >DTCM AT>ITCM :tls :ram_init + PROVIDE(__tls_base = ADDR(.tdata)); + PROVIDE(__tdata_start = ADDR(.tdata)); + PROVIDE(__tdata_source = LOADADDR(.tdata)); + PROVIDE(__tdata_source_end = LOADADDR(.tdata) + SIZEOF(.tdata)); + PROVIDE(__data_source_end = __tdata_source_end); + PROVIDE(__tdata_size = SIZEOF(.tdata)); + PROVIDE(__tls_align = MAX(ALIGNOF(.tdata), ALIGNOF(.tbss))); + + PROVIDE(__edata = __data_end); + PROVIDE(_edata = __data_end); + PROVIDE(edata = __data_end); + PROVIDE(__data_size = __data_end - __data_start); + PROVIDE(__data_source_size = __data_source_end - __data_source); + + .tbss (NOLOAD) : { + *(.tbss .tbss.* .gnu.linkonce.tb.*) + *(.tcommon) + PROVIDE(__tls_end = .); + PROVIDE(__tbss_end = .); + } >DTCM AT>DTCM :tls :ram + PROVIDE(__bss_start = ADDR(.tbss)); + PROVIDE(__tbss_start = ADDR(.tbss)); + PROVIDE(__tbss_offset = ADDR(.tbss) - ADDR(.tdata)); + PROVIDE(__tbss_size = SIZEOF(.tbss)); + PROVIDE(__tls_size = __tls_end - __tls_base); + PROVIDE(__tls_align = MAX(ALIGNOF(.tdata), ALIGNOF(.tbss))); + PROVIDE(__arm32_tls_tcb_offset = MAX(8, __tls_align)); + PROVIDE(__arm64_tls_tcb_offset = MAX(16, __tls_align)); + + .bss (NOLOAD) : { + *(.sbss*) + *(.gnu.linkonce.sb.*) + *(.bss .bss.*) + *(.gnu.linkonce.b.*) + *(COMMON) + . = ALIGN(8); + __bss_end = .; + } >DTCM AT>DTCM :ram + PROVIDE(__non_tls_bss_start = ADDR(.bss)); + PROVIDE(__end = __bss_end); + PROVIDE(_end = __bss_end); + PROVIDE(end = __bss_end); + PROVIDE(__bss_size = __bss_end - __bss_start); + + PROVIDE(__heap_start = __end); +#ifdef HEAP_SIZE + PROVIDE(__heap_end = __heap_start + HEAP_SIZE); +#else + PROVIDE(__heap_end = __stack - STACK_SIZE); + PROVIDE(HEAP_SIZE = __heap_end - __heap_start); +#endif + .heap (NOLOAD) : { + . += HEAP_SIZE; + } >DTCM :ram + + PROVIDE(__stack = ORIGIN(DTCM) + LENGTH(DTCM) - __STACKSEAL_SIZE); + PROVIDE(__stack_limit = __stack - STACK_SIZE); + .stack (__stack_limit) (NOLOAD) : { + . += STACK_SIZE; + } >DTCM :ram + +#if __STACKSEAL_SIZE > 0 + PROVIDE(__stack_seal = __stack); + .stackseal (__stack) (NOLOAD) : { + . += __STACKSEAL_SIZE; + } >DTCM :ram +#endif + + .ddr : { + *(ifm) + *(nn_model) + *(labels) + *(.bss.NoInit.activation_buf_dram) + } > DDR : ddr + + .stab 0 : { *(.stab) } + .stabstr 0 : { *(.stabstr) } + .stab.excl 0 : { *(.stab.excl) } + .stab.exclstr 0 : { *(.stab.exclstr) } + .stab.index 0 : { *(.stab.index) } + .stab.indexstr 0 : { *(.stab.indexstr) } + .comment 0 : { *(.comment) } + .gnu.build.attributes : { *(.gnu.build.attributes .gnu.build.attributes.*) } + .debug 0 : { *(.debug) } + .line 0 : { *(.line) } + .debug_srcinfo 0 : { *(.debug_srcinfo) } + .debug_sfnames 0 : { *(.debug_sfnames) } + .debug_aranges 0 : { *(.debug_aranges) } + .debug_pubnames 0 : { *(.debug_pubnames) } + .debug_info 0 : { *(.debug_info .gnu.linkonce.wi.*) } + .debug_abbrev 0 : { *(.debug_abbrev) } + .debug_line 0 : { *(.debug_line .debug_line.* .debug_line_end) } + .debug_frame 0 : { *(.debug_frame) } + .debug_str 0 : { *(.debug_str) } + .debug_loc 0 : { *(.debug_loc) } + .debug_macinfo 0 : { *(.debug_macinfo) } + .debug_weaknames 0 : { *(.debug_weaknames) } + .debug_funcnames 0 : { *(.debug_funcnames) } + .debug_typenames 0 : { *(.debug_typenames) } + .debug_varnames 0 : { *(.debug_varnames) } + .debug_pubtypes 0 : { *(.debug_pubtypes) } + .debug_ranges 0 : { *(.debug_ranges) } + .debug_addr 0 : { *(.debug_addr) } + .debug_line_str 0 : { *(.debug_line_str) } + .debug_loclists 0 : { *(.debug_loclists) } + .debug_macro 0 : { *(.debug_macro) } + .debug_names 0 : { *(.debug_names) } + .debug_rnglists 0 : { *(.debug_rnglists) } + .debug_str_offsets 0 : { *(.debug_str_offsets) } + .debug_sup 0 : { *(.debug_sup) } + .gnu.attributes 0 : { KEEP (*(.gnu.attributes)) } +} + +ASSERT(__data_size == __data_source_size, + "ERROR: .data/.tdata flash size does not match RAM size"); diff --git a/Tests/UnitTest/Corstone-300/region_defs.h b/Tests/UnitTest/Corstone-300/region_defs.h new file mode 100644 index 00000000..076e5276 --- /dev/null +++ b/Tests/UnitTest/Corstone-300/region_defs.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2026 Arm Limited. All rights reserved. + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TEST_UNITTEST_CORSTONE300_REGION_DEFS_H +#define TEST_UNITTEST_CORSTONE300_REGION_DEFS_H + +#include "region_limits.h" + +/* + * This header is parsed both by the compiler and by the linker script + * preprocessor. Keep expressions simple and avoid C-only syntax. + */ + +#define S_CODE_START (S_ROM_ALIAS) +#define S_CODE_SIZE (TOTAL_S_ROM_SIZE) +#define S_CODE_LIMIT (S_CODE_START + S_CODE_SIZE) + +#define S_DATA_START (S_RAM_ALIAS) +#define S_DATA_SIZE (TOTAL_S_RAM_SIZE) +#define S_DATA_LIMIT (S_DATA_START + S_DATA_SIZE) + +#define S_DDR4_START (S_DDR4_ALIAS) +#define S_DDR4_SIZE (TOTAL_S_DDR4_SIZE) +#define S_DDR4_LIMIT (S_DDR4_START + S_DDR4_SIZE) + +#define S_ISRAM_START (S_ISRAM_ALIAS) +#define S_ISRAM_SIZE (TOTAL_S_ISRAM_SIZE) +#define S_ISRAM_LIMIT (S_ISRAM_START + S_ISRAM_SIZE) + +#define __ROM0_BASE (S_CODE_START) +#define __ROM0_SIZE (S_CODE_SIZE) +#define __RAM0_BASE (S_DATA_START) +#define __RAM0_SIZE (S_DATA_SIZE) + +#ifndef HEAP_SIZE + #define HEAP_SIZE (0x00010000) +#endif + +#ifndef STACK_SIZE + #define STACK_SIZE (0x00008000) +#endif + +#define __HEAP_SIZE (HEAP_SIZE) +#define __STACK_SIZE (STACK_SIZE) + +#endif diff --git a/Tests/UnitTest/Corstone-300/region_limits.h b/Tests/UnitTest/Corstone-300/region_limits.h new file mode 100644 index 00000000..53c79992 --- /dev/null +++ b/Tests/UnitTest/Corstone-300/region_limits.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2026 Arm Limited. All rights reserved. + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TEST_UNITTEST_CORSTONE300_REGION_LIMITS_H +#define TEST_UNITTEST_CORSTONE300_REGION_LIMITS_H + +/* Secure Code */ +#define S_ROM_ALIAS (0x10000000) +#define TOTAL_S_ROM_SIZE (0x00080000) + +/* Secure Data */ +#define S_RAM_ALIAS (0x30000000) +#define TOTAL_S_RAM_SIZE (0x00080000) + +/* Secure DDR */ +#define S_DDR4_ALIAS (0x70000000) +#define TOTAL_S_DDR4_SIZE (0x02000000) + +/* Secure SRAM */ +#define S_ISRAM_ALIAS (0x31000000) +#define TOTAL_S_ISRAM_SIZE (0x00200000) + +#endif diff --git a/Tests/UnitTest/Corstone-300/retarget.c b/Tests/UnitTest/Corstone-300/retarget.c index ce0ec8f4..a03ccc26 100644 --- a/Tests/UnitTest/Corstone-300/retarget.c +++ b/Tests/UnitTest/Corstone-300/retarget.c @@ -1,5 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright 2010-2021, 2024 Arm Limited and/or its affiliates + * SPDX-FileCopyrightText: Copyright 2010-2021, 2024, 2026 Arm Limited and/or its affiliates + * * * SPDX-License-Identifier: Apache-2.0 * @@ -29,6 +30,18 @@ #include #endif +#if defined(__has_include) + #if __has_include("RTE_Components.h") + #include "RTE_Components.h" + #endif +#endif + +#if defined(RTE_CMSIS_Compiler_STDOUT) || defined(RTE_CMSIS_Compiler_STDERR) || defined(RTE_CMSIS_Compiler_STDIN) + #define CMSIS_COMPILER_CUSTOM_STDIO 1 +#else + #define CMSIS_COMPILER_CUSTOM_STDIO 0 +#endif + #include "uart.h" unsigned char UartPutc(unsigned char ch) { return uart_putc(ch); } @@ -52,6 +65,24 @@ void exit(int code) } } +#if CMSIS_COMPILER_CUSTOM_STDIO && defined(__clang__) +__attribute__((noreturn)) void _exit(int code) +{ + UartEndSimulation(code); + while (1) + { + } +} + +__attribute__((noreturn)) void _sys_exit(int code) +{ + UartEndSimulation(code); + while (1) + { + } +} +#endif + #if defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6100100) && !defined(GCCCOMPILER) int fputc(int ch, FILE *f) { @@ -79,14 +110,17 @@ int SER_GetChar(void) { return UartPutc(UartGetc()); } #define FH_STDOUT 0x8002 #define FH_STDERR 0x8003 + #if !CMSIS_COMPILER_CUSTOM_STDIO const char __stdin_name[] = ":STDIN"; const char __stdout_name[] = ":STDOUT"; const char __stderr_name[] = ":STDERR"; + #endif /** The following _sys_xxx functions are defined in rt_sys.h. */ + #if !CMSIS_COMPILER_CUSTOM_STDIO __attribute__((weak)) FILEHANDLE _sys_open(const char *name, int openmode) { (void)openmode; @@ -227,12 +261,14 @@ __attribute__((weak)) char *(_sys_command_string)(char *cmd, int len) } __attribute__((weak)) void(_sys_exit)(int return_code) { exit(return_code); } + #endif #else /** Copied from CMSIS/DSP/DSP_Lib_TestSuite/Common/platform/GCC/Retarget.c */ + #if !CMSIS_COMPILER_CUSTOM_STDIO int _open(const char *path, int flags, ...) { (void)path; @@ -294,4 +330,5 @@ int _write(int fd, char *ptr, int len) SER_PutChar(*ptr++); return (i); } + #endif #endif diff --git a/Tests/UnitTest/Corstone-300/stdout_uart.c b/Tests/UnitTest/Corstone-300/stdout_uart.c new file mode 100644 index 00000000..b28289ee --- /dev/null +++ b/Tests/UnitTest/Corstone-300/stdout_uart.c @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2026 Arm Limited. All rights reserved. + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "uart.h" + +static int stdout_initialized; + +/* + * Bring up the UART on first use so stdio output works even if no explicit + * board-level stdout init was performed before the test starts. + */ +static int stdout_ensure_initialized(void) +{ + if (!stdout_initialized) + { + uart_init(); + stdout_initialized = 1; + } + + return 0; +} + +int stdout_putchar(int ch) +{ + if (stdout_ensure_initialized() != 0) + { + return -1; + } + + return uart_putc((unsigned char)ch); +} + +int stderr_putchar(int ch) { return stdout_putchar(ch); } diff --git a/Tests/UnitTest/Corstone-300/uart.c b/Tests/UnitTest/Corstone-300/uart.c index 4302a6e8..5ff686fb 100644 --- a/Tests/UnitTest/Corstone-300/uart.c +++ b/Tests/UnitTest/Corstone-300/uart.c @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. All rights reserved. + * Copyright (c) 2019-2021, 2026 Arm Limited. All rights reserved. * * SPDX-License-Identifier: Apache-2.0 * @@ -70,6 +70,8 @@ unsigned char uart_putc(unsigned char my_ch) } CMSDK_UART0->DATA = my_ch; // write to transmit holding register + while ((CMSDK_UART0->STATE & 1)) + ; // Wait until transmit holding register accepts the byte return (my_ch); } diff --git a/Tests/UnitTest/Corstone-300/wait.c b/Tests/UnitTest/Corstone-300/wait.c new file mode 100644 index 00000000..338acd79 --- /dev/null +++ b/Tests/UnitTest/Corstone-300/wait.c @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2026 Arm Limited. All rights reserved. + * + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the License); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +/* + * The board device-definition layer expects a sleep hook for a few peripheral + * descriptors. The unit tests do not exercise those timed peripherals, so a + * coarse busy-wait is sufficient here. + */ +__attribute__((weak)) void wait_us(uint32_t usec) +{ + while (usec-- != 0U) + { + for (volatile uint32_t i = 0; i < 25U; ++i) + { + __asm volatile("nop"); + } + } +} diff --git a/Tests/UnitTest/README.md b/Tests/UnitTest/README.md index 566e3bc4..69c94bb0 100644 --- a/Tests/UnitTest/README.md +++ b/Tests/UnitTest/README.md @@ -4,6 +4,22 @@ Unit test CMSIS-NN functions on any [Arm Mbed OS](https://os.mbed.com/mbed-os/) The [Unity test framework](http://www.throwtheswitch.org/unity) is used for running the actual unit tests. +Float unit tests are handled a bit differently from the legacy integer flow: + +- float reference data is generated with the dedicated PyTorch-based scripts such as + `*_settings_flt.py` +- current float unit-test coverage is summarized in + [float_unit_test_coverage.md](./float_unit_test_coverage.md) + with the machine-readable source of truth in + [float_unit_test_coverage.yml](./float_unit_test_coverage.yml) +- float Corstone-300/FVP execution also has a dedicated CMSIS-Toolbox setup under + [cmsis/README.md](./cmsis/README.md) + +The unified float runner prints a summary table at the end of the run and exits +with a non-zero status if any selected generation, host build, host execution, +CMSIS build, or FVP execution step fails. This makes it suitable for CI while +still preserving a full per-family pass/fail summary in the log. + For a quick setup, it is reccomended to the helper script targetting the Arm Corstone-300 softwware. See the section " Using FVP based on Arm Corstone-300 software ". ## Requirements @@ -70,15 +86,26 @@ Use the -h flag to get more info. ### Using FVP based on Arm Corstone-300 software -The easiest way to run the unit tests on Corstone-300 is to use the build_and_run_tests.sh script. This script will install required packages, build unit tests and run unit tests. This script has been designed for Linux hosts with both aarch64 and x86_64 architectures. For more help use the '-h' flag on the script. +For the newer float unit tests, prefer the dedicated CMSIS-Toolbox workflow documented in +[cmsis/README.md](./cmsis/README.md). The instructions below describe the legacy integer unit-test +flow. + +For the legacy integer unit tests, `run_integer_unit_tests.py` provides the +scripted way to build and run the existing Corstone-300/FVP test suites. It +does not introduce a separate integer test set; it automates the existing +integer unit-test targets so they can be driven more easily from CI or from a +reproducible local command line. -Sample usage: +Example: ``` -./build_and_run_tests.sh -c cortex-m3,cortex-m7,cortex-m55 -o '-Ofast' +python3 run_integer_unit_tests.py \ + --tests all \ + --build-fvp \ + --run-fvp \ + --toolchains GCC \ + --cmsis-pack-root /home/$USER/.cache/arm/packs \ + --fvp-bin FVP_Corstone_SSE-300 ``` -By default the script will download and target gcc. To use arm compiler ensure that arm compilers folder is located in path, export CC and use the -a option on the script. - -Downloaded dependencies including python venv can be found in Tests/UnitTests/downloads. Test elfs can be found in Tests/UnitTests/build-($cpu) directories. Otherwise, you can build it manually: @@ -120,6 +147,9 @@ FVP_Corstone_SSE-300_Ethos-U55 --cpulimit 2 -C mps3_board.visualisation.disable- ## Generating new test data **NOTE:** The data generation scrips are being reworked, see *Refactoring of generate_test_data* +For float operator coverage, prefer the dedicated `*_settings_flt.py` generators. Those scripts use +PyTorch to create reproducible reference samples and write them into `TestCases/TestData/`. + Generating new test data is done with the following script. Use the -h flag to get more info. ``` diff --git a/Tests/UnitTest/TestCases/Common/float_packed_test_utils.h b/Tests/UnitTest/TestCases/Common/float_packed_test_utils.h new file mode 100755 index 00000000..6f3bf6f6 --- /dev/null +++ b/Tests/UnitTest/TestCases/Common/float_packed_test_utils.h @@ -0,0 +1,71 @@ +/* + * SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#ifndef FLOAT_PACKED_TEST_UTILS_H +#define FLOAT_PACKED_TEST_UTILS_H + +#include + +#include +#include + +#if ARM_NN_ENABLE_F16 +static inline float16_t *pack_rhs_nt_n_from_nt_t_f16(const float16_t *rhs_nt_t, int32_t rhs_rows, int32_t rhs_cols) +{ + const int32_t block_cols = 8; + const int32_t packed_cols = ((rhs_rows + block_cols - 1) / block_cols) * block_cols; + float16_t *packed = (float16_t *)calloc((size_t)rhs_cols * packed_cols, sizeof(float16_t)); + + TEST_ASSERT_NOT_NULL(packed); + + for (int32_t n_base = 0; n_base < packed_cols; n_base += block_cols) + { + for (int32_t k = 0; k < rhs_cols; ++k) + { + float16_t *dst = packed + (size_t)n_base * rhs_cols + (size_t)k * block_cols; + for (int32_t lane = 0; lane < block_cols; ++lane) + { + const int32_t n = n_base + lane; + if (n < rhs_rows) + { + dst[lane] = rhs_nt_t[(size_t)n * rhs_cols + k]; + } + } + } + } + + return packed; +} +#endif + +static inline float32_t *pack_rhs_nt_n_from_nt_t_f32(const float32_t *rhs_nt_t, int32_t rhs_rows, int32_t rhs_cols) +{ + const int32_t block_cols = 4; + const int32_t packed_cols = ((rhs_rows + block_cols - 1) / block_cols) * block_cols; + float32_t *packed = (float32_t *)calloc((size_t)rhs_cols * packed_cols, sizeof(float32_t)); + + TEST_ASSERT_NOT_NULL(packed); + + for (int32_t n_base = 0; n_base < packed_cols; n_base += block_cols) + { + for (int32_t k = 0; k < rhs_cols; ++k) + { + float32_t *dst = packed + (size_t)n_base * rhs_cols + (size_t)k * block_cols; + for (int32_t lane = 0; lane < block_cols; ++lane) + { + const int32_t n = n_base + lane; + if (n < rhs_rows) + { + dst[lane] = rhs_nt_t[(size_t)n * rhs_cols + k]; + } + } + } + } + + return packed; +} + +#endif diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/config_data.h new file mode 100644 index 00000000..d525e7a9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/config_data.h @@ -0,0 +1,3 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ACTIVATION_F16_SIZE 9 +#define ACTIVATION_F16_LEAKY_RELU_PARAM (float16_t)0.125f diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/input_data.h new file mode 100644 index 00000000..2c4df187 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/input_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t activation_f16_input[9] = {(float16_t)-5.0f, + (float16_t)-3.0f, + (float16_t)-1.25f, + (float16_t)-0.0999755859f, + (float16_t)0.0f, + (float16_t)0.5f, + (float16_t)1.5f, + (float16_t)3.0f, + (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_hardswish_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_hardswish_data.h new file mode 100644 index 00000000..589239b5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_hardswish_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t activation_f16_output_ref_hardswish[9] = {(float16_t)-0.0f, + (float16_t)-0.0f, + (float16_t)-0.364501953f, + (float16_t)-0.0483398438f, + (float16_t)0.0f, + (float16_t)0.291748047f, + (float16_t)1.125f, + (float16_t)3.0f, + (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_leaky_relu_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_leaky_relu_data.h new file mode 100644 index 00000000..d67ec3f2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_leaky_relu_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t activation_f16_output_ref_leaky_relu[9] = {(float16_t)-0.625f, + (float16_t)-0.375f, + (float16_t)-0.15625f, + (float16_t)-0.0124969482f, + (float16_t)0.0f, + (float16_t)0.5f, + (float16_t)1.5f, + (float16_t)3.0f, + (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_sigmoid_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_sigmoid_data.h new file mode 100644 index 00000000..8ca8c616 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_sigmoid_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t activation_f16_output_ref_sigmoid[9] = {(float16_t)0.006690979f, + (float16_t)0.0474243164f, + (float16_t)0.22265625f, + (float16_t)0.475097656f, + (float16_t)0.5f, + (float16_t)0.622558594f, + (float16_t)0.817382812f, + (float16_t)0.952636719f, + (float16_t)0.997558594f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_tanh_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_tanh_data.h new file mode 100644 index 00000000..d150fa98 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/output_ref_tanh_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t activation_f16_output_ref_tanh[9] = {(float16_t)-1.0f, + (float16_t)-0.995117188f, + (float16_t)-0.848144531f, + (float16_t)-0.0996704102f, + (float16_t)0.0f, + (float16_t)0.462158203f, + (float16_t)0.905273438f, + (float16_t)0.995117188f, + (float16_t)1.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/activation_f16/test_data.h new file mode 100644 index 00000000..3b6e3603 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_data.h" +#include "output_ref_hardswish_data.h" +#include "output_ref_leaky_relu_data.h" +#include "output_ref_sigmoid_data.h" +#include "output_ref_tanh_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/config_data.h new file mode 100644 index 00000000..2b994bcd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/config_data.h @@ -0,0 +1,3 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ACTIVATION_F32_SIZE 9 +#define ACTIVATION_F32_LEAKY_RELU_PARAM 0.125f diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/input_data.h new file mode 100644 index 00000000..0a5b0ea4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/input_data.h @@ -0,0 +1,2 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t activation_f32_input[9] = {-5.0f, -3.0f, -1.25f, -0.100000001f, 0.0f, 0.5f, 1.5f, 3.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_hardswish_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_hardswish_data.h new file mode 100644 index 00000000..26c08598 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_hardswish_data.h @@ -0,0 +1,3 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t activation_f32_output_ref_hardswish[9] = + {-0.0f, -0.0f, -0.364583343f, -0.0483333357f, 0.0f, 0.291666657f, 1.125f, 3.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_leaky_relu_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_leaky_relu_data.h new file mode 100644 index 00000000..4f6868cb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_leaky_relu_data.h @@ -0,0 +1,3 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t activation_f32_output_ref_leaky_relu[9] = + {-0.625f, -0.375f, -0.15625f, -0.0125000002f, 0.0f, 0.5f, 1.5f, 3.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_sigmoid_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_sigmoid_data.h new file mode 100644 index 00000000..926ffa9c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_sigmoid_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t activation_f32_output_ref_sigmoid[9] = {0.00669285096f, + 0.0474258736f, + 0.222700134f, + 0.475020796f, + 0.5f, + 0.622459352f, + 0.817574441f, + 0.952574134f, + 0.997527421f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_tanh_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_tanh_data.h new file mode 100644 index 00000000..17cfac7c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/output_ref_tanh_data.h @@ -0,0 +1,10 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t activation_f32_output_ref_tanh[9] = {-0.999909222f, + -0.995054781f, + -0.848283648f, + -0.0996679962f, + 0.0f, + 0.462117165f, + 0.905148268f, + 0.995054781f, + 0.999987721f}; diff --git a/Tests/UnitTest/TestCases/TestData/activation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/activation_f32/test_data.h new file mode 100644 index 00000000..3b6e3603 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/activation_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by activation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_data.h" +#include "output_ref_hardswish_data.h" +#include "output_ref_leaky_relu_data.h" +#include "output_ref_sigmoid_data.h" +#include "output_ref_tanh_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/add_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/add_f16/config_data.h new file mode 100644 index 00000000..3143ba22 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ADD_F16_DST_SIZE 128 +#define ADD_F16_OUT_ACTIVATION_MIN (float16_t) - 10.0f +#define ADD_F16_OUT_ACTIVATION_MAX (float16_t)10.0f +#define ADD_F16_INPUT1_OFFSET 0 +#define ADD_F16_INPUT2_OFFSET 0 +#define ADD_F16_OUTPUT_MULT 0 +#define ADD_F16_OUTPUT_SHIFT 0 +#define ADD_F16_OUTPUT_OFFSET 0 +#define ADD_F16_LEFT_SHIFT 0 +#define ADD_F16_INPUT1_SHIFT 0 +#define ADD_F16_INPUT2_SHIFT 0 +#define ADD_F16_INPUT1_MULT 0 +#define ADD_F16_INPUT2_MULT 0 diff --git a/Tests/UnitTest/TestCases/TestData/add_f16/input1_data.h b/Tests/UnitTest/TestCases/TestData/add_f16/input1_data.h new file mode 100644 index 00000000..1c492ff8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16/input1_data.h @@ -0,0 +1,34 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_input1[128] = { + (float16_t)-3.13476562f, (float16_t)-0.35546875f, (float16_t)3.76757812f, (float16_t)-0.076965332f, + (float16_t)-1.3359375f, (float16_t)0.356201172f, (float16_t)-2.91992188f, (float16_t)0.108764648f, + (float16_t)2.328125f, (float16_t)-0.536621094f, (float16_t)0.581542969f, (float16_t)-3.33203125f, + (float16_t)2.5859375f, (float16_t)-0.912109375f, (float16_t)-2.80273438f, (float16_t)3.1796875f, + (float16_t)-2.77539062f, (float16_t)-3.17773438f, (float16_t)-2.68164062f, (float16_t)0.736816406f, + (float16_t)1.546875f, (float16_t)0.840820312f, (float16_t)0.416015625f, (float16_t)-0.979003906f, + (float16_t)-1.3203125f, (float16_t)0.943847656f, (float16_t)-0.878417969f, (float16_t)-1.43261719f, + (float16_t)3.3046875f, (float16_t)1.27929688f, (float16_t)0.779296875f, (float16_t)1.76171875f, + (float16_t)-1.31445312f, (float16_t)-3.10742188f, (float16_t)0.287841797f, (float16_t)3.82421875f, + (float16_t)-1.34667969f, (float16_t)-1.54980469f, (float16_t)-2.80664062f, (float16_t)-3.54101562f, + (float16_t)-2.35742188f, (float16_t)-2.12109375f, (float16_t)3.91796875f, (float16_t)0.831542969f, + (float16_t)-0.112670898f, (float16_t)-0.0972900391f, (float16_t)-3.296875f, (float16_t)-3.55078125f, + (float16_t)-2.56835938f, (float16_t)-0.524414062f, (float16_t)2.57421875f, (float16_t)-3.03125f, + (float16_t)3.15429688f, (float16_t)-0.557128906f, (float16_t)0.275878906f, (float16_t)0.869628906f, + (float16_t)-3.9140625f, (float16_t)1.1875f, (float16_t)-0.416259766f, (float16_t)-3.83203125f, + (float16_t)3.21875f, (float16_t)2.51757812f, (float16_t)0.252441406f, (float16_t)1.15136719f, + (float16_t)1.75488281f, (float16_t)1.390625f, (float16_t)0.14050293f, (float16_t)-1.20214844f, + (float16_t)0.607910156f, (float16_t)-0.525390625f, (float16_t)3.36914062f, (float16_t)0.0938720703f, + (float16_t)-3.1796875f, (float16_t)-2.0078125f, (float16_t)-3.02539062f, (float16_t)0.233398438f, + (float16_t)3.37890625f, (float16_t)2.3671875f, (float16_t)-2.7109375f, (float16_t)2.24609375f, + (float16_t)2.09960938f, (float16_t)-0.5078125f, (float16_t)3.9453125f, (float16_t)-1.6171875f, + (float16_t)0.458984375f, (float16_t)3.49414062f, (float16_t)1.57226562f, (float16_t)2.98046875f, + (float16_t)-3.39648438f, (float16_t)-3.27929688f, (float16_t)-0.0529785156f, (float16_t)2.5546875f, + (float16_t)-1.36621094f, (float16_t)3.4765625f, (float16_t)1.71972656f, (float16_t)-3.83984375f, + (float16_t)-0.375976562f, (float16_t)3.12304688f, (float16_t)-3.63867188f, (float16_t)-0.891601562f, + (float16_t)-1.24707031f, (float16_t)2.33789062f, (float16_t)-3.1953125f, (float16_t)2.61328125f, + (float16_t)-3.296875f, (float16_t)-0.365722656f, (float16_t)2.94921875f, (float16_t)-0.397705078f, + (float16_t)3.03125f, (float16_t)1.57421875f, (float16_t)-1.31152344f, (float16_t)1.65917969f, + (float16_t)-1.46582031f, (float16_t)-0.627441406f, (float16_t)3.96875f, (float16_t)0.562988281f, + (float16_t)2.75976562f, (float16_t)-1.71777344f, (float16_t)-2.65039062f, (float16_t)1.11328125f, + (float16_t)1.48144531f, (float16_t)-3.03320312f, (float16_t)-3.90820312f, (float16_t)3.74609375f, + (float16_t)-2.27148438f, (float16_t)-3.90820312f, (float16_t)1.65234375f, (float16_t)0.345214844f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16/input2_data.h b/Tests/UnitTest/TestCases/TestData/add_f16/input2_data.h new file mode 100644 index 00000000..02a36d89 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16/input2_data.h @@ -0,0 +1,34 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_input2[128] = { + (float16_t)-1.72265625f, (float16_t)0.857421875f, (float16_t)3.71875f, (float16_t)-1.69335938f, + (float16_t)-3.29492188f, (float16_t)1.8828125f, (float16_t)-1.15332031f, (float16_t)-1.45996094f, + (float16_t)1.50488281f, (float16_t)-2.9453125f, (float16_t)-2.1640625f, (float16_t)-0.6171875f, + (float16_t)0.125976562f, (float16_t)-0.876953125f, (float16_t)0.354248047f, (float16_t)3.52929688f, + (float16_t)1.39746094f, (float16_t)2.87890625f, (float16_t)2.65039062f, (float16_t)-3.01171875f, + (float16_t)0.665527344f, (float16_t)-1.28417969f, (float16_t)-0.110534668f, (float16_t)3.06835938f, + (float16_t)3.0625f, (float16_t)2.84570312f, (float16_t)0.922363281f, (float16_t)-2.44921875f, + (float16_t)0.724121094f, (float16_t)-0.895019531f, (float16_t)-1.21777344f, (float16_t)-3.66601562f, + (float16_t)-0.784667969f, (float16_t)-0.667480469f, (float16_t)3.9296875f, (float16_t)1.85058594f, + (float16_t)2.5f, (float16_t)2.875f, (float16_t)3.50585938f, (float16_t)3.09960938f, + (float16_t)3.6015625f, (float16_t)-1.36425781f, (float16_t)-2.359375f, (float16_t)-1.14160156f, + (float16_t)3.63476562f, (float16_t)-1.78613281f, (float16_t)1.45800781f, (float16_t)0.375732422f, + (float16_t)-3.31054688f, (float16_t)0.272705078f, (float16_t)-1.12597656f, (float16_t)-0.00375175476f, + (float16_t)-3.7890625f, (float16_t)3.60351562f, (float16_t)-3.31640625f, (float16_t)-1.46777344f, + (float16_t)3.88085938f, (float16_t)3.79296875f, (float16_t)-3.16992188f, (float16_t)-0.982910156f, + (float16_t)3.40429688f, (float16_t)-0.716796875f, (float16_t)-2.5f, (float16_t)0.760253906f, + (float16_t)-3.35742188f, (float16_t)0.797851562f, (float16_t)-0.626953125f, (float16_t)3.29492188f, + (float16_t)2.1015625f, (float16_t)2.38085938f, (float16_t)0.414794922f, (float16_t)-2.9375f, + (float16_t)-1.25292969f, (float16_t)1.36816406f, (float16_t)1.00390625f, (float16_t)3.06835938f, + (float16_t)3.80078125f, (float16_t)3.65039062f, (float16_t)-2.76171875f, (float16_t)-1.72851562f, + (float16_t)0.0997314453f, (float16_t)0.7109375f, (float16_t)-1.25585938f, (float16_t)-0.743652344f, + (float16_t)-2.6015625f, (float16_t)3.77148438f, (float16_t)-1.70507812f, (float16_t)3.54882812f, + (float16_t)-3.85546875f, (float16_t)-3.00390625f, (float16_t)-2.88476562f, (float16_t)-3.81445312f, + (float16_t)-2.95117188f, (float16_t)3.18164062f, (float16_t)1.32519531f, (float16_t)0.157470703f, + (float16_t)-1.30078125f, (float16_t)1.74511719f, (float16_t)2.63867188f, (float16_t)0.888183594f, + (float16_t)-0.541992188f, (float16_t)-0.65625f, (float16_t)0.202880859f, (float16_t)1.14160156f, + (float16_t)3.63867188f, (float16_t)2.45507812f, (float16_t)-2.78710938f, (float16_t)-0.211669922f, + (float16_t)3.98632812f, (float16_t)1.45605469f, (float16_t)0.849121094f, (float16_t)0.280273438f, + (float16_t)-2.0546875f, (float16_t)-2.62304688f, (float16_t)2.1015625f, (float16_t)0.0858764648f, + (float16_t)3.46679688f, (float16_t)1.45800781f, (float16_t)1.72949219f, (float16_t)1.42675781f, + (float16_t)-3.38867188f, (float16_t)3.70898438f, (float16_t)0.509765625f, (float16_t)-3.02929688f, + (float16_t)-2.796875f, (float16_t)1.27929688f, (float16_t)-0.131591797f, (float16_t)-3.3125f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/add_f16/output_ref_data.h new file mode 100644 index 00000000..3b60db65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16/output_ref_data.h @@ -0,0 +1,34 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_output_ref[128] = { + (float16_t)-4.85546875f, (float16_t)0.501953125f, (float16_t)7.48828125f, (float16_t)-1.77050781f, + (float16_t)-4.6328125f, (float16_t)2.23828125f, (float16_t)-4.07421875f, (float16_t)-1.35058594f, + (float16_t)3.83398438f, (float16_t)-3.48046875f, (float16_t)-1.58203125f, (float16_t)-3.94921875f, + (float16_t)2.71289062f, (float16_t)-1.7890625f, (float16_t)-2.44921875f, (float16_t)6.7109375f, + (float16_t)-1.37890625f, (float16_t)-0.298095703f, (float16_t)-0.031829834f, (float16_t)-2.27539062f, + (float16_t)2.21289062f, (float16_t)-0.443603516f, (float16_t)0.305419922f, (float16_t)2.08984375f, + (float16_t)1.7421875f, (float16_t)3.7890625f, (float16_t)0.0438232422f, (float16_t)-3.88085938f, + (float16_t)4.03125f, (float16_t)0.384033203f, (float16_t)-0.438232422f, (float16_t)-1.90429688f, + (float16_t)-2.09960938f, (float16_t)-3.77539062f, (float16_t)4.21875f, (float16_t)5.67578125f, + (float16_t)1.15332031f, (float16_t)1.32617188f, (float16_t)0.698242188f, (float16_t)-0.442382812f, + (float16_t)1.24414062f, (float16_t)-3.484375f, (float16_t)1.55957031f, (float16_t)-0.310058594f, + (float16_t)3.5234375f, (float16_t)-1.8828125f, (float16_t)-1.83789062f, (float16_t)-3.17382812f, + (float16_t)-5.87890625f, (float16_t)-0.251708984f, (float16_t)1.44824219f, (float16_t)-3.03515625f, + (float16_t)-0.635253906f, (float16_t)3.046875f, (float16_t)-3.0390625f, (float16_t)-0.598632812f, + (float16_t)-0.0325927734f, (float16_t)4.98046875f, (float16_t)-3.5859375f, (float16_t)-4.81640625f, + (float16_t)6.62109375f, (float16_t)1.79980469f, (float16_t)-2.24609375f, (float16_t)1.91210938f, + (float16_t)-1.60253906f, (float16_t)2.18945312f, (float16_t)-0.486328125f, (float16_t)2.09179688f, + (float16_t)2.70898438f, (float16_t)1.85644531f, (float16_t)3.78320312f, (float16_t)-2.84375f, + (float16_t)-4.43359375f, (float16_t)-0.640136719f, (float16_t)-2.02148438f, (float16_t)3.30078125f, + (float16_t)7.1796875f, (float16_t)6.01953125f, (float16_t)-5.47265625f, (float16_t)0.517089844f, + (float16_t)2.19921875f, (float16_t)0.20324707f, (float16_t)2.6875f, (float16_t)-2.36132812f, + (float16_t)-2.14257812f, (float16_t)7.265625f, (float16_t)-0.133300781f, (float16_t)6.53125f, + (float16_t)-7.25390625f, (float16_t)-6.28125f, (float16_t)-2.9375f, (float16_t)-1.25976562f, + (float16_t)-4.31640625f, (float16_t)6.65625f, (float16_t)3.04492188f, (float16_t)-3.68164062f, + (float16_t)-1.67675781f, (float16_t)4.8671875f, (float16_t)-0.999023438f, (float16_t)-0.00335693359f, + (float16_t)-1.7890625f, (float16_t)1.68261719f, (float16_t)-2.9921875f, (float16_t)3.75585938f, + (float16_t)0.341796875f, (float16_t)2.08789062f, (float16_t)0.161010742f, (float16_t)-0.609375f, + (float16_t)7.01953125f, (float16_t)3.02929688f, (float16_t)-0.462646484f, (float16_t)1.93945312f, + (float16_t)-3.52148438f, (float16_t)-3.25195312f, (float16_t)6.0703125f, (float16_t)0.648925781f, + (float16_t)6.2265625f, (float16_t)-0.259765625f, (float16_t)-0.920898438f, (float16_t)2.5390625f, + (float16_t)-1.90625f, (float16_t)0.676757812f, (float16_t)-3.3984375f, (float16_t)0.717773438f, + (float16_t)-5.0703125f, (float16_t)-2.62890625f, (float16_t)1.52050781f, (float16_t)-2.96679688f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/add_f16/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/add_f16_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/add_f16_spill/config_data.h new file mode 100644 index 00000000..c2314a36 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16_spill/config_data.h @@ -0,0 +1,14 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ADD_F16_SPILL_DST_SIZE 105 +#define ADD_F16_SPILL_OUT_ACTIVATION_MIN (float16_t) - 2.0f +#define ADD_F16_SPILL_OUT_ACTIVATION_MAX (float16_t)3.0f +#define ADD_F16_SPILL_INPUT1_OFFSET 0 +#define ADD_F16_SPILL_INPUT2_OFFSET 0 +#define ADD_F16_SPILL_OUTPUT_MULT 0 +#define ADD_F16_SPILL_OUTPUT_SHIFT 0 +#define ADD_F16_SPILL_OUTPUT_OFFSET 0 +#define ADD_F16_SPILL_LEFT_SHIFT 0 +#define ADD_F16_SPILL_INPUT1_SHIFT 0 +#define ADD_F16_SPILL_INPUT2_SHIFT 0 +#define ADD_F16_SPILL_INPUT1_MULT 0 +#define ADD_F16_SPILL_INPUT2_MULT 0 diff --git a/Tests/UnitTest/TestCases/TestData/add_f16_spill/input1_data.h b/Tests/UnitTest/TestCases/TestData/add_f16_spill/input1_data.h new file mode 100644 index 00000000..b642d6a4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16_spill/input1_data.h @@ -0,0 +1,29 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_spill_input1[105] = { + (float16_t)-2.8359375f, (float16_t)-3.77148438f, (float16_t)2.74023438f, (float16_t)1.67675781f, + (float16_t)-1.24707031f, (float16_t)3.16601562f, (float16_t)-2.7734375f, (float16_t)1.92773438f, + (float16_t)-0.483642578f, (float16_t)-0.823730469f, (float16_t)1.21484375f, (float16_t)1.9140625f, + (float16_t)2.63867188f, (float16_t)0.181640625f, (float16_t)0.424316406f, (float16_t)-3.265625f, + (float16_t)3.03515625f, (float16_t)0.0925292969f, (float16_t)-2.89453125f, (float16_t)-3.9453125f, + (float16_t)2.12890625f, (float16_t)-2.32226562f, (float16_t)3.1015625f, (float16_t)3.66015625f, + (float16_t)-1.40332031f, (float16_t)-0.889648438f, (float16_t)3.03125f, (float16_t)-3.5859375f, + (float16_t)-1.81738281f, (float16_t)-3.59765625f, (float16_t)2.2734375f, (float16_t)2.44726562f, + (float16_t)-1.35546875f, (float16_t)-0.0458068848f, (float16_t)1.54785156f, (float16_t)-0.415039062f, + (float16_t)-3.890625f, (float16_t)-1.69042969f, (float16_t)-0.637695312f, (float16_t)1.10742188f, + (float16_t)-1.00097656f, (float16_t)-2.45898438f, (float16_t)-3.02539062f, (float16_t)-1.40039062f, + (float16_t)1.23925781f, (float16_t)-3.32226562f, (float16_t)-0.343261719f, (float16_t)-2.41992188f, + (float16_t)-0.0793457031f, (float16_t)0.964355469f, (float16_t)-3.27929688f, (float16_t)2.59570312f, + (float16_t)0.273193359f, (float16_t)2.640625f, (float16_t)0.197387695f, (float16_t)-3.6484375f, + (float16_t)2.5390625f, (float16_t)-2.81835938f, (float16_t)-3.96289062f, (float16_t)3.1640625f, + (float16_t)-3.67382812f, (float16_t)-0.755859375f, (float16_t)-1.80273438f, (float16_t)-0.909667969f, + (float16_t)-1.45410156f, (float16_t)1.90332031f, (float16_t)0.931640625f, (float16_t)-1.04199219f, + (float16_t)-2.53515625f, (float16_t)-2.5f, (float16_t)-3.63671875f, (float16_t)-0.506347656f, + (float16_t)-2.328125f, (float16_t)0.58203125f, (float16_t)-2.06835938f, (float16_t)1.12109375f, + (float16_t)-0.274414062f, (float16_t)0.0380859375f, (float16_t)-2.49804688f, (float16_t)-0.220703125f, + (float16_t)2.55078125f, (float16_t)1.90625f, (float16_t)-2.97460938f, (float16_t)3.2734375f, + (float16_t)-1.62988281f, (float16_t)-2.70898438f, (float16_t)2.74414062f, (float16_t)-2.3359375f, + (float16_t)3.83203125f, (float16_t)2.76171875f, (float16_t)2.04101562f, (float16_t)2.26757812f, + (float16_t)3.52539062f, (float16_t)-0.96875f, (float16_t)0.170776367f, (float16_t)3.74609375f, + (float16_t)0.161376953f, (float16_t)0.530761719f, (float16_t)-1.6796875f, (float16_t)2.34375f, + (float16_t)-3.06640625f, (float16_t)-2.40039062f, (float16_t)-3.79101562f, (float16_t)2.98242188f, + (float16_t)-1.39160156f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16_spill/input2_data.h b/Tests/UnitTest/TestCases/TestData/add_f16_spill/input2_data.h new file mode 100644 index 00000000..c244e0fe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16_spill/input2_data.h @@ -0,0 +1,29 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_spill_input2[105] = { + (float16_t)-3.8359375f, (float16_t)2.20507812f, (float16_t)3.78320312f, (float16_t)1.34570312f, + (float16_t)-1.83007812f, (float16_t)-2.23632812f, (float16_t)-0.764648438f, (float16_t)0.149047852f, + (float16_t)2.83203125f, (float16_t)-1.97070312f, (float16_t)1.0f, (float16_t)-1.36621094f, + (float16_t)-1.81542969f, (float16_t)-0.735351562f, (float16_t)-2.796875f, (float16_t)3.546875f, + (float16_t)0.447509766f, (float16_t)-1.88476562f, (float16_t)3.53320312f, (float16_t)2.15625f, + (float16_t)0.553710938f, (float16_t)-1.9765625f, (float16_t)1.34765625f, (float16_t)-1.17089844f, + (float16_t)-0.456298828f, (float16_t)0.310058594f, (float16_t)2.57421875f, (float16_t)1.38964844f, + (float16_t)1.1953125f, (float16_t)0.795410156f, (float16_t)2.58203125f, (float16_t)3.59765625f, + (float16_t)-1.81640625f, (float16_t)1.99804688f, (float16_t)-1.640625f, (float16_t)1.31445312f, + (float16_t)-0.904785156f, (float16_t)-2.61132812f, (float16_t)-1.25390625f, (float16_t)1.31347656f, + (float16_t)2.85351562f, (float16_t)-1.01464844f, (float16_t)-2.90234375f, (float16_t)-2.65820312f, + (float16_t)-2.4296875f, (float16_t)3.39257812f, (float16_t)-0.965820312f, (float16_t)0.888183594f, + (float16_t)-0.0197296143f, (float16_t)2.77539062f, (float16_t)-0.998535156f, (float16_t)-0.353515625f, + (float16_t)1.8203125f, (float16_t)-3.19140625f, (float16_t)-2.87890625f, (float16_t)0.676757812f, + (float16_t)-2.57421875f, (float16_t)-0.889648438f, (float16_t)-1.56640625f, (float16_t)-1.16503906f, + (float16_t)-3.20507812f, (float16_t)-2.45507812f, (float16_t)0.253662109f, (float16_t)-0.280029297f, + (float16_t)-1.49414062f, (float16_t)-0.519042969f, (float16_t)0.470703125f, (float16_t)1.10546875f, + (float16_t)-0.739746094f, (float16_t)1.77929688f, (float16_t)-2.88085938f, (float16_t)2.13671875f, + (float16_t)-0.806640625f, (float16_t)2.859375f, (float16_t)2.15039062f, (float16_t)-1.05664062f, + (float16_t)-0.640136719f, (float16_t)-1.08789062f, (float16_t)3.875f, (float16_t)3.19140625f, + (float16_t)-1.24511719f, (float16_t)1.47949219f, (float16_t)0.240356445f, (float16_t)-1.41015625f, + (float16_t)1.91015625f, (float16_t)-1.13574219f, (float16_t)-0.448974609f, (float16_t)3.02148438f, + (float16_t)0.0458068848f, (float16_t)0.791992188f, (float16_t)2.12890625f, (float16_t)-3.46484375f, + (float16_t)2.92382812f, (float16_t)-1.72558594f, (float16_t)2.79882812f, (float16_t)1.92773438f, + (float16_t)3.81445312f, (float16_t)-1.68847656f, (float16_t)-1.8515625f, (float16_t)0.887695312f, + (float16_t)-3.8203125f, (float16_t)-0.00433731079f, (float16_t)-1.47070312f, (float16_t)-0.823730469f, + (float16_t)-2.0703125f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/add_f16_spill/output_ref_data.h new file mode 100644 index 00000000..04186318 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16_spill/output_ref_data.h @@ -0,0 +1,29 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t add_f16_spill_output_ref[105] = { + (float16_t)-2.0f, (float16_t)-1.56640625f, (float16_t)3.0f, (float16_t)3.0f, + (float16_t)-2.0f, (float16_t)0.930175781f, (float16_t)-2.0f, (float16_t)2.07617188f, + (float16_t)2.34765625f, (float16_t)-2.0f, (float16_t)2.21484375f, (float16_t)0.547851562f, + (float16_t)0.82421875f, (float16_t)-0.553710938f, (float16_t)-2.0f, (float16_t)0.279785156f, + (float16_t)3.0f, (float16_t)-1.79199219f, (float16_t)0.638183594f, (float16_t)-1.78808594f, + (float16_t)2.68359375f, (float16_t)-2.0f, (float16_t)3.0f, (float16_t)2.48828125f, + (float16_t)-1.86035156f, (float16_t)-0.579589844f, (float16_t)3.0f, (float16_t)-2.0f, + (float16_t)-0.622558594f, (float16_t)-2.0f, (float16_t)3.0f, (float16_t)3.0f, + (float16_t)-2.0f, (float16_t)1.95214844f, (float16_t)-0.092590332f, (float16_t)0.898925781f, + (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)-1.89160156f, (float16_t)2.421875f, + (float16_t)1.8515625f, (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)-2.0f, + (float16_t)-1.18945312f, (float16_t)0.0707397461f, (float16_t)-1.30957031f, (float16_t)-1.53222656f, + (float16_t)-0.0990600586f, (float16_t)3.0f, (float16_t)-2.0f, (float16_t)2.24023438f, + (float16_t)2.09375f, (float16_t)-0.551757812f, (float16_t)-2.0f, (float16_t)-2.0f, + (float16_t)-0.0367431641f, (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)1.99902344f, + (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)-1.54882812f, (float16_t)-1.18945312f, + (float16_t)-2.0f, (float16_t)1.38378906f, (float16_t)1.40234375f, (float16_t)0.0634155273f, + (float16_t)-2.0f, (float16_t)-0.719726562f, (float16_t)-2.0f, (float16_t)1.63085938f, + (float16_t)-2.0f, (float16_t)3.0f, (float16_t)0.0817260742f, (float16_t)0.064453125f, + (float16_t)-0.914550781f, (float16_t)-1.04980469f, (float16_t)1.37792969f, (float16_t)2.97070312f, + (float16_t)1.30664062f, (float16_t)3.0f, (float16_t)-2.0f, (float16_t)1.86328125f, + (float16_t)0.280029297f, (float16_t)-2.0f, (float16_t)2.29492188f, (float16_t)0.684570312f, + (float16_t)3.0f, (float16_t)3.0f, (float16_t)3.0f, (float16_t)-1.19824219f, + (float16_t)3.0f, (float16_t)-2.0f, (float16_t)2.96875f, (float16_t)3.0f, + (float16_t)3.0f, (float16_t)-1.15722656f, (float16_t)-2.0f, (float16_t)3.0f, + (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)-2.0f, (float16_t)2.15820312f, + (float16_t)-2.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f16_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/add_f16_spill/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f16_spill/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/add_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/add_f32/config_data.h new file mode 100644 index 00000000..89342567 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ADD_F32_DST_SIZE 128 +#define ADD_F32_OUT_ACTIVATION_MIN -10.0f +#define ADD_F32_OUT_ACTIVATION_MAX 10.0f +#define ADD_F32_INPUT1_OFFSET 0 +#define ADD_F32_INPUT2_OFFSET 0 +#define ADD_F32_OUTPUT_MULT 0 +#define ADD_F32_OUTPUT_SHIFT 0 +#define ADD_F32_OUTPUT_OFFSET 0 +#define ADD_F32_LEFT_SHIFT 0 +#define ADD_F32_INPUT1_SHIFT 0 +#define ADD_F32_INPUT2_SHIFT 0 +#define ADD_F32_INPUT1_MULT 0 +#define ADD_F32_INPUT2_MULT 0 diff --git a/Tests/UnitTest/TestCases/TestData/add_f32/input1_data.h b/Tests/UnitTest/TestCases/TestData/add_f32/input1_data.h new file mode 100644 index 00000000..47f9c579 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32/input1_data.h @@ -0,0 +1,21 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_input1[128] = { + -0.610641599f, -2.37020397f, -0.199197963f, 3.69718766f, -0.544168472f, -1.30090404f, 3.98786664f, + 2.26375175f, -2.2391603f, 0.791596711f, 3.91222048f, -0.387672096f, -3.19580841f, 0.113849923f, + -2.69787955f, -2.07169867f, -0.215423554f, 1.85809982f, 2.73657227f, 0.455435216f, -0.781454384f, + 2.82527375f, 2.78560567f, -1.96481252f, 0.708302081f, 2.92609596f, -3.53329086f, -2.39830327f, + -2.75665998f, 1.69754815f, -2.26368523f, -3.94373441f, 3.65580487f, -3.56856537f, -3.09860921f, + 0.604970694f, 2.61694598f, 2.24806929f, 2.21536255f, -0.168886662f, -0.254338324f, 0.334726363f, + 0.995252907f, 1.85318124f, 2.40845275f, -1.44731414f, 3.77728391f, 2.55055952f, 0.328673273f, + 0.814584792f, -3.54550171f, -1.97505319f, 3.11073685f, 3.81998563f, 2.52378011f, 2.47467756f, + -0.0110008791f, 0.587434947f, 1.21571803f, 2.46164703f, -2.01592803f, -0.210268512f, -3.93889594f, + -3.13022017f, 2.49321413f, 3.20774031f, 3.02935576f, -3.51254058f, 1.89028966f, -1.47258162f, + -3.0709095f, 0.0941980109f, 3.74255872f, 0.165453583f, 3.10686255f, 3.24420428f, 0.372705162f, + -0.268915057f, -0.578726172f, 2.23869705f, -3.24121761f, -3.66987371f, 3.56542659f, 3.29768968f, + 2.30101538f, 2.04914331f, -0.509491384f, -3.80549312f, 0.205819353f, -0.217502251f, 2.63586879f, + 1.70795143f, 1.37001538f, 2.47397923f, -3.72643161f, -2.613585f, -3.79318333f, -2.09307814f, + -1.59956312f, 0.658090472f, 1.27208829f, -0.669014215f, 3.90943909f, 0.511472225f, -3.7227366f, + -1.9182384f, 2.93331742f, -1.3244338f, 1.57260823f, -1.20745146f, -1.45804286f, 0.318681091f, + 3.13189435f, 2.92977595f, -1.12901461f, 0.505177557f, 0.967447877f, 0.122214146f, 3.1798532f, + 0.889128387f, 2.37104321f, 0.0975314304f, 0.815194309f, -2.60927296f, 1.46379542f, -3.81777382f, + 3.9720726f, -2.87272358f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32/input2_data.h b/Tests/UnitTest/TestCases/TestData/add_f32/input2_data.h new file mode 100644 index 00000000..41326cae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32/input2_data.h @@ -0,0 +1,21 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_input2[128] = { + 0.765687108f, 1.32953036f, 0.931642294f, 2.87097025f, -2.52224064f, -2.59325624f, 3.97749925f, + -1.80447221f, -0.733763576f, 0.736046791f, -0.769182026f, 1.96783102f, 3.57063198f, 3.63216591f, + 1.05689979f, -1.98782158f, 0.12349721f, -1.40094864f, 1.62869322f, -2.78574228f, 3.10022211f, + 0.114083305f, 2.30972743f, 0.900927484f, -2.81998897f, 0.889069855f, 3.41902494f, 0.247340262f, + -2.68412042f, 0.128206447f, 3.70634031f, 0.523505211f, 3.80951309f, -0.561804831f, 2.5264411f, + -0.578745663f, -0.408406675f, 0.317644954f, 0.0643290207f, 3.26548696f, -0.742997766f, -2.86710143f, + -2.13237977f, 2.0683217f, 0.493352354f, -1.1246022f, -3.48473358f, -2.97482753f, 2.39822698f, + 3.29883885f, 1.12079966f, 0.607739925f, 1.70874f, 3.50486326f, 2.9430654f, -0.0552027114f, + 2.68281054f, -3.75131989f, 2.29895329f, -3.56815314f, 1.44659126f, 0.478999436f, 1.38791382f, + 2.91297793f, -0.480575413f, -2.29224658f, -2.74410868f, -3.27148557f, -2.9197309f, -1.60864627f, + -0.882075131f, -0.122918569f, -0.635876119f, -0.834777117f, -0.359754711f, 2.08108139f, 1.67200446f, + 2.70199752f, -0.514572918f, -2.64091086f, -1.04082108f, 0.114748083f, -3.37229395f, -0.634167731f, + 1.59288883f, -3.92726851f, 1.7277236f, 3.30928302f, -0.249759957f, -0.941654265f, 2.46326303f, + -2.76428223f, -1.56502712f, 1.82107246f, 3.49739933f, 0.0620016381f, -1.02168834f, 0.298816651f, + -2.00433421f, 2.87800479f, 1.65670395f, 2.69770002f, 1.63932371f, 0.364167601f, -3.248734f, + 0.125766918f, 0.680270672f, -1.38938987f, 3.07126904f, -1.36476481f, -2.15320253f, 1.06233168f, + 0.647840321f, 3.80185604f, 1.85025299f, 1.82312465f, -0.607135713f, 0.711749017f, 0.388674706f, + -3.44015718f, -0.489644051f, 0.425157279f, -1.86266124f, 0.512695014f, -2.43438673f, -1.08844614f, + 3.40913844f, 2.88343358f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/add_f32/output_ref_data.h new file mode 100644 index 00000000..0fc8bb7f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32/output_ref_data.h @@ -0,0 +1,21 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_output_ref[128] = { + 0.155045509f, -1.04067361f, 0.732444346f, 6.56815815f, -3.06640911f, -3.89416027f, 7.96536589f, + 0.459279537f, -2.97292376f, 1.52764344f, 3.14303851f, 1.58015895f, 0.37482357f, 3.74601579f, + -1.64097977f, -4.05952024f, -0.0919263437f, 0.457151175f, 4.36526537f, -2.33030701f, 2.31876779f, + 2.93935704f, 5.0953331f, -1.06388497f, -2.11168694f, 3.81516576f, -0.114265919f, -2.15096307f, + -5.44078064f, 1.82575464f, 1.44265509f, -3.4202292f, 7.46531773f, -4.13037014f, -0.572168112f, + 0.0262250304f, 2.20853925f, 2.56571436f, 2.27969146f, 3.09660029f, -0.99733609f, -2.5323751f, + -1.13712692f, 3.92150307f, 2.90180516f, -2.57191634f, 0.292550325f, -0.424268007f, 2.72690034f, + 4.11342382f, -2.42470217f, -1.36731327f, 4.81947708f, 7.32484913f, 5.46684551f, 2.41947484f, + 2.67180967f, -3.16388488f, 3.51467133f, -1.10650611f, -0.569336772f, 0.268730938f, -2.550982f, + -0.217242241f, 2.01263881f, 0.915493727f, 0.285247087f, -6.78402615f, -1.02944124f, -3.08122778f, + -3.95298457f, -0.0287205577f, 3.10668254f, -0.669323564f, 2.74710774f, 5.32528591f, 2.04470968f, + 2.43308258f, -1.09329915f, -0.402213812f, -4.28203869f, -3.55512571f, 0.193132639f, 2.66352201f, + 3.89390421f, -1.87812519f, 1.21823215f, -0.496210098f, -0.0439406037f, -1.15915656f, 5.09913158f, + -1.0563308f, -0.195011735f, 4.29505157f, -0.229032278f, -2.55158329f, -4.81487179f, -1.79426146f, + -3.60389733f, 3.53609514f, 2.92879224f, 2.02868581f, 5.5487628f, 0.875639796f, -6.97147083f, + -1.79247153f, 3.61358809f, -2.7138238f, 4.64387703f, -2.57221627f, -3.61124539f, 1.3810128f, + 3.77973461f, 6.73163223f, 0.721238375f, 2.32830215f, 0.360312164f, 0.833963156f, 3.56852794f, + -2.55102873f, 1.88139915f, 0.522688687f, -1.04746699f, -2.09657788f, -0.970591307f, -4.90621996f, + 7.38121128f, 0.010710001f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/add_f32/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/add_f32_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/add_f32_spill/config_data.h new file mode 100644 index 00000000..be49c5c1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32_spill/config_data.h @@ -0,0 +1,14 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define ADD_F32_SPILL_DST_SIZE 105 +#define ADD_F32_SPILL_OUT_ACTIVATION_MIN -2.0f +#define ADD_F32_SPILL_OUT_ACTIVATION_MAX 3.0f +#define ADD_F32_SPILL_INPUT1_OFFSET 0 +#define ADD_F32_SPILL_INPUT2_OFFSET 0 +#define ADD_F32_SPILL_OUTPUT_MULT 0 +#define ADD_F32_SPILL_OUTPUT_SHIFT 0 +#define ADD_F32_SPILL_OUTPUT_OFFSET 0 +#define ADD_F32_SPILL_LEFT_SHIFT 0 +#define ADD_F32_SPILL_INPUT1_SHIFT 0 +#define ADD_F32_SPILL_INPUT2_SHIFT 0 +#define ADD_F32_SPILL_INPUT1_MULT 0 +#define ADD_F32_SPILL_INPUT2_MULT 0 diff --git a/Tests/UnitTest/TestCases/TestData/add_f32_spill/input1_data.h b/Tests/UnitTest/TestCases/TestData/add_f32_spill/input1_data.h new file mode 100644 index 00000000..eb1824e6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32_spill/input1_data.h @@ -0,0 +1,17 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_spill_input1[105] = { + 3.11007476f, 3.68568993f, 1.26490092f, -2.78994799f, 1.54017615f, -2.78302026f, -1.39774013f, + -2.6269803f, 3.17151403f, -1.16053843f, 3.46946311f, -3.70725441f, -3.13612533f, -0.72786814f, + 2.6311419f, -3.15282845f, -0.358771443f, 0.915083349f, -2.9412179f, -3.27149916f, -2.46749258f, + 2.70085549f, 3.65058613f, -2.90860391f, 3.2137053f, -2.0201056f, 2.10675931f, 0.560749471f, + 0.585530758f, 0.353547722f, 1.44201744f, -0.479753613f, -2.93135929f, -1.4770025f, 2.17102528f, + 0.748626649f, -2.73763442f, -1.85857177f, -1.63312745f, -0.698328197f, 1.18399656f, -2.21228719f, + 2.50751472f, -1.99499321f, -0.33098191f, -2.56248808f, -2.54946232f, -2.90067768f, -3.19952774f, + -2.01429892f, 3.04570246f, 2.00161386f, -0.375138879f, -3.45453715f, 0.532073498f, 1.44499683f, + 3.02249646f, 3.58522415f, 3.26597738f, 0.0368963927f, 0.982761979f, 3.9078474f, 2.22800779f, + 1.44512272f, -3.6139853f, 1.4266454f, -0.401819348f, 1.33385491f, 0.314515978f, 2.86282659f, + -0.380744308f, -0.790554941f, 0.274620563f, 3.86887121f, -3.0739367f, 1.63242042f, 0.313244253f, + -2.04941487f, 0.00622445904f, -1.25062144f, -2.26331282f, -1.94617808f, 2.41838527f, -2.40489411f, + -3.23540401f, 1.8285743f, 0.512215734f, 1.11218095f, 0.12378189f, 3.20742106f, 2.27016783f, + 3.77332735f, -3.44534373f, -2.29795861f, 1.56532753f, -3.98537683f, 0.207606301f, -1.63283169f, + -2.25341225f, -1.11649907f, 1.88687885f, -3.33317065f, -2.17663836f, 1.71801686f, 3.61984277f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32_spill/input2_data.h b/Tests/UnitTest/TestCases/TestData/add_f32_spill/input2_data.h new file mode 100644 index 00000000..c841b3ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32_spill/input2_data.h @@ -0,0 +1,17 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_spill_input2[105] = { + 2.93020272f, 2.84017563f, 2.40269995f, 1.79385841f, -1.80703866f, -0.565583169f, -0.833481669f, + -0.212546706f, 2.28735352f, 0.326218337f, 1.81706119f, -1.76153719f, -3.84276175f, 2.19550848f, + 3.67457294f, 3.66202831f, -0.811990499f, -3.25573707f, 1.39767647f, -2.27754283f, 1.96012747f, + -3.9279139f, 0.237975836f, 3.83701563f, 1.43945658f, -0.241684228f, -3.82256913f, 2.92526436f, + 3.74976373f, -3.54412317f, 0.754135609f, 3.74154639f, -2.07427859f, -3.48555779f, -1.93008029f, + -0.329987079f, -0.235870317f, -1.51018739f, -3.32120657f, -0.496669054f, 3.16232443f, -3.12187266f, + 3.22331977f, 2.60068178f, -2.73182344f, 2.13104486f, 2.64809299f, 3.86698484f, 2.11349654f, + -1.44610059f, -3.75644588f, 2.84891868f, -2.44750738f, -0.269615829f, 3.18830943f, 2.53467751f, + -2.11401129f, -0.729150355f, -2.27885723f, -3.16261244f, -1.92461884f, -0.410743713f, -0.538286805f, + 1.17494762f, -1.36503959f, -1.75980031f, -0.431109279f, 0.13243404f, -3.40225816f, 2.34864831f, + 2.40860224f, -3.50952888f, 1.19512105f, 1.63744915f, 0.337614119f, -0.777255297f, 0.950873554f, + 1.82885289f, -0.132459834f, 0.557164371f, 2.66758847f, -1.98596668f, -1.95379746f, 2.19318604f, + 1.99176812f, 3.80774069f, -1.28134692f, 1.26710248f, 3.41031146f, 0.859876156f, -0.404920608f, + -3.2801981f, -1.46658492f, 0.94242847f, -0.87958163f, 0.763088942f, -0.150665358f, 3.25778985f, + 1.4996444f, 3.77833986f, -3.06940484f, 0.611247718f, -3.16348791f, -2.01379132f, -0.788389266f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/add_f32_spill/output_ref_data.h new file mode 100644 index 00000000..2028c8a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32_spill/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t add_f32_spill_output_ref[105] = { + 3.0f, 3.0f, 3.0f, -0.996089578f, -0.266862512f, -2.0f, -2.0f, + -2.0f, 3.0f, -0.834320068f, 3.0f, -2.0f, -2.0f, 1.4676404f, + 3.0f, 0.509199858f, -1.17076194f, -2.0f, -1.54354143f, -2.0f, -0.507365108f, + -1.22705841f, 3.0f, 0.928411722f, 3.0f, -2.0f, -1.71580982f, 3.0f, + 3.0f, -2.0f, 2.19615316f, 3.0f, -2.0f, -2.0f, 0.240944982f, + 0.41863957f, -2.0f, -2.0f, -2.0f, -1.19499731f, 3.0f, -2.0f, + 3.0f, 0.605688572f, -2.0f, -0.431443214f, 0.0986306667f, 0.966307163f, -1.0860312f, + -2.0f, -0.710743427f, 3.0f, -2.0f, -2.0f, 3.0f, 3.0f, + 0.908485174f, 2.85607386f, 0.987120152f, -2.0f, -0.941856861f, 3.0f, 1.68972099f, + 2.62007046f, -2.0f, -0.333154917f, -0.832928658f, 1.46628892f, -2.0f, 3.0f, + 2.02785802f, -2.0f, 1.46974158f, 3.0f, -2.0f, 0.855165124f, 1.26411784f, + -0.220561981f, -0.126235381f, -0.693457067f, 0.404275656f, -2.0f, 0.464587808f, -0.211708069f, + -1.24363589f, 3.0f, -0.769131184f, 2.37928343f, 3.0f, 3.0f, 1.86524725f, + 0.493129253f, -2.0f, -1.35553014f, 0.685745895f, -2.0f, 0.056940943f, 1.62495816f, + -0.753767848f, 2.66184092f, -1.18252599f, -2.0f, -2.0f, -0.29577446f, 2.83145356f}; diff --git a/Tests/UnitTest/TestCases/TestData/add_f32_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/add_f32_spill/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/add_f32_spill/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/config_data.h new file mode 100644 index 00000000..a7bb86ea --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F16_BATCH_SIZE 1 +#define AVGPOOLING_F16_INPUT_N 1 +#define AVGPOOLING_F16_INPUT_W 22 +#define AVGPOOLING_F16_INPUT_H 12 +#define AVGPOOLING_F16_INPUT_C 20 +#define AVGPOOLING_F16_FILTER_W 6 +#define AVGPOOLING_F16_FILTER_H 5 +#define AVGPOOLING_F16_STRIDE_W 9 +#define AVGPOOLING_F16_STRIDE_H 5 +#define AVGPOOLING_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_F16_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_F16_OUTPUT_C 20 +#define AVGPOOLING_F16_OUTPUT_W 3 +#define AVGPOOLING_F16_OUTPUT_H 3 +#define AVGPOOLING_F16_PADDING_H 1 +#define AVGPOOLING_F16_PADDING_W 1 +#define AVGPOOLING_F16_DST_SIZE 180 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/input_tensor_data.h new file mode 100644 index 00000000..4de56e49 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/input_tensor_data.h @@ -0,0 +1,5281 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_input_tensor[5280] = {(float16_t)4.65625f, + (float16_t)-5.1484375f, + (float16_t)3.77929688f, + (float16_t)4.74609375f, + (float16_t)-0.28125f, + (float16_t)0.642578125f, + (float16_t)2.42382812f, + (float16_t)4.30078125f, + (float16_t)4.734375f, + (float16_t)0.0364685059f, + (float16_t)2.0f, + (float16_t)1.95800781f, + (float16_t)2.04296875f, + (float16_t)-5.5f, + (float16_t)-4.98046875f, + (float16_t)-4.62890625f, + (float16_t)0.0190887451f, + (float16_t)-4.8828125f, + (float16_t)5.2890625f, + (float16_t)-0.790039062f, + (float16_t)-4.6171875f, + (float16_t)2.09179688f, + (float16_t)-0.806152344f, + (float16_t)-3.20117188f, + (float16_t)-4.31640625f, + (float16_t)-0.942382812f, + (float16_t)0.530273438f, + (float16_t)4.2578125f, + (float16_t)-4.77734375f, + (float16_t)1.58691406f, + (float16_t)3.38671875f, + (float16_t)-2.16796875f, + (float16_t)2.578125f, + (float16_t)2.21679688f, + (float16_t)-5.1640625f, + (float16_t)-4.32421875f, + (float16_t)4.5625f, + (float16_t)-3.0390625f, + (float16_t)-1.70117188f, + (float16_t)0.74609375f, + (float16_t)-1.51757812f, + (float16_t)-0.0997924805f, + (float16_t)4.82421875f, + (float16_t)-0.487304688f, + (float16_t)1.45996094f, + (float16_t)5.8359375f, + (float16_t)2.60546875f, + (float16_t)-3.68164062f, + (float16_t)-5.28125f, + (float16_t)2.83203125f, + (float16_t)5.33984375f, + (float16_t)5.35546875f, + (float16_t)4.90625f, + (float16_t)4.953125f, + (float16_t)-0.991699219f, + (float16_t)1.15136719f, + (float16_t)-3.0f, + (float16_t)-1.95800781f, + (float16_t)3.390625f, + (float16_t)-4.84375f, + (float16_t)2.99023438f, + (float16_t)1.40527344f, + (float16_t)-3.25585938f, + (float16_t)-5.64453125f, + (float16_t)1.984375f, + (float16_t)1.73339844f, + (float16_t)-5.36328125f, + (float16_t)-3.07617188f, + (float16_t)-3.59179688f, + (float16_t)0.592773438f, + (float16_t)3.2578125f, + (float16_t)0.430664062f, + (float16_t)-0.690917969f, + (float16_t)2.3359375f, + (float16_t)4.84375f, + (float16_t)5.78125f, + (float16_t)4.6171875f, + (float16_t)1.37011719f, + (float16_t)4.5234375f, + (float16_t)2.484375f, + (float16_t)3.90039062f, + (float16_t)-5.7265625f, + (float16_t)1.34277344f, + (float16_t)-5.45703125f, + (float16_t)-2.22851562f, + (float16_t)2.79101562f, + (float16_t)3.0703125f, + (float16_t)3.75390625f, + (float16_t)-4.04296875f, + (float16_t)-4.91796875f, + (float16_t)-2.70703125f, + (float16_t)-5.6640625f, + (float16_t)5.1796875f, + (float16_t)-1.15527344f, + (float16_t)5.41796875f, + (float16_t)4.25390625f, + (float16_t)1.86132812f, + (float16_t)-2.625f, + (float16_t)1.9921875f, + (float16_t)-3.92382812f, + (float16_t)-0.98046875f, + (float16_t)-1.53515625f, + (float16_t)-1.27050781f, + (float16_t)-3.84179688f, + (float16_t)1.54394531f, + (float16_t)-0.112304688f, + (float16_t)-5.828125f, + (float16_t)-1.54296875f, + (float16_t)1.11914062f, + (float16_t)-4.5390625f, + (float16_t)4.6015625f, + (float16_t)-5.69921875f, + (float16_t)-0.962402344f, + (float16_t)-3.2734375f, + (float16_t)0.467041016f, + (float16_t)1.96289062f, + (float16_t)3.671875f, + (float16_t)0.666503906f, + (float16_t)2.9140625f, + (float16_t)1.88378906f, + (float16_t)-0.276855469f, + (float16_t)-3.88476562f, + (float16_t)4.65234375f, + (float16_t)-2.03515625f, + (float16_t)3.65820312f, + (float16_t)2.12695312f, + (float16_t)0.946289062f, + (float16_t)5.5703125f, + (float16_t)-2.73046875f, + (float16_t)3.16992188f, + (float16_t)2.7265625f, + (float16_t)0.518066406f, + (float16_t)4.26171875f, + (float16_t)-5.87109375f, + (float16_t)-3.84765625f, + (float16_t)-5.20703125f, + (float16_t)1.82128906f, + (float16_t)1.60546875f, + (float16_t)-1.95898438f, + (float16_t)-3.078125f, + (float16_t)-3.32617188f, + (float16_t)-0.578125f, + (float16_t)2.32421875f, + (float16_t)-2.8125f, + (float16_t)5.31640625f, + (float16_t)-5.16015625f, + (float16_t)2.06640625f, + (float16_t)-2.03320312f, + (float16_t)-5.109375f, + (float16_t)0.206787109f, + (float16_t)5.90625f, + (float16_t)3.84765625f, + (float16_t)1.16503906f, + (float16_t)-1.22265625f, + (float16_t)-1.6171875f, + (float16_t)-3.13085938f, + (float16_t)-4.46875f, + (float16_t)5.17578125f, + (float16_t)-0.762207031f, + (float16_t)-2.8828125f, + (float16_t)0.748046875f, + (float16_t)-3.7109375f, + (float16_t)-1.50195312f, + (float16_t)2.76367188f, + (float16_t)-1.25390625f, + (float16_t)-4.7265625f, + (float16_t)2.89257812f, + (float16_t)-0.973144531f, + (float16_t)-3.45117188f, + (float16_t)4.76953125f, + (float16_t)2.296875f, + (float16_t)-3.59375f, + (float16_t)-0.525390625f, + (float16_t)5.62109375f, + (float16_t)5.296875f, + (float16_t)3.57421875f, + (float16_t)5.09765625f, + (float16_t)2.5625f, + (float16_t)-5.125f, + (float16_t)3.93945312f, + (float16_t)-0.263183594f, + (float16_t)5.91015625f, + (float16_t)2.31445312f, + (float16_t)0.352539062f, + (float16_t)1.63671875f, + (float16_t)-0.817382812f, + (float16_t)5.87109375f, + (float16_t)-1.22070312f, + (float16_t)0.861328125f, + (float16_t)3.31445312f, + (float16_t)3.38671875f, + (float16_t)2.94726562f, + (float16_t)-4.66015625f, + (float16_t)-5.453125f, + (float16_t)1.60351562f, + (float16_t)2.33789062f, + (float16_t)3.61328125f, + (float16_t)-0.397460938f, + (float16_t)-3.99609375f, + (float16_t)5.18359375f, + (float16_t)5.1953125f, + (float16_t)-0.131591797f, + (float16_t)-2.8671875f, + (float16_t)-0.455810547f, + (float16_t)4.63671875f, + (float16_t)-3.54296875f, + (float16_t)-2.67773438f, + (float16_t)4.2265625f, + (float16_t)1.97070312f, + (float16_t)2.46484375f, + (float16_t)2.28125f, + (float16_t)5.0234375f, + (float16_t)0.365478516f, + (float16_t)-0.636230469f, + (float16_t)-3.33984375f, + (float16_t)-0.614746094f, + (float16_t)-4.796875f, + (float16_t)-4.6484375f, + (float16_t)-1.31445312f, + (float16_t)-4.53125f, + (float16_t)-5.59375f, + (float16_t)-1.39453125f, + (float16_t)-2.625f, + (float16_t)-0.402587891f, + (float16_t)-1.10449219f, + (float16_t)4.2578125f, + (float16_t)-0.648925781f, + (float16_t)0.606933594f, + (float16_t)-3.29492188f, + (float16_t)5.64453125f, + (float16_t)-3.125f, + (float16_t)5.98046875f, + (float16_t)2.04492188f, + (float16_t)2.52734375f, + (float16_t)5.9140625f, + (float16_t)5.515625f, + (float16_t)-2.2265625f, + (float16_t)-3.50585938f, + (float16_t)-1.07519531f, + (float16_t)-0.943359375f, + (float16_t)3.23242188f, + (float16_t)-3.94921875f, + (float16_t)4.359375f, + 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(float16_t)-4.49609375f, + (float16_t)-3.83398438f, + (float16_t)0.638671875f, + (float16_t)4.5078125f, + (float16_t)5.5f, + (float16_t)5.8359375f, + (float16_t)-0.252197266f, + (float16_t)-2.63867188f, + (float16_t)-1.72460938f, + (float16_t)-5.8828125f, + (float16_t)-0.520996094f, + (float16_t)-5.59375f, + (float16_t)5.20703125f, + (float16_t)4.5078125f, + (float16_t)-3.42382812f, + (float16_t)0.0116195679f, + (float16_t)0.350585938f, + (float16_t)-3.5390625f, + (float16_t)-2.26953125f, + (float16_t)3.67578125f, + (float16_t)4.37109375f, + (float16_t)2.76757812f, + (float16_t)-3.375f, + (float16_t)-2.18359375f, + (float16_t)-3.859375f, + (float16_t)-4.0703125f, + (float16_t)-4.75390625f, + (float16_t)-1.64648438f, + (float16_t)2.4765625f, + (float16_t)1.61035156f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/output_ref_data.h new file mode 100644 index 00000000..8a35d5e4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/output_ref_data.h @@ -0,0 +1,47 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_output_ref[180] = { + (float16_t)0.188598633f, (float16_t)-0.403564453f, (float16_t)0.607421875f, (float16_t)-0.245605469f, + (float16_t)-0.155639648f, (float16_t)0.389648438f, (float16_t)-0.608886719f, (float16_t)1.58984375f, + (float16_t)-0.813964844f, (float16_t)-0.546386719f, (float16_t)-0.437988281f, (float16_t)0.380126953f, + (float16_t)0.646972656f, (float16_t)-0.125976562f, (float16_t)0.0518493652f, (float16_t)-0.494628906f, + (float16_t)1.16796875f, (float16_t)-1.33496094f, (float16_t)0.0104217529f, (float16_t)-0.203735352f, + (float16_t)0.904785156f, (float16_t)0.110656738f, (float16_t)-0.1015625f, (float16_t)-0.575195312f, + (float16_t)1.24902344f, (float16_t)-1.02441406f, (float16_t)0.497558594f, (float16_t)-0.120544434f, + (float16_t)0.343994141f, (float16_t)0.413330078f, (float16_t)1.15332031f, (float16_t)1.21875f, + (float16_t)0.16015625f, (float16_t)-0.427978516f, (float16_t)0.506347656f, (float16_t)0.641601562f, + (float16_t)-0.166137695f, (float16_t)0.147338867f, (float16_t)0.807617188f, (float16_t)0.234619141f, + (float16_t)-1.3828125f, (float16_t)-0.849121094f, (float16_t)-0.138061523f, (float16_t)-0.487792969f, + (float16_t)-0.576660156f, (float16_t)-0.681152344f, (float16_t)-0.382568359f, (float16_t)0.138305664f, + (float16_t)0.572265625f, (float16_t)-0.439941406f, (float16_t)1.03710938f, (float16_t)-0.00216674805f, + (float16_t)-0.932617188f, (float16_t)-1.27441406f, (float16_t)0.541992188f, (float16_t)1.56445312f, + (float16_t)0.593261719f, (float16_t)0.153076172f, (float16_t)-0.151489258f, (float16_t)0.0239562988f, + (float16_t)1.12695312f, (float16_t)0.573242188f, (float16_t)-1.0859375f, (float16_t)0.304931641f, + (float16_t)-0.791992188f, (float16_t)-0.444335938f, (float16_t)0.306640625f, (float16_t)0.709472656f, + (float16_t)0.173461914f, (float16_t)0.666015625f, (float16_t)0.37109375f, (float16_t)0.184204102f, + (float16_t)0.117797852f, (float16_t)-1.0703125f, (float16_t)-0.0176391602f, (float16_t)0.12878418f, + (float16_t)-0.384521484f, (float16_t)-0.994628906f, (float16_t)-0.987792969f, (float16_t)-0.269042969f, + (float16_t)-0.657226562f, (float16_t)-0.530273438f, (float16_t)-0.410644531f, (float16_t)1.05664062f, + (float16_t)0.773925781f, (float16_t)-0.334716797f, (float16_t)0.723632812f, (float16_t)-1.03320312f, + (float16_t)0.550292969f, (float16_t)0.32421875f, (float16_t)-0.425537109f, (float16_t)-0.0177764893f, + (float16_t)0.611816406f, (float16_t)-0.7265625f, (float16_t)-0.528320312f, (float16_t)0.182250977f, + (float16_t)0.486572266f, (float16_t)-0.477783203f, (float16_t)-0.805175781f, (float16_t)0.136108398f, + (float16_t)-0.765136719f, (float16_t)-0.0698852539f, (float16_t)1.32910156f, (float16_t)-0.897949219f, + (float16_t)0.442871094f, (float16_t)-0.154052734f, (float16_t)-1.2890625f, (float16_t)0.767089844f, + (float16_t)-0.13671875f, (float16_t)-0.653320312f, (float16_t)-0.270507812f, (float16_t)0.73828125f, + (float16_t)1.07226562f, (float16_t)0.643554688f, (float16_t)-0.127075195f, (float16_t)0.415527344f, + (float16_t)1.66796875f, (float16_t)-1.02539062f, (float16_t)-0.773925781f, (float16_t)-0.533691406f, + (float16_t)-0.0876464844f, (float16_t)-0.665039062f, (float16_t)-0.242919922f, (float16_t)0.666015625f, + (float16_t)-1.18359375f, (float16_t)-0.0645141602f, (float16_t)0.508300781f, (float16_t)0.721679688f, + (float16_t)-0.869628906f, (float16_t)2.07617188f, (float16_t)1.0390625f, (float16_t)0.580078125f, + (float16_t)1.05371094f, (float16_t)0.896484375f, (float16_t)-1.34375f, (float16_t)-0.522949219f, + (float16_t)-0.961914062f, (float16_t)-0.115356445f, (float16_t)-0.572265625f, (float16_t)0.37890625f, + (float16_t)0.125732422f, (float16_t)0.364013672f, (float16_t)0.266845703f, (float16_t)0.393554688f, + (float16_t)-0.083190918f, (float16_t)-0.461181641f, (float16_t)0.0230255127f, (float16_t)-0.418212891f, + (float16_t)0.315185547f, (float16_t)-1.45800781f, (float16_t)0.00961303711f, (float16_t)-0.412841797f, + (float16_t)0.296386719f, (float16_t)-0.0263824463f, (float16_t)-0.532714844f, (float16_t)-0.322021484f, + (float16_t)-1.16503906f, (float16_t)0.514648438f, (float16_t)0.145751953f, (float16_t)0.332763672f, + (float16_t)-0.299560547f, (float16_t)-1.40625f, (float16_t)-0.561035156f, (float16_t)0.241088867f, + (float16_t)-1.1796875f, (float16_t)0.33984375f, (float16_t)0.00561523438f, (float16_t)-0.978027344f, + (float16_t)-0.492431641f, (float16_t)0.512207031f, (float16_t)0.415039062f, (float16_t)-1.06054688f, + (float16_t)1.26757812f, (float16_t)-0.164306641f, (float16_t)-0.00415420532f, (float16_t)0.122375488f, + (float16_t)-1.99121094f, (float16_t)-0.147827148f, (float16_t)-1.36132812f, (float16_t)1.15917969f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/config_data.h new file mode 100644 index 00000000..c1e29f58 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F16_1_BATCH_SIZE 1 +#define AVGPOOLING_F16_1_INPUT_N 1 +#define AVGPOOLING_F16_1_INPUT_W 9 +#define AVGPOOLING_F16_1_INPUT_H 5 +#define AVGPOOLING_F16_1_INPUT_C 3 +#define AVGPOOLING_F16_1_FILTER_W 9 +#define AVGPOOLING_F16_1_FILTER_H 5 +#define AVGPOOLING_F16_1_STRIDE_W 1 +#define AVGPOOLING_F16_1_STRIDE_H 2 +#define AVGPOOLING_F16_1_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_F16_1_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_F16_1_OUTPUT_C 3 +#define AVGPOOLING_F16_1_OUTPUT_W 1 +#define AVGPOOLING_F16_1_OUTPUT_H 1 +#define AVGPOOLING_F16_1_PADDING_H 0 +#define AVGPOOLING_F16_1_PADDING_W 0 +#define AVGPOOLING_F16_1_DST_SIZE 3 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/input_tensor_data.h new file mode 100644 index 00000000..8886a710 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/input_tensor_data.h @@ -0,0 +1,36 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_1_input_tensor[135] = { + (float16_t)0.521972656f, (float16_t)-5.3671875f, (float16_t)3.8359375f, (float16_t)-2.34765625f, + (float16_t)-0.711914062f, (float16_t)-4.46484375f, (float16_t)0.969238281f, (float16_t)-4.73046875f, + (float16_t)-4.69921875f, (float16_t)0.362792969f, (float16_t)2.54101562f, (float16_t)-1.40429688f, + (float16_t)-1.44921875f, (float16_t)-0.747070312f, (float16_t)4.52734375f, (float16_t)5.38671875f, + (float16_t)5.7421875f, (float16_t)0.814453125f, (float16_t)1.0078125f, (float16_t)-1.42675781f, + (float16_t)-5.09765625f, (float16_t)-2.74609375f, (float16_t)1.18554688f, (float16_t)-5.28125f, + (float16_t)2.75195312f, (float16_t)-2.76757812f, (float16_t)0.052154541f, (float16_t)-0.0708007812f, + (float16_t)5.7265625f, (float16_t)-0.495849609f, (float16_t)-4.70703125f, (float16_t)1.91992188f, + (float16_t)-2.65429688f, (float16_t)-5.6796875f, (float16_t)1.30664062f, (float16_t)4.328125f, + (float16_t)-3.4921875f, (float16_t)3.40429688f, (float16_t)-1.05664062f, (float16_t)4.80078125f, + (float16_t)-5.7265625f, (float16_t)-2.390625f, (float16_t)-2.04882812f, (float16_t)-0.590332031f, + (float16_t)-2.48632812f, (float16_t)4.07421875f, (float16_t)1.171875f, (float16_t)4.921875f, + (float16_t)3.06445312f, (float16_t)0.275146484f, (float16_t)2.59765625f, (float16_t)-1.55761719f, + (float16_t)5.12109375f, (float16_t)-4.33203125f, (float16_t)0.3984375f, (float16_t)2.26367188f, + (float16_t)-4.125f, (float16_t)1.11035156f, (float16_t)-2.25390625f, (float16_t)-3.84179688f, + (float16_t)-0.493164062f, (float16_t)-2.41992188f, (float16_t)-1.89257812f, (float16_t)-4.78515625f, + (float16_t)4.7578125f, (float16_t)-2.671875f, (float16_t)5.60546875f, (float16_t)-3.13085938f, + (float16_t)-2.703125f, (float16_t)4.64453125f, (float16_t)4.53125f, (float16_t)5.26953125f, + (float16_t)-1.24414062f, (float16_t)-2.66601562f, (float16_t)-3.078125f, (float16_t)1.05859375f, + (float16_t)1.76757812f, (float16_t)-3.17578125f, (float16_t)1.80761719f, (float16_t)-5.53125f, + (float16_t)-2.88476562f, (float16_t)4.15234375f, (float16_t)4.6171875f, (float16_t)2.26367188f, + (float16_t)0.700683594f, (float16_t)4.5859375f, (float16_t)3.38671875f, (float16_t)-0.266845703f, + (float16_t)0.821777344f, (float16_t)0.879394531f, (float16_t)0.0491638184f, (float16_t)5.9296875f, + (float16_t)-3.89453125f, (float16_t)1.90332031f, (float16_t)1.41015625f, (float16_t)4.54296875f, + (float16_t)4.85546875f, (float16_t)-4.6640625f, (float16_t)1.98046875f, (float16_t)3.25976562f, + (float16_t)-4.2578125f, (float16_t)-2.11132812f, (float16_t)-3.59960938f, (float16_t)-0.510253906f, + (float16_t)2.99804688f, (float16_t)-2.50976562f, (float16_t)0.899414062f, (float16_t)-5.7734375f, + (float16_t)5.640625f, (float16_t)-2.9765625f, (float16_t)-1.29785156f, (float16_t)5.90234375f, + (float16_t)-3.2109375f, (float16_t)-1.74316406f, (float16_t)3.0546875f, (float16_t)-5.03125f, + (float16_t)-4.57421875f, (float16_t)-5.65625f, (float16_t)2.37304688f, (float16_t)5.6796875f, + (float16_t)2.69335938f, (float16_t)-3.22070312f, (float16_t)3.71875f, (float16_t)-1.88671875f, + (float16_t)0.132202148f, (float16_t)0.652832031f, (float16_t)-2.42773438f, (float16_t)-3.640625f, + (float16_t)2.34179688f, (float16_t)-5.8125f, (float16_t)-1.93164062f, (float16_t)0.159912109f, + (float16_t)-5.9453125f, (float16_t)-2.51367188f, (float16_t)4.55859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/output_ref_data.h new file mode 100644 index 00000000..3960720a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/output_ref_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_1_output_ref[3] = {(float16_t)0.245605469f, + (float16_t)-0.167724609f, + (float16_t)-0.413818359f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_1/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/config_data.h new file mode 100644 index 00000000..ec42072f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F16_2_BATCH_SIZE 1 +#define AVGPOOLING_F16_2_INPUT_N 1 +#define AVGPOOLING_F16_2_INPUT_W 3 +#define AVGPOOLING_F16_2_INPUT_H 3 +#define AVGPOOLING_F16_2_INPUT_C 1 +#define AVGPOOLING_F16_2_FILTER_W 1 +#define AVGPOOLING_F16_2_FILTER_H 3 +#define AVGPOOLING_F16_2_STRIDE_W 1 +#define AVGPOOLING_F16_2_STRIDE_H 1 +#define AVGPOOLING_F16_2_ACTIVATION_MIN (float16_t)0.0f +#define AVGPOOLING_F16_2_ACTIVATION_MAX (float16_t)6.0f +#define AVGPOOLING_F16_2_OUTPUT_C 1 +#define AVGPOOLING_F16_2_OUTPUT_W 3 +#define AVGPOOLING_F16_2_OUTPUT_H 3 +#define AVGPOOLING_F16_2_PADDING_H 1 +#define AVGPOOLING_F16_2_PADDING_W 0 +#define AVGPOOLING_F16_2_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/input_tensor_data.h new file mode 100644 index 00000000..0aa93ef5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/input_tensor_data.h @@ -0,0 +1,10 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_2_input_tensor[9] = {(float16_t)-0.257324219f, + (float16_t)5.93359375f, + (float16_t)3.98632812f, + (float16_t)-0.883300781f, + (float16_t)7.15234375f, + (float16_t)3.27734375f, + (float16_t)-2.1875f, + (float16_t)6.83984375f, + (float16_t)6.61328125f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/output_ref_data.h new file mode 100644 index 00000000..df97e561 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_2_output_ref[9] = {(float16_t)0.0f, + (float16_t)6.0f, + (float16_t)3.63085938f, + (float16_t)0.0f, + (float16_t)6.0f, + (float16_t)4.625f, + (float16_t)0.0f, + (float16_t)6.0f, + (float16_t)4.9453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_2/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/config_data.h new file mode 100644 index 00000000..069bee97 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F16_GLOBAL_BATCH_SIZE 1 +#define AVGPOOLING_F16_GLOBAL_INPUT_N 1 +#define AVGPOOLING_F16_GLOBAL_INPUT_W 5 +#define AVGPOOLING_F16_GLOBAL_INPUT_H 25 +#define AVGPOOLING_F16_GLOBAL_INPUT_C 64 +#define AVGPOOLING_F16_GLOBAL_FILTER_W 5 +#define AVGPOOLING_F16_GLOBAL_FILTER_H 25 +#define AVGPOOLING_F16_GLOBAL_STRIDE_W 1 +#define AVGPOOLING_F16_GLOBAL_STRIDE_H 1 +#define AVGPOOLING_F16_GLOBAL_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_F16_GLOBAL_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_F16_GLOBAL_OUTPUT_C 64 +#define AVGPOOLING_F16_GLOBAL_OUTPUT_W 1 +#define AVGPOOLING_F16_GLOBAL_OUTPUT_H 1 +#define AVGPOOLING_F16_GLOBAL_PADDING_H 0 +#define AVGPOOLING_F16_GLOBAL_PADDING_W 0 +#define AVGPOOLING_F16_GLOBAL_DST_SIZE 64 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/input_tensor_data.h new file mode 100644 index 00000000..6a85c8b8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/input_tensor_data.h @@ -0,0 +1,8001 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_global_input_tensor[8000] = {(float16_t)-5.1328125f, + (float16_t)-5.84765625f, + (float16_t)1.86328125f, + (float16_t)2.91992188f, + (float16_t)-0.122375488f, + (float16_t)-5.5234375f, + (float16_t)-4.7265625f, + (float16_t)5.37890625f, + (float16_t)-3.94335938f, + (float16_t)5.4140625f, + (float16_t)4.953125f, + (float16_t)3.2734375f, + (float16_t)1.18359375f, + (float16_t)2.84765625f, + (float16_t)0.754394531f, + (float16_t)2.67773438f, + (float16_t)-4.5390625f, + (float16_t)-5.11328125f, + (float16_t)4.2109375f, + (float16_t)-1.22753906f, + (float16_t)5.2265625f, + (float16_t)-4.515625f, + (float16_t)-1.12304688f, + (float16_t)3.65234375f, + (float16_t)-5.30078125f, + (float16_t)5.61328125f, + (float16_t)-5.99609375f, + (float16_t)5.49609375f, + (float16_t)-5.03125f, + (float16_t)-4.86328125f, + (float16_t)-5.2734375f, + (float16_t)-1.90136719f, + (float16_t)-5.09765625f, + (float16_t)1.35839844f, + (float16_t)5.6171875f, + (float16_t)-4.60546875f, + (float16_t)2.2265625f, + (float16_t)-0.959960938f, + (float16_t)3.09375f, + (float16_t)-2.28515625f, + (float16_t)5.35546875f, + (float16_t)-0.49609375f, + (float16_t)-4.62109375f, + (float16_t)-4.515625f, + (float16_t)2.89453125f, + (float16_t)-4.328125f, + (float16_t)-2.64648438f, + (float16_t)-3.55273438f, + (float16_t)-5.76171875f, + (float16_t)0.503417969f, + (float16_t)-0.643554688f, + (float16_t)-5.37109375f, + (float16_t)-4.8359375f, + (float16_t)-4.48046875f, + (float16_t)-0.953613281f, + (float16_t)-2.65820312f, + (float16_t)-4.078125f, + (float16_t)2.7734375f, + (float16_t)0.402099609f, + (float16_t)-5.46484375f, + (float16_t)3.95117188f, + (float16_t)4.1953125f, + (float16_t)3.15429688f, + (float16_t)-0.584472656f, + (float16_t)3.74414062f, + (float16_t)0.746582031f, + (float16_t)3.71289062f, + (float16_t)4.3828125f, + (float16_t)-1.07324219f, + (float16_t)1.93554688f, + (float16_t)-3.09375f, + (float16_t)-2.33203125f, + (float16_t)-2.47851562f, + (float16_t)2.109375f, + (float16_t)-5.12890625f, + (float16_t)-5.05859375f, + (float16_t)-2.50195312f, + (float16_t)-5.93359375f, + (float16_t)0.62109375f, + (float16_t)2.73828125f, + (float16_t)-5.71484375f, + (float16_t)5.6015625f, + (float16_t)1.37988281f, + (float16_t)3.26953125f, + (float16_t)-2.390625f, + (float16_t)2.38867188f, + (float16_t)3.84375f, + (float16_t)3.26171875f, + (float16_t)0.118103027f, + (float16_t)5.00390625f, + (float16_t)4.8046875f, + (float16_t)3.58203125f, + (float16_t)3.46875f, + (float16_t)2.23242188f, + (float16_t)0.666503906f, + (float16_t)3.87109375f, + (float16_t)-0.109863281f, + (float16_t)-1.94042969f, + (float16_t)3.75585938f, + (float16_t)-3.30273438f, + (float16_t)3.58007812f, + (float16_t)-0.520996094f, + (float16_t)-3.8203125f, + (float16_t)-5.01171875f, + (float16_t)-5.8515625f, + (float16_t)0.546875f, + (float16_t)-3.29492188f, + (float16_t)4.62109375f, + (float16_t)-3.25195312f, + (float16_t)-2.57617188f, + (float16_t)5.859375f, + (float16_t)3.22460938f, + (float16_t)-0.671875f, + (float16_t)4.34375f, + (float16_t)-2.96679688f, + (float16_t)2.02148438f, + (float16_t)0.5234375f, + (float16_t)0.862304688f, + (float16_t)-5.0078125f, + (float16_t)2.81835938f, + (float16_t)-3.234375f, + (float16_t)3.96679688f, + (float16_t)-0.0863647461f, + (float16_t)-4.36328125f, + (float16_t)-5.1328125f, + (float16_t)-0.309082031f, + (float16_t)-3.78125f, + (float16_t)0.982421875f, + (float16_t)-1.99121094f, + (float16_t)-3.3515625f, + (float16_t)5.03125f, + (float16_t)-3.3515625f, + (float16_t)5.06640625f, + (float16_t)2.53125f, + (float16_t)2.3671875f, + (float16_t)-2.37890625f, + (float16_t)5.05859375f, + (float16_t)-0.297363281f, + (float16_t)0.235351562f, + (float16_t)4.3984375f, + (float16_t)-1.49316406f, + (float16_t)-4.60546875f, + (float16_t)2.18164062f, + (float16_t)-5.55859375f, + (float16_t)5.078125f, + (float16_t)4.8203125f, + (float16_t)3.90039062f, + (float16_t)-0.191772461f, + (float16_t)-3.35742188f, + (float16_t)1.06835938f, + (float16_t)-2.5f, + (float16_t)3.20898438f, + (float16_t)0.92578125f, + (float16_t)5.8515625f, + (float16_t)4.76953125f, + (float16_t)4.3359375f, + (float16_t)4.50390625f, + (float16_t)2.9140625f, + (float16_t)1.15625f, + (float16_t)3.21875f, + (float16_t)5.07421875f, + (float16_t)-0.285644531f, + (float16_t)-1.88476562f, + (float16_t)1.25976562f, + (float16_t)-1.21679688f, + (float16_t)4.05859375f, + (float16_t)0.328369141f, + (float16_t)-0.733886719f, + (float16_t)1.96289062f, + (float16_t)-2.43359375f, + (float16_t)-2.140625f, + (float16_t)3.359375f, + (float16_t)3.17382812f, + (float16_t)5.109375f, + (float16_t)-0.332275391f, + (float16_t)-0.761230469f, + (float16_t)-2.94726562f, + (float16_t)0.950683594f, + (float16_t)-0.644042969f, + (float16_t)-2.10742188f, + (float16_t)-4.703125f, + (float16_t)-0.260253906f, + (float16_t)-2.61914062f, + (float16_t)-5.2890625f, + (float16_t)3.30078125f, + (float16_t)-3.50585938f, + (float16_t)-5.26171875f, + (float16_t)2.66601562f, + (float16_t)3.6640625f, + (float16_t)1.25292969f, + (float16_t)5.7734375f, + (float16_t)-5.03125f, + (float16_t)-3.46679688f, + (float16_t)-4.31640625f, + (float16_t)0.00353240967f, + (float16_t)5.1328125f, + (float16_t)-2.59570312f, + (float16_t)2.3984375f, + (float16_t)2.59375f, + (float16_t)1.37792969f, + (float16_t)-4.29296875f, + (float16_t)-0.997558594f, + (float16_t)4.859375f, + (float16_t)-2.66015625f, + (float16_t)4.68359375f, + (float16_t)-5.5078125f, + (float16_t)0.223999023f, + (float16_t)-0.889160156f, + (float16_t)0.0841064453f, + (float16_t)-1.60449219f, + (float16_t)5.62109375f, + (float16_t)-0.587402344f, + (float16_t)5.9140625f, + (float16_t)1.08105469f, + (float16_t)-0.544921875f, + (float16_t)-2.9609375f, + (float16_t)1.14453125f, + (float16_t)-0.90234375f, + (float16_t)1.20019531f, + (float16_t)5.5234375f, + (float16_t)-1.25683594f, + (float16_t)5.4296875f, + (float16_t)4.48046875f, + (float16_t)0.01121521f, + (float16_t)-2.3828125f, + (float16_t)1.41210938f, + (float16_t)0.0133361816f, + (float16_t)0.325439453f, + (float16_t)-0.942382812f, + (float16_t)4.30078125f, + (float16_t)-4.33203125f, + (float16_t)0.949707031f, + (float16_t)0.0467834473f, + (float16_t)3.83007812f, + (float16_t)4.47265625f, + (float16_t)-3.82226562f, + (float16_t)-1.88964844f, + (float16_t)-2.0234375f, + (float16_t)3.76171875f, + (float16_t)0.524414062f, + (float16_t)3.23046875f, + (float16_t)-1.08300781f, + (float16_t)-1.06054688f, + (float16_t)3.02929688f, + (float16_t)3.45117188f, + (float16_t)4.2734375f, + (float16_t)4.9609375f, + (float16_t)-4.23046875f, + (float16_t)2.76367188f, + (float16_t)-1.24804688f, + (float16_t)2.46289062f, + (float16_t)0.862304688f, + (float16_t)4.86328125f, + (float16_t)-4.96875f, + (float16_t)-4.24609375f, + (float16_t)-0.918457031f, + (float16_t)-1.77246094f, + (float16_t)4.49609375f, + (float16_t)0.547851562f, + (float16_t)-2.015625f, + (float16_t)-0.639648438f, + (float16_t)5.61328125f, + (float16_t)1.05273438f, + (float16_t)-4.703125f, + (float16_t)-4.4921875f, + (float16_t)0.6640625f, + (float16_t)-0.647460938f, + (float16_t)-5.7421875f, + (float16_t)-0.457275391f, + (float16_t)5.46484375f, + (float16_t)1.20996094f, + (float16_t)0.909667969f, + (float16_t)5.37890625f, + (float16_t)-3.890625f, + (float16_t)3.8046875f, + (float16_t)-5.43359375f, + (float16_t)5.52734375f, + (float16_t)0.172241211f, + (float16_t)-5.3828125f, + (float16_t)-0.79296875f, + (float16_t)-1.95703125f, + (float16_t)3.68554688f, + (float16_t)-1.11914062f, + (float16_t)-0.125244141f, + (float16_t)3.08007812f, + (float16_t)-2.15625f, + (float16_t)1.51367188f, + (float16_t)1.23828125f, + (float16_t)4.52734375f, + (float16_t)-1.16113281f, + (float16_t)5.97265625f, + (float16_t)1.78125f, + (float16_t)-0.671386719f, + (float16_t)-0.942382812f, + (float16_t)-0.502929688f, + (float16_t)-1.99414062f, + (float16_t)-3.1953125f, + (float16_t)-2.36914062f, + (float16_t)-2.41601562f, + (float16_t)5.9296875f, + (float16_t)0.532226562f, + (float16_t)5.19140625f, + (float16_t)5.88671875f, + (float16_t)-5.24609375f, + (float16_t)2.23828125f, + (float16_t)-5.328125f, + (float16_t)1.05957031f, + (float16_t)-4.46484375f, + (float16_t)0.287109375f, + (float16_t)2.84570312f, + (float16_t)2.14257812f, + (float16_t)-1.81152344f, + (float16_t)-0.823730469f, + (float16_t)-2.74609375f, + (float16_t)2.32226562f, + (float16_t)4.40625f, + (float16_t)-2.59375f, + (float16_t)0.795410156f, + (float16_t)-3.40039062f, + (float16_t)-1.60058594f, + (float16_t)2.56054688f, + (float16_t)-1.01660156f, + (float16_t)3.84765625f, + (float16_t)1.27441406f, + (float16_t)1.80078125f, + (float16_t)2.72460938f, + (float16_t)2.10351562f, + (float16_t)2.9140625f, + (float16_t)-3.48242188f, + (float16_t)2.59765625f, + (float16_t)-1.76464844f, + (float16_t)2.18164062f, + (float16_t)5.29296875f, + (float16_t)-5.66796875f, + (float16_t)-0.505859375f, + (float16_t)-3.39257812f, + (float16_t)-0.391113281f, + (float16_t)2.23046875f, + (float16_t)2.74023438f, + (float16_t)2.21679688f, + (float16_t)-0.987792969f, + (float16_t)0.414794922f, + (float16_t)-3.93554688f, + (float16_t)3.23828125f, + (float16_t)1.8125f, + (float16_t)1.75488281f, + (float16_t)3.59375f, + (float16_t)-4.68359375f, + (float16_t)2.41796875f, + (float16_t)0.967773438f, + (float16_t)4.6171875f, + (float16_t)-3.3515625f, + (float16_t)-5.19140625f, + (float16_t)4.609375f, + 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(float16_t)-4.58984375f, + (float16_t)-4.51171875f, + (float16_t)-5.35546875f, + (float16_t)-4.5078125f, + (float16_t)0.799804688f, + (float16_t)-0.6953125f, + (float16_t)4.71484375f, + (float16_t)4.86328125f, + (float16_t)1.96679688f, + (float16_t)2.0546875f, + (float16_t)-2.51171875f, + (float16_t)-2.33007812f, + (float16_t)-3.1796875f, + (float16_t)-3.84765625f, + (float16_t)-2.74414062f, + (float16_t)-2.66796875f, + (float16_t)5.5234375f, + (float16_t)1.95996094f, + (float16_t)-3.12109375f, + (float16_t)-0.217041016f, + (float16_t)4.51953125f, + (float16_t)2.99414062f, + (float16_t)-3.671875f, + (float16_t)-1.99511719f, + (float16_t)-0.409912109f, + (float16_t)3.15234375f, + (float16_t)3.6171875f, + (float16_t)-5.734375f, + (float16_t)-4.09765625f, + (float16_t)3.66796875f, + (float16_t)0.836425781f, + (float16_t)-2.73242188f, + (float16_t)-4.109375f, + (float16_t)3.3046875f, + (float16_t)-1.30859375f, + (float16_t)4.87890625f, + (float16_t)-1.11328125f, + (float16_t)-1.09667969f, + (float16_t)-5.12109375f, + (float16_t)1.86230469f, + (float16_t)3.03710938f, + (float16_t)5.375f, + (float16_t)1.24902344f, + (float16_t)4.703125f, + (float16_t)-5.62109375f, + (float16_t)-0.114746094f, + (float16_t)-5.27734375f, + (float16_t)3.25976562f, + (float16_t)-5.421875f, + (float16_t)1.26953125f, + (float16_t)1.82128906f, + (float16_t)-4.45703125f, + (float16_t)-4.30859375f, + (float16_t)-1.20898438f, + (float16_t)-1.94726562f, + (float16_t)3.71484375f, + (float16_t)-5.78515625f, + (float16_t)-1.71191406f, + (float16_t)5.1640625f, + (float16_t)-4.48046875f, + (float16_t)-2.01757812f, + (float16_t)0.400146484f, + (float16_t)-1.484375f, + (float16_t)-0.8046875f, + (float16_t)5.51953125f, + (float16_t)-1.87695312f, + (float16_t)0.0994262695f, + (float16_t)-4.19140625f, + (float16_t)-4.375f, + (float16_t)-5.015625f, + (float16_t)0.852539062f, + (float16_t)-3.76171875f, + (float16_t)-4.80078125f, + (float16_t)2.08007812f, + (float16_t)-5.94140625f, + (float16_t)-3.88085938f, + (float16_t)-1.90527344f, + (float16_t)3.34765625f, + (float16_t)-0.982421875f, + (float16_t)3.50976562f, + (float16_t)4.7734375f, + (float16_t)3.81640625f, + (float16_t)4.515625f, + (float16_t)-0.859863281f, + (float16_t)4.4140625f, + (float16_t)-2.28320312f, + (float16_t)4.11328125f, + (float16_t)-3.44335938f, + (float16_t)3.22851562f, + (float16_t)4.58203125f, + (float16_t)-0.523925781f, + (float16_t)4.640625f, + (float16_t)5.5703125f, + (float16_t)-0.304931641f, + (float16_t)5.89453125f, + (float16_t)0.631835938f, + (float16_t)3.84375f, + (float16_t)-2.50976562f, + (float16_t)-3.98242188f, + (float16_t)-4.5f, + (float16_t)1.82910156f, + (float16_t)-4.8359375f, + (float16_t)3.65429688f, + (float16_t)-0.90625f, + (float16_t)0.110656738f, + (float16_t)3.39648438f, + (float16_t)-0.58203125f, + (float16_t)0.713378906f, + (float16_t)-1.45117188f, + (float16_t)0.8515625f, + (float16_t)-3.56054688f, + (float16_t)-4.67578125f, + (float16_t)-2.45117188f, + (float16_t)-1.07714844f, + (float16_t)2.23046875f, + (float16_t)5.93359375f, + (float16_t)4.94921875f, + (float16_t)0.92578125f, + (float16_t)4.9453125f, + (float16_t)5.5078125f, + (float16_t)-2.4609375f, + (float16_t)5.25390625f, + (float16_t)-4.50390625f, + (float16_t)5.109375f, + (float16_t)2.41796875f, + (float16_t)3.72851562f, + (float16_t)-2.0546875f, + (float16_t)-0.189575195f, + (float16_t)2.00390625f, + (float16_t)3.32226562f, + (float16_t)-0.798339844f, + (float16_t)-0.314697266f, + (float16_t)4.88671875f, + (float16_t)-0.869628906f, + (float16_t)-4.01171875f, + (float16_t)5.83984375f, + (float16_t)2.96484375f, + (float16_t)-0.557128906f, + (float16_t)3.4609375f, + (float16_t)-2.70507812f, + (float16_t)-3.33398438f, + (float16_t)2.28320312f, + (float16_t)-0.239990234f, + (float16_t)0.494140625f, + (float16_t)-1.51171875f, + (float16_t)-5.40625f, + (float16_t)-2.17578125f, + (float16_t)-2.33203125f, + (float16_t)2.55078125f, + (float16_t)-0.23815918f, + (float16_t)4.73046875f, + (float16_t)-0.188110352f, + (float16_t)-3.47070312f, + (float16_t)-4.35546875f, + (float16_t)-3.703125f, + (float16_t)4.25f, + (float16_t)-1.22265625f, + (float16_t)1.25878906f, + (float16_t)-5.73828125f, + (float16_t)-5.0f, + (float16_t)5.90625f, + (float16_t)-3.63476562f, + (float16_t)2.84765625f, + (float16_t)5.82421875f, + (float16_t)-2.69921875f, + (float16_t)-2.09179688f, + (float16_t)-5.54296875f, + (float16_t)1.71875f, + (float16_t)3.11328125f, + (float16_t)4.53125f, + (float16_t)2.4140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/output_ref_data.h new file mode 100644 index 00000000..b9c19bca --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/output_ref_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_f16_global_output_ref[64] = { + (float16_t)0.431884766f, (float16_t)-0.609375f, (float16_t)0.20715332f, (float16_t)0.437011719f, + (float16_t)-0.0850830078f, (float16_t)0.368408203f, (float16_t)-0.0322570801f, (float16_t)0.23059082f, + (float16_t)0.0767211914f, (float16_t)-0.0109710693f, (float16_t)0.160766602f, (float16_t)-0.666015625f, + (float16_t)0.256347656f, (float16_t)0.0915527344f, (float16_t)-0.0246429443f, (float16_t)0.299072266f, + (float16_t)-0.120239258f, (float16_t)0.421386719f, (float16_t)0.533203125f, (float16_t)-0.134887695f, + (float16_t)-0.331787109f, (float16_t)-0.0963745117f, (float16_t)-0.256103516f, (float16_t)-0.159179688f, + (float16_t)-0.418701172f, (float16_t)-0.328857422f, (float16_t)0.025604248f, (float16_t)-0.223388672f, + (float16_t)0.462158203f, (float16_t)0.0320129395f, (float16_t)0.225463867f, (float16_t)-0.0393676758f, + (float16_t)0.363769531f, (float16_t)-0.18347168f, (float16_t)-0.345703125f, (float16_t)0.16027832f, + (float16_t)0.0443115234f, (float16_t)-0.101196289f, (float16_t)-0.413818359f, (float16_t)0.260986328f, + (float16_t)0.252197266f, (float16_t)0.216308594f, (float16_t)0.3125f, (float16_t)0.490234375f, + (float16_t)-0.837402344f, (float16_t)0.221923828f, (float16_t)-0.0914306641f, (float16_t)-0.0165557861f, + (float16_t)-0.280273438f, (float16_t)-0.140014648f, (float16_t)0.193847656f, (float16_t)0.180786133f, + (float16_t)-0.674316406f, (float16_t)0.129760742f, (float16_t)0.347412109f, (float16_t)0.216064453f, + (float16_t)0.131347656f, (float16_t)-0.218139648f, (float16_t)-0.0665893555f, (float16_t)-0.174194336f, + (float16_t)0.23034668f, (float16_t)0.220947266f, (float16_t)-0.075012207f, (float16_t)-0.413818359f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f16_global/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/config_data.h new file mode 100644 index 00000000..bb36a3af --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F32_BATCH_SIZE 1 +#define AVGPOOLING_F32_INPUT_N 1 +#define AVGPOOLING_F32_INPUT_W 22 +#define AVGPOOLING_F32_INPUT_H 12 +#define AVGPOOLING_F32_INPUT_C 20 +#define AVGPOOLING_F32_FILTER_W 6 +#define AVGPOOLING_F32_FILTER_H 5 +#define AVGPOOLING_F32_STRIDE_W 9 +#define AVGPOOLING_F32_STRIDE_H 5 +#define AVGPOOLING_F32_ACTIVATION_MIN -100.0f +#define AVGPOOLING_F32_ACTIVATION_MAX 100.0f +#define AVGPOOLING_F32_OUTPUT_C 20 +#define AVGPOOLING_F32_OUTPUT_W 3 +#define AVGPOOLING_F32_OUTPUT_H 3 +#define AVGPOOLING_F32_PADDING_H 1 +#define AVGPOOLING_F32_PADDING_W 1 +#define AVGPOOLING_F32_DST_SIZE 180 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/input_tensor_data.h new file mode 100644 index 00000000..2e5e6a31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/input_tensor_data.h @@ -0,0 +1,757 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_input_tensor[5280] = { + 1.35113919f, -5.81159449f, -3.74772501f, 4.2946806f, -5.08561659f, -3.58691716f, 1.56121194f, + -4.81725454f, -4.17354727f, -3.83705997f, -4.41685915f, 5.8094039f, 3.18183851f, -2.95838499f, + -0.112548195f, -3.46700716f, -1.67417455f, 4.95750332f, -5.24679947f, -3.58577013f, -2.80731559f, + 5.47597456f, 3.40689206f, -4.69771004f, 4.67088127f, -1.43954003f, -2.0515039f, -5.14858198f, + -1.55219913f, 5.73344183f, -1.32535601f, -1.46868277f, -1.33356154f, -5.45594215f, -1.70166409f, + -4.50793982f, -4.9565115f, -2.74713111f, -1.19873047f, 2.6167984f, -3.54697561f, 3.92164516f, + -3.23469281f, -0.773711264f, 0.136844411f, 3.63934374f, -1.27698505f, 3.42918634f, 2.30084038f, + -1.14746237f, 1.49044609f, -4.79582739f, -0.786987305f, -4.74746323f, -4.11194038f, 1.80594206f, + 2.11518884f, -1.18385434f, 5.70529938f, 2.93758559f, -5.7076683f, -4.65101624f, -2.91392994f, + 0.43101272f, -1.68154418f, 0.65210259f, 0.313765228f, 5.10580158f, 1.90922058f, 3.83334517f, + -2.25211978f, 0.718568325f, 5.56094599f, 2.6955781f, -3.71517062f, 0.128764704f, -4.47651625f, + -1.41724336f, 1.95848489f, -2.85411787f, 1.99246776f, -2.05743122f, 1.76013005f, -0.309979737f, + 0.612135828f, 5.59050131f, 3.33752155f, 0.00609081658f, -0.932406008f, 3.88900781f, -4.78945398f, + 2.81646776f, 2.25882673f, 3.53829956f, -5.68037987f, -2.81342721f, 3.58381486f, -5.82659578f, + -1.94355869f, 0.571843147f, -3.48697686f, -5.24432659f, -3.17459369f, -0.89235419f, -2.28309631f, + 4.05475998f, -4.65223455f, -4.05625296f, -5.67132235f, 2.45836306f, -2.50099492f, -1.89288425f, + -1.76661086f, -4.93467426f, -5.06749916f, -1.39980829f, -0.308970153f, -1.70740879f, -4.65877962f, + 1.94179177f, -0.59315753f, 4.52208853f, 2.73996973f, -3.79640675f, -3.12721634f, -0.623365343f, + -2.43233156f, 5.26611614f, -1.09458387f, -3.86562157f, -0.81082499f, 2.85635805f, 1.42696214f, + 3.36476064f, 0.553787887f, 5.26994324f, -1.35243416f, 5.73446512f, 1.88668919f, 3.04291773f, + -3.43739724f, 4.19316292f, -5.85850668f, 2.7316103f, -0.52449429f, 4.13822508f, 1.99412441f, + -2.97159648f, -3.97838998f, -1.36038983f, -5.22479105f, -5.30755234f, -3.94819856f, 4.27792692f, + -5.79886055f, 0.672231317f, 2.51054621f, 1.98513246f, 2.90708852f, 4.95670843f, -0.0491955578f, + 0.817239285f, 2.79788637f, -1.28584731f, -5.74572611f, 3.43676114f, -2.1916399f, 4.3029027f, + -1.34779966f, -3.99038839f, -0.99765408f, 5.42639971f, 2.04167962f, -4.71175098f, 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-1.89125168f, 5.11383438f, 4.04908514f, -1.55888343f, 4.40557337f, 1.30509531f, + 2.14374089f, 1.08268106f, -1.88342881f, 5.877388f, 5.40529537f, 5.91757631f, -1.45326281f, + -2.91097593f, -3.7094686f, 3.07055902f, -4.46686363f, 4.8285594f, -5.22615957f, -4.14059925f, + 3.78464317f, 3.64243364f, 0.234957859f, 0.688060224f, -1.49097311f, -3.06622815f, -5.15472937f, + 2.34140658f, 2.51581573f, 2.79273844f, -4.51207924f, 1.0233891f, -2.63819385f, 0.703317642f, + -5.60488749f, -5.70455742f, 3.82693028f, 0.902605176f, -3.74842787f, -4.45981073f, -4.32507896f, + -4.50554276f, -1.28324914f, -5.89346409f, 3.42626739f, 1.67855227f, 3.55707145f, 4.80074596f, + -4.38429832f, -5.5230546f, -1.4383285f, 0.453878909f, -3.93712544f, -3.44558358f, 0.855044603f, + -3.45491219f, 0.549736977f, 3.50528932f, 0.300530225f, -4.40048838f, -5.91065979f, -0.18032223f, + 0.587809741f, -1.01112425f, 2.41880774f, -3.31107211f, 3.08505177f, 4.14780951f, 0.872081935f, + -0.531781256f, -3.73928833f, -3.03578067f, -5.60918713f, -4.23362732f, -1.84019637f, -2.96763492f, + 1.61340153f, 4.75035429f, 0.719539225f, -5.05167389f, -1.81302285f, 4.52375126f, -2.31446958f, + -1.10243833f, -3.42977762f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/output_ref_data.h new file mode 100644 index 00000000..c6376893 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/output_ref_data.h @@ -0,0 +1,28 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_output_ref[180] = { + -1.24020636f, 0.0404633507f, -0.131761253f, -0.110739902f, 1.1943655f, -0.165762872f, -0.603693604f, + 0.769303501f, -0.00848667603f, -0.231027201f, -1.26690388f, -0.310634077f, -0.128557399f, -1.09487438f, + 0.465724051f, 1.37315023f, 0.845588565f, 0.133221939f, -0.603125215f, 0.494126976f, 0.151072174f, + -0.533360183f, -0.203611016f, 1.17989969f, -0.368306905f, 0.0475158393f, -0.942069471f, -0.257567674f, + -0.423888773f, 0.16587691f, -0.591055155f, 1.35789645f, 0.225325122f, -0.658600867f, -0.560902119f, + 0.497436196f, 0.375958174f, 0.0440581851f, 0.0707144961f, -0.269548923f, 0.294920176f, 0.017519284f, + 0.392955661f, -1.56433082f, -0.835401416f, 1.44263077f, 0.962070823f, -0.363872617f, 0.931519866f, + -1.10440707f, -1.12750089f, -0.103198566f, -0.695525885f, -0.924846292f, 0.801591516f, 1.26787782f, + 0.464171976f, -0.377844512f, 0.976350009f, 0.249736071f, -1.47737062f, -0.402576834f, 0.693693757f, + -1.28873837f, -0.232885927f, -0.940111518f, -0.961156487f, 0.436004609f, -0.769557595f, -0.620499194f, + 1.2379775f, -0.40747872f, -0.426782578f, 0.20765613f, 0.686801732f, 0.117744215f, 0.620374262f, + 1.4177804f, 0.617693424f, -0.240732059f, 0.409224033f, -0.615983069f, -0.0793741494f, 0.334699333f, + 0.0168176964f, 0.0582920872f, 0.197789654f, -0.0813294873f, -0.528628111f, 0.0916188583f, 0.304904103f, + -0.272925854f, 0.575717092f, -0.371011168f, 0.187767372f, 0.126241401f, -1.38818669f, -0.00411630468f, + -0.11793676f, -0.201816872f, -0.61781013f, 0.463542551f, -1.12905347f, -1.100003f, 0.240652919f, + 0.391991258f, 0.699228883f, -0.53830117f, 0.118328817f, -0.552612484f, -0.575484633f, 0.534677744f, + -0.204705462f, -0.150047615f, 0.48502174f, -0.775656641f, -0.00765246386f, 0.834083557f, 1.51185024f, + 0.351435006f, 0.410358697f, -2.39573264f, -0.401829451f, 0.459854543f, 0.628241479f, -0.0326407105f, + 1.0113529f, -0.947725296f, 0.0395053849f, 1.2766906f, -0.657649696f, -1.91393292f, 0.435266644f, + 0.886994421f, -0.856829345f, -0.410110414f, 0.80095613f, -0.547126412f, 0.294484824f, -0.746282697f, + 0.680671155f, -1.16645837f, 0.00457953103f, -0.434668958f, -0.328558117f, -1.48324835f, -1.05258167f, + -0.176033437f, 0.191425264f, 0.586199701f, -0.311280042f, 0.119849309f, -0.137930945f, 0.124357276f, + -1.66083884f, 1.25980806f, -1.23078191f, 1.25006247f, -0.47817862f, 0.369710118f, 0.0573162884f, + -0.088247709f, 0.143538609f, 0.419243544f, 0.0712874606f, 0.210107535f, -0.202483863f, -1.85364914f, + -0.880717635f, -1.07181418f, -0.876441121f, -0.266413629f, -0.0561491661f, -0.862130165f, -2.69141793f, + -1.92035377f, 1.11225164f, 0.688270569f, -2.00249004f, 1.05109882f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/config_data.h new file mode 100644 index 00000000..7dc49016 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F32_1_BATCH_SIZE 1 +#define AVGPOOLING_F32_1_INPUT_N 1 +#define AVGPOOLING_F32_1_INPUT_W 9 +#define AVGPOOLING_F32_1_INPUT_H 5 +#define AVGPOOLING_F32_1_INPUT_C 3 +#define AVGPOOLING_F32_1_FILTER_W 9 +#define AVGPOOLING_F32_1_FILTER_H 5 +#define AVGPOOLING_F32_1_STRIDE_W 1 +#define AVGPOOLING_F32_1_STRIDE_H 2 +#define AVGPOOLING_F32_1_ACTIVATION_MIN -100.0f +#define AVGPOOLING_F32_1_ACTIVATION_MAX 100.0f +#define AVGPOOLING_F32_1_OUTPUT_C 3 +#define AVGPOOLING_F32_1_OUTPUT_W 1 +#define AVGPOOLING_F32_1_OUTPUT_H 1 +#define AVGPOOLING_F32_1_PADDING_H 0 +#define AVGPOOLING_F32_1_PADDING_W 0 +#define AVGPOOLING_F32_1_DST_SIZE 3 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/input_tensor_data.h new file mode 100644 index 00000000..7cbddff1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_1_input_tensor[135] = { + -1.4301461f, -1.71372795f, 2.97134781f, -1.32609701f, -1.95442581f, 0.658734381f, -3.75337911f, -4.56417131f, + 4.084198f, -1.16759932f, 5.14621735f, -1.82504153f, -1.60855508f, 5.87703228f, -2.60308123f, -5.61514997f, + -5.96609354f, 3.80532098f, -4.27061749f, -4.147192f, -2.97292256f, 3.08850741f, -2.00745845f, -5.73794365f, + -5.10050154f, 1.13329971f, -5.71489096f, -3.16787434f, -4.3951726f, -3.46183419f, 2.58782721f, 5.12214518f, + -3.03947043f, -1.14160478f, 1.25473666f, -4.38748407f, -2.38006377f, 2.96942639f, -1.04791737f, -1.83173573f, + 0.763053358f, -0.392412513f, 4.41353893f, 2.11729431f, -0.449243516f, -1.06398046f, -4.24817324f, 2.510741f, + -2.87694001f, -5.78445148f, -4.28717136f, 1.63768983f, -0.851417482f, -1.78721738f, 1.47868419f, -5.30453825f, + -3.54148769f, -4.3604908f, -5.43673515f, -2.1177392f, 1.81457126f, 5.5481329f, 5.77875757f, -5.97350502f, + 4.06650066f, 5.45986032f, -2.12896085f, 3.36846685f, 0.278495908f, -2.92108226f, 2.50868297f, 3.49404573f, + -0.501316607f, -2.24306011f, -1.1393615f, 4.04511881f, -0.24292554f, -0.6577456f, 2.75090981f, 2.27595735f, + 5.6852541f, 2.01300859f, 3.67360115f, 4.08316708f, 2.21748352f, 1.6718235f, -5.53485727f, -2.05798793f, + -5.44156361f, -0.885561407f, 3.15594339f, 0.380305499f, 1.83812976f, 1.45520961f, -3.88890791f, 5.5293889f, + -1.53199291f, -1.39461827f, -3.46403909f, -5.23103189f, 0.517564237f, 4.23479128f, -0.433209896f, -4.5950737f, + 4.70497465f, -3.12229228f, 1.03587818f, -4.51437473f, 0.0185405649f, -2.4843061f, 1.78736401f, -5.61038256f, + -5.22479343f, -5.83969831f, 3.41363931f, -4.67401934f, 5.88960791f, -3.64446115f, -3.89811039f, -1.83774042f, + 4.14202118f, -1.48322201f, -2.28457189f, -5.74487162f, -2.47532654f, -1.48954201f, -2.43887949f, 1.82621729f, + 0.18489939f, -5.18600178f, 1.42017782f, 4.31047344f, -5.86494017f, 3.02233028f, 3.13056755f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/output_ref_data.h new file mode 100644 index 00000000..17fde0cc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/output_ref_data.h @@ -0,0 +1,2 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_1_output_ref[3] = {-1.22784352f, -0.638236403f, -0.0131829157f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_1/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/config_data.h new file mode 100644 index 00000000..6cebefa4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F32_2_BATCH_SIZE 1 +#define AVGPOOLING_F32_2_INPUT_N 1 +#define AVGPOOLING_F32_2_INPUT_W 3 +#define AVGPOOLING_F32_2_INPUT_H 3 +#define AVGPOOLING_F32_2_INPUT_C 1 +#define AVGPOOLING_F32_2_FILTER_W 1 +#define AVGPOOLING_F32_2_FILTER_H 3 +#define AVGPOOLING_F32_2_STRIDE_W 1 +#define AVGPOOLING_F32_2_STRIDE_H 1 +#define AVGPOOLING_F32_2_ACTIVATION_MIN 0.0f +#define AVGPOOLING_F32_2_ACTIVATION_MAX 6.0f +#define AVGPOOLING_F32_2_OUTPUT_C 1 +#define AVGPOOLING_F32_2_OUTPUT_W 3 +#define AVGPOOLING_F32_2_OUTPUT_H 3 +#define AVGPOOLING_F32_2_PADDING_H 1 +#define AVGPOOLING_F32_2_PADDING_W 0 +#define AVGPOOLING_F32_2_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/input_tensor_data.h new file mode 100644 index 00000000..75a438fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/input_tensor_data.h @@ -0,0 +1,10 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_2_input_tensor[9] = {-0.746911585f, + 0.0804697201f, + 2.93615198f, + 4.92949867f, + 6.02535439f, + 5.67717457f, + 1.08524644f, + 4.17048693f, + 8.64692497f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/output_ref_data.h new file mode 100644 index 00000000..3c06b49b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/output_ref_data.h @@ -0,0 +1,3 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_2_output_ref[9] = + {2.09129357f, 3.052912f, 4.30666351f, 1.75594461f, 3.42543697f, 5.75341749f, 3.00737262f, 5.09792042f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_2/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/config_data.h new file mode 100644 index 00000000..03235e29 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_F32_GLOBAL_BATCH_SIZE 1 +#define AVGPOOLING_F32_GLOBAL_INPUT_N 1 +#define AVGPOOLING_F32_GLOBAL_INPUT_W 5 +#define AVGPOOLING_F32_GLOBAL_INPUT_H 25 +#define AVGPOOLING_F32_GLOBAL_INPUT_C 64 +#define AVGPOOLING_F32_GLOBAL_FILTER_W 5 +#define AVGPOOLING_F32_GLOBAL_FILTER_H 25 +#define AVGPOOLING_F32_GLOBAL_STRIDE_W 1 +#define AVGPOOLING_F32_GLOBAL_STRIDE_H 1 +#define AVGPOOLING_F32_GLOBAL_ACTIVATION_MIN -100.0f +#define AVGPOOLING_F32_GLOBAL_ACTIVATION_MAX 100.0f +#define AVGPOOLING_F32_GLOBAL_OUTPUT_C 64 +#define AVGPOOLING_F32_GLOBAL_OUTPUT_W 1 +#define AVGPOOLING_F32_GLOBAL_OUTPUT_H 1 +#define AVGPOOLING_F32_GLOBAL_PADDING_H 0 +#define AVGPOOLING_F32_GLOBAL_PADDING_W 0 +#define AVGPOOLING_F32_GLOBAL_DST_SIZE 64 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/input_tensor_data.h new file mode 100644 index 00000000..fbc889f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/input_tensor_data.h @@ -0,0 +1,1336 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_global_input_tensor[8000] = { + -5.98943472f, -4.43818426f, -4.33827162f, -1.4321444f, 1.44808567f, 4.04511309f, + -4.49409199f, 0.725719988f, -2.70900512f, 3.89984393f, 1.70100284f, -3.42349625f, + -5.51393032f, -0.10143742f, -2.10373211f, 5.58335924f, 3.35491157f, 3.08208108f, + 5.96113682f, 3.24556422f, 3.04683208f, 0.0318144895f, -1.3228476f, -4.95008564f, + -5.5696187f, 3.44611287f, 2.13320684f, -0.69141531f, -3.43887496f, 3.60327029f, + -4.26427698f, 5.23097372f, 3.66150975f, 1.33388841f, -1.96253061f, -1.04833841f, + -5.61131382f, 4.39190292f, 2.91668415f, -4.83364058f, 0.349196136f, -2.02829289f, + 0.315698534f, 1.18203259f, -0.285555899f, 3.41593695f, 1.03375292f, 0.884206235f, + 2.25585127f, 3.16869831f, -1.22799623f, -2.14452624f, 1.10970592f, 4.04853344f, + 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3.76236296f, -1.33555031f, -3.76542068f, 0.143675104f, + 5.71728468f, 4.72663975f, 5.37082195f, -0.933130443f, -5.23207378f, 5.12205076f, + 0.671938002f, 1.21853602f, -1.93289733f, 2.2784543f, 1.70209098f, -5.99342871f, + -2.24953556f, -5.91423464f, -2.68162847f, -0.114143595f, 2.08438849f, -0.148023158f, + -3.40210938f, 4.74675179f, -3.91418505f, -0.0393624268f, 5.09702015f, -4.36680412f, + -3.10659194f, 5.33009529f, -2.73320699f, 2.73042941f, 4.63212252f, 2.65222502f, + 4.96594906f, -0.641511858f, -1.28960049f, -4.51006508f, 4.89953756f, 0.640745759f, + -2.57191539f, -5.38540697f, 1.17587352f, 5.74322271f, 3.1586504f, -5.3344183f, + 1.53533769f, 5.30440283f, 2.01674151f, -4.62897396f, 3.17523217f, 0.158335388f, + -0.956074774f, 2.19645119f, 0.0605138168f, 0.691229761f, 5.25398064f, -4.79254532f, + 2.23390794f, 5.71911001f, -1.8180716f, 5.60641861f, -0.162733838f, 0.447760135f, + 2.63663483f, -5.36797047f, 1.15239215f, 1.93531239f, -2.48145032f, 2.48636198f, + -1.4474107f, -5.96748018f, 4.90939045f, -2.96910763f, -1.23989761f, 3.81525302f, + 0.696101308f, 0.526476681f, -2.83025289f, -3.31284881f, 0.937196732f, 5.65547848f, + 2.476367f, -1.16996253f, -2.9856863f, 5.52467871f, -5.85514593f, -0.616576076f, + 5.67829466f, -3.58238435f, -3.59213185f, 4.60870552f, -0.301582366f, -3.08216095f, + -1.89541411f, -1.7117753f, -4.53422737f, -3.83890581f, 4.41857529f, -3.42435598f, + -5.3093524f, -1.96314299f, 1.90821528f, 4.63756037f, 0.481082171f, 1.25304103f, + -5.69253302f, 5.51127052f, 0.432384521f, 5.7170949f, 1.18120205f, 4.1506362f, + -1.28905249f, -4.26192856f, 3.40105724f, -1.3335458f, 5.32364082f, -5.26902342f, + -3.48200989f, 0.568158925f, -4.89005518f, -1.54373491f, -4.54943085f, -5.36540222f, + -4.74796581f, -3.38216209f, 2.26958585f, 4.44730139f, -4.05065823f, -1.89298308f, + -4.66780186f, -0.438623369f, 1.72832286f, 0.217397809f, 3.49854851f, 2.20692635f, + 0.392037928f, -5.71180344f, -4.7262249f, -2.05783939f, 1.50586045f, -3.05094481f, + -1.26459503f, 5.69907856f, 4.69878292f, 1.82600987f, 0.235243693f, 5.76897097f, + 4.88069439f, 5.64483976f, -1.73183918f, 5.06218624f, -0.716016412f, -0.688747406f, + 1.3923068f, 0.144069195f, -0.274592698f, -0.511044204f, 1.58098519f, -3.99452591f, + -0.473982155f, -0.558149576f, 1.42638457f, -4.57977915f, -5.7436924f, 4.13916159f, + 0.19529511f, 3.25386763f, -0.526809812f, -1.68888462f, 4.65679312f, 5.3545661f, + 4.04279089f, 5.16723871f, 4.87898159f, -1.81339693f, -3.55348325f, 2.50423574f, + -4.54807472f, 1.01891196f, 5.40058756f, -0.677663684f, -4.74431562f, 3.96708632f, + -0.661576629f, -5.55054569f, -4.49859238f, -3.83588791f, -2.66482568f, 0.140221268f, + 5.37383175f, -3.95536447f, -1.40553057f, 5.90955734f, -4.26794291f, 3.37469125f, + -1.02051306f, -3.13241243f, 5.92464685f, -4.31624365f, -2.25582838f, -3.71672463f, + 1.56082118f, 1.98214495f, 0.141524225f, -1.40380764f, -1.49814904f, -4.19810343f, + 0.279491216f, 1.17532742f, -0.111853309f, -0.296155363f, -0.332512856f, 3.80939674f, + 0.0327966772f, -3.6659317f, -1.04490471f, -0.49123764f, 1.11493659f, -4.98168135f, + 2.05832791f, -0.9854182f, 5.66707373f, -0.802718759f, 3.21422577f, 4.35436535f, + 1.28341031f, 2.5471015f, -3.76315665f, -0.544174016f, 1.97606766f, 0.509316862f, + 3.18651581f, 3.00365138f, 4.76405764f, -5.95384884f, 2.665411f, -0.144519225f, + 5.78733253f, 3.86911798f, 1.84261584f, -1.29359317f, -1.39200044f, 2.32389307f, + 2.18222284f, -5.32589245f, 3.63745832f, 3.40259743f, 3.35936117f, -5.24851274f, + -0.278493553f, 4.0120182f, 5.26434469f, -2.91859794f, 5.98236561f, 4.94319344f, + -2.63899446f, -1.52386057f, 5.70872688f, 3.57119155f, 0.595033944f, 5.40274811f, + 1.68977141f, 1.9866215f, -2.25612807f, 3.67269731f, -2.92427063f, -3.0116334f, + -2.4867723f, 0.21578829f, 0.813319802f, 2.46228504f, 5.53193331f, 5.057271f, + -1.79121828f, -4.42506742f, 2.47650385f, 4.69136477f, -4.77066708f, -5.08028126f, + 4.77351999f, -3.61976814f, -2.45903969f, 5.97068119f, 5.13043308f, 3.73650622f, + 2.62066364f, 4.62605524f, 1.8991425f, 5.1542716f, -0.401760966f, -4.67846441f, + -3.32382536f, -3.10185742f, -5.55696249f, 4.35171461f, -3.32048631f, -1.60764158f, + 3.69431853f, 1.02697515f, -3.12872148f, -4.06708622f, 0.849937379f, 3.11360312f, + -2.66453338f, 5.40335274f, 0.274850607f, -5.45793438f, 4.71255922f, 1.88279545f, + 3.81373167f, 3.36913395f, 4.47262764f, 5.02532578f, -2.41413045f, -5.53872919f, + -5.42430162f, -0.320155919f, 1.58581102f, -2.27628469f, 3.14013457f, -0.657812297f, + 0.471161574f, 3.80438852f, 4.47664404f, -4.25343323f, 2.16017342f, 1.52985811f, + 4.85317421f, -2.08700514f, 2.79544449f, -0.131629854f, -2.04925299f, -2.19093871f, + 5.90014076f, 0.93015039f, -5.6632638f, 2.10119939f, 3.59227061f, 4.15988398f, + 1.66451335f, -0.66658932f, 5.12114763f, -2.83874416f, 2.65152431f, -2.1095891f, + 3.60512996f, 2.09116244f, 3.15734363f, -0.936518192f, 1.64570391f, 0.44748047f, + -2.23487973f, 0.269457787f, -2.35365462f, 1.70004237f, 3.7541852f, 3.18405819f, + 1.62937415f, -2.18847895f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/output_ref_data.h new file mode 100644 index 00000000..7629f2ac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/output_ref_data.h @@ -0,0 +1,12 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_f32_global_output_ref[64] = { + -0.107985295f, 0.53640455f, 0.110826626f, 0.155489251f, 0.00322953798f, 0.414643139f, -0.281608164f, + 0.543765247f, 0.0427337773f, -0.134288758f, -0.0610762499f, 0.192884579f, 0.0649194196f, 0.198676154f, + 0.117913678f, 0.0745798051f, 0.0368041024f, 0.107101932f, 0.377870381f, -0.10379111f, -0.0133168567f, + -0.206171364f, 0.116966367f, 0.157006964f, 0.133311972f, 0.0896379054f, -0.0239603873f, 0.417930305f, + -0.168411136f, 0.126405835f, 0.681754887f, 0.043441236f, -0.410361409f, 0.347709566f, 0.0176381376f, + 0.195540652f, -0.141738608f, 0.270273924f, -0.119991243f, 0.107289024f, -0.276811779f, 0.236507118f, + -0.0415062197f, -0.0306457672f, -0.0712655708f, 0.233424574f, -0.219552666f, 0.285768688f, 0.341217458f, + 0.0659245327f, 0.594608128f, 0.240299299f, 0.408569187f, -0.121885762f, 0.0724834204f, -0.190734088f, + -0.0345060118f, 0.00774922175f, 0.0486385152f, -0.604849577f, -0.0716980621f, -0.0770024881f, 0.331921577f, + 0.38177529f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_f32_global/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/config_data.h new file mode 100644 index 00000000..48b03ad1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_2_F16_BATCH_SIZE 1 +#define AVGPOOLING_MATCH_2_F16_INPUT_N 1 +#define AVGPOOLING_MATCH_2_F16_INPUT_W 12 +#define AVGPOOLING_MATCH_2_F16_INPUT_H 1 +#define AVGPOOLING_MATCH_2_F16_INPUT_C 5 +#define AVGPOOLING_MATCH_2_F16_FILTER_W 3 +#define AVGPOOLING_MATCH_2_F16_FILTER_H 1 +#define AVGPOOLING_MATCH_2_F16_STRIDE_W 1 +#define AVGPOOLING_MATCH_2_F16_STRIDE_H 2 +#define AVGPOOLING_MATCH_2_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_MATCH_2_F16_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_MATCH_2_F16_OUTPUT_C 5 +#define AVGPOOLING_MATCH_2_F16_OUTPUT_W 12 +#define AVGPOOLING_MATCH_2_F16_OUTPUT_H 1 +#define AVGPOOLING_MATCH_2_F16_PADDING_H 0 +#define AVGPOOLING_MATCH_2_F16_PADDING_W 1 +#define AVGPOOLING_MATCH_2_F16_DST_SIZE 60 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/input_tensor_data.h new file mode 100644 index 00000000..48a4e0de --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/input_tensor_data.h @@ -0,0 +1,17 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_2_f16_input_tensor[60] = { + (float16_t)1.83203125f, (float16_t)0.349365234f, (float16_t)-0.268310547f, (float16_t)-1.0625f, + (float16_t)3.2109375f, (float16_t)-0.943847656f, (float16_t)0.107910156f, (float16_t)-4.3359375f, + (float16_t)-2.63671875f, (float16_t)-1.66601562f, (float16_t)3.703125f, (float16_t)3.11328125f, + (float16_t)0.153442383f, (float16_t)-4.703125f, (float16_t)-5.828125f, (float16_t)3.67382812f, + (float16_t)4.40234375f, (float16_t)0.0573120117f, (float16_t)-1.76367188f, (float16_t)-2.35742188f, + (float16_t)-2.09570312f, (float16_t)1.08007812f, (float16_t)0.0273742676f, (float16_t)-4.640625f, + (float16_t)2.80273438f, (float16_t)3.1796875f, (float16_t)3.30273438f, (float16_t)1.32519531f, + (float16_t)-3.94921875f, (float16_t)3.29101562f, (float16_t)0.418701172f, (float16_t)-4.8046875f, + (float16_t)-2.140625f, (float16_t)-0.382080078f, (float16_t)-1.09863281f, (float16_t)-4.546875f, + (float16_t)2.32617188f, (float16_t)5.0234375f, (float16_t)0.0540771484f, (float16_t)3.84765625f, + (float16_t)-1.93847656f, (float16_t)-0.755371094f, (float16_t)4.203125f, (float16_t)-1.18066406f, + (float16_t)4.97265625f, (float16_t)4.59375f, (float16_t)0.353027344f, (float16_t)-4.71484375f, + (float16_t)-0.506347656f, (float16_t)-4.4375f, (float16_t)-5.60546875f, (float16_t)5.67578125f, + (float16_t)5.296875f, (float16_t)2.09765625f, (float16_t)-1.78027344f, (float16_t)-2.0625f, + (float16_t)-5.765625f, (float16_t)-1.60351562f, (float16_t)-2.72851562f, (float16_t)-0.369140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/output_ref_data.h new file mode 100644 index 00000000..b64716d8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_2_f16_output_ref[60] = { + (float16_t)0.444335938f, (float16_t)0.228637695f, (float16_t)-2.30273438f, (float16_t)-1.84960938f, + (float16_t)0.772460938f, (float16_t)1.53027344f, (float16_t)1.19042969f, (float16_t)-1.48339844f, + (float16_t)-2.80078125f, (float16_t)-1.42675781f, (float16_t)2.14453125f, (float16_t)2.54101562f, + (float16_t)-1.375f, (float16_t)-3.03515625f, (float16_t)-3.28320312f, (float16_t)1.75976562f, + (float16_t)2.86523438f, (float16_t)0.0794067383f, (float16_t)-3.703125f, (float16_t)-1.79394531f, + (float16_t)1.5859375f, (float16_t)2.92773438f, (float16_t)0.469970703f, (float16_t)-3.45117188f, + (float16_t)1.24511719f, (float16_t)0.500488281f, (float16_t)-0.141357422f, (float16_t)-0.262695312f, + (float16_t)-2.9921875f, (float16_t)1.66503906f, (float16_t)-0.316162109f, (float16_t)0.273925781f, + (float16_t)1.40332031f, (float16_t)-1.42578125f, (float16_t)2.01367188f, (float16_t)-2.02148438f, + (float16_t)-1.078125f, (float16_t)2.36132812f, (float16_t)-0.502929688f, (float16_t)2.57421875f, + (float16_t)-0.630371094f, (float16_t)0.641113281f, (float16_t)1.50390625f, (float16_t)-0.543945312f, + (float16_t)1.4609375f, (float16_t)-0.983398438f, (float16_t)1.7578125f, (float16_t)1.59472656f, + (float16_t)0.13684082f, (float16_t)-0.415283203f, (float16_t)-1.02539062f, (float16_t)0.0876464844f, + (float16_t)-0.340087891f, (float16_t)-0.379150391f, (float16_t)-2.1953125f, (float16_t)-3.8359375f, + (float16_t)-0.0450744629f, (float16_t)1.84667969f, (float16_t)-0.315429688f, (float16_t)-1.07421875f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/config_data.h new file mode 100644 index 00000000..3d9e1a25 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_2_F32_BATCH_SIZE 1 +#define AVGPOOLING_MATCH_2_F32_INPUT_N 1 +#define AVGPOOLING_MATCH_2_F32_INPUT_W 12 +#define AVGPOOLING_MATCH_2_F32_INPUT_H 1 +#define AVGPOOLING_MATCH_2_F32_INPUT_C 5 +#define AVGPOOLING_MATCH_2_F32_FILTER_W 3 +#define AVGPOOLING_MATCH_2_F32_FILTER_H 1 +#define AVGPOOLING_MATCH_2_F32_STRIDE_W 1 +#define AVGPOOLING_MATCH_2_F32_STRIDE_H 2 +#define AVGPOOLING_MATCH_2_F32_ACTIVATION_MIN -100.0f +#define AVGPOOLING_MATCH_2_F32_ACTIVATION_MAX 100.0f +#define AVGPOOLING_MATCH_2_F32_OUTPUT_C 5 +#define AVGPOOLING_MATCH_2_F32_OUTPUT_W 12 +#define AVGPOOLING_MATCH_2_F32_OUTPUT_H 1 +#define AVGPOOLING_MATCH_2_F32_PADDING_H 0 +#define AVGPOOLING_MATCH_2_F32_PADDING_W 1 +#define AVGPOOLING_MATCH_2_F32_DST_SIZE 60 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/input_tensor_data.h new file mode 100644 index 00000000..a42683d4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/input_tensor_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_2_f32_input_tensor[60] = { + -5.01337004f, 5.16674471f, -2.55259156f, 1.6626966f, 4.0175128f, 5.72066402f, -4.05318356f, + -0.0383441411f, 2.60192561f, -1.91995573f, 5.33510447f, -3.95294976f, 2.01346445f, -0.0457156226f, + -0.635247171f, 5.44589472f, 2.93025064f, -3.99942946f, 3.4861908f, 3.98281002f, 0.159095466f, + -5.73154783f, 4.28126192f, 3.50744939f, -5.33561325f, 4.39140511f, 1.2423631f, -0.830849588f, + 4.79027414f, -3.44987464f, 5.29691553f, -0.256621867f, 0.803159714f, -1.45758569f, 0.223231092f, + 3.40791941f, -5.03890753f, -0.845049798f, 2.80626154f, 3.87645674f, -4.30531311f, -4.72046995f, + -1.33787882f, -2.15717316f, 4.02400017f, -1.41894317f, -2.55557823f, -3.53257895f, -3.64973044f, + 2.0192194f, -0.758259892f, -4.54896402f, -0.319442242f, -5.11529207f, -5.5373373f, -0.435510725f, + -0.82645452f, 1.37303972f, -3.54779768f, 5.35604715f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/output_ref_data.h new file mode 100644 index 00000000..e772f691 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_2_f32_output_ref[60] = { + 0.353646994f, 0.556780577f, -1.29546785f, 2.13231111f, 1.04877853f, 2.01413274f, -0.94646287f, + -0.192490414f, 1.40630209f, 0.487436622f, 5.50055456f, -1.69196081f, -0.6747697f, 2.01413369f, + 0.47586903f, 3.64669824f, -2.25141549f, 0.765098989f, 2.31597495f, -0.662683487f, 3.33213162f, + -0.519644678f, -0.183005705f, 3.9279716f, -1.60089266f, 3.2824719f, -1.58193552f, 1.41785729f, + 2.28004599f, -2.85408568f, 4.36541319f, -1.3510555f, -0.290913224f, 2.04631662f, 0.216604397f, + 1.46650732f, -3.3386662f, -0.45992294f, -0.269499093f, 2.70789599f, -0.77211231f, -4.10498524f, + -1.90516913f, -1.00021398f, 3.30655861f, -2.16083884f, -3.94167066f, -1.72996664f, -3.64073181f, + 0.168627426f, -0.870904624f, -2.64366555f, -0.826327145f, -4.10427332f, 0.612643063f, -0.596885324f, + -2.68770933f, 0.526798725f, -4.33154488f, -0.0906450748f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_2_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/config_data.h new file mode 100644 index 00000000..e0bd8d3d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_3_F16_BATCH_SIZE 1 +#define AVGPOOLING_MATCH_3_F16_INPUT_N 1 +#define AVGPOOLING_MATCH_3_F16_INPUT_W 9 +#define AVGPOOLING_MATCH_3_F16_INPUT_H 1 +#define AVGPOOLING_MATCH_3_F16_INPUT_C 2 +#define AVGPOOLING_MATCH_3_F16_FILTER_W 1 +#define AVGPOOLING_MATCH_3_F16_FILTER_H 1 +#define AVGPOOLING_MATCH_3_F16_STRIDE_W 2 +#define AVGPOOLING_MATCH_3_F16_STRIDE_H 1 +#define AVGPOOLING_MATCH_3_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_MATCH_3_F16_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_MATCH_3_F16_OUTPUT_C 2 +#define AVGPOOLING_MATCH_3_F16_OUTPUT_W 5 +#define AVGPOOLING_MATCH_3_F16_OUTPUT_H 1 +#define AVGPOOLING_MATCH_3_F16_PADDING_H 0 +#define AVGPOOLING_MATCH_3_F16_PADDING_W 0 +#define AVGPOOLING_MATCH_3_F16_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/input_tensor_data.h new file mode 100644 index 00000000..5eacf62a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_3_f16_input_tensor[18] = {(float16_t)3.40234375f, + (float16_t)4.84765625f, + (float16_t)-3.83007812f, + (float16_t)-1.26953125f, + (float16_t)1.21289062f, + (float16_t)5.6015625f, + (float16_t)3.33203125f, + (float16_t)5.22265625f, + (float16_t)5.32421875f, + (float16_t)2.921875f, + (float16_t)4.28515625f, + (float16_t)0.58203125f, + (float16_t)4.421875f, + (float16_t)-1.8203125f, + (float16_t)3.84179688f, + (float16_t)-2.25976562f, + (float16_t)-2.93945312f, + (float16_t)-5.8671875f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/output_ref_data.h new file mode 100644 index 00000000..c50cef64 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_3_f16_output_ref[10] = {(float16_t)3.40234375f, + (float16_t)4.84765625f, + (float16_t)1.21289062f, + (float16_t)5.6015625f, + (float16_t)5.32421875f, + (float16_t)2.921875f, + (float16_t)4.421875f, + (float16_t)-1.8203125f, + (float16_t)-2.93945312f, + (float16_t)-5.8671875f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/config_data.h new file mode 100644 index 00000000..8e2eaff9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_3_F32_BATCH_SIZE 1 +#define AVGPOOLING_MATCH_3_F32_INPUT_N 1 +#define AVGPOOLING_MATCH_3_F32_INPUT_W 9 +#define AVGPOOLING_MATCH_3_F32_INPUT_H 1 +#define AVGPOOLING_MATCH_3_F32_INPUT_C 2 +#define AVGPOOLING_MATCH_3_F32_FILTER_W 1 +#define AVGPOOLING_MATCH_3_F32_FILTER_H 1 +#define AVGPOOLING_MATCH_3_F32_STRIDE_W 2 +#define AVGPOOLING_MATCH_3_F32_STRIDE_H 1 +#define AVGPOOLING_MATCH_3_F32_ACTIVATION_MIN -100.0f +#define AVGPOOLING_MATCH_3_F32_ACTIVATION_MAX 100.0f +#define AVGPOOLING_MATCH_3_F32_OUTPUT_C 2 +#define AVGPOOLING_MATCH_3_F32_OUTPUT_W 5 +#define AVGPOOLING_MATCH_3_F32_OUTPUT_H 1 +#define AVGPOOLING_MATCH_3_F32_PADDING_H 0 +#define AVGPOOLING_MATCH_3_F32_PADDING_W 0 +#define AVGPOOLING_MATCH_3_F32_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/input_tensor_data.h new file mode 100644 index 00000000..c9a2b76a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_3_f32_input_tensor[18] = {-1.9569428f, + 1.21028328f, + -2.42602444f, + -3.14913058f, + 3.5972383f, + -1.97945809f, + -1.1307193f, + -1.72277176f, + 2.09824729f, + -0.132652447f, + 4.35493708f, + 3.26238608f, + -5.00376797f, + -0.419882268f, + 3.53926635f, + -5.76042461f, + -1.21378183f, + 4.64763975f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/output_ref_data.h new file mode 100644 index 00000000..f91f46c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_3_f32_output_ref[10] = {-1.9569428f, + 1.21028328f, + 3.5972383f, + -1.97945809f, + 2.09824729f, + -0.132652447f, + -5.00376797f, + -0.419882268f, + -1.21378183f, + 4.64763975f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_3_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/config_data.h new file mode 100644 index 00000000..942553c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_4_F16_BATCH_SIZE 3 +#define AVGPOOLING_MATCH_4_F16_INPUT_N 3 +#define AVGPOOLING_MATCH_4_F16_INPUT_W 1 +#define AVGPOOLING_MATCH_4_F16_INPUT_H 20 +#define AVGPOOLING_MATCH_4_F16_INPUT_C 2 +#define AVGPOOLING_MATCH_4_F16_FILTER_W 1 +#define AVGPOOLING_MATCH_4_F16_FILTER_H 3 +#define AVGPOOLING_MATCH_4_F16_STRIDE_W 1 +#define AVGPOOLING_MATCH_4_F16_STRIDE_H 3 +#define AVGPOOLING_MATCH_4_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define AVGPOOLING_MATCH_4_F16_ACTIVATION_MAX (float16_t)100.0f +#define AVGPOOLING_MATCH_4_F16_OUTPUT_C 2 +#define AVGPOOLING_MATCH_4_F16_OUTPUT_W 1 +#define AVGPOOLING_MATCH_4_F16_OUTPUT_H 7 +#define AVGPOOLING_MATCH_4_F16_PADDING_H 0 +#define AVGPOOLING_MATCH_4_F16_PADDING_W 0 +#define AVGPOOLING_MATCH_4_F16_DST_SIZE 42 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/input_tensor_data.h new file mode 100644 index 00000000..ca1b37ae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/input_tensor_data.h @@ -0,0 +1,32 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_4_f16_input_tensor[120] = { + (float16_t)2.796875f, (float16_t)0.0915527344f, (float16_t)-3.97460938f, (float16_t)1.49316406f, + (float16_t)4.34375f, (float16_t)2.40039062f, (float16_t)4.015625f, (float16_t)5.08984375f, + (float16_t)-1.78320312f, (float16_t)-0.277832031f, (float16_t)4.36328125f, (float16_t)-0.718261719f, + (float16_t)2.71875f, (float16_t)0.0406494141f, (float16_t)-4.77734375f, (float16_t)-3.16992188f, + (float16_t)-3.88867188f, (float16_t)4.375f, (float16_t)0.20300293f, (float16_t)-2.91210938f, + (float16_t)-0.628417969f, (float16_t)5.16015625f, (float16_t)-1.85644531f, (float16_t)-3.03320312f, + (float16_t)3.87695312f, (float16_t)2.19726562f, (float16_t)3.35742188f, (float16_t)2.875f, + (float16_t)-1.0703125f, (float16_t)-1.88964844f, (float16_t)3.34375f, (float16_t)2.43554688f, + (float16_t)-4.5078125f, (float16_t)5.46875f, (float16_t)-0.677246094f, (float16_t)4.19921875f, + (float16_t)-0.120727539f, (float16_t)-2.72851562f, (float16_t)2.91015625f, (float16_t)5.3359375f, + (float16_t)-2.91015625f, (float16_t)-0.725097656f, (float16_t)-2.25976562f, (float16_t)5.734375f, + (float16_t)-3.16796875f, (float16_t)-2.62695312f, (float16_t)-4.01171875f, (float16_t)-1.64453125f, + (float16_t)-4.5078125f, (float16_t)4.796875f, (float16_t)-0.834472656f, (float16_t)-1.421875f, + (float16_t)-1.203125f, (float16_t)4.16796875f, (float16_t)-3.54101562f, (float16_t)-4.7421875f, + (float16_t)-4.21875f, (float16_t)-5.9609375f, (float16_t)-2.9609375f, (float16_t)-0.312988281f, + (float16_t)0.749023438f, (float16_t)-2.83984375f, (float16_t)-2.65820312f, (float16_t)4.54296875f, + (float16_t)-3.65039062f, (float16_t)2.2421875f, (float16_t)-2.68945312f, (float16_t)-2.39453125f, + (float16_t)-3.76757812f, (float16_t)5.4765625f, (float16_t)2.76757812f, (float16_t)1.40429688f, + (float16_t)5.91796875f, (float16_t)3.4453125f, (float16_t)4.1953125f, (float16_t)-4.2734375f, + (float16_t)2.68945312f, (float16_t)-5.18359375f, (float16_t)4.1953125f, (float16_t)5.7421875f, + (float16_t)1.25097656f, (float16_t)-2.61132812f, (float16_t)-4.6328125f, (float16_t)0.183837891f, + (float16_t)4.4453125f, (float16_t)0.636230469f, (float16_t)-1.36816406f, (float16_t)-2.27539062f, + (float16_t)4.046875f, (float16_t)-5.30078125f, (float16_t)4.6640625f, (float16_t)0.557617188f, + (float16_t)4.82421875f, (float16_t)1.90722656f, (float16_t)-1.04394531f, (float16_t)2.73828125f, + (float16_t)-2.73242188f, (float16_t)3.91601562f, (float16_t)4.1640625f, (float16_t)-0.662109375f, + (float16_t)-3.9921875f, (float16_t)2.06835938f, (float16_t)-2.0859375f, (float16_t)-2.40625f, + (float16_t)-2.64648438f, (float16_t)0.724121094f, (float16_t)2.1640625f, (float16_t)-2.88671875f, + (float16_t)-4.8203125f, (float16_t)-1.79101562f, (float16_t)3.24609375f, (float16_t)-1.73339844f, + (float16_t)5.51171875f, (float16_t)-1.86621094f, (float16_t)-2.12109375f, (float16_t)-1.4375f, + (float16_t)3.296875f, (float16_t)4.0703125f, (float16_t)4.4140625f, (float16_t)3.71679688f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/output_ref_data.h new file mode 100644 index 00000000..ae57c4cf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t avgpooling_match_4_f16_output_ref[42] = { + (float16_t)1.0546875f, (float16_t)1.32910156f, (float16_t)2.19921875f, (float16_t)1.36425781f, + (float16_t)-1.98242188f, (float16_t)0.414794922f, (float16_t)-0.760253906f, (float16_t)-0.26171875f, + (float16_t)2.0546875f, (float16_t)1.06152344f, (float16_t)-0.61328125f, (float16_t)4.03515625f, + (float16_t)1.39453125f, (float16_t)1.30371094f, (float16_t)-2.77929688f, (float16_t)0.794433594f, + (float16_t)-3.1171875f, (float16_t)0.576660156f, (float16_t)-2.98632812f, (float16_t)-2.17773438f, + (float16_t)-1.62304688f, (float16_t)0.463867188f, (float16_t)-3.36914062f, (float16_t)1.77441406f, + (float16_t)4.29296875f, (float16_t)0.192260742f, (float16_t)3.44335938f, (float16_t)0.279785156f, + (float16_t)0.354492188f, (float16_t)-0.597167969f, (float16_t)2.44726562f, (float16_t)-2.33984375f, + (float16_t)0.349365234f, (float16_t)2.85351562f, (float16_t)-0.638671875f, (float16_t)-0.333496094f, + (float16_t)-1.76757812f, (float16_t)-1.31835938f, (float16_t)2.21289062f, (float16_t)-1.67871094f, + (float16_t)3.85546875f, (float16_t)3.89453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/config_data.h new file mode 100644 index 00000000..968aa272 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define AVGPOOLING_MATCH_4_F32_BATCH_SIZE 3 +#define AVGPOOLING_MATCH_4_F32_INPUT_N 3 +#define AVGPOOLING_MATCH_4_F32_INPUT_W 1 +#define AVGPOOLING_MATCH_4_F32_INPUT_H 20 +#define AVGPOOLING_MATCH_4_F32_INPUT_C 2 +#define AVGPOOLING_MATCH_4_F32_FILTER_W 1 +#define AVGPOOLING_MATCH_4_F32_FILTER_H 3 +#define AVGPOOLING_MATCH_4_F32_STRIDE_W 1 +#define AVGPOOLING_MATCH_4_F32_STRIDE_H 3 +#define AVGPOOLING_MATCH_4_F32_ACTIVATION_MIN -100.0f +#define AVGPOOLING_MATCH_4_F32_ACTIVATION_MAX 100.0f +#define AVGPOOLING_MATCH_4_F32_OUTPUT_C 2 +#define AVGPOOLING_MATCH_4_F32_OUTPUT_W 1 +#define AVGPOOLING_MATCH_4_F32_OUTPUT_H 7 +#define AVGPOOLING_MATCH_4_F32_PADDING_H 0 +#define AVGPOOLING_MATCH_4_F32_PADDING_W 0 +#define AVGPOOLING_MATCH_4_F32_DST_SIZE 42 diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/input_tensor_data.h new file mode 100644 index 00000000..a46c8d6d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/input_tensor_data.h @@ -0,0 +1,20 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_4_f32_input_tensor[120] = { + -5.14530087f, 3.79176426f, 1.71514213f, -3.64085698f, 1.06582558f, 4.74404573f, -0.585829675f, + -3.62857723f, -1.70411587f, -5.48449183f, 3.79732251f, -0.181487903f, 3.70769477f, 0.716073751f, + 5.67111254f, 3.99197316f, 1.26850295f, -3.25806665f, 4.02511168f, -1.96652806f, 1.61105049f, + 1.57698107f, 2.66884613f, -1.03043234f, 2.99885774f, 4.35013723f, 2.89541221f, -4.38720798f, + -4.45197201f, -0.655935347f, -3.76075768f, -1.72290015f, -3.57278633f, 5.07179117f, 3.4268682f, + 5.95821762f, 1.48151696f, 5.13378382f, 3.03316665f, 1.28921628f, -1.37553966f, -4.94022942f, + 1.41874242f, -0.943456352f, -2.93146181f, 5.05794382f, -1.93901896f, -1.09735703f, 3.981529f, + 0.986481071f, -3.92160082f, 1.52352488f, 2.28613353f, -3.34831476f, -3.39131665f, 2.57046962f, + 1.63119519f, 3.95895457f, -5.68887091f, -4.62288952f, -4.8260107f, 3.53328347f, 3.0964427f, + 5.52532434f, 5.24900723f, 2.76867437f, 0.890103996f, 5.08541059f, 1.67796421f, 3.35529828f, + -2.6705215f, -2.2468915f, 5.16752481f, 4.04738855f, 3.54139471f, 1.90305841f, 2.37745905f, + -1.1518923f, 3.23822761f, -0.053487245f, 5.46541786f, 4.78845167f, 4.2333293f, -0.271893948f, + -4.34429693f, -2.54936671f, -4.60348511f, 3.69231009f, -5.8221035f, 2.01125073f, 3.83026958f, + -3.8573482f, -3.91866469f, 3.91363263f, 4.71543264f, -1.57884169f, 3.9396584f, -2.67126918f, + -4.88947153f, -4.01039314f, 3.61142635f, 1.22464705f, 0.212334007f, 5.0674758f, -0.535635591f, + 5.75510025f, -4.85726213f, -0.46914199f, 0.878638685f, -3.26299763f, -1.76300526f, 0.701467574f, + 2.30146718f, -0.129263893f, -2.91787457f, 0.853458464f, -4.39155626f, 5.44789028f, -4.01712894f, + -3.2200768f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/output_ref_data.h new file mode 100644 index 00000000..04d76ce6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/output_ref_data.h @@ -0,0 +1,8 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t avgpooling_match_4_f32_output_ref[42] = { + -0.788111031f, 1.63165092f, 0.50245899f, -3.09818578f, 3.5491035f, 0.483326763f, 2.76833606f, + -0.473326445f, 0.480765969f, -0.231002033f, -1.30222523f, 3.10236955f, 2.25734186f, 3.21150017f, + -0.962752998f, -0.275247246f, -0.626363575f, 0.470882982f, 0.175337359f, 1.06036985f, -2.47281289f, + 1.47857285f, 2.60569167f, 3.73646092f, 2.01279926f, 1.23451853f, 2.80784321f, -0.602689803f, + 1.78481686f, 0.655730307f, -2.1984396f, 0.615404189f, 1.57880878f, -0.112159409f, -0.355237037f, + 0.760576546f, -1.50475299f, 0.674320221f, -0.79313755f, 0.47522071f, -4.20434284f, 1.11390674f}; diff --git a/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/avgpooling_match_4_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/config_data.h new file mode 100644 index 00000000..d05827aa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_1_F16_LHS_BATCH 2 +#define BATCH_MATMUL_1_F16_LHS_HEIGHT 2 +#define BATCH_MATMUL_1_F16_LHS_ROWS 8 +#define BATCH_MATMUL_1_F16_LHS_COLS 5 +#define BATCH_MATMUL_1_F16_RHS_BATCH 2 +#define BATCH_MATMUL_1_F16_RHS_HEIGHT 2 +#define BATCH_MATMUL_1_F16_RHS_ROWS 5 +#define BATCH_MATMUL_1_F16_RHS_COLS 7 +#define BATCH_MATMUL_1_F16_ADJ_X 0 +#define BATCH_MATMUL_1_F16_ADJ_Y 0 +#define BATCH_MATMUL_1_F16_DST_SIZE 224 +#define BATCH_MATMUL_1_F16_OUTPUT_BATCH 2 +#define BATCH_MATMUL_1_F16_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_1_F16_OUTPUT_ROWS 8 +#define BATCH_MATMUL_1_F16_OUTPUT_COLS 7 +#define BATCH_MATMUL_1_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_1_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_input_tensor.h new file mode 100644 index 00000000..ef72bfc5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_input_tensor.h @@ -0,0 +1,42 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_1_f16_lhs_input_tensor[160] = { + (float16_t)-1.85742188f, (float16_t)-0.82421875f, (float16_t)0.611816406f, (float16_t)1.09375f, + (float16_t)0.592773438f, (float16_t)-1.45507812f, (float16_t)1.88769531f, (float16_t)-1.63574219f, + (float16_t)1.7890625f, (float16_t)-0.801757812f, (float16_t)-1.23339844f, (float16_t)-0.560546875f, + (float16_t)1.14355469f, (float16_t)1.7109375f, (float16_t)-1.49511719f, (float16_t)-0.713378906f, + (float16_t)-0.369384766f, (float16_t)1.33984375f, (float16_t)-0.249023438f, (float16_t)-1.01660156f, + (float16_t)-1.67675781f, (float16_t)0.612792969f, (float16_t)-0.502929688f, (float16_t)0.576660156f, + (float16_t)-1.5234375f, (float16_t)-0.903808594f, (float16_t)-0.6484375f, (float16_t)1.96582031f, + (float16_t)1.93554688f, (float16_t)0.510742188f, (float16_t)1.30273438f, (float16_t)1.85742188f, + (float16_t)-0.0811157227f, (float16_t)0.725097656f, (float16_t)1.99609375f, (float16_t)1.74902344f, + (float16_t)-0.559082031f, (float16_t)1.75292969f, (float16_t)0.885742188f, (float16_t)-0.784179688f, + (float16_t)-0.764160156f, (float16_t)-1.97070312f, (float16_t)1.6640625f, (float16_t)-0.593261719f, + (float16_t)1.30273438f, (float16_t)-1.56640625f, (float16_t)-1.61816406f, (float16_t)1.27929688f, + (float16_t)1.34570312f, (float16_t)-1.79882812f, (float16_t)-1.57910156f, (float16_t)1.20019531f, + (float16_t)-0.916992188f, (float16_t)0.341064453f, (float16_t)-1.75683594f, (float16_t)-0.919921875f, + (float16_t)-0.399658203f, (float16_t)1.64355469f, (float16_t)1.10742188f, (float16_t)0.0790405273f, + (float16_t)-0.827636719f, (float16_t)-0.0824584961f, (float16_t)-1.94042969f, (float16_t)1.92578125f, + (float16_t)-1.96191406f, (float16_t)0.870117188f, (float16_t)0.351318359f, (float16_t)-0.680175781f, + (float16_t)-1.36914062f, (float16_t)0.830078125f, (float16_t)-0.401123047f, (float16_t)0.709472656f, + (float16_t)0.8203125f, (float16_t)1.24414062f, (float16_t)-0.990722656f, (float16_t)-1.5234375f, + (float16_t)-0.515136719f, (float16_t)-1.953125f, (float16_t)-0.470703125f, (float16_t)-1.45214844f, + (float16_t)-0.111755371f, (float16_t)0.0677490234f, (float16_t)-0.918945312f, (float16_t)-0.93359375f, + (float16_t)1.0703125f, (float16_t)0.890625f, (float16_t)-1.37988281f, (float16_t)-1.76660156f, + (float16_t)-0.836914062f, (float16_t)1.39257812f, (float16_t)-1.56152344f, (float16_t)0.48046875f, + (float16_t)1.26953125f, (float16_t)1.46386719f, (float16_t)1.84375f, (float16_t)-0.509765625f, + (float16_t)0.167480469f, (float16_t)0.844726562f, (float16_t)0.0434570312f, (float16_t)0.485839844f, + (float16_t)-1.22070312f, (float16_t)0.159301758f, (float16_t)0.19152832f, (float16_t)0.811035156f, + (float16_t)-0.991210938f, (float16_t)-0.311279297f, (float16_t)1.95019531f, (float16_t)1.98632812f, + (float16_t)1.61621094f, (float16_t)-1.37695312f, (float16_t)-0.514160156f, (float16_t)1.61425781f, + (float16_t)-1.48144531f, (float16_t)-0.435058594f, (float16_t)-0.503417969f, (float16_t)-0.428955078f, + (float16_t)-0.153808594f, (float16_t)-0.883789062f, (float16_t)1.83300781f, (float16_t)0.437744141f, + (float16_t)1.50488281f, (float16_t)-0.767578125f, (float16_t)1.18457031f, (float16_t)0.652832031f, + (float16_t)-1.59179688f, (float16_t)0.883300781f, (float16_t)0.138916016f, (float16_t)-1.95117188f, + (float16_t)-0.659179688f, (float16_t)0.713867188f, (float16_t)0.614746094f, (float16_t)0.978027344f, + (float16_t)0.311767578f, (float16_t)1.08886719f, (float16_t)0.946289062f, (float16_t)1.82128906f, + (float16_t)0.235961914f, (float16_t)1.91992188f, (float16_t)0.389892578f, (float16_t)1.96386719f, + (float16_t)0.937011719f, (float16_t)-1.28125f, (float16_t)1.25683594f, (float16_t)-0.195068359f, + (float16_t)-0.975097656f, (float16_t)-1.23925781f, (float16_t)1.16699219f, (float16_t)1.77539062f, + (float16_t)1.71679688f, (float16_t)-1.42773438f, (float16_t)-1.79882812f, (float16_t)0.534667969f, + (float16_t)-1.34179688f, (float16_t)-1.21679688f, (float16_t)-0.806152344f, (float16_t)-1.640625f, + (float16_t)-0.836914062f, (float16_t)1.859375f, (float16_t)-0.165527344f, (float16_t)1.86230469f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..19576f99 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/lhs_transposed_tensor.h @@ -0,0 +1,42 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_1_f16_lhs_transposed_tensor[160] = { + (float16_t)-1.85742188f, (float16_t)-1.45507812f, (float16_t)-1.23339844f, (float16_t)-0.713378906f, + (float16_t)-1.67675781f, (float16_t)-0.903808594f, (float16_t)1.30273438f, (float16_t)1.74902344f, + (float16_t)-0.82421875f, (float16_t)1.88769531f, (float16_t)-0.560546875f, (float16_t)-0.369384766f, + (float16_t)0.612792969f, (float16_t)-0.6484375f, (float16_t)1.85742188f, (float16_t)-0.559082031f, + (float16_t)0.611816406f, (float16_t)-1.63574219f, (float16_t)1.14355469f, (float16_t)1.33984375f, + (float16_t)-0.502929688f, (float16_t)1.96582031f, (float16_t)-0.0811157227f, (float16_t)1.75292969f, + (float16_t)1.09375f, (float16_t)1.7890625f, (float16_t)1.7109375f, (float16_t)-0.249023438f, + (float16_t)0.576660156f, (float16_t)1.93554688f, (float16_t)0.725097656f, (float16_t)0.885742188f, + (float16_t)0.592773438f, (float16_t)-0.801757812f, (float16_t)-1.49511719f, (float16_t)-1.01660156f, + (float16_t)-1.5234375f, (float16_t)0.510742188f, (float16_t)1.99609375f, (float16_t)-0.784179688f, + (float16_t)-0.764160156f, (float16_t)-1.56640625f, (float16_t)-1.57910156f, (float16_t)-0.919921875f, + (float16_t)-0.827636719f, (float16_t)0.870117188f, (float16_t)-0.401123047f, (float16_t)-1.5234375f, + (float16_t)-1.97070312f, (float16_t)-1.61816406f, (float16_t)1.20019531f, (float16_t)-0.399658203f, + (float16_t)-0.0824584961f, (float16_t)0.351318359f, (float16_t)0.709472656f, (float16_t)-0.515136719f, + (float16_t)1.6640625f, (float16_t)1.27929688f, (float16_t)-0.916992188f, (float16_t)1.64355469f, + (float16_t)-1.94042969f, (float16_t)-0.680175781f, (float16_t)0.8203125f, (float16_t)-1.953125f, + (float16_t)-0.593261719f, (float16_t)1.34570312f, (float16_t)0.341064453f, (float16_t)1.10742188f, + (float16_t)1.92578125f, (float16_t)-1.36914062f, (float16_t)1.24414062f, (float16_t)-0.470703125f, + (float16_t)1.30273438f, (float16_t)-1.79882812f, (float16_t)-1.75683594f, (float16_t)0.0790405273f, + (float16_t)-1.96191406f, (float16_t)0.830078125f, (float16_t)-0.990722656f, (float16_t)-1.45214844f, + (float16_t)-0.111755371f, (float16_t)0.890625f, (float16_t)-1.56152344f, (float16_t)-0.509765625f, + (float16_t)-1.22070312f, (float16_t)-0.311279297f, (float16_t)-0.514160156f, (float16_t)-0.428955078f, + (float16_t)0.0677490234f, (float16_t)-1.37988281f, (float16_t)0.48046875f, (float16_t)0.167480469f, + (float16_t)0.159301758f, (float16_t)1.95019531f, (float16_t)1.61425781f, (float16_t)-0.153808594f, + (float16_t)-0.918945312f, (float16_t)-1.76660156f, (float16_t)1.26953125f, (float16_t)0.844726562f, + (float16_t)0.19152832f, (float16_t)1.98632812f, (float16_t)-1.48144531f, (float16_t)-0.883789062f, + (float16_t)-0.93359375f, (float16_t)-0.836914062f, (float16_t)1.46386719f, (float16_t)0.0434570312f, + (float16_t)0.811035156f, (float16_t)1.61621094f, (float16_t)-0.435058594f, (float16_t)1.83300781f, + (float16_t)1.0703125f, (float16_t)1.39257812f, (float16_t)1.84375f, (float16_t)0.485839844f, + (float16_t)-0.991210938f, (float16_t)-1.37695312f, (float16_t)-0.503417969f, (float16_t)0.437744141f, + (float16_t)1.50488281f, (float16_t)0.883300781f, (float16_t)0.614746094f, (float16_t)1.82128906f, + (float16_t)0.937011719f, (float16_t)-1.23925781f, (float16_t)-1.79882812f, (float16_t)-1.640625f, + (float16_t)-0.767578125f, (float16_t)0.138916016f, (float16_t)0.978027344f, (float16_t)0.235961914f, + (float16_t)-1.28125f, (float16_t)1.16699219f, (float16_t)0.534667969f, (float16_t)-0.836914062f, + (float16_t)1.18457031f, (float16_t)-1.95117188f, (float16_t)0.311767578f, (float16_t)1.91992188f, + (float16_t)1.25683594f, (float16_t)1.77539062f, (float16_t)-1.34179688f, (float16_t)1.859375f, + (float16_t)0.652832031f, (float16_t)-0.659179688f, (float16_t)1.08886719f, (float16_t)0.389892578f, + (float16_t)-0.195068359f, (float16_t)1.71679688f, (float16_t)-1.21679688f, (float16_t)-0.165527344f, + (float16_t)-1.59179688f, (float16_t)0.713867188f, (float16_t)0.946289062f, (float16_t)1.96386719f, + (float16_t)-0.975097656f, (float16_t)-1.42773438f, (float16_t)-0.806152344f, (float16_t)1.86230469f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/output.h new file mode 100644 index 00000000..f02ddfb1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/output.h @@ -0,0 +1,58 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_1_f16_output[224] = { + (float16_t)0.00169372559f, (float16_t)-1.51269531f, (float16_t)0.119689941f, (float16_t)-2.24609375f, + (float16_t)-0.0968017578f, (float16_t)-0.0426330566f, (float16_t)-0.213500977f, (float16_t)2.11523438f, + (float16_t)2.53320312f, (float16_t)-3.09375f, (float16_t)-3.35546875f, (float16_t)2.40039062f, + (float16_t)-2.66992188f, (float16_t)-4.71484375f, (float16_t)2.71289062f, (float16_t)-1.49609375f, + (float16_t)2.16601562f, (float16_t)-2.06445312f, (float16_t)-0.990722656f, (float16_t)1.72949219f, + (float16_t)4.28515625f, (float16_t)3.00976562f, (float16_t)0.396484375f, (float16_t)2.31054688f, + (float16_t)-0.550292969f, (float16_t)0.786132812f, (float16_t)-0.584960938f, (float16_t)2.078125f, + (float16_t)3.28515625f, (float16_t)2.375f, (float16_t)0.0065536499f, (float16_t)-3.28320312f, + (float16_t)4.140625f, (float16_t)-3.67773438f, (float16_t)-2.33984375f, (float16_t)0.740234375f, + (float16_t)-3.41796875f, (float16_t)1.00488281f, (float16_t)-0.182617188f, (float16_t)-5.2109375f, + (float16_t)4.62890625f, (float16_t)4.55078125f, (float16_t)-1.41601562f, (float16_t)-0.279785156f, + (float16_t)-3.72851562f, (float16_t)2.96679688f, (float16_t)-5.6875f, (float16_t)3.52539062f, + (float16_t)-1.64746094f, (float16_t)0.356933594f, (float16_t)-3.359375f, (float16_t)2.68554688f, + (float16_t)2.88671875f, (float16_t)-6.38671875f, (float16_t)6.77734375f, (float16_t)8.6640625f, + (float16_t)5.20703125f, (float16_t)9.1328125f, (float16_t)-1.89257812f, (float16_t)0.205810547f, + (float16_t)0.962402344f, (float16_t)3.98828125f, (float16_t)1.92285156f, (float16_t)0.961914062f, + (float16_t)-0.446289062f, (float16_t)-5.73046875f, (float16_t)-6.75f, (float16_t)4.84765625f, + (float16_t)5.5078125f, (float16_t)2.078125f, (float16_t)-2.30664062f, (float16_t)-5.1484375f, + (float16_t)-1.47265625f, (float16_t)-0.611816406f, (float16_t)3.27539062f, (float16_t)0.682617188f, + (float16_t)3.23828125f, (float16_t)0.672851562f, (float16_t)2.33203125f, (float16_t)-1.34277344f, + (float16_t)-2.84960938f, (float16_t)1.61816406f, (float16_t)1.15039062f, (float16_t)1.74511719f, + (float16_t)-2.50195312f, (float16_t)-9.5859375f, (float16_t)-0.788085938f, (float16_t)-5.21484375f, + (float16_t)4.79296875f, (float16_t)-0.116516113f, (float16_t)-1.94335938f, (float16_t)0.532714844f, + (float16_t)1.625f, (float16_t)1.84667969f, (float16_t)4.09375f, (float16_t)-2.62695312f, + (float16_t)-1.24316406f, (float16_t)-0.7421875f, (float16_t)-2.51953125f, (float16_t)-3.00195312f, + (float16_t)-1.10058594f, (float16_t)-3.11523438f, (float16_t)0.849121094f, (float16_t)-0.46484375f, + (float16_t)0.936035156f, (float16_t)0.961425781f, (float16_t)-3.24804688f, (float16_t)-2.25976562f, + (float16_t)-0.13671875f, (float16_t)4.53125f, (float16_t)3.296875f, (float16_t)1.74023438f, + (float16_t)-0.518066406f, (float16_t)-3.20703125f, (float16_t)3.55859375f, (float16_t)0.250488281f, + (float16_t)-0.41015625f, (float16_t)-0.00932312012f, (float16_t)-2.47070312f, (float16_t)-1.14257812f, + (float16_t)-0.718261719f, (float16_t)3.18359375f, (float16_t)-3.42382812f, (float16_t)2.31640625f, + (float16_t)2.171875f, (float16_t)-4.87109375f, (float16_t)4.83203125f, (float16_t)-2.35742188f, + (float16_t)1.58398438f, (float16_t)3.18164062f, (float16_t)-1.28125f, (float16_t)-0.337402344f, + (float16_t)5.16015625f, (float16_t)0.684570312f, (float16_t)-1.38378906f, (float16_t)1.08398438f, + (float16_t)0.980957031f, (float16_t)-0.461425781f, (float16_t)-0.949707031f, (float16_t)1.83007812f, + (float16_t)-0.0301055908f, (float16_t)0.0752563477f, (float16_t)-2.34179688f, (float16_t)2.171875f, + (float16_t)-1.6875f, (float16_t)0.46875f, (float16_t)0.75390625f, (float16_t)3.49804688f, + (float16_t)2.95507812f, (float16_t)-4.84765625f, (float16_t)3.4765625f, (float16_t)-1.96582031f, + (float16_t)-2.19921875f, (float16_t)7.15234375f, (float16_t)-0.0277099609f, (float16_t)-2.41601562f, + (float16_t)0.386230469f, (float16_t)3.31640625f, (float16_t)-3.03710938f, (float16_t)-0.310546875f, + (float16_t)-2.10546875f, (float16_t)3.47851562f, (float16_t)2.01367188f, (float16_t)-2.22460938f, + (float16_t)0.666015625f, (float16_t)-0.00324440002f, (float16_t)2.71484375f, (float16_t)0.815429688f, + (float16_t)-3.10742188f, (float16_t)-1.56152344f, (float16_t)1.39453125f, (float16_t)-1.54882812f, + (float16_t)-2.13671875f, (float16_t)2.02929688f, (float16_t)2.1953125f, (float16_t)3.54492188f, + (float16_t)4.23828125f, (float16_t)-3.80273438f, (float16_t)-1.84472656f, (float16_t)2.23828125f, + (float16_t)4.5546875f, (float16_t)-5.125f, (float16_t)-1.50878906f, (float16_t)2.41992188f, + (float16_t)4.1171875f, (float16_t)2.46289062f, (float16_t)-0.905273438f, (float16_t)0.212890625f, + (float16_t)-0.821289062f, (float16_t)-4.6953125f, (float16_t)2.43359375f, (float16_t)7.78515625f, + (float16_t)4.953125f, (float16_t)-2.22070312f, (float16_t)-2.68164062f, (float16_t)-4.75f, + (float16_t)-2.67773438f, (float16_t)-2.046875f, (float16_t)0.833007812f, (float16_t)-0.699707031f, + (float16_t)-1.46679688f, (float16_t)-0.645019531f, (float16_t)0.624023438f, (float16_t)-3.26757812f, + (float16_t)-4.859375f, (float16_t)4.22265625f, (float16_t)1.09570312f, (float16_t)-2.81835938f, + (float16_t)-2.296875f, (float16_t)8.8984375f, (float16_t)4.4921875f, (float16_t)-2.70898438f, + (float16_t)-6.39453125f, (float16_t)-3.34570312f, (float16_t)2.10546875f, (float16_t)1.31640625f, + (float16_t)2.12109375f, (float16_t)-3.38085938f, (float16_t)-1.57714844f, (float16_t)5.8828125f, + (float16_t)6.3203125f, (float16_t)-0.875976562f, (float16_t)-10.0f, (float16_t)-0.235839844f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_input_tensor.h new file mode 100644 index 00000000..478ee4a2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_input_tensor.h @@ -0,0 +1,37 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_1_f16_rhs_input_tensor[140] = { + (float16_t)-0.857421875f, (float16_t)-0.767578125f, (float16_t)0.162597656f, (float16_t)1.35058594f, + (float16_t)-1.7578125f, (float16_t)1.87011719f, (float16_t)1.76953125f, (float16_t)1.35839844f, + (float16_t)1.37597656f, (float16_t)-0.972167969f, (float16_t)0.259277344f, (float16_t)-0.109130859f, + (float16_t)-0.579101562f, (float16_t)-1.49804688f, (float16_t)1.16503906f, (float16_t)-0.334716797f, + (float16_t)0.780761719f, (float16_t)0.6953125f, (float16_t)-1.47851562f, (float16_t)1.09082031f, + (float16_t)1.56152344f, (float16_t)-0.42578125f, (float16_t)-1.19042969f, (float16_t)-0.276367188f, + (float16_t)-0.205566406f, (float16_t)-1.77929688f, (float16_t)1.84277344f, (float16_t)1.35839844f, + (float16_t)-1.21191406f, (float16_t)-0.500976562f, (float16_t)-0.937011719f, (float16_t)0.466552734f, + (float16_t)-1.01464844f, (float16_t)0.459960938f, (float16_t)-1.015625f, (float16_t)-0.875488281f, + (float16_t)-1.00683594f, (float16_t)0.377197266f, (float16_t)-0.387451172f, (float16_t)-1.73925781f, + (float16_t)-0.900878906f, (float16_t)-1.95117188f, (float16_t)-1.42578125f, (float16_t)-0.997558594f, + (float16_t)1.01757812f, (float16_t)1.125f, (float16_t)-0.804199219f, (float16_t)-1.61425781f, + (float16_t)0.732910156f, (float16_t)0.0146331787f, (float16_t)1.71386719f, (float16_t)-0.926269531f, + (float16_t)-0.443359375f, (float16_t)-0.623535156f, (float16_t)0.627929688f, (float16_t)0.954589844f, + (float16_t)-0.725585938f, (float16_t)-1.77246094f, (float16_t)0.712890625f, (float16_t)-1.92578125f, + (float16_t)0.679199219f, (float16_t)-1.13671875f, (float16_t)-1.17578125f, (float16_t)0.978027344f, + (float16_t)1.91699219f, (float16_t)1.81542969f, (float16_t)1.32226562f, (float16_t)-0.392822266f, + (float16_t)-1.22949219f, (float16_t)-0.315429688f, (float16_t)0.446777344f, (float16_t)1.94140625f, + (float16_t)-0.629882812f, (float16_t)-1.52636719f, (float16_t)1.23828125f, (float16_t)-0.0319824219f, + (float16_t)0.355957031f, (float16_t)1.07617188f, (float16_t)-0.572265625f, (float16_t)0.0897827148f, + (float16_t)1.53125f, (float16_t)-1.10058594f, (float16_t)-0.883300781f, (float16_t)1.01855469f, + (float16_t)0.0889892578f, (float16_t)0.149780273f, (float16_t)-0.00649261475f, (float16_t)-0.0709838867f, + (float16_t)0.256347656f, (float16_t)-1.00585938f, (float16_t)1.84277344f, (float16_t)1.85253906f, + (float16_t)1.82519531f, (float16_t)-1.765625f, (float16_t)0.0996704102f, (float16_t)0.257080078f, + (float16_t)0.919433594f, (float16_t)1.39257812f, (float16_t)1.18652344f, (float16_t)-1.03710938f, + (float16_t)1.70898438f, (float16_t)0.0038356781f, (float16_t)0.260253906f, (float16_t)-0.0176391602f, + (float16_t)0.460205078f, (float16_t)-0.344482422f, (float16_t)1.09570312f, (float16_t)0.182373047f, + (float16_t)-0.509765625f, (float16_t)-0.253173828f, (float16_t)1.99316406f, (float16_t)-1.59667969f, + (float16_t)0.471435547f, (float16_t)-0.409667969f, (float16_t)-0.00497055054f, (float16_t)-0.370117188f, + (float16_t)-0.173339844f, (float16_t)1.47460938f, (float16_t)0.428222656f, (float16_t)-1.73144531f, + (float16_t)-1.50585938f, (float16_t)1.97363281f, (float16_t)1.02734375f, (float16_t)-1.04296875f, + (float16_t)-1.77929688f, (float16_t)0.771484375f, (float16_t)-0.895507812f, (float16_t)1.07226562f, + (float16_t)1.80761719f, (float16_t)0.989746094f, (float16_t)-0.435302734f, (float16_t)-0.373779297f, + (float16_t)1.27832031f, (float16_t)-0.257568359f, (float16_t)1.53320312f, (float16_t)1.50878906f, + (float16_t)1.83984375f, (float16_t)0.231323242f, (float16_t)-1.578125f, (float16_t)-1.99707031f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..95420f55 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/rhs_transposed_tensor.h @@ -0,0 +1,37 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_1_f16_rhs_transposed_tensor[140] = { + (float16_t)-0.857421875f, (float16_t)1.35839844f, (float16_t)1.16503906f, (float16_t)-0.42578125f, + (float16_t)-1.21191406f, (float16_t)-0.767578125f, (float16_t)1.37597656f, (float16_t)-0.334716797f, + (float16_t)-1.19042969f, (float16_t)-0.500976562f, (float16_t)0.162597656f, (float16_t)-0.972167969f, + (float16_t)0.780761719f, (float16_t)-0.276367188f, (float16_t)-0.937011719f, (float16_t)1.35058594f, + (float16_t)0.259277344f, (float16_t)0.6953125f, (float16_t)-0.205566406f, (float16_t)0.466552734f, + (float16_t)-1.7578125f, (float16_t)-0.109130859f, (float16_t)-1.47851562f, (float16_t)-1.77929688f, + (float16_t)-1.01464844f, (float16_t)1.87011719f, (float16_t)-0.579101562f, (float16_t)1.09082031f, + (float16_t)1.84277344f, (float16_t)0.459960938f, (float16_t)1.76953125f, (float16_t)-1.49804688f, + (float16_t)1.56152344f, (float16_t)1.35839844f, (float16_t)-1.015625f, (float16_t)-0.875488281f, + (float16_t)-1.42578125f, (float16_t)0.0146331787f, (float16_t)-0.725585938f, (float16_t)0.978027344f, + (float16_t)-1.00683594f, (float16_t)-0.997558594f, (float16_t)1.71386719f, (float16_t)-1.77246094f, + (float16_t)1.91699219f, (float16_t)0.377197266f, (float16_t)1.01757812f, (float16_t)-0.926269531f, + (float16_t)0.712890625f, (float16_t)1.81542969f, (float16_t)-0.387451172f, (float16_t)1.125f, + (float16_t)-0.443359375f, (float16_t)-1.92578125f, (float16_t)1.32226562f, (float16_t)-1.73925781f, + (float16_t)-0.804199219f, (float16_t)-0.623535156f, (float16_t)0.679199219f, (float16_t)-0.392822266f, + (float16_t)-0.900878906f, (float16_t)-1.61425781f, (float16_t)0.627929688f, (float16_t)-1.13671875f, + (float16_t)-1.22949219f, (float16_t)-1.95117188f, (float16_t)0.732910156f, (float16_t)0.954589844f, + (float16_t)-1.17578125f, (float16_t)-0.315429688f, (float16_t)0.446777344f, (float16_t)1.07617188f, + (float16_t)0.0889892578f, (float16_t)1.85253906f, (float16_t)1.18652344f, (float16_t)1.94140625f, + (float16_t)-0.572265625f, (float16_t)0.149780273f, (float16_t)1.82519531f, (float16_t)-1.03710938f, + (float16_t)-0.629882812f, (float16_t)0.0897827148f, (float16_t)-0.00649261475f, (float16_t)-1.765625f, + (float16_t)1.70898438f, (float16_t)-1.52636719f, (float16_t)1.53125f, (float16_t)-0.0709838867f, + (float16_t)0.0996704102f, (float16_t)0.0038356781f, (float16_t)1.23828125f, (float16_t)-1.10058594f, + (float16_t)0.256347656f, (float16_t)0.257080078f, (float16_t)0.260253906f, (float16_t)-0.0319824219f, + (float16_t)-0.883300781f, (float16_t)-1.00585938f, (float16_t)0.919433594f, (float16_t)-0.0176391602f, + (float16_t)0.355957031f, (float16_t)1.01855469f, (float16_t)1.84277344f, (float16_t)1.39257812f, + (float16_t)0.460205078f, (float16_t)-0.344482422f, (float16_t)0.471435547f, (float16_t)-1.73144531f, + (float16_t)-0.895507812f, (float16_t)-0.257568359f, (float16_t)1.09570312f, (float16_t)-0.409667969f, + (float16_t)-1.50585938f, (float16_t)1.07226562f, (float16_t)1.53320312f, (float16_t)0.182373047f, + (float16_t)-0.00497055054f, (float16_t)1.97363281f, (float16_t)1.80761719f, (float16_t)1.50878906f, + (float16_t)-0.509765625f, (float16_t)-0.370117188f, (float16_t)1.02734375f, (float16_t)0.989746094f, + (float16_t)1.83984375f, (float16_t)-0.253173828f, (float16_t)-0.173339844f, (float16_t)-1.04296875f, + (float16_t)-0.435302734f, (float16_t)0.231323242f, (float16_t)1.99316406f, (float16_t)1.47460938f, + (float16_t)-1.77929688f, (float16_t)-0.373779297f, (float16_t)-1.578125f, (float16_t)-1.59667969f, + (float16_t)0.428222656f, (float16_t)0.771484375f, (float16_t)1.27832031f, (float16_t)-1.99707031f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/config_data.h new file mode 100644 index 00000000..0a523fa9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_1_F32_LHS_BATCH 2 +#define BATCH_MATMUL_1_F32_LHS_HEIGHT 2 +#define BATCH_MATMUL_1_F32_LHS_ROWS 8 +#define BATCH_MATMUL_1_F32_LHS_COLS 5 +#define BATCH_MATMUL_1_F32_RHS_BATCH 2 +#define BATCH_MATMUL_1_F32_RHS_HEIGHT 2 +#define BATCH_MATMUL_1_F32_RHS_ROWS 5 +#define BATCH_MATMUL_1_F32_RHS_COLS 7 +#define BATCH_MATMUL_1_F32_ADJ_X 0 +#define BATCH_MATMUL_1_F32_ADJ_Y 0 +#define BATCH_MATMUL_1_F32_DST_SIZE 224 +#define BATCH_MATMUL_1_F32_OUTPUT_BATCH 2 +#define BATCH_MATMUL_1_F32_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_1_F32_OUTPUT_ROWS 8 +#define BATCH_MATMUL_1_F32_OUTPUT_COLS 7 +#define BATCH_MATMUL_1_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_1_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_input_tensor.h new file mode 100644 index 00000000..315ea712 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_input_tensor.h @@ -0,0 +1,25 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_1_f32_lhs_input_tensor[160] = { + 0.568875492f, -0.936525762f, -0.808807909f, -1.95427108f, -0.750050843f, 1.7905618f, -1.29726231f, + 1.66943181f, 1.74106789f, 1.1791383f, -0.484546214f, 0.927707374f, -1.95066929f, -0.0722658485f, + -1.08744514f, 0.151429445f, -0.815707922f, 0.505989492f, 1.74285388f, -1.31928194f, -0.4186019f, + -1.15368831f, 0.46181339f, -1.79304838f, 1.43664718f, -0.766655922f, 0.0237910524f, -1.42422271f, + 1.3412379f, -1.23754025f, -0.0500263982f, 0.510212302f, -1.54132426f, 0.397919416f, -1.2414155f, + -0.104410298f, -0.914736092f, 1.09848785f, -1.90478313f, -0.0640158728f, 1.03242254f, 0.0423574597f, + 1.79718506f, 0.412097603f, 1.84102547f, 1.50098991f, -0.622870564f, -1.75417197f, -1.88861418f, + 0.587481618f, 1.88718796f, -0.471772879f, -1.31407762f, 1.23935902f, 0.681894004f, 1.64668572f, + 0.196000621f, -0.561933756f, 0.72762531f, 0.862672269f, 1.11284876f, 0.147806257f, 1.93843281f, + -0.105472147f, 1.08231127f, 1.26832783f, -1.5097791f, -0.579242408f, -1.82295918f, -0.255664021f, + 0.823918939f, 1.65693736f, 0.35033074f, -0.228007466f, 1.12247849f, 0.151464507f, 1.96011949f, + -1.65915608f, 1.59273672f, 0.0722328797f, -0.380191237f, -1.56310785f, 0.992460907f, 0.628524601f, + -0.666286469f, -0.695524395f, 0.15970844f, 0.263615191f, 0.634918332f, -0.826898694f, -1.30869722f, + -0.849232852f, -1.09096885f, 1.69628894f, -0.525678456f, -0.658304751f, 0.0329595208f, 0.327334344f, + 1.16686237f, -0.184265316f, 0.232026339f, 1.21711659f, -1.35266304f, 1.08035612f, -1.20575202f, + 0.291449398f, -1.88685501f, -1.64522171f, -0.410854697f, 0.761460125f, 0.898696482f, -1.45595157f, + 0.806679904f, -0.464909911f, -1.88227665f, -0.208667859f, -0.289227605f, 1.76738739f, 0.988060236f, + -1.73495388f, 0.0681542754f, 0.615274668f, 1.25711787f, -1.73796725f, -0.984173834f, 1.20092058f, + 0.187998816f, -1.93253446f, 0.0867304727f, 0.658425689f, 0.751098812f, -1.66111457f, 1.40123308f, + -1.77234983f, 0.596184492f, 0.888464034f, -0.772616625f, -1.52783871f, -1.88522708f, -1.81625473f, + 0.173521385f, -1.44181418f, -1.70897877f, 0.606381238f, 1.36352623f, -0.562596679f, 1.55379844f, + 0.137584373f, 1.81056702f, -1.95865273f, 0.368139088f, -1.72601736f, 1.92210722f, 1.80692124f, + 1.40590537f, 1.81939161f, 0.748393893f, -0.225166649f, -1.21164715f, 0.737874269f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..4ef4fa3f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/lhs_transposed_tensor.h @@ -0,0 +1,25 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_1_f32_lhs_transposed_tensor[160] = { + 0.568875492f, 1.7905618f, -0.484546214f, 0.151429445f, -0.4186019f, -0.766655922f, -0.0500263982f, + -0.104410298f, -0.936525762f, -1.29726231f, 0.927707374f, -0.815707922f, -1.15368831f, 0.0237910524f, + 0.510212302f, -0.914736092f, -0.808807909f, 1.66943181f, -1.95066929f, 0.505989492f, 0.46181339f, + -1.42422271f, -1.54132426f, 1.09848785f, -1.95427108f, 1.74106789f, -0.0722658485f, 1.74285388f, + -1.79304838f, 1.3412379f, 0.397919416f, -1.90478313f, -0.750050843f, 1.1791383f, -1.08744514f, + -1.31928194f, 1.43664718f, -1.23754025f, -1.2414155f, -0.0640158728f, 1.03242254f, 1.50098991f, + 1.88718796f, 1.64668572f, 1.11284876f, 1.26832783f, 0.823918939f, 0.151464507f, 0.0423574597f, + -0.622870564f, -0.471772879f, 0.196000621f, 0.147806257f, -1.5097791f, 1.65693736f, 1.96011949f, + 1.79718506f, -1.75417197f, -1.31407762f, -0.561933756f, 1.93843281f, -0.579242408f, 0.35033074f, + -1.65915608f, 0.412097603f, -1.88861418f, 1.23935902f, 0.72762531f, -0.105472147f, -1.82295918f, + -0.228007466f, 1.59273672f, 1.84102547f, 0.587481618f, 0.681894004f, 0.862672269f, 1.08231127f, + -0.255664021f, 1.12247849f, 0.0722328797f, -0.380191237f, -0.695524395f, -1.30869722f, -0.658304751f, + 0.232026339f, 0.291449398f, 0.898696482f, -0.208667859f, -1.56310785f, 0.15970844f, -0.849232852f, + 0.0329595208f, 1.21711659f, -1.88685501f, -1.45595157f, -0.289227605f, 0.992460907f, 0.263615191f, + -1.09096885f, 0.327334344f, -1.35266304f, -1.64522171f, 0.806679904f, 1.76738739f, 0.628524601f, + 0.634918332f, 1.69628894f, 1.16686237f, 1.08035612f, -0.410854697f, -0.464909911f, 0.988060236f, + -0.666286469f, -0.826898694f, -0.525678456f, -0.184265316f, -1.20575202f, 0.761460125f, -1.88227665f, + -1.73495388f, 0.0681542754f, 1.20092058f, 0.751098812f, 0.888464034f, 0.173521385f, -0.562596679f, + 0.368139088f, 1.81939161f, 0.615274668f, 0.187998816f, -1.66111457f, -0.772616625f, -1.44181418f, + 1.55379844f, -1.72601736f, 0.748393893f, 1.25711787f, -1.93253446f, 1.40123308f, -1.52783871f, + -1.70897877f, 0.137584373f, 1.92210722f, -0.225166649f, -1.73796725f, 0.0867304727f, -1.77234983f, + -1.88522708f, 0.606381238f, 1.81056702f, 1.80692124f, -1.21164715f, -0.984173834f, 0.658425689f, + 0.596184492f, -1.81625473f, 1.36352623f, -1.95865273f, 1.40590537f, 0.737874269f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/output.h new file mode 100644 index 00000000..0d94304a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/output.h @@ -0,0 +1,34 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_1_f32_output[224] = { + -0.710764766f, 0.688858628f, -0.515824258f, 1.10290432f, -5.31326103f, 0.439270824f, 2.3854661f, + -3.46828485f, 0.427778602f, 3.0293045f, 3.91001296f, -2.11067486f, -5.50832081f, 1.8725853f, + -0.251090139f, 0.757270336f, -1.99118042f, -5.04752254f, 1.83165801f, 1.68475378f, -2.02422333f, + -3.56810284f, -0.427259445f, -2.92173862f, -2.58210754f, 0.180799067f, -2.51282597f, 1.37508082f, + 2.00897503f, 0.00852900743f, 1.66414618f, 4.50906467f, -2.85932803f, -0.00922882557f, -0.76664722f, + -2.43554115f, 0.506658077f, -3.55146456f, -6.27774382f, 2.74100018f, -0.911930859f, -2.88358974f, + -1.54500747f, 0.7291978f, -2.03859615f, -4.62680912f, 1.00096381f, 0.386377424f, -0.930037856f, + 1.68641353f, -0.978790581f, -0.0696993843f, 4.30178499f, -4.06023264f, 1.45057821f, 3.34076548f, + 1.88709021f, 1.70446539f, -0.391286373f, 1.43408501f, -3.14889002f, 2.86726785f, 1.31635022f, + 2.83465123f, 3.1520915f, -9.17411423f, -3.49564338f, -2.38977337f, -2.91951442f, 0.257523984f, + 2.01042366f, -1.04288983f, -3.77338696f, 0.466646671f, -0.294789046f, -2.41486669f, 5.00425911f, + 1.45720947f, -0.115201235f, -3.34618306f, -0.760656834f, -2.050632f, -1.81182098f, 2.91500187f, + 1.75010431f, 1.46369195f, -0.183795452f, -0.0929462314f, -3.48544765f, 2.11780834f, 0.695497692f, + 3.31517196f, 2.1615746f, -5.6175189f, -1.700881f, -0.601210594f, -0.938278913f, 1.2633189f, + -0.278524876f, 1.51461029f, -2.68472886f, -3.27142382f, -4.88943005f, -1.22712135f, -1.70652103f, + -2.68827701f, -2.33782315f, -0.397358328f, -2.06887674f, -0.973969579f, -4.12312746f, 0.0652742833f, + 2.29790854f, 0.447164297f, -3.78897071f, 1.07426727f, -0.30449605f, 2.32109094f, 3.46786928f, + 0.623078227f, -0.0651292801f, -0.465808272f, -0.919067621f, -2.15241671f, 1.7956512f, -0.713270664f, + 4.66070223f, -2.83680773f, 0.350039363f, -0.860643983f, -3.95804453f, 1.40841377f, -1.3313098f, + -0.405098319f, -1.18944836f, -0.241589293f, 0.115917966f, -2.84307814f, 2.85362577f, 0.443952084f, + 1.53125477f, 1.40066099f, 6.19079494f, -3.02129388f, -3.05100155f, -2.34966516f, -6.28355169f, + 5.59595537f, -1.52059531f, -0.286829591f, 0.858554065f, 2.59770751f, -3.92076302f, 0.6233567f, + 4.00771856f, 5.05726719f, -1.9526279f, -0.533158779f, 2.69369268f, -2.21800518f, 0.998765945f, + -0.201085567f, 2.83699274f, -2.10281014f, -0.325010657f, -1.95151782f, 3.40187311f, 1.52388024f, + -2.15923452f, 6.46925068f, 2.7244556f, 2.73544288f, -0.421098709f, -3.93294787f, 2.96039891f, + 0.982616186f, -5.09123564f, -2.85174036f, 1.62545025f, -2.41623068f, 0.995615125f, -2.39638233f, + -4.65754461f, 1.66312575f, 0.141381294f, 4.03487778f, 1.93234825f, 1.60474348f, 0.35052222f, + -5.31753349f, 1.14509964f, -3.22657514f, 6.8216486f, -1.92607999f, -6.80287838f, -2.67828941f, + -0.596920252f, -7.40615177f, -4.22567177f, -0.0805444717f, 1.49607134f, 2.75146484f, -3.35906696f, + 3.73813295f, 2.88745189f, 1.77551055f, -3.37754869f, -2.89567089f, -1.69046414f, -0.276409388f, + 1.10806251f, -2.95510578f, 0.420927763f, -3.12810397f, 1.89322543f, 10.0f, -1.904603f, + 0.296797574f, -0.40897727f, 0.319096237f, 3.41832781f, -2.99757433f, 1.38733757f, 0.565648913f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_input_tensor.h new file mode 100644 index 00000000..4e153545 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_input_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_1_f32_rhs_input_tensor[140] = { + -0.973276496f, 0.490267992f, 1.29363465f, 1.00952077f, -1.63830638f, -0.792002797f, 1.61004984f, + 1.33560288f, -0.243720815f, 0.623840988f, -0.196809411f, 1.49809301f, 1.61719608f, 0.218881726f, + 0.708480597f, -0.831177294f, 0.116639331f, 1.66603684f, -0.162749052f, 0.475524068f, 1.662889f, + -1.08516502f, 0.112564199f, -0.320376545f, -1.29612255f, 1.39052999f, -1.20069826f, -0.934826016f, + 0.60522157f, 0.360742778f, 1.59890747f, 1.12149096f, 0.523199737f, -0.58994025f, -1.59001362f, + 1.00140691f, -0.0891183689f, -1.54060936f, -1.12928605f, -1.30285358f, -1.05043089f, 1.3703841f, + -1.03592694f, -0.0797055736f, -0.0636710078f, -1.80997682f, -1.59611011f, -1.06798232f, -1.33537269f, + 0.150133535f, 0.0454836786f, 1.6275593f, 0.396690756f, -0.490004778f, 1.29433215f, -0.122332327f, + -0.371014982f, -1.37008882f, 1.73082995f, 1.41514063f, 0.997423708f, 0.134020016f, 1.43682313f, + 0.423769802f, 1.23991597f, -1.32335246f, 0.749880254f, -0.687982917f, 0.877559662f, -0.2249614f, + -0.477824509f, 1.85034728f, 1.82119429f, 0.247007549f, 1.06079948f, -1.69732273f, -0.265185893f, + -1.98599792f, 0.103593282f, 1.52043021f, -0.668074071f, -0.817239344f, 0.240209296f, -1.51655388f, + -1.85193443f, 0.492580116f, -1.67983568f, 0.786614776f, 0.0866317749f, 1.87356555f, 1.963678f, + -0.285018027f, -0.366360515f, 1.20348942f, 0.226268247f, -1.80902815f, 1.06946599f, -0.0604547262f, + -1.54442322f, -1.58186865f, -0.28632766f, 1.199175f, 0.191483095f, 0.720965385f, 1.37233126f, + 0.202535048f, -0.61581552f, -0.278936863f, 1.32997596f, -1.84075701f, 1.76286495f, -1.0724777f, + 1.63642001f, 0.985847831f, 1.20133126f, -0.698963523f, -0.990965247f, -0.616176009f, 1.35701442f, + 0.0863976553f, 1.76601887f, 1.43993425f, -0.414207876f, 0.270080507f, 1.21812439f, 0.88696599f, + 1.48824418f, -1.24618304f, -0.157996938f, -1.64003634f, -0.451290727f, 1.98324347f, -0.999498487f, + 0.713266492f, -1.54315686f, 0.0817365348f, -0.757216454f, 0.822800756f, 1.78676856f, 0.664069891f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..1b11f089 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/rhs_transposed_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_1_f32_rhs_transposed_tensor[140] = { + -0.973276496f, 1.33560288f, 0.708480597f, -1.08516502f, 0.60522157f, 0.490267992f, -0.243720815f, + -0.831177294f, 0.112564199f, 0.360742778f, 1.29363465f, 0.623840988f, 0.116639331f, -0.320376545f, + 1.59890747f, 1.00952077f, -0.196809411f, 1.66603684f, -1.29612255f, 1.12149096f, -1.63830638f, + 1.49809301f, -0.162749052f, 1.39052999f, 0.523199737f, -0.792002797f, 1.61719608f, 0.475524068f, + -1.20069826f, -0.58994025f, 1.61004984f, 0.218881726f, 1.662889f, -0.934826016f, -1.59001362f, + 1.00140691f, -1.03592694f, 0.150133535f, -0.371014982f, 0.423769802f, -0.0891183689f, -0.0797055736f, + 0.0454836786f, -1.37008882f, 1.23991597f, -1.54060936f, -0.0636710078f, 1.6275593f, 1.73082995f, + -1.32335246f, -1.12928605f, -1.80997682f, 0.396690756f, 1.41514063f, 0.749880254f, -1.30285358f, + -1.59611011f, -0.490004778f, 0.997423708f, -0.687982917f, -1.05043089f, -1.06798232f, 1.29433215f, + 0.134020016f, 0.877559662f, 1.3703841f, -1.33537269f, -0.122332327f, 1.43682313f, -0.2249614f, + -0.477824509f, -1.98599792f, -1.85193443f, -0.285018027f, -1.54442322f, 1.85034728f, 0.103593282f, + 0.492580116f, -0.366360515f, -1.58186865f, 1.82119429f, 1.52043021f, -1.67983568f, 1.20348942f, + -0.28632766f, 0.247007549f, -0.668074071f, 0.786614776f, 0.226268247f, 1.199175f, 1.06079948f, + -0.817239344f, 0.0866317749f, -1.80902815f, 0.191483095f, -1.69732273f, 0.240209296f, 1.87356555f, + 1.06946599f, 0.720965385f, -0.265185893f, -1.51655388f, 1.963678f, -0.0604547262f, 1.37233126f, + 0.202535048f, 1.63642001f, 0.0863976553f, 1.48824418f, 0.713266492f, -0.61581552f, 0.985847831f, + 1.76601887f, -1.24618304f, -1.54315686f, -0.278936863f, 1.20133126f, 1.43993425f, -0.157996938f, + 0.0817365348f, 1.32997596f, -0.698963523f, -0.414207876f, -1.64003634f, -0.757216454f, -1.84075701f, + -0.990965247f, 0.270080507f, -0.451290727f, 0.822800756f, 1.76286495f, -0.616176009f, 1.21812439f, + 1.98324347f, 1.78676856f, -1.0724777f, 1.35701442f, 0.88696599f, -0.999498487f, 0.664069891f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_1_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/config_data.h new file mode 100644 index 00000000..d165e4d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_2_F16_LHS_BATCH 2 +#define BATCH_MATMUL_2_F16_LHS_HEIGHT 2 +#define BATCH_MATMUL_2_F16_LHS_ROWS 8 +#define BATCH_MATMUL_2_F16_LHS_COLS 5 +#define BATCH_MATMUL_2_F16_RHS_BATCH 1 +#define BATCH_MATMUL_2_F16_RHS_HEIGHT 1 +#define BATCH_MATMUL_2_F16_RHS_ROWS 7 +#define BATCH_MATMUL_2_F16_RHS_COLS 5 +#define BATCH_MATMUL_2_F16_ADJ_X 0 +#define BATCH_MATMUL_2_F16_ADJ_Y 1 +#define BATCH_MATMUL_2_F16_DST_SIZE 224 +#define BATCH_MATMUL_2_F16_OUTPUT_BATCH 2 +#define BATCH_MATMUL_2_F16_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_2_F16_OUTPUT_ROWS 8 +#define BATCH_MATMUL_2_F16_OUTPUT_COLS 7 +#define BATCH_MATMUL_2_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_2_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_input_tensor.h new file mode 100644 index 00000000..0b11c854 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_input_tensor.h @@ -0,0 +1,42 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_2_f16_lhs_input_tensor[160] = { + (float16_t)-0.973144531f, (float16_t)0.490234375f, (float16_t)1.29394531f, (float16_t)1.00976562f, + (float16_t)-1.63867188f, (float16_t)-0.791992188f, (float16_t)1.61035156f, (float16_t)1.3359375f, + (float16_t)-0.243774414f, (float16_t)0.624023438f, (float16_t)-0.196777344f, (float16_t)1.49804688f, + (float16_t)1.6171875f, (float16_t)0.21887207f, (float16_t)0.708496094f, (float16_t)-0.831054688f, + (float16_t)0.116638184f, (float16_t)1.66601562f, (float16_t)-0.162719727f, (float16_t)0.475585938f, + (float16_t)1.66308594f, (float16_t)-1.08496094f, (float16_t)0.112548828f, (float16_t)-0.3203125f, + (float16_t)-1.29589844f, (float16_t)1.390625f, (float16_t)-1.20117188f, (float16_t)-0.935058594f, + (float16_t)0.604980469f, (float16_t)0.360839844f, (float16_t)1.59863281f, (float16_t)1.12109375f, + (float16_t)0.5234375f, (float16_t)-0.58984375f, (float16_t)-1.58984375f, (float16_t)1.00097656f, + (float16_t)-0.0891113281f, (float16_t)-1.54101562f, (float16_t)-1.12890625f, (float16_t)-1.30273438f, + (float16_t)-1.05078125f, (float16_t)1.37011719f, (float16_t)-1.03613281f, (float16_t)-0.0797119141f, + (float16_t)-0.063659668f, (float16_t)-1.80957031f, (float16_t)-1.59570312f, (float16_t)-1.06835938f, + (float16_t)-1.33496094f, (float16_t)0.150146484f, (float16_t)0.0454711914f, (float16_t)1.62792969f, + (float16_t)0.396728516f, (float16_t)-0.489990234f, (float16_t)1.29394531f, (float16_t)-0.122314453f, + (float16_t)-0.37109375f, (float16_t)-1.37011719f, (float16_t)1.73046875f, (float16_t)1.41503906f, + (float16_t)0.997558594f, (float16_t)0.134033203f, (float16_t)1.43652344f, (float16_t)0.423828125f, + (float16_t)1.24023438f, (float16_t)-1.32324219f, (float16_t)0.75f, (float16_t)-0.687988281f, + (float16_t)0.877441406f, (float16_t)-0.224975586f, (float16_t)-0.477783203f, (float16_t)1.85058594f, + (float16_t)1.82128906f, (float16_t)0.246948242f, (float16_t)1.06054688f, (float16_t)-1.69726562f, + (float16_t)-0.265136719f, (float16_t)-1.98632812f, (float16_t)0.10357666f, (float16_t)1.52050781f, + (float16_t)-0.66796875f, (float16_t)-0.817382812f, (float16_t)0.240234375f, (float16_t)-1.51660156f, + (float16_t)-1.8515625f, (float16_t)0.492675781f, (float16_t)-1.6796875f, (float16_t)0.786621094f, + (float16_t)0.0866088867f, (float16_t)1.87402344f, (float16_t)1.96386719f, (float16_t)-0.284912109f, + (float16_t)-0.366455078f, (float16_t)1.203125f, (float16_t)0.226318359f, (float16_t)-1.80859375f, + (float16_t)1.06933594f, (float16_t)-0.0604553223f, (float16_t)-1.54394531f, (float16_t)-1.58203125f, + (float16_t)-0.286376953f, (float16_t)1.19921875f, (float16_t)0.19152832f, (float16_t)0.721191406f, + (float16_t)1.37207031f, (float16_t)0.202514648f, (float16_t)-0.615722656f, (float16_t)-0.279052734f, + (float16_t)1.33007812f, (float16_t)-1.84082031f, (float16_t)1.76269531f, (float16_t)-1.07226562f, + (float16_t)1.63671875f, (float16_t)0.985839844f, (float16_t)1.20117188f, (float16_t)-0.698730469f, + (float16_t)-0.990722656f, (float16_t)-0.616210938f, (float16_t)1.35742188f, (float16_t)0.0864257812f, + (float16_t)1.765625f, (float16_t)1.43945312f, (float16_t)-0.414306641f, (float16_t)0.270019531f, + (float16_t)1.21777344f, (float16_t)0.887207031f, (float16_t)1.48828125f, (float16_t)-1.24609375f, + (float16_t)-0.157958984f, (float16_t)-1.63964844f, (float16_t)-0.451171875f, (float16_t)1.98339844f, + (float16_t)-0.999511719f, (float16_t)0.713378906f, (float16_t)-1.54296875f, (float16_t)0.0817260742f, + (float16_t)-0.757324219f, (float16_t)0.822753906f, (float16_t)1.78710938f, (float16_t)0.6640625f, + (float16_t)1.03222656f, (float16_t)-1.49023438f, (float16_t)1.88964844f, (float16_t)-1.16210938f, + (float16_t)-0.406982422f, (float16_t)-0.260253906f, (float16_t)-1.83984375f, (float16_t)-0.873046875f, + (float16_t)1.49023438f, (float16_t)-1.74121094f, (float16_t)1.609375f, (float16_t)0.858398438f, + (float16_t)0.934082031f, (float16_t)-1.9296875f, (float16_t)0.240356445f, (float16_t)-0.747558594f, + (float16_t)-0.520019531f, (float16_t)-0.312255859f, (float16_t)0.354248047f, (float16_t)0.0193328857f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..da990331 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/lhs_transposed_tensor.h @@ -0,0 +1,42 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_2_f16_lhs_transposed_tensor[160] = { + (float16_t)-0.973144531f, (float16_t)-0.791992188f, (float16_t)-0.196777344f, (float16_t)-0.831054688f, + (float16_t)1.66308594f, (float16_t)1.390625f, (float16_t)1.59863281f, (float16_t)1.00097656f, + (float16_t)0.490234375f, (float16_t)1.61035156f, (float16_t)1.49804688f, (float16_t)0.116638184f, + (float16_t)-1.08496094f, (float16_t)-1.20117188f, (float16_t)1.12109375f, (float16_t)-0.0891113281f, + (float16_t)1.29394531f, (float16_t)1.3359375f, (float16_t)1.6171875f, (float16_t)1.66601562f, + (float16_t)0.112548828f, (float16_t)-0.935058594f, (float16_t)0.5234375f, (float16_t)-1.54101562f, + (float16_t)1.00976562f, (float16_t)-0.243774414f, (float16_t)0.21887207f, (float16_t)-0.162719727f, + (float16_t)-0.3203125f, (float16_t)0.604980469f, (float16_t)-0.58984375f, (float16_t)-1.12890625f, + (float16_t)-1.63867188f, (float16_t)0.624023438f, (float16_t)0.708496094f, (float16_t)0.475585938f, + (float16_t)-1.29589844f, (float16_t)0.360839844f, (float16_t)-1.58984375f, (float16_t)-1.30273438f, + (float16_t)-1.05078125f, (float16_t)-1.80957031f, (float16_t)0.0454711914f, (float16_t)-0.122314453f, + (float16_t)0.997558594f, (float16_t)-1.32324219f, (float16_t)-0.477783203f, (float16_t)-1.69726562f, + (float16_t)1.37011719f, (float16_t)-1.59570312f, (float16_t)1.62792969f, (float16_t)-0.37109375f, + (float16_t)0.134033203f, (float16_t)0.75f, (float16_t)1.85058594f, (float16_t)-0.265136719f, + (float16_t)-1.03613281f, (float16_t)-1.06835938f, (float16_t)0.396728516f, (float16_t)-1.37011719f, + (float16_t)1.43652344f, (float16_t)-0.687988281f, (float16_t)1.82128906f, (float16_t)-1.98632812f, + (float16_t)-0.0797119141f, (float16_t)-1.33496094f, (float16_t)-0.489990234f, (float16_t)1.73046875f, + (float16_t)0.423828125f, (float16_t)0.877441406f, (float16_t)0.246948242f, (float16_t)0.10357666f, + (float16_t)-0.063659668f, (float16_t)0.150146484f, (float16_t)1.29394531f, (float16_t)1.41503906f, + (float16_t)1.24023438f, (float16_t)-0.224975586f, (float16_t)1.06054688f, (float16_t)1.52050781f, + (float16_t)-0.66796875f, (float16_t)0.492675781f, (float16_t)1.96386719f, (float16_t)-1.80859375f, + (float16_t)-0.286376953f, (float16_t)0.202514648f, (float16_t)1.76269531f, (float16_t)-0.698730469f, + (float16_t)-0.817382812f, (float16_t)-1.6796875f, (float16_t)-0.284912109f, (float16_t)1.06933594f, + (float16_t)1.19921875f, (float16_t)-0.615722656f, (float16_t)-1.07226562f, (float16_t)-0.990722656f, + (float16_t)0.240234375f, (float16_t)0.786621094f, (float16_t)-0.366455078f, (float16_t)-0.0604553223f, + (float16_t)0.19152832f, (float16_t)-0.279052734f, (float16_t)1.63671875f, (float16_t)-0.616210938f, + (float16_t)-1.51660156f, (float16_t)0.0866088867f, (float16_t)1.203125f, (float16_t)-1.54394531f, + (float16_t)0.721191406f, (float16_t)1.33007812f, (float16_t)0.985839844f, (float16_t)1.35742188f, + (float16_t)-1.8515625f, (float16_t)1.87402344f, (float16_t)0.226318359f, (float16_t)-1.58203125f, + (float16_t)1.37207031f, (float16_t)-1.84082031f, (float16_t)1.20117188f, (float16_t)0.0864257812f, + (float16_t)1.765625f, (float16_t)0.887207031f, (float16_t)-0.451171875f, (float16_t)0.0817260742f, + (float16_t)1.03222656f, (float16_t)-0.260253906f, (float16_t)1.609375f, (float16_t)-0.747558594f, + (float16_t)1.43945312f, (float16_t)1.48828125f, (float16_t)1.98339844f, (float16_t)-0.757324219f, + (float16_t)-1.49023438f, (float16_t)-1.83984375f, (float16_t)0.858398438f, (float16_t)-0.520019531f, + (float16_t)-0.414306641f, (float16_t)-1.24609375f, (float16_t)-0.999511719f, (float16_t)0.822753906f, + (float16_t)1.88964844f, (float16_t)-0.873046875f, (float16_t)0.934082031f, (float16_t)-0.312255859f, + (float16_t)0.270019531f, (float16_t)-0.157958984f, (float16_t)0.713378906f, (float16_t)1.78710938f, + (float16_t)-1.16210938f, (float16_t)1.49023438f, (float16_t)-1.9296875f, (float16_t)0.354248047f, + (float16_t)1.21777344f, (float16_t)-1.63964844f, (float16_t)-1.54296875f, (float16_t)0.6640625f, + (float16_t)-0.406982422f, (float16_t)-1.74121094f, (float16_t)0.240356445f, (float16_t)0.0193328857f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/output.h new file mode 100644 index 00000000..89d4d7d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/output.h @@ -0,0 +1,58 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_2_f16_output[224] = { + (float16_t)-0.752441406f, (float16_t)-0.288818359f, (float16_t)2.31445312f, (float16_t)0.590820312f, + (float16_t)-0.161132812f, (float16_t)1.01464844f, (float16_t)5.02734375f, (float16_t)0.910644531f, + (float16_t)0.574707031f, (float16_t)2.20117188f, (float16_t)-3.9765625f, (float16_t)0.434082031f, + (float16_t)-0.92578125f, (float16_t)0.0291900635f, (float16_t)0.121948242f, (float16_t)1.765625f, + (float16_t)2.1015625f, (float16_t)-5.81640625f, (float16_t)1.70605469f, (float16_t)-0.723144531f, + (float16_t)-0.334228516f, (float16_t)-2.328125f, (float16_t)2.94335938f, (float16_t)1.0546875f, + (float16_t)-2.015625f, (float16_t)-0.10736084f, (float16_t)-1.41601562f, (float16_t)-2.14648438f, + (float16_t)1.45898438f, (float16_t)0.720703125f, (float16_t)-3.44921875f, (float16_t)0.852050781f, + (float16_t)-0.729003906f, (float16_t)1.26269531f, (float16_t)-1.82226562f, (float16_t)-1.70898438f, + (float16_t)1.16601562f, (float16_t)-1.70996094f, (float16_t)-0.172607422f, (float16_t)1.69042969f, + (float16_t)0.461425781f, (float16_t)-1.54101562f, (float16_t)6.2265625f, (float16_t)-1.88378906f, + (float16_t)-1.88964844f, (float16_t)-2.35351562f, (float16_t)-0.523925781f, (float16_t)1.96582031f, + (float16_t)1.03417969f, (float16_t)5.625f, (float16_t)-4.13671875f, (float16_t)-3.046875f, + (float16_t)3.84570312f, (float16_t)-2.4609375f, (float16_t)1.66601562f, (float16_t)0.779296875f, + (float16_t)2.84570312f, (float16_t)-4.203125f, (float16_t)2.0703125f, (float16_t)1.59277344f, + (float16_t)-0.678222656f, (float16_t)0.446289062f, (float16_t)4.34375f, (float16_t)-1.87792969f, + (float16_t)-0.984863281f, (float16_t)-1.15136719f, (float16_t)8.171875f, (float16_t)-4.1875f, + (float16_t)-1.50195312f, (float16_t)-2.11132812f, (float16_t)2.14453125f, (float16_t)-0.0603637695f, + (float16_t)1.15039062f, (float16_t)-4.796875f, (float16_t)1.10351562f, (float16_t)-0.86328125f, + (float16_t)-1.33007812f, (float16_t)-4.3046875f, (float16_t)0.294433594f, (float16_t)2.26367188f, + (float16_t)-0.674316406f, (float16_t)3.41796875f, (float16_t)-0.196044922f, (float16_t)2.140625f, + (float16_t)-2.296875f, (float16_t)4.44921875f, (float16_t)0.0770263672f, (float16_t)-6.546875f, + (float16_t)2.85742188f, (float16_t)-0.984375f, (float16_t)-3.91796875f, (float16_t)-0.355224609f, + (float16_t)-2.72265625f, (float16_t)2.99609375f, (float16_t)1.90136719f, (float16_t)0.293457031f, + (float16_t)0.480957031f, (float16_t)5.4375f, (float16_t)-0.106628418f, (float16_t)1.96875f, + (float16_t)2.85351562f, (float16_t)-6.76171875f, (float16_t)2.01367188f, (float16_t)-1.08886719f, + (float16_t)-0.329589844f, (float16_t)-2.4375f, (float16_t)-2.15039062f, (float16_t)1.97753906f, + (float16_t)4.09765625f, (float16_t)-0.336181641f, (float16_t)-1.27441406f, (float16_t)1.29003906f, + (float16_t)2.37304688f, (float16_t)-1.55273438f, (float16_t)-2.40234375f, (float16_t)6.17578125f, + (float16_t)-4.765625f, (float16_t)0.353271484f, (float16_t)-0.544433594f, (float16_t)-6.015625f, + (float16_t)6.0234375f, (float16_t)-1.21972656f, (float16_t)-2.58398438f, (float16_t)1.66113281f, + (float16_t)-2.21289062f, (float16_t)-7.09765625f, (float16_t)-0.690917969f, (float16_t)1.32226562f, + (float16_t)-0.78515625f, (float16_t)-3.76367188f, (float16_t)3.41992188f, (float16_t)1.12402344f, + (float16_t)-0.0413818359f, (float16_t)5.01171875f, (float16_t)-4.74609375f, (float16_t)0.429199219f, + (float16_t)5.39453125f, (float16_t)-4.79296875f, (float16_t)0.387451172f, (float16_t)3.28710938f, + (float16_t)-0.967773438f, (float16_t)0.534667969f, (float16_t)2.640625f, (float16_t)-4.37109375f, + (float16_t)2.59570312f, (float16_t)-0.682128906f, (float16_t)0.857910156f, (float16_t)-0.743652344f, + (float16_t)-1.24707031f, (float16_t)0.338134766f, (float16_t)2.79492188f, (float16_t)0.336425781f, + (float16_t)2.03515625f, (float16_t)5.0546875f, (float16_t)-5.03515625f, (float16_t)6.88671875f, + (float16_t)-1.05664062f, (float16_t)-6.7890625f, (float16_t)3.87109375f, (float16_t)-0.904785156f, + (float16_t)-5.58203125f, (float16_t)-4.46875f, (float16_t)0.4921875f, (float16_t)1.546875f, + (float16_t)2.4453125f, (float16_t)1.14257812f, (float16_t)0.0454101562f, (float16_t)2.49609375f, + (float16_t)2.92578125f, (float16_t)-0.289794922f, (float16_t)0.0278625488f, (float16_t)-6.72265625f, + (float16_t)3.32617188f, (float16_t)0.454345703f, (float16_t)-0.774414062f, (float16_t)7.171875f, + (float16_t)-5.73046875f, (float16_t)-0.454833984f, (float16_t)0.900390625f, (float16_t)-0.662109375f, + (float16_t)2.6796875f, (float16_t)5.3984375f, (float16_t)5.08984375f, (float16_t)-6.08984375f, + (float16_t)2.50585938f, (float16_t)1.2109375f, (float16_t)-0.0201568604f, (float16_t)2.43164062f, + (float16_t)8.7578125f, (float16_t)-5.96484375f, (float16_t)3.94726562f, (float16_t)1.74804688f, + (float16_t)-2.84960938f, (float16_t)3.30273438f, (float16_t)-0.435546875f, (float16_t)0.122802734f, + (float16_t)-1.18359375f, (float16_t)4.8515625f, (float16_t)-3.75976562f, (float16_t)-0.981933594f, + (float16_t)-1.55761719f, (float16_t)-0.889160156f, (float16_t)-7.1484375f, (float16_t)-3.4765625f, + (float16_t)-0.75f, (float16_t)-0.0386962891f, (float16_t)6.12109375f, (float16_t)-0.277587891f, + (float16_t)1.63574219f, (float16_t)4.55859375f, (float16_t)5.34375f, (float16_t)0.818359375f, + (float16_t)-3.20507812f, (float16_t)-4.37890625f, (float16_t)-1.03027344f, (float16_t)-0.321044922f, + (float16_t)-5.71484375f, (float16_t)-2.08007812f, (float16_t)-0.0604858398f, (float16_t)0.763183594f, + (float16_t)2.26953125f, (float16_t)-0.275390625f, (float16_t)-0.250732422f, (float16_t)1.03125f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_input_tensor.h new file mode 100644 index 00000000..e13c0dbc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_input_tensor.h @@ -0,0 +1,11 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_2_f16_rhs_input_tensor[35] = { + (float16_t)0.943847656f, (float16_t)0.44921875f, (float16_t)-1.05371094f, (float16_t)-1.74511719f, + (float16_t)0.778320312f, (float16_t)0.418212891f, (float16_t)-1.03027344f, (float16_t)1.98144531f, + (float16_t)-1.29785156f, (float16_t)0.852050781f, (float16_t)-1.51367188f, (float16_t)0.384521484f, + (float16_t)0.335693359f, (float16_t)1.49609375f, (float16_t)-0.876953125f, (float16_t)1.78515625f, + (float16_t)0.0836791992f, (float16_t)-1.61035156f, (float16_t)0.200683594f, (float16_t)-0.387695312f, + (float16_t)-1.21289062f, (float16_t)-1.6796875f, (float16_t)0.383056641f, (float16_t)1.23828125f, + (float16_t)-0.834472656f, (float16_t)1.55957031f, (float16_t)0.371582031f, (float16_t)1.96191406f, + (float16_t)-1.23535156f, (float16_t)1.16699219f, (float16_t)0.297607422f, (float16_t)-1.5625f, + (float16_t)0.870605469f, (float16_t)-0.844238281f, (float16_t)-1.75878906f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..0f1234b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/rhs_transposed_tensor.h @@ -0,0 +1,11 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_2_f16_rhs_transposed_tensor[35] = { + (float16_t)0.943847656f, (float16_t)0.418212891f, (float16_t)-1.51367188f, (float16_t)1.78515625f, + (float16_t)-1.21289062f, (float16_t)1.55957031f, (float16_t)0.297607422f, (float16_t)0.44921875f, + (float16_t)-1.03027344f, (float16_t)0.384521484f, (float16_t)0.0836791992f, (float16_t)-1.6796875f, + (float16_t)0.371582031f, (float16_t)-1.5625f, (float16_t)-1.05371094f, (float16_t)1.98144531f, + (float16_t)0.335693359f, (float16_t)-1.61035156f, (float16_t)0.383056641f, (float16_t)1.96191406f, + (float16_t)0.870605469f, (float16_t)-1.74511719f, (float16_t)-1.29785156f, (float16_t)1.49609375f, + (float16_t)0.200683594f, (float16_t)1.23828125f, (float16_t)-1.23535156f, (float16_t)-0.844238281f, + (float16_t)0.778320312f, (float16_t)0.852050781f, (float16_t)-0.876953125f, (float16_t)-0.387695312f, + (float16_t)-0.834472656f, (float16_t)1.16699219f, (float16_t)-1.75878906f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/config_data.h new file mode 100644 index 00000000..cfd73720 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_2_F32_LHS_BATCH 2 +#define BATCH_MATMUL_2_F32_LHS_HEIGHT 2 +#define BATCH_MATMUL_2_F32_LHS_ROWS 8 +#define BATCH_MATMUL_2_F32_LHS_COLS 5 +#define BATCH_MATMUL_2_F32_RHS_BATCH 1 +#define BATCH_MATMUL_2_F32_RHS_HEIGHT 1 +#define BATCH_MATMUL_2_F32_RHS_ROWS 7 +#define BATCH_MATMUL_2_F32_RHS_COLS 5 +#define BATCH_MATMUL_2_F32_ADJ_X 0 +#define BATCH_MATMUL_2_F32_ADJ_Y 1 +#define BATCH_MATMUL_2_F32_DST_SIZE 224 +#define BATCH_MATMUL_2_F32_OUTPUT_BATCH 2 +#define BATCH_MATMUL_2_F32_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_2_F32_OUTPUT_ROWS 8 +#define BATCH_MATMUL_2_F32_OUTPUT_COLS 7 +#define BATCH_MATMUL_2_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_2_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_input_tensor.h new file mode 100644 index 00000000..203739f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_input_tensor.h @@ -0,0 +1,25 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_2_f32_lhs_input_tensor[160] = { + 1.29245794f, 1.05051064f, 0.124528669f, -1.08420873f, -1.48942184f, 0.204222307f, -1.72768545f, + 0.920766294f, 0.760215044f, -1.54672468f, 0.975623369f, 0.490862548f, -1.48205566f, -1.93722272f, + -0.638405442f, -0.650745988f, 0.604279816f, -1.01583982f, 1.56049585f, -1.49298739f, -1.57077253f, + 1.79904592f, -0.42564258f, 1.01278925f, -1.96408498f, 1.70493948f, 1.9526335f, 0.456044763f, + -0.0403864793f, 1.02539694f, -0.581074476f, 0.749837101f, -0.57546401f, 1.20999384f, -1.74268389f, + 1.65225291f, -1.5013144f, 0.917438328f, 0.488708913f, -0.412259877f, 1.37512684f, 1.01986432f, + -0.664546609f, -0.967662513f, -0.508761764f, 0.17651628f, 0.694765925f, 0.0349008888f, -0.682080984f, + -1.04417908f, 0.714799583f, 0.572525203f, -0.306538224f, -0.65391463f, -0.979529083f, -1.85405457f, + -0.20494324f, 0.787081182f, 0.878634572f, -1.34687448f, -1.84662986f, -1.60621572f, 1.99857628f, + 1.05941987f, 0.156581193f, -0.887056053f, -1.82193625f, -0.324210972f, -0.880303979f, 1.07060206f, + 0.860017061f, 0.713762879f, 0.2734873f, -0.0305217095f, 1.89753306f, 1.46733725f, -1.44254005f, + 1.22286797f, 0.00467150658f, 0.552538335f, -1.98664868f, 1.91401637f, 0.309779912f, -1.35302043f, + -0.135895446f, -1.00041187f, 0.247399703f, 1.21640241f, -1.35742331f, -1.48523724f, -1.14349234f, + -1.06141698f, 1.6612345f, 1.06788003f, 1.84059596f, -1.07982242f, -0.0789010227f, 0.386994928f, + 0.724697292f, -1.78126991f, -1.5283953f, -0.644048631f, 0.540646791f, 0.0732444674f, -1.22659349f, + -1.17858827f, 1.30697334f, -0.54981637f, -1.3045634f, 1.40706444f, 0.2608684f, 0.674976587f, + -0.434082121f, -1.16122019f, 0.934462488f, 1.68748128f, 1.04372215f, 1.56736279f, -0.650241673f, + 0.683312595f, -0.7861076f, -1.61272895f, -0.0217272248f, 1.17381704f, -1.91514683f, 0.745653093f, + 0.749640107f, -1.05558431f, -0.0632227212f, -0.993469775f, 0.690659881f, 0.0995075628f, -0.857378304f, + 1.06735444f, -0.539503455f, 1.33239496f, -1.13495207f, 0.112784319f, 1.77510047f, 1.03575623f, + -0.279479146f, 1.40878606f, 1.62743783f, -0.983402312f, -0.0270566922f, 1.50152802f, -0.926781058f, + 1.07988858f, -1.31953156f, 0.787229419f, 1.57632363f, 1.7807945f, 0.512734711f, 0.487995595f, + -0.0500309095f, -1.37846744f, 0.786647916f, 1.64739656f, -1.85474753f, -0.69865036f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..aa989b66 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/lhs_transposed_tensor.h @@ -0,0 +1,25 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_2_f32_lhs_transposed_tensor[160] = { + 1.29245794f, 0.204222307f, 0.975623369f, -0.650745988f, -1.57077253f, 1.70493948f, -0.581074476f, + 1.65225291f, 1.05051064f, -1.72768545f, 0.490862548f, 0.604279816f, 1.79904592f, 1.9526335f, + 0.749837101f, -1.5013144f, 0.124528669f, 0.920766294f, -1.48205566f, -1.01583982f, -0.42564258f, + 0.456044763f, -0.57546401f, 0.917438328f, -1.08420873f, 0.760215044f, -1.93722272f, 1.56049585f, + 1.01278925f, -0.0403864793f, 1.20999384f, 0.488708913f, -1.48942184f, -1.54672468f, -0.638405442f, + -1.49298739f, -1.96408498f, 1.02539694f, -1.74268389f, -0.412259877f, 1.37512684f, 0.17651628f, + 0.714799583f, -1.85405457f, -1.84662986f, -0.887056053f, 0.860017061f, 1.46733725f, 1.01986432f, + 0.694765925f, 0.572525203f, -0.20494324f, -1.60621572f, -1.82193625f, 0.713762879f, -1.44254005f, + -0.664546609f, 0.0349008888f, -0.306538224f, 0.787081182f, 1.99857628f, -0.324210972f, 0.2734873f, + 1.22286797f, -0.967662513f, -0.682080984f, -0.65391463f, 0.878634572f, 1.05941987f, -0.880303979f, + -0.0305217095f, 0.00467150658f, -0.508761764f, -1.04417908f, -0.979529083f, -1.34687448f, 0.156581193f, + 1.07060206f, 1.89753306f, 0.552538335f, -1.98664868f, -1.00041187f, -1.14349234f, -1.07982242f, + -1.5283953f, -1.17858827f, 0.2608684f, 1.68748128f, 1.91401637f, 0.247399703f, -1.06141698f, + -0.0789010227f, -0.644048631f, 1.30697334f, 0.674976587f, 1.04372215f, 0.309779912f, 1.21640241f, + 1.6612345f, 0.386994928f, 0.540646791f, -0.54981637f, -0.434082121f, 1.56736279f, -1.35302043f, + -1.35742331f, 1.06788003f, 0.724697292f, 0.0732444674f, -1.3045634f, -1.16122019f, -0.650241673f, + -0.135895446f, -1.48523724f, 1.84059596f, -1.78126991f, -1.22659349f, 1.40706444f, 0.934462488f, + 0.683312595f, -0.7861076f, 0.745653093f, 0.690659881f, 1.33239496f, -0.279479146f, 1.50152802f, + 1.57632363f, -1.37846744f, -1.61272895f, 0.749640107f, 0.0995075628f, -1.13495207f, 1.40878606f, + -0.926781058f, 1.7807945f, 0.786647916f, -0.0217272248f, -1.05558431f, -0.857378304f, 0.112784319f, + 1.62743783f, 1.07988858f, 0.512734711f, 1.64739656f, 1.17381704f, -0.0632227212f, 1.06735444f, + 1.77510047f, -0.983402312f, -1.31953156f, 0.487995595f, -1.85474753f, -1.91514683f, -0.993469775f, + -0.539503455f, 1.03575623f, -0.0270566922f, 0.787229419f, -0.0500309095f, -0.69865036f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/output.h new file mode 100644 index 00000000..d12084e8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/output.h @@ -0,0 +1,34 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_2_f32_output[224] = { + 3.06846809f, 1.87535357f, 0.160112381f, -5.9650631f, -0.668806255f, 1.02156758f, -4.40273523f, + 1.43951678f, -3.41861534f, -0.311411977f, 2.16890001f, -2.98020029f, 0.632607937f, -4.87534475f, + 1.07268071f, 1.30770493f, 0.225289047f, -7.77126598f, -2.01280355f, 2.88667727f, -2.33112478f, + -3.06555462f, 2.42743111f, -7.18253613f, 0.906296849f, 1.83219039f, -5.86130524f, 2.22247362f, + -1.67661667f, 4.07507801f, -8.08699322f, 0.78902185f, 5.0411973f, -7.97338581f, 1.46702886f, + 1.00398421f, 3.63438463f, 2.53194332f, -2.9072485f, 1.5349592f, 0.474445134f, 2.16835427f, + -1.7647351f, 2.34254169f, -6.39051342f, 0.367174864f, 1.95759463f, -5.39818764f, 0.682906866f, + 1.87478876f, -2.97724342f, 3.09205198f, 0.130786091f, -4.37074375f, 3.52723002f, -3.88028026f, + 1.19687963f, 2.25458956f, 0.292964399f, -5.98058367f, -0.85854429f, 1.40529478f, -1.37062109f, + 1.60041952f, 1.18646812f, -0.83199966f, -2.89621305f, 0.449637055f, -0.344347775f, -2.47071338f, + 1.27970672f, 1.24198461f, -0.637211621f, -3.97450614f, -0.593333662f, 0.416393399f, -2.29295421f, + -0.102825999f, -0.680612028f, -3.81985164f, 4.53365088f, 2.48869038f, -4.36691332f, -1.23140776f, + 0.699554086f, -4.19377041f, 1.10528481f, 8.66437626f, 0.946519554f, -1.11894965f, -1.32673287f, + -0.386766434f, -3.89676332f, 2.81375527f, 1.76723051f, -2.23812127f, 3.58687282f, -0.33120811f, + -0.189353704f, 1.1451354f, 3.57284474f, -0.0503571033f, 0.645766139f, 1.67370534f, 3.29252982f, + 2.32657027f, -3.3558588f, 5.42050076f, 0.697695732f, -3.87268829f, 4.84774637f, -3.12342644f, + 1.25325775f, 2.70388579f, -1.40656531f, -0.458471239f, 5.52514887f, -3.03086543f, 0.386125147f, + 3.94115734f, -0.783871949f, 0.442947388f, -0.825522304f, 1.3194077f, -0.0024228096f, -5.76725388f, + -0.432520032f, -2.99352074f, 3.32578087f, 8.15111637f, 1.23171759f, 0.13770926f, 2.4128561f, + 0.118887782f, -0.0962797254f, -4.11932182f, 2.06040168f, 1.34227371f, -3.72422981f, -2.07958817f, + 0.662395656f, -1.65158749f, -2.1661067f, 2.700562f, 0.87376225f, -1.9375267f, -2.62857008f, + -0.578072369f, 2.04078603f, 0.764770389f, -0.92815125f, 3.43347692f, -0.361732721f, 3.38885617f, + 0.423841059f, 1.06759202f, 2.10007071f, -2.76412773f, 0.390160441f, 1.89596653f, 1.01098609f, + 3.6937418f, 1.00472069f, 5.35639238f, -2.23127699f, 0.0663500726f, 2.94506836f, -1.95501792f, + -0.835165024f, -2.58098626f, -4.06727409f, 2.97515631f, -1.73513758f, -2.16965294f, -2.82132101f, + -0.609343588f, 2.26929235f, -2.78845477f, -4.00856733f, -0.372817487f, -1.09071243f, -0.145765424f, + -2.0358808f, 1.27605784f, -3.00141573f, -0.754691839f, -0.767194986f, -1.76273894f, 1.59711337f, + -2.0038445f, -1.47880459f, 1.43637109f, 2.94619036f, -2.72276783f, 1.15239537f, 2.60034704f, + 3.6112411f, 1.31960249f, 2.50405073f, -0.47524941f, 3.05443621f, -0.11312896f, -2.27261782f, + 3.68982935f, -2.79994655f, 7.0810523f, -2.11659002f, -3.57980418f, 6.54098606f, -4.10694504f, + 0.955382884f, 3.55672503f, 0.297481447f, -2.45990372f, 1.39494634f, -1.12415612f, 0.848125517f, + 4.59687614f, -0.327459902f, 2.13968754f, -0.342406929f, 2.91728306f, 0.316604555f, -4.91342354f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_input_tensor.h new file mode 100644 index 00000000..4dce174f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_input_tensor.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_2_f32_rhs_input_tensor[35] = { + 0.50797987f, 0.149908721f, 1.07281554f, -1.61163616f, -1.34174836f, 1.32574165f, -0.630426466f, + -0.0172511004f, 1.93058097f, -0.794350803f, -0.771640182f, 1.74777699f, -1.42249f, 1.04840732f, + 1.53297484f, -0.604207754f, 1.5598408f, 1.20327318f, 0.394313663f, 0.369642109f, -1.40534103f, + -1.28879094f, 0.383974463f, -1.44759417f, 1.803792f, 0.425053239f, -1.70805275f, 1.61530375f, + -0.565206051f, -0.0973911658f, 1.44735467f, 0.849745631f, 0.241011828f, 0.735502541f, 1.8550601f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..b160b423 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/rhs_transposed_tensor.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_2_f32_rhs_transposed_tensor[35] = { + 0.50797987f, 1.32574165f, -0.771640182f, -0.604207754f, -1.40534103f, 0.425053239f, 1.44735467f, + 0.149908721f, -0.630426466f, 1.74777699f, 1.5598408f, -1.28879094f, -1.70805275f, 0.849745631f, + 1.07281554f, -0.0172511004f, -1.42249f, 1.20327318f, 0.383974463f, 1.61530375f, 0.241011828f, + -1.61163616f, 1.93058097f, 1.04840732f, 0.394313663f, -1.44759417f, -0.565206051f, 0.735502541f, + -1.34174836f, -0.794350803f, 1.53297484f, 0.369642109f, 1.803792f, -0.0973911658f, 1.8550601f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_2_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/config_data.h new file mode 100644 index 00000000..295ec995 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_3_F16_LHS_BATCH 1 +#define BATCH_MATMUL_3_F16_LHS_HEIGHT 1 +#define BATCH_MATMUL_3_F16_LHS_ROWS 5 +#define BATCH_MATMUL_3_F16_LHS_COLS 8 +#define BATCH_MATMUL_3_F16_RHS_BATCH 2 +#define BATCH_MATMUL_3_F16_RHS_HEIGHT 2 +#define BATCH_MATMUL_3_F16_RHS_ROWS 5 +#define BATCH_MATMUL_3_F16_RHS_COLS 7 +#define BATCH_MATMUL_3_F16_ADJ_X 1 +#define BATCH_MATMUL_3_F16_ADJ_Y 0 +#define BATCH_MATMUL_3_F16_DST_SIZE 224 +#define BATCH_MATMUL_3_F16_OUTPUT_BATCH 2 +#define BATCH_MATMUL_3_F16_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_3_F16_OUTPUT_ROWS 8 +#define BATCH_MATMUL_3_F16_OUTPUT_COLS 7 +#define BATCH_MATMUL_3_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_3_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_input_tensor.h new file mode 100644 index 00000000..b0bf3630 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_input_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_3_f16_lhs_input_tensor[40] = { + (float16_t)0.5078125f, (float16_t)0.149902344f, (float16_t)1.07324219f, (float16_t)-1.61132812f, + (float16_t)-1.34179688f, (float16_t)1.32617188f, (float16_t)-0.630371094f, (float16_t)-0.0172576904f, + (float16_t)1.93066406f, (float16_t)-0.794433594f, (float16_t)-0.771484375f, (float16_t)1.74804688f, + (float16_t)-1.42285156f, (float16_t)1.04882812f, (float16_t)1.53320312f, (float16_t)-0.604003906f, + (float16_t)1.55957031f, (float16_t)1.203125f, (float16_t)0.394287109f, (float16_t)0.369628906f, + (float16_t)-1.40527344f, (float16_t)-1.2890625f, (float16_t)0.384033203f, (float16_t)-1.44726562f, + (float16_t)1.80371094f, (float16_t)0.425048828f, (float16_t)-1.70800781f, (float16_t)1.61523438f, + (float16_t)-0.565429688f, (float16_t)-0.0974121094f, (float16_t)1.44726562f, (float16_t)0.849609375f, + (float16_t)0.240966797f, (float16_t)0.735351562f, (float16_t)1.85546875f, (float16_t)1.36914062f, + (float16_t)-0.0921020508f, (float16_t)-0.784179688f, (float16_t)0.0403137207f, (float16_t)1.86621094f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..75d59db6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/lhs_transposed_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_3_f16_lhs_transposed_tensor[40] = { + (float16_t)0.5078125f, (float16_t)1.93066406f, (float16_t)1.55957031f, (float16_t)1.80371094f, + (float16_t)0.240966797f, (float16_t)0.149902344f, (float16_t)-0.794433594f, (float16_t)1.203125f, + (float16_t)0.425048828f, (float16_t)0.735351562f, (float16_t)1.07324219f, (float16_t)-0.771484375f, + (float16_t)0.394287109f, (float16_t)-1.70800781f, (float16_t)1.85546875f, (float16_t)-1.61132812f, + (float16_t)1.74804688f, (float16_t)0.369628906f, (float16_t)1.61523438f, (float16_t)1.36914062f, + (float16_t)-1.34179688f, (float16_t)-1.42285156f, (float16_t)-1.40527344f, (float16_t)-0.565429688f, + (float16_t)-0.0921020508f, (float16_t)1.32617188f, (float16_t)1.04882812f, (float16_t)-1.2890625f, + (float16_t)-0.0974121094f, (float16_t)-0.784179688f, (float16_t)-0.630371094f, (float16_t)1.53320312f, + (float16_t)0.384033203f, (float16_t)1.44726562f, (float16_t)0.0403137207f, (float16_t)-0.0172576904f, + (float16_t)-0.604003906f, (float16_t)-1.44726562f, (float16_t)0.849609375f, (float16_t)1.86621094f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/output.h new file mode 100644 index 00000000..b980ef61 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/output.h @@ -0,0 +1,58 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_3_f16_output[224] = { + (float16_t)-0.567871094f, (float16_t)0.65625f, (float16_t)-1.3515625f, (float16_t)-2.85546875f, + (float16_t)-0.60546875f, (float16_t)-1.96484375f, (float16_t)-0.157226562f, (float16_t)3.40429688f, + (float16_t)-1.21875f, (float16_t)-4.36328125f, (float16_t)0.10168457f, (float16_t)2.35351562f, + (float16_t)-2.859375f, (float16_t)-1.23242188f, (float16_t)1.96875f, (float16_t)0.402832031f, + (float16_t)-4.5078125f, (float16_t)-4.265625f, (float16_t)2.89648438f, (float16_t)-1.73925781f, + (float16_t)0.250488281f, (float16_t)0.790527344f, (float16_t)5.9140625f, (float16_t)-2.96484375f, + (float16_t)-4.27734375f, (float16_t)-3.06445312f, (float16_t)1.56640625f, (float16_t)-4.05859375f, + (float16_t)0.845703125f, (float16_t)-0.09375f, (float16_t)0.485107422f, (float16_t)1.1953125f, + (float16_t)1.95019531f, (float16_t)2.65429688f, (float16_t)-0.942871094f, (float16_t)1.14257812f, + (float16_t)4.63671875f, (float16_t)-0.355957031f, (float16_t)-0.500488281f, (float16_t)-1.63085938f, + (float16_t)5.05859375f, (float16_t)-1.54785156f, (float16_t)-2.07617188f, (float16_t)-5.08203125f, + (float16_t)5.04296875f, (float16_t)5.375f, (float16_t)1.8125f, (float16_t)-0.120117188f, + (float16_t)-1.05078125f, (float16_t)4.15625f, (float16_t)0.000863075256f, (float16_t)-4.29296875f, + (float16_t)2.71679688f, (float16_t)-1.29199219f, (float16_t)-3.21484375f, (float16_t)-0.517578125f, + (float16_t)-1.07324219f, (float16_t)-0.162841797f, (float16_t)-0.371337891f, (float16_t)2.3828125f, + (float16_t)-0.321289062f, (float16_t)-4.41015625f, (float16_t)2.25976562f, (float16_t)-1.44726562f, + (float16_t)-1.43066406f, (float16_t)-1.29980469f, (float16_t)2.02929688f, (float16_t)-2.390625f, + (float16_t)-0.994628906f, (float16_t)-1.11328125f, (float16_t)-1.16894531f, (float16_t)-3.08203125f, + (float16_t)-1.51953125f, (float16_t)0.121154785f, (float16_t)0.104553223f, (float16_t)-4.94140625f, + (float16_t)0.127197266f, (float16_t)-2.703125f, (float16_t)0.132324219f, (float16_t)-2.28710938f, + (float16_t)4.75f, (float16_t)1.73144531f, (float16_t)-6.2734375f, (float16_t)-0.527832031f, + (float16_t)1.73535156f, (float16_t)-0.0409240723f, (float16_t)-2.453125f, (float16_t)-3.25976562f, + (float16_t)0.599609375f, (float16_t)1.01757812f, (float16_t)-3.83007812f, (float16_t)-0.872070312f, + (float16_t)-1.03515625f, (float16_t)0.344482422f, (float16_t)-0.908691406f, (float16_t)3.5f, + (float16_t)2.16796875f, (float16_t)-2.95703125f, (float16_t)4.6953125f, (float16_t)4.5859375f, + (float16_t)-4.23828125f, (float16_t)-3.1328125f, (float16_t)-3.29492188f, (float16_t)1.125f, + (float16_t)-2.796875f, (float16_t)-4.17578125f, (float16_t)-1.58300781f, (float16_t)4.25f, + (float16_t)5.34375f, (float16_t)-2.15820312f, (float16_t)5.54296875f, (float16_t)1.08886719f, + (float16_t)-1.81542969f, (float16_t)1.43457031f, (float16_t)0.461181641f, (float16_t)-3.359375f, + (float16_t)2.90234375f, (float16_t)-3.078125f, (float16_t)1.39453125f, (float16_t)-1.8359375f, + (float16_t)-3.44921875f, (float16_t)0.649902344f, (float16_t)-4.703125f, (float16_t)4.15234375f, + (float16_t)-4.01953125f, (float16_t)-0.860839844f, (float16_t)-2.68359375f, (float16_t)-1.0234375f, + (float16_t)1.78320312f, (float16_t)-5.6171875f, (float16_t)3.30859375f, (float16_t)-3.74609375f, + (float16_t)2.56835938f, (float16_t)0.963867188f, (float16_t)4.5078125f, (float16_t)-3.05664062f, + (float16_t)-0.539550781f, (float16_t)3.44335938f, (float16_t)-1.60644531f, (float16_t)3.64257812f, + (float16_t)0.592773438f, (float16_t)-1.65429688f, (float16_t)-1.05859375f, (float16_t)1.23144531f, + (float16_t)-2.77734375f, (float16_t)1.35058594f, (float16_t)1.1484375f, (float16_t)4.265625f, + (float16_t)2.63476562f, (float16_t)-2.140625f, (float16_t)5.98828125f, (float16_t)-2.54882812f, + (float16_t)5.28515625f, (float16_t)1.22558594f, (float16_t)-2.125f, (float16_t)-3.90039062f, + (float16_t)-1.66308594f, (float16_t)-0.981445312f, (float16_t)-3.57226562f, (float16_t)-1.97167969f, + (float16_t)-0.731445312f, (float16_t)1.96386719f, (float16_t)-1.96777344f, (float16_t)0.76171875f, + (float16_t)0.586425781f, (float16_t)4.93359375f, (float16_t)0.0606689453f, (float16_t)-5.55078125f, + (float16_t)-2.10742188f, (float16_t)2.55078125f, (float16_t)-2.40429688f, (float16_t)-0.0767822266f, + (float16_t)-1.12695312f, (float16_t)2.71679688f, (float16_t)0.328613281f, (float16_t)-1.20898438f, + (float16_t)-2.03515625f, (float16_t)-0.659667969f, (float16_t)1.59277344f, (float16_t)0.283203125f, + (float16_t)2.50585938f, (float16_t)2.66210938f, (float16_t)-0.49609375f, (float16_t)2.95507812f, + (float16_t)-1.95507812f, (float16_t)0.377685547f, (float16_t)-2.14453125f, (float16_t)3.7421875f, + (float16_t)-0.291748047f, (float16_t)-6.578125f, (float16_t)3.40429688f, (float16_t)-0.671386719f, + (float16_t)1.56738281f, (float16_t)-1.21972656f, (float16_t)-2.671875f, (float16_t)1.82226562f, + (float16_t)1.97949219f, (float16_t)-1.17871094f, (float16_t)2.67773438f, (float16_t)0.461914062f, + (float16_t)1.92578125f, (float16_t)-2.91210938f, (float16_t)-0.9140625f, (float16_t)-1.31542969f, + (float16_t)0.639648438f, (float16_t)2.72851562f, (float16_t)0.604003906f, (float16_t)-1.37890625f, + (float16_t)-6.0546875f, (float16_t)-0.4296875f, (float16_t)3.6875f, (float16_t)-4.43359375f, + (float16_t)2.51757812f, (float16_t)0.0096206665f, (float16_t)7.09765625f, (float16_t)-1.1640625f, + (float16_t)-0.338134766f, (float16_t)-3.171875f, (float16_t)-4.65234375f, (float16_t)-0.752441406f, + (float16_t)1.68554688f, (float16_t)-2.1015625f, (float16_t)1.08691406f, (float16_t)4.9453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_input_tensor.h new file mode 100644 index 00000000..4eea1622 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_input_tensor.h @@ -0,0 +1,37 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_3_f16_rhs_input_tensor[140] = { + (float16_t)1.41113281f, (float16_t)1.48046875f, (float16_t)-1.85742188f, (float16_t)-0.971679688f, + (float16_t)0.436523438f, (float16_t)0.789550781f, (float16_t)-1.49121094f, (float16_t)-0.477783203f, + (float16_t)-1.5f, (float16_t)0.754394531f, (float16_t)0.225097656f, (float16_t)1.61425781f, + (float16_t)-0.0915527344f, (float16_t)-0.00323295593f, (float16_t)-0.528808594f, (float16_t)0.525390625f, + (float16_t)-0.603515625f, (float16_t)-1.81445312f, (float16_t)-0.590820312f, (float16_t)-1.01464844f, + (float16_t)0.541992188f, (float16_t)1.08789062f, (float16_t)-1.04589844f, (float16_t)-1.3984375f, + (float16_t)-0.127197266f, (float16_t)1.40722656f, (float16_t)-1.11035156f, (float16_t)1.64550781f, + (float16_t)-0.149291992f, (float16_t)-1.23046875f, (float16_t)0.514160156f, (float16_t)1.83203125f, + (float16_t)-0.915039062f, (float16_t)-1.70605469f, (float16_t)-0.505859375f, (float16_t)-0.629394531f, + (float16_t)-0.671386719f, (float16_t)-1.4609375f, (float16_t)0.502929688f, (float16_t)0.388183594f, + (float16_t)-1.421875f, (float16_t)-1.63769531f, (float16_t)1.58007812f, (float16_t)0.545898438f, + (float16_t)-1.98046875f, (float16_t)-1.74316406f, (float16_t)-0.647460938f, (float16_t)-1.29003906f, + (float16_t)-0.963378906f, (float16_t)-0.791992188f, (float16_t)-0.448242188f, (float16_t)0.701660156f, + (float16_t)1.16699219f, (float16_t)0.246582031f, (float16_t)-1.90136719f, (float16_t)1.85546875f, + (float16_t)-0.566894531f, (float16_t)-1.86425781f, (float16_t)1.65527344f, (float16_t)-0.723144531f, + (float16_t)-0.126831055f, (float16_t)1.61914062f, (float16_t)0.653320312f, (float16_t)-0.24206543f, + (float16_t)1.45214844f, (float16_t)0.360839844f, (float16_t)1.98144531f, (float16_t)-1.78710938f, + (float16_t)0.867675781f, (float16_t)0.832519531f, (float16_t)0.199829102f, (float16_t)-0.033203125f, + (float16_t)-0.842773438f, (float16_t)-0.625976562f, (float16_t)0.904296875f, (float16_t)-0.587890625f, + (float16_t)1.20507812f, (float16_t)-1.50097656f, (float16_t)-1.35546875f, (float16_t)0.0500183105f, + (float16_t)-1.72949219f, (float16_t)-0.884765625f, (float16_t)-1.37890625f, (float16_t)0.984863281f, + (float16_t)-0.669433594f, (float16_t)1.23242188f, (float16_t)0.671875f, (float16_t)-1.23730469f, + (float16_t)1.36816406f, (float16_t)-0.962402344f, (float16_t)0.547363281f, (float16_t)-0.276123047f, + (float16_t)-1.41308594f, (float16_t)1.88085938f, (float16_t)-0.728515625f, (float16_t)0.559082031f, + (float16_t)0.244018555f, (float16_t)-1.12402344f, (float16_t)-0.348876953f, (float16_t)-0.669921875f, + (float16_t)-0.346923828f, (float16_t)-0.551757812f, (float16_t)1.25097656f, (float16_t)-1.27148438f, + (float16_t)-1.8125f, (float16_t)-1.30371094f, (float16_t)0.280517578f, (float16_t)0.534667969f, + (float16_t)0.884765625f, (float16_t)-0.0394287109f, (float16_t)-1.10058594f, (float16_t)0.649902344f, + (float16_t)1.57714844f, (float16_t)-0.0713500977f, (float16_t)0.372802734f, (float16_t)-0.135986328f, + (float16_t)1.65820312f, (float16_t)0.562988281f, (float16_t)-0.837890625f, (float16_t)-0.944824219f, + (float16_t)1.81738281f, (float16_t)-1.56738281f, (float16_t)-0.356933594f, (float16_t)-1.38769531f, + (float16_t)1.69726562f, (float16_t)-0.163696289f, (float16_t)1.34277344f, (float16_t)-0.249755859f, + (float16_t)-0.612792969f, (float16_t)-0.280273438f, (float16_t)-1.59179688f, (float16_t)1.99023438f, + (float16_t)-0.198242188f, (float16_t)-0.886230469f, (float16_t)-1.97949219f, (float16_t)-0.397460938f, + (float16_t)0.337402344f, (float16_t)1.21777344f, (float16_t)0.657226562f, (float16_t)1.62109375f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..29a6085c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/rhs_transposed_tensor.h @@ -0,0 +1,37 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_3_f16_rhs_transposed_tensor[140] = { + (float16_t)1.41113281f, (float16_t)-0.477783203f, (float16_t)-0.528808594f, (float16_t)1.08789062f, + (float16_t)-0.149291992f, (float16_t)1.48046875f, (float16_t)-1.5f, (float16_t)0.525390625f, + (float16_t)-1.04589844f, (float16_t)-1.23046875f, (float16_t)-1.85742188f, (float16_t)0.754394531f, + (float16_t)-0.603515625f, (float16_t)-1.3984375f, (float16_t)0.514160156f, (float16_t)-0.971679688f, + (float16_t)0.225097656f, (float16_t)-1.81445312f, (float16_t)-0.127197266f, (float16_t)1.83203125f, + (float16_t)0.436523438f, (float16_t)1.61425781f, (float16_t)-0.590820312f, (float16_t)1.40722656f, + (float16_t)-0.915039062f, (float16_t)0.789550781f, (float16_t)-0.0915527344f, (float16_t)-1.01464844f, + (float16_t)-1.11035156f, (float16_t)-1.70605469f, (float16_t)-1.49121094f, (float16_t)-0.00323295593f, + (float16_t)0.541992188f, (float16_t)1.64550781f, (float16_t)-0.505859375f, (float16_t)-0.629394531f, + (float16_t)1.58007812f, (float16_t)-0.791992188f, (float16_t)-0.566894531f, (float16_t)-0.24206543f, + (float16_t)-0.671386719f, (float16_t)0.545898438f, (float16_t)-0.448242188f, (float16_t)-1.86425781f, + (float16_t)1.45214844f, (float16_t)-1.4609375f, (float16_t)-1.98046875f, (float16_t)0.701660156f, + (float16_t)1.65527344f, (float16_t)0.360839844f, (float16_t)0.502929688f, (float16_t)-1.74316406f, + (float16_t)1.16699219f, (float16_t)-0.723144531f, (float16_t)1.98144531f, (float16_t)0.388183594f, + (float16_t)-0.647460938f, (float16_t)0.246582031f, (float16_t)-0.126831055f, (float16_t)-1.78710938f, + (float16_t)-1.421875f, (float16_t)-1.29003906f, (float16_t)-1.90136719f, (float16_t)1.61914062f, + (float16_t)0.867675781f, (float16_t)-1.63769531f, (float16_t)-0.963378906f, (float16_t)1.85546875f, + (float16_t)0.653320312f, (float16_t)0.832519531f, (float16_t)0.199829102f, (float16_t)-1.50097656f, + (float16_t)-0.669433594f, (float16_t)-0.276123047f, (float16_t)-0.348876953f, (float16_t)-0.033203125f, + (float16_t)-1.35546875f, (float16_t)1.23242188f, (float16_t)-1.41308594f, (float16_t)-0.669921875f, + (float16_t)-0.842773438f, (float16_t)0.0500183105f, (float16_t)0.671875f, (float16_t)1.88085938f, + (float16_t)-0.346923828f, (float16_t)-0.625976562f, (float16_t)-1.72949219f, (float16_t)-1.23730469f, + (float16_t)-0.728515625f, (float16_t)-0.551757812f, (float16_t)0.904296875f, (float16_t)-0.884765625f, + (float16_t)1.36816406f, (float16_t)0.559082031f, (float16_t)1.25097656f, (float16_t)-0.587890625f, + (float16_t)-1.37890625f, (float16_t)-0.962402344f, (float16_t)0.244018555f, (float16_t)-1.27148438f, + (float16_t)1.20507812f, (float16_t)0.984863281f, (float16_t)0.547363281f, (float16_t)-1.12402344f, + (float16_t)-1.8125f, (float16_t)-1.30371094f, (float16_t)1.57714844f, (float16_t)-0.944824219f, + (float16_t)1.34277344f, (float16_t)-0.886230469f, (float16_t)0.280517578f, (float16_t)-0.0713500977f, + (float16_t)1.81738281f, (float16_t)-0.249755859f, (float16_t)-1.97949219f, (float16_t)0.534667969f, + (float16_t)0.372802734f, (float16_t)-1.56738281f, (float16_t)-0.612792969f, (float16_t)-0.397460938f, + (float16_t)0.884765625f, (float16_t)-0.135986328f, (float16_t)-0.356933594f, (float16_t)-0.280273438f, + (float16_t)0.337402344f, (float16_t)-0.0394287109f, (float16_t)1.65820312f, (float16_t)-1.38769531f, + (float16_t)-1.59179688f, (float16_t)1.21777344f, (float16_t)-1.10058594f, (float16_t)0.562988281f, + (float16_t)1.69726562f, (float16_t)1.99023438f, (float16_t)0.657226562f, (float16_t)0.649902344f, + (float16_t)-0.837890625f, (float16_t)-0.163696289f, (float16_t)-0.198242188f, (float16_t)1.62109375f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/config_data.h new file mode 100644 index 00000000..7044d3ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_3_F32_LHS_BATCH 1 +#define BATCH_MATMUL_3_F32_LHS_HEIGHT 1 +#define BATCH_MATMUL_3_F32_LHS_ROWS 5 +#define BATCH_MATMUL_3_F32_LHS_COLS 8 +#define BATCH_MATMUL_3_F32_RHS_BATCH 2 +#define BATCH_MATMUL_3_F32_RHS_HEIGHT 2 +#define BATCH_MATMUL_3_F32_RHS_ROWS 5 +#define BATCH_MATMUL_3_F32_RHS_COLS 7 +#define BATCH_MATMUL_3_F32_ADJ_X 1 +#define BATCH_MATMUL_3_F32_ADJ_Y 0 +#define BATCH_MATMUL_3_F32_DST_SIZE 224 +#define BATCH_MATMUL_3_F32_OUTPUT_BATCH 2 +#define BATCH_MATMUL_3_F32_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_3_F32_OUTPUT_ROWS 8 +#define BATCH_MATMUL_3_F32_OUTPUT_COLS 7 +#define BATCH_MATMUL_3_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_3_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_input_tensor.h new file mode 100644 index 00000000..43ad2493 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_input_tensor.h @@ -0,0 +1,8 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_3_f32_lhs_input_tensor[40] = { + -1.00855041f, 0.737538993f, -1.94636345f, -0.92148447f, -0.630164027f, 1.81013834f, -1.4329102f, + 0.468716383f, 1.5665729f, -0.866617203f, -1.81250131f, 1.19787467f, 0.997959137f, 0.945478678f, + 1.70249093f, 0.702513218f, -0.716794193f, -1.67396748f, -0.527817905f, -1.77222323f, -0.533124506f, + 0.570860684f, -0.273757249f, -0.40920788f, 0.735898435f, 0.447614253f, -1.71360648f, -0.129373416f, + -0.0641292781f, -0.941820621f, -0.00215054606f, 1.33747208f, 0.713955641f, 1.75226748f, 0.614841402f, + -1.35323763f, -1.78724754f, 0.620450735f, -1.7088908f, -0.0130688762f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..6fbc558b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/lhs_transposed_tensor.h @@ -0,0 +1,8 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_3_f32_lhs_transposed_tensor[40] = { + -1.00855041f, 1.5665729f, -0.716794193f, 0.735898435f, 0.713955641f, 0.737538993f, -0.866617203f, + -1.67396748f, 0.447614253f, 1.75226748f, -1.94636345f, -1.81250131f, -0.527817905f, -1.71360648f, + 0.614841402f, -0.92148447f, 1.19787467f, -1.77222323f, -0.129373416f, -1.35323763f, -0.630164027f, + 0.997959137f, -0.533124506f, -0.0641292781f, -1.78724754f, 1.81013834f, 0.945478678f, 0.570860684f, + -0.941820621f, 0.620450735f, -1.4329102f, 1.70249093f, -0.273757249f, -0.00215054606f, -1.7088908f, + 0.468716383f, 0.702513218f, -0.40920788f, 1.33747208f, -0.0130688762f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/output.h new file mode 100644 index 00000000..cfb5550e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/output.h @@ -0,0 +1,34 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_3_f32_output[224] = { + 2.63756466f, 3.66952109f, -1.43147004f, -3.87826562f, -1.16600776f, 2.71169758f, -0.22823149f, + -0.10977044f, -8.87057304f, 2.93319106f, 9.25947952f, 3.72833681f, -3.55866528f, 0.699992061f, + -5.6165638f, 2.60639095f, 0.458977222f, 0.905253649f, -0.7635988f, -3.78901482f, 1.37831521f, + -4.70959854f, -0.574116707f, 2.21758914f, -0.112483561f, -2.57653046f, -3.57493758f, -0.871650517f, + -1.24312091f, -0.269436002f, 2.00132799f, -1.78941464f, -3.04089355f, -1.27517068f, -0.967609763f, + -3.69531202f, 2.93456888f, 0.0479587913f, -2.75885558f, -1.98083985f, -0.593215823f, 2.30895042f, + 3.6673975f, 0.540281117f, -0.997361362f, -2.59017611f, -0.656314433f, 2.51113963f, -3.41553783f, + -0.00639340468f, 2.24035764f, -3.6641829f, 0.124869011f, 3.02617526f, 1.59923208f, -1.28124833f, + 0.218995273f, -3.61100149f, -0.201978952f, -2.88972688f, -0.344631881f, -0.955134928f, 2.4672296f, + 3.1488688f, 3.63885927f, -0.165781647f, 7.16850328f, 3.39113975f, 0.998603523f, -5.84599829f, + -4.32086182f, 1.52075803f, -1.55028486f, -3.87043238f, -4.55088997f, 0.880268037f, 5.2808733f, + -2.80386424f, 3.57818794f, 0.628417313f, -1.74475026f, -1.73692656f, 1.897452f, -0.309563875f, + -0.977293789f, 1.14566934f, 0.277486265f, -1.26330471f, -0.678091288f, 0.656396031f, -0.96279645f, + -3.58674169f, 1.44792795f, -3.02214622f, -0.34142971f, -5.60885239f, -2.46669102f, 3.21068525f, + 2.24806499f, -2.14293551f, 3.87134266f, -2.32347417f, 4.60168982f, 2.19704676f, -2.34620595f, + -0.0616105124f, -0.593124092f, 2.91781425f, -1.86684263f, 2.10315537f, 1.87059486f, 1.25765109f, + 5.11711073f, 2.26419163f, -4.5343585f, 1.78968084f, -3.78988123f, 0.409601927f, -1.1373378f, + -10.0f, -1.1878413f, 6.2207737f, -0.381187737f, 0.7378968f, -0.25562048f, -2.98519993f, + 3.90505791f, -1.30875778f, 2.63256502f, 0.0259377956f, 5.2631197f, -4.49000502f, 4.17782259f, + -1.03494525f, -0.621029556f, 1.86066747f, -0.819495976f, 5.2831049f, 0.383899033f, 3.31884456f, + -0.467365026f, 1.25127363f, -1.1732862f, 1.41546988f, 0.364734828f, 1.9996556f, 0.0412239134f, + 2.69386721f, -3.47178698f, 1.93203735f, 1.4528861f, 1.37355804f, -1.72881198f, 1.25069284f, + 1.04128313f, 3.72162437f, -4.98670816f, -1.92944491f, -1.20491707f, 3.68908906f, 0.348683447f, + 2.77865171f, -1.27877843f, 0.3519333f, -5.98954248f, 4.55434132f, -1.46808624f, 4.85191727f, + 2.40668011f, 6.0361867f, -4.30102444f, -3.53392959f, -4.4657135f, -1.08825386f, 0.851327956f, + -0.977100432f, -10.0f, 3.50342607f, 0.56044817f, 1.8635658f, 4.06008434f, 3.26257014f, + -5.94654655f, 1.09899616f, 4.36275768f, 3.81737065f, -0.209721804f, -1.37089741f, -6.05331707f, + -2.51490259f, -2.18946171f, 2.70123792f, 3.13732719f, 5.15499353f, -3.98927855f, -1.54782271f, + 1.21499741f, 0.103392899f, -1.20736623f, -0.414161354f, 2.29813528f, -3.63902497f, 1.6689353f, + -1.82011008f, 0.937779784f, 4.07637548f, 6.06364536f, 3.96010351f, 0.86105603f, -4.85058212f, + 3.34831667f, 2.92606163f, -5.80609941f, -5.65827084f, -1.89774179f, -3.76192856f, 4.30433893f, + -3.24256325f, 1.21448171f, 1.56257463f, 1.13540328f, -0.377768427f, 0.695285857f, -3.66074085f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_input_tensor.h new file mode 100644 index 00000000..5446a8d9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_input_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_3_f32_rhs_input_tensor[140] = { + -1.08075857f, -1.22456622f, 1.51999283f, 0.469075501f, -0.929381788f, -1.1097331f, 1.05478907f, + 1.85997927f, -1.57330549f, -0.166560695f, 1.61684239f, 1.44498563f, 0.651252806f, 0.0326931514f, + 0.930711091f, 1.67819643f, -1.29698658f, -1.85421681f, 0.192431241f, 1.9645015f, 0.905501783f, + -0.0127704823f, -0.674317181f, -0.804892302f, 1.3268435f, 1.65374494f, 0.289689332f, 0.327338696f, + 1.54402268f, 0.280257046f, -1.06356966f, -1.69353044f, 0.0912827402f, 1.95251286f, -1.17366815f, + -0.509880722f, 1.26663828f, -1.6321038f, 1.64885974f, -1.6203835f, -1.08331001f, -0.556594551f, + 1.76382601f, -0.62403518f, 0.184107423f, 1.72492206f, 1.73040473f, -0.146510616f, -1.35356474f, + -0.0530636273f, -1.02096939f, -0.981360197f, 0.634574413f, -1.07349801f, -1.77992845f, 0.940692544f, + 0.422612548f, 0.518150449f, 0.183733955f, 1.48516202f, 0.551759124f, -0.11139024f, -0.51859349f, + 0.971232772f, -0.346476972f, 1.55803156f, 0.382298261f, 1.75841165f, 0.179276168f, -1.67324936f, + -1.88418257f, -0.902378082f, 1.42055249f, 1.94216192f, -0.710961699f, 0.0900102705f, -1.43829083f, + -1.80916309f, 0.411375701f, 0.0955084711f, 0.0298864301f, -1.68004143f, 0.25722298f, -1.64556861f, + 1.61734796f, -1.21364224f, -0.497230679f, 0.336261898f, -1.68237305f, -0.163313001f, -0.681034803f, + -1.14024472f, -1.4617523f, 1.6263963f, -1.59323299f, 1.0019182f, -0.625674486f, 0.522236705f, + -0.187558413f, -0.0410512686f, -1.30766201f, -1.77080309f, -0.186430261f, 1.96291256f, 0.373272508f, + 0.363204569f, -1.83785069f, 1.29644728f, 1.63822687f, 1.91560805f, 0.608946681f, 0.22318241f, + 0.994792223f, -1.11599505f, -0.825732708f, -1.54442894f, -1.47087288f, 1.60873461f, 1.67603743f, + 0.869380355f, 1.55316925f, 0.132811993f, 1.11761343f, 0.0845132172f, 1.8238641f, -1.06616712f, + -0.96404916f, -1.63635302f, 1.74137902f, 1.4805721f, 0.880008578f, 1.94390535f, -1.05582249f, + 1.59519482f, 0.37592873f, -1.20289338f, -0.427389622f, 1.47497654f, -0.348069429f, 1.03625095f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..342c3123 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/rhs_transposed_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_3_f32_rhs_transposed_tensor[140] = { + -1.08075857f, 1.85997927f, 0.930711091f, -0.0127704823f, 1.54402268f, -1.22456622f, -1.57330549f, + 1.67819643f, -0.674317181f, 0.280257046f, 1.51999283f, -0.166560695f, -1.29698658f, -0.804892302f, + -1.06356966f, 0.469075501f, 1.61684239f, -1.85421681f, 1.3268435f, -1.69353044f, -0.929381788f, + 1.44498563f, 0.192431241f, 1.65374494f, 0.0912827402f, -1.1097331f, 0.651252806f, 1.9645015f, + 0.289689332f, 1.95251286f, 1.05478907f, 0.0326931514f, 0.905501783f, 0.327338696f, -1.17366815f, + -0.509880722f, 1.76382601f, -0.0530636273f, 0.422612548f, 0.971232772f, 1.26663828f, -0.62403518f, + -1.02096939f, 0.518150449f, -0.346476972f, -1.6321038f, 0.184107423f, -0.981360197f, 0.183733955f, + 1.55803156f, 1.64885974f, 1.72492206f, 0.634574413f, 1.48516202f, 0.382298261f, -1.6203835f, + 1.73040473f, -1.07349801f, 0.551759124f, 1.75841165f, -1.08331001f, -0.146510616f, -1.77992845f, + -0.11139024f, 0.179276168f, -0.556594551f, -1.35356474f, 0.940692544f, -0.51859349f, -1.67324936f, + -1.88418257f, -1.80916309f, 1.61734796f, -1.14024472f, -0.187558413f, -0.902378082f, 0.411375701f, + -1.21364224f, -1.4617523f, -0.0410512686f, 1.42055249f, 0.0955084711f, -0.497230679f, 1.6263963f, + -1.30766201f, 1.94216192f, 0.0298864301f, 0.336261898f, -1.59323299f, -1.77080309f, -0.710961699f, + -1.68004143f, -1.68237305f, 1.0019182f, -0.186430261f, 0.0900102705f, 0.25722298f, -0.163313001f, + -0.625674486f, 1.96291256f, -1.43829083f, -1.64556861f, -0.681034803f, 0.522236705f, 0.373272508f, + 0.363204569f, 0.994792223f, 0.869380355f, -0.96404916f, 1.59519482f, -1.83785069f, -1.11599505f, + 1.55316925f, -1.63635302f, 0.37592873f, 1.29644728f, -0.825732708f, 0.132811993f, 1.74137902f, + -1.20289338f, 1.63822687f, -1.54442894f, 1.11761343f, 1.4805721f, -0.427389622f, 1.91560805f, + -1.47087288f, 0.0845132172f, 0.880008578f, 1.47497654f, 0.608946681f, 1.60873461f, 1.8238641f, + 1.94390535f, -0.348069429f, 0.22318241f, 1.67603743f, -1.06616712f, -1.05582249f, 1.03625095f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_3_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/config_data.h new file mode 100644 index 00000000..eb608b4e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_4_F16_LHS_BATCH 2 +#define BATCH_MATMUL_4_F16_LHS_HEIGHT 1 +#define BATCH_MATMUL_4_F16_LHS_ROWS 5 +#define BATCH_MATMUL_4_F16_LHS_COLS 8 +#define BATCH_MATMUL_4_F16_RHS_BATCH 1 +#define BATCH_MATMUL_4_F16_RHS_HEIGHT 2 +#define BATCH_MATMUL_4_F16_RHS_ROWS 7 +#define BATCH_MATMUL_4_F16_RHS_COLS 5 +#define BATCH_MATMUL_4_F16_ADJ_X 1 +#define BATCH_MATMUL_4_F16_ADJ_Y 1 +#define BATCH_MATMUL_4_F16_DST_SIZE 224 +#define BATCH_MATMUL_4_F16_OUTPUT_BATCH 2 +#define BATCH_MATMUL_4_F16_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_4_F16_OUTPUT_ROWS 8 +#define BATCH_MATMUL_4_F16_OUTPUT_COLS 7 +#define BATCH_MATMUL_4_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_4_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_input_tensor.h new file mode 100644 index 00000000..df5890fc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_input_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_4_f16_lhs_input_tensor[80] = { + (float16_t)-1.08105469f, (float16_t)-1.22460938f, (float16_t)1.51953125f, (float16_t)0.468994141f, + (float16_t)-0.929199219f, (float16_t)-1.109375f, (float16_t)1.0546875f, (float16_t)1.86035156f, + (float16_t)-1.57324219f, (float16_t)-0.166503906f, (float16_t)1.6171875f, (float16_t)1.4453125f, + (float16_t)0.651367188f, (float16_t)0.0326843262f, (float16_t)0.930664062f, (float16_t)1.67773438f, + (float16_t)-1.296875f, (float16_t)-1.85449219f, (float16_t)0.192382812f, (float16_t)1.96484375f, + (float16_t)0.905273438f, (float16_t)-0.0127716064f, (float16_t)-0.674316406f, (float16_t)-0.8046875f, + (float16_t)1.32714844f, (float16_t)1.65332031f, (float16_t)0.289794922f, (float16_t)0.327392578f, + (float16_t)1.54394531f, (float16_t)0.280273438f, (float16_t)-1.06347656f, (float16_t)-1.69335938f, + (float16_t)0.0913085938f, (float16_t)1.95214844f, (float16_t)-1.17382812f, (float16_t)-0.509765625f, + (float16_t)1.26660156f, (float16_t)-1.63183594f, (float16_t)1.6484375f, (float16_t)-1.62011719f, + (float16_t)-1.08300781f, (float16_t)-0.556640625f, (float16_t)1.76367188f, (float16_t)-0.624023438f, + (float16_t)0.184082031f, (float16_t)1.72460938f, (float16_t)1.73046875f, (float16_t)-0.146484375f, + (float16_t)-1.35351562f, (float16_t)-0.0530700684f, (float16_t)-1.02050781f, (float16_t)-0.981445312f, + (float16_t)0.634765625f, (float16_t)-1.07324219f, (float16_t)-1.78027344f, (float16_t)0.940917969f, + (float16_t)0.422607422f, (float16_t)0.518066406f, (float16_t)0.18371582f, (float16_t)1.48535156f, + (float16_t)0.551757812f, (float16_t)-0.11138916f, (float16_t)-0.518554688f, (float16_t)0.971191406f, + (float16_t)-0.346435547f, (float16_t)1.55761719f, (float16_t)0.382324219f, (float16_t)1.75878906f, + (float16_t)0.179321289f, (float16_t)-1.67285156f, (float16_t)-1.88378906f, (float16_t)-0.90234375f, + (float16_t)1.42089844f, (float16_t)1.94238281f, (float16_t)-0.7109375f, (float16_t)0.0900268555f, + (float16_t)-1.43847656f, (float16_t)-1.80957031f, (float16_t)0.411376953f, (float16_t)0.0955200195f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..23a9c174 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/lhs_transposed_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_4_f16_lhs_transposed_tensor[80] = { + (float16_t)-1.08105469f, (float16_t)-1.57324219f, (float16_t)-1.296875f, (float16_t)1.32714844f, + (float16_t)0.0913085938f, (float16_t)-1.22460938f, (float16_t)-0.166503906f, (float16_t)-1.85449219f, + (float16_t)1.65332031f, (float16_t)1.95214844f, (float16_t)1.51953125f, (float16_t)1.6171875f, + (float16_t)0.192382812f, (float16_t)0.289794922f, (float16_t)-1.17382812f, (float16_t)0.468994141f, + (float16_t)1.4453125f, (float16_t)1.96484375f, (float16_t)0.327392578f, (float16_t)-0.509765625f, + (float16_t)-0.929199219f, (float16_t)0.651367188f, (float16_t)0.905273438f, (float16_t)1.54394531f, + (float16_t)1.26660156f, (float16_t)-1.109375f, (float16_t)0.0326843262f, (float16_t)-0.0127716064f, + (float16_t)0.280273438f, (float16_t)-1.63183594f, (float16_t)1.0546875f, (float16_t)0.930664062f, + (float16_t)-0.674316406f, (float16_t)-1.06347656f, (float16_t)1.6484375f, (float16_t)1.86035156f, + (float16_t)1.67773438f, (float16_t)-0.8046875f, (float16_t)-1.69335938f, (float16_t)-1.62011719f, + (float16_t)-1.08300781f, (float16_t)-1.35351562f, (float16_t)0.422607422f, (float16_t)-0.346435547f, + (float16_t)1.42089844f, (float16_t)-0.556640625f, (float16_t)-0.0530700684f, (float16_t)0.518066406f, + (float16_t)1.55761719f, (float16_t)1.94238281f, (float16_t)1.76367188f, (float16_t)-1.02050781f, + (float16_t)0.18371582f, (float16_t)0.382324219f, (float16_t)-0.7109375f, (float16_t)-0.624023438f, + (float16_t)-0.981445312f, (float16_t)1.48535156f, (float16_t)1.75878906f, (float16_t)0.0900268555f, + (float16_t)0.184082031f, (float16_t)0.634765625f, (float16_t)0.551757812f, (float16_t)0.179321289f, + (float16_t)-1.43847656f, (float16_t)1.72460938f, (float16_t)-1.07324219f, (float16_t)-0.11138916f, + (float16_t)-1.67285156f, (float16_t)-1.80957031f, (float16_t)1.73046875f, (float16_t)-1.78027344f, + (float16_t)-0.518554688f, (float16_t)-1.88378906f, (float16_t)0.411376953f, (float16_t)-0.146484375f, + (float16_t)0.940917969f, (float16_t)0.971191406f, (float16_t)-0.90234375f, (float16_t)0.0955200195f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/output.h new file mode 100644 index 00000000..94dfe707 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/output.h @@ -0,0 +1,58 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_4_f16_output[224] = { + (float16_t)2.22851562f, (float16_t)-0.190063477f, (float16_t)-2.24804688f, (float16_t)-4.19921875f, + (float16_t)-0.68359375f, (float16_t)5.14453125f, (float16_t)1.0859375f, (float16_t)0.913574219f, + (float16_t)-2.09765625f, (float16_t)2.45898438f, (float16_t)-1.11425781f, (float16_t)-3.2578125f, + (float16_t)7.14453125f, (float16_t)1.33691406f, (float16_t)-3.05859375f, (float16_t)-5.69921875f, + (float16_t)-3.12890625f, (float16_t)0.729003906f, (float16_t)-2.61523438f, (float16_t)2.09179688f, + (float16_t)-3.29101562f, (float16_t)-1.45019531f, (float16_t)-2.16992188f, (float16_t)-7.3203125f, + (float16_t)0.0475463867f, (float16_t)-2.41015625f, (float16_t)-3.3046875f, (float16_t)-6.64453125f, + (float16_t)-2.69726562f, (float16_t)-2.95703125f, (float16_t)-3.37109375f, (float16_t)1.61621094f, + (float16_t)-0.663085938f, (float16_t)-2.61523438f, (float16_t)-3.70898438f, (float16_t)-0.732421875f, + (float16_t)-1.83886719f, (float16_t)5.21875f, (float16_t)2.1328125f, (float16_t)-1.96191406f, + (float16_t)3.640625f, (float16_t)1.54492188f, (float16_t)3.421875f, (float16_t)4.51953125f, + (float16_t)2.84765625f, (float16_t)-3.359375f, (float16_t)3.34375f, (float16_t)2.13085938f, + (float16_t)6.08203125f, (float16_t)2.4296875f, (float16_t)0.662597656f, (float16_t)2.78125f, + (float16_t)-3.23828125f, (float16_t)0.619140625f, (float16_t)6.55859375f, (float16_t)4.8203125f, + (float16_t)3.78320312f, (float16_t)0.363037109f, (float16_t)4.3984375f, (float16_t)2.35351562f, + (float16_t)3.65820312f, (float16_t)-7.23828125f, (float16_t)1.03125f, (float16_t)1.79296875f, + (float16_t)-1.93652344f, (float16_t)3.29882812f, (float16_t)6.41796875f, (float16_t)0.0561828613f, + (float16_t)-10.0f, (float16_t)4.078125f, (float16_t)2.62304688f, (float16_t)-0.153076172f, + (float16_t)4.7109375f, (float16_t)5.94140625f, (float16_t)-1.95898438f, (float16_t)-1.92382812f, + (float16_t)0.7265625f, (float16_t)4.375f, (float16_t)2.21484375f, (float16_t)3.61523438f, + (float16_t)3.56445312f, (float16_t)-6.3046875f, (float16_t)3.58789062f, (float16_t)-1.32910156f, + (float16_t)0.93359375f, (float16_t)0.83203125f, (float16_t)1.19140625f, (float16_t)1.29980469f, + (float16_t)-2.69140625f, (float16_t)4.78515625f, (float16_t)-1.171875f, (float16_t)-1.91699219f, + (float16_t)-2.51367188f, (float16_t)-0.966308594f, (float16_t)3.40625f, (float16_t)-0.913574219f, + (float16_t)-6.1328125f, (float16_t)3.45898438f, (float16_t)-1.54785156f, (float16_t)-0.13684082f, + (float16_t)-1.28320312f, (float16_t)-5.015625f, (float16_t)7.13671875f, (float16_t)-2.24609375f, + (float16_t)-1.03320312f, (float16_t)0.438232422f, (float16_t)-1.234375f, (float16_t)1.60644531f, + (float16_t)0.345703125f, (float16_t)6.625f, (float16_t)-8.5625f, (float16_t)1.91601562f, + (float16_t)-1.55175781f, (float16_t)-3.6484375f, (float16_t)2.52148438f, (float16_t)4.12109375f, + (float16_t)-4.4609375f, (float16_t)1.66796875f, (float16_t)-4.3046875f, (float16_t)4.2109375f, + (float16_t)1.46191406f, (float16_t)-1.16113281f, (float16_t)-5.3359375f, (float16_t)-0.585449219f, + (float16_t)6.72265625f, (float16_t)2.06054688f, (float16_t)0.441162109f, (float16_t)-1.0703125f, + (float16_t)-2.56054688f, (float16_t)-1.83886719f, (float16_t)-2.58007812f, (float16_t)2.46875f, + (float16_t)-1.14453125f, (float16_t)-0.380859375f, (float16_t)0.329833984f, (float16_t)2.515625f, + (float16_t)0.545898438f, (float16_t)1.32421875f, (float16_t)0.200439453f, (float16_t)2.46679688f, + (float16_t)-1.19921875f, (float16_t)-0.407958984f, (float16_t)-1.15527344f, (float16_t)1.50488281f, + (float16_t)-1.43554688f, (float16_t)-2.58007812f, (float16_t)-2.21679688f, (float16_t)-2.86523438f, + (float16_t)-3.78320312f, (float16_t)1.59082031f, (float16_t)2.58203125f, (float16_t)-2.265625f, + (float16_t)1.24902344f, (float16_t)-0.801757812f, (float16_t)2.76953125f, (float16_t)3.43359375f, + (float16_t)-0.338623047f, (float16_t)-3.75976562f, (float16_t)3.08398438f, (float16_t)1.10839844f, + (float16_t)3.90039062f, (float16_t)-1.06347656f, (float16_t)-0.0805664062f, (float16_t)2.95703125f, + (float16_t)2.31445312f, (float16_t)-0.565917969f, (float16_t)-0.973632812f, (float16_t)0.623046875f, + (float16_t)0.206054688f, (float16_t)-1.94238281f, (float16_t)-2.20703125f, (float16_t)5.4296875f, + (float16_t)-6.1015625f, (float16_t)-4.5390625f, (float16_t)5.8125f, (float16_t)4.14453125f, + (float16_t)0.14831543f, (float16_t)3.25585938f, (float16_t)1.4375f, (float16_t)5.1875f, + (float16_t)-10.0f, (float16_t)2.56640625f, (float16_t)3.328125f, (float16_t)0.330078125f, + (float16_t)5.09375f, (float16_t)5.34375f, (float16_t)-2.7265625f, (float16_t)-5.4453125f, + (float16_t)0.188232422f, (float16_t)-2.25195312f, (float16_t)-0.721679688f, (float16_t)-1.22167969f, + (float16_t)-1.65625f, (float16_t)2.18164062f, (float16_t)0.702148438f, (float16_t)-0.537109375f, + (float16_t)0.155151367f, (float16_t)0.370605469f, (float16_t)0.798828125f, (float16_t)2.1484375f, + (float16_t)-5.25390625f, (float16_t)1.99023438f, (float16_t)-1.01171875f, (float16_t)-0.823242188f, + (float16_t)-1.42578125f, (float16_t)1.72265625f, (float16_t)4.8515625f, (float16_t)-3.25976562f, + (float16_t)-2.07617188f, (float16_t)0.953613281f, (float16_t)0.174072266f, (float16_t)0.852050781f, + (float16_t)0.24609375f, (float16_t)-4.44921875f, (float16_t)6.375f, (float16_t)-0.262939453f, + (float16_t)-1.86816406f, (float16_t)-2.37109375f, (float16_t)-1.03710938f, (float16_t)-1.47949219f, + (float16_t)0.782226562f, (float16_t)-2.640625f, (float16_t)0.374023438f, (float16_t)0.18371582f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_input_tensor.h new file mode 100644 index 00000000..00d7ec0f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_input_tensor.h @@ -0,0 +1,20 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_4_f16_rhs_input_tensor[70] = { + (float16_t)0.928222656f, (float16_t)1.67871094f, (float16_t)-1.49511719f, (float16_t)-0.828125f, + (float16_t)0.325927734f, (float16_t)-1.57617188f, (float16_t)-0.523925781f, (float16_t)0.240112305f, + (float16_t)-0.697265625f, (float16_t)-1.56640625f, (float16_t)-0.281982422f, (float16_t)-1.45019531f, + (float16_t)0.572753906f, (float16_t)-1.96875f, (float16_t)0.600097656f, (float16_t)0.518066406f, + (float16_t)-0.165649414f, (float16_t)-1.62597656f, (float16_t)0.52734375f, (float16_t)1.37988281f, + (float16_t)1.875f, (float16_t)0.764648438f, (float16_t)0.744140625f, (float16_t)-1.6484375f, + (float16_t)-1.58496094f, (float16_t)0.968261719f, (float16_t)-0.0150375366f, (float16_t)0.324462891f, + (float16_t)-1.48046875f, (float16_t)-1.61035156f, (float16_t)-1.56445312f, (float16_t)0.404785156f, + (float16_t)-0.116333008f, (float16_t)-0.772460938f, (float16_t)-0.971679688f, (float16_t)0.704589844f, + (float16_t)0.908203125f, (float16_t)0.0511169434f, (float16_t)-1.00683594f, (float16_t)-0.916992188f, + (float16_t)1.46777344f, (float16_t)-1.07128906f, (float16_t)1.84570312f, (float16_t)0.273681641f, + (float16_t)1.046875f, (float16_t)1.99511719f, (float16_t)-1.78515625f, (float16_t)-1.68945312f, + (float16_t)1.07128906f, (float16_t)0.368408203f, (float16_t)0.0917358398f, (float16_t)1.93652344f, + (float16_t)0.228759766f, (float16_t)1.83203125f, (float16_t)-1.74511719f, (float16_t)-1.02050781f, + (float16_t)0.946777344f, (float16_t)0.778808594f, (float16_t)0.756347656f, (float16_t)-1.35742188f, + (float16_t)1.99121094f, (float16_t)0.734375f, (float16_t)-0.956054688f, (float16_t)0.352294922f, + (float16_t)0.3359375f, (float16_t)1.53320312f, (float16_t)0.954101562f, (float16_t)-0.666992188f, + (float16_t)1.87695312f, (float16_t)-1.16796875f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..3188b62c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/rhs_transposed_tensor.h @@ -0,0 +1,20 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_4_f16_rhs_transposed_tensor[70] = { + (float16_t)0.928222656f, (float16_t)-1.57617188f, (float16_t)-0.281982422f, (float16_t)0.518066406f, + (float16_t)1.875f, (float16_t)0.968261719f, (float16_t)-1.56445312f, (float16_t)1.67871094f, + (float16_t)-0.523925781f, (float16_t)-1.45019531f, (float16_t)-0.165649414f, (float16_t)0.764648438f, + (float16_t)-0.0150375366f, (float16_t)0.404785156f, (float16_t)-1.49511719f, (float16_t)0.240112305f, + (float16_t)0.572753906f, (float16_t)-1.62597656f, (float16_t)0.744140625f, (float16_t)0.324462891f, + (float16_t)-0.116333008f, (float16_t)-0.828125f, (float16_t)-0.697265625f, (float16_t)-1.96875f, + (float16_t)0.52734375f, (float16_t)-1.6484375f, (float16_t)-1.48046875f, (float16_t)-0.772460938f, + (float16_t)0.325927734f, (float16_t)-1.56640625f, (float16_t)0.600097656f, (float16_t)1.37988281f, + (float16_t)-1.58496094f, (float16_t)-1.61035156f, (float16_t)-0.971679688f, (float16_t)0.704589844f, + (float16_t)1.46777344f, (float16_t)1.99511719f, (float16_t)0.0917358398f, (float16_t)-1.02050781f, + (float16_t)1.99121094f, (float16_t)1.53320312f, (float16_t)0.908203125f, (float16_t)-1.07128906f, + (float16_t)-1.78515625f, (float16_t)1.93652344f, (float16_t)0.946777344f, (float16_t)0.734375f, + (float16_t)0.954101562f, (float16_t)0.0511169434f, (float16_t)1.84570312f, (float16_t)-1.68945312f, + (float16_t)0.228759766f, (float16_t)0.778808594f, (float16_t)-0.956054688f, (float16_t)-0.666992188f, + (float16_t)-1.00683594f, (float16_t)0.273681641f, (float16_t)1.07128906f, (float16_t)1.83203125f, + (float16_t)0.756347656f, (float16_t)0.352294922f, (float16_t)1.87695312f, (float16_t)-0.916992188f, + (float16_t)1.046875f, (float16_t)0.368408203f, (float16_t)-1.74511719f, (float16_t)-1.35742188f, + (float16_t)0.3359375f, (float16_t)-1.16796875f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/config_data.h new file mode 100644 index 00000000..0e3cae36 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_4_F32_LHS_BATCH 2 +#define BATCH_MATMUL_4_F32_LHS_HEIGHT 1 +#define BATCH_MATMUL_4_F32_LHS_ROWS 5 +#define BATCH_MATMUL_4_F32_LHS_COLS 8 +#define BATCH_MATMUL_4_F32_RHS_BATCH 1 +#define BATCH_MATMUL_4_F32_RHS_HEIGHT 2 +#define BATCH_MATMUL_4_F32_RHS_ROWS 7 +#define BATCH_MATMUL_4_F32_RHS_COLS 5 +#define BATCH_MATMUL_4_F32_ADJ_X 1 +#define BATCH_MATMUL_4_F32_ADJ_Y 1 +#define BATCH_MATMUL_4_F32_DST_SIZE 224 +#define BATCH_MATMUL_4_F32_OUTPUT_BATCH 2 +#define BATCH_MATMUL_4_F32_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_4_F32_OUTPUT_ROWS 8 +#define BATCH_MATMUL_4_F32_OUTPUT_COLS 7 +#define BATCH_MATMUL_4_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_4_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_input_tensor.h new file mode 100644 index 00000000..fc83c547 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_input_tensor.h @@ -0,0 +1,14 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_4_f32_lhs_input_tensor[80] = { + -0.102637179f, 1.56566191f, 1.35098624f, 0.684624135f, -1.11301374f, 1.41324711f, -0.623643637f, + -0.933929205f, -0.829163492f, -1.62716508f, -0.395851403f, 1.35085011f, -1.87126982f, -1.2235682f, + -1.55177581f, -1.21133387f, -1.28165674f, 1.93731725f, 1.67591286f, 0.146418482f, -1.23100507f, + -0.457072467f, -1.09031928f, -1.78746951f, 0.499405533f, -1.02552319f, 1.1317656f, -1.18757427f, + -1.38509607f, 0.148608506f, -1.65328419f, -1.83122814f, -0.0443772152f, -0.209327191f, -0.549218774f, + 1.45037997f, 1.14737058f, -0.0561073832f, -1.38099945f, 1.43834984f, 1.44511795f, 0.797140956f, + 0.888439119f, -0.440563023f, 0.957007825f, 0.174350753f, 1.52873123f, 0.46728462f, -1.5028398f, + -0.994481921f, 1.205899f, 1.58054698f, -1.15833867f, 0.533609271f, -0.90154475f, -0.0866636634f, + 1.36405313f, -1.82770562f, -0.565051913f, -0.65561676f, 1.343799f, -1.64189827f, 1.30853522f, + -1.37121511f, -1.32555044f, 0.0760860145f, 1.37997031f, 0.089787811f, -0.559332788f, -0.77716279f, + 1.3268013f, -0.38560918f, -0.97346276f, 1.44094718f, 0.748055637f, 0.296175599f, -1.0430634f, + -0.650796771f, 1.41006112f, 0.922989905f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..446cc46e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/lhs_transposed_tensor.h @@ -0,0 +1,14 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_4_f32_lhs_transposed_tensor[80] = { + -0.102637179f, -0.829163492f, -1.28165674f, 0.499405533f, -0.0443772152f, 1.56566191f, -1.62716508f, + 1.93731725f, -1.02552319f, -0.209327191f, 1.35098624f, -0.395851403f, 1.67591286f, 1.1317656f, + -0.549218774f, 0.684624135f, 1.35085011f, 0.146418482f, -1.18757427f, 1.45037997f, -1.11301374f, + -1.87126982f, -1.23100507f, -1.38509607f, 1.14737058f, 1.41324711f, -1.2235682f, -0.457072467f, + 0.148608506f, -0.0561073832f, -0.623643637f, -1.55177581f, -1.09031928f, -1.65328419f, -1.38099945f, + -0.933929205f, -1.21133387f, -1.78746951f, -1.83122814f, 1.43834984f, 1.44511795f, -1.5028398f, + 1.36405313f, -1.32555044f, -0.97346276f, 0.797140956f, -0.994481921f, -1.82770562f, 0.0760860145f, + 1.44094718f, 0.888439119f, 1.205899f, -0.565051913f, 1.37997031f, 0.748055637f, -0.440563023f, + 1.58054698f, -0.65561676f, 0.089787811f, 0.296175599f, 0.957007825f, -1.15833867f, 1.343799f, + -0.559332788f, -1.0430634f, 0.174350753f, 0.533609271f, -1.64189827f, -0.77716279f, -0.650796771f, + 1.52873123f, -0.90154475f, 1.30853522f, 1.3268013f, 1.41006112f, 0.46728462f, -0.0866636634f, + -1.37121511f, -0.38560918f, 0.922989905f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/output.h new file mode 100644 index 00000000..9a354366 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/output.h @@ -0,0 +1,34 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_4_f32_output[224] = { + 1.57078767f, 1.19093561f, 2.13545895f, 0.73082912f, -0.55482465f, 2.25693393f, 0.779851615f, + 3.52403355f, -1.80505466f, 2.99547148f, 1.0092566f, -2.84989738f, 1.48288727f, 1.90899789f, + 2.57262039f, -3.95427775f, 0.554859042f, 0.488184541f, -0.653271079f, 3.8342216f, 1.43551362f, + 0.144662261f, 2.33408523f, 1.66740251f, -0.480950117f, 1.89848876f, 4.13504982f, 0.441285372f, + 2.36409616f, -2.5856483f, 3.60699129f, -1.15689123f, -2.69018984f, -0.529179811f, 2.65542674f, + 4.29404736f, 0.516922534f, 5.7631321f, -4.01542044f, -2.58487225f, -1.48788619f, 1.51034641f, + 4.85560226f, -2.85538125f, -0.430729866f, 1.35881186f, -0.738142908f, 4.978405f, -0.550627232f, + -2.28169775f, 2.6278851f, -1.11152005f, -3.39299583f, 1.87609935f, -3.9234674f, -1.99055457f, + 2.21968222f, -2.36529613f, -2.9926846f, 1.34184992f, 1.64727139f, -1.2533536f, -0.302112818f, + -0.559385717f, -0.802660584f, -1.27870584f, -2.55999947f, 3.10433078f, -3.23415041f, 1.70763958f, + -4.73994493f, 3.27711034f, 1.8302145f, -5.8064599f, 0.331431389f, -1.04746258f, 4.9193902f, + 1.51265192f, -2.20851803f, -1.66472626f, 1.04761469f, 0.39778614f, 0.352238417f, -0.140986919f, + -3.56607938f, -0.580116272f, 3.30347872f, -6.35512877f, 3.45922112f, -3.42477322f, 1.67044449f, + -3.02988124f, -0.76587224f, 6.33466101f, -6.6663723f, 7.9248457f, -6.39672089f, -0.500840306f, + -2.96581125f, 5.3900857f, -0.0199829936f, -3.06886005f, 1.51925015f, -2.03111839f, 4.94529867f, + -0.604454041f, 0.827902794f, 5.12449884f, 0.221845865f, 0.381974697f, 0.566537142f, -2.98177576f, + -4.59556007f, 3.9060452f, -1.01676285f, -1.27923608f, 3.749547f, 0.0845906734f, -0.749328613f, + -2.33938122f, -4.65380621f, -2.71290064f, 2.18121028f, -1.45573902f, -2.82970929f, 1.14427245f, + -3.10514879f, 1.38383222f, 0.822068691f, -1.65707135f, -0.494484663f, -5.45261526f, 0.644720376f, + -2.36073875f, -2.79999018f, -2.91331959f, 1.76426268f, 2.08250761f, 3.37117195f, 0.579725027f, + -0.69887799f, -0.279722542f, -2.27649069f, 1.8371768f, 2.16926384f, 4.27685404f, -0.665128708f, + 2.50035644f, -0.45824188f, 3.84873247f, -2.56626868f, -1.166538f, 0.500721931f, 1.65733457f, + -0.545050144f, -2.43189955f, 1.94777441f, 1.98777723f, -2.14378071f, 0.127153158f, 3.22163677f, + -0.916268885f, -2.76445699f, -5.40974522f, 2.82582307f, 1.27944434f, 1.58021271f, -1.94454193f, + 2.77880025f, -3.22950721f, -0.746884346f, 4.04676151f, -3.04814482f, 3.74575758f, -3.14718342f, + -2.43545842f, 1.41564739f, -0.125853002f, -1.72010255f, -4.15379429f, 2.34964323f, 1.89976895f, + 1.02424932f, -3.5372715f, 1.878479f, 0.564228654f, 0.0295138359f, 0.363559961f, -3.74339771f, + -2.28649735f, 1.90946031f, -1.09128869f, -1.00635433f, -5.36058235f, 4.07563925f, 3.41129017f, + 0.558371961f, 1.39851844f, -3.07471919f, 2.06442976f, -3.37267947f, 2.8066256f, 2.00520158f, + -3.11303234f, -0.388579965f, 4.39574957f, -5.6958437f, 4.3559289f, -3.55343604f, 0.857661724f, + 0.212072536f, -3.63654256f, -2.97720051f, -1.29607248f, -0.908978939f, 0.0755296946f, 0.864304423f, + -1.14413261f, 5.25338173f, -1.90266824f, 1.728091f, -5.03347921f, 3.50468469f, 3.0725739f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_input_tensor.h new file mode 100644 index 00000000..2699248b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_input_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_4_f32_rhs_input_tensor[70] = { + -0.103780694f, 1.80160892f, 1.29512787f, -1.51676166f, 0.19074221f, -1.07835937f, -0.110805355f, + -0.62024945f, -1.05438972f, 1.00276816f, -0.224638224f, -0.612435699f, -0.61312747f, 1.4239037f, + -1.06562042f, 0.585231245f, 0.203599766f, 0.403256148f, 0.831081331f, 1.29579294f, 0.544498682f, + 0.366709828f, -0.411863595f, 1.01817369f, 0.156953424f, -1.60674572f, -1.91060388f, 0.700387359f, + -1.78186071f, 0.0215102267f, -1.69995522f, 0.348559409f, 0.405796707f, -1.796929f, -0.632461607f, + 0.676084161f, -1.39536238f, 1.29038358f, 0.145473719f, 1.6587249f, -0.911953628f, -1.74626672f, + -1.08918023f, -1.23923194f, 0.784367502f, -1.9564141f, -0.111652277f, -0.0717673823f, 0.273971289f, + 0.988140762f, -1.64172983f, -1.58841407f, 1.03412747f, -1.17478347f, 1.17046905f, -0.198921859f, + 0.381928742f, -1.1921649f, -0.158473298f, -0.315037519f, 1.10355854f, -1.14786339f, -0.74280405f, + 0.551168501f, 0.177734554f, -0.249175355f, 1.54546809f, -1.85613716f, 1.54510391f, -0.844343722f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..defaaa16 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/rhs_transposed_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_4_f32_rhs_transposed_tensor[70] = { + -0.103780694f, -1.07835937f, -0.224638224f, 0.585231245f, 0.544498682f, -1.60674572f, -1.69995522f, + 1.80160892f, -0.110805355f, -0.612435699f, 0.203599766f, 0.366709828f, -1.91060388f, 0.348559409f, + 1.29512787f, -0.62024945f, -0.61312747f, 0.403256148f, -0.411863595f, 0.700387359f, 0.405796707f, + -1.51676166f, -1.05438972f, 1.4239037f, 0.831081331f, 1.01817369f, -1.78186071f, -1.796929f, + 0.19074221f, 1.00276816f, -1.06562042f, 1.29579294f, 0.156953424f, 0.0215102267f, -0.632461607f, + 0.676084161f, -0.911953628f, -1.9564141f, -1.64172983f, -0.198921859f, 1.10355854f, -0.249175355f, + -1.39536238f, -1.74626672f, -0.111652277f, -1.58841407f, 0.381928742f, -1.14786339f, 1.54546809f, + 1.29038358f, -1.08918023f, -0.0717673823f, 1.03412747f, -1.1921649f, -0.74280405f, -1.85613716f, + 0.145473719f, -1.23923194f, 0.273971289f, -1.17478347f, -0.158473298f, 0.551168501f, 1.54510391f, + 1.6587249f, 0.784367502f, 0.988140762f, 1.17046905f, -0.315037519f, 0.177734554f, -0.844343722f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_4_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/config_data.h new file mode 100644 index 00000000..279e29d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_5_F16_LHS_BATCH 1 +#define BATCH_MATMUL_5_F16_LHS_HEIGHT 2 +#define BATCH_MATMUL_5_F16_LHS_ROWS 8 +#define BATCH_MATMUL_5_F16_LHS_COLS 5 +#define BATCH_MATMUL_5_F16_RHS_BATCH 2 +#define BATCH_MATMUL_5_F16_RHS_HEIGHT 1 +#define BATCH_MATMUL_5_F16_RHS_ROWS 7 +#define BATCH_MATMUL_5_F16_RHS_COLS 5 +#define BATCH_MATMUL_5_F16_ADJ_X 0 +#define BATCH_MATMUL_5_F16_ADJ_Y 1 +#define BATCH_MATMUL_5_F16_DST_SIZE 224 +#define BATCH_MATMUL_5_F16_OUTPUT_BATCH 2 +#define BATCH_MATMUL_5_F16_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_5_F16_OUTPUT_ROWS 8 +#define BATCH_MATMUL_5_F16_OUTPUT_COLS 7 +#define BATCH_MATMUL_5_F16_ACTIVATION_MIN (float16_t) - 1.0f +#define BATCH_MATMUL_5_F16_ACTIVATION_MAX (float16_t)1.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_input_tensor.h new file mode 100644 index 00000000..4f999900 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_input_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_5_f16_lhs_input_tensor[80] = { + (float16_t)-0.103759766f, (float16_t)1.80175781f, (float16_t)1.29492188f, (float16_t)-1.51660156f, + (float16_t)0.190795898f, (float16_t)-1.078125f, (float16_t)-0.110778809f, (float16_t)-0.620117188f, + (float16_t)-1.0546875f, (float16_t)1.00292969f, (float16_t)-0.224609375f, (float16_t)-0.612304688f, + (float16_t)-0.61328125f, (float16_t)1.42382812f, (float16_t)-1.06542969f, (float16_t)0.585449219f, + (float16_t)0.203613281f, (float16_t)0.403320312f, (float16_t)0.831054688f, (float16_t)1.29589844f, + (float16_t)0.544433594f, (float16_t)0.366699219f, (float16_t)-0.411865234f, (float16_t)1.01855469f, + (float16_t)0.156982422f, (float16_t)-1.60644531f, (float16_t)-1.91015625f, (float16_t)0.700195312f, + (float16_t)-1.78222656f, (float16_t)0.0215148926f, (float16_t)-1.70019531f, (float16_t)0.348632812f, + (float16_t)0.405761719f, (float16_t)-1.796875f, (float16_t)-0.632324219f, (float16_t)0.676269531f, + (float16_t)-1.39550781f, (float16_t)1.29003906f, (float16_t)0.145507812f, (float16_t)1.65917969f, + (float16_t)-0.912109375f, (float16_t)-1.74609375f, (float16_t)-1.08886719f, (float16_t)-1.23925781f, + (float16_t)0.784179688f, (float16_t)-1.95605469f, (float16_t)-0.111633301f, (float16_t)-0.0717773438f, + (float16_t)0.273925781f, (float16_t)0.98828125f, (float16_t)-1.64160156f, (float16_t)-1.58886719f, + (float16_t)1.03417969f, (float16_t)-1.17480469f, (float16_t)1.17089844f, (float16_t)-0.198974609f, + (float16_t)0.381835938f, (float16_t)-1.19238281f, (float16_t)-0.158447266f, (float16_t)-0.314941406f, + (float16_t)1.10351562f, (float16_t)-1.14746094f, (float16_t)-0.742675781f, (float16_t)0.551269531f, + (float16_t)0.177734375f, (float16_t)-0.249145508f, (float16_t)1.54589844f, (float16_t)-1.85644531f, + (float16_t)1.54492188f, (float16_t)-0.844238281f, (float16_t)-0.576171875f, (float16_t)0.700683594f, + (float16_t)-0.4296875f, (float16_t)-0.174194336f, (float16_t)-0.577636719f, (float16_t)0.217041016f, + (float16_t)-1.05273438f, (float16_t)-0.639160156f, (float16_t)-0.691894531f, (float16_t)1.60742188f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..f827171d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/lhs_transposed_tensor.h @@ -0,0 +1,22 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_5_f16_lhs_transposed_tensor[80] = { + (float16_t)-0.103759766f, (float16_t)-1.078125f, (float16_t)-0.224609375f, (float16_t)0.585449219f, + (float16_t)0.544433594f, (float16_t)-1.60644531f, (float16_t)-1.70019531f, (float16_t)0.676269531f, + (float16_t)1.80175781f, (float16_t)-0.110778809f, (float16_t)-0.612304688f, (float16_t)0.203613281f, + (float16_t)0.366699219f, (float16_t)-1.91015625f, (float16_t)0.348632812f, (float16_t)-1.39550781f, + (float16_t)1.29492188f, (float16_t)-0.620117188f, (float16_t)-0.61328125f, (float16_t)0.403320312f, + (float16_t)-0.411865234f, (float16_t)0.700195312f, (float16_t)0.405761719f, (float16_t)1.29003906f, + (float16_t)-1.51660156f, (float16_t)-1.0546875f, (float16_t)1.42382812f, (float16_t)0.831054688f, + (float16_t)1.01855469f, (float16_t)-1.78222656f, (float16_t)-1.796875f, (float16_t)0.145507812f, + (float16_t)0.190795898f, (float16_t)1.00292969f, (float16_t)-1.06542969f, (float16_t)1.29589844f, + (float16_t)0.156982422f, (float16_t)0.0215148926f, (float16_t)-0.632324219f, (float16_t)1.65917969f, + (float16_t)-0.912109375f, (float16_t)-1.95605469f, (float16_t)-1.64160156f, (float16_t)-0.198974609f, + (float16_t)1.10351562f, (float16_t)-0.249145508f, (float16_t)-0.576171875f, (float16_t)0.217041016f, + (float16_t)-1.74609375f, (float16_t)-0.111633301f, (float16_t)-1.58886719f, (float16_t)0.381835938f, + (float16_t)-1.14746094f, (float16_t)1.54589844f, (float16_t)0.700683594f, (float16_t)-1.05273438f, + (float16_t)-1.08886719f, (float16_t)-0.0717773438f, (float16_t)1.03417969f, (float16_t)-1.19238281f, + (float16_t)-0.742675781f, (float16_t)-1.85644531f, (float16_t)-0.4296875f, (float16_t)-0.639160156f, + (float16_t)-1.23925781f, (float16_t)0.273925781f, (float16_t)-1.17480469f, (float16_t)-0.158447266f, + (float16_t)0.551269531f, (float16_t)1.54492188f, (float16_t)-0.174194336f, (float16_t)-0.691894531f, + (float16_t)0.784179688f, (float16_t)0.98828125f, (float16_t)1.17089844f, (float16_t)-0.314941406f, + (float16_t)0.177734375f, (float16_t)-0.844238281f, (float16_t)-0.577636719f, (float16_t)1.60742188f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/output.h new file mode 100644 index 00000000..17dc9715 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/output.h @@ -0,0 +1,58 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_5_f16_output[224] = { + (float16_t)1.0f, (float16_t)-0.698730469f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-0.434326172f, (float16_t)0.983886719f, (float16_t)-0.387695312f, (float16_t)1.0f, + (float16_t)-0.78125f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-0.359130859f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-0.115539551f, (float16_t)-0.30078125f, + (float16_t)0.322509766f, (float16_t)0.453369141f, (float16_t)-1.0f, (float16_t)-0.0908813477f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-0.0456542969f, + (float16_t)-0.958496094f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)0.382324219f, (float16_t)1.0f, (float16_t)0.7890625f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)0.850585938f, (float16_t)1.0f, + (float16_t)0.576660156f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-0.841796875f, (float16_t)-0.66796875f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)0.3515625f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)0.191162109f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-0.986328125f, + (float16_t)0.151000977f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-0.620117188f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)-0.0350646973f, (float16_t)-0.69921875f, (float16_t)-1.0f, + (float16_t)-0.2578125f, (float16_t)0.801757812f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)0.955078125f, (float16_t)1.0f, (float16_t)-0.091003418f, (float16_t)0.425537109f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)0.0129394531f, (float16_t)0.347900391f, (float16_t)0.158447266f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)-0.970214844f, (float16_t)1.0f, (float16_t)-0.259277344f, + (float16_t)-0.891601562f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)-0.358886719f, (float16_t)-0.227172852f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-0.0606994629f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-0.819335938f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)0.131103516f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)0.702148438f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)-1.0f, (float16_t)0.966796875f, (float16_t)-1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-0.821289062f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)-1.0f, (float16_t)1.0f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)1.0f, (float16_t)0.394287109f, (float16_t)1.0f, + (float16_t)1.0f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)-0.321533203f, + (float16_t)-0.549316406f, (float16_t)1.0f, (float16_t)1.0f, (float16_t)-1.0f, + (float16_t)-1.0f, (float16_t)0.927246094f, (float16_t)0.879882812f, (float16_t)0.651367188f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_input_tensor.h new file mode 100644 index 00000000..f691b1c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_input_tensor.h @@ -0,0 +1,20 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_5_f16_rhs_input_tensor[70] = { + (float16_t)-1.85253906f, (float16_t)0.842773438f, (float16_t)-0.982910156f, (float16_t)0.907714844f, + (float16_t)-0.185424805f, (float16_t)0.58984375f, (float16_t)1.77246094f, (float16_t)1.86816406f, + (float16_t)-1.59179688f, (float16_t)-0.0146789551f, (float16_t)0.642578125f, (float16_t)-1.67773438f, + (float16_t)-1.04882812f, (float16_t)1.91308594f, (float16_t)0.745117188f, (float16_t)0.339111328f, + (float16_t)1.05859375f, (float16_t)0.690429688f, (float16_t)-0.642089844f, (float16_t)1.01855469f, + (float16_t)-0.284423828f, (float16_t)-0.470703125f, (float16_t)-1.30761719f, (float16_t)-0.313476562f, + (float16_t)-1.72167969f, (float16_t)-1.91015625f, (float16_t)0.682128906f, (float16_t)0.285644531f, + (float16_t)0.962890625f, (float16_t)-0.867675781f, (float16_t)0.247680664f, (float16_t)-0.723144531f, + (float16_t)-1.07128906f, (float16_t)1.08691406f, (float16_t)-1.23046875f, (float16_t)-0.321044922f, + (float16_t)-1.40820312f, (float16_t)-0.884277344f, (float16_t)-1.90820312f, (float16_t)-1.81933594f, + (float16_t)-0.744140625f, (float16_t)1.5859375f, (float16_t)-0.577636719f, (float16_t)1.93359375f, + (float16_t)0.603027344f, (float16_t)-0.124267578f, (float16_t)0.214477539f, (float16_t)-0.0344238281f, + (float16_t)-1.71289062f, (float16_t)-1.86132812f, (float16_t)-1.22265625f, (float16_t)-0.548828125f, + (float16_t)1.82910156f, (float16_t)-0.204589844f, (float16_t)1.30566406f, (float16_t)-1.90625f, + (float16_t)0.638671875f, (float16_t)-1.93261719f, (float16_t)1.23339844f, (float16_t)0.371582031f, + (float16_t)-1.89355469f, (float16_t)-1.171875f, (float16_t)0.540527344f, (float16_t)0.128173828f, + (float16_t)1.77734375f, (float16_t)-1.35449219f, (float16_t)-1.78222656f, (float16_t)0.0664672852f, + (float16_t)0.639648438f, (float16_t)-1.45507812f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..d75dd61e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/rhs_transposed_tensor.h @@ -0,0 +1,20 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_5_f16_rhs_transposed_tensor[70] = { + (float16_t)-1.85253906f, (float16_t)0.58984375f, (float16_t)0.642578125f, (float16_t)0.339111328f, + (float16_t)-0.284423828f, (float16_t)-1.91015625f, (float16_t)0.247680664f, (float16_t)0.842773438f, + (float16_t)1.77246094f, (float16_t)-1.67773438f, (float16_t)1.05859375f, (float16_t)-0.470703125f, + (float16_t)0.682128906f, (float16_t)-0.723144531f, (float16_t)-0.982910156f, (float16_t)1.86816406f, + (float16_t)-1.04882812f, (float16_t)0.690429688f, (float16_t)-1.30761719f, (float16_t)0.285644531f, + (float16_t)-1.07128906f, (float16_t)0.907714844f, (float16_t)-1.59179688f, (float16_t)1.91308594f, + (float16_t)-0.642089844f, (float16_t)-0.313476562f, (float16_t)0.962890625f, (float16_t)1.08691406f, + (float16_t)-0.185424805f, (float16_t)-0.0146789551f, (float16_t)0.745117188f, (float16_t)1.01855469f, + (float16_t)-1.72167969f, (float16_t)-0.867675781f, (float16_t)-1.23046875f, (float16_t)-0.321044922f, + (float16_t)-0.744140625f, (float16_t)-0.124267578f, (float16_t)-1.22265625f, (float16_t)-1.90625f, + (float16_t)-1.89355469f, (float16_t)-1.35449219f, (float16_t)-1.40820312f, (float16_t)1.5859375f, + (float16_t)0.214477539f, (float16_t)-0.548828125f, (float16_t)0.638671875f, (float16_t)-1.171875f, + (float16_t)-1.78222656f, (float16_t)-0.884277344f, (float16_t)-0.577636719f, (float16_t)-0.0344238281f, + (float16_t)1.82910156f, (float16_t)-1.93261719f, (float16_t)0.540527344f, (float16_t)0.0664672852f, + (float16_t)-1.90820312f, (float16_t)1.93359375f, (float16_t)-1.71289062f, (float16_t)-0.204589844f, + (float16_t)1.23339844f, (float16_t)0.128173828f, (float16_t)0.639648438f, (float16_t)-1.81933594f, + (float16_t)0.603027344f, (float16_t)-1.86132812f, (float16_t)1.30566406f, (float16_t)0.371582031f, + (float16_t)1.77734375f, (float16_t)-1.45507812f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/config_data.h new file mode 100644 index 00000000..bb6ea888 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_5_F32_LHS_BATCH 1 +#define BATCH_MATMUL_5_F32_LHS_HEIGHT 2 +#define BATCH_MATMUL_5_F32_LHS_ROWS 8 +#define BATCH_MATMUL_5_F32_LHS_COLS 5 +#define BATCH_MATMUL_5_F32_RHS_BATCH 2 +#define BATCH_MATMUL_5_F32_RHS_HEIGHT 1 +#define BATCH_MATMUL_5_F32_RHS_ROWS 7 +#define BATCH_MATMUL_5_F32_RHS_COLS 5 +#define BATCH_MATMUL_5_F32_ADJ_X 0 +#define BATCH_MATMUL_5_F32_ADJ_Y 1 +#define BATCH_MATMUL_5_F32_DST_SIZE 224 +#define BATCH_MATMUL_5_F32_OUTPUT_BATCH 2 +#define BATCH_MATMUL_5_F32_OUTPUT_HEIGHT 2 +#define BATCH_MATMUL_5_F32_OUTPUT_ROWS 8 +#define BATCH_MATMUL_5_F32_OUTPUT_COLS 7 +#define BATCH_MATMUL_5_F32_ACTIVATION_MIN -1.0f +#define BATCH_MATMUL_5_F32_ACTIVATION_MAX 1.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_input_tensor.h new file mode 100644 index 00000000..9ded9761 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_input_tensor.h @@ -0,0 +1,14 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_5_f32_lhs_input_tensor[80] = { + 0.0441367626f, -0.428453565f, 1.81048942f, -0.796921551f, -0.483504325f, -1.20451033f, 0.297253609f, + 0.404585451f, -1.43768561f, 0.952830017f, -1.30674136f, -0.229773387f, -0.0448473021f, 1.25397432f, + 1.57463193f, -1.50165474f, -1.03763509f, 1.29145432f, 0.0185944866f, 0.594885349f, -0.0367894731f, + -0.927315176f, 1.58253121f, 1.95356834f, -0.551086664f, -1.60472608f, -1.20796978f, 0.109570764f, + -0.897545397f, 0.503980041f, 0.739135981f, 1.56718004f, -1.13564992f, -0.764307678f, -1.19965732f, + -0.436067492f, -1.31833637f, 1.18250585f, 1.52748907f, -1.35231757f, -0.0728239194f, 0.330377758f, + 0.0629822016f, 1.79084325f, -0.807220042f, -0.881611347f, -1.35969448f, 0.352968335f, 0.846705019f, + 1.77058232f, 0.819207966f, 1.45088661f, -1.12073231f, 1.71606016f, 0.17254211f, -1.58227479f, + -1.81469393f, 0.181735367f, -1.55762792f, -1.80457485f, -1.37078238f, 1.39386356f, -0.974356592f, + 0.316028208f, 0.510329366f, -0.996890068f, -1.87615991f, -1.91803002f, 1.26027966f, 0.730854392f, + -1.09620917f, 0.0310724713f, 1.00641835f, 0.705288649f, 1.14255095f, 0.928770304f, 1.33664274f, + 0.217990637f, -1.5376873f, 1.94513679f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..dbcea75f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/lhs_transposed_tensor.h @@ -0,0 +1,14 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_5_f32_lhs_transposed_tensor[80] = { + 0.0441367626f, -1.20451033f, -1.30674136f, -1.50165474f, -0.0367894731f, -1.60472608f, 0.739135981f, + -0.436067492f, -0.428453565f, 0.297253609f, -0.229773387f, -1.03763509f, -0.927315176f, -1.20796978f, + 1.56718004f, -1.31833637f, 1.81048942f, 0.404585451f, -0.0448473021f, 1.29145432f, 1.58253121f, + 0.109570764f, -1.13564992f, 1.18250585f, -0.796921551f, -1.43768561f, 1.25397432f, 0.0185944866f, + 1.95356834f, -0.897545397f, -0.764307678f, 1.52748907f, -0.483504325f, 0.952830017f, 1.57463193f, + 0.594885349f, -0.551086664f, 0.503980041f, -1.19965732f, -1.35231757f, -0.0728239194f, -0.881611347f, + 0.819207966f, -1.58227479f, -1.37078238f, -0.996890068f, -1.09620917f, 0.928770304f, 0.330377758f, + -1.35969448f, 1.45088661f, -1.81469393f, 1.39386356f, -1.87615991f, 0.0310724713f, 1.33664274f, + 0.0629822016f, 0.352968335f, -1.12073231f, 0.181735367f, -0.974356592f, -1.91803002f, 1.00641835f, + 0.217990637f, 1.79084325f, 0.846705019f, 1.71606016f, -1.55762792f, 0.316028208f, 1.26027966f, + 0.705288649f, -1.5376873f, -0.807220042f, 1.77058232f, 0.17254211f, -1.80457485f, 0.510329366f, + 0.730854392f, 1.14255095f, 1.94513679f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/output.h new file mode 100644 index 00000000..0b05abe2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/output.h @@ -0,0 +1,34 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_5_f32_output[224] = { + 1.0f, 1.0f, 1.0f, -1.0f, 1.0f, 1.0f, 0.174033999f, + 1.0f, 1.0f, -1.0f, 0.619474292f, -1.0f, 1.0f, -1.0f, + 0.789828777f, -0.827107549f, -1.0f, 0.489275098f, -1.0f, -0.495299697f, -1.0f, + 1.0f, 0.92290318f, -1.0f, -0.922037125f, -0.738471031f, 1.0f, -1.0f, + 1.0f, -1.0f, 1.0f, -1.0f, 1.0f, -1.0f, -1.0f, + 1.0f, 1.0f, -1.0f, 0.788615584f, -1.0f, 1.0f, -1.0f, + -1.0f, -0.667096138f, 0.892907262f, -0.0503627062f, 0.00701975822f, -1.0f, 1.0f, + 1.0f, -1.0f, 1.0f, -1.0f, 1.0f, -1.0f, -1.0f, + -0.513330519f, -1.0f, 1.0f, -1.0f, 1.0f, -1.0f, -1.0f, + 1.0f, 0.907287836f, -1.0f, 1.0f, -1.0f, 0.595421672f, -1.0f, + -1.0f, -1.0f, 1.0f, 0.0575206131f, -0.740008533f, -1.0f, -0.331113428f, + 1.0f, 1.0f, 0.0425639153f, -0.919541478f, 1.0f, -0.817348242f, 1.0f, + -1.0f, -1.0f, -1.0f, 0.0674448311f, -1.0f, -1.0f, -1.0f, + 1.0f, -1.0f, -1.0f, 1.0f, -0.393150687f, -1.0f, -1.0f, + 1.0f, -0.209848642f, -1.0f, -0.810890079f, -1.0f, 1.0f, -1.0f, + -1.0f, 1.0f, -1.0f, 1.0f, -1.0f, 1.0f, 0.348543406f, + 0.0872933343f, -0.56637013f, 1.0f, -1.0f, -1.0f, 1.0f, 1.0f, + -1.0f, 0.556084931f, -0.201235771f, 1.0f, -1.0f, 1.0f, -0.0698494762f, + 1.0f, -1.0f, -1.0f, 1.0f, 1.0f, 1.0f, -1.0f, + 1.0f, -1.0f, -0.21605736f, 1.0f, -0.756745636f, 1.0f, 0.0783025697f, + 1.0f, -1.0f, 1.0f, 0.161843777f, 1.0f, 0.802119792f, -0.532226443f, + 1.0f, -0.279353231f, -1.0f, 1.0f, -1.0f, 1.0f, 0.97967881f, + -1.0f, 1.0f, 1.0f, -1.0f, -1.0f, -1.0f, -0.657737792f, + 1.0f, -1.0f, 1.0f, 0.043879509f, -0.468272686f, 1.0f, 0.0617522299f, + 1.0f, -1.0f, 1.0f, -0.452677846f, 0.0739454031f, -0.933497429f, -1.0f, + 1.0f, -0.632073641f, -1.0f, 1.0f, 1.0f, 1.0f, -0.0396710336f, + -0.736990333f, -0.312089086f, -0.0499570966f, -0.826211572f, 1.0f, -1.0f, -1.0f, + 0.958057404f, -1.0f, 1.0f, 0.693099499f, -1.0f, 1.0f, 1.0f, + -1.0f, -0.998083353f, 0.379666269f, 1.0f, -1.0f, -0.593681037f, -1.0f, + 1.0f, -0.296240509f, -1.0f, 1.0f, 1.0f, 1.0f, -0.196762249f, + 1.0f, -1.0f, -0.167961121f, 1.0f, 0.56537044f, 1.0f, -1.0f, + -1.0f, 1.0f, -1.0f, -0.79955411f, 1.0f, -1.0f, 0.5948838f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_input_tensor.h new file mode 100644 index 00000000..dbf779bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_input_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_5_f32_rhs_input_tensor[70] = { + -1.56735122f, 0.282033205f, 1.14080727f, 0.437756658f, 1.02499378f, 0.326294065f, 1.10831487f, + -1.64457536f, -0.503127396f, 0.033932399f, -0.511769891f, -0.998259246f, 0.350467294f, 0.275072098f, + 1.16673112f, 0.140866295f, 1.24527574f, -1.06901085f, 0.375448406f, 1.36531866f, -0.879013419f, + -0.398395747f, -1.88282466f, 1.07656085f, -0.54147619f, 0.291274488f, -1.41269553f, -1.3750807f, + -0.688586771f, 1.13878214f, -1.86181366f, 1.00009f, -1.21940088f, 1.17127383f, -1.52756727f, + -0.124273054f, 1.03758299f, -0.417539418f, -1.52171504f, 1.50433862f, -0.790973961f, 0.976314425f, + -1.58363426f, -0.16123195f, 1.52902365f, -0.875719488f, -0.671663105f, -0.988481641f, -1.34353554f, + 0.223479688f, -0.655332029f, 1.42093849f, -0.564901412f, -0.063571021f, 0.334914625f, 0.175699219f, + 1.08931208f, -1.03537226f, -0.0100855296f, 0.47971791f, 0.888487458f, -0.281211168f, -1.06110287f, + 0.252834827f, 0.661596656f, -1.83460307f, 1.35984945f, 1.46396029f, 0.225484297f, -0.0956262648f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..7dbb63d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/rhs_transposed_tensor.h @@ -0,0 +1,12 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_5_f32_rhs_transposed_tensor[70] = { + -1.56735122f, 0.326294065f, -0.511769891f, 0.140866295f, -0.879013419f, 0.291274488f, -1.86181366f, + 0.282033205f, 1.10831487f, -0.998259246f, 1.24527574f, -0.398395747f, -1.41269553f, 1.00009f, + 1.14080727f, -1.64457536f, 0.350467294f, -1.06901085f, -1.88282466f, -1.3750807f, -1.21940088f, + 0.437756658f, -0.503127396f, 0.275072098f, 0.375448406f, 1.07656085f, -0.688586771f, 1.17127383f, + 1.02499378f, 0.033932399f, 1.16673112f, 1.36531866f, -0.54147619f, 1.13878214f, -1.52756727f, + -0.124273054f, -0.790973961f, -0.875719488f, -0.655332029f, 0.175699219f, 0.888487458f, -1.83460307f, + 1.03758299f, 0.976314425f, -0.671663105f, 1.42093849f, 1.08931208f, -0.281211168f, 1.35984945f, + -0.417539418f, -1.58363426f, -0.988481641f, -0.564901412f, -1.03537226f, -1.06110287f, 1.46396029f, + -1.52171504f, -0.16123195f, -1.34353554f, -0.063571021f, -0.0100855296f, 0.252834827f, 0.225484297f, + 1.50433862f, 1.52902365f, 0.223479688f, 0.334914625f, 0.47971791f, 0.661596656f, -0.0956262648f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_5_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/config_data.h new file mode 100644 index 00000000..8151bc00 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_6_F16_LHS_BATCH 3 +#define BATCH_MATMUL_6_F16_LHS_HEIGHT 3 +#define BATCH_MATMUL_6_F16_LHS_ROWS 32 +#define BATCH_MATMUL_6_F16_LHS_COLS 16 +#define BATCH_MATMUL_6_F16_RHS_BATCH 3 +#define BATCH_MATMUL_6_F16_RHS_HEIGHT 3 +#define BATCH_MATMUL_6_F16_RHS_ROWS 16 +#define BATCH_MATMUL_6_F16_RHS_COLS 24 +#define BATCH_MATMUL_6_F16_ADJ_X 0 +#define BATCH_MATMUL_6_F16_ADJ_Y 0 +#define BATCH_MATMUL_6_F16_DST_SIZE 6912 +#define BATCH_MATMUL_6_F16_OUTPUT_BATCH 3 +#define BATCH_MATMUL_6_F16_OUTPUT_HEIGHT 3 +#define BATCH_MATMUL_6_F16_OUTPUT_ROWS 32 +#define BATCH_MATMUL_6_F16_OUTPUT_COLS 24 +#define BATCH_MATMUL_6_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_6_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_input_tensor.h new file mode 100644 index 00000000..2f04114a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_input_tensor.h @@ -0,0 +1,4609 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_6_f16_lhs_input_tensor[4608] = {(float16_t)-1.56738281f, + (float16_t)0.281982422f, + (float16_t)1.140625f, + (float16_t)0.437744141f, + (float16_t)1.02539062f, + (float16_t)0.326416016f, + (float16_t)1.10839844f, + (float16_t)-1.64453125f, + (float16_t)-0.502929688f, + (float16_t)0.0339355469f, + (float16_t)-0.51171875f, + (float16_t)-0.998046875f, + (float16_t)0.350585938f, + (float16_t)0.275146484f, + (float16_t)1.16699219f, + (float16_t)0.140869141f, + (float16_t)1.24511719f, + (float16_t)-1.06933594f, + (float16_t)0.375488281f, + (float16_t)1.36523438f, + (float16_t)-0.87890625f, + (float16_t)-0.3984375f, + (float16_t)-1.8828125f, + (float16_t)1.07617188f, + (float16_t)-0.541503906f, + (float16_t)0.291259766f, + (float16_t)-1.41308594f, + (float16_t)-1.375f, + (float16_t)-0.688476562f, + (float16_t)1.13867188f, + (float16_t)-1.86132812f, + (float16_t)1.0f, + (float16_t)-1.21972656f, + (float16_t)1.17089844f, + (float16_t)-1.52734375f, + (float16_t)-0.124267578f, + (float16_t)1.03710938f, + 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+ (float16_t)-0.417724609f, + (float16_t)-1.70898438f, + (float16_t)-0.7578125f, + (float16_t)-1.6640625f, + (float16_t)1.89355469f, + (float16_t)1.96582031f, + (float16_t)1.71582031f, + (float16_t)-0.102966309f, + (float16_t)1.84472656f, + (float16_t)0.388183594f, + (float16_t)-1.57128906f, + (float16_t)-1.58789062f, + (float16_t)-0.250244141f, + (float16_t)-1.79101562f, + (float16_t)0.516601562f, + (float16_t)0.322998047f, + (float16_t)0.0936279297f, + (float16_t)-1.47753906f, + (float16_t)-1.89746094f, + (float16_t)-1.68457031f, + (float16_t)-0.885742188f, + (float16_t)0.690917969f, + (float16_t)-0.818359375f, + (float16_t)0.00766372681f, + (float16_t)-1.88183594f, + (float16_t)-1.74707031f, + (float16_t)1.390625f, + (float16_t)0.579101562f, + (float16_t)-1.20703125f, + (float16_t)1.51367188f, + (float16_t)0.915039062f, + (float16_t)-0.98046875f, + (float16_t)-0.169189453f, + (float16_t)0.0238189697f, + (float16_t)1.23144531f, + (float16_t)1.28417969f, + (float16_t)1.08691406f, + (float16_t)1.52246094f, + (float16_t)-0.913574219f, + (float16_t)0.55859375f, + (float16_t)1.06152344f, + (float16_t)-0.181396484f, + (float16_t)1.16113281f, + (float16_t)-0.774414062f, + (float16_t)-1.14257812f, + (float16_t)-1.06445312f, + (float16_t)0.243774414f, + (float16_t)-0.298828125f, + (float16_t)-0.53515625f, + (float16_t)1.78710938f, + (float16_t)-1.28417969f, + (float16_t)-0.443115234f, + (float16_t)-1.75390625f, + (float16_t)-1.20800781f, + (float16_t)1.46679688f, + (float16_t)-0.0192565918f, + (float16_t)1.68457031f, + (float16_t)-1.02539062f, + (float16_t)1.99609375f, + (float16_t)1.15136719f, + (float16_t)-1.68457031f, + (float16_t)1.62890625f, + (float16_t)-1.35351562f, + (float16_t)-1.32226562f, + (float16_t)-1.97753906f, + (float16_t)0.593261719f, + (float16_t)-0.9765625f, + (float16_t)-0.41796875f, + (float16_t)1.45019531f, + (float16_t)-0.861816406f, + (float16_t)-1.71679688f, + (float16_t)1.72167969f, + (float16_t)0.874511719f, + (float16_t)-1.01855469f, + (float16_t)1.01074219f, + (float16_t)-1.84765625f, + (float16_t)-0.486328125f, + (float16_t)-1.08300781f, + (float16_t)-0.297363281f, + (float16_t)-1.61523438f, + (float16_t)1.6328125f, + (float16_t)1.19921875f, + (float16_t)1.50390625f, + (float16_t)0.223999023f, + (float16_t)-1.88671875f, + (float16_t)-0.764648438f, + (float16_t)-1.83007812f, + (float16_t)-0.5703125f, + (float16_t)-0.727539062f, + (float16_t)0.558105469f, + (float16_t)1.22558594f, + (float16_t)-0.891113281f, + (float16_t)0.0699462891f, + (float16_t)1.84375f, + (float16_t)0.0997924805f, + (float16_t)0.220581055f, + (float16_t)-0.977050781f, + (float16_t)-1.52539062f, + (float16_t)1.015625f, + (float16_t)0.557617188f, + (float16_t)-1.73046875f, + (float16_t)-1.76367188f, + (float16_t)0.928710938f, + (float16_t)-0.565429688f, + (float16_t)0.834960938f, + (float16_t)-1.1875f, + (float16_t)-0.143188477f, + (float16_t)1.16015625f, + (float16_t)1.65429688f, + (float16_t)0.0215454102f, + (float16_t)0.474609375f, + (float16_t)-1.91992188f, + (float16_t)-0.352294922f, + (float16_t)0.753417969f, + (float16_t)-0.192260742f, + (float16_t)1.83300781f, + (float16_t)1.01953125f, + (float16_t)0.12298584f, + (float16_t)-0.237548828f, + (float16_t)1.74023438f, + (float16_t)1.48632812f, + (float16_t)1.09765625f, + (float16_t)-0.123291016f, + (float16_t)-1.81933594f, + (float16_t)-1.94628906f, + (float16_t)1.02539062f, + (float16_t)-1.71582031f, + (float16_t)1.140625f, + (float16_t)1.99609375f, + (float16_t)0.653808594f, + (float16_t)0.00687408447f, + (float16_t)-0.28125f, + (float16_t)-1.21972656f, + (float16_t)1.77246094f, + (float16_t)0.28515625f, + (float16_t)-0.404296875f, + (float16_t)-0.0287322998f, + (float16_t)0.49609375f, + (float16_t)1.87792969f, + (float16_t)0.221191406f, + (float16_t)-0.806640625f, + (float16_t)-0.430908203f, + (float16_t)1.74414062f, + (float16_t)-0.0425415039f, + (float16_t)0.390136719f, + (float16_t)1.74609375f, + (float16_t)-0.991699219f, + (float16_t)-0.6171875f, + (float16_t)-0.680175781f, + (float16_t)0.00637435913f, + (float16_t)0.0902099609f, + (float16_t)-1.18554688f, + (float16_t)0.814453125f, + (float16_t)-1.203125f, + (float16_t)-1.60058594f, + (float16_t)-1.12988281f, + (float16_t)-1.78125f, + (float16_t)0.465576172f, + (float16_t)1.56835938f, + (float16_t)0.58984375f, + (float16_t)0.275390625f, + (float16_t)0.0170288086f, + (float16_t)1.87695312f, + (float16_t)0.839355469f, + (float16_t)-0.585449219f, + (float16_t)1.35449219f, + (float16_t)-1.40820312f, + (float16_t)-0.14050293f, + (float16_t)1.96972656f, + (float16_t)-1.66015625f, + (float16_t)1.19433594f, + (float16_t)-0.388671875f, + (float16_t)0.526855469f, + (float16_t)0.720214844f, + (float16_t)-1.67578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..710fb0a0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/lhs_transposed_tensor.h @@ -0,0 +1,2306 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_6_f16_lhs_transposed_tensor[4608] = { + (float16_t)-1.56738281f, (float16_t)1.24511719f, + (float16_t)-1.21972656f, (float16_t)-1.34375f, + (float16_t)0.661621094f, (float16_t)-0.0114135742f, + (float16_t)0.0670776367f, (float16_t)0.469482422f, + (float16_t)0.208251953f, (float16_t)0.418457031f, + (float16_t)0.400634766f, (float16_t)1.84570312f, + (float16_t)1.89160156f, (float16_t)0.151245117f, + (float16_t)1.39746094f, (float16_t)0.390625f, + (float16_t)-0.248168945f, (float16_t)-1.68847656f, + (float16_t)1.2578125f, (float16_t)-0.61328125f, + (float16_t)1.0625f, (float16_t)-0.98828125f, + (float16_t)1.99023438f, (float16_t)0.320800781f, + (float16_t)-0.209594727f, (float16_t)-1.72558594f, + (float16_t)0.961914062f, (float16_t)-0.846679688f, + (float16_t)0.436523438f, (float16_t)-1.37988281f, + (float16_t)0.235473633f, (float16_t)-0.96875f, + (float16_t)0.281982422f, (float16_t)-1.06933594f, + (float16_t)1.17089844f, (float16_t)0.223510742f, + (float16_t)-1.83496094f, (float16_t)0.811523438f, + (float16_t)-0.610351562f, (float16_t)-0.282714844f, + (float16_t)1.01171875f, (float16_t)-0.0305175781f, + (float16_t)0.787109375f, (float16_t)-1.3203125f, + (float16_t)0.339111328f, (float16_t)0.375732422f, + (float16_t)1.765625f, (float16_t)-1.2109375f, + (float16_t)-0.532226562f, (float16_t)-1.90039062f, + (float16_t)1.77050781f, (float16_t)0.936523438f, + (float16_t)0.133178711f, (float16_t)-1.69433594f, + (float16_t)1.99707031f, (float16_t)0.538085938f, + (float16_t)0.495117188f, (float16_t)1.83007812f, + (float16_t)-0.153930664f, (float16_t)-1.76953125f, + (float16_t)-0.814453125f, (float16_t)-1.61132812f, + (float16_t)0.152832031f, (float16_t)0.681152344f, + (float16_t)1.140625f, (float16_t)0.375488281f, + (float16_t)-1.52734375f, (float16_t)-0.655273438f, + (float16_t)1.359375f, (float16_t)1.02246094f, + (float16_t)1.57910156f, (float16_t)-0.335693359f, + (float16_t)-0.303955078f, (float16_t)0.483398438f, + (float16_t)0.460449219f, (float16_t)1.73242188f, + (float16_t)-0.320068359f, (float16_t)-1.97167969f, + (float16_t)1.92480469f, (float16_t)-1.890625f, + (float16_t)1.78613281f, (float16_t)-1.08789062f, + (float16_t)0.886230469f, (float16_t)0.729003906f, + (float16_t)-1.13964844f, (float16_t)-1.36132812f, + (float16_t)0.336914062f, (float16_t)-0.216674805f, + (float16_t)1.96777344f, (float16_t)-0.604003906f, + (float16_t)-1.61328125f, (float16_t)0.68359375f, + (float16_t)0.680664062f, (float16_t)-0.180419922f, + (float16_t)1.22070312f, (float16_t)1.47167969f, + (float16_t)0.437744141f, (float16_t)1.36523438f, + (float16_t)-0.124267578f, (float16_t)1.42089844f, + (float16_t)1.46386719f, (float16_t)0.981933594f, + (float16_t)0.887695312f, (float16_t)-1.29199219f, + (float16_t)-1.6640625f, (float16_t)-1.98632812f, + (float16_t)0.113037109f, (float16_t)-1.27734375f, + (float16_t)-0.895507812f, (float16_t)-1.06445312f, + (float16_t)0.170776367f, (float16_t)-1.07519531f, + (float16_t)-1.90917969f, (float16_t)0.447021484f, + (float16_t)1.25488281f, (float16_t)-0.19921875f, + (float16_t)1.82910156f, (float16_t)0.887207031f, + (float16_t)0.198120117f, (float16_t)-0.417236328f, + (float16_t)1.43457031f, (float16_t)0.376953125f, + (float16_t)0.194580078f, (float16_t)-1.18359375f, + (float16_t)-1.59765625f, (float16_t)1.16113281f, + (float16_t)1.09082031f, (float16_t)-0.0837402344f, + (float16_t)1.02539062f, (float16_t)-0.87890625f, + (float16_t)1.03710938f, (float16_t)-0.564941406f, + (float16_t)0.225463867f, (float16_t)0.134277344f, + (float16_t)-1.15527344f, (float16_t)-1.60839844f, + (float16_t)1.38183594f, (float16_t)-1.00292969f, + (float16_t)-0.672363281f, (float16_t)0.578613281f, + (float16_t)1.50488281f, (float16_t)0.363525391f, + (float16_t)-0.786132812f, (float16_t)-0.701660156f, + (float16_t)-1.203125f, (float16_t)-1.48730469f, + (float16_t)-0.438720703f, (float16_t)-1.41210938f, + (float16_t)-0.53515625f, (float16_t)0.703613281f, + (float16_t)-1.11425781f, (float16_t)0.993652344f, + (float16_t)-1.359375f, (float16_t)0.466796875f, + (float16_t)-0.931640625f, (float16_t)1.64941406f, + (float16_t)1.63476562f, (float16_t)-0.549316406f, + (float16_t)0.716308594f, (float16_t)1.32519531f, + (float16_t)0.326416016f, (float16_t)-0.3984375f, + (float16_t)-0.417480469f, (float16_t)-0.0635986328f, + (float16_t)-0.0956420898f, (float16_t)0.926269531f, + (float16_t)0.497314453f, (float16_t)0.298095703f, + (float16_t)-1.69042969f, (float16_t)-0.96875f, + (float16_t)-0.80859375f, (float16_t)0.0673217773f, + (float16_t)-0.465087891f, (float16_t)-0.615722656f, + (float16_t)0.7734375f, (float16_t)-1.72851562f, + (float16_t)-0.383544922f, (float16_t)-0.214233398f, + (float16_t)1.92480469f, (float16_t)0.356445312f, + (float16_t)-0.15246582f, (float16_t)-1.72949219f, + (float16_t)-1.49414062f, (float16_t)1.83691406f, + (float16_t)-1.13964844f, (float16_t)0.291748047f, + (float16_t)0.346435547f, (float16_t)-0.386962891f, + (float16_t)-1.99707031f, (float16_t)0.362304688f, + (float16_t)0.15612793f, (float16_t)-0.756347656f, + (float16_t)1.10839844f, (float16_t)-1.8828125f, + (float16_t)-1.52148438f, (float16_t)0.334960938f, + (float16_t)-1.15820312f, (float16_t)-0.445556641f, + (float16_t)1.73925781f, (float16_t)0.946289062f, + (float16_t)-0.550292969f, (float16_t)-1.234375f, + (float16_t)0.658203125f, (float16_t)0.0270690918f, + (float16_t)-0.385742188f, (float16_t)-1.08789062f, + (float16_t)-0.643066406f, (float16_t)-0.415771484f, + (float16_t)0.499511719f, (float16_t)-0.349121094f, + (float16_t)1.68164062f, (float16_t)-0.691894531f, + (float16_t)-1.96875f, (float16_t)1.578125f, + (float16_t)-0.545898438f, (float16_t)-1.80761719f, + (float16_t)1.74023438f, (float16_t)-1.00488281f, + (float16_t)1.69335938f, 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(float16_t)-1.20800781f, (float16_t)-0.861816406f, + (float16_t)-0.764648438f, (float16_t)-1.76367188f, + (float16_t)0.12298584f, (float16_t)1.77246094f, + (float16_t)0.00637435913f, (float16_t)1.35449219f, + (float16_t)1.45507812f, (float16_t)-0.823242188f, + (float16_t)1.41601562f, (float16_t)-0.176757812f, + (float16_t)0.541015625f, (float16_t)-0.537109375f, + (float16_t)-0.153320312f, (float16_t)-0.815429688f, + (float16_t)-0.322753906f, (float16_t)-0.666503906f, + (float16_t)-1.16894531f, (float16_t)1.72460938f, + (float16_t)-1.91992188f, (float16_t)-0.103271484f, + (float16_t)-0.879882812f, (float16_t)-1.66992188f, + (float16_t)-1.75390625f, (float16_t)0.275390625f, + (float16_t)1.76171875f, (float16_t)0.0749511719f, + (float16_t)0.288085938f, (float16_t)1.71582031f, + (float16_t)-0.818359375f, (float16_t)-0.913574219f, + (float16_t)1.46679688f, (float16_t)-1.71679688f, + (float16_t)-1.83007812f, (float16_t)0.928710938f, + (float16_t)-0.237548828f, (float16_t)0.28515625f, + (float16_t)0.0902099609f, (float16_t)-1.40820312f, + (float16_t)1.87597656f, (float16_t)-1.08398438f, + (float16_t)1.20703125f, (float16_t)0.216918945f, + (float16_t)1.58886719f, (float16_t)-1.30175781f, + (float16_t)1.75f, (float16_t)-0.313964844f, + (float16_t)0.127807617f, (float16_t)0.304443359f, + (float16_t)-0.612304688f, (float16_t)-1.80957031f, + (float16_t)0.758789062f, (float16_t)0.869140625f, + (float16_t)-0.0163269043f, (float16_t)-0.876464844f, + (float16_t)-1.48144531f, (float16_t)-1.52148438f, + (float16_t)1.78710938f, (float16_t)-0.527832031f, + (float16_t)-0.111694336f, (float16_t)-0.102966309f, + (float16_t)0.00766372681f, (float16_t)0.55859375f, + (float16_t)-0.0192565918f, (float16_t)1.72167969f, + (float16_t)-0.5703125f, (float16_t)-0.565429688f, + (float16_t)1.74023438f, (float16_t)-0.404296875f, + (float16_t)-1.18554688f, (float16_t)-0.14050293f, + (float16_t)-1.97949219f, (float16_t)-0.827148438f, + (float16_t)-0.375732422f, (float16_t)-0.966308594f, + (float16_t)1.27246094f, (float16_t)-1.14648438f, + (float16_t)1.15332031f, (float16_t)-0.434814453f, + (float16_t)1.90429688f, (float16_t)-1.98925781f, + (float16_t)0.530761719f, (float16_t)-1.3671875f, + (float16_t)0.0737915039f, (float16_t)0.0792236328f, + (float16_t)-1.14941406f, (float16_t)0.0518188477f, + (float16_t)-1.8046875f, (float16_t)0.747070312f, + (float16_t)1.38183594f, (float16_t)1.56542969f, + (float16_t)-0.546875f, (float16_t)1.84472656f, + (float16_t)-1.88183594f, (float16_t)1.06152344f, + (float16_t)1.68457031f, (float16_t)0.874511719f, + (float16_t)-0.727539062f, (float16_t)0.834960938f, + (float16_t)1.48632812f, (float16_t)-0.0287322998f, + (float16_t)0.814453125f, (float16_t)1.96972656f, + (float16_t)-1.984375f, (float16_t)0.479003906f, + (float16_t)0.716796875f, (float16_t)-0.383056641f, + (float16_t)-1.68164062f, (float16_t)1.87792969f, + (float16_t)0.238769531f, (float16_t)0.719238281f, + (float16_t)-1.40136719f, (float16_t)1.8046875f, + (float16_t)0.930664062f, (float16_t)-0.00200462341f, + (float16_t)-1.39550781f, (float16_t)0.872070312f, + (float16_t)-0.372558594f, (float16_t)-1.74804688f, + (float16_t)-1.54589844f, (float16_t)0.640136719f, + (float16_t)1.37988281f, (float16_t)1.87207031f, + (float16_t)0.7890625f, (float16_t)0.388183594f, + (float16_t)-1.74707031f, (float16_t)-0.181396484f, + (float16_t)-1.02539062f, (float16_t)-1.01855469f, + (float16_t)0.558105469f, (float16_t)-1.1875f, + (float16_t)1.09765625f, (float16_t)0.49609375f, + (float16_t)-1.203125f, (float16_t)-1.66015625f, + (float16_t)-1.95214844f, (float16_t)0.0632324219f, + (float16_t)-0.667480469f, (float16_t)0.615234375f, + (float16_t)-0.624023438f, (float16_t)1.85742188f, + (float16_t)-0.321044922f, (float16_t)-1.5078125f, + (float16_t)0.104553223f, (float16_t)-1.64160156f, + (float16_t)-1.79296875f, (float16_t)-0.703613281f, + (float16_t)-1.16796875f, (float16_t)0.415527344f, + (float16_t)1.46875f, (float16_t)-1.29589844f, + (float16_t)-1.05761719f, (float16_t)-1.62890625f, + (float16_t)-1.80761719f, (float16_t)-1.20214844f, + (float16_t)0.788574219f, (float16_t)-1.57128906f, + (float16_t)1.390625f, (float16_t)1.16113281f, + (float16_t)1.99609375f, (float16_t)1.01074219f, + (float16_t)1.22558594f, (float16_t)-0.143188477f, + (float16_t)-0.123291016f, (float16_t)1.87792969f, + (float16_t)-1.60058594f, (float16_t)1.19433594f, + (float16_t)0.7578125f, (float16_t)-1.00976562f, + (float16_t)1.37207031f, (float16_t)-0.235473633f, + (float16_t)-1.5234375f, (float16_t)1.85449219f, + (float16_t)-1.77441406f, (float16_t)1.12011719f, + (float16_t)0.288574219f, (float16_t)1.16503906f, + (float16_t)-0.117675781f, (float16_t)-0.741699219f, + (float16_t)-0.890136719f, (float16_t)-0.428710938f, + (float16_t)0.00674057007f, (float16_t)1.79199219f, + (float16_t)1.2421875f, (float16_t)-0.726074219f, + (float16_t)1.06347656f, (float16_t)-1.29882812f, + (float16_t)-1.08691406f, (float16_t)-1.58789062f, + (float16_t)0.579101562f, (float16_t)-0.774414062f, + (float16_t)1.15136719f, (float16_t)-1.84765625f, + (float16_t)-0.891113281f, (float16_t)1.16015625f, + (float16_t)-1.81933594f, (float16_t)0.221191406f, + (float16_t)-1.12988281f, (float16_t)-0.388671875f, + (float16_t)-1.30957031f, (float16_t)0.465820312f, + (float16_t)-1.5546875f, (float16_t)-1.2109375f, + (float16_t)0.9140625f, (float16_t)0.253173828f, + (float16_t)1.52636719f, (float16_t)0.850585938f, + (float16_t)-1.70800781f, (float16_t)0.699707031f, + (float16_t)-0.194702148f, (float16_t)-0.0321960449f, + (float16_t)1.79492188f, (float16_t)0.702636719f, + (float16_t)-0.470947266f, (float16_t)0.556640625f, + (float16_t)-0.173217773f, (float16_t)1.93457031f, + (float16_t)-0.689941406f, (float16_t)-0.635253906f, + (float16_t)-0.745605469f, (float16_t)-0.250244141f, + (float16_t)-1.20703125f, (float16_t)-1.14257812f, + (float16_t)-1.68457031f, (float16_t)-0.486328125f, + (float16_t)0.0699462891f, (float16_t)1.65429688f, + (float16_t)-1.94628906f, (float16_t)-0.806640625f, + (float16_t)-1.78125f, (float16_t)0.526855469f, + (float16_t)0.414306641f, (float16_t)-0.939453125f, + (float16_t)0.364257812f, (float16_t)-1.43261719f, + (float16_t)0.259765625f, (float16_t)-0.0424499512f, + (float16_t)-0.706054688f, (float16_t)1.11523438f, + (float16_t)-1.95410156f, (float16_t)0.332275391f, + (float16_t)-0.301757812f, (float16_t)-0.684570312f, + (float16_t)-0.289306641f, (float16_t)0.686523438f, + (float16_t)0.8359375f, (float16_t)0.554199219f, + (float16_t)0.495849609f, (float16_t)-1.01464844f, + (float16_t)-0.948242188f, (float16_t)-0.977050781f, + (float16_t)0.809570312f, (float16_t)-1.79101562f, + (float16_t)1.51367188f, (float16_t)-1.06445312f, + (float16_t)1.62890625f, (float16_t)-1.08300781f, + (float16_t)1.84375f, (float16_t)0.0215454102f, + (float16_t)1.02539062f, (float16_t)-0.430908203f, + (float16_t)0.465576172f, (float16_t)0.720214844f, + (float16_t)0.0287017822f, (float16_t)-1.32226562f, + (float16_t)-0.784667969f, (float16_t)-1.6015625f, + (float16_t)1.02441406f, (float16_t)1.51660156f, + (float16_t)1.07226562f, (float16_t)0.948730469f, + (float16_t)0.603027344f, (float16_t)1.41308594f, + (float16_t)-1.00390625f, (float16_t)-0.629882812f, + (float16_t)-0.886230469f, (float16_t)-0.812988281f, + (float16_t)-0.135620117f, (float16_t)1.21972656f, + (float16_t)1.07910156f, (float16_t)-0.147705078f, + (float16_t)0.697265625f, (float16_t)0.479492188f, + (float16_t)-0.726074219f, (float16_t)0.516601562f, + (float16_t)0.915039062f, (float16_t)0.243774414f, + (float16_t)-1.35351562f, (float16_t)-0.297363281f, + (float16_t)0.0997924805f, (float16_t)0.474609375f, + (float16_t)-1.71582031f, (float16_t)1.74414062f, + (float16_t)1.56835938f, (float16_t)-1.67578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/output.h new file mode 100644 index 00000000..7474dfbc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/output.h @@ -0,0 +1,6913 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_6_f16_output[6912] = {(float16_t)0.169311523f, + (float16_t)2.25195312f, + (float16_t)-2.6796875f, + (float16_t)-2.22460938f, + (float16_t)8.0078125f, + (float16_t)0.796875f, + (float16_t)-2.46289062f, + (float16_t)3.14648438f, + (float16_t)-1.19140625f, + (float16_t)-2.84375f, + (float16_t)9.0390625f, + (float16_t)-2.01171875f, + (float16_t)1.94335938f, + (float16_t)2.41210938f, + (float16_t)-0.633300781f, + (float16_t)4.05859375f, + (float16_t)4.34375f, + (float16_t)-1.90332031f, + (float16_t)-1.56347656f, + (float16_t)-8.2265625f, + (float16_t)-0.0778808594f, + (float16_t)3.38671875f, + (float16_t)1.15722656f, + (float16_t)-2.68359375f, + (float16_t)-0.8671875f, + (float16_t)2.43554688f, + (float16_t)-4.67578125f, + (float16_t)1.47167969f, + (float16_t)-3.38085938f, + (float16_t)-4.15234375f, + (float16_t)5.859375f, + (float16_t)-3.67773438f, + (float16_t)-3.33203125f, + (float16_t)4.0546875f, + (float16_t)-3.74023438f, + (float16_t)5.46484375f, + (float16_t)-2.5f, + (float16_t)-3.72460938f, + (float16_t)-3.88476562f, + (float16_t)10.0f, + (float16_t)-10.0f, + (float16_t)1.00292969f, + (float16_t)9.25f, + (float16_t)-0.936035156f, + (float16_t)0.535644531f, + (float16_t)9.2421875f, + (float16_t)6.03515625f, + (float16_t)8.1796875f, + (float16_t)3.27148438f, + (float16_t)4.609375f, + (float16_t)-0.111999512f, + (float16_t)-4.39453125f, + (float16_t)3.82421875f, + (float16_t)-2.94726562f, + (float16_t)-1.73339844f, + (float16_t)6.375f, + (float16_t)0.829589844f, + (float16_t)5.2890625f, + (float16_t)-0.624023438f, + (float16_t)-2.08007812f, + (float16_t)5.25f, + (float16_t)-10.0f, + (float16_t)-1.08398438f, + (float16_t)-10.0f, + (float16_t)-5.1171875f, + (float16_t)-0.997558594f, + (float16_t)-0.591796875f, + (float16_t)4.26953125f, + 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(float16_t)-3.0859375f, + (float16_t)4.77734375f, + (float16_t)-0.266113281f, + (float16_t)-2.546875f, + (float16_t)0.815917969f, + (float16_t)4.69140625f, + (float16_t)3.29101562f, + (float16_t)-2.8359375f, + (float16_t)-6.609375f, + (float16_t)4.2109375f, + (float16_t)-2.25390625f, + (float16_t)-1.0703125f, + (float16_t)-4.92578125f, + (float16_t)3.6796875f, + (float16_t)-2.21484375f, + (float16_t)-6.08984375f, + (float16_t)-5.90234375f, + (float16_t)-3.22265625f, + (float16_t)-0.791992188f, + (float16_t)3.90429688f, + (float16_t)-2.18359375f, + (float16_t)3.2890625f, + (float16_t)1.58789062f, + (float16_t)10.0f, + (float16_t)2.2578125f, + (float16_t)-0.849609375f, + (float16_t)9.4609375f, + (float16_t)5.10546875f, + (float16_t)-1.04882812f, + (float16_t)-4.453125f, + (float16_t)0.951660156f, + (float16_t)5.015625f, + (float16_t)2.69921875f, + (float16_t)6.64453125f, + (float16_t)-10.0f, + (float16_t)0.404785156f, + (float16_t)-1.31835938f, + (float16_t)10.0f, + (float16_t)-4.265625f, + (float16_t)-3.44921875f, + (float16_t)0.628417969f, + (float16_t)-3.84570312f, + (float16_t)-3.28320312f, + (float16_t)-5.625f, + (float16_t)-7.7109375f, + (float16_t)3.33203125f, + (float16_t)6.96484375f, + (float16_t)3.66210938f, + (float16_t)-10.0f, + (float16_t)-6.5390625f, + (float16_t)-6.46484375f, + (float16_t)-4.0390625f, + (float16_t)-2.1484375f, + (float16_t)3.85351562f, + (float16_t)0.12890625f, + (float16_t)8.28125f, + (float16_t)-0.918457031f, + (float16_t)3.75585938f, + (float16_t)-2.49804688f, + (float16_t)1.125f, + (float16_t)-0.464111328f, + (float16_t)4.4453125f, + (float16_t)-6.4609375f, + (float16_t)-4.984375f, + (float16_t)7.28125f, + (float16_t)-3.59570312f, + (float16_t)-1.62207031f, + (float16_t)3.64257812f, + (float16_t)-9.2578125f, + (float16_t)6.45703125f, + (float16_t)-10.0f, + (float16_t)-0.422363281f, + (float16_t)2.86132812f, + (float16_t)10.0f, + (float16_t)1.44042969f, + (float16_t)-4.640625f, + (float16_t)-0.00602722168f, + (float16_t)-7.1171875f, + (float16_t)-4.7265625f, + (float16_t)-1.29589844f, + (float16_t)-8.015625f, + (float16_t)-10.0f, + (float16_t)-5.3515625f, + (float16_t)0.108642578f, + (float16_t)1.61816406f, + (float16_t)-10.0f, + (float16_t)-5.20703125f, + (float16_t)10.0f, + (float16_t)-1.84277344f, + (float16_t)-10.0f, + (float16_t)-3.16601562f, + (float16_t)9.90625f, + (float16_t)1.67773438f, + (float16_t)1.17480469f, + (float16_t)-5.73046875f, + (float16_t)-7.84375f, + (float16_t)-1.83398438f, + (float16_t)3.28515625f, + (float16_t)5.19921875f, + (float16_t)-6.99609375f, + (float16_t)9.859375f, + (float16_t)4.30859375f, + (float16_t)4.98046875f, + (float16_t)-2.078125f, + (float16_t)-4.2421875f, + (float16_t)-1.84472656f, + (float16_t)-2.66210938f, + (float16_t)-6.1484375f, + (float16_t)-3.1015625f, + (float16_t)-6.17578125f, + (float16_t)-1.89648438f, + (float16_t)-0.380126953f, + (float16_t)3.85742188f, + (float16_t)8.1328125f, + (float16_t)5.3125f, + (float16_t)0.823730469f, + (float16_t)10.0f, + (float16_t)-5.1171875f, + (float16_t)5.1484375f, + (float16_t)2.40039062f, + (float16_t)-1.25195312f, + (float16_t)0.8984375f, + (float16_t)4.52734375f, + (float16_t)-2.09765625f, + (float16_t)-8.6328125f, + (float16_t)1.30859375f, + (float16_t)1.65429688f, + (float16_t)7.16796875f, + (float16_t)-5.5703125f, + (float16_t)1.52148438f, + (float16_t)-8.234375f, + (float16_t)1.79980469f, + (float16_t)0.30078125f, + (float16_t)7.58984375f, + (float16_t)-3.18945312f, + (float16_t)0.993652344f, + (float16_t)-6.01171875f, + (float16_t)-10.0f, + (float16_t)-5.93359375f, + (float16_t)2.70507812f, + (float16_t)-9.875f, + (float16_t)10.0f, + (float16_t)-4.08984375f, + (float16_t)3.41601562f, + (float16_t)5.40625f, + (float16_t)0.200561523f, + (float16_t)0.0116500854f, + (float16_t)3.4921875f, + (float16_t)1.16992188f, + (float16_t)-1.62207031f, + (float16_t)-0.61328125f, + (float16_t)-9.78125f, + (float16_t)1.47460938f, + (float16_t)3.953125f, + (float16_t)1.59960938f, + (float16_t)4.04296875f, + (float16_t)-0.0133590698f, + (float16_t)2.25f, + (float16_t)3.43554688f, + (float16_t)1.48535156f, + (float16_t)-9.390625f, + (float16_t)-0.0587158203f, + (float16_t)-2.4921875f, + (float16_t)-4.53125f, + (float16_t)-1.22753906f, + (float16_t)-1.86230469f, + (float16_t)1.30859375f, + (float16_t)10.0f, + (float16_t)-4.82421875f, + (float16_t)-2.71289062f, + (float16_t)1.90527344f, + (float16_t)9.53125f, + (float16_t)-2.4609375f, + (float16_t)-1.5625f, + (float16_t)0.354492188f, + (float16_t)4.06640625f, + (float16_t)-10.0f, + (float16_t)-0.32421875f, + (float16_t)1.42285156f, + (float16_t)0.872070312f, + (float16_t)7.16015625f, + (float16_t)0.420654297f, + (float16_t)-10.0f, + (float16_t)2.59765625f, + (float16_t)0.517578125f, + (float16_t)7.640625f, + (float16_t)-0.67578125f, + (float16_t)-4.6875f, + (float16_t)-5.08984375f, + (float16_t)3.63867188f, + (float16_t)0.292480469f, + (float16_t)0.512207031f, + (float16_t)-4.04296875f, + (float16_t)9.1796875f, + (float16_t)9.34375f, + (float16_t)-10.0f, + (float16_t)3.85546875f, + (float16_t)-0.264404297f, + (float16_t)1.83398438f, + (float16_t)5.328125f, + (float16_t)-9.6953125f, + (float16_t)-1.19628906f, + (float16_t)-9.859375f, + (float16_t)-6.078125f, + (float16_t)-4.73046875f, + (float16_t)-2.38085938f, + (float16_t)0.241577148f, + (float16_t)-2.296875f, + (float16_t)4.640625f, + (float16_t)-4.0f, + (float16_t)6.11328125f, + (float16_t)2.37304688f, + (float16_t)4.625f, + (float16_t)2.703125f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_input_tensor.h new file mode 100644 index 00000000..3c8cfb7c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_input_tensor.h @@ -0,0 +1,866 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_6_f16_rhs_input_tensor[3456] = { + (float16_t)-0.0897827148f, 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(float16_t)0.429199219f, (float16_t)-0.0375976562f, + (float16_t)1.94042969f, (float16_t)1.77832031f, (float16_t)1.69921875f, (float16_t)0.134155273f, + (float16_t)-1.62402344f, (float16_t)-1.75878906f, (float16_t)0.302246094f, (float16_t)1.92871094f, + (float16_t)1.53222656f, (float16_t)-0.577148438f, (float16_t)-0.226196289f, (float16_t)1.46289062f, + (float16_t)1.52636719f, (float16_t)-1.89941406f, (float16_t)-1.83886719f, (float16_t)0.626953125f, + (float16_t)-0.737304688f, (float16_t)-1.30273438f, (float16_t)-0.376953125f, (float16_t)-0.2734375f, + (float16_t)0.615722656f, (float16_t)-0.3203125f, (float16_t)-0.983886719f, (float16_t)-1.40136719f, + (float16_t)-1.30664062f, (float16_t)0.891601562f, (float16_t)1.83984375f, (float16_t)-0.335205078f, + (float16_t)1.34375f, (float16_t)-0.996582031f, (float16_t)-1.46777344f, (float16_t)-1.22363281f, + (float16_t)-1.65820312f, (float16_t)0.702148438f, (float16_t)-1.00683594f, (float16_t)0.470458984f, + (float16_t)-0.452148438f, (float16_t)1.07128906f, (float16_t)1.95410156f, (float16_t)1.48144531f, + (float16_t)0.172119141f, (float16_t)-0.220092773f, (float16_t)-0.497070312f, (float16_t)1.90429688f, + (float16_t)1.53320312f, (float16_t)-1.92089844f, (float16_t)-0.0114135742f, (float16_t)-0.220703125f, + (float16_t)1.3515625f, (float16_t)1.67382812f, (float16_t)0.676757812f, (float16_t)-1.47460938f, + (float16_t)1.64257812f, (float16_t)1.26074219f, (float16_t)1.18457031f, (float16_t)1.99804688f, + (float16_t)0.168579102f, (float16_t)1.8984375f, (float16_t)0.345947266f, (float16_t)1.06054688f, + (float16_t)-0.994140625f, (float16_t)-0.948730469f, (float16_t)-1.19726562f, (float16_t)-0.0565185547f, + (float16_t)0.482177734f, (float16_t)0.490478516f, (float16_t)-0.998046875f, (float16_t)0.321044922f, + (float16_t)0.958984375f, (float16_t)-0.291748047f, (float16_t)-1.44726562f, (float16_t)0.894042969f, + (float16_t)-0.990234375f, (float16_t)-0.900878906f, (float16_t)-0.847167969f, (float16_t)0.562988281f, + (float16_t)1.75976562f, (float16_t)-0.229248047f, (float16_t)1.63085938f, (float16_t)-1.47949219f, + (float16_t)1.47460938f, (float16_t)1.18945312f, (float16_t)0.488525391f, (float16_t)0.694824219f, + (float16_t)0.0103683472f, (float16_t)-1.33105469f, (float16_t)1.76367188f, (float16_t)1.78320312f, + (float16_t)-1.54492188f, (float16_t)1.49316406f, (float16_t)-0.122802734f, (float16_t)1.97460938f, + (float16_t)0.579101562f, (float16_t)0.5234375f, (float16_t)-1.09863281f, (float16_t)1.41894531f, + (float16_t)0.7421875f, (float16_t)-1.22851562f, (float16_t)0.575195312f, (float16_t)-1.11621094f, + (float16_t)0.954589844f, (float16_t)-0.737792969f, (float16_t)-0.685058594f, (float16_t)-1.57519531f, + (float16_t)-1.96582031f, (float16_t)-1.37011719f, (float16_t)-1.76464844f, (float16_t)0.755371094f, + (float16_t)0.959960938f, (float16_t)-0.198852539f, (float16_t)1.48242188f, (float16_t)-0.677734375f, + (float16_t)-0.0453491211f, (float16_t)1.4296875f, (float16_t)-0.085144043f, (float16_t)1.13574219f, + (float16_t)1.3359375f, (float16_t)1.11035156f, (float16_t)-0.258056641f, (float16_t)-1.453125f, + (float16_t)-0.424560547f, (float16_t)0.90625f, (float16_t)-1.65625f, (float16_t)1.99511719f, + (float16_t)1.58691406f, (float16_t)0.6171875f, (float16_t)-1.99511719f, (float16_t)-0.414794922f, + (float16_t)1.984375f, (float16_t)-0.978515625f, (float16_t)1.01660156f, (float16_t)0.606445312f, + (float16_t)1.12304688f, (float16_t)1.91210938f, (float16_t)-0.633789062f, (float16_t)1.72363281f, + (float16_t)-1.43261719f, (float16_t)-1.08886719f, (float16_t)-1.13964844f, (float16_t)1.92773438f, + (float16_t)-0.737304688f, (float16_t)-0.813964844f, (float16_t)-0.587890625f, (float16_t)0.681640625f, + (float16_t)1.33789062f, (float16_t)-1.57910156f, (float16_t)-0.685546875f, (float16_t)1.47949219f, + (float16_t)-0.480712891f, (float16_t)-1.03320312f, (float16_t)1.99902344f, (float16_t)0.342041016f, + (float16_t)0.699707031f, (float16_t)0.119445801f, (float16_t)-0.190917969f, (float16_t)-1.12207031f, + (float16_t)-0.403076172f, (float16_t)-0.739257812f, (float16_t)1.73632812f, (float16_t)0.581054688f, + (float16_t)0.508300781f, (float16_t)1.49804688f, (float16_t)-0.280761719f, (float16_t)1.31152344f, + (float16_t)1.50097656f, (float16_t)1.05664062f, (float16_t)-1.70703125f, (float16_t)-1.89453125f, + (float16_t)-1.55175781f, (float16_t)-1.25f, (float16_t)-1.20996094f, (float16_t)1.34082031f, + (float16_t)-0.871582031f, (float16_t)-0.544433594f, (float16_t)-0.184936523f, (float16_t)0.126586914f, + (float16_t)-0.0586242676f, (float16_t)-0.754882812f, (float16_t)0.864257812f, (float16_t)0.0957641602f, + (float16_t)-0.767578125f, (float16_t)-0.934570312f, (float16_t)1.421875f, (float16_t)1.69628906f, + (float16_t)-1.58789062f, (float16_t)0.409912109f, (float16_t)-1.09277344f, (float16_t)0.985351562f, + (float16_t)1.74121094f, (float16_t)-1.20996094f, (float16_t)-0.715332031f, (float16_t)-1.85546875f, + (float16_t)-0.488525391f, (float16_t)0.759277344f, (float16_t)0.404785156f, (float16_t)-1.328125f, + (float16_t)-0.838378906f, (float16_t)-0.87890625f, (float16_t)1.83203125f, (float16_t)1.62597656f, + (float16_t)-0.797851562f, (float16_t)1.59179688f, (float16_t)0.200317383f, (float16_t)-0.210083008f, + (float16_t)1.01171875f, (float16_t)-1.28222656f, (float16_t)1.95117188f, (float16_t)0.182373047f, + (float16_t)1.35839844f, (float16_t)-1.41015625f, (float16_t)-1.87402344f, (float16_t)0.896972656f, + (float16_t)1.36816406f, (float16_t)-1.95410156f, (float16_t)-0.828613281f, (float16_t)0.875976562f, + (float16_t)-0.290771484f, (float16_t)0.12512207f, (float16_t)-0.0228729248f, (float16_t)-0.809082031f, + (float16_t)1.12988281f, (float16_t)-1.71289062f, (float16_t)-1.45410156f, (float16_t)1.04589844f, + (float16_t)0.30078125f, (float16_t)-1.13769531f, (float16_t)1.81738281f, (float16_t)0.7890625f, + (float16_t)-1.14648438f, (float16_t)-0.579101562f, (float16_t)1.66601562f, (float16_t)-0.504394531f, + (float16_t)-0.855957031f, (float16_t)-0.260253906f, (float16_t)-1.24414062f, (float16_t)-1.74902344f, + (float16_t)1.98828125f, (float16_t)1.98535156f, (float16_t)1.73242188f, (float16_t)0.585449219f, + (float16_t)1.12988281f, (float16_t)0.47265625f, (float16_t)1.99609375f, (float16_t)-1.40722656f, + (float16_t)-1.22265625f, (float16_t)0.349609375f, (float16_t)0.200561523f, (float16_t)1.60351562f, + (float16_t)0.128540039f, (float16_t)0.998046875f, (float16_t)-0.974121094f, (float16_t)-0.205810547f, + (float16_t)-1.54394531f, (float16_t)1.94726562f, (float16_t)-1.61621094f, (float16_t)-1.97851562f, + (float16_t)-1.14257812f, (float16_t)0.060333252f, (float16_t)0.587890625f, (float16_t)-0.422851562f, + (float16_t)0.833984375f, (float16_t)1.03320312f, (float16_t)-0.488769531f, (float16_t)-1.5546875f, + (float16_t)-0.586914062f, (float16_t)1.96289062f, (float16_t)1.26367188f, (float16_t)-0.199707031f, + (float16_t)-1.60546875f, (float16_t)-0.602050781f, (float16_t)-1.4453125f, (float16_t)0.158569336f, + (float16_t)-1.98632812f, (float16_t)1.47949219f, (float16_t)0.76171875f, (float16_t)1.54882812f, + (float16_t)0.210571289f, (float16_t)-1.85058594f, (float16_t)-1.30078125f, (float16_t)-0.0852661133f, + (float16_t)1.55175781f, (float16_t)0.561523438f, (float16_t)0.151367188f, (float16_t)-0.289550781f, + (float16_t)-0.826660156f, (float16_t)0.369384766f, (float16_t)-1.62890625f, (float16_t)1.92382812f, + (float16_t)-0.5234375f, (float16_t)1.03320312f, (float16_t)1.10546875f, (float16_t)-0.90234375f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..0a4923bd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/rhs_transposed_tensor.h @@ -0,0 +1,3457 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_6_f16_rhs_transposed_tensor[3456] = {(float16_t)-0.0897827148f, + (float16_t)-0.0631713867f, + (float16_t)0.625f, + (float16_t)1.68066406f, + (float16_t)0.587890625f, + (float16_t)1.37402344f, + (float16_t)-0.538574219f, + (float16_t)1.77832031f, + (float16_t)1.07910156f, + (float16_t)-0.251953125f, + (float16_t)-0.348144531f, + (float16_t)0.484375f, + (float16_t)1.47851562f, + (float16_t)-1.74511719f, + (float16_t)1.45605469f, + (float16_t)1.33105469f, + (float16_t)-1.25585938f, + (float16_t)-1.69726562f, + (float16_t)-0.779296875f, + (float16_t)1.20703125f, + (float16_t)0.458740234f, + (float16_t)1.21289062f, + (float16_t)-0.980957031f, + (float16_t)1.03710938f, + (float16_t)-0.980957031f, + (float16_t)-0.0444641113f, + (float16_t)0.399414062f, + (float16_t)-1.3125f, + (float16_t)0.401611328f, + (float16_t)0.795410156f, + (float16_t)1.12207031f, + (float16_t)-1.54589844f, + (float16_t)-1.20605469f, + (float16_t)-0.582519531f, + (float16_t)-0.383056641f, + (float16_t)-1.91113281f, + (float16_t)-0.953125f, + (float16_t)-1.21582031f, + (float16_t)0.224975586f, + (float16_t)1.78125f, + (float16_t)-0.312255859f, + (float16_t)-0.0914306641f, + (float16_t)0.420166016f, + (float16_t)0.646972656f, + (float16_t)-1.53613281f, + (float16_t)1.1953125f, + (float16_t)1.77246094f, + (float16_t)-1.63867188f, + (float16_t)1.13671875f, + (float16_t)0.651367188f, + (float16_t)-1.109375f, + (float16_t)-0.525390625f, + (float16_t)-1.83789062f, + (float16_t)0.5390625f, + (float16_t)1.50292969f, + (float16_t)-1.05859375f, + (float16_t)0.421875f, + (float16_t)-0.491699219f, + (float16_t)-0.464599609f, + (float16_t)-0.417480469f, + (float16_t)0.590820312f, + (float16_t)-0.0347290039f, + (float16_t)-1.41503906f, + (float16_t)1.40820312f, + (float16_t)-0.491943359f, + (float16_t)0.266113281f, + (float16_t)0.402832031f, + (float16_t)0.546386719f, + (float16_t)1.74121094f, + (float16_t)1.74804688f, + (float16_t)1.58398438f, + (float16_t)-1.05175781f, + 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(float16_t)-0.685546875f, + (float16_t)-1.20996094f, + (float16_t)0.404785156f, + (float16_t)-0.0228729248f, + (float16_t)1.99609375f, + (float16_t)1.26367188f, + (float16_t)1.10546875f, + (float16_t)0.0354003906f, + (float16_t)-0.967285156f, + (float16_t)1.015625f, + (float16_t)0.134155273f, + (float16_t)-0.335205078f, + (float16_t)-1.47460938f, + (float16_t)0.562988281f, + (float16_t)-1.11621094f, + (float16_t)1.99511719f, + (float16_t)1.47949219f, + (float16_t)1.34082031f, + (float16_t)-1.328125f, + (float16_t)-0.809082031f, + (float16_t)-1.40722656f, + (float16_t)-0.199707031f, + (float16_t)-0.90234375f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/config_data.h new file mode 100644 index 00000000..9def92b0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_6_F32_LHS_BATCH 3 +#define BATCH_MATMUL_6_F32_LHS_HEIGHT 3 +#define BATCH_MATMUL_6_F32_LHS_ROWS 32 +#define BATCH_MATMUL_6_F32_LHS_COLS 16 +#define BATCH_MATMUL_6_F32_RHS_BATCH 3 +#define BATCH_MATMUL_6_F32_RHS_HEIGHT 3 +#define BATCH_MATMUL_6_F32_RHS_ROWS 16 +#define BATCH_MATMUL_6_F32_RHS_COLS 24 +#define BATCH_MATMUL_6_F32_ADJ_X 0 +#define BATCH_MATMUL_6_F32_ADJ_Y 0 +#define BATCH_MATMUL_6_F32_DST_SIZE 6912 +#define BATCH_MATMUL_6_F32_OUTPUT_BATCH 3 +#define BATCH_MATMUL_6_F32_OUTPUT_HEIGHT 3 +#define BATCH_MATMUL_6_F32_OUTPUT_ROWS 32 +#define BATCH_MATMUL_6_F32_OUTPUT_COLS 24 +#define BATCH_MATMUL_6_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_6_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/lhs_input_tensor.h new file mode 100644 index 00000000..75e43cd4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/lhs_input_tensor.h @@ -0,0 +1,770 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_6_f32_lhs_input_tensor[4608] = { + -0.865710855f, -0.195706889f, -0.0526264347f, 0.726250947f, -1.85677469f, -1.84106553f, + 1.65166259f, 1.25477219f, -0.416927248f, -1.21305954f, -0.531593621f, -0.316975951f, + -1.73004603f, 1.752823f, -1.89028716f, 0.250143975f, 1.86359322f, 1.74877095f, + -0.605475664f, 0.568108976f, 0.0250058603f, 0.168702304f, 1.0871197f, -1.21334684f, + -1.57629192f, 1.22336817f, -0.463142127f, 0.0157578923f, 0.925049067f, -0.43631798f, + 1.87910187f, -0.756545901f, -0.861651599f, 1.13881552f, 0.676303923f, -0.628701985f, + 0.749259472f, 0.124414898f, -1.0161742f, -1.04299748f, 1.83018661f, 0.796519637f, + 0.480270773f, -1.49348366f, 1.71594381f, 1.64444125f, 0.658096254f, -1.62005758f, + 0.503682733f, 0.442301303f, -0.863211632f, -0.925940633f, -1.54219425f, -0.99224633f, + -1.91528332f, 1.17478049f, -1.87540984f, 1.82128203f, 0.660266817f, 1.00396264f, + -1.7123276f, -1.15006733f, -1.75427556f, 0.251863271f, 1.1298424f, -0.578014672f, + 0.729131103f, 0.70707041f, -1.79065847f, 1.85276067f, 0.40114221f, -1.46430469f, + 0.269411564f, 1.50178528f, 1.12903333f, 1.9745121f, -1.99263096f, 1.91090953f, + 0.287161559f, -1.54691994f, 1.4038353f, -1.44984484f, 1.83664763f, -1.70989823f, + -0.906947732f, 0.966156781f, 0.788883448f, -0.871058047f, -0.0528332442f, 0.0863320976f, + 0.423176885f, 1.27646816f, 1.73140085f, -1.09205067f, 1.4147104f, -0.776523054f, + 1.2415154f, 0.285712302f, 0.804749966f, -1.66108477f, 1.18587065f, -0.831435919f, + -0.755197704f, 1.32767189f, 0.617107928f, -1.23959243f, -0.14049834f, -0.6341061f, + 1.2724607f, 1.8677845f, -1.57377124f, 1.34388804f, 0.510679841f, -0.36205411f, + -0.342786819f, -1.39566219f, -1.55422401f, 0.0742290765f, 0.904363275f, -0.646872818f, + -1.3153609f, -1.48929906f, -1.40924191f, 1.14855909f, -1.03061831f, 0.9596138f, + -1.6631707f, 0.468402475f, -0.523424029f, -1.19882357f, 0.885521352f, -1.45548904f, + -0.499413162f, 1.27984583f, 1.1517086f, 0.49312371f, 1.74488306f, 0.616540909f, + -0.0593676977f, -1.66464579f, -1.68481517f, 1.47970402f, 1.58059359f, 0.944114923f, + -0.732816815f, -0.563765109f, 1.35073459f, -1.7083087f, 1.3031888f, 0.664607465f, + -1.26110089f, -0.635311306f, -0.713199973f, -1.98252416f, 1.38173556f, 0.58106333f, + 0.94402945f, -1.16792572f, -1.01215243f, -1.02045286f, 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b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..fa93450d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/lhs_transposed_tensor.h @@ -0,0 +1,661 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_6_f32_lhs_transposed_tensor[4608] = { + -0.865710855f, 1.86359322f, -0.861651599f, 0.503682733f, 1.1298424f, 1.4038353f, 1.2415154f, + 0.510679841f, -0.523424029f, -0.732816815f, 1.9288857f, 1.61489105f, 1.93727481f, -1.6693809f, + 1.8352437f, -1.47323132f, 1.40523052f, -1.87271261f, 0.249887109f, -0.419895589f, 1.67449582f, + -1.48220468f, -1.39390421f, -1.34464669f, -1.16351831f, -0.934829831f, -1.1121434f, -1.67776752f, + 0.275309652f, -1.54087269f, -1.13899159f, 0.777137399f, -0.195706889f, 1.74877095f, 1.13881552f, + 0.442301303f, -0.578014672f, -1.44984484f, 0.285712302f, -0.36205411f, -1.19882357f, -0.563765109f, + -1.00203121f, 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-1.15415537f, 0.863703549f, -1.00828373f, -1.33085513f, -0.143888324f, 1.53218007f, + 1.31364739f, 0.295455426f, -1.76264822f, 0.290293515f, 0.561067462f, 1.72845209f, 1.82666099f, + -1.08616829f, -0.39073962f, -0.117865868f, 0.334044039f, -0.895748258f, 1.59488618f, 0.680108964f, + -1.63964391f, 1.1290338f, -1.21014822f, -1.23737323f, 1.74740434f, -0.495843381f, -1.97760749f, + -0.820897162f, -0.0306983273f, 0.0363300256f, 1.58454967f, 0.418177307f, -1.28393936f, -1.20587873f, + 1.51388383f, -0.656974137f, -0.39956677f, -1.07033467f, -1.87141073f, -1.65957224f, 1.01002574f, + 1.86157763f, -1.58078885f, -0.731415391f, -0.670836031f, 1.06140983f, -0.893366158f, -1.80360115f, + -0.0408879258f, 0.460278749f, -1.9844557f, -0.31151095f, 0.486991763f, -1.61917353f, -1.38692796f, + -1.00332904f, -1.29200459f, 0.792852521f, 1.45719755f, 0.350323468f, 0.819793463f, -0.142670348f, + -1.53062582f, 0.0352407284f, -0.956065178f, 0.45301035f, -1.19011104f, 0.487250835f, -1.11714399f, + -0.307405949f, -0.0222718287f, 0.991405785f, -0.23361735f, -1.52538276f, -1.00651729f, -0.709549367f, + -1.9165262f, -0.0426301397f, -1.76081157f, -0.798852384f, 1.09251964f, -0.865250587f, -0.0290909372f, + -0.0524875149f, -1.32725835f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/output.h new file mode 100644 index 00000000..78fc855b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/output.h @@ -0,0 +1,990 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_6_f32_output[6912] = { + 3.95474076f, 1.47584593f, -3.9702847f, 2.24166441f, -7.10499239f, -3.29380631f, 2.95984817f, + -3.60855246f, -0.0661541671f, -0.719352782f, 0.250574738f, 4.40851927f, -7.90430689f, -6.19138527f, + -0.166577965f, -7.72934723f, 1.09808052f, 6.03258514f, 6.65668106f, -0.268593132f, -1.31846404f, + -10.0f, 7.74792528f, -3.62131119f, -10.0f, 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-0.459675431f, + -10.0f, -5.76493454f, -2.09320807f, -1.45596874f, 7.30914783f, 4.11028957f, -2.00594187f, + -3.95341873f, 2.59070969f, 2.10935307f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_input_tensor.h new file mode 100644 index 00000000..4b9e2031 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_input_tensor.h @@ -0,0 +1,578 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_6_f32_rhs_input_tensor[3456] = { + -1.62761271f, 1.36200726f, -0.801319957f, 0.469965279f, 1.99066067f, 0.763923228f, + 1.03942132f, -0.348432571f, -0.473024756f, 0.830744743f, -0.557051837f, -0.546183884f, + 1.7433871f, -0.818175316f, -0.0329718255f, 0.774340034f, -0.682633698f, 1.74955475f, + 0.724930763f, -1.11402941f, 1.28473997f, 1.45565987f, -1.73343563f, -1.84015405f, + -0.641185641f, 0.422584623f, -0.85959667f, 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-0.54268086f, + -1.33387399f, 0.0246083867f, -0.339895308f, -0.0738984272f, 1.96332014f, -1.100914f, + 1.68194473f, -0.669345379f, 0.866838217f, -0.0594200939f, 0.0750961378f, 1.79276526f, + 1.0526607f, -1.89458811f, 0.436351687f, 1.82306087f, -0.105518535f, -0.788122118f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..1c5cfef2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/rhs_transposed_tensor.h @@ -0,0 +1,578 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_6_f32_rhs_transposed_tensor[3456] = { + -1.62761271f, -0.641185641f, 0.583442628f, -0.327879757f, -0.745362043f, -0.120321274f, + -1.71249521f, 1.75451541f, 1.4035058f, -1.41632712f, -1.13557208f, -1.85532379f, + 0.657091141f, 1.3183161f, 0.901741028f, -0.334258407f, 1.36200726f, 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-1.04033256f, -1.71586263f, + -0.31919384f, -0.695000947f, -1.41180229f, 1.16476023f, 1.80382645f, -1.8630724f, + 1.71509838f, -0.105518535f, -0.0113600502f, -1.60591865f, -1.03631139f, 1.66906989f, + 0.878088772f, 0.858517468f, -1.87536299f, -0.987262368f, 1.69629383f, -1.00622654f, + 0.62818557f, 0.357538193f, 0.424131542f, -0.394556165f, -1.58974874f, -0.788122118f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_6_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/config_data.h new file mode 100644 index 00000000..9c099a5f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_ET_SMALL_F16_LHS_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F16_LHS_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F16_LHS_ROWS 2 +#define BATCH_MATMUL_ET_SMALL_F16_LHS_COLS 3 +#define BATCH_MATMUL_ET_SMALL_F16_RHS_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F16_RHS_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F16_RHS_ROWS 3 +#define BATCH_MATMUL_ET_SMALL_F16_RHS_COLS 4 +#define BATCH_MATMUL_ET_SMALL_F16_ADJ_X 0 +#define BATCH_MATMUL_ET_SMALL_F16_ADJ_Y 0 +#define BATCH_MATMUL_ET_SMALL_F16_DST_SIZE 8 +#define BATCH_MATMUL_ET_SMALL_F16_OUTPUT_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F16_OUTPUT_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F16_OUTPUT_ROWS 2 +#define BATCH_MATMUL_ET_SMALL_F16_OUTPUT_COLS 4 +#define BATCH_MATMUL_ET_SMALL_F16_ACTIVATION_MIN (float16_t) - 10.0f +#define BATCH_MATMUL_ET_SMALL_F16_ACTIVATION_MAX (float16_t)10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_input_tensor.h new file mode 100644 index 00000000..1fa001b3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_input_tensor.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_et_small_f16_lhs_input_tensor[6] = {(float16_t)-0.821289062f, + (float16_t)-0.650390625f, + (float16_t)-0.815917969f, + (float16_t)-1.21289062f, + (float16_t)-1.0859375f, + (float16_t)-0.4765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_transposed_tensor.h new file mode 100644 index 00000000..dd696700 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/lhs_transposed_tensor.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_et_small_f16_lhs_transposed_tensor[6] = {(float16_t)-0.821289062f, + (float16_t)-1.21289062f, + (float16_t)-0.650390625f, + (float16_t)-1.0859375f, + (float16_t)-0.815917969f, + (float16_t)-0.4765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/output.h new file mode 100644 index 00000000..6a8da0c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/output.h @@ -0,0 +1,9 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_et_small_f16_output[8] = {(float16_t)-2.41992188f, + (float16_t)-0.548828125f, + (float16_t)0.373291016f, + (float16_t)1.4140625f, + (float16_t)-2.65234375f, + (float16_t)-2.24023438f, + (float16_t)-0.0537414551f, + (float16_t)1.71484375f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_input_tensor.h new file mode 100644 index 00000000..ede514e1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_input_tensor.h @@ -0,0 +1,13 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_et_small_f16_rhs_input_tensor[12] = {(float16_t)0.845214844f, + (float16_t)1.84765625f, + (float16_t)-0.435791016f, + (float16_t)-0.901855469f, + (float16_t)0.875976562f, + (float16_t)0.798828125f, + (float16_t)0.837402344f, + (float16_t)-0.321777344f, + (float16_t)1.41796875f, + (float16_t)-1.82421875f, + (float16_t)-0.686523438f, + (float16_t)-0.569335938f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_transposed_tensor.h new file mode 100644 index 00000000..c12587ca --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/rhs_transposed_tensor.h @@ -0,0 +1,13 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t batch_matmul_et_small_f16_rhs_transposed_tensor[12] = {(float16_t)0.845214844f, + (float16_t)0.875976562f, + (float16_t)1.41796875f, + (float16_t)1.84765625f, + (float16_t)0.798828125f, + (float16_t)-1.82421875f, + (float16_t)-0.435791016f, + (float16_t)0.837402344f, + (float16_t)-0.686523438f, + (float16_t)-0.901855469f, + (float16_t)-0.321777344f, + (float16_t)-0.569335938f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f16/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/config_data.h new file mode 100644 index 00000000..5fd0fd23 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BATCH_MATMUL_ET_SMALL_F32_LHS_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F32_LHS_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F32_LHS_ROWS 2 +#define BATCH_MATMUL_ET_SMALL_F32_LHS_COLS 3 +#define BATCH_MATMUL_ET_SMALL_F32_RHS_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F32_RHS_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F32_RHS_ROWS 3 +#define BATCH_MATMUL_ET_SMALL_F32_RHS_COLS 4 +#define BATCH_MATMUL_ET_SMALL_F32_ADJ_X 0 +#define BATCH_MATMUL_ET_SMALL_F32_ADJ_Y 0 +#define BATCH_MATMUL_ET_SMALL_F32_DST_SIZE 8 +#define BATCH_MATMUL_ET_SMALL_F32_OUTPUT_BATCH 1 +#define BATCH_MATMUL_ET_SMALL_F32_OUTPUT_HEIGHT 1 +#define BATCH_MATMUL_ET_SMALL_F32_OUTPUT_ROWS 2 +#define BATCH_MATMUL_ET_SMALL_F32_OUTPUT_COLS 4 +#define BATCH_MATMUL_ET_SMALL_F32_ACTIVATION_MIN -10.0f +#define BATCH_MATMUL_ET_SMALL_F32_ACTIVATION_MAX 10.0f diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_input_tensor.h new file mode 100644 index 00000000..671a23b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_input_tensor.h @@ -0,0 +1,3 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_et_small_f32_lhs_input_tensor[6] = + {-1.19223011f, 0.727761507f, -1.27740252f, 0.657582879f, -1.87273502f, -1.64384687f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_transposed_tensor.h new file mode 100644 index 00000000..2d196260 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/lhs_transposed_tensor.h @@ -0,0 +1,3 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_et_small_f32_lhs_transposed_tensor[6] = + {-1.19223011f, 0.657582879f, 0.727761507f, -1.87273502f, -1.27740252f, -1.64384687f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/output.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/output.h new file mode 100644 index 00000000..59733a78 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/output.h @@ -0,0 +1,3 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_et_small_f32_output[8] = + {0.580903649f, 2.43280125f, -2.44782233f, 1.7580483f, 2.54052758f, -0.338131189f, 3.21111798f, 3.0767405f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_input_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_input_tensor.h new file mode 100644 index 00000000..744e788e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_input_tensor.h @@ -0,0 +1,13 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_et_small_f32_rhs_input_tensor[12] = {1.3838129f, + -0.0879031345f, + 1.24959612f, + 0.154308945f, + 0.441429287f, + 1.16619527f, + -1.28939128f, + -0.169492006f, + -1.49480832f, + -1.1580441f, + 0.0153803807f, + -1.61685133f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_transposed_tensor.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_transposed_tensor.h new file mode 100644 index 00000000..aec798d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/rhs_transposed_tensor.h @@ -0,0 +1,13 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t batch_matmul_et_small_f32_rhs_transposed_tensor[12] = {1.3838129f, + 0.441429287f, + -1.49480832f, + -0.0879031345f, + 1.16619527f, + -1.1580441f, + 1.24959612f, + -1.28939128f, + 0.0153803807f, + 0.154308945f, + -0.169492006f, + -1.61685133f}; diff --git a/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/test_data.h new file mode 100644 index 00000000..7513298e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/batch_matmul_et_small_f32/test_data.h @@ -0,0 +1,7 @@ +// Generated by batch_matmul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_tensor.h" +#include "lhs_transposed_tensor.h" +#include "output.h" +#include "rhs_input_tensor.h" +#include "rhs_transposed_tensor.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/bias_data.h new file mode 100644 index 00000000..0eefc332 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/bias_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_f16_bias[6] = {(float16_t)0.0762939453f, + (float16_t)0.0362243652f, + (float16_t)0.0104751587f, + (float16_t)-0.00966644287f, + (float16_t)-0.0278778076f, + (float16_t)-0.0459594727f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/config_data.h new file mode 100644 index 00000000..2ebb17ac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_BASIC_F16_INPUT_N 1 +#define BN_BASIC_F16_INPUT_H 8 +#define BN_BASIC_F16_INPUT_W 8 +#define BN_BASIC_F16_INPUT_C 6 +#define BN_BASIC_F16_SIZE 384 +#define BN_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/input_data.h new file mode 100644 index 00000000..406d0a28 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/input_data.h @@ -0,0 +1,98 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_f16_input[384] = { + (float16_t)3.18164062f, (float16_t)0.426513672f, (float16_t)3.015625f, (float16_t)0.375488281f, + (float16_t)-3.30078125f, (float16_t)0.862792969f, (float16_t)1.30761719f, (float16_t)-0.647949219f, + (float16_t)2.00585938f, (float16_t)-1.73144531f, (float16_t)0.200683594f, (float16_t)-1.81054688f, + (float16_t)-1.37792969f, (float16_t)-1.70703125f, (float16_t)-2.703125f, (float16_t)-1.99707031f, + (float16_t)3.42578125f, (float16_t)-3.72460938f, (float16_t)-1.80859375f, (float16_t)-0.97265625f, + (float16_t)-0.90625f, (float16_t)-2.109375f, (float16_t)-3.703125f, (float16_t)-2.67578125f, + (float16_t)1.14648438f, (float16_t)-1.25976562f, (float16_t)3.9375f, (float16_t)-1.75878906f, + (float16_t)-3.62304688f, (float16_t)1.53808594f, (float16_t)1.22070312f, (float16_t)2.96679688f, + (float16_t)-3.09179688f, (float16_t)0.700195312f, (float16_t)2.18359375f, (float16_t)-1.52636719f, + (float16_t)-0.907714844f, (float16_t)-2.890625f, (float16_t)0.939453125f, (float16_t)1.37792969f, + (float16_t)2.23242188f, (float16_t)-3.31835938f, (float16_t)3.34375f, (float16_t)-1.44335938f, + (float16_t)-1.95410156f, (float16_t)2.59179688f, (float16_t)2.6953125f, (float16_t)3.19921875f, + (float16_t)2.23242188f, (float16_t)3.69921875f, (float16_t)2.99804688f, (float16_t)3.73242188f, + (float16_t)2.40039062f, (float16_t)-1.54980469f, (float16_t)-0.488037109f, (float16_t)0.743652344f, + (float16_t)0.685058594f, (float16_t)0.841796875f, (float16_t)-1.97949219f, (float16_t)2.85742188f, + (float16_t)3.16015625f, (float16_t)-0.0506896973f, (float16_t)0.753417969f, (float16_t)-2.6875f, + (float16_t)0.373046875f, (float16_t)-1.80078125f, (float16_t)-0.0069694519f, (float16_t)3.30078125f, + (float16_t)-0.484863281f, (float16_t)3.30273438f, (float16_t)-2.53710938f, (float16_t)2.43359375f, + (float16_t)2.60351562f, (float16_t)-0.480224609f, (float16_t)-2.5390625f, (float16_t)1.0390625f, + (float16_t)0.319091797f, (float16_t)3.45507812f, (float16_t)1.81054688f, (float16_t)3.42773438f, + (float16_t)1.04785156f, (float16_t)2.25195312f, (float16_t)-0.0947875977f, (float16_t)1.57226562f, + (float16_t)-3.6875f, (float16_t)-0.109375f, (float16_t)-1.3671875f, (float16_t)-0.17578125f, + (float16_t)-1.27929688f, (float16_t)-1.93261719f, (float16_t)-1.90136719f, (float16_t)-2.2734375f, + (float16_t)0.0843505859f, (float16_t)-1.44824219f, (float16_t)-0.100158691f, (float16_t)2.9375f, + (float16_t)0.854492188f, (float16_t)1.46289062f, (float16_t)0.154541016f, (float16_t)-3.83398438f, + (float16_t)-0.723632812f, (float16_t)0.152709961f, (float16_t)1.0390625f, (float16_t)-2.25f, + (float16_t)3.79492188f, (float16_t)3.09960938f, (float16_t)3.39648438f, (float16_t)-3.70117188f, + (float16_t)0.34765625f, (float16_t)3.9609375f, (float16_t)-3.84765625f, (float16_t)2.0546875f, + (float16_t)3.8984375f, (float16_t)3.13671875f, (float16_t)3.95117188f, (float16_t)1.484375f, + (float16_t)-0.169799805f, (float16_t)-1.29394531f, (float16_t)1.0546875f, (float16_t)-3.71679688f, + (float16_t)-3.95117188f, (float16_t)-0.99609375f, (float16_t)-0.69921875f, (float16_t)2.11523438f, + (float16_t)1.5625f, (float16_t)1.56054688f, (float16_t)3.57226562f, (float16_t)-3.04882812f, + (float16_t)3.69335938f, (float16_t)-2.2421875f, (float16_t)-2.765625f, (float16_t)-2.20507812f, + (float16_t)1.70019531f, (float16_t)-3.39257812f, (float16_t)1.49316406f, (float16_t)3.40429688f, + (float16_t)2.69726562f, (float16_t)0.134399414f, (float16_t)0.211303711f, (float16_t)-0.656738281f, + (float16_t)-2.3046875f, (float16_t)2.60742188f, (float16_t)-0.785644531f, (float16_t)0.176879883f, + (float16_t)-3.00976562f, (float16_t)2.265625f, (float16_t)-3.52929688f, (float16_t)3.10546875f, + (float16_t)1.59960938f, (float16_t)0.593261719f, (float16_t)3.8203125f, (float16_t)2.83203125f, + (float16_t)-2.18945312f, (float16_t)-2.51367188f, (float16_t)-2.10351562f, (float16_t)-1.30566406f, + (float16_t)-1.18457031f, (float16_t)-2.98242188f, (float16_t)3.5234375f, (float16_t)-0.271240234f, + (float16_t)-1.04394531f, (float16_t)-1.50488281f, (float16_t)-3.0078125f, (float16_t)-3.48632812f, + (float16_t)2.82617188f, (float16_t)-1.77734375f, (float16_t)-2.23242188f, (float16_t)-2.1953125f, + (float16_t)-1.54589844f, (float16_t)1.953125f, (float16_t)1.37988281f, (float16_t)-2.00195312f, + (float16_t)3.65429688f, (float16_t)-3.08007812f, (float16_t)1.70800781f, (float16_t)1.73144531f, + (float16_t)2.88085938f, (float16_t)-3.83398438f, (float16_t)-1.34960938f, (float16_t)-0.725097656f, + (float16_t)3.359375f, (float16_t)3.75585938f, (float16_t)3.6015625f, (float16_t)-1.20703125f, + (float16_t)-1.61621094f, (float16_t)-3.95117188f, (float16_t)-0.404541016f, (float16_t)2.22070312f, + (float16_t)-1.26953125f, (float16_t)0.371337891f, (float16_t)-2.04492188f, (float16_t)2.37890625f, + (float16_t)-0.513183594f, (float16_t)2.81640625f, (float16_t)1.2578125f, (float16_t)3.6796875f, + (float16_t)-2.69921875f, (float16_t)-0.176879883f, (float16_t)3.12890625f, (float16_t)-0.0385742188f, + (float16_t)3.98828125f, (float16_t)-2.94335938f, (float16_t)-0.524902344f, (float16_t)3.39453125f, + (float16_t)1.31542969f, (float16_t)-2.36328125f, (float16_t)-3.828125f, (float16_t)1.97558594f, + (float16_t)3.02734375f, (float16_t)-3.51953125f, (float16_t)-3.65039062f, (float16_t)-1.39257812f, + (float16_t)-2.57421875f, (float16_t)-3.18554688f, (float16_t)-1.6171875f, (float16_t)-1.82519531f, + (float16_t)-3.29101562f, (float16_t)2.5390625f, (float16_t)-3.09179688f, (float16_t)-0.725585938f, + (float16_t)-3.828125f, (float16_t)1.91210938f, (float16_t)-0.687011719f, (float16_t)-1.18554688f, + (float16_t)2.45117188f, (float16_t)2.734375f, (float16_t)3.203125f, (float16_t)-2.21875f, + (float16_t)-2.34570312f, (float16_t)3.73242188f, (float16_t)-0.609863281f, (float16_t)2.41015625f, + (float16_t)-2.0703125f, (float16_t)1.45996094f, (float16_t)2.94921875f, (float16_t)-3.67578125f, + (float16_t)0.8203125f, (float16_t)-0.420166016f, (float16_t)-2.25195312f, (float16_t)1.06933594f, + (float16_t)3.22265625f, (float16_t)0.838378906f, (float16_t)0.827636719f, (float16_t)3.16015625f, + (float16_t)2.28125f, (float16_t)-3.74414062f, (float16_t)-0.196655273f, (float16_t)-1.77050781f, + (float16_t)-2.37109375f, (float16_t)-2.23632812f, (float16_t)-0.348632812f, (float16_t)-1.47363281f, + (float16_t)-3.86328125f, (float16_t)2.3828125f, (float16_t)-3.19921875f, (float16_t)2.734375f, + (float16_t)-2.08007812f, (float16_t)2.24804688f, (float16_t)3.3984375f, (float16_t)-2.62304688f, + (float16_t)0.331054688f, (float16_t)3.35546875f, (float16_t)0.867675781f, (float16_t)-3.36328125f, + (float16_t)0.453369141f, (float16_t)3.75976562f, (float16_t)-0.821289062f, (float16_t)0.89453125f, + (float16_t)-0.754394531f, (float16_t)0.374267578f, (float16_t)-0.440917969f, (float16_t)2.1171875f, + (float16_t)3.39453125f, (float16_t)0.0986938477f, (float16_t)-3.93554688f, (float16_t)-2.5546875f, + (float16_t)1.08984375f, (float16_t)3.46289062f, (float16_t)-1.7734375f, (float16_t)-3.68554688f, + (float16_t)-3.61328125f, (float16_t)0.521972656f, (float16_t)-1.05859375f, (float16_t)-1.33789062f, + (float16_t)-1.1796875f, (float16_t)-2.5234375f, (float16_t)-0.559570312f, (float16_t)-2.35742188f, + (float16_t)-0.951171875f, (float16_t)-2.796875f, (float16_t)-1.78417969f, (float16_t)0.329833984f, + (float16_t)0.532714844f, (float16_t)2.81445312f, (float16_t)0.801269531f, (float16_t)-1.8046875f, + (float16_t)-3.6328125f, (float16_t)3.578125f, (float16_t)-3.71679688f, (float16_t)2.32421875f, + (float16_t)1.95605469f, (float16_t)2.609375f, (float16_t)-1.11132812f, (float16_t)-3.65625f, + (float16_t)-2.29101562f, (float16_t)3.53125f, (float16_t)-0.439941406f, (float16_t)2.8125f, + (float16_t)1.14550781f, (float16_t)2.81054688f, (float16_t)0.227050781f, (float16_t)1.02050781f, + (float16_t)-0.147338867f, (float16_t)-0.705566406f, (float16_t)-3.4453125f, (float16_t)-3.20117188f, + (float16_t)2.81640625f, (float16_t)1.86035156f, (float16_t)-3.63476562f, (float16_t)-2.7734375f, + (float16_t)0.449951172f, (float16_t)0.969726562f, (float16_t)-3.48828125f, (float16_t)-1.23339844f, + (float16_t)-0.486816406f, (float16_t)1.66699219f, (float16_t)0.310058594f, (float16_t)0.0907592773f, + (float16_t)2.64648438f, (float16_t)-0.572753906f, (float16_t)-2.09960938f, (float16_t)-3.72460938f, + (float16_t)3.5703125f, (float16_t)3.05664062f, (float16_t)2.51953125f, (float16_t)2.78710938f, + (float16_t)-0.951660156f, (float16_t)1.609375f, (float16_t)-2.21289062f, (float16_t)-1.92675781f, + (float16_t)2.56054688f, (float16_t)3.30078125f, (float16_t)-2.61914062f, (float16_t)3.6796875f, + (float16_t)-3.43554688f, (float16_t)3.6875f, (float16_t)2.25585938f, (float16_t)-0.973144531f, + (float16_t)-1.61035156f, (float16_t)-3.41796875f, (float16_t)-3.65039062f, (float16_t)-3.07421875f, + (float16_t)1.70996094f, (float16_t)-3.40234375f, (float16_t)0.278564453f, (float16_t)3.0625f, + (float16_t)-2.96289062f, (float16_t)-3.57226562f, (float16_t)-2.671875f, (float16_t)2.4765625f, + (float16_t)-2.10546875f, (float16_t)-0.494873047f, (float16_t)-0.0694580078f, (float16_t)-0.596679688f, + (float16_t)1.22265625f, (float16_t)-0.410644531f, (float16_t)0.62890625f, (float16_t)2.84570312f, + (float16_t)1.26171875f, (float16_t)-0.438720703f, (float16_t)3.19726562f, (float16_t)-0.132080078f, + (float16_t)-3.671875f, (float16_t)-1.97167969f, (float16_t)3.41796875f, (float16_t)-0.497314453f, + (float16_t)-1.28027344f, (float16_t)-3.39453125f, (float16_t)3.2265625f, (float16_t)-0.328369141f, + (float16_t)-0.350341797f, (float16_t)-2.640625f, (float16_t)-3.390625f, (float16_t)3.38671875f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/output_ref_data.h new file mode 100644 index 00000000..fe6f642b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/output_ref_data.h @@ -0,0 +1,98 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_f16_output_ref[384] = { + (float16_t)3.67578125f, (float16_t)0.484863281f, (float16_t)3.0625f, (float16_t)0.362548828f, + (float16_t)-3.26953125f, (float16_t)0.799316406f, (float16_t)1.55566406f, (float16_t)-0.645507812f, + (float16_t)2.04101562f, (float16_t)-1.7265625f, (float16_t)0.169311523f, (float16_t)-1.8203125f, + (float16_t)-1.48339844f, (float16_t)-1.75976562f, (float16_t)-2.7265625f, (float16_t)-1.99023438f, + (float16_t)3.33789062f, (float16_t)-3.6953125f, (float16_t)-1.96972656f, (float16_t)-0.986816406f, + (float16_t)-0.906738281f, (float16_t)-2.1015625f, (float16_t)-3.66601562f, (float16_t)-2.66601562f, + (float16_t)1.37304688f, (float16_t)-1.2890625f, (float16_t)3.99609375f, (float16_t)-1.75390625f, + (float16_t)-3.5859375f, (float16_t)1.46191406f, (float16_t)1.45703125f, (float16_t)3.15820312f, + (float16_t)-3.11914062f, (float16_t)0.684570312f, (float16_t)2.1171875f, (float16_t)-1.54199219f, + (float16_t)-0.950683594f, (float16_t)-3.00390625f, (float16_t)0.961425781f, (float16_t)1.35644531f, + (float16_t)2.1640625f, (float16_t)-3.296875f, (float16_t)3.859375f, (float16_t)-1.48242188f, + (float16_t)-1.96679688f, (float16_t)2.56054688f, (float16_t)2.62109375f, (float16_t)3.08984375f, + (float16_t)2.6015625f, (float16_t)3.92773438f, (float16_t)3.04492188f, (float16_t)3.69140625f, + (float16_t)2.33007812f, (float16_t)-1.56445312f, (float16_t)-0.475830078f, (float16_t)0.818359375f, + (float16_t)0.703613281f, (float16_t)0.825195312f, (float16_t)-1.97167969f, (float16_t)2.75390625f, + (float16_t)3.65234375f, (float16_t)-0.0170898438f, (float16_t)0.772949219f, (float16_t)-2.67382812f, + (float16_t)0.338623047f, (float16_t)-1.81054688f, (float16_t)0.068359375f, (float16_t)3.5078125f, + (float16_t)-0.480224609f, (float16_t)3.265625f, (float16_t)-2.51953125f, (float16_t)2.33789062f, + (float16_t)3.02148438f, (float16_t)-0.468994141f, (float16_t)-2.56054688f, (float16_t)1.02050781f, + (float16_t)0.285644531f, (float16_t)3.33984375f, (float16_t)2.125f, (float16_t)3.640625f, + (float16_t)1.0703125f, (float16_t)2.22265625f, (float16_t)-0.12097168f, (float16_t)1.49511719f, + (float16_t)-4.09375f, (float16_t)-0.0788574219f, (float16_t)-1.37304688f, (float16_t)-0.184082031f, + (float16_t)-1.28515625f, (float16_t)-1.93945312f, (float16_t)-2.07617188f, (float16_t)-2.35351562f, + (float16_t)0.0958251953f, (float16_t)-1.4453125f, (float16_t)-0.126220703f, (float16_t)2.83203125f, + (float16_t)1.04296875f, (float16_t)1.57519531f, (float16_t)0.166748047f, (float16_t)-3.8125f, + (float16_t)-0.738769531f, (float16_t)0.10369873f, (float16_t)1.25097656f, (float16_t)-2.33203125f, + (float16_t)3.8515625f, (float16_t)3.0625f, (float16_t)3.30859375f, (float16_t)-3.671875f, + (float16_t)0.469726562f, (float16_t)4.203125f, (float16_t)-3.8828125f, (float16_t)2.02734375f, + (float16_t)3.80078125f, (float16_t)3.02734375f, (float16_t)4.546875f, (float16_t)1.59765625f, + (float16_t)-0.161376953f, (float16_t)-1.29199219f, (float16_t)1.00878906f, (float16_t)-3.6875f, + (float16_t)-4.39453125f, (float16_t)-1.01171875f, (float16_t)-0.696777344f, (float16_t)2.08789062f, + (float16_t)1.50683594f, (float16_t)1.48242188f, (float16_t)4.1171875f, (float16_t)-3.16992188f, + (float16_t)3.74804688f, (float16_t)-2.23242188f, (float16_t)-2.74414062f, (float16_t)-2.20703125f, + (float16_t)1.99902344f, (float16_t)-3.53320312f, (float16_t)1.52148438f, (float16_t)3.36523438f, + (float16_t)2.62109375f, (float16_t)0.0856933594f, (float16_t)0.315429688f, (float16_t)-0.654785156f, + (float16_t)-2.32226562f, (float16_t)2.57617188f, (float16_t)-0.799804688f, (float16_t)0.127319336f, + (float16_t)-3.33007812f, (float16_t)2.41796875f, (float16_t)-3.5625f, (float16_t)3.0703125f, + (float16_t)1.54394531f, (float16_t)0.535644531f, (float16_t)4.3984375f, (float16_t)3.015625f, + (float16_t)-2.20507812f, (float16_t)-2.50195312f, (float16_t)-2.09375f, (float16_t)-1.32519531f, + (float16_t)-1.26367188f, (float16_t)-3.1015625f, (float16_t)3.57421875f, (float16_t)-0.278564453f, + (float16_t)-1.05371094f, (float16_t)-1.52050781f, (float16_t)-3.32617188f, (float16_t)-3.63085938f, + (float16_t)2.87109375f, (float16_t)-1.77246094f, (float16_t)-2.22070312f, (float16_t)-2.19726562f, + (float16_t)-1.67285156f, (float16_t)2.08984375f, (float16_t)1.40625f, (float16_t)-1.99511719f, + (float16_t)3.56054688f, (float16_t)-3.0625f, (float16_t)2.00976562f, (float16_t)1.85644531f, + (float16_t)2.92578125f, (float16_t)-3.8125f, (float16_t)-1.35351562f, (float16_t)-0.756347656f, + (float16_t)3.87695312f, (float16_t)3.98632812f, (float16_t)3.65429688f, (float16_t)-1.20703125f, + (float16_t)-1.61523438f, (float16_t)-3.91601562f, (float16_t)-0.381347656f, (float16_t)2.37304688f, + (float16_t)-1.27441406f, (float16_t)0.358398438f, (float16_t)-2.03515625f, (float16_t)2.28320312f, + (float16_t)-0.504394531f, (float16_t)2.99804688f, (float16_t)1.28320312f, (float16_t)3.63867188f, + (float16_t)-2.67773438f, (float16_t)-0.219238281f, (float16_t)3.61523438f, (float16_t)-0.00435638428f, + (float16_t)4.046875f, (float16_t)-2.92773438f, (float16_t)-0.543457031f, (float16_t)3.28125f, + (float16_t)1.56445312f, (float16_t)-2.44921875f, (float16_t)-3.86328125f, (float16_t)1.94921875f, + (float16_t)2.9453125f, (float16_t)-3.49414062f, (float16_t)-4.0546875f, (float16_t)-1.42871094f, + (float16_t)-2.59570312f, (float16_t)-3.16796875f, (float16_t)-1.61621094f, (float16_t)-1.83496094f, + (float16_t)-3.6484375f, (float16_t)2.70703125f, (float16_t)-3.1171875f, (float16_t)-0.729003906f, + (float16_t)-3.7890625f, (float16_t)1.82714844f, (float16_t)-0.701171875f, (float16_t)-1.2109375f, + (float16_t)2.4921875f, (float16_t)2.70117188f, (float16_t)3.11914062f, (float16_t)-2.21875f, + (float16_t)-2.578125f, (float16_t)3.96289062f, (float16_t)-0.606933594f, (float16_t)2.37890625f, + (float16_t)-2.06054688f, (float16_t)1.38476562f, (float16_t)3.41210938f, (float16_t)-3.83007812f, + (float16_t)0.840332031f, (float16_t)-0.426269531f, (float16_t)-2.24023438f, (float16_t)1.00097656f, + (float16_t)3.72265625f, (float16_t)0.91796875f, (float16_t)0.848144531f, (float16_t)3.125f, + (float16_t)2.21289062f, (float16_t)-3.71484375f, (float16_t)-0.146240234f, (float16_t)-1.82617188f, + (float16_t)-2.38867188f, (float16_t)-2.2265625f, (float16_t)-0.370361328f, (float16_t)-1.48925781f, + (float16_t)-4.296875f, (float16_t)2.54101562f, (float16_t)-3.2265625f, (float16_t)2.703125f, + (float16_t)-2.0703125f, (float16_t)2.15625f, (float16_t)3.921875f, (float16_t)-2.72265625f, + (float16_t)0.345458984f, (float16_t)3.31640625f, (float16_t)0.824707031f, (float16_t)-3.34179688f, + (float16_t)0.589355469f, (float16_t)3.99023438f, (float16_t)-0.820800781f, (float16_t)0.876953125f, + (float16_t)-0.768554688f, (float16_t)0.320800781f, (float16_t)-0.422607422f, (float16_t)2.26171875f, + (float16_t)3.4453125f, (float16_t)0.0881958008f, (float16_t)-3.89257812f, (float16_t)-2.54882812f, + (float16_t)1.30859375f, (float16_t)3.6796875f, (float16_t)-1.78417969f, (float16_t)-3.6640625f, + (float16_t)-3.578125f, (float16_t)0.465576172f, (float16_t)-1.12207031f, (float16_t)-1.37109375f, + (float16_t)-1.18359375f, (float16_t)-2.51171875f, (float16_t)-0.577636719f, (float16_t)-2.35546875f, + (float16_t)-0.999511719f, (float16_t)-2.90429688f, (float16_t)-1.79492188f, (float16_t)0.317382812f, + (float16_t)0.495361328f, (float16_t)2.7109375f, (float16_t)0.982910156f, (float16_t)-1.86230469f, + (float16_t)-3.66601562f, (float16_t)3.53710938f, (float16_t)-3.6796875f, (float16_t)2.23046875f, + (float16_t)2.2890625f, (float16_t)2.77929688f, (float16_t)-1.11425781f, (float16_t)-3.63671875f, + (float16_t)-2.27929688f, (float16_t)3.4140625f, (float16_t)-0.421630859f, (float16_t)2.99414062f, + (float16_t)1.16992188f, (float16_t)2.77734375f, (float16_t)0.19519043f, (float16_t)0.953613281f, + (float16_t)-0.0904541016f, (float16_t)-0.706054688f, (float16_t)-3.4765625f, (float16_t)-3.18359375f, + (float16_t)2.73828125f, (float16_t)1.77734375f, (float16_t)-4.03515625f, (float16_t)-2.88085938f, + (float16_t)0.465820312f, (float16_t)0.952148438f, (float16_t)-3.453125f, (float16_t)-1.25390625f, + (float16_t)-0.474365234f, (float16_t)1.7890625f, (float16_t)0.324462891f, (float16_t)0.0803222656f, + (float16_t)2.57226562f, (float16_t)-0.607421875f, (float16_t)-2.29882812f, (float16_t)-3.88085938f, + (float16_t)3.625f, (float16_t)3.02148438f, (float16_t)2.44726562f, (float16_t)2.68554688f, + (float16_t)-1.0f, (float16_t)1.72851562f, (float16_t)-2.22851562f, (float16_t)-1.92089844f, + (float16_t)2.48632812f, (float16_t)3.18945312f, (float16_t)-2.88671875f, (float16_t)3.90625f, + (float16_t)-3.46679688f, (float16_t)3.64648438f, (float16_t)2.1875f, (float16_t)-0.999511719f, + (float16_t)-1.74609375f, (float16_t)-3.55859375f, (float16_t)-3.68359375f, (float16_t)-3.05859375f, + (float16_t)1.65136719f, (float16_t)-3.37890625f, (float16_t)0.391357422f, (float16_t)3.25585938f, + (float16_t)-2.98632812f, (float16_t)-3.55273438f, (float16_t)-2.65234375f, (float16_t)2.38085938f, + (float16_t)-2.3046875f, (float16_t)-0.484130859f, (float16_t)-0.0598144531f, (float16_t)-0.6015625f, + (float16_t)1.17285156f, (float16_t)-0.448242188f, (float16_t)0.787597656f, (float16_t)3.02929688f, + (float16_t)1.28710938f, (float16_t)-0.444580078f, (float16_t)3.11328125f, (float16_t)-0.175415039f, + (float16_t)-4.078125f, (float16_t)-2.03710938f, (float16_t)3.47070312f, (float16_t)-0.502929688f, + (float16_t)-1.28515625f, (float16_t)-3.37109375f, (float16_t)3.72460938f, (float16_t)-0.309082031f, + (float16_t)-0.343994141f, (float16_t)-2.62695312f, (float16_t)-3.359375f, (float16_t)3.2734375f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/scale_data.h new file mode 100644 index 00000000..8ff5b1f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/scale_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_f16_scale[6] = {(float16_t)1.13183594f, + (float16_t)1.05175781f, + (float16_t)1.01171875f, + (float16_t)0.991699219f, + (float16_t)0.982421875f, + (float16_t)0.979980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/bias_data.h new file mode 100644 index 00000000..35348225 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/bias_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_f32_bias[6] = + {0.0762719065f, 0.0362152457f, 0.0104769394f, -0.00966387242f, -0.0278761461f, -0.0459585339f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/config_data.h new file mode 100644 index 00000000..5b78ad65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_BASIC_F32_INPUT_N 1 +#define BN_BASIC_F32_INPUT_H 8 +#define BN_BASIC_F32_INPUT_W 8 +#define BN_BASIC_F32_INPUT_C 6 +#define BN_BASIC_F32_SIZE 384 +#define BN_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/input_data.h new file mode 100644 index 00000000..8281b4f1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/input_data.h @@ -0,0 +1,57 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_f32_input[384] = { + -2.28438139f, 2.87192249f, 0.649432957f, -1.70332277f, 2.3170023f, -0.356217265f, -2.50128484f, + 0.646951735f, -2.39875436f, -2.38964772f, 3.08183169f, -1.14788425f, 2.30381346f, -2.9781177f, + -3.23294306f, 2.73814034f, 0.642948329f, 3.08948445f, -2.31297922f, -3.27769661f, -2.09245896f, + 1.14144433f, 1.08386993f, 3.26089954f, -3.70916271f, 0.400170863f, 3.31357861f, -2.86910439f, + -2.67528343f, -1.07854474f, -0.441715926f, -1.666242f, 1.66989923f, -1.9910171f, 1.70345592f, + -3.36220622f, 1.1507237f, 2.78196669f, 1.51765227f, 3.27022815f, 3.30990624f, -0.665856719f, + -3.09491181f, -0.910251379f, -3.27641964f, 0.0989072099f, 2.50180483f, 0.864079535f, -0.594065845f, + -0.298415363f, 2.74499702f, 1.13779879f, -0.0155478045f, 3.4738884f, 2.04954123f, -2.35864282f, + 2.9399085f, 1.67157829f, -0.220355392f, 0.512697875f, 2.1824441f, -0.433336943f, -0.247400522f, + 0.329130441f, -2.73251343f, 0.628067672f, -1.26270473f, 2.69248629f, 1.10930014f, -0.50271064f, + 2.14535952f, 1.85042143f, 3.7155664f, -1.46434391f, -3.43882513f, -2.72167587f, -0.522908747f, + 2.77708721f, 3.98171687f, 1.21893942f, 2.98155069f, 3.93076801f, -3.77029634f, -0.682204187f, + -1.79917514f, 3.24169588f, 1.68223059f, 0.8879053f, -3.98167992f, 0.843762815f, 0.39445737f, + -3.828264f, -1.3647778f, -1.65411866f, -0.330917031f, -1.27701664f, -3.29841328f, 3.02630115f, + 3.93007588f, 0.998677611f, -0.597996771f, 2.70648503f, 3.96551871f, 2.86876082f, -0.449194849f, + -0.90813601f, 3.05076241f, 1.55538702f, 1.75881934f, -0.567197204f, -3.54835749f, -1.33172596f, + 0.020309506f, 3.43940592f, -0.918101132f, -3.96177864f, -2.62957454f, 2.89941025f, 0.56515795f, + -3.76918864f, 0.109465219f, 2.99964929f, -3.01535702f, -1.14806199f, 1.04724503f, -2.24793935f, + 3.02043486f, 1.42528772f, 3.40793562f, -2.45302558f, -1.32785714f, -3.25161123f, 0.737537146f, + -2.2311604f, 0.970035613f, -2.0212779f, 1.51462162f, -1.80865073f, 1.03666854f, 2.76329446f, + -3.09990239f, 3.8622005f, 2.09198165f, -0.469780326f, 0.825247407f, 3.14561296f, -2.22928476f, + 0.171827301f, 2.85036802f, 3.09345722f, -3.1332798f, -3.09474921f, 1.64739096f, -1.46563733f, + -0.333505481f, 2.01183152f, 0.863502562f, 0.0509212911f, 2.11064029f, 3.7018702f, 2.11034346f, + -0.0611718893f, -0.828420043f, 3.86693597f, -0.548705757f, 1.59456944f, -1.74416792f, 0.605124712f, + 1.10543728f, 2.13046098f, 1.27198887f, 2.3287878f, 1.36514711f, 1.08565319f, -2.83458924f, + 0.795243204f, 0.00955751911f, 1.33508027f, 3.59864759f, -0.34202522f, -2.94337273f, -3.46291566f, + 2.07871962f, 1.16413057f, -1.05074847f, 2.60190582f, -3.39149976f, -1.23170543f, 0.107467525f, + 1.77201521f, 1.97881687f, 0.751767159f, -0.12741749f, -1.47537184f, 2.81300783f, 1.52414525f, + -2.57565022f, 0.230603427f, -2.02818489f, 2.4697454f, 2.31918693f, -2.28882313f, 2.06651568f, + 0.333007842f, -0.583278f, -3.17186594f, -1.45399296f, -2.52351618f, 1.99581468f, -2.42138577f, + -0.966990054f, 3.91075134f, -1.01661956f, -2.55110097f, 0.258079529f, -1.59610856f, 3.02313256f, + 2.45431089f, -2.76962852f, -3.48044276f, -1.0051688f, -0.291639566f, 3.23167706f, 3.05144358f, + 0.970734715f, -0.75633049f, 3.61227298f, 0.98739773f, -3.83542371f, 0.661178589f, 3.30086255f, + 1.15861857f, 1.38732445f, 1.55084574f, -2.01610565f, -0.553699374f, -2.01874566f, 2.37721968f, + 2.4659245f, -2.27326751f, -0.205644339f, 0.379158467f, -2.99894977f, -0.174703225f, 3.64696383f, + -0.629766941f, -3.73031545f, -3.89481235f, -3.21639061f, -3.12103677f, -2.03635144f, 2.22187757f, + 0.193021774f, 2.1141994f, 1.92724097f, 2.01125288f, -0.285990804f, -1.78530741f, 3.3974309f, + -1.88485539f, -2.65637374f, 2.66098833f, 3.49554253f, 2.36797905f, 2.55817795f, -3.50870562f, + 3.20638037f, 1.59062064f, -3.57659364f, -0.0257401653f, 2.82071137f, 0.37936914f, 1.13910198f, + -2.57421184f, -0.448940903f, -1.03830969f, -2.28526545f, -0.933316588f, 2.44778943f, 3.00161099f, + 1.26639187f, 2.68590236f, -1.29158366f, 0.897539079f, -0.502159297f, 3.0555253f, -0.97653085f, + -3.59304094f, -0.0476075374f, 3.11162782f, -1.82138634f, -3.72117662f, 1.60680139f, 3.94811964f, + 0.819082797f, -0.0307091475f, -3.24966121f, -1.93168688f, 3.24924088f, 2.17668247f, -2.89835691f, + -3.65872169f, 1.01428604f, -3.48773122f, 0.079014428f, 1.11099517f, 3.76804161f, 0.592788398f, + 3.42015266f, -0.71397531f, -0.254616767f, 2.43915677f, 0.518309057f, 3.15108633f, 0.114227682f, + -0.71200943f, -3.52354455f, -0.58664f, 3.61892724f, 0.454927385f, -0.928201675f, -0.438569784f, + 0.725730956f, -2.75967646f, 1.76735497f, -1.90205181f, 3.64020777f, 1.60715079f, 1.85676444f, + 1.31676757f, -3.70983434f, 2.84130621f, 1.9207803f, -1.21517277f, 2.79032087f, -3.14247799f, + 3.91326571f, 1.61830056f, -0.261145681f, 2.92154765f, -0.156625703f, 0.972042263f, 3.43788195f, + 3.28437018f, 3.91017652f, -3.64695692f, 0.720436811f, -3.1890595f, -1.1009531f, 0.0340912566f, + 3.52157807f, 2.52479529f, -3.31208825f, 2.36744165f, 0.0818168521f, -2.57386851f, 1.43157184f, + 1.76185215f, -2.5674963f, 3.35448551f, -1.15021026f, 1.59793067f, 1.71288264f, 2.08056116f, + 2.23391318f, -0.282180279f, -3.91238546f, -0.41034463f, 0.228933141f, -1.2112298f, 3.48258233f, + 2.69093251f, -3.30652618f, -3.09629035f, -3.9040041f, 2.69542336f, -3.98076773f, 2.49780178f, + 2.09863305f, 2.47569227f, -2.29584885f, 2.7085042f, -0.654321492f, 1.00971878f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/output_ref_data.h new file mode 100644 index 00000000..21f4b3ce --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/output_ref_data.h @@ -0,0 +1,57 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_f32_output_ref[384] = { + -2.50818467f, 3.05688524f, 0.667653263f, -1.69867313f, 2.24811816f, -0.394977599f, -2.75358033f, + 0.716674984f, -2.41687846f, -2.37923074f, 2.99941087f, -1.17064703f, 2.68271303f, -3.09615016f, + -3.26101351f, 2.70546699f, 0.60369277f, 2.98109555f, -2.54053903f, -3.41124535f, -2.10693097f, + 1.12218857f, 1.03681064f, 3.1490469f, -4.12012434f, 0.457112432f, 3.36356449f, -2.85465837f, + -2.65581036f, -1.1027087f, -0.423467606f, -1.71632755f, 1.70028687f, -1.9839499f, 1.64543068f, + -3.34022355f, 1.37815404f, 2.96227026f, 1.54622471f, 3.23308349f, 3.22344875f, -0.698360026f, + -3.42518592f, -0.92118144f, -3.30500841f, 0.088412188f, 2.42964983f, 0.800660253f, -0.595830142f, + -0.277656138f, 2.78820348f, 1.11857367f, -0.0431487784f, 3.35773182f, 2.3950398f, -2.44459033f, + 2.98543882f, 1.64786768f, -0.244331524f, 0.456379086f, 2.54540062f, -0.419565827f, -0.239873424f, + 0.316700786f, -2.71202755f, 0.569417536f, -1.352301f, 2.86815524f, 1.13300359f, -0.508150101f, + 2.07951307f, 1.76707077f, 4.27991295f, -1.50397241f, -3.46935034f, -2.70846868f, -0.541530073f, + 2.67501116f, 4.58102512f, 1.31828809f, 3.02757764f, 3.88807273f, -3.73144293f, -0.714377165f, + -1.95924187f, 3.44581056f, 1.71276534f, 0.87078011f, -3.93908525f, 0.780754089f, 0.52254498f, + -3.99032879f, -1.37057352f, -1.64988244f, -0.352936357f, -1.29717004f, -3.65541911f, 3.21925974f, + 3.98741245f, 0.980621576f, -0.615289092f, 2.60583568f, 4.56269884f, 3.05355978f, -0.444073796f, + -0.910168529f, 2.96889162f, 1.47799826f, 2.06612873f, -0.56035918f, -3.58018875f, -1.33019888f, + -0.00792609714f, 3.32394624f, -0.962430537f, -4.13075876f, -2.65045094f, 2.86538172f, 0.527279198f, + -3.73898196f, 0.200116396f, 3.19122744f, -3.04083323f, -1.14807832f, 1.00083399f, -2.24847293f, + 3.49346948f, 1.53532386f, 3.45904636f, -2.44207597f, -1.33223188f, -3.23186326f, 0.9106915f, + -2.31050515f, 0.992078602f, -2.01395631f, 1.45993853f, -1.81806123f, 1.24911666f, 2.94263101f, + -3.12638664f, 3.82008147f, 2.02708006f, -0.506245852f, 1.00992334f, 3.34475112f, -2.24538827f, + 0.160719514f, 2.77204394f, 2.98498821f, -3.46859384f, -3.21882248f, 1.67751026f, -1.46298504f, + -0.355479002f, 1.92521918f, 1.0532037f, 0.0897739381f, 2.14628315f, 3.66109848f, 2.0451169f, + -0.105894297f, -0.860968947f, 4.10343409f, -0.544771194f, 1.5715059f, -1.74117446f, 0.546938241f, + 1.32691884f, 2.27702069f, 1.2976321f, 2.29955435f, 1.31310952f, 1.01775646f, -3.13066745f, + 0.872647047f, 0.0201484133f, 1.3141973f, 3.50707984f, -0.381072313f, -3.25374079f, -3.60605764f, + 2.11398172f, 1.1446842f, -1.06002772f, 2.50336957f, -3.76073337f, -1.25928473f, 0.11922583f, + 1.7474606f, 1.91591811f, 0.690617442f, -0.0678830892f, -1.51557159f, 2.85702515f, 1.50167358f, + -2.55794048f, 0.179985017f, -2.2183342f, 2.63387799f, 2.35731626f, -2.27925324f, 2.0020647f, + 0.280320108f, -0.583625197f, -3.29993343f, -1.46085238f, -2.5119741f, 1.93261504f, -2.41841459f, + -1.01774144f, 4.14951897f, -1.01826406f, -2.53932714f, 0.225635692f, -1.60981393f, 3.49652147f, + 2.61764407f, -2.79217482f, -3.46085954f, -1.01525474f, -0.331704825f, 3.73246026f, 3.24570441f, + 0.99278605f, -0.759638667f, 3.52046394f, 0.921486497f, -4.26297092f, 0.73163873f, 3.3506968f, + 1.13921845f, 1.33489442f, 1.47354877f, -2.20466828f, -0.546162248f, -2.03233886f, 2.34757924f, + 2.39440465f, -2.27328944f, -0.156385779f, 0.435011744f, -3.02423024f, -0.182899013f, 3.55454087f, + -0.66299957f, -4.14405584f, -4.06032372f, -3.24426365f, -3.10447359f, -2.0281868f, 2.13102078f, + 0.294648916f, 2.25991678f, 1.96069705f, 1.98468781f, -0.308805257f, -1.79518962f, 3.91998744f, + -1.94626379f, -2.67756963f, 2.62896347f, 3.40579963f, 2.27416992f, 2.97049069f, -3.65421915f, + 3.25508809f, 1.56759036f, -3.54116845f, -0.071178548f, 3.26751041f, 0.435233325f, 1.1631608f, + -2.56224394f, -0.468871355f, -1.06328678f, -2.50918484f, -0.945441246f, 2.48745203f, 2.96672368f, + 1.21610212f, 2.5856688f, -1.38497353f, 0.980241179f, -0.497669756f, 3.02018499f, -0.987123609f, + -3.56639361f, 0.0224106535f, 3.30900574f, -1.83262634f, -3.69957042f, 1.5504868f, 3.82238007f, + 1.002949f, 0.0039155581f, -3.27793097f, -1.92511821f, 3.16385722f, 2.08673906f, -3.20281172f, + -3.81200504f, 1.03685665f, -3.46808672f, 0.049739819f, 1.04258633f, 4.33928156f, 0.659706354f, + 3.47140908f, -0.717639446f, -0.277986526f, 2.34390926f, 0.662665725f, 3.35050797f, 0.126066595f, + -0.715690076f, -3.48905826f, -0.620744109f, 4.17057943f, 0.514705002f, -0.928792f, -0.444548219f, + 0.685010254f, -2.74986935f, 2.07578564f, -1.96435082f, 3.69408798f, 1.58398151f, 1.79602587f, + 1.24420071f, -4.12088442f, 3.02468324f, 1.95415926f, -1.21462512f, 2.71305943f, -3.12493539f, + 4.503582f, 1.73833358f, -0.253782451f, 2.88733315f, -0.181729779f, 0.906441331f, 3.96575212f, + 3.490695f, 3.96727562f, -3.62597442f, 0.679809809f, -3.17057562f, -1.16930187f, 0.0720722154f, + 3.57404399f, 2.49391484f, -3.28134441f, 2.27364349f, 0.168836161f, -2.67096353f, 1.45911789f, + 1.73738289f, -2.54993105f, 3.24074173f, -1.22502935f, 1.71690869f, 1.74378276f, 2.05341363f, + 2.16649961f, -0.32243669f, -4.35004234f, -0.395382643f, 0.242139727f, -1.21071529f, 3.39306879f, + 2.59059739f, -3.66459775f, -3.22044349f, -3.94007587f, 2.66310906f, -3.93818927f, 2.40136933f, + 2.45058036f, 2.64013267f, -2.31274605f, 2.67607999f, -0.670616984f, 0.943356514f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/scale_data.h new file mode 100644 index 00000000..8876411d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/scale_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_f32_scale[6] = + {1.13135958f, 1.05179369f, 1.01192331f, 0.991596699f, 0.982301235f, 0.979792655f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/bias_data.h new file mode 100644 index 00000000..54212ca1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/bias_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_nhwc_f16_bias[6] = {(float16_t)0.0762939453f, + (float16_t)0.0362243652f, + (float16_t)0.0104751587f, + (float16_t)-0.00966644287f, + (float16_t)-0.0278778076f, + (float16_t)-0.0459594727f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/config_data.h new file mode 100644 index 00000000..46711c50 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_BASIC_NHWC_F16_INPUT_N 1 +#define BN_BASIC_NHWC_F16_INPUT_H 8 +#define BN_BASIC_NHWC_F16_INPUT_W 8 +#define BN_BASIC_NHWC_F16_INPUT_C 6 +#define BN_BASIC_NHWC_F16_SIZE 384 +#define BN_BASIC_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/input_data.h new file mode 100644 index 00000000..642ade91 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/input_data.h @@ -0,0 +1,98 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_nhwc_f16_input[384] = { + (float16_t)-1.52636719f, (float16_t)-2.73828125f, (float16_t)3.65039062f, (float16_t)1.89160156f, + (float16_t)0.161499023f, (float16_t)-2.59765625f, (float16_t)3.99804688f, (float16_t)1.79785156f, + (float16_t)2.9765625f, (float16_t)-0.578613281f, (float16_t)2.81640625f, (float16_t)3.61328125f, + (float16_t)1.78417969f, (float16_t)1.48632812f, (float16_t)2.90429688f, (float16_t)-0.267822266f, + (float16_t)3.02734375f, (float16_t)3.9296875f, (float16_t)2.55859375f, (float16_t)-1.28710938f, + (float16_t)-0.390869141f, (float16_t)-0.72265625f, (float16_t)3.1875f, (float16_t)3.30664062f, + (float16_t)3.72460938f, (float16_t)1.44433594f, (float16_t)1.72070312f, (float16_t)-2.94726562f, + (float16_t)-3.64257812f, (float16_t)-3.90234375f, (float16_t)0.921875f, (float16_t)-1.87402344f, + (float16_t)2.43359375f, (float16_t)-2.21875f, (float16_t)3.15820312f, (float16_t)-3.828125f, + (float16_t)-2.86132812f, (float16_t)0.250976562f, (float16_t)2.64257812f, (float16_t)0.134521484f, + (float16_t)-1.12695312f, (float16_t)0.168212891f, (float16_t)1.84667969f, (float16_t)-0.647460938f, + (float16_t)-0.625976562f, (float16_t)-2.83789062f, (float16_t)-1.86914062f, (float16_t)-2.09570312f, + (float16_t)2.65429688f, (float16_t)-3.8203125f, (float16_t)2.86914062f, (float16_t)-3.609375f, + (float16_t)-3.75585938f, (float16_t)-2.72851562f, (float16_t)3.8203125f, (float16_t)-0.179443359f, + (float16_t)1.99316406f, (float16_t)-3.49804688f, (float16_t)2.4765625f, (float16_t)3.21289062f, + (float16_t)-3.51171875f, (float16_t)-3.36328125f, (float16_t)0.958984375f, (float16_t)3.91210938f, + (float16_t)-1.16796875f, (float16_t)0.501953125f, (float16_t)-2.73046875f, (float16_t)1.83300781f, + (float16_t)-2.63085938f, (float16_t)-1.44042969f, (float16_t)1.30371094f, (float16_t)-0.599121094f, + (float16_t)0.844726562f, (float16_t)-2.01171875f, (float16_t)3.96875f, (float16_t)3.90039062f, + (float16_t)-0.0388183594f, (float16_t)-0.713867188f, (float16_t)-4.0f, (float16_t)-3.8359375f, + (float16_t)-2.4609375f, (float16_t)-1.84570312f, (float16_t)2.08203125f, (float16_t)3.11132812f, + (float16_t)-1.39355469f, (float16_t)0.630371094f, (float16_t)0.858398438f, (float16_t)0.757324219f, + (float16_t)2.1171875f, (float16_t)1.13183594f, (float16_t)-3.96679688f, (float16_t)-1.05175781f, + (float16_t)2.8828125f, (float16_t)3.34570312f, (float16_t)2.390625f, (float16_t)-3.09375f, + (float16_t)2.32421875f, (float16_t)0.263427734f, (float16_t)-1.05273438f, (float16_t)2.97070312f, + (float16_t)-3.09179688f, (float16_t)3.65625f, (float16_t)2.30859375f, (float16_t)0.387695312f, + (float16_t)1.55078125f, (float16_t)1.41308594f, (float16_t)-0.0643310547f, (float16_t)2.88085938f, + (float16_t)-0.22277832f, (float16_t)1.30761719f, (float16_t)1.8046875f, (float16_t)-3.4609375f, + (float16_t)3.23046875f, (float16_t)-1.41894531f, (float16_t)-0.173461914f, (float16_t)0.548339844f, + (float16_t)1.09667969f, (float16_t)2.52539062f, (float16_t)-1.859375f, (float16_t)-2.56054688f, + (float16_t)0.0424194336f, (float16_t)1.95605469f, (float16_t)-2.60742188f, (float16_t)2.51367188f, + (float16_t)2.40429688f, (float16_t)-1.71386719f, (float16_t)-2.80664062f, (float16_t)-0.00381660461f, + (float16_t)1.04589844f, (float16_t)0.587402344f, (float16_t)-3.828125f, (float16_t)2.41015625f, + (float16_t)0.884765625f, (float16_t)-0.268310547f, (float16_t)-2.61914062f, (float16_t)-1.45898438f, + (float16_t)2.65820312f, (float16_t)-1.85839844f, (float16_t)-2.49414062f, (float16_t)1.1796875f, + (float16_t)1.01660156f, (float16_t)-3.31445312f, (float16_t)0.774414062f, (float16_t)1.52636719f, + (float16_t)-2.70703125f, (float16_t)-2.40429688f, (float16_t)-1.19238281f, (float16_t)-1.44726562f, + (float16_t)2.2734375f, (float16_t)-3.484375f, (float16_t)-1.33105469f, (float16_t)3.46289062f, + (float16_t)-0.436523438f, (float16_t)-3.41796875f, (float16_t)3.5078125f, (float16_t)-2.15234375f, + (float16_t)-3.30859375f, (float16_t)-2.89453125f, (float16_t)2.03515625f, (float16_t)1.875f, + (float16_t)-0.823242188f, (float16_t)-0.418212891f, (float16_t)-2.1484375f, (float16_t)1.59472656f, + (float16_t)-1.54296875f, (float16_t)1.89257812f, (float16_t)2.06445312f, (float16_t)-2.50390625f, + (float16_t)0.526367188f, (float16_t)1.81347656f, (float16_t)3.93945312f, (float16_t)-2.74414062f, + (float16_t)3.09179688f, (float16_t)0.643554688f, (float16_t)-3.08789062f, (float16_t)-2.4765625f, + (float16_t)1.83300781f, (float16_t)2.38476562f, (float16_t)0.015335083f, (float16_t)0.385009766f, + (float16_t)0.370849609f, (float16_t)1.46875f, (float16_t)-3.6796875f, (float16_t)-1.12988281f, + (float16_t)1.14160156f, (float16_t)3.70117188f, (float16_t)0.729980469f, (float16_t)-1.47167969f, + (float16_t)2.52734375f, (float16_t)-0.862792969f, (float16_t)-2.3203125f, (float16_t)1.66503906f, + (float16_t)2.52148438f, (float16_t)-0.699707031f, (float16_t)-2.6484375f, (float16_t)-2.75976562f, + (float16_t)-1.83105469f, (float16_t)3.55859375f, (float16_t)1.00195312f, (float16_t)2.25976562f, + (float16_t)-0.924316406f, (float16_t)1.75976562f, (float16_t)-3.22265625f, (float16_t)3.58789062f, + (float16_t)3.06054688f, (float16_t)-1.08007812f, (float16_t)1.76367188f, (float16_t)-3.08398438f, + (float16_t)2.06445312f, (float16_t)-3.03125f, (float16_t)-0.797851562f, (float16_t)-0.587890625f, + (float16_t)-0.354980469f, (float16_t)0.553222656f, (float16_t)3.26953125f, (float16_t)-2.52148438f, + (float16_t)0.5f, (float16_t)-0.277832031f, (float16_t)-1.3359375f, (float16_t)2.11523438f, + (float16_t)-0.306884766f, (float16_t)2.328125f, (float16_t)-1.94042969f, (float16_t)3.7578125f, + (float16_t)3.10546875f, (float16_t)-1.76855469f, (float16_t)-1.20800781f, (float16_t)1.2578125f, + (float16_t)-1.265625f, (float16_t)2.7109375f, (float16_t)-3.92578125f, (float16_t)-1.265625f, + (float16_t)1.10742188f, (float16_t)0.796875f, (float16_t)-0.200683594f, (float16_t)3.40039062f, + (float16_t)-3.26757812f, (float16_t)1.48339844f, (float16_t)2.25976562f, (float16_t)-0.673339844f, + (float16_t)-2.85742188f, (float16_t)3.234375f, (float16_t)0.812988281f, (float16_t)-1.37402344f, + (float16_t)-2.87109375f, (float16_t)2.87890625f, (float16_t)1.68164062f, (float16_t)-1.7421875f, + (float16_t)3.96679688f, (float16_t)3.3125f, (float16_t)-2.24609375f, (float16_t)1.0703125f, + (float16_t)-1.88476562f, (float16_t)-3.00390625f, (float16_t)-3.55664062f, (float16_t)-0.0756835938f, + (float16_t)3.63085938f, (float16_t)3.53125f, (float16_t)-2.46289062f, (float16_t)1.32128906f, + (float16_t)2.72851562f, (float16_t)-2.9296875f, (float16_t)-1.43847656f, (float16_t)0.506835938f, + (float16_t)-1.97167969f, (float16_t)3.36328125f, (float16_t)-3.88085938f, (float16_t)-0.116088867f, + (float16_t)3.74414062f, (float16_t)-3.54296875f, (float16_t)3.296875f, (float16_t)2.98242188f, + (float16_t)3.77148438f, (float16_t)-3.8671875f, (float16_t)2.34375f, (float16_t)0.5703125f, + (float16_t)-3.44335938f, (float16_t)3.6015625f, (float16_t)-0.806152344f, (float16_t)0.600585938f, + (float16_t)3.58984375f, (float16_t)-2.97460938f, (float16_t)-2.59375f, (float16_t)1.74121094f, + (float16_t)3.36328125f, (float16_t)1.95117188f, (float16_t)-3.16601562f, (float16_t)-1.75976562f, + (float16_t)-1.29101562f, (float16_t)2.91210938f, (float16_t)3.45898438f, (float16_t)0.685546875f, + (float16_t)-3.96484375f, (float16_t)1.36035156f, (float16_t)1.28320312f, (float16_t)0.307128906f, + (float16_t)-2.79492188f, (float16_t)-1.72753906f, (float16_t)0.469726562f, (float16_t)-0.00146007538f, + (float16_t)-0.459472656f, (float16_t)-1.61035156f, (float16_t)0.942871094f, (float16_t)1.22265625f, + (float16_t)-2.52929688f, (float16_t)-0.305664062f, (float16_t)-3.76367188f, (float16_t)-3.68554688f, + (float16_t)1.95507812f, (float16_t)1.02734375f, (float16_t)-2.52929688f, (float16_t)1.20507812f, + (float16_t)1.45605469f, (float16_t)-0.754394531f, (float16_t)-3.421875f, (float16_t)-2.78710938f, + (float16_t)-3.91210938f, (float16_t)-1.90917969f, (float16_t)-1.3203125f, (float16_t)-0.392089844f, + (float16_t)0.522460938f, (float16_t)3.90429688f, (float16_t)3.62695312f, (float16_t)-3.859375f, + (float16_t)-1.76074219f, (float16_t)-3.79882812f, (float16_t)-3.94726562f, (float16_t)-3.11328125f, + (float16_t)-1.96386719f, (float16_t)-1.40039062f, (float16_t)1.26074219f, (float16_t)-1.45507812f, + (float16_t)0.0123214722f, (float16_t)-2.02148438f, (float16_t)1.63183594f, (float16_t)3.8828125f, + (float16_t)-1.46777344f, (float16_t)0.404296875f, (float16_t)1.64355469f, (float16_t)-0.99609375f, + (float16_t)-1.55371094f, (float16_t)-3.06640625f, (float16_t)1.60351562f, (float16_t)3.40234375f, + (float16_t)-3.19140625f, (float16_t)-3.88085938f, (float16_t)1.81933594f, (float16_t)2.65039062f, + (float16_t)3.43945312f, (float16_t)-3.5859375f, (float16_t)1.3203125f, (float16_t)-1.65332031f, + (float16_t)2.73046875f, (float16_t)-2.84960938f, (float16_t)1.49121094f, (float16_t)2.13867188f, + (float16_t)-3.22265625f, (float16_t)-0.440185547f, (float16_t)2.8671875f, (float16_t)2.20703125f, + (float16_t)1.03613281f, (float16_t)-1.21875f, (float16_t)-2.3671875f, (float16_t)-1.3515625f, + (float16_t)1.81347656f, (float16_t)2.046875f, (float16_t)2.26757812f, (float16_t)1.66699219f, + (float16_t)-2.46484375f, (float16_t)-3.5390625f, (float16_t)3.765625f, (float16_t)-0.8046875f, + (float16_t)-2.40820312f, (float16_t)2.07617188f, (float16_t)-0.486572266f, (float16_t)3.70898438f, + (float16_t)-1.70898438f, (float16_t)-0.580566406f, (float16_t)-3.83007812f, (float16_t)2.4921875f, + (float16_t)-0.624511719f, (float16_t)-0.593261719f, (float16_t)-3.16992188f, (float16_t)-1.8515625f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..ec9143df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/output_ref_data.h @@ -0,0 +1,98 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_nhwc_f16_output_ref[384] = { + (float16_t)-1.65136719f, (float16_t)-2.84375f, (float16_t)3.703125f, (float16_t)1.86621094f, + (float16_t)0.130737305f, (float16_t)-2.59179688f, (float16_t)4.59765625f, (float16_t)1.92773438f, + (float16_t)3.0234375f, (float16_t)-0.583496094f, (float16_t)2.73828125f, (float16_t)3.49414062f, + (float16_t)2.09375f, (float16_t)1.59960938f, (float16_t)2.94921875f, (float16_t)-0.275146484f, + (float16_t)2.94726562f, (float16_t)3.8046875f, (float16_t)2.97070312f, (float16_t)-1.31835938f, + (float16_t)-0.385009766f, (float16_t)-0.726074219f, (float16_t)3.10351562f, (float16_t)3.1953125f, + (float16_t)4.2890625f, (float16_t)1.5546875f, (float16_t)1.75195312f, (float16_t)-2.93164062f, + (float16_t)-3.60546875f, (float16_t)-3.86914062f, (float16_t)1.11914062f, (float16_t)-1.93457031f, + (float16_t)2.47460938f, (float16_t)-2.2109375f, (float16_t)3.07421875f, (float16_t)-3.796875f, + (float16_t)-3.16210938f, (float16_t)0.300292969f, (float16_t)2.68554688f, (float16_t)0.123657227f, + (float16_t)-1.13476562f, (float16_t)0.118774414f, (float16_t)2.16601562f, (float16_t)-0.645019531f, + (float16_t)-0.623046875f, (float16_t)-2.82226562f, (float16_t)-1.86328125f, (float16_t)-2.09960938f, + (float16_t)3.078125f, (float16_t)-3.98242188f, (float16_t)2.9140625f, (float16_t)-3.58789062f, + (float16_t)-3.71679688f, (float16_t)-2.71875f, (float16_t)4.3984375f, (float16_t)-0.15246582f, + (float16_t)2.02734375f, (float16_t)-3.47851562f, (float16_t)2.40625f, (float16_t)3.1015625f, + (float16_t)-3.8984375f, (float16_t)-3.50195312f, (float16_t)0.980957031f, (float16_t)3.86914062f, + (float16_t)-1.17480469f, (float16_t)0.445800781f, (float16_t)-3.01367188f, (float16_t)1.96484375f, + (float16_t)-2.65234375f, (float16_t)-1.4375f, (float16_t)1.25292969f, (float16_t)-0.633300781f, + (float16_t)1.03125f, (float16_t)-2.078125f, (float16_t)4.02734375f, (float16_t)3.85742188f, + (float16_t)-0.0659790039f, (float16_t)-0.745605469f, (float16_t)-4.44921875f, (float16_t)-3.99804688f, + (float16_t)-2.48046875f, (float16_t)-1.83984375f, (float16_t)2.015625f, (float16_t)3.00195312f, + (float16_t)-1.50097656f, (float16_t)0.69921875f, (float16_t)0.879394531f, (float16_t)0.741699219f, + (float16_t)2.05273438f, (float16_t)1.0625f, (float16_t)-4.4140625f, (float16_t)-1.0703125f, + (float16_t)2.92773438f, (float16_t)3.30859375f, (float16_t)2.3203125f, (float16_t)-3.07617188f, + (float16_t)2.70507812f, (float16_t)0.313476562f, (float16_t)-1.0546875f, (float16_t)2.93554688f, + (float16_t)-3.06445312f, (float16_t)3.53515625f, (float16_t)2.6875f, (float16_t)0.444091797f, + (float16_t)1.58007812f, (float16_t)1.39160156f, (float16_t)-0.0910644531f, (float16_t)2.77734375f, + (float16_t)-0.17578125f, (float16_t)1.41113281f, (float16_t)1.8359375f, (float16_t)-3.44140625f, + (float16_t)3.14453125f, (float16_t)-1.43554688f, (float16_t)-0.119995117f, (float16_t)0.61328125f, + (float16_t)1.12011719f, (float16_t)2.49414062f, (float16_t)-1.85449219f, (float16_t)-2.5546875f, + (float16_t)0.124267578f, (float16_t)2.09375f, (float16_t)-2.62695312f, (float16_t)2.48242188f, + (float16_t)2.33203125f, (float16_t)-1.72460938f, (float16_t)-3.09765625f, (float16_t)0.0321960449f, + (float16_t)1.06835938f, (float16_t)0.572753906f, (float16_t)-3.7890625f, (float16_t)2.31445312f, + (float16_t)1.07714844f, (float16_t)-0.24609375f, (float16_t)-2.640625f, (float16_t)-1.45605469f, + (float16_t)2.58203125f, (float16_t)-1.86621094f, (float16_t)-2.74609375f, (float16_t)1.27636719f, + (float16_t)1.0390625f, (float16_t)-3.296875f, (float16_t)0.732421875f, (float16_t)1.44921875f, + (float16_t)-2.98632812f, (float16_t)-2.49414062f, (float16_t)-1.19628906f, (float16_t)-1.44433594f, + (float16_t)2.20507812f, (float16_t)-3.45898438f, (float16_t)-1.4296875f, (float16_t)3.67773438f, + (float16_t)-0.431396484f, (float16_t)-3.40039062f, (float16_t)3.41796875f, (float16_t)-2.15625f, + (float16_t)-3.66601562f, (float16_t)-3.0078125f, (float16_t)2.0703125f, (float16_t)1.84960938f, + (float16_t)-0.836914062f, (float16_t)-0.455566406f, (float16_t)-2.35351562f, (float16_t)1.71289062f, + (float16_t)-1.55078125f, (float16_t)1.8671875f, (float16_t)2.0f, (float16_t)-2.5f, + (float16_t)0.671875f, (float16_t)1.94335938f, (float16_t)3.99804688f, (float16_t)-2.73046875f, + (float16_t)3.00976562f, (float16_t)0.584960938f, (float16_t)-3.41796875f, (float16_t)-2.56835938f, + (float16_t)1.86523438f, (float16_t)2.35546875f, (float16_t)-0.0128173828f, (float16_t)0.331298828f, + (float16_t)0.495849609f, (float16_t)1.58105469f, (float16_t)-3.71289062f, (float16_t)-1.12988281f, + (float16_t)1.09375f, (float16_t)3.58007812f, (float16_t)0.901855469f, (float16_t)-1.51171875f, + (float16_t)2.56835938f, (float16_t)-0.865234375f, (float16_t)-2.30664062f, (float16_t)1.58496094f, + (float16_t)2.92773438f, (float16_t)-0.699707031f, (float16_t)-2.66992188f, (float16_t)-2.74609375f, + (float16_t)-1.82617188f, (float16_t)3.43945312f, (float16_t)1.20996094f, (float16_t)2.41210938f, + (float16_t)-0.925292969f, (float16_t)1.73535156f, (float16_t)-3.19335938f, (float16_t)3.46875f, + (float16_t)3.53710938f, (float16_t)-1.09960938f, (float16_t)1.79589844f, (float16_t)-3.06835938f, + (float16_t)2.0f, (float16_t)-3.015625f, (float16_t)-0.826660156f, (float16_t)-0.58203125f, + (float16_t)-0.348632812f, (float16_t)0.5390625f, (float16_t)3.18554688f, (float16_t)-2.515625f, + (float16_t)0.642089844f, (float16_t)-0.255859375f, (float16_t)-1.34179688f, (float16_t)2.08789062f, + (float16_t)-0.329345703f, (float16_t)2.234375f, (float16_t)-2.11914062f, (float16_t)3.98828125f, + (float16_t)3.15429688f, (float16_t)-1.76367188f, (float16_t)-1.21386719f, (float16_t)1.18652344f, + (float16_t)-1.35546875f, (float16_t)2.88671875f, (float16_t)-3.9609375f, (float16_t)-1.26464844f, + (float16_t)1.05957031f, (float16_t)0.734863281f, (float16_t)-0.150756836f, (float16_t)3.61132812f, + (float16_t)-3.29492188f, (float16_t)1.46191406f, (float16_t)2.19335938f, (float16_t)-0.705566406f, + (float16_t)-3.15625f, (float16_t)3.43945312f, (float16_t)0.833007812f, (float16_t)-1.37207031f, + (float16_t)-2.84765625f, (float16_t)2.77539062f, (float16_t)1.97949219f, (float16_t)-1.79589844f, + (float16_t)4.0234375f, (float16_t)3.27539062f, (float16_t)-2.234375f, (float16_t)1.00292969f, + (float16_t)-2.05664062f, (float16_t)-3.12304688f, (float16_t)-3.58984375f, (float16_t)-0.0847167969f, + (float16_t)3.5390625f, (float16_t)3.4140625f, (float16_t)-2.70898438f, (float16_t)1.42578125f, + (float16_t)2.77148438f, (float16_t)-2.9140625f, (float16_t)-1.44042969f, (float16_t)0.450927734f, + (float16_t)-2.15429688f, (float16_t)3.57226562f, (float16_t)-3.91601562f, (float16_t)-0.124755859f, + (float16_t)3.65039062f, (float16_t)-3.51757812f, (float16_t)3.80664062f, (float16_t)3.17382812f, + (float16_t)3.828125f, (float16_t)-3.84570312f, (float16_t)2.2734375f, (float16_t)0.512695312f, + (float16_t)-3.81835938f, (float16_t)3.82421875f, (float16_t)-0.805664062f, (float16_t)0.585449219f, + (float16_t)3.49804688f, (float16_t)-2.9609375f, (float16_t)-2.85742188f, (float16_t)1.8671875f, + (float16_t)3.41210938f, (float16_t)1.92480469f, (float16_t)-3.13867188f, (float16_t)-1.77050781f, + (float16_t)-1.38476562f, (float16_t)3.09960938f, (float16_t)3.50976562f, (float16_t)0.669921875f, + (float16_t)-3.921875f, (float16_t)1.28613281f, (float16_t)1.52832031f, (float16_t)0.359130859f, + (float16_t)-2.81835938f, (float16_t)-1.72265625f, (float16_t)0.43359375f, (float16_t)-0.0473937988f, + (float16_t)-0.443603516f, (float16_t)-1.65722656f, (float16_t)0.964355469f, (float16_t)1.203125f, + (float16_t)-2.51171875f, (float16_t)-0.345458984f, (float16_t)-4.1796875f, (float16_t)-3.83984375f, + (float16_t)1.98925781f, (float16_t)1.00878906f, (float16_t)-2.51367188f, (float16_t)1.13476562f, + (float16_t)1.72363281f, (float16_t)-0.757324219f, (float16_t)-3.453125f, (float16_t)-2.7734375f, + (float16_t)-3.86914062f, (float16_t)-1.91699219f, (float16_t)-1.41699219f, (float16_t)-0.376220703f, + (float16_t)0.5390625f, (float16_t)3.86132812f, (float16_t)3.53320312f, (float16_t)-3.828125f, + (float16_t)-1.91503906f, (float16_t)-3.9609375f, (float16_t)-3.98242188f, (float16_t)-3.09570312f, + (float16_t)-1.95703125f, (float16_t)-1.41796875f, (float16_t)1.50195312f, (float16_t)-1.49414062f, + (float16_t)0.0229492188f, (float16_t)-2.01367188f, (float16_t)1.57519531f, (float16_t)3.75976562f, + (float16_t)-1.58496094f, (float16_t)0.461425781f, (float16_t)1.67285156f, (float16_t)-0.997558594f, + (float16_t)-1.5546875f, (float16_t)-3.04882812f, (float16_t)1.890625f, (float16_t)3.61523438f, + (float16_t)-3.21875f, (float16_t)-3.85742188f, (float16_t)1.75976562f, (float16_t)2.55078125f, + (float16_t)3.96875f, (float16_t)-3.73632812f, (float16_t)1.34667969f, (float16_t)-1.6484375f, + (float16_t)2.65429688f, (float16_t)-2.83789062f, (float16_t)1.76367188f, (float16_t)2.28710938f, + (float16_t)-3.25195312f, (float16_t)-0.446044922f, (float16_t)2.7890625f, (float16_t)2.1171875f, + (float16_t)1.24804688f, (float16_t)-1.24609375f, (float16_t)-2.38476562f, (float16_t)-1.34960938f, + (float16_t)1.75390625f, (float16_t)1.95898438f, (float16_t)2.64257812f, (float16_t)1.7890625f, + (float16_t)-2.484375f, (float16_t)-3.51953125f, (float16_t)3.671875f, (float16_t)-0.834472656f, + (float16_t)-2.6484375f, (float16_t)2.22070312f, (float16_t)-0.481933594f, (float16_t)3.66796875f, + (float16_t)-1.70703125f, (float16_t)-0.614746094f, (float16_t)-4.2578125f, (float16_t)2.65820312f, + (float16_t)-0.621582031f, (float16_t)-0.59765625f, (float16_t)-3.14257812f, (float16_t)-1.86035156f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/scale_data.h new file mode 100644 index 00000000..c0e0eedd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/scale_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_basic_nhwc_f16_scale[6] = {(float16_t)1.13183594f, + (float16_t)1.05175781f, + (float16_t)1.01171875f, + (float16_t)0.991699219f, + (float16_t)0.982421875f, + (float16_t)0.979980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/bias_data.h new file mode 100644 index 00000000..cddc8f01 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/bias_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_nhwc_f32_bias[6] = + {0.0762719065f, 0.0362152457f, 0.0104769394f, -0.00966387242f, -0.0278761461f, -0.0459585339f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/config_data.h new file mode 100644 index 00000000..fa786904 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_BASIC_NHWC_F32_INPUT_N 1 +#define BN_BASIC_NHWC_F32_INPUT_H 8 +#define BN_BASIC_NHWC_F32_INPUT_W 8 +#define BN_BASIC_NHWC_F32_INPUT_C 6 +#define BN_BASIC_NHWC_F32_SIZE 384 +#define BN_BASIC_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/input_data.h new file mode 100644 index 00000000..2e621189 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/input_data.h @@ -0,0 +1,57 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_nhwc_f32_input[384] = { + 3.926723f, 3.08336377f, 3.64056873f, 1.85182428f, 1.06154633f, 1.00426042f, -2.41852164f, + 1.90767455f, -1.92815697f, 2.11791253f, 3.68760443f, 2.58827925f, -0.597665668f, 3.17799711f, + -2.82507706f, -1.43272078f, 1.45240891f, -2.11141515f, 2.29288435f, 2.57259059f, 1.26389229f, + -3.31673646f, 2.65664744f, -0.678809822f, -2.64510751f, -1.55882919f, -3.10906529f, 0.151522189f, + 1.88098586f, -2.32983899f, 0.164801165f, 1.55553842f, 2.52231002f, 0.830010533f, 0.131203845f, + -2.51973486f, 1.59241283f, -0.759438455f, -2.60164046f, -3.0359056f, 3.88686514f, -2.76089859f, + 1.61404371f, 3.1964643f, 3.08231688f, 2.9250555f, -0.0362773649f, 2.08614731f, -0.0482972749f, + -2.82510591f, -2.89239836f, -0.73613137f, -2.96813416f, -1.86878192f, -3.00795293f, 2.84457469f, + -1.24339521f, 1.44482267f, 0.497001886f, -1.16747916f, 0.193949491f, -1.60541487f, -2.30779934f, + 0.902385175f, 3.79672241f, 0.571163535f, -1.14202321f, -2.28188562f, -0.670181215f, -1.72628558f, + -2.6664474f, 1.53311014f, 2.66265345f, 2.18577003f, -3.08589578f, -1.02076542f, -0.537626624f, + -1.2441572f, 0.40801236f, 2.70672035f, 3.11491942f, 2.80081248f, 3.14619875f, -1.2452749f, + 3.84908485f, 0.537195683f, 3.27608585f, 3.68322611f, -1.7279762f, 2.87710428f, 2.28658605f, + -2.17378783f, 0.500544965f, 1.81741309f, 2.84224772f, -1.30071187f, 2.71889043f, 1.75191081f, + 2.22188807f, 1.37966573f, -1.42379236f, -2.63024402f, -0.465184003f, -3.16240144f, -2.67726302f, + -0.159586892f, 2.09096718f, -1.59159648f, 2.42799044f, -0.814275265f, -0.921460211f, -2.32512903f, + 3.40464282f, 1.40953326f, -1.8053484f, -1.93828881f, 2.57383657f, -3.75260806f, -3.72737312f, + -3.75118279f, 0.887329638f, -2.10323882f, 1.27341068f, 2.54629898f, 1.01937485f, 0.749736786f, + -2.45444322f, 1.61090922f, 1.48999655f, 0.930893004f, -0.427954137f, 2.5723455f, 0.561731219f, + -2.60121226f, 2.44795561f, 3.66192508f, -3.10643578f, 2.17756486f, -0.892143369f, -2.61897302f, + -3.36272764f, -0.397191733f, 2.31897163f, -0.477533996f, -2.3162291f, 3.52934813f, 2.25964499f, + -1.14460289f, 2.42283988f, -0.281905591f, -1.9558301f, 2.79721689f, -0.233815745f, 2.54834032f, + -1.47385919f, -0.830891788f, -0.958778024f, -0.114782996f, -1.30887878f, 2.56997418f, -1.85533607f, + 2.98034358f, 0.451094478f, 3.64503217f, -1.64232814f, 2.64425969f, -1.29059839f, 1.69926548f, + -0.624443948f, -2.66171646f, 2.38370442f, 1.64511788f, -3.16812968f, 1.12244689f, -2.9498229f, + 2.68789673f, -3.58766699f, 0.302023023f, 1.14111698f, 3.4003253f, 2.91583538f, 3.33106184f, + 2.7545166f, -0.748811185f, 0.309818387f, 3.66534996f, 3.70729589f, -0.87915349f, -3.2476325f, + -3.93260121f, 0.229057267f, 2.3466928f, 2.90007544f, -1.02898824f, -3.90065122f, -2.68628216f, + 3.60647964f, -3.42979765f, 2.87499595f, -1.7421608f, 3.20551181f, -1.84191632f, -0.418661714f, + -2.52538729f, -0.570540488f, -0.531470835f, 1.59229755f, -3.82465434f, 3.52273822f, -0.81962204f, + -3.87743473f, 1.0749836f, -3.08194447f, 3.84336829f, 1.0020479f, 3.63840318f, 2.87942362f, + 3.87049937f, 3.2067287f, -2.56090808f, 2.25934219f, -2.83970284f, -3.71828842f, -0.822978854f, + -0.552967727f, -1.20456791f, -0.863323033f, 2.80950928f, -2.45224762f, -3.66838312f, -1.76723933f, + 3.49336386f, 1.67839479f, 1.77428854f, -1.82292783f, 0.783405602f, 1.8290118f, 1.8863765f, + -1.04616261f, 2.17057753f, -0.060002625f, 0.315769464f, -0.548525512f, -1.11991584f, -0.621580899f, + -2.37134027f, -2.81176019f, -1.28903675f, -3.23592257f, -1.67060184f, -1.74778175f, -1.37297428f, + -0.899201989f, 2.81804967f, 0.0543534197f, -0.176301718f, -0.1965276f, -0.145579383f, 1.86392093f, + -0.0750460774f, 3.05863309f, -1.71417034f, 2.49324703f, -0.171783254f, -2.3797791f, -3.53206635f, + 0.603073299f, 2.95530701f, -2.04023337f, -2.32643509f, 3.04127622f, 1.19698167f, 2.17318439f, + -3.81650233f, 0.279462904f, -1.03504169f, -2.48656988f, 1.76981068f, -2.22886729f, -0.307230204f, + 0.169388056f, -3.0387907f, 3.45176959f, -0.249323651f, 1.85398388f, 2.39615655f, -3.04703546f, + 0.879375696f, -0.293964624f, 0.919452429f, 2.27818942f, 0.0103632379f, 1.4450289f, 1.38069165f, + -2.38054681f, -3.7062242f, -3.54238439f, 3.80986953f, -1.76689863f, 1.20723879f, 0.25210914f, + 3.76565409f, -3.48574662f, -1.76892149f, 2.52669311f, 1.81091321f, 2.24505115f, 3.42543435f, + 1.56399143f, 0.274349153f, 1.8414619f, 1.29845369f, 2.62833285f, 1.66980743f, -1.63514125f, + 0.958280981f, -2.19197559f, -3.26746607f, -2.51181245f, -2.28647566f, -0.144202605f, 3.04065323f, + -1.48651493f, -0.408806175f, -1.45176315f, -0.588685572f, 0.265847206f, 0.192848951f, 0.48856619f, + -0.157779232f, -3.08608031f, 2.63830566f, 3.80981183f, 2.58075547f, -1.17444205f, 2.2310102f, + -1.61279941f, -1.54377031f, 2.21919751f, 1.77979648f, -0.967546642f, 1.56575203f, -2.79707146f, + 1.31925821f, 3.49396467f, 0.397069365f, -2.65689802f, -0.0368123576f, 3.63769007f, 1.77948391f, + 1.81565106f, 0.125777066f, 3.57598734f, 0.279781759f, 2.37929726f, -2.97632146f, -0.26359725f, + 0.0771098733f, -2.06606245f, -3.90231991f, 2.14355826f, -3.87245226f, -0.44025144f, 1.04356945f, + -0.562664211f, 2.01118159f, -1.45979869f, 3.22301507f, 0.642982423f, 1.42989194f, -3.30366707f, + 0.047878623f, -2.32008624f, 1.27079618f, -0.265273243f, 1.26838899f, -1.68222797f, 2.10129857f, + 2.73226666f, -2.39358306f, -1.97974741f, -2.96565104f, 2.77414703f, -2.75055337f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..b2c16023 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/output_ref_data.h @@ -0,0 +1,57 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_nhwc_f32_output_ref[384] = { + 4.51880693f, 3.2792778f, 3.69445324f, 1.826599f, 1.01488209f, 0.938008428f, -2.65994549f, + 2.04269528f, -1.94067001f, 2.09045124f, 3.59446216f, 2.49001837f, -0.599902809f, 3.37881255f, + -2.84828448f, -1.43034506f, 1.39882696f, -2.11470747f, 2.67034841f, 2.74204969f, 1.28943896f, + -3.29852891f, 2.58175182f, -0.711051404f, -2.91629553f, -1.60335147f, -3.13565874f, 0.140585035f, + 1.81981862f, -2.32871771f, 0.262721241f, 1.67232072f, 2.5628612f, 0.813371837f, 0.101005547f, + -2.51477623f, 1.87786317f, -0.762557328f, -2.6221838f, -3.02005792f, 3.79019618f, -2.75106668f, + 1.90233552f, 3.39823627f, 3.12954521f, 2.89081144f, -0.0635114536f, 1.99803329f, 0.0216303132f, + -2.93521333f, -2.9164083f, -0.739609301f, -2.94347811f, -1.87697732f, -3.32680416f, 3.02812099f, + -1.24774361f, 1.4230175f, 0.460329413f, -1.18984604f, 0.295698494f, -1.65234995f, -2.32483912f, + 0.885138273f, 3.70164895f, 0.513663292f, -1.21576691f, -2.36385775f, -0.667695045f, -1.72144294f, + -2.64713073f, 1.45617151f, 3.08869004f, 2.33519435f, -3.1122129f, -1.02185154f, -0.555987418f, + -1.26497459f, 0.53788054f, 2.88312674f, 3.16253662f, 2.76761246f, 3.06263876f, -1.26606977f, + 4.43097067f, 0.601234257f, 3.3256247f, 3.64261103f, -1.72526932f, 2.77300715f, 2.66322255f, + -2.25016117f, 0.516990066f, 1.79247689f, 2.76406741f, -1.32038641f, 3.15231419f, 1.87886393f, + 2.25885725f, 1.35840809f, -1.42646909f, -2.62305236f, -0.450018436f, -3.28997874f, -2.69870782f, + -0.167909712f, 2.02608347f, -1.60539305f, 2.82320189f, -0.820234358f, -0.921970129f, -2.31525421f, + 3.31650877f, 1.33509183f, -1.9662261f, -2.00246477f, 2.61500216f, -3.73073769f, -3.68927932f, + -3.72133994f, 1.08016062f, -2.17595816f, 1.29907084f, 2.51523781f, 0.973457038f, 0.688628078f, + -2.7005856f, 1.73055947f, 1.51823914f, 0.913406551f, -0.448256016f, 2.47440672f, 0.711791813f, + -2.69972348f, 2.48762035f, 3.62148905f, -3.07933187f, 2.08760357f, -0.933062971f, -2.71840405f, + -3.39234567f, -0.403517872f, 2.25005245f, -0.513842821f, -2.54421592f, 3.74836135f, 2.2970643f, + -1.14464831f, 2.35208249f, -0.322167546f, -2.13647509f, 2.97831035f, -0.226126671f, 2.51726198f, + -1.47564983f, -0.860060215f, -1.00845075f, -0.0845127851f, -1.314008f, 2.53871393f, -1.85037506f, + 2.87416029f, 0.586621881f, 3.87003708f, -1.65143323f, 2.61237526f, -1.2956326f, 1.61896932f, + -0.630198658f, -2.76336145f, 2.42260313f, 1.6216296f, -3.13993382f, 1.05380666f, -3.26103806f, + 2.86332798f, -3.61996698f, 0.289821148f, 1.09304452f, 3.28565526f, 3.37512994f, 3.53980517f, + 2.79783654f, -0.752182543f, 0.27645883f, 3.54532433f, 4.27055597f, -0.888472855f, -3.27587819f, + -3.90921831f, 0.197127089f, 2.25331378f, 3.35729957f, -1.04606807f, -3.93668294f, -2.67337251f, + 3.51477337f, -3.40644908f, 3.32892585f, -1.79617846f, 3.25420904f, -1.83610201f, -0.439128071f, + -2.52031446f, -0.569214463f, -0.522782445f, 1.62176001f, -3.80217838f, 3.43251395f, -0.849018157f, + -4.31050062f, 1.1668762f, -3.10821462f, 3.80140734f, 0.956436753f, 3.51892209f, 3.33393502f, + 4.10718203f, 3.25544047f, -2.54905176f, 2.19147849f, -2.82827854f, -4.13044882f, -0.829388738f, + -0.549084008f, -1.20410943f, -0.875919402f, 2.70677805f, -2.69810176f, -3.82216692f, -1.7778337f, + 3.45434427f, 1.62081313f, 1.69247639f, -1.98611474f, 0.860196292f, 1.86129665f, 1.86086082f, + -1.05552292f, 2.08075738f, 0.0083873542f, 0.368339568f, -0.544588804f, -1.12016869f, -0.638455808f, + -2.36938024f, -3.10483956f, -1.31958544f, -3.26402855f, -1.6662271f, -1.74472427f, -1.39118862f, + -0.941048801f, 3.00022221f, 0.0654784292f, -0.184484079f, -0.22092545f, -0.188596129f, 2.18503642f, + -0.0427177437f, 3.10557914f, -1.7094295f, 2.42124343f, -0.214270502f, -2.61611366f, -3.67878985f, + 0.620740891f, 2.92080879f, -2.03199983f, -2.32538247f, 3.5170486f, 1.29519308f, 2.20957279f, + -3.79409504f, 0.246640608f, -1.06008482f, -2.73693252f, 1.89769101f, -2.24496579f, -0.314312339f, + 0.138513952f, -3.02334332f, 3.98146415f, -0.226021796f, 1.8865664f, 2.36635709f, -3.02098274f, + 0.815647304f, -0.256307781f, 1.00328946f, 2.31582999f, 0.000612279167f, 1.39157748f, 1.30683303f, + -2.61698222f, -3.86196804f, -3.57414436f, 3.76819015f, -1.76350284f, 1.13688517f, 0.361497939f, + 3.99690652f, -3.5168314f, -1.76372063f, 2.45409751f, 1.72836089f, 2.61623168f, 3.6390655f, + 1.59311628f, 0.262379855f, 1.78099418f, 1.22625685f, 3.04986119f, 1.79250813f, -1.64416063f, + 0.940564394f, -2.1810565f, -3.24739766f, -2.76549077f, -2.36868548f, -0.135445043f, 3.00543785f, + -1.48808157f, -0.446503818f, -1.56619406f, -0.582960546f, 0.279493928f, 0.18156451f, 0.452043027f, + -0.200549453f, -3.41519427f, 2.81116843f, 3.86571431f, 2.54940462f, -1.18153203f, 2.13996887f, + -1.748384f, -1.58751261f, 2.25613475f, 1.75517642f, -0.978298426f, 1.48815382f, -3.08822131f, + 1.42380273f, 3.54610133f, 0.384068787f, -2.63775039f, -0.0820270032f, 4.19180679f, 1.90786517f, + 1.84777653f, 0.115056247f, 3.4848206f, 0.22816959f, 2.76811242f, -3.09426093f, -0.256263256f, + 0.0667980239f, -2.05737185f, -3.86942291f, 2.50140691f, -4.03680563f, -0.435023755f, 1.02513611f, + -0.580581903f, 1.92458248f, -1.5752852f, 3.42616224f, 0.661125839f, 1.4082123f, -3.27307248f, + 0.000952596718f, -2.54857969f, 1.37283063f, -0.257959247f, 1.24806643f, -1.68033075f, 2.01287842f, + 3.16744757f, -2.48134041f, -1.99287558f, -2.95039368f, 2.69717193f, -2.74093056f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/scale_data.h new file mode 100644 index 00000000..4158c454 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/scale_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_basic_nhwc_f32_scale[6] = + {1.13135958f, 1.05179369f, 1.01192331f, 0.991596699f, 0.982301235f, 0.979792655f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_basic_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/bias_data.h new file mode 100644 index 00000000..de1dab8b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/bias_data.h @@ -0,0 +1,8 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_f16_bias[7] = {(float16_t)0.0762939453f, + (float16_t)0.0415344238f, + (float16_t)0.018157959f, + (float16_t)0.0f, + (float16_t)-0.015838623f, + (float16_t)-0.0308685303f, + (float16_t)-0.0459594727f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/config_data.h new file mode 100644 index 00000000..b08e1610 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_OP_F16_INPUT_N 1 +#define BN_OP_F16_INPUT_H 5 +#define BN_OP_F16_INPUT_W 6 +#define BN_OP_F16_INPUT_C 7 +#define BN_OP_F16_SIZE 210 +#define BN_OP_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/input_data.h new file mode 100644 index 00000000..52ee83a2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/input_data.h @@ -0,0 +1,55 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_f16_input[210] = { + (float16_t)-3.1953125f, (float16_t)-2.78515625f, (float16_t)0.502929688f, (float16_t)1.20800781f, + (float16_t)-0.0342712402f, (float16_t)2.77734375f, (float16_t)2.6796875f, (float16_t)2.41015625f, + (float16_t)-3.79492188f, (float16_t)-1.16601562f, (float16_t)-2.14257812f, (float16_t)3.41015625f, + (float16_t)3.3671875f, (float16_t)2.99609375f, (float16_t)3.13085938f, (float16_t)-3.14648438f, + (float16_t)0.930175781f, (float16_t)-2.30664062f, (float16_t)1.07226562f, (float16_t)2.41992188f, + (float16_t)-2.18164062f, (float16_t)-2.28320312f, (float16_t)2.875f, (float16_t)-1.76757812f, + (float16_t)1.921875f, (float16_t)-1.55859375f, (float16_t)0.37109375f, (float16_t)0.383789062f, + (float16_t)3.72265625f, (float16_t)-0.337402344f, (float16_t)2.8359375f, (float16_t)-1.94433594f, + (float16_t)-2.40625f, (float16_t)-2.69140625f, (float16_t)1.46289062f, (float16_t)3.671875f, + (float16_t)2.55859375f, (float16_t)2.63476562f, (float16_t)-3.21289062f, (float16_t)3.9140625f, + (float16_t)-0.326416016f, (float16_t)3.87304688f, (float16_t)2.671875f, (float16_t)3.4296875f, + (float16_t)-1.44824219f, (float16_t)2.34570312f, (float16_t)-0.691894531f, (float16_t)0.547363281f, + (float16_t)3.43554688f, (float16_t)-0.27734375f, (float16_t)-3.58203125f, (float16_t)1.78515625f, + (float16_t)1.07910156f, (float16_t)-0.0703125f, (float16_t)3.63867188f, (float16_t)0.837890625f, + (float16_t)1.31347656f, (float16_t)-3.56445312f, (float16_t)3.33789062f, (float16_t)1.16601562f, + (float16_t)0.8828125f, (float16_t)3.52734375f, (float16_t)-1.25976562f, (float16_t)-3.95117188f, + (float16_t)1.70996094f, (float16_t)0.176025391f, (float16_t)-2.82421875f, (float16_t)1.69628906f, + (float16_t)-3.34765625f, (float16_t)1.97363281f, (float16_t)2.59179688f, (float16_t)-3.63476562f, + (float16_t)-2.14257812f, (float16_t)2.57226562f, (float16_t)-2.66796875f, (float16_t)-3.30859375f, + (float16_t)-0.511230469f, (float16_t)0.748535156f, (float16_t)3.8671875f, (float16_t)-2.68359375f, + (float16_t)-3.4453125f, (float16_t)-3.26953125f, (float16_t)-1.68457031f, (float16_t)3.171875f, + (float16_t)3.69335938f, (float16_t)3.81445312f, (float16_t)1.97070312f, (float16_t)-1.25878906f, + (float16_t)-3.22460938f, (float16_t)3.73046875f, (float16_t)-1.32714844f, (float16_t)1.47167969f, + (float16_t)-0.831542969f, (float16_t)-3.18945312f, (float16_t)-3.75976562f, (float16_t)-1.66015625f, + (float16_t)2.53710938f, (float16_t)-1.49804688f, (float16_t)3.61132812f, (float16_t)1.484375f, + (float16_t)-1.51660156f, (float16_t)-1.48046875f, (float16_t)0.0191955566f, (float16_t)3.3359375f, + (float16_t)-1.4765625f, (float16_t)-2.20898438f, (float16_t)-3.5859375f, (float16_t)0.330810547f, + (float16_t)0.434814453f, (float16_t)1.74023438f, (float16_t)-1.40527344f, (float16_t)0.915039062f, + (float16_t)-1.171875f, (float16_t)-3.37304688f, (float16_t)-3.59765625f, (float16_t)-3.18554688f, + (float16_t)-1.71972656f, (float16_t)-2.45898438f, (float16_t)-3.45507812f, (float16_t)3.74609375f, + (float16_t)-0.0638427734f, (float16_t)2.61328125f, (float16_t)-1.42773438f, (float16_t)-1.00390625f, + (float16_t)3.61523438f, (float16_t)0.548339844f, (float16_t)3.80859375f, (float16_t)-3.56054688f, + (float16_t)2.89257812f, (float16_t)-3.36914062f, (float16_t)-1.29980469f, (float16_t)0.329833984f, + (float16_t)-0.123779297f, (float16_t)3.89648438f, (float16_t)-0.995605469f, (float16_t)-3.53320312f, + (float16_t)3.61914062f, (float16_t)2.5546875f, (float16_t)-1.9453125f, (float16_t)-0.931640625f, + (float16_t)2.765625f, (float16_t)2.79492188f, (float16_t)3.09179688f, (float16_t)2.359375f, + (float16_t)0.786132812f, (float16_t)-2.9765625f, (float16_t)-2.91992188f, (float16_t)0.284667969f, + (float16_t)2.60742188f, (float16_t)0.33203125f, (float16_t)-0.112731934f, (float16_t)2.74414062f, + (float16_t)0.440429688f, (float16_t)2.72460938f, (float16_t)3.3125f, (float16_t)-3.80859375f, + (float16_t)-3.60351562f, (float16_t)3.35742188f, (float16_t)-0.990722656f, (float16_t)-2.97851562f, + (float16_t)-1.34667969f, (float16_t)-3.15429688f, (float16_t)-2.70703125f, (float16_t)-0.781738281f, + (float16_t)2.81445312f, (float16_t)0.0433959961f, (float16_t)-0.261474609f, (float16_t)-2.96679688f, + (float16_t)-0.297851562f, (float16_t)-0.415527344f, (float16_t)3.515625f, (float16_t)-2.50585938f, + (float16_t)-2.328125f, (float16_t)-1.32226562f, (float16_t)-2.9296875f, (float16_t)2.31835938f, + (float16_t)-1.83984375f, (float16_t)0.548339844f, (float16_t)2.74609375f, (float16_t)-2.91796875f, + (float16_t)2.92773438f, (float16_t)-0.638671875f, (float16_t)-1.25390625f, (float16_t)3.625f, + (float16_t)3.94140625f, (float16_t)-3.41015625f, (float16_t)1.44921875f, (float16_t)-0.287597656f, + (float16_t)-1.76660156f, (float16_t)-3.24023438f, (float16_t)0.989746094f, (float16_t)-0.73046875f, + (float16_t)1.01367188f, (float16_t)0.117492676f, (float16_t)-2.26367188f, (float16_t)-1.53125f, + (float16_t)-1.08203125f, (float16_t)-1.875f, (float16_t)-2.08203125f, (float16_t)-1.75585938f, + (float16_t)-0.810546875f, (float16_t)3.4453125f, (float16_t)-1.98339844f, (float16_t)1.15429688f, + (float16_t)3.1484375f, (float16_t)1.04980469f, (float16_t)-2.53320312f, (float16_t)-2.7265625f, + (float16_t)-1.51464844f, (float16_t)3.31835938f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/output_ref_data.h new file mode 100644 index 00000000..4584c8a7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/output_ref_data.h @@ -0,0 +1,55 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_f16_output_ref[210] = { + (float16_t)-3.5390625f, (float16_t)-2.9140625f, (float16_t)0.532226562f, (float16_t)1.20800781f, + (float16_t)-0.0496826172f, (float16_t)2.6953125f, (float16_t)2.58007812f, (float16_t)2.80273438f, + (float16_t)-3.98632812f, (float16_t)-1.17382812f, (float16_t)-2.14257812f, (float16_t)3.3515625f, + (float16_t)3.27539062f, (float16_t)2.890625f, (float16_t)3.61914062f, (float16_t)-3.29882812f, + (float16_t)0.969238281f, (float16_t)-2.30664062f, (float16_t)1.04296875f, (float16_t)2.34375f, + (float16_t)-2.18359375f, (float16_t)-2.50585938f, (float16_t)3.09375f, (float16_t)-1.7890625f, + (float16_t)1.921875f, (float16_t)-1.55566406f, (float16_t)0.333251953f, (float16_t)0.330078125f, + (float16_t)4.2890625f, (float16_t)-0.316650391f, (float16_t)2.91796875f, (float16_t)-1.94433594f, + (float16_t)-2.39257812f, (float16_t)-2.671875f, (float16_t)1.38769531f, (float16_t)4.23046875f, + (float16_t)2.7578125f, (float16_t)2.7109375f, (float16_t)-3.21289062f, (float16_t)3.84960938f, + (float16_t)-0.351318359f, (float16_t)3.75f, (float16_t)3.09960938f, (float16_t)3.68164062f, + (float16_t)-1.46191406f, (float16_t)2.34570312f, (float16_t)-0.69921875f, (float16_t)0.506347656f, + (float16_t)3.31835938f, (float16_t)-0.237548828f, (float16_t)-3.76171875f, (float16_t)1.84375f, + (float16_t)1.07910156f, (float16_t)-0.0852661133f, (float16_t)3.54101562f, (float16_t)0.775390625f, + (float16_t)1.5625f, (float16_t)-3.7421875f, (float16_t)3.4296875f, (float16_t)1.16601562f, + (float16_t)0.855957031f, (float16_t)3.43164062f, (float16_t)-1.28027344f, (float16_t)-4.39453125f, + (float16_t)1.85644531f, (float16_t)0.198120117f, (float16_t)-2.82421875f, (float16_t)1.65917969f, + (float16_t)-3.31640625f, (float16_t)1.88769531f, (float16_t)3.0078125f, (float16_t)-3.81640625f, + (float16_t)-2.17382812f, (float16_t)2.57226562f, (float16_t)-2.65039062f, (float16_t)-3.27734375f, + (float16_t)-0.546875f, (float16_t)0.922851562f, (float16_t)4.14453125f, (float16_t)-2.7265625f, + (float16_t)-3.4453125f, (float16_t)-3.24414062f, (float16_t)-1.68457031f, (float16_t)3.0625f, + (float16_t)4.25390625f, (float16_t)4.08984375f, (float16_t)2.03320312f, (float16_t)-1.25878906f, + (float16_t)-3.19921875f, (float16_t)3.63085938f, (float16_t)-1.34667969f, (float16_t)1.74121094f, + (float16_t)-0.840820312f, (float16_t)-3.2421875f, (float16_t)-3.75976562f, (float16_t)-1.65527344f, + (float16_t)2.45898438f, (float16_t)-1.51367188f, (float16_t)4.16015625f, (float16_t)1.6171875f, + (float16_t)-1.53222656f, (float16_t)-1.48046875f, (float16_t)0.00312423706f, (float16_t)3.2421875f, + (float16_t)-1.4921875f, (float16_t)-2.421875f, (float16_t)-3.765625f, (float16_t)0.356445312f, + (float16_t)0.434814453f, (float16_t)1.703125f, (float16_t)-1.41015625f, (float16_t)0.850585938f, + (float16_t)-1.25f, (float16_t)-3.5390625f, (float16_t)-3.65820312f, (float16_t)-3.18554688f, + (float16_t)-1.71386719f, (float16_t)-2.4453125f, (float16_t)-3.43164062f, (float16_t)4.31640625f, + (float16_t)-0.0262451172f, (float16_t)2.68945312f, (float16_t)-1.42773438f, (float16_t)-1.0078125f, + (float16_t)3.51757812f, (float16_t)0.491455078f, (float16_t)4.38671875f, (float16_t)-3.73828125f, + (float16_t)2.9765625f, (float16_t)-3.36914062f, (float16_t)-1.29882812f, (float16_t)0.29296875f, + (float16_t)-0.167236328f, (float16_t)4.484375f, (float16_t)-1.015625f, (float16_t)-3.59570312f, + (float16_t)3.61914062f, (float16_t)2.50585938f, (float16_t)-1.94042969f, (float16_t)-0.958496094f, + (float16_t)3.20507812f, (float16_t)3.0078125f, (float16_t)3.1796875f, (float16_t)2.359375f, + (float16_t)0.760253906f, (float16_t)-2.953125f, (float16_t)-2.90625f, (float16_t)0.3984375f, + (float16_t)2.80859375f, (float16_t)0.357421875f, (float16_t)-0.112731934f, (float16_t)2.69335938f, + (float16_t)0.401367188f, (float16_t)2.62304688f, (float16_t)3.82421875f, (float16_t)-4.0f, + (float16_t)-3.66601562f, (float16_t)3.35742188f, (float16_t)-0.994140625f, (float16_t)-2.95507812f, + (float16_t)-1.36523438f, (float16_t)-3.4921875f, (float16_t)-2.83203125f, (float16_t)-0.78125f, + (float16_t)2.81445312f, (float16_t)0.0270080566f, (float16_t)-0.287597656f, (float16_t)-2.953125f, + (float16_t)-0.260742188f, (float16_t)-0.399414062f, (float16_t)3.61132812f, (float16_t)-2.50585938f, + (float16_t)-2.31640625f, (float16_t)-1.328125f, (float16_t)-2.91601562f, (float16_t)2.69921875f, + (float16_t)-1.91210938f, (float16_t)0.578613281f, (float16_t)2.74609375f, (float16_t)-2.89648438f, + (float16_t)2.84179688f, (float16_t)-0.671875f, (float16_t)-1.34277344f, (float16_t)3.88867188f, + (float16_t)4.046875f, (float16_t)-3.41015625f, (float16_t)1.41503906f, (float16_t)-0.313232422f, + (float16_t)-1.77636719f, (float16_t)-3.58984375f, (float16_t)1.09179688f, (float16_t)-0.729003906f, + (float16_t)1.01367188f, (float16_t)0.100219727f, (float16_t)-2.25195312f, (float16_t)-1.54589844f, + (float16_t)-1.1484375f, (float16_t)-1.94921875f, (float16_t)-2.11132812f, (float16_t)-1.75585938f, + (float16_t)-0.81640625f, (float16_t)3.34960938f, (float16_t)-1.98925781f, (float16_t)1.38183594f, + (float16_t)3.3828125f, (float16_t)1.09179688f, (float16_t)-2.53320312f, (float16_t)-2.70898438f, + (float16_t)-1.51757812f, (float16_t)3.20507812f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/scale_data.h new file mode 100644 index 00000000..8110da86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/scale_data.h @@ -0,0 +1,8 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_f16_scale[7] = {(float16_t)1.13183594f, + (float16_t)1.06152344f, + (float16_t)1.02246094f, + (float16_t)1.0f, + (float16_t)0.987304688f, + (float16_t)0.981445312f, + (float16_t)0.979980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f16/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/bias_data.h new file mode 100644 index 00000000..c55be3ef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/bias_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_f32_bias[7] = + {0.0762719065f, 0.0415249765f, 0.0181606114f, 1.11758336e-08f, -0.0158358067f, -0.03086555f, -0.0459585339f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/config_data.h new file mode 100644 index 00000000..165a2c03 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_OP_F32_INPUT_N 1 +#define BN_OP_F32_INPUT_H 5 +#define BN_OP_F32_INPUT_W 6 +#define BN_OP_F32_INPUT_C 7 +#define BN_OP_F32_SIZE 210 +#define BN_OP_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/input_data.h new file mode 100644 index 00000000..0ae8f3cd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/input_data.h @@ -0,0 +1,32 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_f32_input[210] = { + -1.85936916f, -1.51402366f, -3.60758567f, -0.414930195f, 1.60886204f, 3.64239454f, 2.02603698f, + -3.8899343f, 3.14046693f, -0.266106099f, 1.5904845f, -0.697453976f, -0.435917675f, -1.73107564f, + -2.3183639f, 1.3295548f, 3.06246829f, -3.41701937f, 2.46731067f, -0.518087327f, 1.65745628f, + -0.917640269f, -3.78198314f, 2.41308069f, -0.351518005f, 1.43495548f, -2.59081316f, -1.06681502f, + 3.51034856f, -1.40191591f, 3.95717907f, 0.651433945f, 0.405041128f, 3.72257233f, -2.68814349f, + -3.28919768f, -2.28621864f, 0.890180886f, -2.13204455f, 2.43053293f, 3.50570869f, -3.84946847f, + -3.52346539f, 2.51055217f, -3.94139481f, -0.198885471f, 1.15121591f, 1.69525206f, 0.66446805f, + -2.64171958f, -1.16623294f, 0.247384563f, -3.19031715f, -3.11436796f, -2.86856031f, -2.83749843f, + 2.95315433f, 3.08520985f, -0.943990946f, 0.61571306f, -2.78034782f, -2.26959991f, 3.91706181f, + -0.0383264832f, -3.84510636f, 1.69867504f, -0.027660979f, -0.209468454f, 1.36031711f, -1.0220238f, + 1.71472824f, -3.40589452f, -1.44849837f, -1.29038036f, -2.12696123f, -3.89606333f, 2.05234051f, + -0.937272251f, 1.57039499f, -3.18077493f, 3.04074907f, 0.415949911f, -3.68942857f, 1.90182662f, + 1.29460859f, -2.68338728f, -0.462709576f, 3.17299557f, -1.44711113f, -3.98501635f, -0.14543654f, + 0.0151415905f, 0.309503824f, 2.17711401f, 2.6200664f, 2.49644089f, -3.19075441f, -3.18937182f, + 1.85452425f, -1.84156644f, 2.8669312f, -2.80660272f, 1.59523046f, 0.766566217f, -3.50304484f, + -0.865838766f, 3.44570327f, -3.4823947f, 1.9664737f, -2.14624f, 0.252279907f, 1.76416326f, + -1.07703543f, 2.72998238f, 3.98241544f, 0.197673976f, -0.446976155f, 2.86848903f, 1.11817765f, + -3.55525541f, 2.72045279f, 0.895412385f, 1.80421066f, 2.07796526f, 1.09242499f, -2.40862656f, + -1.67090225f, -3.20791006f, 0.856910706f, -3.04197955f, -1.6010344f, 1.07616353f, -2.23669934f, + -2.13542724f, 3.33447194f, 3.77777553f, -0.776512504f, -2.44047308f, -1.0661093f, 2.20446491f, + 1.29247129f, -3.78777122f, 3.10695982f, -1.4100467f, -2.95185494f, 0.967218518f, 1.98838842f, + -2.17025733f, 0.995683849f, -3.57371354f, -3.4051156f, 3.46947956f, 2.22926354f, -0.0726928636f, + -0.147865221f, 0.605116367f, 2.49796581f, 2.06993556f, 3.45521665f, -2.04781628f, -0.318387717f, + -1.96391451f, -3.08256841f, -2.55697322f, -3.34739971f, 1.98154056f, -2.05784845f, -0.927564442f, + -0.526460052f, -0.396470159f, 2.67696548f, 0.802780747f, 3.60474586f, -3.26432037f, -0.15051344f, + 3.17898917f, 0.212910786f, 2.17500663f, 1.85644424f, -2.24004483f, -3.86394286f, 2.50344181f, + -2.06094933f, -1.02812612f, 2.74336243f, 3.55386472f, -3.90967941f, 3.74659252f, -2.92477727f, + -0.769266486f, 0.594403088f, 1.7643218f, 3.08890891f, -1.50404859f, 0.844702542f, 3.01633978f, + -1.61994946f, -0.647582233f, -0.778870165f, 2.07156515f, 1.14501953f, -0.138907298f, -1.95859551f, + 3.57862258f, 2.04377937f, -1.10984015f, 3.92514277f, -2.59886956f, 2.00076532f, 2.1739552f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/output_ref_data.h new file mode 100644 index 00000000..4932a2a2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_f32_output_ref[210] = { + -2.02734303f, -1.56551647f, -3.67026806f, -0.414928108f, 1.5729754f, 3.54411459f, 1.93913758f, + -4.32464218f, 3.3749342f, -0.253908724f, 1.59047651f, -0.704597592f, -0.458715141f, -1.74205375f, + -2.5466311f, 1.45276427f, 3.14925647f, -3.4170022f, 2.42072535f, -0.53936398f, 1.57800496f, + -0.961909115f, -3.97281361f, 2.48531651f, -0.351516247f, 1.40123618f, -2.57372737f, -1.09121609f, + 4.04773808f, -1.44652116f, 4.06401682f, 0.651430666f, 0.384157389f, 3.62280846f, -2.67978168f, + -3.64499307f, -2.38515329f, 0.928289711f, -2.13203382f, 2.38440609f, 3.4099586f, -3.81763935f, + -3.91003394f, 2.70631909f, -4.01155758f, -0.198884457f, 1.12103283f, 1.63301039f, 0.605082393f, + -2.91246247f, -1.19635844f, 0.271088868f, -3.19030118f, -3.09139013f, -2.84633374f, -2.82611871f, + 3.41735101f, 3.31628227f, -0.94698441f, 0.61570996f, -2.76153278f, -2.25845909f, 3.79194975f, + 0.0329108611f, -4.03981495f, 1.75490165f, -0.0276608299f, -0.222693712f, 1.30427444f, -1.0473299f, + 2.01624584f, -3.57361913f, -1.4627974f, -1.29037392f, -2.11628938f, -3.85481954f, 1.96490967f, + -0.984119952f, 1.70840108f, -3.23389292f, 3.04073381f, 0.394930243f, -3.65200925f, 1.81743717f, + 1.54093957f, -2.80672288f, -0.454917938f, 3.17297959f, -1.44491196f, -3.94212627f, -0.188456178f, + 0.0934024751f, 0.370043933f, 2.24406195f, 2.62005329f, 2.44949269f, -3.16256475f, -3.17088151f, + 2.17440534f, -1.91318274f, 2.94933748f, -2.80658865f, 1.55951369f, 0.721512854f, -3.47821617f, + -0.903302968f, 3.69892359f, -3.54227185f, 1.96646392f, -2.13532782f, 0.216745093f, 1.68255568f, + -1.14224231f, 2.93923068f, 4.08981895f, 0.197672993f, -0.457241416f, 2.78453279f, 1.04962373f, + -3.94599986f, 2.92911553f, 0.933638453f, 1.8042016f, 2.03623223f, 1.04134059f, -2.40591311f, + -1.81411922f, -3.36347103f, 0.894273996f, -3.04196429f, -1.59691691f, 1.02538002f, -2.23746014f, + -2.33966398f, 3.58085847f, 3.88059306f, -0.776508629f, -2.42589402f, -1.07724297f, 2.11396003f, + 1.53852153f, -3.97895741f, 3.19474483f, -1.41003966f, -2.93090248f, 0.918451428f, 1.90224981f, + -2.37906933f, 1.09838116f, -3.63563704f, -3.40509844f, 3.41040444f, 2.15713811f, -0.117182463f, + -0.0910168216f, 0.683818161f, 2.57210374f, 2.06992531f, 3.39631939f, -2.04078031f, -0.357912481f, + -2.1456213f, -3.2304287f, -2.59611225f, -3.34738302f, 1.94100928f, -2.05062675f, -0.954779327f, + -0.519343674f, -0.379303306f, 2.75511479f, 0.802776754f, 3.54398513f, -3.23476911f, -0.193430483f, + 3.67285132f, 0.267516464f, 2.24190736f, 1.85643494f, -2.22796345f, -3.82329369f, 2.4068954f, + -2.25540257f, -1.04976666f, 2.82299948f, 3.55384684f, -3.87678981f, 3.646384f, -2.91163373f, + -0.794045031f, 0.672446728f, 1.82201946f, 3.08889341f, -1.50113988f, 0.798203051f, 2.90942907f, + -1.7564733f, -0.645843089f, -0.778163433f, 2.0715549f, 1.1149137f, -0.167201921f, -1.96497607f, + 4.12498045f, 2.21086907f, -1.11655021f, 3.92512321f, -2.5823164f, 1.93286908f, 2.08406687f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/scale_data.h new file mode 100644 index 00000000..d0852514 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/scale_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_f32_scale[7] = + {1.13135958f, 1.06143749f, 1.02240932f, 0.999994993f, 0.987537265f, 0.981491745f, 0.979792655f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_f32/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/bias_data.h new file mode 100644 index 00000000..e203f141 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/bias_data.h @@ -0,0 +1,8 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_nhwc_f16_bias[7] = {(float16_t)0.0762939453f, + (float16_t)0.0415344238f, + (float16_t)0.018157959f, + (float16_t)0.0f, + (float16_t)-0.015838623f, + (float16_t)-0.0308685303f, + (float16_t)-0.0459594727f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/config_data.h new file mode 100644 index 00000000..55de46fe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_OP_NHWC_F16_INPUT_N 1 +#define BN_OP_NHWC_F16_INPUT_H 5 +#define BN_OP_NHWC_F16_INPUT_W 6 +#define BN_OP_NHWC_F16_INPUT_C 7 +#define BN_OP_NHWC_F16_SIZE 210 +#define BN_OP_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/input_data.h new file mode 100644 index 00000000..0f4b1d9a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/input_data.h @@ -0,0 +1,55 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_nhwc_f16_input[210] = { + (float16_t)-1.40527344f, (float16_t)-1.29199219f, (float16_t)0.128662109f, (float16_t)2.94726562f, + (float16_t)0.921386719f, (float16_t)3.09570312f, (float16_t)2.76953125f, (float16_t)0.482910156f, + (float16_t)0.116943359f, (float16_t)-0.75f, (float16_t)-0.455810547f, (float16_t)-2.29296875f, + (float16_t)-0.414306641f, (float16_t)0.615234375f, (float16_t)-1.38085938f, (float16_t)-3.4609375f, + (float16_t)1.22460938f, (float16_t)0.495605469f, (float16_t)-1.23046875f, (float16_t)1.74902344f, + (float16_t)1.01269531f, (float16_t)-3.38671875f, (float16_t)1.71582031f, (float16_t)0.165405273f, + (float16_t)-2.89453125f, (float16_t)-0.571289062f, (float16_t)1.08691406f, (float16_t)3.3671875f, + (float16_t)0.508789062f, (float16_t)3.45703125f, (float16_t)-2.421875f, (float16_t)1.69042969f, + (float16_t)0.552246094f, (float16_t)3.19140625f, (float16_t)-2.70507812f, (float16_t)1.76464844f, + (float16_t)2.85742188f, (float16_t)0.633789062f, (float16_t)2.79492188f, (float16_t)-3.93359375f, + (float16_t)-1.26855469f, (float16_t)3.9375f, (float16_t)3.08007812f, (float16_t)-1.74609375f, + (float16_t)0.146362305f, (float16_t)-3.57226562f, (float16_t)3.14257812f, (float16_t)-3.58984375f, + (float16_t)-0.0314941406f, (float16_t)3.57617188f, (float16_t)-2.97851562f, (float16_t)1.82910156f, + (float16_t)-1.36132812f, (float16_t)3.015625f, (float16_t)-3.90234375f, (float16_t)-0.0334472656f, + (float16_t)-1.26757812f, (float16_t)-3.74804688f, (float16_t)-2.7265625f, (float16_t)2.27929688f, + (float16_t)0.285888672f, (float16_t)-2.76367188f, (float16_t)0.726074219f, (float16_t)-1.77539062f, + (float16_t)-0.190185547f, (float16_t)-0.599609375f, (float16_t)-2.95703125f, (float16_t)1.75683594f, + (float16_t)0.66015625f, (float16_t)3.79882812f, (float16_t)-0.653808594f, (float16_t)3.5390625f, + (float16_t)-2.22265625f, (float16_t)-3.74414062f, (float16_t)-2.0078125f, (float16_t)-3.14648438f, + (float16_t)1.08691406f, (float16_t)3.48242188f, (float16_t)-1.16601562f, (float16_t)-3.140625f, + (float16_t)-3.44726562f, (float16_t)2.55078125f, (float16_t)0.0880737305f, (float16_t)1.26855469f, + (float16_t)-2.58789062f, (float16_t)-3.87304688f, (float16_t)1.31933594f, (float16_t)-2.86132812f, + (float16_t)-3.734375f, (float16_t)2.08398438f, (float16_t)-3.015625f, (float16_t)3.35351562f, + (float16_t)1.83300781f, (float16_t)-3.86914062f, (float16_t)3.78710938f, (float16_t)-1.68554688f, + (float16_t)0.892578125f, (float16_t)-2.30859375f, (float16_t)0.496582031f, (float16_t)0.438964844f, + (float16_t)2.26171875f, (float16_t)0.734863281f, (float16_t)3.33984375f, (float16_t)1.1015625f, + (float16_t)2.18554688f, (float16_t)-2.30664062f, (float16_t)-2.92578125f, (float16_t)-0.158081055f, + (float16_t)0.930175781f, (float16_t)-0.486816406f, (float16_t)-0.138183594f, (float16_t)3.19921875f, + (float16_t)-3.82226562f, (float16_t)-2.00585938f, (float16_t)1.50097656f, (float16_t)1.3203125f, + (float16_t)1.74609375f, (float16_t)0.224243164f, (float16_t)-2.43945312f, (float16_t)1.38378906f, + (float16_t)2.21484375f, (float16_t)2.6875f, (float16_t)-0.561523438f, (float16_t)3.828125f, + (float16_t)-1.27539062f, (float16_t)2.59375f, (float16_t)1.70605469f, (float16_t)-0.431884766f, + (float16_t)-0.143554688f, (float16_t)3.43945312f, (float16_t)-3.58984375f, (float16_t)-2.46484375f, + (float16_t)-0.907714844f, (float16_t)-2.25195312f, (float16_t)-2.87109375f, (float16_t)3.83789062f, + (float16_t)0.151245117f, (float16_t)1.66015625f, (float16_t)-3.73828125f, (float16_t)-2.56054688f, + (float16_t)2.2578125f, (float16_t)-3.96679688f, (float16_t)-2.70117188f, (float16_t)1.73046875f, + (float16_t)-3.6328125f, (float16_t)2.22265625f, (float16_t)0.404296875f, (float16_t)-0.809570312f, + (float16_t)0.921386719f, (float16_t)1.61621094f, (float16_t)-1.69433594f, (float16_t)3.40820312f, + (float16_t)3.55859375f, (float16_t)-3.45703125f, (float16_t)-1.45605469f, (float16_t)-2.52148438f, + (float16_t)0.0207519531f, (float16_t)1.44042969f, (float16_t)0.525390625f, (float16_t)0.238769531f, + (float16_t)2.18164062f, (float16_t)0.713867188f, (float16_t)-0.401367188f, (float16_t)2.93359375f, + (float16_t)-3.43945312f, (float16_t)2.65234375f, (float16_t)-0.777832031f, (float16_t)-2.3828125f, + (float16_t)2.12890625f, (float16_t)2.39453125f, (float16_t)1.38183594f, (float16_t)1.45019531f, + (float16_t)2.54882812f, (float16_t)0.561523438f, (float16_t)-1.34960938f, (float16_t)2.68945312f, + (float16_t)-0.794921875f, (float16_t)-2.69921875f, (float16_t)2.08007812f, (float16_t)-0.90625f, + (float16_t)2.49804688f, (float16_t)-0.814941406f, (float16_t)0.14465332f, (float16_t)0.0199584961f, + (float16_t)-1.83496094f, (float16_t)2.078125f, (float16_t)0.211181641f, (float16_t)3.484375f, + (float16_t)1.07910156f, (float16_t)-2.11132812f, (float16_t)1.66796875f, (float16_t)-1.8984375f, + (float16_t)-1.61328125f, (float16_t)1.86523438f, (float16_t)-3.265625f, (float16_t)1.15625f, + (float16_t)-3.27929688f, (float16_t)1.50878906f, (float16_t)1.828125f, (float16_t)-1.03320312f, + (float16_t)-2.79492188f, (float16_t)-0.928222656f, (float16_t)3.22265625f, (float16_t)-1.28613281f, + (float16_t)-0.796386719f, (float16_t)3.87304688f, (float16_t)-1.44433594f, (float16_t)3.0859375f, + (float16_t)2.40039062f, (float16_t)-2.25f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..6619ccc2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/output_ref_data.h @@ -0,0 +1,55 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_nhwc_f16_output_ref[210] = { + (float16_t)-1.51367188f, (float16_t)-1.33007812f, (float16_t)0.149658203f, (float16_t)2.94726562f, + (float16_t)0.894042969f, (float16_t)3.0078125f, (float16_t)2.66796875f, (float16_t)0.622558594f, + (float16_t)0.165649414f, (float16_t)-0.749023438f, (float16_t)-0.455810547f, (float16_t)-2.28125f, + (float16_t)-0.4375f, (float16_t)0.556640625f, (float16_t)-1.48535156f, (float16_t)-3.6328125f, + (float16_t)1.27050781f, (float16_t)0.495605469f, (float16_t)-1.23046875f, (float16_t)1.68554688f, + (float16_t)0.946289062f, (float16_t)-3.75585938f, (float16_t)1.86328125f, (float16_t)0.187255859f, + (float16_t)-2.89453125f, (float16_t)-0.580078125f, (float16_t)1.03613281f, (float16_t)3.25195312f, + (float16_t)0.651855469f, (float16_t)3.7109375f, (float16_t)-2.45703125f, (float16_t)1.69042969f, + (float16_t)0.529785156f, (float16_t)3.1015625f, (float16_t)-2.6953125f, (float16_t)2.07226562f, + (float16_t)3.07421875f, (float16_t)0.666015625f, (float16_t)2.79492188f, (float16_t)-3.90039062f, + (float16_t)-1.27636719f, (float16_t)3.8125f, (float16_t)3.56054688f, (float16_t)-1.81152344f, + (float16_t)0.16784668f, (float16_t)-3.57226562f, (float16_t)3.0859375f, (float16_t)-3.55273438f, + (float16_t)-0.0768432617f, (float16_t)4.12109375f, (float16_t)-3.12109375f, (float16_t)1.88867188f, + (float16_t)-1.36132812f, (float16_t)2.96289062f, (float16_t)-3.86132812f, (float16_t)-0.0787353516f, + (float16_t)-1.35742188f, (float16_t)-3.9375f, (float16_t)-2.76953125f, (float16_t)2.27929688f, + (float16_t)0.266601562f, (float16_t)-2.74414062f, (float16_t)0.665527344f, (float16_t)-1.93164062f, + (float16_t)-0.160400391f, (float16_t)-0.594726562f, (float16_t)-2.95703125f, (float16_t)1.71972656f, + (float16_t)0.6171875f, (float16_t)3.67578125f, (float16_t)-0.663574219f, (float16_t)3.796875f, + (float16_t)-2.25390625f, (float16_t)-3.74414062f, (float16_t)-1.99804688f, (float16_t)-3.11914062f, + (float16_t)1.01953125f, (float16_t)4.015625f, (float16_t)-1.19628906f, (float16_t)-3.19335938f, + (float16_t)-3.44726562f, (float16_t)2.50390625f, (float16_t)0.0555725098f, (float16_t)1.19726562f, + (float16_t)-2.8515625f, (float16_t)-4.0703125f, (float16_t)1.3671875f, (float16_t)-2.86132812f, + (float16_t)-3.703125f, (float16_t)2.01367188f, (float16_t)-3.0f, (float16_t)3.86914062f, + (float16_t)1.98730469f, (float16_t)-3.9375f, (float16_t)3.78710938f, (float16_t)-1.68066406f, + (float16_t)0.845214844f, (float16_t)-2.30859375f, (float16_t)0.638183594f, (float16_t)0.507324219f, + (float16_t)2.33007812f, (float16_t)0.734863281f, (float16_t)3.28125f, (float16_t)1.05078125f, + (float16_t)2.09570312f, (float16_t)-2.53320312f, (float16_t)-3.06445312f, (float16_t)-0.143432617f, + (float16_t)0.930175781f, (float16_t)-0.496582031f, (float16_t)-0.166503906f, (float16_t)3.08984375f, + (float16_t)-4.25f, (float16_t)-2.0859375f, (float16_t)1.55273438f, (float16_t)1.3203125f, + (float16_t)1.70800781f, (float16_t)0.189208984f, (float16_t)-2.43554688f, (float16_t)1.64257812f, + (float16_t)2.39257812f, (float16_t)2.765625f, (float16_t)-0.561523438f, (float16_t)3.76367188f, + (float16_t)-1.28320312f, (float16_t)2.49609375f, (float16_t)2.0078125f, (float16_t)-0.416992188f, + (float16_t)-0.128662109f, (float16_t)3.43945312f, (float16_t)-3.5625f, (float16_t)-2.44921875f, + (float16_t)-0.935058594f, (float16_t)-2.47265625f, (float16_t)-3.00585938f, (float16_t)3.94335938f, + (float16_t)0.151245117f, (float16_t)1.62304688f, (float16_t)-3.70117188f, (float16_t)-2.5546875f, + (float16_t)2.62890625f, (float16_t)-4.16796875f, (float16_t)-2.7421875f, (float16_t)1.73046875f, + (float16_t)-3.60351562f, (float16_t)2.15039062f, (float16_t)0.350097656f, (float16_t)-0.839355469f, + (float16_t)1.01953125f, (float16_t)1.67089844f, (float16_t)-1.69433594f, (float16_t)3.34960938f, + (float16_t)3.4609375f, (float16_t)-3.43359375f, (float16_t)-1.5703125f, (float16_t)-2.63476562f, + (float16_t)0.0393676758f, (float16_t)1.44042969f, (float16_t)0.502929688f, (float16_t)0.203369141f, + (float16_t)2.09179688f, (float16_t)0.883789062f, (float16_t)-0.384521484f, (float16_t)3.01757812f, + (float16_t)-3.43945312f, (float16_t)2.60351562f, (float16_t)-0.794433594f, (float16_t)-2.38085938f, + (float16_t)2.48632812f, (float16_t)2.58203125f, (float16_t)1.43066406f, (float16_t)1.45019531f, + (float16_t)2.50195312f, (float16_t)0.520019531f, (float16_t)-1.36914062f, (float16_t)3.11914062f, + (float16_t)-0.802246094f, (float16_t)-2.74023438f, (float16_t)2.08007812f, (float16_t)-0.910644531f, + (float16_t)2.41992188f, (float16_t)-0.844238281f, (float16_t)0.239990234f, (float16_t)0.0626831055f, + (float16_t)-1.85742188f, (float16_t)2.078125f, (float16_t)0.192749023f, (float16_t)3.390625f, + (float16_t)1.01074219f, (float16_t)-2.3125f, (float16_t)1.81152344f, (float16_t)-1.92285156f, + (float16_t)-1.61328125f, (float16_t)1.82617188f, (float16_t)-3.23632812f, (float16_t)1.08691406f, + (float16_t)-3.63476562f, (float16_t)1.64257812f, (float16_t)1.88671875f, (float16_t)-1.03320312f, + (float16_t)-2.77539062f, (float16_t)-0.941894531f, (float16_t)3.11328125f, (float16_t)-1.37890625f, + (float16_t)-0.803710938f, (float16_t)3.9765625f, (float16_t)-1.44433594f, (float16_t)3.03125f, + (float16_t)2.32421875f, (float16_t)-2.25f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/scale_data.h new file mode 100644 index 00000000..6b5ed8c1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/scale_data.h @@ -0,0 +1,8 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t bn_op_nhwc_f16_scale[7] = {(float16_t)1.13183594f, + (float16_t)1.06152344f, + (float16_t)1.02246094f, + (float16_t)1.0f, + (float16_t)0.987304688f, + (float16_t)0.981445312f, + (float16_t)0.979980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/bias_data.h new file mode 100644 index 00000000..75a9d873 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/bias_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_nhwc_f32_bias[7] = + {0.0762719065f, 0.0415249765f, 0.0181606114f, 1.11758336e-08f, -0.0158358067f, -0.03086555f, -0.0459585339f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/config_data.h new file mode 100644 index 00000000..bcb0ec17 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/config_data.h @@ -0,0 +1,7 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define BN_OP_NHWC_F32_INPUT_N 1 +#define BN_OP_NHWC_F32_INPUT_H 5 +#define BN_OP_NHWC_F32_INPUT_W 6 +#define BN_OP_NHWC_F32_INPUT_C 7 +#define BN_OP_NHWC_F32_SIZE 210 +#define BN_OP_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/input_data.h new file mode 100644 index 00000000..8e1706b8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/input_data.h @@ -0,0 +1,32 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_nhwc_f32_input[210] = { + -3.6768105f, 3.09227467f, -3.83728123f, -0.439721256f, -2.88790107f, -0.185499638f, 3.24495506f, + 3.53077507f, 0.460196793f, 0.54359591f, 3.09284544f, 1.3112309f, 2.49883556f, -2.67167687f, + -2.83765721f, -0.0710553229f, -3.76145625f, 3.28855944f, 2.50595284f, -0.785657823f, -3.85380054f, + -0.54263103f, -1.175771f, 2.83223844f, -3.89722896f, -3.96349692f, -2.61792707f, 3.62479067f, + 0.923055232f, 3.89194798f, -1.83437741f, -0.788333237f, -2.98545146f, -3.19485021f, -3.95038247f, + 1.18796194f, -3.61176872f, 1.93154275f, -1.31760728f, 3.11915374f, -0.975669801f, 0.526004195f, + -2.83455276f, -3.21029592f, 2.13293839f, 0.00964942481f, 3.61073828f, -1.43903244f, -3.41376424f, + -3.86434841f, 3.6323359f, -1.40012455f, -1.74681616f, -0.360287547f, 2.7386086f, -0.914750934f, + -2.2638154f, -1.72827876f, -0.0617222302f, -0.255154938f, -1.16007411f, 1.15007532f, -0.509103477f, + -1.91047812f, -1.73519361f, 0.900359333f, -0.254626662f, 0.702941477f, 2.35668778f, -1.76246834f, + -1.17801046f, 2.38847089f, -2.48898339f, -2.75421119f, 3.59722281f, 2.20472956f, -3.26026011f, + -3.72765017f, 3.73636842f, 3.78690815f, -0.589680195f, 1.56415999f, -2.48840165f, 2.6273303f, + -1.13065863f, 2.63457704f, 3.12045813f, -3.65881538f, -1.9938364f, 2.56945753f, 2.32951856f, + 2.23669696f, -2.65705013f, 1.71700656f, 1.42978215f, 2.55183768f, 2.88116121f, -0.996058047f, + 3.25594068f, 0.174079835f, -1.31649983f, -0.950457275f, 2.02433538f, -0.658488214f, -1.33434796f, + 3.28390884f, -1.61844075f, -2.40435076f, -0.407074958f, 1.93274045f, 0.115546532f, 0.121696196f, + -2.39315009f, -2.73242426f, -1.07436955f, 3.23549652f, -2.20207715f, -1.48780811f, 3.53770041f, + 0.880358994f, -1.19730139f, -0.141749978f, 3.07093096f, -3.09960699f, 0.407106906f, -2.37703037f, + -0.0108424583f, -1.1089685f, 0.658592284f, 0.154298648f, 2.17549896f, 2.72429085f, -3.08494186f, + 0.165856555f, -2.9428606f, 0.0494765826f, 0.872338116f, -0.178502783f, 1.79929924f, 0.134222746f, + 1.37721515f, 2.5564723f, -1.15912282f, -0.530788124f, -2.6200335f, 3.9966805f, 2.14327049f, + 1.9546473f, -3.81182599f, -0.317181766f, 2.17899823f, -1.16195059f, 2.96477532f, 2.08627462f, + 0.982428908f, -2.1717658f, -0.910770059f, 2.00942945f, 0.857451499f, 3.0439713f, -0.247154251f, + -3.51997328f, 2.27587152f, 3.08917785f, 3.42901373f, 0.361363679f, 0.378967851f, 0.365139455f, + -1.32064593f, -3.33646846f, -2.34207726f, -2.29076576f, 1.39710689f, -1.55507088f, -0.236410484f, + 2.06510377f, -2.88227725f, 1.62430322f, 0.649200439f, 3.70900154f, 2.22953987f, -0.451320231f, + -3.11741471f, 2.6761806f, 0.463247061f, 2.14113879f, -1.59538996f, 1.82778656f, -1.66139293f, + -2.78423643f, -3.36342502f, 0.79915005f, -3.12978292f, -1.76448011f, -3.5881412f, 0.0995579585f, + -1.16235006f, 0.384780109f, -2.51742506f, -0.443280131f, 2.26558614f, 2.22141099f, 3.54125476f, + 0.926825583f, 3.09191656f, -3.14075136f, -0.166989177f, 3.30451322f, 0.316634834f, 1.55526519f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..797ad9a3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_nhwc_f32_output_ref[210] = { + -4.08352232f, 3.32378125f, -3.90511107f, -0.439719051f, -2.86774564f, -0.212931916f, 3.13342452f, + 4.07084751f, 0.529995084f, 0.573938072f, 3.09282994f, 1.27905357f, 2.42172098f, -2.66364789f, + -3.13413835f, -0.0338958018f, -3.82758689f, 3.28854299f, 2.45888591f, -0.801982224f, -3.82188392f, + -0.537638843f, -1.20648241f, 2.91386724f, -3.89720941f, -3.92993665f, -2.60033941f, 3.50558472f, + 1.12057912f, 4.17258453f, -1.85732377f, -0.788329303f, -2.96408033f, -3.16658473f, -3.91651416f, + 1.42028391f, -3.79214168f, 1.99298763f, -1.31760073f, 3.06444478f, -0.988477409f, 0.469416529f, + -3.1306262f, -3.36600351f, 2.19889641f, 0.00964938756f, 3.54990268f, -1.44326401f, -3.39073968f, + -4.2956953f, 3.89702249f, -1.41333961f, -1.74680746f, -0.371633202f, 2.65705609f, -0.942224801f, + -2.48491716f, -1.79293489f, -0.0449447706f, -0.255153656f, -1.16145217f, 1.09792387f, -0.544774354f, + -2.0851655f, -1.80027461f, 0.938696265f, -0.25462538f, 0.678345084f, 2.28220415f, -1.77281201f, + -1.25648141f, 2.5767374f, -2.52659893f, -2.75419736f, 3.53655577f, 2.13305831f, -3.24033737f, + -4.14104033f, 4.00744629f, 3.88993025f, -0.589677215f, 1.52883053f, -2.47321129f, 2.5282805f, + -1.20290947f, 2.83796382f, 3.20854568f, -3.65879703f, -1.98482358f, 2.4910357f, 2.23648667f, + 2.60678005f, -2.77876759f, 1.77364397f, 1.429775f, 2.50419903f, 2.79697037f, -1.02188885f, + 3.75991106f, 0.226299837f, -1.32784092f, -0.950452507f, 1.98327076f, -0.677166283f, -1.35334289f, + 3.79155326f, -1.67634869f, -2.44006968f, -0.407072902f, 1.89281738f, 0.0825424194f, 0.0732785165f, + -2.63124108f, -2.85877252f, -1.08028471f, 3.23548031f, -2.19046903f, -1.49113691f, 3.42025447f, + 1.07227433f, -1.22933555f, -0.126765877f, 3.0709157f, -3.07681322f, 0.368706524f, -2.37495542f, + 0.0640051737f, -1.13557577f, 0.691511393f, 0.154297888f, 2.13255048f, 2.64300346f, -3.06856179f, + 0.263915271f, -3.08213758f, 0.0687459186f, 0.872333765f, -0.192113966f, 1.73513174f, 0.0855519325f, + 1.63439727f, 2.75506043f, -1.16693723f, -0.530785441f, -2.60321641f, 3.89184332f, 2.05400205f, + 2.28768063f, -4.0044899f, -0.306128949f, 2.17898726f, -1.16330528f, 2.8790369f, 1.99815798f, + 1.18775213f, -2.26366878f, -0.913019061f, 2.00941944f, 0.830929518f, 2.95676708f, -0.288118452f, + -3.90608311f, 2.45722032f, 3.17656446f, 3.42899656f, 0.34102428f, 0.341088265f, 0.311802417f, + -1.41785336f, -3.49992776f, -2.37640071f, -2.29075432f, 1.3638593f, -1.55715477f, -0.277591795f, + 2.41264653f, -3.01783204f, 1.67886317f, 0.649197221f, 3.64694142f, 2.15740943f, -0.488158762f, + -3.45064473f, 2.88212347f, 0.491788656f, 2.14112806f, -1.59134281f, 1.76309192f, -1.67377913f, + -3.07370043f, -3.52854037f, 0.835218966f, -3.12976718f, -1.7583257f, -3.55259657f, 0.0515876301f, + -1.23876381f, 0.449945003f, -2.55567789f, -0.443277895f, 2.22151494f, 2.14943099f, 3.42373681f, + 1.12484479f, 3.32340121f, -3.19297242f, -0.166988328f, 3.24749422f, 0.279908925f, 1.47787893f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/scale_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/scale_data.h new file mode 100644 index 00000000..ddb024d4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/scale_data.h @@ -0,0 +1,3 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t bn_op_nhwc_f32_scale[7] = + {1.13135958f, 1.06143749f, 1.02240932f, 0.999994993f, 0.987537265f, 0.981491745f, 0.979792655f}; diff --git a/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/test_data.h new file mode 100644 index 00000000..77d6d456 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/bn_op_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by batch_norm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "scale_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f16/config_data.h new file mode 100644 index 00000000..63912745 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONCAT_C_F16_INPUT_N 1 +#define CONCAT_C_F16_INPUT_H 8 +#define CONCAT_C_F16_INPUT_W 8 +#define CONCAT_C_F16_LHS_C 5 +#define CONCAT_C_F16_RHS_C 3 +#define CONCAT_C_F16_OUTPUT_C 8 +#define CONCAT_C_F16_SIZE 512 +#define CONCAT_C_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f16/lhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f16/lhs_input_data.h new file mode 100644 index 00000000..ca64a42a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f16/lhs_input_data.h @@ -0,0 +1,82 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_f16_lhs_input[320] = { + (float16_t)-0.701171875f, (float16_t)1.9296875f, (float16_t)0.0797729492f, (float16_t)-1.68164062f, + (float16_t)-1.66113281f, (float16_t)0.310058594f, (float16_t)-0.455810547f, (float16_t)-0.506347656f, + (float16_t)-1.70019531f, (float16_t)1.75f, (float16_t)1.31640625f, (float16_t)1.45117188f, + (float16_t)1.62109375f, (float16_t)1.13085938f, (float16_t)0.876953125f, (float16_t)-1.97753906f, + (float16_t)-1.1328125f, (float16_t)-0.201904297f, (float16_t)-0.247558594f, (float16_t)-0.378173828f, + (float16_t)0.309082031f, (float16_t)-0.789550781f, (float16_t)-1.84765625f, (float16_t)-0.127929688f, + (float16_t)1.14550781f, (float16_t)-1.41992188f, (float16_t)0.413818359f, (float16_t)0.013381958f, + (float16_t)-1.09375f, (float16_t)-1.24414062f, (float16_t)-1.28125f, (float16_t)1.24414062f, + (float16_t)1.14746094f, (float16_t)1.16699219f, (float16_t)-1.08984375f, (float16_t)1.93359375f, + (float16_t)-1.91699219f, (float16_t)-1.29003906f, (float16_t)-0.0726928711f, (float16_t)0.501464844f, + (float16_t)1.32617188f, (float16_t)-0.173583984f, (float16_t)-0.0638427734f, (float16_t)1.61816406f, + (float16_t)-0.378173828f, (float16_t)1.23144531f, (float16_t)-0.118530273f, (float16_t)1.8046875f, + (float16_t)0.987792969f, (float16_t)0.848144531f, (float16_t)0.135131836f, (float16_t)-0.541015625f, + (float16_t)-1.88085938f, (float16_t)-1.84472656f, (float16_t)1.16015625f, (float16_t)-0.858886719f, + (float16_t)-1.23339844f, (float16_t)-0.205444336f, (float16_t)1.03515625f, (float16_t)-1.71484375f, + (float16_t)-1.47167969f, (float16_t)0.905761719f, (float16_t)1.84765625f, (float16_t)-1.94433594f, + (float16_t)0.202636719f, (float16_t)1.05371094f, (float16_t)0.8359375f, (float16_t)0.112060547f, + (float16_t)-0.0452270508f, (float16_t)-0.284423828f, (float16_t)-0.221435547f, (float16_t)1.33398438f, + (float16_t)1.85644531f, (float16_t)0.329833984f, (float16_t)0.871582031f, (float16_t)0.400390625f, + (float16_t)-0.0437316895f, (float16_t)-0.257324219f, (float16_t)-0.433837891f, (float16_t)-1.9609375f, + (float16_t)-1.54296875f, (float16_t)1.20703125f, (float16_t)-0.174194336f, (float16_t)1.84082031f, + (float16_t)1.0703125f, (float16_t)-1.76953125f, (float16_t)1.19238281f, (float16_t)1.15429688f, + (float16_t)-1.90136719f, (float16_t)1.93164062f, (float16_t)-1.90429688f, (float16_t)-1.18164062f, + (float16_t)1.81835938f, (float16_t)1.06835938f, (float16_t)-0.416992188f, (float16_t)-0.61328125f, + (float16_t)1.671875f, (float16_t)-1.83691406f, (float16_t)0.255615234f, (float16_t)-1.64648438f, + (float16_t)0.681152344f, (float16_t)0.00813293457f, (float16_t)1.67675781f, (float16_t)-0.7421875f, + (float16_t)0.55859375f, (float16_t)1.703125f, (float16_t)-1.49414062f, (float16_t)0.687988281f, + (float16_t)1.92089844f, (float16_t)-0.562011719f, (float16_t)-1.68457031f, (float16_t)-0.621582031f, + (float16_t)1.81445312f, (float16_t)1.76953125f, (float16_t)-0.385253906f, (float16_t)0.810546875f, + (float16_t)-1.92480469f, (float16_t)0.972167969f, (float16_t)0.693359375f, (float16_t)-1.75488281f, + (float16_t)0.672363281f, (float16_t)0.506347656f, (float16_t)0.714355469f, (float16_t)-0.238037109f, + (float16_t)-1.31640625f, (float16_t)-1.74902344f, (float16_t)-0.0395812988f, (float16_t)0.515625f, + (float16_t)-1.8125f, (float16_t)0.527832031f, (float16_t)-1.74023438f, (float16_t)1.60253906f, + (float16_t)-1.97851562f, (float16_t)-0.682617188f, (float16_t)-0.788574219f, (float16_t)0.322509766f, + (float16_t)-0.900390625f, (float16_t)-1.07226562f, (float16_t)0.0556640625f, (float16_t)-0.800292969f, + (float16_t)1.81640625f, (float16_t)-0.23059082f, (float16_t)-0.422851562f, (float16_t)1.359375f, + (float16_t)0.248046875f, (float16_t)1.01757812f, (float16_t)0.754882812f, (float16_t)1.11914062f, + (float16_t)-0.168823242f, (float16_t)1.68359375f, (float16_t)-0.414306641f, (float16_t)-0.327636719f, + (float16_t)1.06152344f, (float16_t)-0.926757812f, (float16_t)-1.25f, (float16_t)-0.112792969f, + (float16_t)-1.98925781f, (float16_t)1.65039062f, (float16_t)0.406005859f, (float16_t)-0.591308594f, + (float16_t)0.370361328f, (float16_t)-0.0891113281f, (float16_t)1.61425781f, (float16_t)-1.41210938f, + (float16_t)-1.8046875f, (float16_t)-1.78125f, (float16_t)1.32128906f, (float16_t)0.243408203f, + (float16_t)1.58496094f, (float16_t)-0.321533203f, (float16_t)0.470214844f, (float16_t)-0.538085938f, + (float16_t)-1.25097656f, (float16_t)0.143676758f, (float16_t)0.916503906f, (float16_t)-0.805175781f, + (float16_t)-1.58203125f, (float16_t)-1.58300781f, (float16_t)0.584960938f, (float16_t)0.000428438187f, + (float16_t)0.248413086f, (float16_t)0.936523438f, (float16_t)-0.951660156f, (float16_t)0.456787109f, + (float16_t)0.417480469f, (float16_t)-1.02636719f, (float16_t)0.613769531f, (float16_t)1.87597656f, + (float16_t)-0.0553588867f, (float16_t)1.8515625f, (float16_t)0.943359375f, (float16_t)1.90332031f, + (float16_t)0.673339844f, (float16_t)1.62597656f, (float16_t)0.927734375f, (float16_t)0.168212891f, + (float16_t)-0.962402344f, (float16_t)-0.967285156f, (float16_t)0.728027344f, (float16_t)-1.82910156f, + (float16_t)-0.638671875f, (float16_t)0.0942993164f, (float16_t)-1.44824219f, (float16_t)-1.68945312f, + (float16_t)-0.570800781f, (float16_t)-1.18457031f, (float16_t)-1.20117188f, (float16_t)1.77929688f, + (float16_t)1.61621094f, (float16_t)-0.438964844f, (float16_t)-0.456542969f, (float16_t)-1.09863281f, + (float16_t)-1.54785156f, (float16_t)1.2421875f, (float16_t)1.21777344f, (float16_t)-0.212280273f, + (float16_t)-0.458496094f, (float16_t)1.33105469f, (float16_t)0.663574219f, (float16_t)0.48046875f, + (float16_t)-0.347167969f, (float16_t)1.98828125f, (float16_t)-0.499267578f, (float16_t)0.697753906f, + (float16_t)1.015625f, (float16_t)-1.74121094f, (float16_t)0.365722656f, (float16_t)-0.990234375f, + (float16_t)0.423583984f, (float16_t)-0.779296875f, (float16_t)-0.956054688f, (float16_t)-0.365478516f, + (float16_t)0.995117188f, (float16_t)1.29394531f, (float16_t)1.21777344f, (float16_t)0.493652344f, + (float16_t)-0.591308594f, (float16_t)0.742675781f, (float16_t)-1.43554688f, (float16_t)1.87402344f, + (float16_t)-1.0390625f, (float16_t)-0.815917969f, (float16_t)1.38671875f, (float16_t)1.31542969f, + (float16_t)-0.627929688f, (float16_t)-0.978027344f, (float16_t)0.0156707764f, (float16_t)1.57421875f, + (float16_t)-1.67089844f, (float16_t)-1.97460938f, (float16_t)0.736328125f, (float16_t)0.520996094f, + (float16_t)-1.03417969f, (float16_t)-0.0155029297f, (float16_t)-0.0828857422f, (float16_t)-0.090637207f, + (float16_t)0.86328125f, (float16_t)-0.845703125f, (float16_t)1.68164062f, (float16_t)-1.1328125f, + (float16_t)-0.382080078f, (float16_t)-0.118103027f, (float16_t)0.307128906f, (float16_t)0.442382812f, + (float16_t)-0.327148438f, (float16_t)-0.263427734f, (float16_t)1.14746094f, (float16_t)1.34960938f, + (float16_t)-0.312255859f, (float16_t)0.929199219f, (float16_t)-1.49023438f, (float16_t)-1.04980469f, + (float16_t)-0.861328125f, (float16_t)-1.87304688f, (float16_t)-0.292480469f, (float16_t)-1.59960938f, + (float16_t)-0.503417969f, (float16_t)1.90429688f, (float16_t)0.138305664f, (float16_t)-0.87890625f, + (float16_t)-1.66894531f, (float16_t)-0.115966797f, (float16_t)-0.396728516f, (float16_t)1.17480469f, + (float16_t)-1.8828125f, (float16_t)0.865722656f, (float16_t)0.33984375f, (float16_t)0.322753906f, + (float16_t)-1.0234375f, (float16_t)-0.694824219f, (float16_t)1.50878906f, (float16_t)0.772460938f, + (float16_t)-1.81054688f, (float16_t)1.44140625f, (float16_t)1.42773438f, (float16_t)-0.0352783203f, + (float16_t)1.52050781f, (float16_t)-1.328125f, (float16_t)-1.91308594f, (float16_t)-0.218383789f, + (float16_t)-0.555664062f, (float16_t)-1.75390625f, (float16_t)1.37304688f, (float16_t)1.28808594f, + (float16_t)-0.201293945f, (float16_t)-1.55761719f, (float16_t)-0.934570312f, (float16_t)0.403808594f, + (float16_t)0.009765625f, (float16_t)-0.798339844f, (float16_t)0.163452148f, (float16_t)-1.83886719f, + (float16_t)0.748046875f, (float16_t)1.5234375f, (float16_t)0.359375f, (float16_t)-1.84472656f, + (float16_t)1.44238281f, (float16_t)1.95800781f, (float16_t)1.62597656f, (float16_t)1.4609375f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f16/output_ref_data.h new file mode 100644 index 00000000..94429b9c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f16/output_ref_data.h @@ -0,0 +1,130 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_f16_output_ref[512] = { + (float16_t)-0.701171875f, (float16_t)1.9296875f, (float16_t)0.0797729492f, (float16_t)-1.68164062f, + (float16_t)-1.66113281f, (float16_t)0.656738281f, (float16_t)1.58398438f, (float16_t)1.02832031f, + (float16_t)0.310058594f, (float16_t)-0.455810547f, (float16_t)-0.506347656f, (float16_t)-1.70019531f, + (float16_t)1.75f, (float16_t)-0.417724609f, (float16_t)-0.0461425781f, (float16_t)1.20214844f, + (float16_t)1.31640625f, (float16_t)1.45117188f, (float16_t)1.62109375f, (float16_t)1.13085938f, + (float16_t)0.876953125f, (float16_t)-0.599609375f, (float16_t)-1.87792969f, (float16_t)-1.74707031f, + (float16_t)-1.97753906f, (float16_t)-1.1328125f, (float16_t)-0.201904297f, (float16_t)-0.247558594f, + (float16_t)-0.378173828f, (float16_t)-1.22265625f, (float16_t)1.4453125f, (float16_t)-0.162597656f, + (float16_t)0.309082031f, (float16_t)-0.789550781f, (float16_t)-1.84765625f, (float16_t)-0.127929688f, + (float16_t)1.14550781f, (float16_t)0.905761719f, (float16_t)1.88085938f, (float16_t)0.672363281f, + (float16_t)-1.41992188f, (float16_t)0.413818359f, (float16_t)0.013381958f, (float16_t)-1.09375f, + (float16_t)-1.24414062f, (float16_t)1.92675781f, (float16_t)0.145019531f, (float16_t)-1.68066406f, + (float16_t)-1.28125f, (float16_t)1.24414062f, (float16_t)1.14746094f, (float16_t)1.16699219f, + (float16_t)-1.08984375f, (float16_t)1.64550781f, (float16_t)1.01855469f, (float16_t)1.34277344f, + (float16_t)1.93359375f, (float16_t)-1.91699219f, (float16_t)-1.29003906f, (float16_t)-0.0726928711f, + (float16_t)0.501464844f, (float16_t)0.236694336f, (float16_t)0.845214844f, (float16_t)-0.235595703f, + (float16_t)1.32617188f, (float16_t)-0.173583984f, (float16_t)-0.0638427734f, (float16_t)1.61816406f, + (float16_t)-0.378173828f, (float16_t)-0.984375f, (float16_t)1.18066406f, (float16_t)1.04980469f, + (float16_t)1.23144531f, (float16_t)-0.118530273f, (float16_t)1.8046875f, (float16_t)0.987792969f, + (float16_t)0.848144531f, (float16_t)0.0700683594f, (float16_t)0.0883178711f, (float16_t)0.922851562f, + (float16_t)0.135131836f, (float16_t)-0.541015625f, (float16_t)-1.88085938f, (float16_t)-1.84472656f, + (float16_t)1.16015625f, (float16_t)1.375f, (float16_t)0.0233001709f, (float16_t)-0.153320312f, + (float16_t)-0.858886719f, (float16_t)-1.23339844f, (float16_t)-0.205444336f, (float16_t)1.03515625f, + (float16_t)-1.71484375f, (float16_t)1.17285156f, (float16_t)1.91894531f, (float16_t)-0.873535156f, + (float16_t)-1.47167969f, (float16_t)0.905761719f, (float16_t)1.84765625f, (float16_t)-1.94433594f, + (float16_t)0.202636719f, (float16_t)0.465087891f, (float16_t)1.171875f, (float16_t)-0.831542969f, + (float16_t)1.05371094f, (float16_t)0.8359375f, (float16_t)0.112060547f, (float16_t)-0.0452270508f, + (float16_t)-0.284423828f, (float16_t)-1.79101562f, (float16_t)-1.80371094f, (float16_t)1.91015625f, + (float16_t)-0.221435547f, (float16_t)1.33398438f, (float16_t)1.85644531f, (float16_t)0.329833984f, + (float16_t)0.871582031f, (float16_t)-1.671875f, (float16_t)-1.58886719f, (float16_t)0.437011719f, + (float16_t)0.400390625f, (float16_t)-0.0437316895f, (float16_t)-0.257324219f, (float16_t)-0.433837891f, + (float16_t)-1.9609375f, (float16_t)1.77148438f, (float16_t)-1.46582031f, (float16_t)-1.69726562f, + (float16_t)-1.54296875f, (float16_t)1.20703125f, (float16_t)-0.174194336f, (float16_t)1.84082031f, + (float16_t)1.0703125f, (float16_t)1.34960938f, (float16_t)-1.94921875f, (float16_t)0.154907227f, + (float16_t)-1.76953125f, (float16_t)1.19238281f, (float16_t)1.15429688f, (float16_t)-1.90136719f, + (float16_t)1.93164062f, (float16_t)0.251464844f, (float16_t)1.52441406f, (float16_t)0.427001953f, + (float16_t)-1.90429688f, (float16_t)-1.18164062f, (float16_t)1.81835938f, (float16_t)1.06835938f, + (float16_t)-0.416992188f, (float16_t)-1.27832031f, (float16_t)-1.38378906f, (float16_t)-1.37792969f, + (float16_t)-0.61328125f, (float16_t)1.671875f, (float16_t)-1.83691406f, (float16_t)0.255615234f, + (float16_t)-1.64648438f, (float16_t)-0.890625f, (float16_t)1.24902344f, (float16_t)1.22363281f, + (float16_t)0.681152344f, (float16_t)0.00813293457f, (float16_t)1.67675781f, (float16_t)-0.7421875f, + (float16_t)0.55859375f, (float16_t)1.09570312f, (float16_t)1.88574219f, (float16_t)1.50292969f, + (float16_t)1.703125f, (float16_t)-1.49414062f, (float16_t)0.687988281f, (float16_t)1.92089844f, + (float16_t)-0.562011719f, (float16_t)-1.93066406f, (float16_t)-0.50390625f, (float16_t)1.45507812f, + (float16_t)-1.68457031f, (float16_t)-0.621582031f, (float16_t)1.81445312f, (float16_t)1.76953125f, + (float16_t)-0.385253906f, (float16_t)-0.996582031f, (float16_t)-1.5703125f, (float16_t)0.720703125f, + (float16_t)0.810546875f, (float16_t)-1.92480469f, (float16_t)0.972167969f, (float16_t)0.693359375f, + (float16_t)-1.75488281f, (float16_t)-0.991210938f, (float16_t)0.675292969f, (float16_t)-1.18359375f, + (float16_t)0.672363281f, (float16_t)0.506347656f, (float16_t)0.714355469f, (float16_t)-0.238037109f, + (float16_t)-1.31640625f, (float16_t)-1.94042969f, (float16_t)1.32324219f, (float16_t)-0.0309753418f, + (float16_t)-1.74902344f, (float16_t)-0.0395812988f, (float16_t)0.515625f, (float16_t)-1.8125f, + (float16_t)0.527832031f, (float16_t)1.65722656f, (float16_t)1.41894531f, (float16_t)-1.30859375f, + (float16_t)-1.74023438f, (float16_t)1.60253906f, (float16_t)-1.97851562f, (float16_t)-0.682617188f, + (float16_t)-0.788574219f, (float16_t)0.319580078f, (float16_t)-0.859375f, (float16_t)0.723632812f, + (float16_t)0.322509766f, (float16_t)-0.900390625f, (float16_t)-1.07226562f, (float16_t)0.0556640625f, + (float16_t)-0.800292969f, (float16_t)-0.11126709f, (float16_t)-1.49902344f, (float16_t)-1.06445312f, + (float16_t)1.81640625f, (float16_t)-0.23059082f, (float16_t)-0.422851562f, (float16_t)1.359375f, + (float16_t)0.248046875f, (float16_t)1.93261719f, (float16_t)-0.16394043f, (float16_t)0.255615234f, + (float16_t)1.01757812f, (float16_t)0.754882812f, (float16_t)1.11914062f, (float16_t)-0.168823242f, + (float16_t)1.68359375f, (float16_t)1.12695312f, (float16_t)0.258544922f, (float16_t)-1.77929688f, + (float16_t)-0.414306641f, (float16_t)-0.327636719f, (float16_t)1.06152344f, (float16_t)-0.926757812f, + (float16_t)-1.25f, (float16_t)-1.640625f, (float16_t)-0.375f, (float16_t)0.902832031f, + (float16_t)-0.112792969f, (float16_t)-1.98925781f, (float16_t)1.65039062f, (float16_t)0.406005859f, + (float16_t)-0.591308594f, (float16_t)-0.805175781f, (float16_t)-1.82128906f, (float16_t)-0.763183594f, + (float16_t)0.370361328f, (float16_t)-0.0891113281f, (float16_t)1.61425781f, (float16_t)-1.41210938f, + (float16_t)-1.8046875f, (float16_t)-0.937011719f, (float16_t)-0.626953125f, (float16_t)0.712890625f, + (float16_t)-1.78125f, (float16_t)1.32128906f, (float16_t)0.243408203f, (float16_t)1.58496094f, + (float16_t)-0.321533203f, (float16_t)1.61328125f, (float16_t)-1.93261719f, (float16_t)1.20605469f, + (float16_t)0.470214844f, (float16_t)-0.538085938f, (float16_t)-1.25097656f, (float16_t)0.143676758f, + (float16_t)0.916503906f, (float16_t)-1.89746094f, (float16_t)1.98144531f, (float16_t)1.14355469f, + (float16_t)-0.805175781f, (float16_t)-1.58203125f, (float16_t)-1.58300781f, (float16_t)0.584960938f, + (float16_t)0.000428438187f, (float16_t)0.476806641f, (float16_t)-1.86230469f, (float16_t)-1.70800781f, + (float16_t)0.248413086f, (float16_t)0.936523438f, (float16_t)-0.951660156f, (float16_t)0.456787109f, + (float16_t)0.417480469f, (float16_t)1.58496094f, (float16_t)1.77246094f, (float16_t)1.15917969f, + (float16_t)-1.02636719f, (float16_t)0.613769531f, (float16_t)1.87597656f, (float16_t)-0.0553588867f, + (float16_t)1.8515625f, (float16_t)-0.161254883f, (float16_t)-1.30273438f, (float16_t)-0.840820312f, + (float16_t)0.943359375f, (float16_t)1.90332031f, (float16_t)0.673339844f, (float16_t)1.62597656f, + (float16_t)0.927734375f, (float16_t)0.750488281f, (float16_t)-0.485595703f, (float16_t)1.70703125f, + (float16_t)0.168212891f, (float16_t)-0.962402344f, (float16_t)-0.967285156f, (float16_t)0.728027344f, + (float16_t)-1.82910156f, (float16_t)-0.1328125f, (float16_t)-1.42382812f, (float16_t)1.93066406f, + (float16_t)-0.638671875f, (float16_t)0.0942993164f, (float16_t)-1.44824219f, (float16_t)-1.68945312f, + (float16_t)-0.570800781f, (float16_t)-1.0546875f, (float16_t)1.40136719f, (float16_t)-1.69335938f, + (float16_t)-1.18457031f, (float16_t)-1.20117188f, (float16_t)1.77929688f, (float16_t)1.61621094f, + (float16_t)-0.438964844f, (float16_t)1.12304688f, (float16_t)1.19726562f, (float16_t)1.58105469f, + (float16_t)-0.456542969f, (float16_t)-1.09863281f, (float16_t)-1.54785156f, (float16_t)1.2421875f, + (float16_t)1.21777344f, (float16_t)-1.86914062f, (float16_t)0.983886719f, (float16_t)-0.87109375f, + (float16_t)-0.212280273f, (float16_t)-0.458496094f, (float16_t)1.33105469f, (float16_t)0.663574219f, + (float16_t)0.48046875f, (float16_t)-1.54882812f, (float16_t)1.109375f, (float16_t)1.37792969f, + (float16_t)-0.347167969f, (float16_t)1.98828125f, (float16_t)-0.499267578f, (float16_t)0.697753906f, + (float16_t)1.015625f, (float16_t)0.0338134766f, (float16_t)-1.58886719f, (float16_t)-1.64941406f, + (float16_t)-1.74121094f, (float16_t)0.365722656f, (float16_t)-0.990234375f, (float16_t)0.423583984f, + (float16_t)-0.779296875f, (float16_t)0.889648438f, (float16_t)-1.52929688f, (float16_t)-0.251953125f, + (float16_t)-0.956054688f, (float16_t)-0.365478516f, (float16_t)0.995117188f, (float16_t)1.29394531f, + (float16_t)1.21777344f, (float16_t)0.643066406f, (float16_t)0.855957031f, (float16_t)1.83691406f, + (float16_t)0.493652344f, (float16_t)-0.591308594f, (float16_t)0.742675781f, (float16_t)-1.43554688f, + (float16_t)1.87402344f, (float16_t)-1.65136719f, (float16_t)-0.819824219f, (float16_t)-1.0390625f, + (float16_t)-1.0390625f, (float16_t)-0.815917969f, (float16_t)1.38671875f, (float16_t)1.31542969f, + (float16_t)-0.627929688f, (float16_t)-1.09863281f, (float16_t)1.25097656f, (float16_t)-0.809570312f, + (float16_t)-0.978027344f, (float16_t)0.0156707764f, (float16_t)1.57421875f, (float16_t)-1.67089844f, + (float16_t)-1.97460938f, (float16_t)-1.75683594f, (float16_t)0.358886719f, (float16_t)-1.42773438f, + (float16_t)0.736328125f, (float16_t)0.520996094f, (float16_t)-1.03417969f, (float16_t)-0.0155029297f, + (float16_t)-0.0828857422f, (float16_t)0.132080078f, (float16_t)-1.50683594f, (float16_t)-1.42480469f, + (float16_t)-0.090637207f, (float16_t)0.86328125f, (float16_t)-0.845703125f, (float16_t)1.68164062f, + (float16_t)-1.1328125f, (float16_t)-0.875f, (float16_t)-1.47363281f, (float16_t)0.516601562f, + (float16_t)-0.382080078f, (float16_t)-0.118103027f, (float16_t)0.307128906f, (float16_t)0.442382812f, + (float16_t)-0.327148438f, (float16_t)-0.135498047f, (float16_t)-1.38085938f, (float16_t)1.08984375f, + (float16_t)-0.263427734f, (float16_t)1.14746094f, (float16_t)1.34960938f, (float16_t)-0.312255859f, + (float16_t)0.929199219f, (float16_t)1.90136719f, (float16_t)0.00661468506f, (float16_t)-1.39648438f, + (float16_t)-1.49023438f, (float16_t)-1.04980469f, (float16_t)-0.861328125f, (float16_t)-1.87304688f, + (float16_t)-0.292480469f, (float16_t)1.28417969f, (float16_t)-1.16503906f, (float16_t)0.385253906f, + (float16_t)-1.59960938f, (float16_t)-0.503417969f, (float16_t)1.90429688f, (float16_t)0.138305664f, + (float16_t)-0.87890625f, (float16_t)-1.30175781f, (float16_t)0.51171875f, (float16_t)-0.25f, + (float16_t)-1.66894531f, (float16_t)-0.115966797f, (float16_t)-0.396728516f, (float16_t)1.17480469f, + (float16_t)-1.8828125f, (float16_t)-0.982910156f, (float16_t)-0.6875f, (float16_t)-1.05664062f, + (float16_t)0.865722656f, (float16_t)0.33984375f, (float16_t)0.322753906f, (float16_t)-1.0234375f, + (float16_t)-0.694824219f, (float16_t)1.83203125f, (float16_t)0.371337891f, (float16_t)0.958496094f, + (float16_t)1.50878906f, (float16_t)0.772460938f, (float16_t)-1.81054688f, (float16_t)1.44140625f, + (float16_t)1.42773438f, (float16_t)-1.81640625f, (float16_t)1.0f, (float16_t)0.203735352f, + (float16_t)-0.0352783203f, (float16_t)1.52050781f, (float16_t)-1.328125f, (float16_t)-1.91308594f, + (float16_t)-0.218383789f, (float16_t)0.286376953f, (float16_t)-1.984375f, (float16_t)-0.302490234f, + (float16_t)-0.555664062f, (float16_t)-1.75390625f, (float16_t)1.37304688f, (float16_t)1.28808594f, + (float16_t)-0.201293945f, (float16_t)-0.693847656f, (float16_t)1.71679688f, (float16_t)-1.0703125f, + (float16_t)-1.55761719f, (float16_t)-0.934570312f, (float16_t)0.403808594f, (float16_t)0.009765625f, + (float16_t)-0.798339844f, (float16_t)-1.5234375f, (float16_t)-1.8203125f, (float16_t)-0.867675781f, + (float16_t)0.163452148f, (float16_t)-1.83886719f, (float16_t)0.748046875f, (float16_t)1.5234375f, + (float16_t)0.359375f, (float16_t)1.51660156f, (float16_t)1.33691406f, (float16_t)-1.3046875f, + (float16_t)-1.84472656f, (float16_t)1.44238281f, (float16_t)1.95800781f, (float16_t)1.62597656f, + (float16_t)1.4609375f, (float16_t)1.85742188f, (float16_t)-1.17480469f, (float16_t)0.303222656f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f16/rhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f16/rhs_input_data.h new file mode 100644 index 00000000..aa05978e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f16/rhs_input_data.h @@ -0,0 +1,50 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_f16_rhs_input[192] = { + (float16_t)0.656738281f, (float16_t)1.58398438f, (float16_t)1.02832031f, (float16_t)-0.417724609f, + (float16_t)-0.0461425781f, (float16_t)1.20214844f, (float16_t)-0.599609375f, (float16_t)-1.87792969f, + (float16_t)-1.74707031f, (float16_t)-1.22265625f, (float16_t)1.4453125f, (float16_t)-0.162597656f, + (float16_t)0.905761719f, (float16_t)1.88085938f, (float16_t)0.672363281f, (float16_t)1.92675781f, + (float16_t)0.145019531f, (float16_t)-1.68066406f, (float16_t)1.64550781f, (float16_t)1.01855469f, + (float16_t)1.34277344f, (float16_t)0.236694336f, (float16_t)0.845214844f, (float16_t)-0.235595703f, + (float16_t)-0.984375f, (float16_t)1.18066406f, (float16_t)1.04980469f, (float16_t)0.0700683594f, + (float16_t)0.0883178711f, (float16_t)0.922851562f, (float16_t)1.375f, (float16_t)0.0233001709f, + (float16_t)-0.153320312f, (float16_t)1.17285156f, (float16_t)1.91894531f, (float16_t)-0.873535156f, + (float16_t)0.465087891f, (float16_t)1.171875f, (float16_t)-0.831542969f, (float16_t)-1.79101562f, + (float16_t)-1.80371094f, (float16_t)1.91015625f, (float16_t)-1.671875f, (float16_t)-1.58886719f, + (float16_t)0.437011719f, (float16_t)1.77148438f, (float16_t)-1.46582031f, (float16_t)-1.69726562f, + (float16_t)1.34960938f, (float16_t)-1.94921875f, (float16_t)0.154907227f, (float16_t)0.251464844f, + (float16_t)1.52441406f, (float16_t)0.427001953f, (float16_t)-1.27832031f, (float16_t)-1.38378906f, + (float16_t)-1.37792969f, (float16_t)-0.890625f, (float16_t)1.24902344f, (float16_t)1.22363281f, + (float16_t)1.09570312f, (float16_t)1.88574219f, (float16_t)1.50292969f, (float16_t)-1.93066406f, + (float16_t)-0.50390625f, (float16_t)1.45507812f, (float16_t)-0.996582031f, (float16_t)-1.5703125f, + (float16_t)0.720703125f, (float16_t)-0.991210938f, (float16_t)0.675292969f, (float16_t)-1.18359375f, + (float16_t)-1.94042969f, (float16_t)1.32324219f, (float16_t)-0.0309753418f, (float16_t)1.65722656f, + (float16_t)1.41894531f, (float16_t)-1.30859375f, (float16_t)0.319580078f, (float16_t)-0.859375f, + (float16_t)0.723632812f, (float16_t)-0.11126709f, (float16_t)-1.49902344f, (float16_t)-1.06445312f, + (float16_t)1.93261719f, (float16_t)-0.16394043f, (float16_t)0.255615234f, (float16_t)1.12695312f, + (float16_t)0.258544922f, (float16_t)-1.77929688f, (float16_t)-1.640625f, (float16_t)-0.375f, + (float16_t)0.902832031f, (float16_t)-0.805175781f, (float16_t)-1.82128906f, (float16_t)-0.763183594f, + (float16_t)-0.937011719f, (float16_t)-0.626953125f, (float16_t)0.712890625f, (float16_t)1.61328125f, + (float16_t)-1.93261719f, (float16_t)1.20605469f, (float16_t)-1.89746094f, (float16_t)1.98144531f, + (float16_t)1.14355469f, (float16_t)0.476806641f, (float16_t)-1.86230469f, (float16_t)-1.70800781f, + (float16_t)1.58496094f, (float16_t)1.77246094f, (float16_t)1.15917969f, (float16_t)-0.161254883f, + (float16_t)-1.30273438f, (float16_t)-0.840820312f, (float16_t)0.750488281f, (float16_t)-0.485595703f, + (float16_t)1.70703125f, (float16_t)-0.1328125f, (float16_t)-1.42382812f, (float16_t)1.93066406f, + (float16_t)-1.0546875f, (float16_t)1.40136719f, (float16_t)-1.69335938f, (float16_t)1.12304688f, + (float16_t)1.19726562f, (float16_t)1.58105469f, (float16_t)-1.86914062f, (float16_t)0.983886719f, + (float16_t)-0.87109375f, (float16_t)-1.54882812f, (float16_t)1.109375f, (float16_t)1.37792969f, + (float16_t)0.0338134766f, (float16_t)-1.58886719f, (float16_t)-1.64941406f, (float16_t)0.889648438f, + (float16_t)-1.52929688f, (float16_t)-0.251953125f, (float16_t)0.643066406f, (float16_t)0.855957031f, + (float16_t)1.83691406f, (float16_t)-1.65136719f, (float16_t)-0.819824219f, (float16_t)-1.0390625f, + (float16_t)-1.09863281f, (float16_t)1.25097656f, (float16_t)-0.809570312f, (float16_t)-1.75683594f, + (float16_t)0.358886719f, (float16_t)-1.42773438f, (float16_t)0.132080078f, (float16_t)-1.50683594f, + (float16_t)-1.42480469f, (float16_t)-0.875f, (float16_t)-1.47363281f, (float16_t)0.516601562f, + (float16_t)-0.135498047f, (float16_t)-1.38085938f, (float16_t)1.08984375f, (float16_t)1.90136719f, + (float16_t)0.00661468506f, (float16_t)-1.39648438f, (float16_t)1.28417969f, (float16_t)-1.16503906f, + (float16_t)0.385253906f, (float16_t)-1.30175781f, (float16_t)0.51171875f, (float16_t)-0.25f, + (float16_t)-0.982910156f, (float16_t)-0.6875f, (float16_t)-1.05664062f, (float16_t)1.83203125f, + (float16_t)0.371337891f, (float16_t)0.958496094f, (float16_t)-1.81640625f, (float16_t)1.0f, + (float16_t)0.203735352f, (float16_t)0.286376953f, (float16_t)-1.984375f, (float16_t)-0.302490234f, + (float16_t)-0.693847656f, (float16_t)1.71679688f, (float16_t)-1.0703125f, (float16_t)-1.5234375f, + (float16_t)-1.8203125f, (float16_t)-0.867675781f, (float16_t)1.51660156f, (float16_t)1.33691406f, + (float16_t)-1.3046875f, (float16_t)1.85742188f, (float16_t)-1.17480469f, (float16_t)0.303222656f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f16/test_data.h new file mode 100644 index 00000000..3102dc40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_data.h" +#include "output_ref_data.h" +#include "rhs_input_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f32/config_data.h new file mode 100644 index 00000000..f03541f4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONCAT_C_F32_INPUT_N 1 +#define CONCAT_C_F32_INPUT_H 8 +#define CONCAT_C_F32_INPUT_W 8 +#define CONCAT_C_F32_LHS_C 5 +#define CONCAT_C_F32_RHS_C 3 +#define CONCAT_C_F32_OUTPUT_C 8 +#define CONCAT_C_F32_SIZE 512 +#define CONCAT_C_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f32/lhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f32/lhs_input_data.h new file mode 100644 index 00000000..d73762b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f32/lhs_input_data.h @@ -0,0 +1,48 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_f32_lhs_input[320] = { + 1.5905484f, 0.213247269f, 1.50822258f, 0.18772532f, -1.65022159f, 0.653924048f, -0.324042618f, + 1.00326312f, -0.865900636f, 0.100348517f, -0.689203441f, -0.85363853f, -1.35204029f, -0.998536587f, + 1.71335804f, -0.904162228f, -0.48623544f, -0.453189284f, -1.05511391f, -1.85142446f, 0.573095381f, + -0.630049765f, 1.96921051f, -0.879609466f, -1.81119084f, 0.610224128f, 1.48374665f, -1.54600561f, + 0.350181282f, 1.09185135f, -0.453883797f, -1.44533002f, 0.469801217f, 0.689039528f, 1.11613512f, + 1.6716069f, -0.721725643f, -0.977015555f, 1.29637206f, 1.3479085f, 1.11645722f, 1.84977078f, + 1.49946535f, 1.86610198f, 1.20008397f, -0.243996799f, 0.371835053f, 0.342447937f, 0.421002835f, + -0.989560425f, 1.58035648f, -0.0253394861f, 0.376758128f, -1.34343624f, 0.186506361f, -0.00348492153f, + 1.65051079f, -0.242471248f, 1.6510483f, -1.26842999f, 1.30186164f, -0.240125388f, -1.26992142f, + 0.519313037f, 0.159560367f, 0.90529573f, 1.71360648f, 0.523714423f, 1.125844f, -0.0473834537f, + -1.84363139f, -0.0546940789f, -0.68375504f, -0.0879177675f, -0.639792025f, -0.950858295f, -1.13627851f, + 0.0421634912f, -0.724040747f, -0.0500799865f, 0.427284062f, 0.7314803f, 0.077243343f, -1.9171741f, + -0.361837178f, 0.519341648f, -1.12546301f, 1.89757669f, 1.54933345f, 1.6979022f, 0.173876718f, + 1.98020959f, -1.92358208f, 1.02739656f, 1.94929576f, 1.97522688f, 0.742218435f, -0.0849181041f, + -0.646803439f, 0.527525783f, -1.97531462f, -0.498139381f, -0.349555075f, 1.05780685f, 0.781011283f, + 1.78646421f, -1.52397561f, 1.84637976f, -1.12087047f, -1.38241947f, 0.849916816f, -1.69657326f, + 0.746614397f, 1.70187628f, 1.3481642f, 0.105678484f, -0.328378886f, -1.15257859f, 1.30376685f, + -0.3928653f, -1.50504339f, 1.13257635f, -1.76501691f, 1.55306363f, 0.799861491f, 1.90971541f, + 1.41622698f, -1.0948081f, -1.25665772f, -1.05202937f, -0.592105448f, -1.49134147f, 1.76133847f, + -0.13560985f, -0.521939039f, -1.50386333f, -1.74342549f, 1.41328955f, -0.888677835f, -1.11625218f, + -0.772987306f, 0.97667402f, 0.689771533f, -1.00116873f, 1.82695436f, 0.854154229f, 0.865499973f, + 1.44016635f, -1.91715682f, -0.674742997f, 1.67957544f, 1.87823737f, 1.80071378f, -0.603678346f, + -0.808042705f, -0.202225432f, 1.11050224f, -0.634934008f, 0.185634077f, -1.02219474f, -0.256644219f, + 1.40835166f, 0.628774166f, 1.83950198f, -1.34912169f, 1.56406629f, -0.0192871653f, 1.99430537f, + -1.47155762f, -0.262426615f, 0.657834768f, -1.18130279f, -1.91403079f, 0.987664223f, 1.51335287f, + -1.82511806f, -0.696289599f, -1.287503f, -1.59239793f, -0.808493376f, -1.64585936f, 1.2697506f, + -1.54561913f, -0.362702191f, -1.91409338f, -0.343490124f, -0.59294349f, 1.22589445f, 1.36712289f, + 1.60158813f, -1.17323816f, 1.86622787f, -0.304948092f, 1.20477545f, -1.03508019f, 1.47431147f, + -1.83763158f, 0.410106391f, -0.210118398f, -1.12570572f, 1.61157155f, 0.419266373f, 0.413841218f, + 1.58023572f, 1.14049876f, -0.098340407f, -0.885258734f, -1.18545103f, -1.11772776f, -0.174342304f, + -1.93207622f, 1.19093943f, -1.59933531f, 1.3674897f, -1.03960502f, 1.69916034f, -1.31158209f, + 0.165540278f, 1.67753613f, 0.43388021f, 0.226703614f, 1.87962365f, -0.410655916f, 0.447159231f, + -0.377116621f, -0.220503226f, 1.05832791f, 1.69701421f, 0.0493569933f, -1.96732485f, 0.544707596f, + 1.73170638f, -0.886866391f, -1.84228778f, -1.80681884f, -0.529426396f, -0.669142187f, -0.589991808f, + -1.26139247f, -0.279881895f, -0.475532234f, -1.39828432f, -0.891863823f, 0.164871126f, 0.266283691f, + 0.400581568f, -0.902313292f, -1.81646371f, 1.78873181f, -1.85841835f, 0.977805614f, 1.3044585f, + -0.555653751f, -1.82841945f, -1.1455195f, -0.220016554f, 1.40633345f, 0.572666228f, 1.40525651f, + 0.113541938f, -0.0736934841f, -0.352856904f, -1.72262585f, -1.60049915f, 1.40820944f, -1.81724942f, + -1.38657618f, 0.224931434f, 0.484975666f, -1.74386966f, -0.243373454f, 0.833463252f, 0.155084193f, + 0.0453781188f, 1.32347846f, -1.05006754f, -1.86226237f, 1.78561509f, 1.5280143f, 1.25953865f, + -0.475713611f, 0.804714859f, -1.10624063f, -0.963618755f, 1.28015709f, -1.3098377f, 1.84017324f, + -1.71794987f, 1.84391618f, 1.12753403f, -0.805376053f, -1.70883572f, -1.825665f, -1.53720284f, + 0.854817748f, 0.139262006f, 1.53084302f, -1.4810611f, -1.78636622f, -1.33591771f, -1.05236554f, + -0.247410432f, -0.0347327739f, -0.298438877f, 0.611165404f, 0.314375103f, 1.42290568f, 0.630949795f, + -0.219338074f, 1.59859681f, -1.8361969f, -0.985975564f, 1.70931184f, -0.248637781f, -0.640118897f, + 1.61279774f, -0.164148688f, -0.175171673f, -1.32009006f, -1.69559407f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f32/output_ref_data.h new file mode 100644 index 00000000..204f085a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f32/output_ref_data.h @@ -0,0 +1,76 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_f32_output_ref[512] = { + 1.5905484f, 0.213247269f, 1.50822258f, 0.18772532f, -1.65022159f, 0.43144086f, -1.63304365f, + 1.8930459f, 0.653924048f, -0.324042618f, 1.00326312f, -0.865900636f, 0.100348517f, -0.905451298f, + -0.189345002f, -0.298916698f, -0.689203441f, -0.85363853f, -1.35204029f, -0.998536587f, 1.71335804f, + -1.86241281f, -1.90975368f, -0.194281653f, -0.904162228f, -0.48623544f, -0.453189284f, -1.05511391f, + -1.85142446f, -1.33742642f, 1.90439284f, -1.60281765f, 0.573095381f, -0.630049765f, 1.96921051f, + -0.879609466f, -1.81119084f, 0.769269049f, -0.207030028f, -0.614065289f, 0.610224128f, 1.48374665f, + -1.54600561f, 0.350181282f, 1.09185135f, -0.763406456f, -0.270702988f, 0.512607932f, -0.453883797f, + -1.44533002f, 0.469801217f, 0.689039528f, 1.11613512f, -1.6587379f, 0.831584692f, 0.967389226f, + 1.6716069f, -0.721725643f, -0.977015555f, 1.29637206f, 1.3479085f, 1.59988916f, -1.59813261f, + 1.24281454f, 1.11645722f, 1.84977078f, 1.49946535f, 1.86610198f, 1.20008397f, -0.774700761f, + 1.74245667f, -0.956675291f, -0.243996799f, 0.371835053f, 0.342447937f, 0.421002835f, -0.989560425f, + 1.42851615f, 1.88459158f, 0.984940469f, 1.58035648f, -0.0253394861f, 0.376758128f, -1.34343624f, + 0.186506361f, -0.900537133f, 1.68667853f, -0.86158061f, -0.00348492153f, 1.65051079f, -0.242471248f, + 1.6510483f, -1.26842999f, 1.21681213f, 1.88010669f, -1.57113814f, 1.30186164f, -0.240125388f, + -1.26992142f, 0.519313037f, 0.159560367f, 1.72743607f, -1.2465595f, 1.21507025f, 0.90529573f, + 1.71360648f, 0.523714423f, 1.125844f, -0.0473834537f, 0.786228657f, -1.04375482f, -0.0348958671f, + -1.84363139f, -0.0546940789f, -0.68375504f, -0.0879177675f, -0.639792025f, -0.966073453f, -1.94766414f, + -0.284193367f, -0.950858295f, -1.13627851f, 0.0421634912f, -0.724040747f, -0.0500799865f, 1.46915305f, + -1.34901059f, 1.65719903f, 0.427284062f, 0.7314803f, 0.077243343f, -1.9171741f, -0.361837178f, + 0.0763601437f, -0.76643759f, -1.86975408f, 0.519341648f, -1.12546301f, 1.89757669f, 1.54933345f, + 1.6979022f, -1.85027552f, 0.319137394f, 0.346622676f, 0.173876718f, 1.98020959f, -1.92358208f, + 1.02739656f, 1.94929576f, 1.56844938f, -0.256862849f, 0.839108646f, 1.97522688f, 0.742218435f, + -0.0849181041f, -0.646803439f, 0.527525783f, -1.85867751f, 0.32915324f, -0.00899593439f, -1.97531462f, + -0.498139381f, -0.349555075f, 1.05780685f, 0.781011283f, 0.780029953f, 1.054245f, -1.75493968f, + 1.78646421f, -1.52397561f, 1.84637976f, -1.12087047f, -1.38241947f, -1.10262322f, 1.54511642f, + -0.599570215f, 0.849916816f, -1.69657326f, 0.746614397f, 1.70187628f, 1.3481642f, 0.0671826154f, + 1.38651669f, -1.63464141f, 0.105678484f, -0.328378886f, -1.15257859f, 1.30376685f, -0.3928653f, + 0.0884324983f, 0.123600751f, 0.707298756f, -1.50504339f, 1.13257635f, -1.76501691f, 1.55306363f, + 0.799861491f, 0.296734273f, 1.54963493f, -0.455944538f, 1.90971541f, 1.41622698f, -1.0948081f, + -1.25665772f, -1.05202937f, -0.652952135f, 1.10140872f, 0.307172567f, -0.592105448f, -1.49134147f, + 1.76133847f, -0.13560985f, -0.521939039f, -0.752615392f, 0.089186281f, -0.636471093f, -1.50386333f, + -1.74342549f, 1.41328955f, -0.888677835f, -1.11625218f, -1.09742165f, 0.0308572371f, 0.552951813f, + -0.772987306f, 0.97667402f, 0.689771533f, -1.00116873f, 1.82695436f, -1.53971398f, 1.29037011f, + 0.637752056f, 0.854154229f, 0.865499973f, 1.44016635f, -1.91715682f, -0.674742997f, -0.362458885f, + 1.70008516f, 0.428198099f, 1.67957544f, 1.87823737f, 1.80071378f, -0.603678346f, -0.808042705f, + -1.97513282f, -1.56157053f, 0.837560415f, -0.202225432f, 1.11050224f, -0.634934008f, 0.185634077f, + -1.02219474f, 1.18907428f, -0.0172359608f, -0.377762198f, -0.256644219f, 1.40835166f, 0.628774166f, + 1.83950198f, -1.34912169f, -0.0884539783f, 0.141584769f, -1.74252582f, 1.56406629f, -0.0192871653f, + 1.99430537f, -1.47155762f, -0.262426615f, 1.69769847f, -0.715074062f, 1.41429651f, 0.657834768f, + -1.18130279f, -1.91403079f, 0.987664223f, 1.51335287f, -1.75956035f, 0.692902982f, 0.259749293f, + -1.82511806f, -0.696289599f, -1.287503f, -1.59239793f, -0.808493376f, -0.912701607f, -0.519200146f, + 1.63248765f, -1.64585936f, 1.2697506f, -1.54561913f, -0.362702191f, -1.91409338f, 0.956106007f, + -0.408129871f, -0.706245899f, -0.343490124f, -0.59294349f, 1.22589445f, 1.36712289f, 1.60158813f, + -1.10927975f, 1.79793465f, -1.31823003f, -1.17323816f, 1.86622787f, -0.304948092f, 1.20477545f, + -1.03508019f, 0.730076134f, -0.0337109715f, -0.218696967f, 1.47431147f, -1.83763158f, 0.410106391f, + -0.210118398f, -1.12570572f, 0.534470737f, -1.80403078f, 1.59750724f, 1.61157155f, 0.419266373f, + 0.413841218f, 1.58023572f, 1.14049876f, -1.87210631f, 0.690586388f, 0.827608943f, -0.098340407f, + -0.885258734f, -1.18545103f, -1.11772776f, -0.174342304f, -0.73660928f, -1.02628326f, -1.81650674f, + -1.93207622f, 1.19093943f, -1.59933531f, 1.3674897f, -1.03960502f, 1.1241858f, 0.201277211f, + 1.28129339f, 1.69916034f, -1.31158209f, 0.165540278f, 1.67753613f, 0.43388021f, -1.6814853f, + -1.79042745f, -1.40968776f, 0.226703614f, 1.87962365f, -0.410655916f, 0.447159231f, -0.377116621f, + 0.187125295f, 0.963339508f, -0.141032889f, -0.220503226f, 1.05832791f, 1.69701421f, 0.0493569933f, + -1.96732485f, -1.27746093f, -0.535658121f, -0.419031292f, 0.544707596f, 1.73170638f, -0.886866391f, + -1.84228778f, -1.80681884f, 0.261040688f, -1.5158819f, -1.26564515f, -0.529426396f, -0.669142187f, + -0.589991808f, -1.26139247f, -0.279881895f, -1.17832088f, 0.328306526f, -1.94060636f, -0.475532234f, + -1.39828432f, -0.891863823f, 0.164871126f, 0.266283691f, 1.40736067f, 0.781351864f, -0.789524794f, + 0.400581568f, -0.902313292f, -1.81646371f, 1.78873181f, -1.85841835f, 1.16184783f, 1.2685008f, + 0.207727656f, 0.977805614f, 1.3044585f, -0.555653751f, -1.82841945f, -1.1455195f, 1.76521361f, + 1.74458683f, -0.50146091f, -0.220016554f, 1.40633345f, 0.572666228f, 1.40525651f, 0.113541938f, + 0.510012746f, 0.310624748f, -1.3972106f, -0.0736934841f, -0.352856904f, -1.72262585f, -1.60049915f, + 1.40820944f, 0.930318534f, -1.23154044f, 0.950085402f, -1.81724942f, -1.38657618f, 0.224931434f, + 0.484975666f, -1.74386966f, -0.616506815f, -1.35540855f, -0.544623733f, -0.243373454f, 0.833463252f, + 0.155084193f, 0.0453781188f, 1.32347846f, -0.286479771f, 1.57786179f, -1.48550177f, -1.05006754f, + -1.86226237f, 1.78561509f, 1.5280143f, 1.25953865f, 1.39357758f, -0.424269408f, 0.176141381f, + -0.475713611f, 0.804714859f, -1.10624063f, -0.963618755f, 1.28015709f, 1.65080559f, -0.303229272f, + 0.759256124f, -1.3098377f, 1.84017324f, -1.71794987f, 1.84391618f, 1.12753403f, -0.486522704f, + 0.768299699f, -0.876393139f, -0.805376053f, -1.70883572f, -1.825665f, -1.53720284f, 0.854817748f, + -1.70115888f, 0.32650423f, 1.73931634f, 0.139262006f, 1.53084302f, -1.4810611f, -1.78636622f, + -1.33591771f, 1.23805463f, 0.92971468f, -1.64744043f, -1.05236554f, -0.247410432f, -0.0347327739f, + -0.298438877f, 0.611165404f, -0.20533593f, 1.52000535f, 1.17437744f, 0.314375103f, 1.42290568f, + 0.630949795f, -0.219338074f, 1.59859681f, -0.0660620928f, -1.69021642f, -0.662908912f, -1.8361969f, + -0.985975564f, 1.70931184f, -0.248637781f, -0.640118897f, -1.69704986f, 1.58393562f, -1.356125f, + 1.61279774f, -0.164148688f, -0.175171673f, -1.32009006f, -1.69559407f, 1.69364488f, 1.03740799f, + -0.523169219f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f32/rhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f32/rhs_input_data.h new file mode 100644 index 00000000..c73cef31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f32/rhs_input_data.h @@ -0,0 +1,30 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_f32_rhs_input[192] = { + 0.43144086f, -1.63304365f, 1.8930459f, -0.905451298f, -0.189345002f, -0.298916698f, -1.86241281f, + -1.90975368f, -0.194281653f, -1.33742642f, 1.90439284f, -1.60281765f, 0.769269049f, -0.207030028f, + -0.614065289f, -0.763406456f, -0.270702988f, 0.512607932f, -1.6587379f, 0.831584692f, 0.967389226f, + 1.59988916f, -1.59813261f, 1.24281454f, -0.774700761f, 1.74245667f, -0.956675291f, 1.42851615f, + 1.88459158f, 0.984940469f, -0.900537133f, 1.68667853f, -0.86158061f, 1.21681213f, 1.88010669f, + -1.57113814f, 1.72743607f, -1.2465595f, 1.21507025f, 0.786228657f, -1.04375482f, -0.0348958671f, + -0.966073453f, -1.94766414f, -0.284193367f, 1.46915305f, -1.34901059f, 1.65719903f, 0.0763601437f, + -0.76643759f, -1.86975408f, -1.85027552f, 0.319137394f, 0.346622676f, 1.56844938f, -0.256862849f, + 0.839108646f, -1.85867751f, 0.32915324f, -0.00899593439f, 0.780029953f, 1.054245f, -1.75493968f, + -1.10262322f, 1.54511642f, -0.599570215f, 0.0671826154f, 1.38651669f, -1.63464141f, 0.0884324983f, + 0.123600751f, 0.707298756f, 0.296734273f, 1.54963493f, -0.455944538f, -0.652952135f, 1.10140872f, + 0.307172567f, -0.752615392f, 0.089186281f, -0.636471093f, -1.09742165f, 0.0308572371f, 0.552951813f, + -1.53971398f, 1.29037011f, 0.637752056f, -0.362458885f, 1.70008516f, 0.428198099f, -1.97513282f, + -1.56157053f, 0.837560415f, 1.18907428f, -0.0172359608f, -0.377762198f, -0.0884539783f, 0.141584769f, + -1.74252582f, 1.69769847f, -0.715074062f, 1.41429651f, -1.75956035f, 0.692902982f, 0.259749293f, + -0.912701607f, -0.519200146f, 1.63248765f, 0.956106007f, -0.408129871f, -0.706245899f, -1.10927975f, + 1.79793465f, -1.31823003f, 0.730076134f, -0.0337109715f, -0.218696967f, 0.534470737f, -1.80403078f, + 1.59750724f, -1.87210631f, 0.690586388f, 0.827608943f, -0.73660928f, -1.02628326f, -1.81650674f, + 1.1241858f, 0.201277211f, 1.28129339f, -1.6814853f, -1.79042745f, -1.40968776f, 0.187125295f, + 0.963339508f, -0.141032889f, -1.27746093f, -0.535658121f, -0.419031292f, 0.261040688f, -1.5158819f, + -1.26564515f, -1.17832088f, 0.328306526f, -1.94060636f, 1.40736067f, 0.781351864f, -0.789524794f, + 1.16184783f, 1.2685008f, 0.207727656f, 1.76521361f, 1.74458683f, -0.50146091f, 0.510012746f, + 0.310624748f, -1.3972106f, 0.930318534f, -1.23154044f, 0.950085402f, -0.616506815f, -1.35540855f, + -0.544623733f, -0.286479771f, 1.57786179f, -1.48550177f, 1.39357758f, -0.424269408f, 0.176141381f, + 1.65080559f, -0.303229272f, 0.759256124f, -0.486522704f, 0.768299699f, -0.876393139f, -1.70115888f, + 0.32650423f, 1.73931634f, 1.23805463f, 0.92971468f, -1.64744043f, -0.20533593f, 1.52000535f, + 1.17437744f, -0.0660620928f, -1.69021642f, -0.662908912f, -1.69704986f, 1.58393562f, -1.356125f, + 1.69364488f, 1.03740799f, -0.523169219f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_f32/test_data.h new file mode 100644 index 00000000..3102dc40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_data.h" +#include "output_ref_data.h" +#include "rhs_input_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/config_data.h new file mode 100644 index 00000000..dba2dc07 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONCAT_C_NHWC_F16_INPUT_N 1 +#define CONCAT_C_NHWC_F16_INPUT_H 8 +#define CONCAT_C_NHWC_F16_INPUT_W 8 +#define CONCAT_C_NHWC_F16_LHS_C 5 +#define CONCAT_C_NHWC_F16_RHS_C 3 +#define CONCAT_C_NHWC_F16_OUTPUT_C 8 +#define CONCAT_C_NHWC_F16_SIZE 512 +#define CONCAT_C_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/lhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/lhs_input_data.h new file mode 100644 index 00000000..f6c8e9bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/lhs_input_data.h @@ -0,0 +1,82 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_nhwc_f16_lhs_input[320] = { + (float16_t)0.823730469f, (float16_t)0.551269531f, (float16_t)1.17480469f, (float16_t)-0.985839844f, + (float16_t)1.38183594f, (float16_t)0.716308594f, (float16_t)-0.498291016f, (float16_t)0.0980224609f, + (float16_t)-0.903808594f, (float16_t)0.875976562f, (float16_t)-0.802246094f, (float16_t)0.0605163574f, + (float16_t)1.26660156f, (float16_t)-0.670410156f, (float16_t)-0.834472656f, (float16_t)1.578125f, + (float16_t)-1.47070312f, (float16_t)0.979980469f, (float16_t)-1.06542969f, (float16_t)-1.17285156f, + (float16_t)0.307617188f, (float16_t)-0.848144531f, (float16_t)-0.167114258f, (float16_t)1.18261719f, + (float16_t)1.11035156f, (float16_t)0.51171875f, (float16_t)1.84960938f, (float16_t)-0.945800781f, + (float16_t)1.98925781f, (float16_t)0.819824219f, (float16_t)0.873046875f, (float16_t)1.06347656f, + (float16_t)-1.01269531f, (float16_t)-1.32617188f, (float16_t)-0.241943359f, (float16_t)1.11230469f, + (float16_t)-1.74023438f, (float16_t)0.0492553711f, (float16_t)1.77832031f, (float16_t)-0.761230469f, + (float16_t)1.30859375f, (float16_t)-0.237304688f, (float16_t)1.03417969f, (float16_t)-1.04882812f, + (float16_t)-0.603027344f, (float16_t)0.377197266f, (float16_t)1.81347656f, (float16_t)-0.965332031f, + (float16_t)0.762207031f, (float16_t)-0.144897461f, (float16_t)1.58398438f, (float16_t)0.4765625f, + (float16_t)-1.01660156f, (float16_t)-0.0543823242f, (float16_t)-0.9609375f, (float16_t)-1.69726562f, + (float16_t)-1.81933594f, (float16_t)-1.80664062f, (float16_t)-0.451660156f, (float16_t)1.42480469f, + (float16_t)0.203979492f, (float16_t)0.240844727f, (float16_t)-0.839355469f, (float16_t)0.332519531f, + (float16_t)-1.35253906f, (float16_t)1.6875f, (float16_t)-1.69824219f, (float16_t)-1.51464844f, + (float16_t)-0.447265625f, (float16_t)1.02539062f, (float16_t)-1.39453125f, (float16_t)-0.110290527f, + (float16_t)1.11035156f, (float16_t)1.98242188f, (float16_t)-1.22558594f, (float16_t)-1.62011719f, + (float16_t)1.77050781f, (float16_t)1.87890625f, (float16_t)1.12890625f, (float16_t)-1.42089844f, + (float16_t)1.96679688f, (float16_t)-0.465576172f, (float16_t)1.93945312f, (float16_t)-1.15332031f, + (float16_t)-1.82519531f, (float16_t)0.90625f, (float16_t)-0.0544433594f, (float16_t)1.34960938f, + (float16_t)-1.52539062f, (float16_t)-0.0928955078f, (float16_t)0.447509766f, (float16_t)1.76953125f, + (float16_t)-1.3984375f, (float16_t)0.0743408203f, (float16_t)-1.33496094f, (float16_t)1.05175781f, + (float16_t)-0.495605469f, (float16_t)1.17871094f, (float16_t)1.85839844f, (float16_t)-0.530761719f, + (float16_t)1.04882812f, (float16_t)-0.291259766f, (float16_t)1.53613281f, (float16_t)-1.59082031f, + (float16_t)-1.76367188f, (float16_t)1.95800781f, (float16_t)0.0333557129f, (float16_t)0.310058594f, + (float16_t)0.441894531f, (float16_t)1.33496094f, (float16_t)0.61328125f, (float16_t)1.47949219f, + (float16_t)-1.81347656f, (float16_t)-0.794433594f, (float16_t)0.276855469f, (float16_t)-1.47460938f, + (float16_t)-0.876953125f, (float16_t)0.314453125f, (float16_t)1.05761719f, (float16_t)-1.07714844f, + (float16_t)-1.37890625f, (float16_t)-0.0842285156f, (float16_t)-1.67480469f, (float16_t)0.0941772461f, + (float16_t)-0.268310547f, (float16_t)1.77539062f, (float16_t)1.75976562f, (float16_t)1.84277344f, + (float16_t)1.14257812f, (float16_t)-0.890136719f, (float16_t)-0.846191406f, (float16_t)0.395751953f, + (float16_t)-1.33398438f, (float16_t)-1.20410156f, (float16_t)-0.48828125f, (float16_t)-1.54003906f, + (float16_t)1.55859375f, (float16_t)0.815917969f, (float16_t)0.374755859f, (float16_t)-0.00600814819f, + (float16_t)-0.583984375f, (float16_t)1.06640625f, (float16_t)1.42675781f, (float16_t)0.731933594f, + (float16_t)-1.45410156f, (float16_t)1.05664062f, (float16_t)-0.930664062f, (float16_t)1.97265625f, + (float16_t)1.91210938f, (float16_t)1.58691406f, (float16_t)-1.7578125f, (float16_t)-0.461425781f, + (float16_t)1.98242188f, (float16_t)-1.07714844f, (float16_t)1.05957031f, (float16_t)0.0764160156f, + (float16_t)0.754394531f, (float16_t)-0.522460938f, (float16_t)1.64550781f, (float16_t)-0.951171875f, + (float16_t)0.580078125f, (float16_t)-0.321777344f, (float16_t)-0.437011719f, (float16_t)-0.546875f, + (float16_t)-1.29785156f, (float16_t)-1.4375f, (float16_t)0.038848877f, (float16_t)-0.281005859f, + (float16_t)-0.767578125f, (float16_t)-1.91796875f, (float16_t)0.792480469f, (float16_t)-0.767089844f, + (float16_t)-0.248168945f, (float16_t)0.657714844f, (float16_t)1.51953125f, (float16_t)1.61328125f, + (float16_t)1.94238281f, (float16_t)1.38476562f, (float16_t)1.45800781f, (float16_t)-1.18066406f, + (float16_t)1.40722656f, (float16_t)-0.270263672f, (float16_t)1.58789062f, (float16_t)0.51171875f, + (float16_t)-1.98144531f, (float16_t)-1.56445312f, (float16_t)1.35644531f, (float16_t)0.000752925873f, + (float16_t)1.10644531f, (float16_t)0.824707031f, (float16_t)-0.623046875f, (float16_t)1.59179688f, + (float16_t)-0.278808594f, (float16_t)1.1796875f, (float16_t)-1.09667969f, (float16_t)-0.969726562f, + (float16_t)-0.6953125f, (float16_t)-0.427001953f, (float16_t)1.01855469f, (float16_t)0.531738281f, + (float16_t)-1.87695312f, (float16_t)-0.923828125f, (float16_t)0.189941406f, (float16_t)-0.220214844f, + (float16_t)-0.0795898438f, (float16_t)0.277832031f, (float16_t)-1.69042969f, (float16_t)0.614257812f, + (float16_t)-0.0855102539f, (float16_t)1.13574219f, (float16_t)-0.478759766f, (float16_t)-0.859375f, + (float16_t)1.71191406f, (float16_t)0.5859375f, (float16_t)1.14746094f, (float16_t)1.04785156f, + (float16_t)0.383789062f, (float16_t)1.68457031f, (float16_t)-0.0363769531f, (float16_t)1.15234375f, + (float16_t)-0.932128906f, (float16_t)-0.595703125f, (float16_t)1.21191406f, (float16_t)-0.106628418f, + (float16_t)0.195678711f, (float16_t)0.0141525269f, (float16_t)1.4296875f, (float16_t)0.116516113f, + (float16_t)-0.4609375f, (float16_t)0.770019531f, (float16_t)-1.609375f, (float16_t)0.998046875f, + (float16_t)-0.748046875f, (float16_t)-1.12597656f, (float16_t)-1.10742188f, (float16_t)-1.015625f, + (float16_t)0.969238281f, (float16_t)-0.881835938f, (float16_t)-0.258789062f, (float16_t)0.5703125f, + (float16_t)0.0564880371f, (float16_t)-0.510253906f, (float16_t)-0.212280273f, (float16_t)-1.921875f, + (float16_t)1.98339844f, (float16_t)0.162841797f, (float16_t)-0.928710938f, (float16_t)-1.60253906f, + (float16_t)1.50585938f, (float16_t)1.76367188f, (float16_t)-1.76269531f, (float16_t)-1.9453125f, + (float16_t)-1.08398438f, (float16_t)1.89648438f, (float16_t)0.374511719f, (float16_t)-0.124206543f, + (float16_t)1.23046875f, (float16_t)-0.465576172f, (float16_t)1.12695312f, (float16_t)-1.53808594f, + (float16_t)-0.52734375f, (float16_t)0.314208984f, (float16_t)-0.0739746094f, (float16_t)0.309814453f, + (float16_t)-0.302978516f, (float16_t)0.292480469f, (float16_t)1.02539062f, (float16_t)0.51171875f, + (float16_t)-1.671875f, (float16_t)-1.47363281f, (float16_t)-0.401123047f, (float16_t)-0.037109375f, + (float16_t)0.413818359f, (float16_t)1.84179688f, (float16_t)1.85449219f, (float16_t)-0.610351562f, + (float16_t)-0.312988281f, (float16_t)1.45019531f, (float16_t)0.202392578f, (float16_t)0.749023438f, + (float16_t)0.944335938f, (float16_t)0.00813293457f, (float16_t)0.683105469f, (float16_t)1.78320312f, + (float16_t)-1.46289062f, (float16_t)-1.59570312f, (float16_t)-1.42578125f, (float16_t)0.24621582f, + (float16_t)1.20214844f, (float16_t)-1.55371094f, (float16_t)-1.18554688f, (float16_t)-1.12695312f, + (float16_t)0.376953125f, (float16_t)0.10723877f, (float16_t)-0.831054688f, (float16_t)0.795898438f, + (float16_t)-1.21582031f, (float16_t)-0.19519043f, (float16_t)-1.46679688f, (float16_t)0.838378906f, + (float16_t)0.830566406f, (float16_t)-0.467041016f, (float16_t)-0.158203125f, (float16_t)-0.466064453f, + (float16_t)-0.84765625f, (float16_t)0.887207031f, (float16_t)1.15429688f, (float16_t)-0.421386719f, + (float16_t)-1.34179688f, (float16_t)1.48535156f, (float16_t)-0.854492188f, (float16_t)1.96582031f, + (float16_t)-0.192504883f, (float16_t)-0.813964844f, (float16_t)0.943847656f, (float16_t)0.201416016f, + (float16_t)0.677246094f, (float16_t)0.081237793f, (float16_t)-1.8046875f, (float16_t)-1.12304688f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..08f3c3c0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/output_ref_data.h @@ -0,0 +1,130 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_nhwc_f16_output_ref[512] = { + (float16_t)0.823730469f, (float16_t)0.551269531f, (float16_t)1.17480469f, (float16_t)-0.985839844f, + (float16_t)1.38183594f, (float16_t)-0.864257812f, (float16_t)1.37011719f, (float16_t)1.24316406f, + (float16_t)0.716308594f, (float16_t)-0.498291016f, (float16_t)0.0980224609f, (float16_t)-0.903808594f, + (float16_t)0.875976562f, (float16_t)-1.73144531f, (float16_t)0.191040039f, (float16_t)-1.47851562f, + (float16_t)-0.802246094f, (float16_t)0.0605163574f, (float16_t)1.26660156f, (float16_t)-0.670410156f, + (float16_t)-0.834472656f, (float16_t)0.313232422f, (float16_t)-1.92871094f, (float16_t)-1.68945312f, + (float16_t)1.578125f, (float16_t)-1.47070312f, (float16_t)0.979980469f, (float16_t)-1.06542969f, + (float16_t)-1.17285156f, (float16_t)-1.11816406f, (float16_t)-0.411376953f, (float16_t)-1.20996094f, + (float16_t)0.307617188f, (float16_t)-0.848144531f, (float16_t)-0.167114258f, (float16_t)1.18261719f, + (float16_t)1.11035156f, (float16_t)1.30957031f, (float16_t)1.32421875f, (float16_t)0.687988281f, + (float16_t)0.51171875f, (float16_t)1.84960938f, (float16_t)-0.945800781f, (float16_t)1.98925781f, + (float16_t)0.819824219f, (float16_t)0.545410156f, (float16_t)1.31445312f, (float16_t)0.556152344f, + (float16_t)0.873046875f, (float16_t)1.06347656f, (float16_t)-1.01269531f, (float16_t)-1.32617188f, + (float16_t)-0.241943359f, (float16_t)-0.461425781f, (float16_t)-0.0942993164f, (float16_t)1.6015625f, + (float16_t)1.11230469f, (float16_t)-1.74023438f, (float16_t)0.0492553711f, (float16_t)1.77832031f, + (float16_t)-0.761230469f, (float16_t)-0.175415039f, (float16_t)0.639648438f, (float16_t)-0.467041016f, + (float16_t)1.30859375f, (float16_t)-0.237304688f, (float16_t)1.03417969f, (float16_t)-1.04882812f, + (float16_t)-0.603027344f, (float16_t)1.62304688f, (float16_t)-1.05761719f, (float16_t)-1.26855469f, + (float16_t)0.377197266f, (float16_t)1.81347656f, (float16_t)-0.965332031f, (float16_t)0.762207031f, + (float16_t)-0.144897461f, (float16_t)-1.96972656f, (float16_t)0.563476562f, (float16_t)-0.0329284668f, + (float16_t)1.58398438f, (float16_t)0.4765625f, (float16_t)-1.01660156f, (float16_t)-0.0543823242f, + (float16_t)-0.9609375f, (float16_t)-1.29492188f, (float16_t)-0.948730469f, (float16_t)-1.08691406f, + (float16_t)-1.69726562f, (float16_t)-1.81933594f, (float16_t)-1.80664062f, (float16_t)-0.451660156f, + (float16_t)1.42480469f, (float16_t)0.135253906f, (float16_t)-0.0272827148f, (float16_t)-1.2265625f, + (float16_t)0.203979492f, (float16_t)0.240844727f, (float16_t)-0.839355469f, (float16_t)0.332519531f, + (float16_t)-1.35253906f, (float16_t)0.844726562f, (float16_t)1.05664062f, (float16_t)1.37011719f, + (float16_t)1.6875f, (float16_t)-1.69824219f, (float16_t)-1.51464844f, (float16_t)-0.447265625f, + (float16_t)1.02539062f, (float16_t)-0.142456055f, (float16_t)-1.38378906f, (float16_t)0.868164062f, + (float16_t)-1.39453125f, (float16_t)-0.110290527f, (float16_t)1.11035156f, (float16_t)1.98242188f, + (float16_t)-1.22558594f, (float16_t)-0.284667969f, (float16_t)0.523925781f, (float16_t)0.23840332f, + (float16_t)-1.62011719f, (float16_t)1.77050781f, (float16_t)1.87890625f, (float16_t)1.12890625f, + (float16_t)-1.42089844f, (float16_t)0.165161133f, (float16_t)-0.0136566162f, (float16_t)-0.0426025391f, + (float16_t)1.96679688f, (float16_t)-0.465576172f, (float16_t)1.93945312f, (float16_t)-1.15332031f, + (float16_t)-1.82519531f, (float16_t)-1.09667969f, (float16_t)-0.835449219f, (float16_t)-0.232788086f, + (float16_t)0.90625f, (float16_t)-0.0544433594f, (float16_t)1.34960938f, (float16_t)-1.52539062f, + (float16_t)-0.0928955078f, (float16_t)1.75585938f, (float16_t)-1.25585938f, (float16_t)1.6484375f, + (float16_t)0.447509766f, (float16_t)1.76953125f, (float16_t)-1.3984375f, (float16_t)0.0743408203f, + (float16_t)-1.33496094f, (float16_t)-1.046875f, (float16_t)0.858398438f, (float16_t)0.425292969f, + (float16_t)1.05175781f, (float16_t)-0.495605469f, (float16_t)1.17871094f, (float16_t)1.85839844f, + (float16_t)-0.530761719f, (float16_t)0.6953125f, (float16_t)1.23242188f, (float16_t)1.70019531f, + (float16_t)1.04882812f, (float16_t)-0.291259766f, (float16_t)1.53613281f, (float16_t)-1.59082031f, + (float16_t)-1.76367188f, (float16_t)0.0855102539f, (float16_t)0.59765625f, (float16_t)0.790527344f, + (float16_t)1.95800781f, (float16_t)0.0333557129f, (float16_t)0.310058594f, (float16_t)0.441894531f, + (float16_t)1.33496094f, (float16_t)-1.32617188f, (float16_t)1.56347656f, (float16_t)0.486572266f, + (float16_t)0.61328125f, (float16_t)1.47949219f, (float16_t)-1.81347656f, (float16_t)-0.794433594f, + (float16_t)0.276855469f, (float16_t)-1.35351562f, (float16_t)-0.445068359f, (float16_t)-0.927246094f, + (float16_t)-1.47460938f, (float16_t)-0.876953125f, (float16_t)0.314453125f, (float16_t)1.05761719f, + (float16_t)-1.07714844f, (float16_t)-1.97460938f, (float16_t)-1.70898438f, (float16_t)0.6171875f, + (float16_t)-1.37890625f, (float16_t)-0.0842285156f, (float16_t)-1.67480469f, (float16_t)0.0941772461f, + (float16_t)-0.268310547f, (float16_t)-1.72363281f, (float16_t)1.76367188f, (float16_t)-1.70703125f, + (float16_t)1.77539062f, (float16_t)1.75976562f, (float16_t)1.84277344f, (float16_t)1.14257812f, + (float16_t)-0.890136719f, (float16_t)-0.12878418f, (float16_t)1.515625f, (float16_t)-1.125f, + (float16_t)-0.846191406f, (float16_t)0.395751953f, (float16_t)-1.33398438f, (float16_t)-1.20410156f, + (float16_t)-0.48828125f, (float16_t)0.414306641f, (float16_t)0.377929688f, (float16_t)-1.93359375f, + (float16_t)-1.54003906f, (float16_t)1.55859375f, (float16_t)0.815917969f, (float16_t)0.374755859f, + (float16_t)-0.00600814819f, (float16_t)-1.84667969f, (float16_t)1.62792969f, (float16_t)-1.984375f, + (float16_t)-0.583984375f, (float16_t)1.06640625f, (float16_t)1.42675781f, (float16_t)0.731933594f, + (float16_t)-1.45410156f, (float16_t)-0.941894531f, (float16_t)-1.56152344f, (float16_t)-1.36035156f, + (float16_t)1.05664062f, (float16_t)-0.930664062f, (float16_t)1.97265625f, (float16_t)1.91210938f, + (float16_t)1.58691406f, (float16_t)-0.383789062f, (float16_t)0.831542969f, (float16_t)-0.55078125f, + (float16_t)-1.7578125f, (float16_t)-0.461425781f, (float16_t)1.98242188f, (float16_t)-1.07714844f, + (float16_t)1.05957031f, (float16_t)0.23425293f, (float16_t)-0.489257812f, (float16_t)0.788574219f, + (float16_t)0.0764160156f, (float16_t)0.754394531f, (float16_t)-0.522460938f, (float16_t)1.64550781f, + (float16_t)-0.951171875f, (float16_t)-0.0646972656f, (float16_t)-1.92480469f, (float16_t)1.49707031f, + (float16_t)0.580078125f, (float16_t)-0.321777344f, (float16_t)-0.437011719f, (float16_t)-0.546875f, + (float16_t)-1.29785156f, (float16_t)-1.27929688f, (float16_t)-1.27246094f, (float16_t)1.47949219f, + (float16_t)-1.4375f, (float16_t)0.038848877f, (float16_t)-0.281005859f, (float16_t)-0.767578125f, + (float16_t)-1.91796875f, (float16_t)1.34960938f, (float16_t)1.55566406f, (float16_t)1.98339844f, + (float16_t)0.792480469f, (float16_t)-0.767089844f, (float16_t)-0.248168945f, (float16_t)0.657714844f, + (float16_t)1.51953125f, (float16_t)1.53417969f, (float16_t)1.015625f, (float16_t)-1.37402344f, + (float16_t)1.61328125f, (float16_t)1.94238281f, (float16_t)1.38476562f, (float16_t)1.45800781f, + (float16_t)-1.18066406f, (float16_t)1.70996094f, (float16_t)-0.0812988281f, (float16_t)0.634277344f, + (float16_t)1.40722656f, (float16_t)-0.270263672f, (float16_t)1.58789062f, (float16_t)0.51171875f, + (float16_t)-1.98144531f, (float16_t)1.95214844f, (float16_t)1.26074219f, (float16_t)-1.33300781f, + (float16_t)-1.56445312f, (float16_t)1.35644531f, (float16_t)0.000752925873f, (float16_t)1.10644531f, + (float16_t)0.824707031f, (float16_t)-0.422607422f, (float16_t)-0.190185547f, (float16_t)-0.236572266f, + (float16_t)-0.623046875f, (float16_t)1.59179688f, (float16_t)-0.278808594f, (float16_t)1.1796875f, + (float16_t)-1.09667969f, (float16_t)-0.455078125f, (float16_t)1.95800781f, (float16_t)-1.21777344f, + (float16_t)-0.969726562f, (float16_t)-0.6953125f, (float16_t)-0.427001953f, (float16_t)1.01855469f, + (float16_t)0.531738281f, (float16_t)-0.439208984f, (float16_t)0.403564453f, (float16_t)0.806640625f, + (float16_t)-1.87695312f, (float16_t)-0.923828125f, (float16_t)0.189941406f, (float16_t)-0.220214844f, + (float16_t)-0.0795898438f, (float16_t)-1.71582031f, (float16_t)0.788574219f, (float16_t)-0.131958008f, + (float16_t)0.277832031f, (float16_t)-1.69042969f, (float16_t)0.614257812f, (float16_t)-0.0855102539f, + (float16_t)1.13574219f, (float16_t)-0.909667969f, (float16_t)0.941894531f, (float16_t)-1.10546875f, + (float16_t)-0.478759766f, (float16_t)-0.859375f, (float16_t)1.71191406f, (float16_t)0.5859375f, + (float16_t)1.14746094f, (float16_t)-1.71484375f, (float16_t)1.87109375f, (float16_t)0.783203125f, + (float16_t)1.04785156f, (float16_t)0.383789062f, (float16_t)1.68457031f, (float16_t)-0.0363769531f, + (float16_t)1.15234375f, (float16_t)1.3671875f, (float16_t)-1.29199219f, (float16_t)-0.0859985352f, + (float16_t)-0.932128906f, (float16_t)-0.595703125f, (float16_t)1.21191406f, (float16_t)-0.106628418f, + (float16_t)0.195678711f, (float16_t)-1.69726562f, (float16_t)1.72753906f, (float16_t)-1.24023438f, + (float16_t)0.0141525269f, (float16_t)1.4296875f, (float16_t)0.116516113f, (float16_t)-0.4609375f, + (float16_t)0.770019531f, (float16_t)0.666503906f, (float16_t)-0.765625f, (float16_t)-1.12890625f, + (float16_t)-1.609375f, (float16_t)0.998046875f, (float16_t)-0.748046875f, (float16_t)-1.12597656f, + (float16_t)-1.10742188f, (float16_t)-1.56445312f, (float16_t)-0.527832031f, (float16_t)-0.341308594f, + (float16_t)-1.015625f, (float16_t)0.969238281f, (float16_t)-0.881835938f, (float16_t)-0.258789062f, + (float16_t)0.5703125f, (float16_t)-0.999511719f, (float16_t)-0.0760498047f, (float16_t)-0.958496094f, + (float16_t)0.0564880371f, (float16_t)-0.510253906f, (float16_t)-0.212280273f, (float16_t)-1.921875f, + (float16_t)1.98339844f, (float16_t)-1.63476562f, (float16_t)-1.45214844f, (float16_t)1.71582031f, + (float16_t)0.162841797f, (float16_t)-0.928710938f, (float16_t)-1.60253906f, (float16_t)1.50585938f, + (float16_t)1.76367188f, (float16_t)1.84082031f, (float16_t)-0.00591278076f, (float16_t)1.359375f, + (float16_t)-1.76269531f, (float16_t)-1.9453125f, (float16_t)-1.08398438f, (float16_t)1.89648438f, + (float16_t)0.374511719f, (float16_t)-0.741699219f, (float16_t)0.965332031f, (float16_t)-0.682128906f, + (float16_t)-0.124206543f, (float16_t)1.23046875f, (float16_t)-0.465576172f, (float16_t)1.12695312f, + (float16_t)-1.53808594f, (float16_t)-0.9765625f, (float16_t)-0.354736328f, (float16_t)1.00488281f, + (float16_t)-0.52734375f, (float16_t)0.314208984f, (float16_t)-0.0739746094f, (float16_t)0.309814453f, + (float16_t)-0.302978516f, (float16_t)1.35546875f, (float16_t)1.06640625f, (float16_t)0.202392578f, + (float16_t)0.292480469f, (float16_t)1.02539062f, (float16_t)0.51171875f, (float16_t)-1.671875f, + (float16_t)-1.47363281f, (float16_t)1.828125f, (float16_t)0.780273438f, (float16_t)1.03808594f, + (float16_t)-0.401123047f, (float16_t)-0.037109375f, (float16_t)0.413818359f, (float16_t)1.84179688f, + (float16_t)1.85449219f, (float16_t)-1.90917969f, (float16_t)-1.52636719f, (float16_t)1.72265625f, + (float16_t)-0.610351562f, (float16_t)-0.312988281f, (float16_t)1.45019531f, (float16_t)0.202392578f, + (float16_t)0.749023438f, (float16_t)-1.60546875f, (float16_t)-0.845703125f, (float16_t)0.778808594f, + (float16_t)0.944335938f, (float16_t)0.00813293457f, (float16_t)0.683105469f, (float16_t)1.78320312f, + (float16_t)-1.46289062f, (float16_t)-0.825683594f, (float16_t)-1.38867188f, (float16_t)1.3125f, + (float16_t)-1.59570312f, (float16_t)-1.42578125f, (float16_t)0.24621582f, (float16_t)1.20214844f, + (float16_t)-1.55371094f, (float16_t)-1.51660156f, (float16_t)0.685058594f, (float16_t)-0.839355469f, + (float16_t)-1.18554688f, (float16_t)-1.12695312f, (float16_t)0.376953125f, (float16_t)0.10723877f, + (float16_t)-0.831054688f, (float16_t)-1.15722656f, (float16_t)-0.128540039f, (float16_t)-0.188354492f, + (float16_t)0.795898438f, (float16_t)-1.21582031f, (float16_t)-0.19519043f, (float16_t)-1.46679688f, + (float16_t)0.838378906f, (float16_t)-0.247436523f, (float16_t)1.45117188f, (float16_t)1.24316406f, + (float16_t)0.830566406f, (float16_t)-0.467041016f, (float16_t)-0.158203125f, (float16_t)-0.466064453f, + (float16_t)-0.84765625f, (float16_t)0.00826263428f, (float16_t)-1.32714844f, (float16_t)0.542480469f, + (float16_t)0.887207031f, (float16_t)1.15429688f, (float16_t)-0.421386719f, (float16_t)-1.34179688f, + (float16_t)1.48535156f, (float16_t)0.537597656f, (float16_t)0.112121582f, (float16_t)-1.18066406f, + (float16_t)-0.854492188f, (float16_t)1.96582031f, (float16_t)-0.192504883f, (float16_t)-0.813964844f, + (float16_t)0.943847656f, (float16_t)1.94824219f, (float16_t)-0.814453125f, (float16_t)-1.49414062f, + (float16_t)0.201416016f, (float16_t)0.677246094f, (float16_t)0.081237793f, (float16_t)-1.8046875f, + (float16_t)-1.12304688f, (float16_t)1.92675781f, (float16_t)-1.70410156f, (float16_t)0.675292969f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/rhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/rhs_input_data.h new file mode 100644 index 00000000..88035b38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/rhs_input_data.h @@ -0,0 +1,50 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t concat_c_nhwc_f16_rhs_input[192] = { + (float16_t)-0.864257812f, (float16_t)1.37011719f, (float16_t)1.24316406f, (float16_t)-1.73144531f, + (float16_t)0.191040039f, (float16_t)-1.47851562f, (float16_t)0.313232422f, (float16_t)-1.92871094f, + (float16_t)-1.68945312f, (float16_t)-1.11816406f, (float16_t)-0.411376953f, (float16_t)-1.20996094f, + (float16_t)1.30957031f, (float16_t)1.32421875f, (float16_t)0.687988281f, (float16_t)0.545410156f, + (float16_t)1.31445312f, (float16_t)0.556152344f, (float16_t)-0.461425781f, (float16_t)-0.0942993164f, + (float16_t)1.6015625f, (float16_t)-0.175415039f, (float16_t)0.639648438f, (float16_t)-0.467041016f, + (float16_t)1.62304688f, (float16_t)-1.05761719f, (float16_t)-1.26855469f, (float16_t)-1.96972656f, + (float16_t)0.563476562f, (float16_t)-0.0329284668f, (float16_t)-1.29492188f, (float16_t)-0.948730469f, + (float16_t)-1.08691406f, (float16_t)0.135253906f, (float16_t)-0.0272827148f, (float16_t)-1.2265625f, + (float16_t)0.844726562f, (float16_t)1.05664062f, (float16_t)1.37011719f, (float16_t)-0.142456055f, + (float16_t)-1.38378906f, (float16_t)0.868164062f, (float16_t)-0.284667969f, (float16_t)0.523925781f, + (float16_t)0.23840332f, (float16_t)0.165161133f, (float16_t)-0.0136566162f, (float16_t)-0.0426025391f, + (float16_t)-1.09667969f, (float16_t)-0.835449219f, (float16_t)-0.232788086f, (float16_t)1.75585938f, + (float16_t)-1.25585938f, (float16_t)1.6484375f, (float16_t)-1.046875f, (float16_t)0.858398438f, + (float16_t)0.425292969f, (float16_t)0.6953125f, (float16_t)1.23242188f, (float16_t)1.70019531f, + (float16_t)0.0855102539f, (float16_t)0.59765625f, (float16_t)0.790527344f, (float16_t)-1.32617188f, + (float16_t)1.56347656f, (float16_t)0.486572266f, (float16_t)-1.35351562f, (float16_t)-0.445068359f, + (float16_t)-0.927246094f, (float16_t)-1.97460938f, (float16_t)-1.70898438f, (float16_t)0.6171875f, + (float16_t)-1.72363281f, (float16_t)1.76367188f, (float16_t)-1.70703125f, (float16_t)-0.12878418f, + (float16_t)1.515625f, (float16_t)-1.125f, (float16_t)0.414306641f, (float16_t)0.377929688f, + (float16_t)-1.93359375f, (float16_t)-1.84667969f, (float16_t)1.62792969f, (float16_t)-1.984375f, + (float16_t)-0.941894531f, (float16_t)-1.56152344f, (float16_t)-1.36035156f, (float16_t)-0.383789062f, + (float16_t)0.831542969f, (float16_t)-0.55078125f, (float16_t)0.23425293f, (float16_t)-0.489257812f, + (float16_t)0.788574219f, (float16_t)-0.0646972656f, (float16_t)-1.92480469f, (float16_t)1.49707031f, + (float16_t)-1.27929688f, (float16_t)-1.27246094f, (float16_t)1.47949219f, (float16_t)1.34960938f, + (float16_t)1.55566406f, (float16_t)1.98339844f, (float16_t)1.53417969f, (float16_t)1.015625f, + (float16_t)-1.37402344f, (float16_t)1.70996094f, (float16_t)-0.0812988281f, (float16_t)0.634277344f, + (float16_t)1.95214844f, (float16_t)1.26074219f, (float16_t)-1.33300781f, (float16_t)-0.422607422f, + (float16_t)-0.190185547f, (float16_t)-0.236572266f, (float16_t)-0.455078125f, (float16_t)1.95800781f, + (float16_t)-1.21777344f, (float16_t)-0.439208984f, (float16_t)0.403564453f, (float16_t)0.806640625f, + (float16_t)-1.71582031f, (float16_t)0.788574219f, (float16_t)-0.131958008f, (float16_t)-0.909667969f, + (float16_t)0.941894531f, (float16_t)-1.10546875f, (float16_t)-1.71484375f, (float16_t)1.87109375f, + (float16_t)0.783203125f, (float16_t)1.3671875f, (float16_t)-1.29199219f, (float16_t)-0.0859985352f, + (float16_t)-1.69726562f, (float16_t)1.72753906f, (float16_t)-1.24023438f, (float16_t)0.666503906f, + (float16_t)-0.765625f, (float16_t)-1.12890625f, (float16_t)-1.56445312f, (float16_t)-0.527832031f, + (float16_t)-0.341308594f, (float16_t)-0.999511719f, (float16_t)-0.0760498047f, (float16_t)-0.958496094f, + (float16_t)-1.63476562f, (float16_t)-1.45214844f, (float16_t)1.71582031f, (float16_t)1.84082031f, + (float16_t)-0.00591278076f, (float16_t)1.359375f, (float16_t)-0.741699219f, (float16_t)0.965332031f, + (float16_t)-0.682128906f, (float16_t)-0.9765625f, (float16_t)-0.354736328f, (float16_t)1.00488281f, + (float16_t)1.35546875f, (float16_t)1.06640625f, (float16_t)0.202392578f, (float16_t)1.828125f, + (float16_t)0.780273438f, (float16_t)1.03808594f, (float16_t)-1.90917969f, (float16_t)-1.52636719f, + (float16_t)1.72265625f, (float16_t)-1.60546875f, (float16_t)-0.845703125f, (float16_t)0.778808594f, + (float16_t)-0.825683594f, (float16_t)-1.38867188f, (float16_t)1.3125f, (float16_t)-1.51660156f, + (float16_t)0.685058594f, (float16_t)-0.839355469f, (float16_t)-1.15722656f, (float16_t)-0.128540039f, + (float16_t)-0.188354492f, (float16_t)-0.247436523f, (float16_t)1.45117188f, (float16_t)1.24316406f, + (float16_t)0.00826263428f, (float16_t)-1.32714844f, (float16_t)0.542480469f, (float16_t)0.537597656f, + (float16_t)0.112121582f, (float16_t)-1.18066406f, (float16_t)1.94824219f, (float16_t)-0.814453125f, + (float16_t)-1.49414062f, (float16_t)1.92675781f, (float16_t)-1.70410156f, (float16_t)0.675292969f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/test_data.h new file mode 100644 index 00000000..3102dc40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_data.h" +#include "output_ref_data.h" +#include "rhs_input_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/config_data.h new file mode 100644 index 00000000..2af6f54c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONCAT_C_NHWC_F32_INPUT_N 1 +#define CONCAT_C_NHWC_F32_INPUT_H 8 +#define CONCAT_C_NHWC_F32_INPUT_W 8 +#define CONCAT_C_NHWC_F32_LHS_C 5 +#define CONCAT_C_NHWC_F32_RHS_C 3 +#define CONCAT_C_NHWC_F32_OUTPUT_C 8 +#define CONCAT_C_NHWC_F32_SIZE 512 +#define CONCAT_C_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/lhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/lhs_input_data.h new file mode 100644 index 00000000..16b60c03 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/lhs_input_data.h @@ -0,0 +1,48 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_nhwc_f32_lhs_input[320] = { + -0.763414145f, -1.36924922f, 1.8248347f, 0.945885062f, 0.080747731f, 1.99895942f, 0.899140716f, + 1.4886533f, -0.289399803f, 1.40772974f, 0.89198792f, 0.743072629f, 1.45198822f, -0.133908004f, + 1.51388335f, 1.2795043f, -0.643795311f, -0.195436001f, -0.361330867f, 1.59358382f, 1.86196566f, + 0.722058892f, 0.860412359f, -1.47324562f, -1.82106173f, 0.460947394f, -0.936787724f, 1.21724844f, + -1.10970676f, 1.57865119f, -1.43094933f, 0.12551342f, 1.32141626f, 0.0672352463f, -0.563640416f, + 0.923158467f, -0.323846728f, -0.313074559f, -1.41848564f, -0.934347689f, 1.32689416f, -1.91020584f, + 1.43465543f, -1.8044821f, -1.87813604f, 1.91005874f, -0.0896986052f, 0.996746123f, -1.74897337f, + 1.23876417f, -1.75624907f, -1.68204868f, 0.479429394f, 1.95600665f, -0.583834708f, -1.3654573f, + 0.916659832f, -1.31582165f, -0.720005572f, 0.651748359f, 0.422261685f, -1.00557053f, 1.98422456f, + 1.95033443f, -0.0194023289f, -1.99971807f, -1.91752028f, -1.23051977f, -0.92288512f, 1.04065704f, + -0.696889639f, 0.315269619f, 0.429279059f, 0.378748983f, 1.05879009f, -1.98385119f, -0.525922f, + 1.44124699f, 1.67281663f, 1.19560492f, 1.1622082f, 0.131750554f, -0.526153445f, 1.48527718f, + -1.5457468f, 1.15440178f, 0.193848655f, 0.775539577f, 0.706486762f, -0.032152079f, -0.111369513f, + 0.653753996f, 0.902151763f, -1.73051941f, 1.61475575f, -0.0867261365f, 0.274224728f, 0.548414886f, + 1.26228178f, -0.92953819f, 0.0212145299f, 0.977988601f, -1.30336857f, 1.25700665f, 1.2016747f, + -1.40311491f, -0.00190808158f, 0.522796214f, 0.293679416f, -1.914505f, 0.442329884f, -0.134193391f, + -1.30953026f, -0.72932452f, 1.32893777f, -1.24712574f, 0.589695275f, 0.508129418f, -1.6571542f, + 0.387092322f, -1.3539089f, -1.20248461f, -0.596340239f, -0.723594368f, 1.13649929f, -0.665525079f, + 1.73110259f, -0.218290403f, -1.7094059f, 1.75398195f, -1.65431178f, -1.44716632f, 1.01798296f, + 0.937591136f, -0.411721975f, -1.07404757f, 0.797276318f, -0.771465957f, 0.946379364f, 1.03250968f, + 0.263250202f, 0.906751931f, 1.96986198f, -1.37235439f, 1.54579461f, -1.5442456f, -1.23855019f, + 0.916487515f, 1.19281018f, 0.00766632333f, 0.185414568f, 0.73432368f, -1.83943713f, -0.564963818f, + 0.570866525f, 0.364877492f, -0.735838354f, 1.26373446f, -0.431417793f, -1.16003644f, 1.260602f, + -0.349760503f, -1.32400453f, -1.37960243f, -0.915448129f, 0.500955939f, 1.12989092f, -0.462278575f, + 0.880124986f, -1.61150157f, 1.5298934f, -0.539847255f, 0.881997943f, -1.54246318f, 1.03232741f, + -0.398956448f, -0.293977588f, -0.177484334f, 0.276668876f, 1.63523793f, 0.250020891f, -0.138873324f, + -0.667933106f, 1.05740678f, -0.153403535f, -0.970107138f, 1.87910569f, 1.55315387f, -0.88444829f, + -0.603769302f, -0.632831395f, 1.35510254f, -1.96260965f, -0.632621646f, 0.55363965f, -0.100360408f, + 1.69993091f, -1.63352907f, 0.741941333f, 1.1303333f, -1.42900956f, 1.61756897f, 0.406515419f, + -0.686842203f, -1.43532336f, 0.84099704f, -0.871107399f, 1.9832989f, 1.65670896f, -1.12338972f, + -0.942431033f, -1.50190353f, -1.77863574f, -0.0378405116f, 1.81587434f, -1.23098004f, 0.660546482f, + 1.36409724f, -1.46488225f, -0.71905005f, -0.985832572f, 1.68132329f, -1.94047678f, -0.058037065f, + 1.87212467f, 1.64892471f, 1.49135339f, 1.88601649f, -1.93386662f, 1.17165267f, -1.72152209f, + 1.80097878f, -0.403181225f, 0.300190479f, 1.79489911f, -1.29670823f, 0.870441794f, 1.681229f, + 0.975538135f, -1.5833087f, -0.645527482f, 1.45581234f, 1.72922897f, 0.342693388f, -1.98234391f, + 0.641836584f, 0.153545827f, -1.39775121f, -0.863611877f, 0.234898657f, -0.229751915f, -0.805102706f, + 0.471349061f, 0.611556888f, -1.26422012f, -1.88173091f, -1.84237838f, 0.977647483f, 0.513526261f, + -1.26503396f, 0.727989614f, -0.377280802f, -1.71094024f, -1.39319134f, -1.95558429f, -0.660151005f, + -0.196006402f, 0.261124223f, 1.95225418f, 1.81309795f, -0.880252421f, -1.89980459f, -1.97317231f, + -1.55641067f, -0.981761992f, 0.630197704f, -0.727385461f, 0.00616242038f, -1.01086688f, 0.815971673f, + -0.733964324f, 0.202130809f, 0.821562052f, -0.498163581f, -0.777085543f, 0.801676393f, 1.70116889f, + -1.59533763f, -1.94054163f, 0.909867167f, 1.72001994f, -1.79304373f, 0.660286725f, -0.826469958f, + 1.36516178f, 0.745750904f, 1.06961799f, -1.61153913f, -0.220071092f, 1.43378723f, 0.518011212f, + -0.609508455f, -1.18389606f, -0.675845206f, 0.906968117f, 1.13422966f, 0.833269596f, -1.23230314f, + -1.76970649f, 1.88303733f, -1.20449293f, 1.03826261f, -0.24330005f, 1.85475564f, -0.854602218f, + -1.91552508f, 1.24626613f, -0.312242031f, -0.296554774f, -1.58504176f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..1fee4ff4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/output_ref_data.h @@ -0,0 +1,76 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_nhwc_f32_output_ref[512] = { + -0.763414145f, -1.36924922f, 1.8248347f, 0.945885062f, 0.080747731f, -1.2987169f, -1.74235535f, + -1.45443225f, 1.99895942f, 0.899140716f, 1.4886533f, -0.289399803f, 1.40772974f, 1.80702245f, + 1.64167523f, -1.40829241f, 0.89198792f, 0.743072629f, 1.45198822f, -0.133908004f, 1.51388335f, + 1.96525252f, -1.74380112f, 0.0889729559f, 1.2795043f, -0.643795311f, -0.195436001f, -0.361330867f, + 1.59358382f, 1.65358412f, -0.0870738998f, -0.149656951f, 1.86196566f, 0.722058892f, 0.860412359f, + -1.47324562f, -1.82106173f, -1.95105219f, 0.365082115f, 0.747235358f, 0.460947394f, -0.936787724f, + 1.21724844f, -1.10970676f, 1.57865119f, -1.91436541f, 1.51647687f, -0.172820047f, -1.43094933f, + 0.12551342f, 1.32141626f, 0.0672352463f, -0.563640416f, 0.0840766206f, -1.35669744f, -1.84238374f, + 0.923158467f, -0.323846728f, -0.313074559f, -1.41848564f, -0.934347689f, -1.04831803f, 0.04923217f, + -0.0328106284f, 1.32689416f, -1.91020584f, 1.43465543f, -1.8044821f, -1.87813604f, -1.36398232f, + 1.99772286f, 0.483283579f, 1.91005874f, -0.0896986052f, 0.996746123f, -1.74897337f, 1.23876417f, + 1.60641778f, -0.152677745f, -1.68607378f, -1.75624907f, -1.68204868f, 0.479429394f, 1.95600665f, + -0.583834708f, 0.250966787f, 1.02754354f, -0.0083320681f, -1.3654573f, 0.916659832f, -1.31582165f, + -0.720005572f, 0.651748359f, -0.299613237f, -0.717833221f, 0.828985035f, 0.422261685f, -1.00557053f, + 1.98422456f, 1.95033443f, -0.0194023289f, -0.357031763f, 1.7172004f, -1.18979073f, -1.99971807f, + -1.91752028f, -1.23051977f, -0.92288512f, 1.04065704f, 1.55530429f, -0.320746869f, -1.41195595f, + -0.696889639f, 0.315269619f, 0.429279059f, 0.378748983f, 1.05879009f, 0.565816402f, -1.47202301f, + -0.446693867f, -1.98385119f, -0.525922f, 1.44124699f, 1.67281663f, 1.19560492f, -1.54672647f, + 0.579147577f, 1.04760349f, 1.1622082f, 0.131750554f, -0.526153445f, 1.48527718f, -1.5457468f, + 1.82770395f, 0.27913335f, 1.90060163f, 1.15440178f, 0.193848655f, 0.775539577f, 0.706486762f, + -0.032152079f, 1.44090402f, 1.03312552f, -0.731151044f, -0.111369513f, 0.653753996f, 0.902151763f, + -1.73051941f, 1.61475575f, -0.709240615f, -0.968494236f, 1.41094005f, -0.0867261365f, 0.274224728f, + 0.548414886f, 1.26228178f, -0.92953819f, -1.28046155f, -0.334033519f, 0.885307252f, 0.0212145299f, + 0.977988601f, -1.30336857f, 1.25700665f, 1.2016747f, -0.856745064f, -0.117639758f, -1.50408149f, + -1.40311491f, -0.00190808158f, 0.522796214f, 0.293679416f, -1.914505f, 1.20469642f, 0.358936816f, + 1.39319563f, 0.442329884f, -0.134193391f, -1.30953026f, -0.72932452f, 1.32893777f, -0.929036498f, + 0.0406251624f, 0.0922719315f, -1.24712574f, 0.589695275f, 0.508129418f, -1.6571542f, 0.387092322f, + 0.763181031f, -0.926324368f, -1.96752167f, -1.3539089f, -1.20248461f, -0.596340239f, -0.723594368f, + 1.13649929f, -1.74214506f, -1.22902071f, 1.36291659f, -0.665525079f, 1.73110259f, -0.218290403f, + -1.7094059f, 1.75398195f, -1.07660246f, 0.68219763f, 1.07911718f, -1.65431178f, -1.44716632f, + 1.01798296f, 0.937591136f, -0.411721975f, -0.209049061f, 0.617111206f, -1.1881429f, -1.07404757f, + 0.797276318f, -0.771465957f, 0.946379364f, 1.03250968f, -1.25225246f, 0.221675307f, -0.515305102f, + 0.263250202f, 0.906751931f, 1.96986198f, -1.37235439f, 1.54579461f, 0.3218427f, -0.888367891f, + 1.90738034f, -1.5442456f, -1.23855019f, 0.916487515f, 1.19281018f, 0.00766632333f, 0.192509204f, + -1.75684059f, 0.984118998f, 0.185414568f, 0.73432368f, -1.83943713f, -0.564963818f, 0.570866525f, + 1.85010004f, 1.07144415f, 1.29176378f, 0.364877492f, -0.735838354f, 1.26373446f, -0.431417793f, + -1.16003644f, 0.832500458f, 0.362367839f, 1.96211553f, 1.260602f, -0.349760503f, -1.32400453f, + -1.37960243f, -0.915448129f, 1.77883756f, 1.93473697f, -0.539399505f, 0.500955939f, 1.12989092f, + -0.462278575f, 0.880124986f, -1.61150157f, 1.79351926f, 1.20048738f, -1.82172751f, 1.5298934f, + -0.539847255f, 0.881997943f, -1.54246318f, 1.03232741f, -1.5153476f, 0.25223887f, -1.21682048f, + -0.398956448f, -0.293977588f, -0.177484334f, 0.276668876f, 1.63523793f, -1.26057363f, 0.979886711f, + 0.0991455764f, 0.250020891f, -0.138873324f, -0.667933106f, 1.05740678f, -0.153403535f, 1.16360807f, + -1.1858505f, 0.0844851658f, -0.970107138f, 1.87910569f, 1.55315387f, -0.88444829f, -0.603769302f, + 0.62899965f, 1.41396379f, 1.14603782f, -0.632831395f, 1.35510254f, -1.96260965f, -0.632621646f, + 0.55363965f, 0.398492813f, 1.43153989f, -1.06206238f, -0.100360408f, 1.69993091f, -1.63352907f, + 0.741941333f, 1.1303333f, -0.336674988f, -0.679068029f, 1.45758641f, -1.42900956f, 1.61756897f, + 0.406515419f, -0.686842203f, -1.43532336f, 1.43952167f, -0.348366052f, 1.07724833f, 0.84099704f, + -0.871107399f, 1.9832989f, 1.65670896f, -1.12338972f, 0.535321772f, 0.550226867f, 1.01629221f, + -0.942431033f, -1.50190353f, -1.77863574f, -0.0378405116f, 1.81587434f, 1.76591825f, 1.42596042f, + -0.0244810116f, -1.23098004f, 0.660546482f, 1.36409724f, -1.46488225f, -0.71905005f, 0.253522992f, + -1.37525761f, 1.23898435f, -0.985832572f, 1.68132329f, -1.94047678f, -0.058037065f, 1.87212467f, + -1.77192998f, -0.590990424f, 1.22497618f, 1.64892471f, 1.49135339f, 1.88601649f, -1.93386662f, + 1.17165267f, 0.285204262f, -1.46007824f, 1.74482894f, -1.72152209f, 1.80097878f, -0.403181225f, + 0.300190479f, 1.79489911f, -1.48772681f, 1.59264505f, -1.23856163f, -1.29670823f, 0.870441794f, + 1.681229f, 0.975538135f, -1.5833087f, -0.880010724f, -0.613354981f, -1.24075747f, -0.645527482f, + 1.45581234f, 1.72922897f, 0.342693388f, -1.98234391f, 0.680008233f, -0.285601676f, 1.22742712f, + 0.641836584f, 0.153545827f, -1.39775121f, -0.863611877f, 0.234898657f, -0.000730167434f, -0.096794717f, + 0.673999369f, -0.229751915f, -0.805102706f, 0.471349061f, 0.611556888f, -1.26422012f, -0.152874187f, + -1.46053767f, -0.563289464f, -1.88173091f, -1.84237838f, 0.977647483f, 0.513526261f, -1.26503396f, + 0.602589428f, -1.93031728f, -1.77578342f, 0.727989614f, -0.377280802f, -1.71094024f, -1.39319134f, + -1.95558429f, -0.954657078f, -0.72833699f, 1.30504835f, -0.660151005f, -0.196006402f, 0.261124223f, + 1.95225418f, 1.81309795f, -1.92977834f, 0.416439772f, 0.687434018f, -0.880252421f, -1.89980459f, + -1.97317231f, -1.55641067f, -0.981761992f, -0.700193405f, 1.58048463f, -0.164048329f, 0.630197704f, + -0.727385461f, 0.00616242038f, -1.01086688f, 0.815971673f, 1.94161296f, -1.27171278f, -1.94220281f, + -0.733964324f, 0.202130809f, 0.821562052f, -0.498163581f, -0.777085543f, -1.53284323f, -1.25751686f, + -1.42018175f, 0.801676393f, 1.70116889f, -1.59533763f, -1.94054163f, 0.909867167f, 1.3254931f, + -0.603204787f, -1.8961823f, 1.72001994f, -1.79304373f, 0.660286725f, -0.826469958f, 1.36516178f, + -1.42440474f, 1.45734143f, -0.0789873153f, 0.745750904f, 1.06961799f, -1.61153913f, -0.220071092f, + 1.43378723f, 1.10371017f, -0.607233524f, -1.32497108f, 0.518011212f, -0.609508455f, -1.18389606f, + -0.675845206f, 0.906968117f, 1.02313745f, 1.20898223f, -1.22268999f, 1.13422966f, 0.833269596f, + -1.23230314f, -1.76970649f, 1.88303733f, -0.402274668f, -0.275435418f, -0.236405566f, -1.20449293f, + 1.03826261f, -0.24330005f, 1.85475564f, -0.854602218f, -0.290198922f, -0.182530552f, 0.732818365f, + -1.91552508f, 1.24626613f, -0.312242031f, -0.296554774f, -1.58504176f, -0.926024616f, 0.728593349f, + 1.03517234f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/rhs_input_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/rhs_input_data.h new file mode 100644 index 00000000..ecced193 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/rhs_input_data.h @@ -0,0 +1,30 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t concat_c_nhwc_f32_rhs_input[192] = { + -1.2987169f, -1.74235535f, -1.45443225f, 1.80702245f, 1.64167523f, -1.40829241f, 1.96525252f, + -1.74380112f, 0.0889729559f, 1.65358412f, -0.0870738998f, -0.149656951f, -1.95105219f, 0.365082115f, + 0.747235358f, -1.91436541f, 1.51647687f, -0.172820047f, 0.0840766206f, -1.35669744f, -1.84238374f, + -1.04831803f, 0.04923217f, -0.0328106284f, -1.36398232f, 1.99772286f, 0.483283579f, 1.60641778f, + -0.152677745f, -1.68607378f, 0.250966787f, 1.02754354f, -0.0083320681f, -0.299613237f, -0.717833221f, + 0.828985035f, -0.357031763f, 1.7172004f, -1.18979073f, 1.55530429f, -0.320746869f, -1.41195595f, + 0.565816402f, -1.47202301f, -0.446693867f, -1.54672647f, 0.579147577f, 1.04760349f, 1.82770395f, + 0.27913335f, 1.90060163f, 1.44090402f, 1.03312552f, -0.731151044f, -0.709240615f, -0.968494236f, + 1.41094005f, -1.28046155f, -0.334033519f, 0.885307252f, -0.856745064f, -0.117639758f, -1.50408149f, + 1.20469642f, 0.358936816f, 1.39319563f, -0.929036498f, 0.0406251624f, 0.0922719315f, 0.763181031f, + -0.926324368f, -1.96752167f, -1.74214506f, -1.22902071f, 1.36291659f, -1.07660246f, 0.68219763f, + 1.07911718f, -0.209049061f, 0.617111206f, -1.1881429f, -1.25225246f, 0.221675307f, -0.515305102f, + 0.3218427f, -0.888367891f, 1.90738034f, 0.192509204f, -1.75684059f, 0.984118998f, 1.85010004f, + 1.07144415f, 1.29176378f, 0.832500458f, 0.362367839f, 1.96211553f, 1.77883756f, 1.93473697f, + -0.539399505f, 1.79351926f, 1.20048738f, -1.82172751f, -1.5153476f, 0.25223887f, -1.21682048f, + -1.26057363f, 0.979886711f, 0.0991455764f, 1.16360807f, -1.1858505f, 0.0844851658f, 0.62899965f, + 1.41396379f, 1.14603782f, 0.398492813f, 1.43153989f, -1.06206238f, -0.336674988f, -0.679068029f, + 1.45758641f, 1.43952167f, -0.348366052f, 1.07724833f, 0.535321772f, 0.550226867f, 1.01629221f, + 1.76591825f, 1.42596042f, -0.0244810116f, 0.253522992f, -1.37525761f, 1.23898435f, -1.77192998f, + -0.590990424f, 1.22497618f, 0.285204262f, -1.46007824f, 1.74482894f, -1.48772681f, 1.59264505f, + -1.23856163f, -0.880010724f, -0.613354981f, -1.24075747f, 0.680008233f, -0.285601676f, 1.22742712f, + -0.000730167434f, -0.096794717f, 0.673999369f, -0.152874187f, -1.46053767f, -0.563289464f, 0.602589428f, + -1.93031728f, -1.77578342f, -0.954657078f, -0.72833699f, 1.30504835f, -1.92977834f, 0.416439772f, + 0.687434018f, -0.700193405f, 1.58048463f, -0.164048329f, 1.94161296f, -1.27171278f, -1.94220281f, + -1.53284323f, -1.25751686f, -1.42018175f, 1.3254931f, -0.603204787f, -1.8961823f, -1.42440474f, + 1.45734143f, -0.0789873153f, 1.10371017f, -0.607233524f, -1.32497108f, 1.02313745f, 1.20898223f, + -1.22268999f, -0.402274668f, -0.275435418f, -0.236405566f, -0.290198922f, -0.182530552f, 0.732818365f, + -0.926024616f, 0.728593349f, 1.03517234f}; diff --git a/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/test_data.h new file mode 100644 index 00000000..3102dc40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/concat_c_nhwc_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by concatenation_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "lhs_input_data.h" +#include "output_ref_data.h" +#include "rhs_input_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/biases_data.h new file mode 100644 index 00000000..17f07daf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/biases_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_1x1_stride2_nhwc_f16_biases_data[13] = {(float16_t)-0.424560547f, + (float16_t)0.495117188f, + (float16_t)-0.213989258f, + (float16_t)-0.0149841309f, + (float16_t)0.236206055f, + (float16_t)-0.478027344f, + (float16_t)0.231567383f, + (float16_t)-0.00909423828f, + (float16_t)0.489746094f, + (float16_t)-0.427490234f, + (float16_t)0.162841797f, + (float16_t)0.401611328f, + (float16_t)-0.436523438f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/config_data.h new file mode 100644 index 00000000..c4ab801d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_1X1_STRIDE2_NHWC_F16_INPUT_BATCHES 1 +#define CONV_1X1_STRIDE2_NHWC_F16_INPUT_H 3 +#define CONV_1X1_STRIDE2_NHWC_F16_INPUT_W 17 +#define CONV_1X1_STRIDE2_NHWC_F16_IN_CH 11 +#define CONV_1X1_STRIDE2_NHWC_F16_FILTER_H 1 +#define CONV_1X1_STRIDE2_NHWC_F16_FILTER_W 1 +#define CONV_1X1_STRIDE2_NHWC_F16_OUT_CH 13 +#define CONV_1X1_STRIDE2_NHWC_F16_STRIDE_H 1 +#define CONV_1X1_STRIDE2_NHWC_F16_STRIDE_W 2 +#define CONV_1X1_STRIDE2_NHWC_F16_PADDING_H 0 +#define CONV_1X1_STRIDE2_NHWC_F16_PADDING_W 0 +#define CONV_1X1_STRIDE2_NHWC_F16_DILATION_H 1 +#define CONV_1X1_STRIDE2_NHWC_F16_DILATION_W 1 +#define CONV_1X1_STRIDE2_NHWC_F16_OUTPUT_H 3 +#define CONV_1X1_STRIDE2_NHWC_F16_OUTPUT_W 9 +#define CONV_1X1_STRIDE2_NHWC_F16_OUTPUT_C 13 +#define CONV_1X1_STRIDE2_NHWC_F16_DST_SIZE 351 +#define CONV_1X1_STRIDE2_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_1X1_STRIDE2_NHWC_F16_USE_WRAPPER 1 +#define CONV_1X1_STRIDE2_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 0.75f +#define CONV_1X1_STRIDE2_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)0.75f diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/input_data.h new file mode 100644 index 00000000..26dca634 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/input_data.h @@ -0,0 +1,562 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_1x1_stride2_nhwc_f16_input_data[561] = {(float16_t)0.790527344f, + (float16_t)-0.354980469f, + (float16_t)0.582519531f, + (float16_t)0.696777344f, + (float16_t)-0.620605469f, + (float16_t)-0.180053711f, + (float16_t)0.189086914f, + (float16_t)-0.559570312f, + (float16_t)-0.818359375f, + (float16_t)0.0933227539f, + (float16_t)0.42578125f, + (float16_t)0.371826172f, + (float16_t)0.796875f, + (float16_t)-0.962402344f, + (float16_t)-0.821289062f, + (float16_t)0.208007812f, + (float16_t)0.467773438f, + (float16_t)0.738769531f, + (float16_t)-0.971191406f, + (float16_t)0.865722656f, + (float16_t)-0.62890625f, + (float16_t)0.692382812f, + (float16_t)0.693847656f, + (float16_t)0.707519531f, + (float16_t)0.6015625f, + (float16_t)0.845703125f, + (float16_t)0.817871094f, + (float16_t)-0.515625f, + (float16_t)0.324707031f, + (float16_t)-0.773925781f, + (float16_t)0.455566406f, + (float16_t)0.810546875f, + (float16_t)-0.429443359f, + (float16_t)-0.971191406f, + (float16_t)0.34765625f, + (float16_t)-0.0881347656f, + (float16_t)-0.721679688f, + (float16_t)0.00617218018f, + (float16_t)-0.195922852f, + (float16_t)0.9140625f, + (float16_t)0.750976562f, + (float16_t)0.96875f, + (float16_t)0.0520629883f, + (float16_t)-0.938964844f, + (float16_t)-0.490722656f, + (float16_t)-0.104736328f, + (float16_t)0.107055664f, + (float16_t)0.106384277f, + (float16_t)-0.30859375f, + (float16_t)0.990234375f, + (float16_t)-0.860839844f, + (float16_t)0.215209961f, + (float16_t)0.232177734f, + (float16_t)-0.771484375f, + (float16_t)0.672851562f, + (float16_t)0.5390625f, + (float16_t)-0.133422852f, + (float16_t)0.398193359f, + (float16_t)0.990722656f, + (float16_t)-0.624023438f, + (float16_t)0.268310547f, + (float16_t)-0.887695312f, + (float16_t)0.0761108398f, + (float16_t)0.783691406f, + (float16_t)0.0969848633f, + (float16_t)-0.435058594f, + (float16_t)0.235107422f, + (float16_t)0.0533752441f, + (float16_t)-0.503417969f, + (float16_t)0.361572266f, + (float16_t)-0.485351562f, + (float16_t)-0.108154297f, + (float16_t)-0.952636719f, + (float16_t)-0.962402344f, + (float16_t)0.783203125f, + (float16_t)0.254638672f, + (float16_t)0.773925781f, + (float16_t)0.874511719f, + (float16_t)-0.830566406f, + (float16_t)0.705078125f, + (float16_t)-0.0800170898f, + (float16_t)-0.982421875f, + (float16_t)0.178100586f, + (float16_t)-0.779785156f, + (float16_t)-0.178344727f, + (float16_t)-0.125488281f, + (float16_t)0.620117188f, + (float16_t)-0.218505859f, + (float16_t)-0.875488281f, + (float16_t)-0.367675781f, + (float16_t)-0.141113281f, + (float16_t)0.367675781f, + (float16_t)0.701171875f, + (float16_t)0.778320312f, + (float16_t)-0.797851562f, + (float16_t)0.821289062f, + (float16_t)-0.741210938f, + (float16_t)0.296630859f, + (float16_t)-0.884277344f, + (float16_t)0.178710938f, + (float16_t)-0.749023438f, + (float16_t)-0.610351562f, + (float16_t)-0.000751018524f, + (float16_t)-0.0392150879f, + (float16_t)0.0723876953f, + (float16_t)0.392089844f, + (float16_t)-0.532226562f, + (float16_t)-0.286621094f, + (float16_t)0.0542297363f, + (float16_t)0.00765991211f, + (float16_t)0.766113281f, + (float16_t)0.831542969f, + (float16_t)0.3828125f, + (float16_t)-0.274169922f, + (float16_t)0.221801758f, + (float16_t)0.631347656f, + (float16_t)-0.0512084961f, + (float16_t)0.931640625f, + (float16_t)0.338623047f, + (float16_t)0.584960938f, + (float16_t)0.346435547f, + (float16_t)-0.913574219f, + (float16_t)0.159057617f, + (float16_t)-0.111938477f, + (float16_t)-0.650390625f, + (float16_t)-0.186523438f, + (float16_t)-0.573242188f, + (float16_t)0.0803222656f, + (float16_t)-0.686523438f, + (float16_t)-0.0541381836f, + (float16_t)-0.208007812f, + (float16_t)-0.0831298828f, + (float16_t)-0.391113281f, + (float16_t)0.608398438f, + (float16_t)0.791992188f, + (float16_t)0.977539062f, + (float16_t)0.603027344f, + (float16_t)-0.650878906f, + (float16_t)0.478027344f, + (float16_t)-0.471679688f, + (float16_t)-0.0472717285f, + (float16_t)0.287353516f, + (float16_t)-0.195678711f, + (float16_t)0.740722656f, + (float16_t)-0.725097656f, + (float16_t)0.0714111328f, + (float16_t)0.838378906f, + (float16_t)-0.041809082f, + (float16_t)0.154907227f, + (float16_t)-0.221191406f, + (float16_t)0.636230469f, + (float16_t)-0.631835938f, + (float16_t)0.319335938f, + (float16_t)-0.29296875f, + (float16_t)0.517089844f, + (float16_t)0.555175781f, + (float16_t)-0.95703125f, + (float16_t)-0.728515625f, + (float16_t)-0.135986328f, + (float16_t)-0.662597656f, + (float16_t)0.14831543f, + (float16_t)0.456054688f, + (float16_t)-0.560546875f, + (float16_t)0.250488281f, + (float16_t)-0.756835938f, + (float16_t)0.84375f, + (float16_t)0.756347656f, + (float16_t)0.660644531f, + (float16_t)-0.281738281f, + (float16_t)0.573730469f, + (float16_t)0.884765625f, + (float16_t)-0.297119141f, + (float16_t)0.85546875f, + (float16_t)0.250976562f, + (float16_t)0.848144531f, + (float16_t)0.254882812f, + (float16_t)0.0780029297f, + (float16_t)0.739257812f, + (float16_t)0.611816406f, + (float16_t)0.495605469f, + (float16_t)-0.635253906f, + (float16_t)-0.318847656f, + (float16_t)0.281494141f, + (float16_t)0.228759766f, + (float16_t)0.0407714844f, + (float16_t)0.38671875f, + (float16_t)0.205566406f, + (float16_t)-0.180175781f, + (float16_t)-0.945800781f, + (float16_t)-0.946777344f, + (float16_t)-0.153686523f, + (float16_t)0.86328125f, + (float16_t)0.156738281f, + (float16_t)-0.0470275879f, + (float16_t)-0.59765625f, + (float16_t)0.5f, + (float16_t)0.701660156f, + (float16_t)-0.237670898f, + (float16_t)-0.895507812f, + (float16_t)-0.799804688f, + (float16_t)-0.946777344f, + (float16_t)-0.72265625f, + (float16_t)0.532226562f, + (float16_t)-0.320556641f, + (float16_t)-0.199584961f, + (float16_t)-0.417480469f, + (float16_t)-0.982910156f, + (float16_t)-0.157714844f, + (float16_t)0.843261719f, + (float16_t)0.654296875f, + (float16_t)-0.743652344f, + (float16_t)0.981933594f, + (float16_t)-0.832519531f, + (float16_t)-0.932128906f, + (float16_t)0.770996094f, + (float16_t)-0.627929688f, + (float16_t)-0.412353516f, + (float16_t)-0.141357422f, + (float16_t)0.716796875f, + (float16_t)0.310058594f, + (float16_t)-0.0916137695f, + (float16_t)-0.368164062f, + (float16_t)-0.470947266f, + (float16_t)0.0693359375f, + (float16_t)0.167602539f, + (float16_t)-0.813964844f, + (float16_t)0.309570312f, + (float16_t)0.456298828f, + (float16_t)-0.170776367f, + (float16_t)0.495849609f, + (float16_t)0.459472656f, + (float16_t)-0.901367188f, + (float16_t)-0.131225586f, + (float16_t)0.487548828f, + (float16_t)-0.0141448975f, + (float16_t)-0.0600891113f, + (float16_t)-0.424560547f, + (float16_t)0.924316406f, + (float16_t)-0.980957031f, + (float16_t)0.703613281f, + (float16_t)-0.0651855469f, + (float16_t)-0.631835938f, + (float16_t)-0.144287109f, + (float16_t)-0.962890625f, + 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(float16_t)0.943359375f, + (float16_t)0.716308594f, + (float16_t)-0.755371094f, + (float16_t)-0.169067383f, + (float16_t)0.584960938f, + (float16_t)0.576171875f, + (float16_t)0.890136719f, + (float16_t)-0.0620422363f, + (float16_t)0.829589844f, + (float16_t)-0.751464844f, + (float16_t)-0.741699219f, + (float16_t)0.0809326172f, + (float16_t)0.338378906f, + (float16_t)-0.512695312f, + (float16_t)-0.270751953f, + (float16_t)0.416748047f, + (float16_t)0.0884399414f, + (float16_t)-0.750488281f, + (float16_t)0.568847656f, + (float16_t)0.869140625f, + (float16_t)-0.399658203f, + (float16_t)0.0847167969f, + (float16_t)-0.338378906f, + (float16_t)0.848632812f, + (float16_t)-0.342285156f, + (float16_t)0.476074219f, + (float16_t)0.430175781f, + (float16_t)0.966308594f, + (float16_t)0.983886719f, + (float16_t)0.798828125f, + (float16_t)-0.287597656f, + (float16_t)-0.604980469f, + (float16_t)0.951171875f, + (float16_t)-0.0606384277f, + (float16_t)-0.655273438f, + (float16_t)-0.171386719f, + (float16_t)0.746582031f, + (float16_t)0.532714844f, + (float16_t)-0.409667969f, + (float16_t)-0.0321960449f, + (float16_t)0.985839844f, + (float16_t)-0.296386719f, + (float16_t)0.335693359f, + (float16_t)0.535644531f, + (float16_t)0.121459961f, + (float16_t)0.878417969f, + (float16_t)0.967773438f, + (float16_t)-0.7265625f, + (float16_t)-0.934082031f, + (float16_t)-0.965332031f, + (float16_t)0.572753906f, + (float16_t)0.975097656f, + (float16_t)0.502929688f, + (float16_t)0.318603516f, + (float16_t)-0.594238281f, + (float16_t)0.0543823242f, + (float16_t)0.747558594f, + (float16_t)0.453613281f, + (float16_t)-0.162353516f, + (float16_t)-0.41015625f, + (float16_t)0.595703125f, + (float16_t)0.295166016f, + (float16_t)-0.10369873f, + (float16_t)0.466064453f, + (float16_t)0.240722656f, + (float16_t)0.812988281f, + (float16_t)-0.422607422f, + (float16_t)0.0523986816f, + (float16_t)0.979492188f, + (float16_t)-0.338378906f, + (float16_t)-0.70703125f, + (float16_t)0.536132812f, + (float16_t)-0.994140625f, + (float16_t)0.199951172f, + (float16_t)-0.203979492f, + (float16_t)0.581542969f, + (float16_t)-0.120788574f, + (float16_t)-0.0912475586f, + (float16_t)0.863769531f, + (float16_t)0.578125f, + (float16_t)0.713867188f, + (float16_t)0.865722656f, + (float16_t)-0.514160156f, + (float16_t)-0.728515625f, + (float16_t)0.783203125f, + (float16_t)-0.438476562f, + (float16_t)-0.542480469f, + (float16_t)-0.559570312f, + (float16_t)-0.556640625f, + (float16_t)0.730957031f, + (float16_t)-0.0963134766f, + (float16_t)0.204467773f, + (float16_t)0.567871094f, + (float16_t)-0.715332031f, + (float16_t)-0.385986328f, + (float16_t)0.933105469f, + (float16_t)0.282714844f, + (float16_t)0.332275391f, + (float16_t)0.716308594f, + (float16_t)-0.812011719f, + (float16_t)-0.235717773f, + (float16_t)-0.94921875f, + (float16_t)-0.22253418f, + (float16_t)-0.953613281f, + (float16_t)-0.964355469f, + (float16_t)-0.263183594f, + (float16_t)-0.0626831055f, + (float16_t)0.19909668f, + (float16_t)-0.320800781f, + (float16_t)-0.303955078f, + (float16_t)0.287841797f, + (float16_t)-0.285644531f, + (float16_t)0.212280273f, + (float16_t)-0.609375f, + (float16_t)0.338623047f, + (float16_t)0.792480469f, + (float16_t)-0.448974609f, + (float16_t)0.809570312f, + (float16_t)0.427246094f, + (float16_t)-0.481445312f, + (float16_t)0.290771484f, + (float16_t)-0.10369873f, + (float16_t)0.337402344f, + (float16_t)-0.803710938f, + (float16_t)0.890625f, + (float16_t)-0.655273438f, + (float16_t)-1.0f, + (float16_t)-0.193969727f, + (float16_t)-0.962890625f, + (float16_t)-0.384277344f, + (float16_t)0.0155410767f, + (float16_t)0.117004395f, + (float16_t)-0.6875f, + (float16_t)0.850585938f, + (float16_t)-0.766601562f, + (float16_t)0.499755859f, + (float16_t)-0.778808594f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..43183b43 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/output_ref_data.h @@ -0,0 +1,90 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_1x1_stride2_nhwc_f16_output_ref_data[351] = { + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)-0.205322266f, (float16_t)-0.340087891f, + (float16_t)-0.114379883f, (float16_t)0.100036621f, (float16_t)0.234375f, (float16_t)-0.0806274414f, + (float16_t)0.326416016f, (float16_t)0.163208008f, (float16_t)0.0838012695f, (float16_t)-0.330322266f, + (float16_t)-0.162963867f, (float16_t)-0.347167969f, (float16_t)-0.327148438f, (float16_t)0.263183594f, + (float16_t)-0.31640625f, (float16_t)0.160522461f, (float16_t)0.410644531f, (float16_t)0.0738525391f, + (float16_t)-0.250488281f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)0.393310547f, + (float16_t)0.5859375f, (float16_t)0.357910156f, (float16_t)-0.674316406f, (float16_t)-0.378417969f, + (float16_t)0.434814453f, (float16_t)-0.372314453f, (float16_t)-0.75f, (float16_t)-0.75f, + (float16_t)0.371337891f, (float16_t)-0.00708007812f, (float16_t)0.75f, (float16_t)-0.75f, + (float16_t)0.508300781f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)-0.453369141f, + (float16_t)-0.408935547f, (float16_t)0.75f, (float16_t)-0.435791016f, (float16_t)-0.602050781f, + (float16_t)0.127441406f, (float16_t)0.157470703f, (float16_t)-0.0305633545f, (float16_t)0.75f, + (float16_t)-0.121643066f, (float16_t)-0.255371094f, (float16_t)0.210327148f, (float16_t)0.708496094f, + (float16_t)-0.75f, (float16_t)0.407714844f, (float16_t)-0.75f, (float16_t)-0.142822266f, + (float16_t)0.329345703f, (float16_t)0.416748047f, (float16_t)-0.75f, (float16_t)-0.115722656f, + (float16_t)0.104492188f, (float16_t)-0.75f, (float16_t)-0.75f, (float16_t)0.75f, + (float16_t)-0.75f, (float16_t)-0.439453125f, (float16_t)0.512207031f, (float16_t)-0.596191406f, + (float16_t)-0.653808594f, (float16_t)0.454589844f, (float16_t)-0.401611328f, (float16_t)0.75f, + (float16_t)-0.381835938f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)0.75f, + (float16_t)0.75f, (float16_t)-0.696777344f, (float16_t)0.0128860474f, (float16_t)-0.315429688f, + (float16_t)0.235961914f, (float16_t)-0.37890625f, (float16_t)-0.411865234f, (float16_t)-0.75f, + (float16_t)-0.430175781f, (float16_t)-0.75f, (float16_t)0.708984375f, (float16_t)-0.437744141f, + (float16_t)0.15612793f, (float16_t)0.375732422f, (float16_t)0.379882812f, (float16_t)-0.324707031f, + (float16_t)0.75f, (float16_t)-0.658203125f, (float16_t)0.361816406f, (float16_t)0.75f, + (float16_t)-0.239135742f, (float16_t)-0.0748291016f, (float16_t)0.75f, (float16_t)-0.345214844f, + (float16_t)-0.0527954102f, (float16_t)-0.483398438f, (float16_t)-0.150024414f, (float16_t)-0.489013672f, + (float16_t)-0.375488281f, (float16_t)0.75f, (float16_t)0.307128906f, (float16_t)-0.260009766f, + (float16_t)-0.0261077881f, (float16_t)-0.75f, (float16_t)0.727539062f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)-0.306152344f, (float16_t)0.75f, (float16_t)0.124206543f, + (float16_t)0.3203125f, (float16_t)-0.19128418f, (float16_t)-0.75f, (float16_t)-0.692382812f, + (float16_t)0.143554688f, (float16_t)0.75f, (float16_t)0.729492188f, (float16_t)-0.613769531f, + (float16_t)0.354492188f, (float16_t)0.0435791016f, (float16_t)-0.75f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)-0.00551986694f, (float16_t)-0.75f, (float16_t)0.468017578f, + (float16_t)-0.465576172f, (float16_t)0.620117188f, (float16_t)0.75f, (float16_t)0.75f, + (float16_t)0.75f, (float16_t)0.244873047f, (float16_t)0.75f, (float16_t)0.357421875f, + (float16_t)0.75f, (float16_t)-0.142944336f, (float16_t)-0.75f, (float16_t)0.75f, + (float16_t)-0.449462891f, (float16_t)0.576660156f, (float16_t)0.75f, (float16_t)0.554199219f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)0.100891113f, (float16_t)0.75f, + (float16_t)0.00537872314f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)-0.154296875f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)0.75f, (float16_t)-0.127319336f, + (float16_t)0.324951172f, (float16_t)0.75f, (float16_t)-0.505371094f, (float16_t)0.75f, + (float16_t)0.75f, (float16_t)-0.75f, (float16_t)0.634765625f, (float16_t)0.510742188f, + (float16_t)-0.75f, (float16_t)-0.75f, (float16_t)0.75f, (float16_t)0.416259766f, + (float16_t)0.401855469f, (float16_t)0.311767578f, (float16_t)-0.75f, (float16_t)0.330566406f, + (float16_t)0.75f, (float16_t)-0.302490234f, (float16_t)-0.75f, (float16_t)0.75f, + (float16_t)0.75f, (float16_t)-0.75f, (float16_t)0.393798828f, (float16_t)0.75f, + (float16_t)-0.27734375f, (float16_t)0.59765625f, (float16_t)0.607421875f, (float16_t)-0.75f, + (float16_t)0.0913085938f, (float16_t)0.259521484f, (float16_t)-0.444335938f, (float16_t)-0.362304688f, + (float16_t)0.319824219f, (float16_t)-0.392822266f, (float16_t)-0.335693359f, (float16_t)-0.141845703f, + (float16_t)0.75f, (float16_t)-0.75f, (float16_t)-0.666015625f, (float16_t)0.116882324f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)0.75f, + (float16_t)0.75f, (float16_t)0.171508789f, (float16_t)0.175415039f, (float16_t)-0.75f, + (float16_t)0.136962891f, (float16_t)0.75f, (float16_t)-0.33203125f, (float16_t)-0.75f, + (float16_t)-0.332519531f, (float16_t)-0.75f, (float16_t)-0.369628906f, (float16_t)-0.75f, + (float16_t)-0.00441741943f, (float16_t)0.557128906f, (float16_t)-0.75f, (float16_t)0.10949707f, + (float16_t)0.661621094f, (float16_t)-0.4140625f, (float16_t)-0.75f, (float16_t)0.326171875f, + (float16_t)-0.75f, (float16_t)-0.307617188f, (float16_t)-0.0150222778f, (float16_t)0.75f, + (float16_t)0.151611328f, (float16_t)0.75f, (float16_t)-0.283447266f, (float16_t)0.75f, + (float16_t)0.719726562f, (float16_t)-0.3046875f, (float16_t)-0.0119094849f, (float16_t)0.75f, + (float16_t)-0.671386719f, (float16_t)0.324462891f, (float16_t)0.702636719f, (float16_t)-0.75f, + (float16_t)0.1640625f, (float16_t)-0.75f, (float16_t)-0.0366210938f, (float16_t)-0.311523438f, + (float16_t)-0.00256538391f, (float16_t)0.0574951172f, (float16_t)0.260498047f, (float16_t)-0.164916992f, + (float16_t)0.75f, (float16_t)-0.714355469f, (float16_t)-0.690917969f, (float16_t)0.145019531f, + (float16_t)-0.609375f, (float16_t)-0.379638672f, (float16_t)-0.75f, (float16_t)0.604492188f, + (float16_t)0.537109375f, (float16_t)-0.75f, (float16_t)0.491455078f, (float16_t)-0.286621094f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)-0.640136719f, + (float16_t)0.382080078f, (float16_t)-0.75f, (float16_t)-0.425048828f, (float16_t)-0.254150391f, + (float16_t)-0.75f, (float16_t)-0.6953125f, (float16_t)-0.75f, (float16_t)0.610839844f, + (float16_t)0.190185547f, (float16_t)-0.75f, (float16_t)0.0955810547f, (float16_t)-0.75f, + (float16_t)-0.703125f, (float16_t)-0.481445312f, (float16_t)-0.59765625f, (float16_t)-0.489257812f, + (float16_t)-0.75f, (float16_t)0.297363281f, (float16_t)-0.75f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)0.135864258f, (float16_t)0.4921875f, (float16_t)-0.00139141083f, + (float16_t)-0.38671875f, (float16_t)0.75f, (float16_t)0.287353516f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)-0.0133743286f, (float16_t)0.75f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)0.75f, (float16_t)-0.75f, (float16_t)-0.729003906f, + (float16_t)-0.0484313965f, (float16_t)0.673339844f, (float16_t)0.206787109f, (float16_t)0.192138672f, + (float16_t)-0.123596191f, (float16_t)0.630371094f, (float16_t)0.75f, (float16_t)0.75f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)0.75f, (float16_t)-0.75f, + (float16_t)-0.0424194336f, (float16_t)0.611816406f, (float16_t)-0.75f, (float16_t)-0.0109100342f, + (float16_t)-0.310791016f, (float16_t)-0.75f, (float16_t)0.293212891f, (float16_t)-0.75f, + (float16_t)0.75f, (float16_t)0.298095703f, (float16_t)-0.67578125f, (float16_t)0.476074219f, + (float16_t)0.0210876465f, (float16_t)0.364013672f, (float16_t)-0.131469727f, (float16_t)0.273681641f, + (float16_t)0.155151367f, (float16_t)-0.419189453f, (float16_t)-0.75f, (float16_t)0.204223633f, + (float16_t)-0.484130859f, (float16_t)0.359619141f, (float16_t)0.0277862549f, (float16_t)0.387207031f, + (float16_t)-0.255615234f, (float16_t)0.0520019531f, (float16_t)-0.5546875f, (float16_t)0.75f, + (float16_t)-0.75f, (float16_t)0.225830078f, (float16_t)0.75f, (float16_t)0.0464782715f, + (float16_t)-0.75f, (float16_t)0.0231018066f, (float16_t)-0.170898438f, (float16_t)-0.75f, + (float16_t)-0.75f, (float16_t)0.75f, (float16_t)-0.661132812f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/weights_data.h new file mode 100644 index 00000000..14e1be3a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f16/weights_data.h @@ -0,0 +1,38 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_1x1_stride2_nhwc_f16_weights_data[143] = { + (float16_t)-0.581542969f, (float16_t)0.305175781f, (float16_t)-0.0391235352f, (float16_t)-0.447753906f, + (float16_t)0.296386719f, (float16_t)-0.574707031f, (float16_t)0.236694336f, (float16_t)0.148193359f, + (float16_t)0.741210938f, (float16_t)-0.120361328f, (float16_t)0.172363281f, (float16_t)0.439941406f, + (float16_t)0.41015625f, (float16_t)-0.283447266f, (float16_t)-0.489990234f, (float16_t)-0.611816406f, + (float16_t)-0.509277344f, (float16_t)-0.119140625f, (float16_t)0.376708984f, (float16_t)-0.745117188f, + (float16_t)-0.221313477f, (float16_t)-0.591796875f, (float16_t)-0.239990234f, (float16_t)0.677246094f, + (float16_t)-0.129150391f, (float16_t)0.369140625f, (float16_t)-0.517089844f, (float16_t)0.285644531f, + (float16_t)-0.179443359f, (float16_t)-0.66015625f, (float16_t)0.316162109f, (float16_t)-0.206420898f, + (float16_t)-0.164794922f, (float16_t)-0.121765137f, (float16_t)-0.206420898f, (float16_t)0.133911133f, + (float16_t)-0.443603516f, (float16_t)-0.393066406f, (float16_t)0.0195159912f, (float16_t)0.157836914f, + (float16_t)-0.241455078f, (float16_t)0.198364258f, (float16_t)-0.0998535156f, (float16_t)-0.715820312f, + (float16_t)0.201293945f, (float16_t)-0.0306091309f, (float16_t)-0.269775391f, (float16_t)-0.588867188f, + (float16_t)-0.173950195f, (float16_t)0.183837891f, (float16_t)0.431640625f, (float16_t)-0.0147323608f, + (float16_t)-0.130493164f, (float16_t)0.425537109f, (float16_t)-0.62109375f, (float16_t)0.453857422f, + (float16_t)-0.248535156f, (float16_t)0.145141602f, (float16_t)-0.318847656f, (float16_t)0.204956055f, + (float16_t)0.401855469f, (float16_t)-0.671875f, (float16_t)-0.724609375f, (float16_t)-0.397460938f, + (float16_t)0.744140625f, (float16_t)-0.481201172f, (float16_t)0.385986328f, (float16_t)0.184692383f, + (float16_t)0.518066406f, (float16_t)-0.712402344f, (float16_t)-0.552246094f, (float16_t)0.347167969f, + (float16_t)0.588378906f, (float16_t)-0.107421875f, (float16_t)0.653320312f, (float16_t)-0.182617188f, + (float16_t)0.136352539f, (float16_t)0.481933594f, (float16_t)0.201171875f, (float16_t)-0.541503906f, + (float16_t)-0.30859375f, (float16_t)-0.189941406f, (float16_t)0.707519531f, (float16_t)0.122741699f, + (float16_t)-0.639160156f, (float16_t)-0.165527344f, (float16_t)-0.4765625f, (float16_t)-0.735351562f, + (float16_t)-0.142333984f, (float16_t)0.301269531f, (float16_t)0.712890625f, (float16_t)-0.365966797f, + (float16_t)-0.147827148f, (float16_t)0.191650391f, (float16_t)-0.396240234f, (float16_t)-0.304931641f, + (float16_t)0.376464844f, (float16_t)0.224121094f, (float16_t)0.0708618164f, (float16_t)-0.134521484f, + (float16_t)0.153076172f, (float16_t)0.192504883f, (float16_t)-0.732421875f, (float16_t)-0.242553711f, + (float16_t)-0.541992188f, (float16_t)0.0425109863f, (float16_t)-0.575195312f, (float16_t)-0.454345703f, + (float16_t)-0.0620117188f, (float16_t)0.482666016f, (float16_t)0.114990234f, (float16_t)0.447265625f, + (float16_t)0.588867188f, (float16_t)-0.244750977f, (float16_t)-0.678222656f, (float16_t)0.71875f, + (float16_t)0.745605469f, (float16_t)-0.267822266f, (float16_t)0.6796875f, (float16_t)-0.396484375f, + (float16_t)-0.405517578f, (float16_t)0.0113372803f, (float16_t)-0.117126465f, (float16_t)-0.0919799805f, + (float16_t)0.469238281f, (float16_t)0.712402344f, (float16_t)0.425537109f, (float16_t)-0.385986328f, + (float16_t)0.746582031f, (float16_t)0.174926758f, (float16_t)0.3046875f, (float16_t)0.160522461f, + (float16_t)-0.193603516f, (float16_t)0.0195007324f, (float16_t)-0.307373047f, (float16_t)0.483642578f, + (float16_t)-0.143188477f, (float16_t)-0.742675781f, (float16_t)0.104797363f, (float16_t)-0.00537490845f, + (float16_t)0.308105469f, (float16_t)0.587890625f, (float16_t)0.534667969f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/biases_data.h new file mode 100644 index 00000000..cfe4fea1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/biases_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_1x1_stride2_nhwc_f32_biases_data[13] = {-0.424446285f, + 0.49512291f, + -0.214005142f, + -0.0149858408f, + 0.23616913f, + -0.478109539f, + 0.23161386f, + -0.00909191649f, + 0.489786059f, + -0.427591681f, + 0.162849128f, + 0.401654154f, + -0.436546057f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/config_data.h new file mode 100644 index 00000000..0ff22ea1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_1X1_STRIDE2_NHWC_F32_INPUT_BATCHES 1 +#define CONV_1X1_STRIDE2_NHWC_F32_INPUT_H 3 +#define CONV_1X1_STRIDE2_NHWC_F32_INPUT_W 17 +#define CONV_1X1_STRIDE2_NHWC_F32_IN_CH 11 +#define CONV_1X1_STRIDE2_NHWC_F32_FILTER_H 1 +#define CONV_1X1_STRIDE2_NHWC_F32_FILTER_W 1 +#define CONV_1X1_STRIDE2_NHWC_F32_OUT_CH 13 +#define CONV_1X1_STRIDE2_NHWC_F32_STRIDE_H 1 +#define CONV_1X1_STRIDE2_NHWC_F32_STRIDE_W 2 +#define CONV_1X1_STRIDE2_NHWC_F32_PADDING_H 0 +#define CONV_1X1_STRIDE2_NHWC_F32_PADDING_W 0 +#define CONV_1X1_STRIDE2_NHWC_F32_DILATION_H 1 +#define CONV_1X1_STRIDE2_NHWC_F32_DILATION_W 1 +#define CONV_1X1_STRIDE2_NHWC_F32_OUTPUT_H 3 +#define CONV_1X1_STRIDE2_NHWC_F32_OUTPUT_W 9 +#define CONV_1X1_STRIDE2_NHWC_F32_OUTPUT_C 13 +#define CONV_1X1_STRIDE2_NHWC_F32_DST_SIZE 351 +#define CONV_1X1_STRIDE2_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_1X1_STRIDE2_NHWC_F32_USE_WRAPPER 1 +#define CONV_1X1_STRIDE2_NHWC_F32_OUT_ACTIVATION_MIN -0.75f +#define CONV_1X1_STRIDE2_NHWC_F32_OUT_ACTIVATION_MAX 0.75f diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/input_data.h new file mode 100644 index 00000000..663d166f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/input_data.h @@ -0,0 +1,83 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_1x1_stride2_nhwc_f32_input_data[561] = { + 0.790315032f, -0.354888201f, 0.582464516f, 0.696850002f, -0.620713592f, -0.180058569f, 0.189111292f, + -0.559560478f, -0.81859386f, 0.0933091491f, 0.425667852f, 0.371904045f, 0.79667896f, -0.962198317f, + -0.821271479f, 0.208025917f, 0.467766583f, 0.738686681f, -0.971265078f, 0.865561008f, -0.628688574f, + 0.69262141f, 0.693656445f, 0.70770818f, 0.601763964f, 0.845649838f, 0.817993999f, -0.515406549f, + 0.32463792f, -0.774009049f, 0.45551303f, 0.810356855f, -0.429556638f, -0.971212506f, 0.347542644f, + -0.088152349f, -0.721465826f, 0.0061715059f, -0.195935979f, 0.913932621f, 0.750999808f, 0.968863964f, + 0.0520681329f, -0.938890934f, -0.490645677f, -0.104741536f, 0.107076436f, 0.106368437f, -0.308569342f, + 0.989993334f, -0.860751688f, 0.215258434f, 0.232120037f, -0.771378517f, 0.673010826f, 0.538992643f, + -0.133383527f, 0.398268491f, 0.990656376f, -0.623838186f, 0.268355846f, -0.887476563f, 0.0760867149f, + 0.783519268f, 0.0969773307f, -0.434944004f, 0.235058233f, 0.0533644371f, -0.503499866f, 0.361493826f, + -0.485398829f, -0.108130693f, -0.952833772f, -0.962319672f, 0.783314586f, 0.25472191f, 0.773816705f, + 0.874736309f, -0.830357313f, 0.705140769f, -0.0800331458f, -0.982379973f, 0.178078637f, -0.780023038f, + -0.178360388f, -0.125460789f, 0.61989814f, -0.218513638f, -0.875590086f, -0.36774683f, -0.141074657f, + 0.367628187f, 0.701386869f, 0.778310597f, -0.797882855f, 0.821262836f, -0.7410115f, 0.296734691f, + -0.884230494f, 0.178716049f, -0.749202728f, -0.610431731f, -0.000750877254f, -0.0392121524f, 0.0723656714f, + 0.392102897f, -0.532115459f, -0.286645621f, 0.0542291068f, 0.00765848067f, 0.766117752f, 0.831503808f, + 0.382777393f, -0.274264574f, 0.2218059f, 0.631185293f, -0.0512046553f, 0.93188262f, 0.338621885f, + 0.584926307f, 0.346399903f, -0.913741827f, 0.159055963f, -0.111965246f, -0.650546014f, -0.186571613f, + -0.573425531f, 0.080331482f, -0.686525047f, -0.0541527793f, -0.208052889f, -0.0831440985f, -0.391112f, + 0.608424723f, 0.792021334f, 0.977441609f, 0.60312289f, -0.651011765f, 0.47792542f, -0.471590102f, + -0.0472705588f, 0.287400752f, -0.195633456f, 0.740658462f, -0.724932015f, 0.0713830963f, 0.838259995f, + -0.0417958982f, 0.154923156f, -0.22122103f, 0.636163771f, -0.632004917f, 0.319274545f, -0.292918473f, + 0.517075896f, 0.554933786f, -0.956866026f, -0.728408694f, -0.136010423f, -0.662775636f, 0.148369834f, + 0.45612821f, -0.560325384f, 0.250469327f, -0.756667018f, 0.843717456f, 0.756418765f, 0.660518467f, + -0.281761289f, 0.573490202f, 0.884544075f, -0.297123134f, 0.855492592f, 0.250861913f, 0.848126471f, + 0.254768342f, 0.0779941231f, 0.739085495f, 0.611898482f, 0.495674491f, -0.635403395f, -0.318847716f, + 0.281375557f, 0.228786394f, 0.0407826379f, 0.38661623f, 0.205593377f, -0.180231184f, -0.946022689f, + -0.946981788f, -0.15364781f, 0.863121867f, 0.15669705f, -0.0470359251f, -0.597789288f, 0.49996686f, + 0.701753676f, -0.237719223f, -0.895431578f, -0.799945593f, -0.946732581f, -0.722431898f, 0.532424271f, + -0.320544392f, -0.199605167f, -0.417580336f, -0.983047366f, -0.157671869f, 0.843277276f, 0.65447104f, + -0.743890882f, 0.98203969f, -0.832728803f, -0.932312965f, 0.771080911f, -0.627929926f, -0.412273467f, + -0.14133817f, 0.716984749f, 0.30998224f, -0.0916344076f, -0.368267983f, -0.470911264f, 0.0693320557f, + 0.16756089f, -0.813860774f, 0.309683621f, 0.456203431f, -0.17075032f, 0.495880812f, 0.459517777f, + -0.901548445f, -0.131251782f, 0.48745957f, -0.0141463364f, -0.0600910634f, -0.424495608f, 0.924323142f, + -0.980987251f, 0.703831434f, -0.0652068108f, -0.631734073f, -0.144303665f, -0.962984622f, -0.143234208f, + -0.685864985f, 0.898536623f, -0.524879515f, -0.683270872f, 0.35878095f, 0.773088217f, -0.698496819f, + 0.848170102f, 0.548171282f, 0.77765727f, 0.679166436f, 0.96117574f, 0.0695968345f, 0.705995142f, + -0.365736872f, -0.637575746f, -0.990640521f, 0.319333464f, -0.832837284f, -0.00928667933f, -0.540991008f, + 0.509460986f, -0.403778017f, 0.0973174423f, -0.932356775f, 0.023489235f, 0.717636108f, 0.0558564998f, + 0.0959551632f, 0.275278687f, 0.648583353f, -0.16503951f, -0.476016879f, 0.837599933f, -0.747982919f, + 0.733178437f, -0.270143658f, 0.885316253f, 0.433629006f, -0.843917131f, -0.942772806f, 0.142749324f, + 0.820469558f, -0.641600668f, -0.783222139f, -0.455531895f, 0.571426094f, 0.176290616f, -0.489540458f, + -0.505933404f, 0.819700718f, 0.951171458f, -0.30296737f, 0.0992196128f, -0.483239472f, -0.083140105f, + -0.235652462f, -0.845172346f, 0.576137185f, 0.394097537f, -0.0301406439f, -0.704384267f, -0.681723177f, + -0.990592897f, 0.011222898f, 0.926193297f, 0.893175066f, 0.978856981f, 0.583224475f, -0.25647974f, + -0.160649866f, -0.00809005741f, -0.0775591284f, 0.61367929f, 0.127302855f, 0.892251134f, 0.823537886f, + -0.775469065f, 0.0434908718f, -0.187457457f, 0.250219673f, 0.654653132f, -0.880655646f, -0.0199230202f, + 0.829146743f, 0.979774952f, 0.291879505f, -0.0275566988f, 0.125106499f, -0.492462158f, -0.325818956f, + 0.469025314f, 0.924001396f, -0.725508213f, 0.778390586f, 0.18994154f, -0.0328356475f, 0.295199454f, + -0.733215988f, -0.16089727f, -0.285910606f, -0.870226443f, -0.338449776f, 0.202333018f, -0.251251638f, + -0.175970063f, 0.876423895f, -0.807182193f, 0.975020409f, -0.564171374f, 0.194057003f, -0.386959434f, + -0.759262919f, -0.44505775f, -0.0686002001f, -0.222803101f, 0.521815062f, -0.918375194f, 0.46692732f, + 0.565396369f, 0.398462564f, 0.314130992f, 0.890844703f, 0.623214066f, 0.456689328f, -0.978687823f, + 0.367046297f, 0.227746531f, 0.883165658f, -0.56965524f, -0.221456796f, -0.216930464f, -0.0945994332f, + -0.753939629f, -0.514568806f, 0.468657374f, 0.758212745f, -0.187930971f, 0.285212696f, -0.139803857f, + -0.813436508f, 0.563498557f, -0.475386947f, -0.511384964f, 0.591263473f, -0.231857374f, -0.429659635f, + 0.620791256f, 0.243934184f, -0.956301093f, 0.681779563f, -0.254395217f, 0.863420546f, 0.245494232f, + -0.366010845f, 0.278823763f, -0.441298187f, -0.124465004f, 0.471267134f, -0.956754506f, 0.288417011f, + 0.354462415f, 0.546105742f, -0.711109638f, -0.150063738f, 0.609410822f, 0.581356883f, -0.331887007f, + 0.657436311f, 0.956884205f, -0.596303523f, 0.733645022f, -0.584905207f, 0.0138839725f, -0.689314544f, + -0.974746644f, 0.442348778f, -0.347802401f, -0.00532000838f, 0.943271995f, 0.716396332f, -0.75521934f, + -0.169100896f, 0.585030437f, 0.576354563f, 0.89014554f, -0.0620380715f, 0.829464436f, -0.75164932f, + -0.741745532f, 0.0809365734f, 0.338427722f, -0.512677491f, -0.270779282f, 0.416675597f, 0.0884204954f, + -0.750642836f, 0.568742573f, 0.868963838f, -0.399703413f, 0.0846888945f, -0.338395327f, 0.848490179f, + -0.342235863f, 0.47611773f, 0.430168182f, 0.966531932f, 0.983782947f, 0.798981845f, -0.287514567f, + -0.604786158f, 0.951004386f, -0.0606261268f, -0.655267954f, -0.171333328f, 0.746554852f, 0.532743573f, + -0.409701586f, -0.0321907476f, 0.985996425f, -0.296379596f, 0.335693926f, 0.535509884f, 0.121451393f, + 0.878434956f, 0.967554271f, -0.726452768f, -0.93427974f, -0.965216398f, 0.572690248f, 0.975273311f, + 0.502787709f, 0.318522036f, -0.594367266f, 0.054377038f, 0.747763574f, 0.453583896f, -0.162357777f, + -0.410151303f, 0.59558636f, 0.295059144f, -0.103700161f, 0.4661192f, 0.240666792f, 0.813027859f, + -0.422629029f, 0.0523988567f, 0.979249418f, -0.338288009f, -0.706799448f, 0.536093891f, -0.994374335f, + 0.199967727f, -0.203950986f, 0.581607401f, -0.120780289f, -0.0912234634f, 0.863625586f, 0.57812804f, + 0.713921905f, 0.865803182f, -0.514200807f, -0.728390455f, 0.782977521f, -0.438582361f, -0.542356014f, + -0.559388936f, -0.556529284f, 0.730872989f, -0.0963028669f, 0.204407156f, 0.568112969f, -0.715159118f, + -0.386065245f, 0.933084846f, 0.282627076f, 0.3321639f, 0.716465473f, -0.812105894f, -0.2356866f, + -0.949023485f, -0.222517475f, -0.953568637f, -0.964302838f, -0.263262421f, -0.0626889616f, 0.199109837f, + -0.320711285f, -0.303877771f, 0.287745565f, -0.285629839f, 0.21225372f, -0.609593391f, 0.338598967f, + 0.792694747f, -0.448996276f, 0.809784472f, 0.427312016f, -0.481341332f, 0.290789574f, -0.10367956f, + 0.337462008f, -0.803812027f, 0.890463293f, -0.65525651f, -0.999788523f, -0.193977803f, -0.962978363f, + -0.384169728f, 0.0155414306f, 0.116974927f, -0.687687337f, 0.850610495f, -0.766807318f, 0.499822229f, + -0.77870506f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..a1e81caa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/output_ref_data.h @@ -0,0 +1,53 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_1x1_stride2_nhwc_f32_output_ref_data[351] = { + -0.75f, 0.75f, -0.205369368f, -0.340095073f, -0.11436668f, 0.100018084f, 0.234367058f, + -0.0806457922f, 0.326447546f, 0.163201869f, 0.0837776065f, -0.330305606f, -0.162954956f, -0.347232461f, + -0.327098191f, 0.26311639f, -0.316501379f, 0.160532743f, 0.410528302f, 0.0738774389f, -0.250470191f, + 0.75f, -0.75f, 0.393199861f, 0.586033463f, 0.357880741f, -0.674334884f, -0.378304303f, + 0.434789568f, -0.372364134f, -0.75f, -0.75f, 0.371319026f, -0.0070809545f, 0.75f, + -0.75f, 0.508485436f, 0.75f, -0.75f, -0.453402698f, -0.408861876f, 0.75f, + -0.435712755f, -0.601971388f, 0.127499461f, 0.157506764f, -0.030561775f, 0.75f, -0.121659219f, + -0.255259335f, 0.210292742f, 0.708487749f, -0.75f, 0.407760203f, -0.75f, -0.142865226f, + 0.329312742f, 0.41670388f, -0.75f, -0.115708202f, 0.104500085f, -0.75f, -0.75f, + 0.75f, -0.75f, -0.439374924f, 0.512358427f, -0.596294165f, -0.65399617f, 0.454494596f, + -0.4016397f, 0.75f, -0.381931156f, 0.75f, -0.75f, 0.75f, 0.75f, + -0.696534038f, 0.0128887892f, -0.31549269f, 0.235992759f, -0.378915846f, -0.411750644f, -0.75f, + -0.430183351f, -0.75f, 0.708908916f, -0.43782714f, 0.156157285f, 0.37585029f, 0.379861981f, + -0.324786603f, 0.75f, -0.658240438f, 0.361709267f, 0.75f, -0.239146754f, -0.0748339742f, + 0.75f, -0.345293075f, -0.0527939796f, -0.483350277f, -0.150082141f, -0.489134699f, -0.375564724f, + 0.75f, 0.307068616f, -0.259960234f, -0.0261060596f, -0.75f, 0.727490008f, -0.75f, + 0.75f, -0.306225002f, 0.75f, 0.124206275f, 0.320199698f, -0.191235065f, -0.75f, + -0.692314744f, 0.143594369f, 0.75f, 0.729375541f, -0.613736749f, 0.354538023f, 0.0435799956f, + -0.75f, -0.75f, 0.75f, -0.00551965833f, -0.75f, 0.468041778f, -0.46561262f, + 0.620054245f, 0.75f, 0.75f, 0.75f, 0.24484095f, 0.75f, 0.357369184f, + 0.75f, -0.142916054f, -0.75f, 0.75f, -0.449421465f, 0.576756358f, 0.75f, + 0.554064393f, -0.75f, 0.75f, 0.100919738f, 0.75f, 0.00537723303f, 0.75f, + -0.75f, -0.154290944f, -0.75f, 0.75f, 0.75f, -0.127330303f, 0.325009644f, + 0.75f, -0.505177379f, 0.75f, 0.75f, -0.75f, 0.63482064f, 0.51082921f, + -0.75f, -0.75f, 0.75f, 0.416255862f, 0.401770383f, 0.311862022f, -0.75f, + 0.330463469f, 0.75f, -0.302497655f, -0.75f, 0.75f, 0.75f, -0.75f, + 0.39389205f, 0.75f, -0.277251184f, 0.597774029f, 0.60735321f, -0.75f, 0.0913076699f, + 0.259633094f, -0.444239825f, -0.362271905f, 0.319801748f, -0.392929822f, -0.335662514f, -0.141837239f, + 0.75f, -0.75f, -0.665847301f, 0.116884336f, -0.75f, 0.75f, -0.75f, + 0.75f, 0.75f, 0.171522334f, 0.175408378f, -0.75f, 0.136947632f, 0.75f, + -0.331972063f, -0.75f, -0.332564563f, -0.75f, -0.369561017f, -0.75f, -0.00441625714f, + 0.557166398f, -0.75f, 0.109489501f, 0.661436915f, -0.414099246f, -0.75f, 0.326256305f, + -0.75f, -0.307694405f, -0.0150258839f, 0.75f, 0.151603788f, 0.75f, -0.283471882f, + 0.75f, 0.719734192f, -0.30472073f, -0.0119119585f, 0.75f, -0.671153486f, 0.324434787f, + 0.702852845f, -0.75f, 0.164007723f, -0.75f, -0.0366310179f, -0.311482459f, -0.0025652647f, + 0.057499975f, 0.260556906f, -0.164864898f, 0.75f, -0.714216232f, -0.690898597f, 0.145001814f, + -0.609455645f, -0.379572809f, -0.75f, 0.604408383f, 0.53734231f, -0.75f, 0.49151364f, + -0.286534011f, -0.75f, 0.75f, -0.75f, -0.640279174f, 0.381988883f, -0.75f, + -0.424973309f, -0.254189879f, -0.75f, -0.695281506f, -0.75f, 0.610916376f, 0.19020322f, + -0.75f, 0.0955674052f, -0.75f, -0.702969015f, -0.481408685f, -0.597777545f, -0.489375949f, + -0.75f, 0.297434986f, -0.75f, -0.75f, 0.75f, 0.13580361f, 0.492269874f, + -0.00139179826f, -0.386604518f, 0.75f, 0.287465334f, -0.75f, 0.75f, -0.0133731533f, + 0.75f, -0.75f, 0.75f, 0.75f, -0.75f, -0.729093671f, -0.048445642f, + 0.673348665f, 0.206833705f, 0.192082107f, -0.123601615f, 0.630309463f, 0.75f, 0.75f, + -0.75f, 0.75f, 0.75f, -0.75f, -0.0424094498f, 0.611733675f, -0.75f, + -0.010907039f, -0.310689121f, -0.75f, 0.293333054f, -0.75f, 0.75f, 0.298124611f, + -0.675998092f, 0.476044953f, 0.0210868418f, 0.363907993f, -0.13141042f, 0.273715287f, 0.155131534f, + -0.419219583f, -0.75f, 0.204271331f, -0.484215945f, 0.359726846f, 0.0277828574f, 0.387182653f, + -0.255576402f, 0.051997751f, -0.554574132f, 0.75f, -0.75f, 0.225788951f, 0.75f, + 0.0464918017f, -0.75f, 0.0230999924f, -0.170902163f, -0.75f, -0.75f, 0.75f, + -0.6610623f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/weights_data.h new file mode 100644 index 00000000..de101630 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_1x1_stride2_nhwc_f32/weights_data.h @@ -0,0 +1,23 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_1x1_stride2_nhwc_f32_weights_data[143] = { + -0.581636131f, 0.305103332f, -0.0391200185f, -0.44778493f, 0.29635042f, -0.574931085f, 0.236635372f, + 0.148172662f, 0.741280794f, -0.120349444f, 0.172390312f, 0.4399122f, 0.410050303f, -0.283434927f, + -0.489998341f, -0.61162889f, -0.509178519f, -0.119161606f, 0.376668781f, -0.745037436f, -0.221307322f, + -0.591724813f, -0.240001008f, 0.677475393f, -0.129143909f, 0.369027197f, -0.517194629f, 0.285746276f, + -0.179479152f, -0.660076261f, 0.316101551f, -0.206453934f, -0.164827093f, -0.121736437f, -0.206384629f, + 0.13389954f, -0.44356063f, -0.392954469f, 0.0195235424f, 0.157860264f, -0.24141714f, 0.198389113f, + -0.0998479724f, -0.715721667f, 0.201283723f, -0.0306016169f, -0.269743502f, -0.588802338f, -0.173939511f, + 0.183864132f, 0.431655407f, -0.0147351427f, -0.13045229f, 0.425582707f, -0.620871902f, 0.453949273f, + -0.248556346f, 0.145105496f, -0.318820834f, 0.205004305f, 0.401887268f, -0.671998858f, -0.724577785f, + -0.397533238f, 0.744265914f, -0.481147349f, 0.385977596f, 0.184717774f, 0.518251181f, -0.712517738f, + -0.55230391f, 0.347201675f, 0.588561594f, -0.107420616f, 0.653298318f, -0.182672277f, 0.136330634f, + 0.481955647f, 0.201168507f, -0.541326046f, -0.308508158f, -0.189917624f, 0.707727849f, 0.122752182f, + -0.638994873f, -0.165544957f, -0.476609945f, -0.735398173f, -0.14229168f, 0.301292062f, 0.712784111f, + -0.365919113f, -0.147837088f, 0.191660598f, -0.396118224f, -0.304912776f, 0.376458168f, 0.224064335f, + 0.070851095f, -0.134534061f, 0.153017715f, 0.192506567f, -0.732448041f, -0.242495283f, -0.542157948f, + 0.0425055996f, -0.575205922f, -0.454429656f, -0.0620260946f, 0.482661277f, 0.114969827f, 0.447378278f, + 0.588662922f, -0.244716495f, -0.678048313f, 0.718964934f, 0.745831072f, -0.267791122f, 0.679449916f, + -0.396388024f, -0.405568391f, 0.0113347024f, -0.117117964f, -0.0919553488f, 0.469291121f, 0.712366819f, + 0.425497025f, -0.385900676f, 0.746622801f, 0.174900874f, 0.304730803f, 0.160550609f, -0.193632931f, + 0.0194945224f, -0.307364941f, 0.483623058f, -0.143219367f, -0.742693067f, 0.104802407f, -0.0053744209f, + 0.308192998f, 0.587819934f, 0.534547567f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/biases_data.h new file mode 100644 index 00000000..a1f3e738 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_f16_biases_data[10] = {(float16_t)-0.412841797f, + (float16_t)-0.366455078f, + (float16_t)0.0196075439f, + (float16_t)-0.110351562f, + (float16_t)0.0523986816f, + (float16_t)0.111022949f, + (float16_t)0.187011719f, + (float16_t)-0.484863281f, + (float16_t)0.0728149414f, + (float16_t)0.3203125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/config_data.h new file mode 100644 index 00000000..0a8aefda --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_BASIC_F16_INPUT_BATCHES 1 +#define CONV_BASIC_F16_INPUT_H 9 +#define CONV_BASIC_F16_INPUT_W 11 +#define CONV_BASIC_F16_IN_CH 6 +#define CONV_BASIC_F16_FILTER_H 3 +#define CONV_BASIC_F16_FILTER_W 3 +#define CONV_BASIC_F16_OUT_CH 10 +#define CONV_BASIC_F16_STRIDE_H 1 +#define CONV_BASIC_F16_STRIDE_W 1 +#define CONV_BASIC_F16_PADDING_H 0 +#define CONV_BASIC_F16_PADDING_W 0 +#define CONV_BASIC_F16_DILATION_H 1 +#define CONV_BASIC_F16_DILATION_W 1 +#define CONV_BASIC_F16_OUTPUT_H 7 +#define CONV_BASIC_F16_OUTPUT_W 9 +#define CONV_BASIC_F16_OUTPUT_C 10 +#define CONV_BASIC_F16_DST_SIZE 630 +#define CONV_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_BASIC_F16_USE_WRAPPER 0 +#define CONV_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/input_data.h new file mode 100644 index 00000000..10ff0f9e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/input_data.h @@ -0,0 +1,151 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_f16_input_data[594] = { + (float16_t)-0.152709961f, (float16_t)0.164550781f, (float16_t)-0.220458984f, (float16_t)0.88671875f, + (float16_t)0.421142578f, (float16_t)-0.387207031f, (float16_t)-0.592773438f, (float16_t)0.318115234f, + (float16_t)-0.0307312012f, (float16_t)0.746582031f, (float16_t)0.883789062f, (float16_t)0.0475158691f, + (float16_t)-0.0498046875f, (float16_t)-0.167236328f, (float16_t)-0.158935547f, (float16_t)-0.00579833984f, + (float16_t)0.166015625f, (float16_t)-0.6796875f, (float16_t)0.924316406f, (float16_t)0.977539062f, + (float16_t)-0.208740234f, (float16_t)0.23046875f, (float16_t)0.16394043f, 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(float16_t)-0.716796875f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/output_ref_data.h new file mode 100644 index 00000000..0a192ad3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/output_ref_data.h @@ -0,0 +1,160 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_f16_output_ref_data[630] = { + (float16_t)-1.61621094f, (float16_t)-2.8359375f, (float16_t)1.1328125f, (float16_t)2.45703125f, + (float16_t)2.29296875f, (float16_t)1.14648438f, (float16_t)0.752929688f, (float16_t)-1.85058594f, + (float16_t)0.326171875f, (float16_t)-0.846191406f, (float16_t)0.609375f, (float16_t)1.16113281f, + (float16_t)3.82421875f, (float16_t)2.15820312f, (float16_t)0.267578125f, (float16_t)0.0444946289f, + (float16_t)1.84960938f, (float16_t)2.328125f, (float16_t)0.927734375f, (float16_t)0.748046875f, + (float16_t)-0.210205078f, (float16_t)1.11523438f, (float16_t)1.0078125f, (float16_t)-0.418701172f, + (float16_t)0.0580444336f, (float16_t)-1.72070312f, (float16_t)3.75195312f, (float16_t)-2.0859375f, + (float16_t)-2.09375f, (float16_t)2.06835938f, (float16_t)2.44726562f, (float16_t)-0.364013672f, + (float16_t)-0.543457031f, (float16_t)0.812988281f, (float16_t)3.6171875f, (float16_t)1.05664062f, + (float16_t)-3.32421875f, (float16_t)0.320068359f, (float16_t)2.49414062f, (float16_t)-2.203125f, + (float16_t)-0.522460938f, (float16_t)2.06640625f, (float16_t)1.35644531f, (float16_t)-0.266845703f, + (float16_t)-1.48828125f, (float16_t)-0.590332031f, (float16_t)0.647460938f, (float16_t)0.673828125f, + (float16_t)-1.15917969f, (float16_t)-1.36328125f, (float16_t)-0.413818359f, (float16_t)-3.75976562f, + (float16_t)1.29980469f, (float16_t)0.667480469f, (float16_t)-2.64648438f, (float16_t)-1.00878906f, + (float16_t)2.01953125f, (float16_t)0.671386719f, (float16_t)-1.75292969f, (float16_t)0.912109375f, + (float16_t)-0.625488281f, (float16_t)-0.112731934f, (float16_t)-1.94824219f, (float16_t)-2.84765625f, + (float16_t)0.849609375f, (float16_t)1.37402344f, (float16_t)-1.83007812f, (float16_t)0.323974609f, + (float16_t)1.22460938f, (float16_t)2.32421875f, (float16_t)-0.810546875f, (float16_t)-0.874511719f, + (float16_t)1.04394531f, (float16_t)1.52246094f, (float16_t)-1.75390625f, (float16_t)1.76074219f, + (float16_t)-0.420898438f, (float16_t)-1.85839844f, (float16_t)-1.78125f, (float16_t)-1.80078125f, + (float16_t)-2.0390625f, (float16_t)-3.49023438f, (float16_t)-3.45117188f, (float16_t)-1.35644531f, + (float16_t)2.84765625f, (float16_t)-1.45214844f, (float16_t)-1.76660156f, (float16_t)0.0171356201f, + (float16_t)2.57421875f, (float16_t)1.23046875f, (float16_t)-1.06738281f, (float16_t)-2.21484375f, + (float16_t)-0.711425781f, (float16_t)-2.19921875f, (float16_t)-2.69140625f, (float16_t)1.40332031f, + (float16_t)-0.938476562f, (float16_t)-0.0038356781f, (float16_t)-1.20800781f, (float16_t)1.94726562f, + (float16_t)-0.369384766f, (float16_t)1.77539062f, (float16_t)-1.5390625f, (float16_t)-1.99609375f, + (float16_t)0.854492188f, (float16_t)1.61328125f, (float16_t)0.305419922f, (float16_t)-0.40625f, + (float16_t)1.33984375f, (float16_t)2.38671875f, (float16_t)-0.62109375f, (float16_t)-0.338378906f, + (float16_t)-1.3515625f, (float16_t)3.14453125f, (float16_t)2.24414062f, (float16_t)0.083190918f, + (float16_t)-0.952148438f, (float16_t)-1.08496094f, (float16_t)2.40625f, (float16_t)-1.28222656f, + (float16_t)-0.401611328f, (float16_t)-3.65820312f, (float16_t)-1.63867188f, (float16_t)-0.250976562f, + (float16_t)2.47851562f, (float16_t)0.137451172f, (float16_t)1.00195312f, (float16_t)-0.659667969f, + (float16_t)-0.215820312f, (float16_t)4.328125f, (float16_t)2.74609375f, (float16_t)0.476806641f, + (float16_t)2.3203125f, (float16_t)3.546875f, (float16_t)1.21289062f, (float16_t)0.483154297f, + (float16_t)0.845214844f, (float16_t)1.30957031f, (float16_t)-0.231445312f, (float16_t)-3.921875f, + (float16_t)-1.13476562f, (float16_t)-1.43554688f, (float16_t)-3.08398438f, (float16_t)-1.36230469f, + (float16_t)1.1796875f, (float16_t)-1.80957031f, (float16_t)-0.597167969f, (float16_t)0.404052734f, + (float16_t)0.484863281f, (float16_t)-1.87402344f, (float16_t)0.173706055f, (float16_t)-2.3046875f, + (float16_t)-0.0500183105f, (float16_t)-1.11328125f, (float16_t)-2.64453125f, (float16_t)1.61230469f, + (float16_t)1.26269531f, (float16_t)0.350830078f, (float16_t)-0.0334472656f, (float16_t)1.3125f, + (float16_t)0.143188477f, (float16_t)-0.452392578f, (float16_t)-2.45117188f, (float16_t)-3.203125f, + (float16_t)-3.0703125f, (float16_t)3.38867188f, (float16_t)-1.37695312f, (float16_t)-2.17382812f, + (float16_t)1.95507812f, (float16_t)0.798339844f, (float16_t)-0.732421875f, (float16_t)-0.506835938f, + (float16_t)0.106567383f, (float16_t)1.89160156f, (float16_t)1.23339844f, (float16_t)1.29589844f, + (float16_t)0.865722656f, (float16_t)3.47265625f, (float16_t)-0.43359375f, (float16_t)-0.0698852539f, + (float16_t)3.83007812f, (float16_t)0.860839844f, (float16_t)3.46289062f, (float16_t)3.578125f, + (float16_t)0.0875854492f, (float16_t)2.38671875f, (float16_t)-1.84765625f, (float16_t)0.0533752441f, + (float16_t)-2.140625f, (float16_t)-1.99316406f, (float16_t)0.350341797f, (float16_t)1.2578125f, + (float16_t)5.28125f, (float16_t)2.5625f, (float16_t)-1.24023438f, (float16_t)-0.426757812f, + (float16_t)3.39453125f, (float16_t)2.1875f, (float16_t)-3.26367188f, (float16_t)-1.68164062f, + (float16_t)-0.0690917969f, (float16_t)-1.8203125f, (float16_t)-2.02539062f, (float16_t)0.717285156f, + (float16_t)2.90820312f, (float16_t)-1.9921875f, (float16_t)0.415039062f, (float16_t)-1.828125f, + (float16_t)3.5546875f, (float16_t)4.17578125f, (float16_t)-0.298828125f, (float16_t)-0.278320312f, + (float16_t)-3.765625f, (float16_t)0.717773438f, (float16_t)0.374267578f, (float16_t)-1.58007812f, + (float16_t)0.916015625f, (float16_t)-2.97265625f, (float16_t)-0.276367188f, (float16_t)-0.135986328f, + (float16_t)-2.92578125f, (float16_t)0.0569763184f, (float16_t)-2.64648438f, (float16_t)-3.9765625f, + (float16_t)2.67578125f, (float16_t)-1.65527344f, (float16_t)1.03613281f, (float16_t)2.26171875f, + (float16_t)1.11328125f, (float16_t)4.36328125f, (float16_t)-0.640625f, (float16_t)1.7421875f, + (float16_t)-0.987304688f, (float16_t)-2.515625f, (float16_t)0.164306641f, (float16_t)-0.241333008f, + (float16_t)-0.291992188f, (float16_t)-0.478271484f, (float16_t)0.655761719f, (float16_t)-1.41601562f, + (float16_t)-2.67382812f, (float16_t)2.66796875f, (float16_t)-0.11706543f, (float16_t)-3.625f, + (float16_t)0.115356445f, (float16_t)-0.489257812f, (float16_t)-0.0523681641f, (float16_t)-0.866699219f, + (float16_t)0.329589844f, (float16_t)-1.06054688f, (float16_t)0.709472656f, (float16_t)-1.37597656f, + (float16_t)-0.261962891f, (float16_t)-0.590820312f, (float16_t)-0.0618896484f, (float16_t)3.98046875f, + (float16_t)-0.540527344f, (float16_t)0.345703125f, (float16_t)1.76660156f, (float16_t)3.04296875f, + (float16_t)0.0413208008f, (float16_t)-0.379394531f, (float16_t)-1.70410156f, (float16_t)1.5703125f, + (float16_t)-2.23828125f, (float16_t)2.48242188f, (float16_t)1.87207031f, (float16_t)-1.26757812f, + (float16_t)-2.20507812f, (float16_t)0.0876464844f, (float16_t)-0.705566406f, (float16_t)-2.953125f, + (float16_t)0.722167969f, (float16_t)-0.484375f, (float16_t)0.534667969f, (float16_t)-1.97851562f, + (float16_t)0.208496094f, (float16_t)-1.3671875f, (float16_t)1.20019531f, (float16_t)0.157592773f, + (float16_t)-2.44140625f, (float16_t)2.22265625f, (float16_t)0.828613281f, (float16_t)0.454833984f, + (float16_t)1.4453125f, (float16_t)0.239013672f, (float16_t)-1.52441406f, (float16_t)2.296875f, + (float16_t)-2.05273438f, (float16_t)-0.711425781f, (float16_t)-1.81835938f, (float16_t)1.18652344f, + (float16_t)-1.82714844f, (float16_t)0.848632812f, (float16_t)-2.31054688f, (float16_t)1.77441406f, + (float16_t)0.993652344f, (float16_t)-2.05273438f, (float16_t)-0.690917969f, (float16_t)-0.2890625f, + (float16_t)-1.55175781f, (float16_t)1.43164062f, (float16_t)-1.02929688f, (float16_t)0.769042969f, + (float16_t)1.84570312f, (float16_t)-3.56640625f, (float16_t)0.788574219f, (float16_t)-1.296875f, + (float16_t)1.08789062f, (float16_t)5.890625f, (float16_t)-0.593261719f, (float16_t)-2.24609375f, + (float16_t)-0.530761719f, (float16_t)2.046875f, (float16_t)-0.412597656f, (float16_t)0.608886719f, + (float16_t)0.944335938f, (float16_t)0.0933227539f, (float16_t)-2.35351562f, (float16_t)-1.56640625f, + (float16_t)-2.08789062f, (float16_t)-2.45898438f, (float16_t)-1.77539062f, (float16_t)-2.73828125f, + (float16_t)0.849609375f, (float16_t)-1.83105469f, (float16_t)1.70605469f, (float16_t)-0.576171875f, + (float16_t)3.484375f, (float16_t)2.28515625f, (float16_t)1.91699219f, (float16_t)0.102355957f, + (float16_t)1.95996094f, (float16_t)0.790527344f, (float16_t)2.609375f, (float16_t)-2.02148438f, + (float16_t)-0.425292969f, (float16_t)2.78710938f, (float16_t)-0.335449219f, (float16_t)-0.87109375f, + (float16_t)-1.8515625f, (float16_t)1.64746094f, (float16_t)1.27050781f, (float16_t)0.144775391f, + (float16_t)0.8359375f, (float16_t)-1.11132812f, (float16_t)0.819335938f, (float16_t)-0.654296875f, + (float16_t)0.604003906f, (float16_t)-0.609863281f, (float16_t)-0.646484375f, (float16_t)-1.31835938f, + (float16_t)-2.16210938f, (float16_t)-0.708007812f, (float16_t)-0.665039062f, (float16_t)0.20703125f, + (float16_t)1.02636719f, (float16_t)-1.14453125f, (float16_t)1.78125f, (float16_t)5.76171875f, + (float16_t)1.57421875f, (float16_t)-1.95507812f, (float16_t)-1.00097656f, (float16_t)-0.719726562f, + (float16_t)-1.85644531f, (float16_t)-2.23632812f, (float16_t)-1.25390625f, (float16_t)0.428466797f, + (float16_t)-1.50390625f, (float16_t)-1.35644531f, (float16_t)-0.040222168f, (float16_t)4.41796875f, + (float16_t)0.934570312f, (float16_t)0.101745605f, (float16_t)-0.730957031f, (float16_t)-0.856933594f, + (float16_t)2.44921875f, (float16_t)-2.36523438f, (float16_t)0.731933594f, (float16_t)-1.68847656f, + (float16_t)1.42871094f, (float16_t)-1.44726562f, (float16_t)-1.07128906f, (float16_t)0.362792969f, + (float16_t)-0.715332031f, (float16_t)0.519042969f, (float16_t)-1.13378906f, (float16_t)0.628417969f, + (float16_t)0.45703125f, (float16_t)0.871582031f, (float16_t)-1.95507812f, (float16_t)-0.772949219f, + (float16_t)6.20703125f, (float16_t)0.838867188f, (float16_t)-3.01367188f, (float16_t)0.184204102f, + (float16_t)0.0455932617f, (float16_t)1.1953125f, (float16_t)-2.82617188f, (float16_t)0.150024414f, + (float16_t)-3.80664062f, (float16_t)-0.678710938f, (float16_t)-3.91601562f, (float16_t)-0.453857422f, + (float16_t)2.00585938f, (float16_t)1.3984375f, (float16_t)0.975585938f, (float16_t)-3.453125f, + (float16_t)2.3125f, (float16_t)0.673828125f, (float16_t)-0.969238281f, (float16_t)-1.06445312f, + (float16_t)-1.29589844f, (float16_t)-0.211303711f, (float16_t)2.12304688f, (float16_t)1.97460938f, + (float16_t)0.799316406f, (float16_t)-0.020324707f, (float16_t)1.875f, (float16_t)2.28515625f, + (float16_t)0.405029297f, (float16_t)-0.999023438f, (float16_t)-0.471679688f, (float16_t)0.627929688f, + (float16_t)-1.265625f, (float16_t)-0.356445312f, (float16_t)1.54785156f, (float16_t)0.661621094f, + (float16_t)2.0625f, (float16_t)1.31738281f, (float16_t)1.17382812f, (float16_t)-0.210327148f, + (float16_t)4.78125f, (float16_t)1.70898438f, (float16_t)-1.15527344f, (float16_t)-0.832519531f, + (float16_t)0.380371094f, (float16_t)1.93652344f, (float16_t)-0.518066406f, (float16_t)0.217163086f, + (float16_t)-0.90234375f, (float16_t)-1.78222656f, (float16_t)0.648925781f, (float16_t)1.15136719f, + (float16_t)-0.185302734f, (float16_t)-3.54101562f, (float16_t)1.94726562f, (float16_t)-2.25f, + (float16_t)1.02636719f, (float16_t)-1.24121094f, (float16_t)1.91699219f, (float16_t)0.583984375f, + (float16_t)-2.87109375f, (float16_t)-0.481689453f, (float16_t)-1.27148438f, (float16_t)0.483886719f, + (float16_t)-0.3828125f, (float16_t)-1.984375f, (float16_t)-0.236938477f, (float16_t)-0.957519531f, + (float16_t)2.53710938f, (float16_t)-1.25976562f, (float16_t)-0.189208984f, (float16_t)-0.0918579102f, + (float16_t)2.52539062f, (float16_t)-0.568359375f, (float16_t)-0.437011719f, (float16_t)1.86328125f, + (float16_t)0.80078125f, (float16_t)0.296630859f, (float16_t)-0.704589844f, (float16_t)-2.94140625f, + (float16_t)-0.797851562f, (float16_t)-1.27148438f, (float16_t)-0.0906982422f, (float16_t)-1.16015625f, + (float16_t)1.38964844f, (float16_t)-0.619140625f, (float16_t)-1.50585938f, (float16_t)0.270263672f, + (float16_t)-2.13085938f, (float16_t)-0.241943359f, (float16_t)2.57617188f, (float16_t)-0.688476562f, + (float16_t)-1.66210938f, (float16_t)1.49023438f, (float16_t)-0.195556641f, (float16_t)1.1953125f, + (float16_t)-1.00976562f, (float16_t)0.403076172f, (float16_t)0.0966796875f, (float16_t)1.1171875f, + (float16_t)-0.828613281f, (float16_t)1.95703125f, (float16_t)-0.0809936523f, (float16_t)2.75f, + (float16_t)1.36621094f, (float16_t)1.33984375f, (float16_t)1.8671875f, (float16_t)-0.559570312f, + (float16_t)-0.0107116699f, (float16_t)0.965332031f, (float16_t)-3.15820312f, (float16_t)-0.27734375f, + (float16_t)1.07128906f, (float16_t)0.272460938f, (float16_t)-0.704101562f, (float16_t)-1.921875f, + (float16_t)1.12988281f, (float16_t)2.37695312f, (float16_t)1.31640625f, (float16_t)-2.07226562f, + (float16_t)-2.19921875f, (float16_t)0.0296936035f, (float16_t)0.250244141f, (float16_t)1.89160156f, + (float16_t)0.964355469f, (float16_t)-1.2578125f, (float16_t)0.916015625f, (float16_t)0.45703125f, + (float16_t)-2.60742188f, (float16_t)-0.445556641f, (float16_t)-0.115600586f, (float16_t)-3.45117188f, + (float16_t)-2.265625f, (float16_t)-0.823730469f, (float16_t)-0.17565918f, (float16_t)2.30859375f, + (float16_t)-0.222290039f, (float16_t)2.5390625f, (float16_t)-5.171875f, (float16_t)4.296875f, + (float16_t)0.251464844f, (float16_t)-0.608886719f, (float16_t)-4.2421875f, (float16_t)-3.25195312f, + (float16_t)0.372070312f, (float16_t)0.457519531f, (float16_t)1.78320312f, (float16_t)-0.197387695f, + (float16_t)0.762207031f, (float16_t)-0.731445312f, (float16_t)2.52539062f, (float16_t)-0.929199219f, + (float16_t)0.588378906f, (float16_t)0.41796875f, (float16_t)1.22753906f, (float16_t)-1.36035156f, + (float16_t)-1.00878906f, (float16_t)-0.411621094f, (float16_t)-3.14453125f, (float16_t)-0.157958984f, + (float16_t)-1.39648438f, (float16_t)-0.309570312f, (float16_t)1.7578125f, (float16_t)-0.9296875f, + (float16_t)1.27246094f, (float16_t)-1.76855469f, (float16_t)1.04785156f, (float16_t)2.4609375f, + (float16_t)1.79589844f, (float16_t)0.595214844f, (float16_t)-0.63671875f, (float16_t)2.3828125f, + (float16_t)3.98046875f, (float16_t)1.3984375f, (float16_t)1.03027344f, (float16_t)-2.296875f, + (float16_t)1.8984375f, (float16_t)-0.727539062f, (float16_t)2.79296875f, (float16_t)-1.83984375f, + (float16_t)1.27246094f, (float16_t)0.465087891f, (float16_t)2.65039062f, (float16_t)1.43945312f, + (float16_t)1.24023438f, (float16_t)-1.1484375f, (float16_t)-0.699707031f, (float16_t)-0.789550781f, + (float16_t)-2.07226562f, (float16_t)-0.821777344f, (float16_t)-1.25390625f, (float16_t)-0.729003906f, + (float16_t)2.24023438f, (float16_t)-1.16796875f, (float16_t)0.611328125f, (float16_t)2.2421875f, + (float16_t)1.07421875f, (float16_t)0.0716552734f, (float16_t)0.748535156f, (float16_t)0.277587891f, + (float16_t)-1.28417969f, (float16_t)-2.15625f, (float16_t)-2.14257812f, (float16_t)-0.751464844f, + (float16_t)-1.89648438f, (float16_t)-1.88671875f, (float16_t)-4.68359375f, (float16_t)0.510742188f, + (float16_t)-1.71386719f, (float16_t)2.55273438f, (float16_t)-0.580078125f, (float16_t)-0.467285156f, + (float16_t)1.32714844f, (float16_t)-1.36035156f, (float16_t)-1.40136719f, (float16_t)1.91894531f, + (float16_t)0.844726562f, (float16_t)0.295410156f, (float16_t)-0.000366926193f, (float16_t)1.32421875f, + (float16_t)2.49609375f, (float16_t)-0.794433594f, (float16_t)-6.31640625f, (float16_t)-0.118469238f, + (float16_t)1.24609375f, (float16_t)-1.47070312f, (float16_t)-3.09765625f, (float16_t)1.75585938f, + (float16_t)-0.882324219f, (float16_t)1.16015625f, (float16_t)0.50390625f, (float16_t)-3.09375f, + (float16_t)-2.0859375f, (float16_t)-0.974121094f, (float16_t)-1.38964844f, (float16_t)-0.188720703f, + (float16_t)2.54492188f, (float16_t)-0.17590332f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/weights_data.h new file mode 100644 index 00000000..1351a804 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f16/weights_data.h @@ -0,0 +1,541 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_f16_weights_data[540] = {(float16_t)0.654296875f, + (float16_t)0.130859375f, + (float16_t)0.00868225098f, + (float16_t)-0.423095703f, + (float16_t)0.484863281f, + (float16_t)0.179443359f, + (float16_t)0.0930175781f, + (float16_t)0.125488281f, + (float16_t)0.113098145f, + (float16_t)-0.484375f, + (float16_t)0.711425781f, + (float16_t)-0.0456542969f, + (float16_t)-0.614746094f, + (float16_t)0.17175293f, + (float16_t)0.733398438f, + (float16_t)-0.526855469f, + (float16_t)0.527832031f, + (float16_t)0.56640625f, + (float16_t)-0.668945312f, + (float16_t)0.452148438f, + (float16_t)0.254882812f, + 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(float16_t)0.670898438f, + (float16_t)0.325683594f, + (float16_t)0.431640625f, + (float16_t)0.0331115723f, + (float16_t)-0.404785156f, + (float16_t)-0.0234832764f, + (float16_t)0.190917969f, + (float16_t)-0.112121582f, + (float16_t)-0.495117188f, + (float16_t)-0.352783203f, + (float16_t)0.131225586f, + (float16_t)0.192871094f, + (float16_t)0.579589844f, + (float16_t)-0.538085938f, + (float16_t)-0.147094727f, + (float16_t)0.288085938f, + (float16_t)0.411376953f, + (float16_t)0.194580078f, + (float16_t)-0.129760742f, + (float16_t)0.576171875f, + (float16_t)-0.102783203f, + (float16_t)-0.741699219f, + (float16_t)-0.130615234f, + (float16_t)-0.558105469f, + (float16_t)0.703613281f, + (float16_t)-0.364990234f, + (float16_t)-0.315917969f, + (float16_t)-0.583984375f, + (float16_t)-0.703125f, + (float16_t)-0.314453125f, + (float16_t)-0.0663452148f, + (float16_t)0.212158203f, + (float16_t)-0.281738281f, + (float16_t)-0.313232422f, + (float16_t)0.578125f, + (float16_t)-0.183349609f, + (float16_t)-0.711425781f, + (float16_t)-0.62890625f, + (float16_t)-0.122619629f, + (float16_t)0.607910156f, + (float16_t)-0.593261719f, + (float16_t)0.406982422f, + (float16_t)-0.392822266f, + (float16_t)-0.0581970215f, + (float16_t)-0.675292969f, + (float16_t)-0.526367188f, + (float16_t)0.446289062f, + (float16_t)0.181518555f, + (float16_t)-0.209838867f, + (float16_t)-0.301025391f, + (float16_t)0.164672852f, + (float16_t)-0.368408203f, + (float16_t)0.738769531f, + (float16_t)-0.712890625f, + (float16_t)0.0969238281f, + (float16_t)-0.425048828f, + (float16_t)-0.503417969f, + (float16_t)0.712890625f, + (float16_t)0.296386719f, + (float16_t)0.537597656f, + (float16_t)-0.307373047f, + (float16_t)0.638671875f, + (float16_t)0.396484375f, + (float16_t)0.0715942383f, + (float16_t)-0.190551758f, + (float16_t)-0.549804688f, + (float16_t)-0.557128906f, + (float16_t)-0.640625f, + (float16_t)-0.423828125f, + (float16_t)-0.705078125f, + (float16_t)0.102233887f, + (float16_t)0.596679688f, + (float16_t)0.683105469f, + (float16_t)0.197021484f, + (float16_t)-0.572265625f, + (float16_t)0.325439453f, + (float16_t)0.624023438f, + (float16_t)-0.627929688f, + (float16_t)0.0772094727f, + (float16_t)-0.370361328f, + (float16_t)-0.340576172f, + (float16_t)0.197998047f, + (float16_t)0.215454102f, + (float16_t)0.283691406f, + (float16_t)-0.473632812f, + (float16_t)0.143676758f, + (float16_t)-0.0675048828f, + (float16_t)0.557128906f, + (float16_t)0.110351562f, + (float16_t)-0.394287109f, + (float16_t)0.520507812f, + (float16_t)-0.608886719f, + (float16_t)0.176147461f, + (float16_t)0.527832031f, + (float16_t)-0.5546875f, + (float16_t)0.607421875f, + (float16_t)0.749511719f, + (float16_t)0.217651367f, + (float16_t)0.0755004883f, + (float16_t)-0.399658203f, + (float16_t)-0.00998687744f, + (float16_t)-0.533203125f, + (float16_t)-0.177856445f, + (float16_t)-0.633789062f, + (float16_t)0.00198936462f, + (float16_t)-0.120117188f, + (float16_t)-0.209960938f, + (float16_t)0.446044922f, + (float16_t)0.570800781f, + (float16_t)0.325195312f, + (float16_t)0.310058594f, + (float16_t)0.700195312f, + (float16_t)0.0444641113f, + (float16_t)-0.450683594f, + (float16_t)-0.538574219f, + (float16_t)-0.606445312f, + (float16_t)0.447998047f, + (float16_t)-0.16003418f, + (float16_t)-0.156982422f, + (float16_t)-0.120605469f, + (float16_t)0.428222656f, + (float16_t)-0.655273438f, + (float16_t)0.477539062f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/biases_data.h new file mode 100644 index 00000000..855505cc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_f32_biases_data[10] = {-0.412936568f, + -0.366484016f, + 0.0196087081f, + -0.110348053f, + 0.052388642f, + 0.111013778f, + 0.187041506f, + -0.484796196f, + 0.0728422925f, + 0.320306778f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/config_data.h new file mode 100644 index 00000000..4b6299e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_BASIC_F32_INPUT_BATCHES 1 +#define CONV_BASIC_F32_INPUT_H 9 +#define CONV_BASIC_F32_INPUT_W 11 +#define CONV_BASIC_F32_IN_CH 6 +#define CONV_BASIC_F32_FILTER_H 3 +#define CONV_BASIC_F32_FILTER_W 3 +#define CONV_BASIC_F32_OUT_CH 10 +#define CONV_BASIC_F32_STRIDE_H 1 +#define CONV_BASIC_F32_STRIDE_W 1 +#define CONV_BASIC_F32_PADDING_H 0 +#define CONV_BASIC_F32_PADDING_W 0 +#define CONV_BASIC_F32_DILATION_H 1 +#define CONV_BASIC_F32_DILATION_W 1 +#define CONV_BASIC_F32_OUTPUT_H 7 +#define CONV_BASIC_F32_OUTPUT_W 9 +#define CONV_BASIC_F32_OUTPUT_C 10 +#define CONV_BASIC_F32_DST_SIZE 630 +#define CONV_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_BASIC_F32_USE_WRAPPER 0 +#define CONV_BASIC_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_BASIC_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/input_data.h new file mode 100644 index 00000000..e09fdabc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/input_data.h @@ -0,0 +1,87 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_f32_input_data[594] = { + -0.1526604f, 0.164522618f, -0.220518783f, 0.886897922f, 0.421240747f, -0.387193859f, -0.592550993f, + 0.318022072f, -0.0307296421f, 0.746647537f, 0.883907974f, 0.0475306921f, -0.0497994907f, -0.167253554f, + -0.15896903f, -0.00579841156f, 0.165959686f, -0.679562211f, 0.924296916f, 0.977359772f, -0.208694279f, + 0.230511054f, 0.163986683f, -0.202650115f, -0.136042118f, 0.127868056f, -0.0899386778f, 0.272667378f, + 0.080242835f, -0.165694073f, -0.325226009f, -0.930684149f, 0.520270348f, -0.300356477f, 0.914561093f, + -0.650796473f, 0.99696666f, -0.4795596f, 0.418001115f, 0.664688945f, 0.273287803f, 0.360190451f, + 0.565937936f, 0.733329356f, 0.67549938f, -0.22307992f, -0.66902703f, -0.351163149f, -0.559790075f, + -0.331108451f, -0.12864323f, 0.00305680605f, 0.308508337f, 0.910141468f, 0.197899178f, 0.393152058f, + -0.660227716f, 0.360872507f, 0.00694856653f, 0.566267848f, 0.97805512f, -0.301862866f, -0.260205269f, + 0.673114836f, 0.754273176f, 0.136997059f, -0.0969180241f, -0.364510715f, 0.0286870208f, 0.329843134f, + -0.328068376f, -0.0447729081f, -0.798952103f, 0.0796702728f, -0.843073487f, 0.364419341f, -0.127015203f, + 0.193887204f, 0.0284624808f, 0.782973588f, -0.158541933f, -0.132081896f, -0.336632788f, -0.272300899f, + -0.674469888f, 0.732443988f, 0.398222208f, 0.14183332f, 0.45368132f, -0.195926115f, -0.517924666f, + -0.282253653f, -0.981817126f, 0.408737123f, 0.889216363f, 0.355154634f, -0.0538558885f, 0.126294389f, + 0.4319309f, -0.624213517f, -0.320040792f, 0.207406953f, 0.464524955f, 0.241861969f, 0.827320755f, + 0.707574427f, 0.365622103f, 0.809312344f, 0.684143066f, 0.0305535365f, -0.0624399893f, 0.393065721f, + -0.146210849f, 0.166466668f, 0.113858804f, 0.7949633f, -0.235413566f, -0.923073232f, -0.633158624f, + -0.125359073f, -0.195363596f, 0.222282097f, 0.615815759f, 0.544228077f, -0.912918806f, 0.776591182f, + 0.706318438f, 0.592760801f, -0.691070557f, -0.691589653f, -0.166248828f, 0.185641989f, 0.696401417f, + 0.0243828576f, -0.391256779f, -0.13118495f, -0.0516254008f, 0.612323165f, -0.491203129f, 0.203798577f, + 0.455268115f, -0.970533073f, 0.277692765f, 0.0971159711f, 0.17707552f, -0.652318239f, 0.874349833f, + -0.734745443f, 0.903466642f, -0.810008824f, 0.731523991f, 0.365948856f, 0.0155004095f, 0.709948719f, + -0.867127657f, 0.0727782771f, -0.883322716f, -0.954443455f, -0.255422086f, 0.116596133f, 0.933657348f, + -0.286229283f, -0.599575818f, 0.99301815f, 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0.735766351f, 0.161908001f, 0.0726398006f, 0.067408435f, -0.134430915f, + 0.575253844f, -0.0138006778f, -0.499339193f, 0.241480708f, 0.764153302f, 0.84375453f, 0.512285829f, + 0.670702636f, 0.773791432f, -0.580197692f, -0.226294383f, -0.993002594f, -0.127372846f, -0.937829971f, + 0.62965554f, 0.342731684f, 0.325422913f, 0.819926798f, -0.951373279f, 0.574738324f, 0.956073105f, + -0.39607802f, -0.514887631f, -0.973141909f, 0.0514548384f, -0.892038286f, -0.0227125641f, 0.72127372f, + 0.896060407f, 0.0207078159f, -0.0543755628f, 0.361647815f, 0.580828786f, -0.841035783f, 0.666987181f, + -0.730169058f, 0.658967197f, 0.119749859f, 0.793828428f, -0.625015199f, -0.914164484f, 0.580243051f, + 0.426987857f, 0.346978456f, 0.726022363f, 0.252979517f, 0.200574651f, 0.151913673f, 0.342503846f, + 0.728244483f, -0.842705011f, 0.172636211f, 0.846609116f, -0.512583196f, 0.618494809f, -0.120143853f, + -0.312718391f, 0.0487228334f, 0.729016244f, 0.763647199f, -0.931607902f, -0.573061645f, 0.504196286f, + 0.682170212f, 0.377401352f, -0.244140163f, -0.653396249f, -0.686027169f, 0.485405505f, 0.191110075f, + 0.256261498f, 0.868250132f, -0.948295832f, -0.817871392f, -0.975933254f, -0.126255929f, 0.249300301f, + -0.752654076f, -0.523269534f, -0.729932725f, -0.761045992f, -0.158306301f, 0.099048458f, 0.294286191f, + -0.39989078f, -0.402161568f, -0.141518921f, -0.913335145f, -0.719596863f, -0.716900826f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/output_ref_data.h new file mode 100644 index 00000000..015aa345 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/output_ref_data.h @@ -0,0 +1,92 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_f32_output_ref_data[630] = { + -1.61631596f, -2.83687901f, 1.13320053f, 2.45633602f, 2.29206085f, 1.14614165f, 0.752825081f, + -1.85067439f, 0.326140732f, -0.846411347f, 0.609416485f, 1.16106498f, 3.82436419f, 2.15799141f, + 0.267580181f, 0.0445064381f, 1.84948611f, 2.32883096f, 0.927589834f, 0.748189211f, -0.210221007f, + 1.11551011f, 1.00762916f, -0.418744653f, 0.0580557249f, -1.72039628f, 3.75218844f, -2.08637261f, + -2.09382176f, 2.0687151f, 2.4465518f, -0.363941014f, -0.543645918f, 0.813171029f, 3.61810827f, + 1.05705214f, -3.32504082f, 0.320072681f, 2.49391961f, -2.20342898f, -0.522432029f, 2.06587815f, + 1.35623062f, -0.266723961f, -1.48848498f, -0.59049058f, 0.647279561f, 0.673987269f, -1.1594497f, + -1.36311114f, -0.413906187f, -3.75890589f, 1.2996459f, 0.667339861f, -2.64620924f, -1.0087353f, + 2.01878309f, 0.671196461f, -1.75324667f, 0.911951065f, -0.625389755f, -0.112761587f, -1.94828272f, + -2.84726572f, 0.849574029f, 1.37444377f, -1.83026552f, 0.32405135f, 1.22429395f, 2.3233881f, + -0.810441136f, -0.874356985f, 1.04387343f, 1.52227139f, -1.75370395f, 1.76114547f, -0.42089504f, + -1.85879242f, -1.78095341f, -1.80031753f, -2.03959894f, -3.49059033f, -3.45138621f, 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1.43921864f, 1.24001586f, -1.14851081f, -0.699736774f, -0.789607882f, -2.07212067f, + -0.821639776f, -1.25347626f, -0.729000926f, 2.24119544f, -1.16808248f, 0.611482978f, 2.24288368f, + 1.07387233f, 0.0716284066f, 0.748315454f, 0.27748093f, -1.28425455f, -2.15582776f, -2.14300179f, + -0.751252174f, -1.89617229f, -1.88689554f, -4.68508101f, 0.510530472f, -1.7140851f, 2.55332589f, + -0.580124378f, -0.467213333f, 1.32681227f, -1.36064601f, -1.40108669f, 1.9189173f, 0.844815552f, + 0.295420289f, -0.000366896391f, 1.32453585f, 2.49690413f, -0.794444382f, -6.31720638f, -0.118464492f, + 1.24590516f, -1.47097957f, -3.09798288f, 1.75540423f, -0.882266045f, 1.16025352f, 0.504047453f, + -3.0935061f, -2.08567166f, -0.973917007f, -1.38934076f, -0.188719213f, 2.54432011f, -0.175871938f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/weights_data.h new file mode 100644 index 00000000..005b691e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_f32/weights_data.h @@ -0,0 +1,80 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_f32_weights_data[540] = { + 0.654108584f, 0.130909219f, 0.00868309569f, -0.423151731f, 0.484744132f, 0.179416969f, 0.0930115506f, + 0.125510558f, 0.113125563f, -0.484335095f, 0.711533248f, -0.0456587858f, -0.614504933f, 0.17170085f, + 0.733439684f, -0.52677685f, 0.527986646f, 0.5666278f, -0.668733656f, 0.452226043f, 0.25488317f, + 0.0544884168f, 0.237229303f, 0.313924491f, -0.630856574f, 0.569941044f, 0.537638247f, -0.705402553f, + 0.352445215f, 0.402691782f, 0.25388658f, 0.404797316f, -0.198021099f, -0.229056865f, 0.319855303f, + -0.0662158057f, 0.658302069f, 0.213504955f, -0.0802750885f, -0.287289858f, -0.18526338f, 0.209341571f, + -0.0666131303f, 0.161922365f, -0.156945154f, 0.519890964f, 0.658053875f, -0.615358174f, 0.515616179f, + 0.197212368f, -0.651185155f, 0.232061878f, -0.0560307205f, 0.218811572f, -0.278450102f, 0.61986202f, + -0.535099745f, -0.644833148f, -0.210313037f, -0.481587887f, 0.739839137f, -0.0778629556f, -0.141056269f, + -0.643604815f, -0.722576439f, -0.739987791f, 0.197644234f, -0.0935688391f, 0.123533688f, 0.114377074f, + -0.0620827749f, -0.125843048f, 0.0854957029f, -0.278277427f, 0.376215249f, 0.250015438f, 0.167248175f, + -0.593255103f, 0.370135367f, 0.0247951224f, -0.558740735f, -0.681482255f, 0.265403062f, -0.124682501f, + 0.378164232f, 0.252439737f, 0.617564857f, 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-0.112107404f, -0.495105296f, -0.352897525f, + 0.131207094f, 0.192889348f, 0.57959336f, -0.538277149f, -0.147115558f, 0.288170516f, 0.41132623f, + 0.194620937f, -0.129776657f, 0.575938821f, -0.102809004f, -0.741457283f, -0.130591005f, -0.558178425f, + 0.703390777f, -0.364934832f, -0.315897524f, -0.583822131f, -0.703210413f, -0.314542055f, -0.0663317218f, + 0.21213977f, -0.281780779f, -0.313325167f, 0.578261375f, -0.183366239f, -0.711195052f, -0.628928721f, + -0.122639813f, 0.607919574f, -0.593453348f, 0.40699625f, -0.392705381f, -0.0582027771f, -0.675418317f, + -0.52631253f, 0.446176529f, 0.181487888f, -0.209887058f, -0.300907373f, 0.164665252f, -0.368421316f, + 0.738601625f, -0.712789237f, 0.0968990549f, -0.425145268f, -0.50365901f, 0.712693691f, 0.296326727f, + 0.537376881f, -0.307313591f, 0.638682306f, 0.396418631f, 0.0716193914f, -0.190504849f, -0.550039351f, + -0.557148278f, -0.640496194f, -0.423899889f, -0.704995275f, 0.10222207f, 0.596519291f, 0.682925165f, + 0.197077394f, -0.572160542f, 0.32556057f, 0.62416935f, -0.627715707f, 0.0772336274f, -0.37024653f, + -0.340459019f, 0.198027626f, 0.215473667f, 0.283572286f, -0.473671257f, 0.143663272f, -0.0674864873f, + 0.557315111f, 0.110365093f, -0.394203424f, 0.520702481f, -0.608966768f, 0.176137149f, 0.527758062f, + -0.554757833f, 0.607483745f, 0.749483168f, 0.217630327f, 0.0755309239f, -0.399748832f, -0.00998539384f, + -0.533108473f, -0.177804038f, -0.633755445f, 0.00198874599f, -0.120129295f, -0.209981114f, 0.445927203f, + 0.570706785f, 0.325309396f, 0.310128838f, 0.700438917f, 0.0444560461f, -0.45058164f, -0.538374782f, + -0.606204748f, 0.44789508f, -0.160081998f, -0.156964764f, -0.120575652f, 0.428270876f, -0.655076504f, + 0.477631301f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/biases_data.h new file mode 100644 index 00000000..89c21881 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_nhwc_f16_biases_data[10] = {(float16_t)-0.412841797f, + (float16_t)-0.366455078f, + (float16_t)0.0196075439f, + (float16_t)-0.110351562f, + (float16_t)0.0523986816f, + (float16_t)0.111022949f, + (float16_t)0.187011719f, + (float16_t)-0.484863281f, + (float16_t)0.0728149414f, + (float16_t)0.3203125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/config_data.h new file mode 100644 index 00000000..f3e5ece7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_BASIC_NHWC_F16_INPUT_BATCHES 1 +#define CONV_BASIC_NHWC_F16_INPUT_H 9 +#define CONV_BASIC_NHWC_F16_INPUT_W 11 +#define CONV_BASIC_NHWC_F16_IN_CH 6 +#define CONV_BASIC_NHWC_F16_FILTER_H 3 +#define CONV_BASIC_NHWC_F16_FILTER_W 3 +#define CONV_BASIC_NHWC_F16_OUT_CH 10 +#define CONV_BASIC_NHWC_F16_STRIDE_H 1 +#define CONV_BASIC_NHWC_F16_STRIDE_W 1 +#define CONV_BASIC_NHWC_F16_PADDING_H 0 +#define CONV_BASIC_NHWC_F16_PADDING_W 0 +#define CONV_BASIC_NHWC_F16_DILATION_H 1 +#define CONV_BASIC_NHWC_F16_DILATION_W 1 +#define CONV_BASIC_NHWC_F16_OUTPUT_H 7 +#define CONV_BASIC_NHWC_F16_OUTPUT_W 9 +#define CONV_BASIC_NHWC_F16_OUTPUT_C 10 +#define CONV_BASIC_NHWC_F16_DST_SIZE 630 +#define CONV_BASIC_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_BASIC_NHWC_F16_USE_WRAPPER 1 +#define CONV_BASIC_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_BASIC_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/input_data.h new file mode 100644 index 00000000..014740b3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/input_data.h @@ -0,0 +1,151 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_nhwc_f16_input_data[594] = { + (float16_t)-0.152709961f, (float16_t)0.164550781f, (float16_t)-0.220458984f, (float16_t)0.88671875f, + (float16_t)0.421142578f, (float16_t)-0.387207031f, (float16_t)-0.592773438f, (float16_t)0.318115234f, + (float16_t)-0.0307312012f, (float16_t)0.746582031f, (float16_t)0.883789062f, (float16_t)0.0475158691f, + (float16_t)-0.0498046875f, (float16_t)-0.167236328f, (float16_t)-0.158935547f, (float16_t)-0.00579833984f, + (float16_t)0.166015625f, (float16_t)-0.6796875f, (float16_t)0.924316406f, (float16_t)0.977539062f, + (float16_t)-0.208740234f, (float16_t)0.23046875f, (float16_t)0.16394043f, (float16_t)-0.202636719f, + (float16_t)-0.135986328f, (float16_t)0.127807617f, (float16_t)-0.0899658203f, (float16_t)0.272705078f, + (float16_t)0.0802612305f, (float16_t)-0.165649414f, (float16_t)-0.325195312f, (float16_t)-0.930664062f, + (float16_t)0.520507812f, (float16_t)-0.300292969f, (float16_t)0.914550781f, (float16_t)-0.650878906f, + (float16_t)0.997070312f, (float16_t)-0.479492188f, (float16_t)0.41796875f, (float16_t)0.664550781f, + (float16_t)0.273193359f, (float16_t)0.360107422f, (float16_t)0.565917969f, (float16_t)0.733398438f, + (float16_t)0.675292969f, (float16_t)-0.223022461f, (float16_t)-0.668945312f, (float16_t)-0.351074219f, + (float16_t)-0.559570312f, (float16_t)-0.331054688f, (float16_t)-0.128662109f, (float16_t)0.00305747986f, + (float16_t)0.30859375f, (float16_t)0.91015625f, (float16_t)0.197875977f, (float16_t)0.393066406f, + (float16_t)-0.66015625f, (float16_t)0.360839844f, (float16_t)0.00695037842f, (float16_t)0.56640625f, + (float16_t)0.978027344f, (float16_t)-0.301757812f, (float16_t)-0.260253906f, (float16_t)0.673339844f, + (float16_t)0.754394531f, (float16_t)0.136962891f, (float16_t)-0.0969238281f, (float16_t)-0.364501953f, + (float16_t)0.0286865234f, (float16_t)0.329833984f, (float16_t)-0.328125f, (float16_t)-0.0447692871f, + (float16_t)-0.798828125f, (float16_t)0.0796508789f, (float16_t)-0.843261719f, (float16_t)0.364501953f, + (float16_t)-0.127075195f, (float16_t)0.193847656f, (float16_t)0.0284576416f, (float16_t)0.783203125f, + (float16_t)-0.158569336f, (float16_t)-0.132080078f, (float16_t)-0.336669922f, (float16_t)-0.272216797f, + (float16_t)-0.674316406f, (float16_t)0.732421875f, (float16_t)0.398193359f, (float16_t)0.141845703f, + (float16_t)0.453613281f, (float16_t)-0.195922852f, (float16_t)-0.518066406f, (float16_t)-0.282226562f, + (float16_t)-0.981933594f, (float16_t)0.408691406f, (float16_t)0.889160156f, (float16_t)0.355224609f, + (float16_t)-0.0538635254f, (float16_t)0.126342773f, (float16_t)0.431884766f, (float16_t)-0.624023438f, + (float16_t)-0.320068359f, (float16_t)0.207397461f, (float16_t)0.464599609f, (float16_t)0.241821289f, + (float16_t)0.827148438f, (float16_t)0.707519531f, (float16_t)0.365722656f, (float16_t)0.809082031f, + (float16_t)0.684082031f, (float16_t)0.0305480957f, (float16_t)-0.0624389648f, (float16_t)0.393066406f, + (float16_t)-0.146240234f, (float16_t)0.166503906f, (float16_t)0.113830566f, (float16_t)0.794921875f, + (float16_t)-0.235473633f, (float16_t)-0.922851562f, (float16_t)-0.633300781f, (float16_t)-0.125366211f, + (float16_t)-0.1953125f, (float16_t)0.222290039f, (float16_t)0.615722656f, (float16_t)0.544433594f, + (float16_t)-0.913085938f, (float16_t)0.776367188f, (float16_t)0.706542969f, (float16_t)0.592773438f, + (float16_t)-0.690917969f, (float16_t)-0.69140625f, (float16_t)-0.166259766f, (float16_t)0.185668945f, + (float16_t)0.696289062f, (float16_t)0.0243835449f, (float16_t)-0.391357422f, (float16_t)-0.131225586f, + (float16_t)-0.0516357422f, (float16_t)0.612304688f, (float16_t)-0.491210938f, (float16_t)0.203857422f, + (float16_t)0.455322266f, (float16_t)-0.970703125f, (float16_t)0.277587891f, (float16_t)0.0971069336f, + (float16_t)0.177124023f, (float16_t)-0.65234375f, (float16_t)0.874511719f, (float16_t)-0.734863281f, + (float16_t)0.903320312f, (float16_t)-0.810058594f, (float16_t)0.731445312f, (float16_t)0.365966797f, + (float16_t)0.0155029297f, (float16_t)0.709960938f, (float16_t)-0.8671875f, (float16_t)0.0727539062f, + (float16_t)-0.883300781f, (float16_t)-0.954589844f, (float16_t)-0.255371094f, (float16_t)0.116577148f, + (float16_t)0.93359375f, (float16_t)-0.286132812f, (float16_t)-0.599609375f, (float16_t)0.993164062f, + (float16_t)0.0747070312f, (float16_t)-0.645507812f, (float16_t)-0.594726562f, (float16_t)-0.944335938f, + (float16_t)-0.688964844f, (float16_t)-0.718261719f, (float16_t)-0.500976562f, (float16_t)-0.0419311523f, + (float16_t)-0.758789062f, (float16_t)-0.765136719f, (float16_t)0.424316406f, (float16_t)0.19140625f, + (float16_t)0.719726562f, (float16_t)-0.212036133f, (float16_t)-0.415527344f, (float16_t)0.0781860352f, + (float16_t)-0.565917969f, (float16_t)0.332275391f, (float16_t)0.4140625f, (float16_t)0.970703125f, + (float16_t)-0.90625f, (float16_t)-0.99609375f, (float16_t)-0.985839844f, (float16_t)0.232910156f, + (float16_t)0.674316406f, (float16_t)-0.6640625f, (float16_t)-0.207885742f, (float16_t)0.806152344f, + (float16_t)0.9140625f, (float16_t)0.717773438f, (float16_t)0.409912109f, (float16_t)0.0393981934f, + (float16_t)-0.847167969f, (float16_t)-0.872070312f, (float16_t)-0.892089844f, (float16_t)-0.630371094f, + (float16_t)0.0910644531f, (float16_t)0.946777344f, (float16_t)0.681152344f, (float16_t)0.0820922852f, + (float16_t)-0.774414062f, (float16_t)-0.6484375f, (float16_t)-0.812011719f, (float16_t)-0.276611328f, + (float16_t)0.386474609f, (float16_t)0.658203125f, (float16_t)0.151245117f, (float16_t)0.994140625f, + (float16_t)0.0314331055f, (float16_t)0.202026367f, (float16_t)-0.88671875f, (float16_t)0.96484375f, + (float16_t)0.654296875f, (float16_t)-0.451171875f, (float16_t)0.170043945f, (float16_t)0.663085938f, + (float16_t)0.0720214844f, (float16_t)0.232177734f, (float16_t)0.562011719f, (float16_t)-0.18347168f, + (float16_t)-0.347412109f, (float16_t)-0.3359375f, (float16_t)-0.73046875f, (float16_t)-0.543945312f, + (float16_t)0.553710938f, (float16_t)0.183959961f, (float16_t)0.767578125f, (float16_t)0.74609375f, + (float16_t)0.9765625f, (float16_t)-0.986328125f, (float16_t)-0.0422363281f, (float16_t)-0.192260742f, + (float16_t)-0.341308594f, (float16_t)-0.336669922f, (float16_t)-0.0163726807f, (float16_t)-0.686035156f, + (float16_t)-0.0635986328f, (float16_t)0.491943359f, (float16_t)-0.538085938f, (float16_t)-0.0856323242f, + (float16_t)0.450195312f, (float16_t)-0.828125f, (float16_t)0.0836791992f, (float16_t)0.892578125f, + (float16_t)0.265625f, (float16_t)0.899902344f, (float16_t)0.576660156f, (float16_t)0.499511719f, + (float16_t)0.248779297f, (float16_t)0.908203125f, (float16_t)0.161987305f, (float16_t)0.151977539f, + (float16_t)-0.812988281f, (float16_t)0.658203125f, (float16_t)0.463378906f, (float16_t)0.264160156f, + (float16_t)0.950683594f, (float16_t)-0.955566406f, (float16_t)0.276123047f, (float16_t)-0.405517578f, + (float16_t)0.602050781f, (float16_t)-0.497070312f, (float16_t)0.462646484f, (float16_t)0.039276123f, + (float16_t)-0.251464844f, (float16_t)0.751464844f, (float16_t)-0.361816406f, (float16_t)0.0308685303f, + (float16_t)0.455810547f, (float16_t)-0.763671875f, (float16_t)0.475830078f, (float16_t)-0.729980469f, + (float16_t)0.944335938f, (float16_t)-0.350341797f, (float16_t)-0.151733398f, (float16_t)-0.286132812f, + (float16_t)0.347412109f, (float16_t)0.159667969f, (float16_t)0.637695312f, (float16_t)0.407226562f, + (float16_t)0.177978516f, (float16_t)0.583984375f, (float16_t)-0.0998535156f, (float16_t)0.132568359f, + (float16_t)0.0821533203f, (float16_t)-0.696289062f, (float16_t)0.0971679688f, (float16_t)-0.321777344f, + (float16_t)-0.318847656f, (float16_t)-0.0305328369f, (float16_t)0.203613281f, (float16_t)0.774902344f, + (float16_t)-0.859863281f, (float16_t)0.828125f, (float16_t)-0.79296875f, (float16_t)-0.1171875f, + (float16_t)-0.886230469f, (float16_t)0.0285186768f, (float16_t)-0.122436523f, (float16_t)0.688964844f, + (float16_t)-0.02684021f, (float16_t)-0.577636719f, (float16_t)-0.493652344f, (float16_t)0.577636719f, + (float16_t)0.106262207f, (float16_t)-0.696289062f, (float16_t)0.150756836f, (float16_t)-0.192993164f, + (float16_t)0.777832031f, (float16_t)0.225219727f, (float16_t)-0.465576172f, (float16_t)-0.393066406f, + (float16_t)-0.498046875f, (float16_t)0.435302734f, (float16_t)0.955078125f, (float16_t)-0.705078125f, + (float16_t)0.128173828f, (float16_t)0.52734375f, (float16_t)0.136108398f, (float16_t)0.662109375f, + (float16_t)0.630859375f, (float16_t)0.222290039f, (float16_t)-0.608398438f, (float16_t)-0.123657227f, + (float16_t)0.686035156f, (float16_t)-0.194458008f, (float16_t)0.618652344f, (float16_t)0.854980469f, + (float16_t)-0.272216797f, (float16_t)0.869628906f, (float16_t)0.184448242f, (float16_t)-0.359863281f, + (float16_t)-0.00275039673f, (float16_t)0.0618286133f, (float16_t)0.852050781f, (float16_t)-0.864746094f, + (float16_t)0.977050781f, (float16_t)0.465087891f, (float16_t)0.146850586f, (float16_t)-0.670898438f, + (float16_t)0.720703125f, (float16_t)-0.455322266f, (float16_t)-0.203491211f, (float16_t)-0.27734375f, + (float16_t)0.303955078f, (float16_t)0.032043457f, (float16_t)0.779785156f, (float16_t)0.756835938f, + (float16_t)0.514648438f, (float16_t)-0.0365600586f, (float16_t)0.615234375f, (float16_t)0.926757812f, + (float16_t)-0.780273438f, (float16_t)-0.73828125f, (float16_t)-0.174072266f, (float16_t)0.590820312f, + (float16_t)-0.50390625f, (float16_t)0.130859375f, (float16_t)-0.908691406f, (float16_t)0.747558594f, + (float16_t)-0.881835938f, (float16_t)-0.974609375f, (float16_t)-0.0525817871f, (float16_t)0.952148438f, + (float16_t)0.495849609f, (float16_t)0.530761719f, (float16_t)-0.508300781f, (float16_t)-0.354248047f, + (float16_t)-0.984863281f, (float16_t)-0.14050293f, (float16_t)0.990722656f, (float16_t)-0.177978516f, + (float16_t)0.128417969f, (float16_t)-0.624023438f, (float16_t)-0.782714844f, (float16_t)0.631835938f, + (float16_t)0.478027344f, (float16_t)-0.6328125f, (float16_t)-0.634277344f, (float16_t)0.640625f, + (float16_t)0.623535156f, (float16_t)-0.14465332f, (float16_t)-0.401123047f, (float16_t)-0.95703125f, + (float16_t)-0.721191406f, (float16_t)-0.596679688f, (float16_t)0.801757812f, (float16_t)-0.102111816f, + (float16_t)-0.722167969f, (float16_t)-0.217895508f, (float16_t)-0.890136719f, (float16_t)-0.530761719f, + (float16_t)0.757324219f, (float16_t)0.0794067383f, (float16_t)-0.546386719f, (float16_t)-0.599609375f, + (float16_t)-0.636230469f, (float16_t)-0.967773438f, (float16_t)-0.877929688f, (float16_t)0.0160827637f, + (float16_t)0.123291016f, (float16_t)-0.545410156f, (float16_t)-0.468017578f, (float16_t)0.359130859f, + (float16_t)0.47265625f, (float16_t)0.81640625f, (float16_t)0.465087891f, (float16_t)0.137329102f, + (float16_t)0.504882812f, (float16_t)0.187255859f, (float16_t)-0.368164062f, (float16_t)-0.185791016f, + (float16_t)0.0440673828f, (float16_t)0.148193359f, (float16_t)0.330322266f, (float16_t)-0.539550781f, + (float16_t)-0.767578125f, (float16_t)-0.716796875f, (float16_t)-0.7578125f, (float16_t)0.204467773f, + (float16_t)0.213500977f, (float16_t)0.351074219f, (float16_t)0.0235443115f, (float16_t)-0.533203125f, + (float16_t)0.205810547f, (float16_t)0.472900391f, (float16_t)-0.795410156f, (float16_t)0.615234375f, + (float16_t)0.935546875f, (float16_t)0.517089844f, (float16_t)-0.343505859f, (float16_t)-0.250488281f, + (float16_t)0.656738281f, (float16_t)-0.282714844f, (float16_t)0.0413513184f, (float16_t)0.123352051f, + (float16_t)0.867675781f, (float16_t)0.180664062f, (float16_t)-0.150024414f, (float16_t)0.0422973633f, + (float16_t)0.776855469f, (float16_t)-0.28125f, (float16_t)-0.193603516f, (float16_t)-0.661621094f, + (float16_t)0.301269531f, (float16_t)-0.638183594f, (float16_t)0.811035156f, (float16_t)-0.87109375f, + (float16_t)0.948730469f, (float16_t)-0.770507812f, (float16_t)-0.817871094f, (float16_t)-0.160766602f, + (float16_t)0.0932006836f, (float16_t)-0.743652344f, (float16_t)0.402099609f, (float16_t)-0.0809326172f, + (float16_t)0.283203125f, (float16_t)0.89453125f, (float16_t)-0.067199707f, (float16_t)0.599609375f, + (float16_t)0.423339844f, (float16_t)-0.244628906f, (float16_t)0.260986328f, (float16_t)0.0576171875f, + (float16_t)-0.14465332f, (float16_t)0.824707031f, (float16_t)0.242431641f, (float16_t)0.217285156f, + (float16_t)-0.890136719f, (float16_t)-0.108825684f, (float16_t)0.559570312f, (float16_t)0.280273438f, + (float16_t)-0.299560547f, (float16_t)0.375488281f, (float16_t)-0.501464844f, (float16_t)-0.668945312f, + (float16_t)-0.810546875f, (float16_t)0.151977539f, (float16_t)-0.272216797f, (float16_t)0.490478516f, + (float16_t)-0.410400391f, (float16_t)0.778808594f, (float16_t)-0.917480469f, (float16_t)0.427246094f, + (float16_t)-0.322021484f, (float16_t)0.00192165375f, (float16_t)0.280273438f, (float16_t)-0.0369873047f, + (float16_t)0.891113281f, (float16_t)0.875976562f, (float16_t)0.29296875f, (float16_t)0.441894531f, + (float16_t)0.0170288086f, (float16_t)-0.572753906f, (float16_t)0.82421875f, (float16_t)0.735839844f, + (float16_t)0.161865234f, (float16_t)0.0726318359f, (float16_t)0.0673828125f, (float16_t)-0.134399414f, + (float16_t)0.575195312f, (float16_t)-0.0138015747f, (float16_t)-0.499267578f, (float16_t)0.241455078f, + (float16_t)0.764160156f, (float16_t)0.84375f, (float16_t)0.512207031f, (float16_t)0.670898438f, + (float16_t)0.773925781f, (float16_t)-0.580078125f, (float16_t)-0.226318359f, (float16_t)-0.993164062f, + (float16_t)-0.127319336f, (float16_t)-0.937988281f, (float16_t)0.629882812f, (float16_t)0.342773438f, + (float16_t)0.325439453f, (float16_t)0.819824219f, (float16_t)-0.951171875f, (float16_t)0.574707031f, + (float16_t)0.956054688f, (float16_t)-0.395996094f, (float16_t)-0.514648438f, (float16_t)-0.973144531f, + (float16_t)0.0514526367f, (float16_t)-0.892089844f, (float16_t)-0.0227050781f, (float16_t)0.721191406f, + (float16_t)0.895996094f, (float16_t)0.0207061768f, (float16_t)-0.0543823242f, (float16_t)0.361572266f, + (float16_t)0.581054688f, (float16_t)-0.840820312f, (float16_t)0.666992188f, (float16_t)-0.729980469f, + (float16_t)0.659179688f, (float16_t)0.119750977f, (float16_t)0.793945312f, (float16_t)-0.625f, + (float16_t)-0.9140625f, (float16_t)0.580078125f, (float16_t)0.427001953f, (float16_t)0.346923828f, + (float16_t)0.726074219f, (float16_t)0.252929688f, (float16_t)0.200561523f, (float16_t)0.151855469f, + (float16_t)0.342529297f, (float16_t)0.728027344f, (float16_t)-0.842773438f, (float16_t)0.172607422f, + (float16_t)0.846679688f, (float16_t)-0.512695312f, (float16_t)0.618652344f, (float16_t)-0.120117188f, + (float16_t)-0.312744141f, (float16_t)0.0487365723f, (float16_t)0.729003906f, (float16_t)0.763671875f, + (float16_t)-0.931640625f, (float16_t)-0.573242188f, (float16_t)0.504394531f, (float16_t)0.682128906f, + (float16_t)0.377441406f, (float16_t)-0.244140625f, (float16_t)-0.653320312f, (float16_t)-0.686035156f, + (float16_t)0.485351562f, (float16_t)0.191162109f, (float16_t)0.256347656f, (float16_t)0.868164062f, + (float16_t)-0.948242188f, (float16_t)-0.817871094f, (float16_t)-0.976074219f, (float16_t)-0.126220703f, + (float16_t)0.249267578f, (float16_t)-0.752441406f, (float16_t)-0.5234375f, (float16_t)-0.729980469f, + (float16_t)-0.761230469f, (float16_t)-0.158325195f, (float16_t)0.0990600586f, (float16_t)0.294189453f, + (float16_t)-0.399902344f, (float16_t)-0.402099609f, (float16_t)-0.141479492f, (float16_t)-0.913574219f, + (float16_t)-0.719726562f, (float16_t)-0.716796875f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..2d370eb5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/output_ref_data.h @@ -0,0 +1,160 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_nhwc_f16_output_ref_data[630] = { + (float16_t)-1.61621094f, (float16_t)-2.8359375f, (float16_t)1.1328125f, (float16_t)2.45703125f, + (float16_t)2.29296875f, (float16_t)1.14648438f, (float16_t)0.752929688f, (float16_t)-1.85058594f, + (float16_t)0.326171875f, (float16_t)-0.846191406f, (float16_t)0.609375f, (float16_t)1.16113281f, + (float16_t)3.82421875f, (float16_t)2.15820312f, (float16_t)0.267578125f, (float16_t)0.0444946289f, + (float16_t)1.84960938f, (float16_t)2.328125f, (float16_t)0.927734375f, (float16_t)0.748046875f, + (float16_t)-0.210205078f, (float16_t)1.11523438f, (float16_t)1.0078125f, (float16_t)-0.418701172f, + (float16_t)0.0580444336f, (float16_t)-1.72070312f, (float16_t)3.75195312f, (float16_t)-2.0859375f, + (float16_t)-2.09375f, (float16_t)2.06835938f, (float16_t)2.44726562f, (float16_t)-0.364013672f, + (float16_t)-0.543457031f, (float16_t)0.812988281f, (float16_t)3.6171875f, (float16_t)1.05664062f, + (float16_t)-3.32421875f, (float16_t)0.320068359f, (float16_t)2.49414062f, (float16_t)-2.203125f, + (float16_t)-0.522460938f, (float16_t)2.06640625f, (float16_t)1.35644531f, (float16_t)-0.266845703f, + (float16_t)-1.48828125f, (float16_t)-0.590332031f, (float16_t)0.647460938f, (float16_t)0.673828125f, + (float16_t)-1.15917969f, (float16_t)-1.36328125f, (float16_t)-0.413818359f, (float16_t)-3.75976562f, + (float16_t)1.29980469f, (float16_t)0.667480469f, (float16_t)-2.64648438f, (float16_t)-1.00878906f, + (float16_t)2.01953125f, (float16_t)0.671386719f, (float16_t)-1.75292969f, (float16_t)0.912109375f, + (float16_t)-0.625488281f, (float16_t)-0.112731934f, (float16_t)-1.94824219f, (float16_t)-2.84765625f, + (float16_t)0.849609375f, (float16_t)1.37402344f, (float16_t)-1.83007812f, (float16_t)0.323974609f, + (float16_t)1.22460938f, (float16_t)2.32421875f, (float16_t)-0.810546875f, (float16_t)-0.874511719f, + (float16_t)1.04394531f, (float16_t)1.52246094f, (float16_t)-1.75390625f, (float16_t)1.76074219f, + (float16_t)-0.420898438f, (float16_t)-1.85839844f, (float16_t)-1.78125f, (float16_t)-1.80078125f, + (float16_t)-2.0390625f, (float16_t)-3.49023438f, (float16_t)-3.45117188f, (float16_t)-1.35644531f, + (float16_t)2.84765625f, (float16_t)-1.45214844f, (float16_t)-1.76660156f, (float16_t)0.0171356201f, + (float16_t)2.57421875f, (float16_t)1.23046875f, (float16_t)-1.06738281f, (float16_t)-2.21484375f, + (float16_t)-0.711425781f, (float16_t)-2.19921875f, (float16_t)-2.69140625f, (float16_t)1.40332031f, + (float16_t)-0.938476562f, (float16_t)-0.0038356781f, (float16_t)-1.20800781f, (float16_t)1.94726562f, + (float16_t)-0.369384766f, (float16_t)1.77539062f, (float16_t)-1.5390625f, (float16_t)-1.99609375f, + (float16_t)0.854492188f, (float16_t)1.61328125f, (float16_t)0.305419922f, (float16_t)-0.40625f, + (float16_t)1.33984375f, (float16_t)2.38671875f, (float16_t)-0.62109375f, (float16_t)-0.338378906f, + (float16_t)-1.3515625f, (float16_t)3.14453125f, (float16_t)2.24414062f, (float16_t)0.083190918f, + (float16_t)-0.952148438f, (float16_t)-1.08496094f, (float16_t)2.40625f, (float16_t)-1.28222656f, + (float16_t)-0.401611328f, (float16_t)-3.65820312f, (float16_t)-1.63867188f, (float16_t)-0.250976562f, + (float16_t)2.47851562f, (float16_t)0.137451172f, (float16_t)1.00195312f, (float16_t)-0.659667969f, + (float16_t)-0.215820312f, (float16_t)4.328125f, (float16_t)2.74609375f, (float16_t)0.476806641f, + (float16_t)2.3203125f, (float16_t)3.546875f, (float16_t)1.21289062f, (float16_t)0.483154297f, + (float16_t)0.845214844f, (float16_t)1.30957031f, (float16_t)-0.231445312f, (float16_t)-3.921875f, + (float16_t)-1.13476562f, (float16_t)-1.43554688f, (float16_t)-3.08398438f, (float16_t)-1.36230469f, + (float16_t)1.1796875f, (float16_t)-1.80957031f, (float16_t)-0.597167969f, (float16_t)0.404052734f, + (float16_t)0.484863281f, (float16_t)-1.87402344f, (float16_t)0.173706055f, (float16_t)-2.3046875f, + (float16_t)-0.0500183105f, (float16_t)-1.11328125f, (float16_t)-2.64453125f, (float16_t)1.61230469f, + (float16_t)1.26269531f, (float16_t)0.350830078f, (float16_t)-0.0334472656f, (float16_t)1.3125f, + (float16_t)0.143188477f, (float16_t)-0.452392578f, (float16_t)-2.45117188f, (float16_t)-3.203125f, + (float16_t)-3.0703125f, (float16_t)3.38867188f, (float16_t)-1.37695312f, (float16_t)-2.17382812f, + (float16_t)1.95507812f, (float16_t)0.798339844f, (float16_t)-0.732421875f, (float16_t)-0.506835938f, + (float16_t)0.106567383f, (float16_t)1.89160156f, (float16_t)1.23339844f, (float16_t)1.29589844f, + (float16_t)0.865722656f, (float16_t)3.47265625f, (float16_t)-0.43359375f, (float16_t)-0.0698852539f, + (float16_t)3.83007812f, (float16_t)0.860839844f, (float16_t)3.46289062f, (float16_t)3.578125f, + (float16_t)0.0875854492f, (float16_t)2.38671875f, (float16_t)-1.84765625f, (float16_t)0.0533752441f, + (float16_t)-2.140625f, (float16_t)-1.99316406f, (float16_t)0.350341797f, (float16_t)1.2578125f, + (float16_t)5.28125f, (float16_t)2.5625f, (float16_t)-1.24023438f, (float16_t)-0.426757812f, + (float16_t)3.39453125f, (float16_t)2.1875f, (float16_t)-3.26367188f, (float16_t)-1.68164062f, + (float16_t)-0.0690917969f, (float16_t)-1.8203125f, (float16_t)-2.02539062f, (float16_t)0.717285156f, + (float16_t)2.90820312f, (float16_t)-1.9921875f, (float16_t)0.415039062f, (float16_t)-1.828125f, + (float16_t)3.5546875f, (float16_t)4.17578125f, (float16_t)-0.298828125f, (float16_t)-0.278320312f, + (float16_t)-3.765625f, (float16_t)0.717773438f, (float16_t)0.374267578f, (float16_t)-1.58007812f, + (float16_t)0.916015625f, (float16_t)-2.97265625f, (float16_t)-0.276367188f, (float16_t)-0.135986328f, + (float16_t)-2.92578125f, (float16_t)0.0569763184f, (float16_t)-2.64648438f, (float16_t)-3.9765625f, + (float16_t)2.67578125f, (float16_t)-1.65527344f, (float16_t)1.03613281f, (float16_t)2.26171875f, + (float16_t)1.11328125f, (float16_t)4.36328125f, (float16_t)-0.640625f, (float16_t)1.7421875f, + (float16_t)-0.987304688f, (float16_t)-2.515625f, (float16_t)0.164306641f, (float16_t)-0.241333008f, + (float16_t)-0.291992188f, (float16_t)-0.478271484f, (float16_t)0.655761719f, (float16_t)-1.41601562f, + (float16_t)-2.67382812f, (float16_t)2.66796875f, (float16_t)-0.11706543f, (float16_t)-3.625f, + (float16_t)0.115356445f, (float16_t)-0.489257812f, (float16_t)-0.0523681641f, (float16_t)-0.866699219f, + (float16_t)0.329589844f, (float16_t)-1.06054688f, (float16_t)0.709472656f, (float16_t)-1.37597656f, + (float16_t)-0.261962891f, (float16_t)-0.590820312f, (float16_t)-0.0618896484f, (float16_t)3.98046875f, + (float16_t)-0.540527344f, (float16_t)0.345703125f, (float16_t)1.76660156f, (float16_t)3.04296875f, + (float16_t)0.0413208008f, (float16_t)-0.379394531f, (float16_t)-1.70410156f, (float16_t)1.5703125f, + (float16_t)-2.23828125f, (float16_t)2.48242188f, (float16_t)1.87207031f, (float16_t)-1.26757812f, + (float16_t)-2.20507812f, (float16_t)0.0876464844f, (float16_t)-0.705566406f, (float16_t)-2.953125f, + (float16_t)0.722167969f, (float16_t)-0.484375f, (float16_t)0.534667969f, (float16_t)-1.97851562f, + (float16_t)0.208496094f, (float16_t)-1.3671875f, (float16_t)1.20019531f, (float16_t)0.157592773f, + (float16_t)-2.44140625f, (float16_t)2.22265625f, (float16_t)0.828613281f, (float16_t)0.454833984f, + (float16_t)1.4453125f, (float16_t)0.239013672f, (float16_t)-1.52441406f, (float16_t)2.296875f, + (float16_t)-2.05273438f, (float16_t)-0.711425781f, (float16_t)-1.81835938f, (float16_t)1.18652344f, + (float16_t)-1.82714844f, (float16_t)0.848632812f, (float16_t)-2.31054688f, (float16_t)1.77441406f, + (float16_t)0.993652344f, (float16_t)-2.05273438f, (float16_t)-0.690917969f, (float16_t)-0.2890625f, + (float16_t)-1.55175781f, (float16_t)1.43164062f, (float16_t)-1.02929688f, (float16_t)0.769042969f, + (float16_t)1.84570312f, (float16_t)-3.56640625f, (float16_t)0.788574219f, (float16_t)-1.296875f, + (float16_t)1.08789062f, (float16_t)5.890625f, (float16_t)-0.593261719f, (float16_t)-2.24609375f, + (float16_t)-0.530761719f, (float16_t)2.046875f, (float16_t)-0.412597656f, (float16_t)0.608886719f, + (float16_t)0.944335938f, (float16_t)0.0933227539f, (float16_t)-2.35351562f, (float16_t)-1.56640625f, + (float16_t)-2.08789062f, (float16_t)-2.45898438f, (float16_t)-1.77539062f, (float16_t)-2.73828125f, + (float16_t)0.849609375f, (float16_t)-1.83105469f, (float16_t)1.70605469f, (float16_t)-0.576171875f, + (float16_t)3.484375f, (float16_t)2.28515625f, (float16_t)1.91699219f, (float16_t)0.102355957f, + (float16_t)1.95996094f, (float16_t)0.790527344f, (float16_t)2.609375f, (float16_t)-2.02148438f, + (float16_t)-0.425292969f, (float16_t)2.78710938f, (float16_t)-0.335449219f, (float16_t)-0.87109375f, + (float16_t)-1.8515625f, (float16_t)1.64746094f, (float16_t)1.27050781f, (float16_t)0.144775391f, + (float16_t)0.8359375f, (float16_t)-1.11132812f, (float16_t)0.819335938f, (float16_t)-0.654296875f, + (float16_t)0.604003906f, (float16_t)-0.609863281f, (float16_t)-0.646484375f, (float16_t)-1.31835938f, + (float16_t)-2.16210938f, (float16_t)-0.708007812f, (float16_t)-0.665039062f, (float16_t)0.20703125f, + (float16_t)1.02636719f, (float16_t)-1.14453125f, (float16_t)1.78125f, (float16_t)5.76171875f, + (float16_t)1.57421875f, (float16_t)-1.95507812f, (float16_t)-1.00097656f, (float16_t)-0.719726562f, + (float16_t)-1.85644531f, (float16_t)-2.23632812f, (float16_t)-1.25390625f, (float16_t)0.428466797f, + (float16_t)-1.50390625f, (float16_t)-1.35644531f, (float16_t)-0.040222168f, (float16_t)4.41796875f, + (float16_t)0.934570312f, (float16_t)0.101745605f, (float16_t)-0.730957031f, (float16_t)-0.856933594f, + (float16_t)2.44921875f, (float16_t)-2.36523438f, (float16_t)0.731933594f, (float16_t)-1.68847656f, + (float16_t)1.42871094f, 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(float16_t)2.53710938f, (float16_t)-1.25976562f, (float16_t)-0.189208984f, (float16_t)-0.0918579102f, + (float16_t)2.52539062f, (float16_t)-0.568359375f, (float16_t)-0.437011719f, (float16_t)1.86328125f, + (float16_t)0.80078125f, (float16_t)0.296630859f, (float16_t)-0.704589844f, (float16_t)-2.94140625f, + (float16_t)-0.797851562f, (float16_t)-1.27148438f, (float16_t)-0.0906982422f, (float16_t)-1.16015625f, + (float16_t)1.38964844f, (float16_t)-0.619140625f, (float16_t)-1.50585938f, (float16_t)0.270263672f, + (float16_t)-2.13085938f, (float16_t)-0.241943359f, (float16_t)2.57617188f, (float16_t)-0.688476562f, + (float16_t)-1.66210938f, (float16_t)1.49023438f, (float16_t)-0.195556641f, (float16_t)1.1953125f, + (float16_t)-1.00976562f, (float16_t)0.403076172f, (float16_t)0.0966796875f, (float16_t)1.1171875f, + (float16_t)-0.828613281f, (float16_t)1.95703125f, (float16_t)-0.0809936523f, (float16_t)2.75f, + (float16_t)1.36621094f, (float16_t)1.33984375f, (float16_t)1.8671875f, (float16_t)-0.559570312f, + (float16_t)-0.0107116699f, (float16_t)0.965332031f, (float16_t)-3.15820312f, (float16_t)-0.27734375f, + (float16_t)1.07128906f, (float16_t)0.272460938f, (float16_t)-0.704101562f, (float16_t)-1.921875f, + (float16_t)1.12988281f, (float16_t)2.37695312f, (float16_t)1.31640625f, (float16_t)-2.07226562f, + (float16_t)-2.19921875f, (float16_t)0.0296936035f, (float16_t)0.250244141f, (float16_t)1.89160156f, + (float16_t)0.964355469f, (float16_t)-1.2578125f, (float16_t)0.916015625f, (float16_t)0.45703125f, + (float16_t)-2.60742188f, (float16_t)-0.445556641f, (float16_t)-0.115600586f, (float16_t)-3.45117188f, + (float16_t)-2.265625f, (float16_t)-0.823730469f, (float16_t)-0.17565918f, (float16_t)2.30859375f, + (float16_t)-0.222290039f, (float16_t)2.5390625f, (float16_t)-5.171875f, (float16_t)4.296875f, + (float16_t)0.251464844f, (float16_t)-0.608886719f, (float16_t)-4.2421875f, (float16_t)-3.25195312f, + (float16_t)0.372070312f, (float16_t)0.457519531f, (float16_t)1.78320312f, (float16_t)-0.197387695f, + (float16_t)0.762207031f, (float16_t)-0.731445312f, (float16_t)2.52539062f, (float16_t)-0.929199219f, + (float16_t)0.588378906f, (float16_t)0.41796875f, (float16_t)1.22753906f, (float16_t)-1.36035156f, + (float16_t)-1.00878906f, (float16_t)-0.411621094f, (float16_t)-3.14453125f, (float16_t)-0.157958984f, + (float16_t)-1.39648438f, (float16_t)-0.309570312f, (float16_t)1.7578125f, (float16_t)-0.9296875f, + (float16_t)1.27246094f, (float16_t)-1.76855469f, (float16_t)1.04785156f, (float16_t)2.4609375f, + (float16_t)1.79589844f, (float16_t)0.595214844f, (float16_t)-0.63671875f, (float16_t)2.3828125f, + (float16_t)3.98046875f, (float16_t)1.3984375f, (float16_t)1.03027344f, (float16_t)-2.296875f, + (float16_t)1.8984375f, (float16_t)-0.727539062f, (float16_t)2.79296875f, (float16_t)-1.83984375f, + (float16_t)1.27246094f, (float16_t)0.465087891f, (float16_t)2.65039062f, (float16_t)1.43945312f, + (float16_t)1.24023438f, (float16_t)-1.1484375f, (float16_t)-0.699707031f, (float16_t)-0.789550781f, + (float16_t)-2.07226562f, (float16_t)-0.821777344f, (float16_t)-1.25390625f, (float16_t)-0.729003906f, + (float16_t)2.24023438f, (float16_t)-1.16796875f, (float16_t)0.611328125f, (float16_t)2.2421875f, + (float16_t)1.07421875f, (float16_t)0.0716552734f, (float16_t)0.748535156f, (float16_t)0.277587891f, + (float16_t)-1.28417969f, (float16_t)-2.15625f, (float16_t)-2.14257812f, (float16_t)-0.751464844f, + (float16_t)-1.89648438f, (float16_t)-1.88671875f, (float16_t)-4.68359375f, (float16_t)0.510742188f, + (float16_t)-1.71386719f, (float16_t)2.55273438f, (float16_t)-0.580078125f, (float16_t)-0.467285156f, + (float16_t)1.32714844f, (float16_t)-1.36035156f, (float16_t)-1.40136719f, (float16_t)1.91894531f, + (float16_t)0.844726562f, (float16_t)0.295410156f, (float16_t)-0.000366926193f, (float16_t)1.32421875f, + (float16_t)2.49609375f, (float16_t)-0.794433594f, (float16_t)-6.31640625f, (float16_t)-0.118469238f, + (float16_t)1.24609375f, (float16_t)-1.47070312f, (float16_t)-3.09765625f, (float16_t)1.75585938f, + (float16_t)-0.882324219f, (float16_t)1.16015625f, (float16_t)0.50390625f, (float16_t)-3.09375f, + (float16_t)-2.0859375f, (float16_t)-0.974121094f, (float16_t)-1.38964844f, (float16_t)-0.188720703f, + (float16_t)2.54492188f, (float16_t)-0.17590332f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/weights_data.h new file mode 100644 index 00000000..84be8f12 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f16/weights_data.h @@ -0,0 +1,541 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_basic_nhwc_f16_weights_data[540] = {(float16_t)0.654296875f, + (float16_t)0.130859375f, + (float16_t)0.00868225098f, + (float16_t)-0.423095703f, + (float16_t)0.484863281f, + (float16_t)0.179443359f, + (float16_t)0.0930175781f, + (float16_t)0.125488281f, + (float16_t)0.113098145f, + (float16_t)-0.484375f, + (float16_t)0.711425781f, + (float16_t)-0.0456542969f, + (float16_t)-0.614746094f, + (float16_t)0.17175293f, + (float16_t)0.733398438f, + (float16_t)-0.526855469f, + (float16_t)0.527832031f, + (float16_t)0.56640625f, + (float16_t)-0.668945312f, + (float16_t)0.452148438f, + (float16_t)0.254882812f, + (float16_t)0.054473877f, + (float16_t)0.237182617f, + (float16_t)0.313964844f, + (float16_t)-0.630859375f, + (float16_t)0.569824219f, + 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(float16_t)-0.627929688f, + (float16_t)0.0772094727f, + (float16_t)-0.370361328f, + (float16_t)-0.340576172f, + (float16_t)0.197998047f, + (float16_t)0.215454102f, + (float16_t)0.283691406f, + (float16_t)-0.473632812f, + (float16_t)0.143676758f, + (float16_t)-0.0675048828f, + (float16_t)0.557128906f, + (float16_t)0.110351562f, + (float16_t)-0.394287109f, + (float16_t)0.520507812f, + (float16_t)-0.608886719f, + (float16_t)0.176147461f, + (float16_t)0.527832031f, + (float16_t)-0.5546875f, + (float16_t)0.607421875f, + (float16_t)0.749511719f, + (float16_t)0.217651367f, + (float16_t)0.0755004883f, + (float16_t)-0.399658203f, + (float16_t)-0.00998687744f, + (float16_t)-0.533203125f, + (float16_t)-0.177856445f, + (float16_t)-0.633789062f, + (float16_t)0.00198936462f, + (float16_t)-0.120117188f, + (float16_t)-0.209960938f, + (float16_t)0.446044922f, + (float16_t)0.570800781f, + (float16_t)0.325195312f, + (float16_t)0.310058594f, + (float16_t)0.700195312f, + (float16_t)0.0444641113f, + (float16_t)-0.450683594f, + (float16_t)-0.538574219f, + (float16_t)-0.606445312f, + (float16_t)0.447998047f, + (float16_t)-0.16003418f, + (float16_t)-0.156982422f, + (float16_t)-0.120605469f, + (float16_t)0.428222656f, + (float16_t)-0.655273438f, + (float16_t)0.477539062f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/biases_data.h new file mode 100644 index 00000000..14b013f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_nhwc_f32_biases_data[10] = {-0.412936568f, + -0.366484016f, + 0.0196087081f, + -0.110348053f, + 0.052388642f, + 0.111013778f, + 0.187041506f, + -0.484796196f, + 0.0728422925f, + 0.320306778f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/config_data.h new file mode 100644 index 00000000..438ba1c9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_BASIC_NHWC_F32_INPUT_BATCHES 1 +#define CONV_BASIC_NHWC_F32_INPUT_H 9 +#define CONV_BASIC_NHWC_F32_INPUT_W 11 +#define CONV_BASIC_NHWC_F32_IN_CH 6 +#define CONV_BASIC_NHWC_F32_FILTER_H 3 +#define CONV_BASIC_NHWC_F32_FILTER_W 3 +#define CONV_BASIC_NHWC_F32_OUT_CH 10 +#define CONV_BASIC_NHWC_F32_STRIDE_H 1 +#define CONV_BASIC_NHWC_F32_STRIDE_W 1 +#define CONV_BASIC_NHWC_F32_PADDING_H 0 +#define CONV_BASIC_NHWC_F32_PADDING_W 0 +#define CONV_BASIC_NHWC_F32_DILATION_H 1 +#define CONV_BASIC_NHWC_F32_DILATION_W 1 +#define CONV_BASIC_NHWC_F32_OUTPUT_H 7 +#define CONV_BASIC_NHWC_F32_OUTPUT_W 9 +#define CONV_BASIC_NHWC_F32_OUTPUT_C 10 +#define CONV_BASIC_NHWC_F32_DST_SIZE 630 +#define CONV_BASIC_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_BASIC_NHWC_F32_USE_WRAPPER 1 +#define CONV_BASIC_NHWC_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_BASIC_NHWC_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/input_data.h new file mode 100644 index 00000000..f4466164 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/input_data.h @@ -0,0 +1,87 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_nhwc_f32_input_data[594] = { + -0.1526604f, 0.164522618f, -0.220518783f, 0.886897922f, 0.421240747f, -0.387193859f, -0.592550993f, + 0.318022072f, -0.0307296421f, 0.746647537f, 0.883907974f, 0.0475306921f, -0.0497994907f, -0.167253554f, + -0.15896903f, -0.00579841156f, 0.165959686f, -0.679562211f, 0.924296916f, 0.977359772f, -0.208694279f, + 0.230511054f, 0.163986683f, -0.202650115f, -0.136042118f, 0.127868056f, -0.0899386778f, 0.272667378f, + 0.080242835f, -0.165694073f, -0.325226009f, -0.930684149f, 0.520270348f, -0.300356477f, 0.914561093f, + -0.650796473f, 0.99696666f, -0.4795596f, 0.418001115f, 0.664688945f, 0.273287803f, 0.360190451f, + 0.565937936f, 0.733329356f, 0.67549938f, -0.22307992f, -0.66902703f, -0.351163149f, -0.559790075f, + -0.331108451f, -0.12864323f, 0.00305680605f, 0.308508337f, 0.910141468f, 0.197899178f, 0.393152058f, + -0.660227716f, 0.360872507f, 0.00694856653f, 0.566267848f, 0.97805512f, -0.301862866f, -0.260205269f, + 0.673114836f, 0.754273176f, 0.136997059f, -0.0969180241f, -0.364510715f, 0.0286870208f, 0.329843134f, + -0.328068376f, -0.0447729081f, -0.798952103f, 0.0796702728f, -0.843073487f, 0.364419341f, -0.127015203f, + 0.193887204f, 0.0284624808f, 0.782973588f, -0.158541933f, -0.132081896f, -0.336632788f, -0.272300899f, + -0.674469888f, 0.732443988f, 0.398222208f, 0.14183332f, 0.45368132f, -0.195926115f, -0.517924666f, + -0.282253653f, -0.981817126f, 0.408737123f, 0.889216363f, 0.355154634f, -0.0538558885f, 0.126294389f, + 0.4319309f, -0.624213517f, -0.320040792f, 0.207406953f, 0.464524955f, 0.241861969f, 0.827320755f, + 0.707574427f, 0.365622103f, 0.809312344f, 0.684143066f, 0.0305535365f, -0.0624399893f, 0.393065721f, + -0.146210849f, 0.166466668f, 0.113858804f, 0.7949633f, -0.235413566f, -0.923073232f, -0.633158624f, + -0.125359073f, -0.195363596f, 0.222282097f, 0.615815759f, 0.544228077f, -0.912918806f, 0.776591182f, + 0.706318438f, 0.592760801f, -0.691070557f, -0.691589653f, -0.166248828f, 0.185641989f, 0.696401417f, + 0.0243828576f, -0.391256779f, -0.13118495f, -0.0516254008f, 0.612323165f, -0.491203129f, 0.203798577f, + 0.455268115f, -0.970533073f, 0.277692765f, 0.0971159711f, 0.17707552f, -0.652318239f, 0.874349833f, + -0.734745443f, 0.903466642f, -0.810008824f, 0.731523991f, 0.365948856f, 0.0155004095f, 0.709948719f, + -0.867127657f, 0.0727782771f, -0.883322716f, -0.954443455f, -0.255422086f, 0.116596133f, 0.933657348f, + -0.286229283f, -0.599575818f, 0.99301815f, 0.0747041628f, -0.645602643f, -0.594783843f, -0.944146395f, + -0.689164996f, -0.718180895f, -0.501083553f, -0.0419231243f, -0.759021461f, -0.765182734f, 0.424387038f, + 0.191421777f, 0.719501197f, -0.212042585f, -0.415513843f, 0.0781709701f, -0.565921307f, 0.33238259f, + 0.414175987f, 0.97071892f, -0.906152666f, -0.99614042f, -0.985933602f, 0.232910573f, 0.674425006f, + -0.66383338f, -0.207906798f, 0.806344688f, 0.913951218f, 0.717742562f, 0.409830928f, 0.039387513f, + -0.84719032f, -0.871959388f, -0.892141342f, -0.63056016f, 0.0910419002f, 0.946685314f, 0.681270838f, + 0.082097359f, -0.774652302f, -0.648314059f, -0.812183499f, -0.276715875f, 0.386583298f, 0.658233583f, + 0.151242673f, 0.994374812f, 0.0314417295f, 0.202001065f, -0.886548638f, 0.964856505f, 0.654236495f, + -0.451118052f, 0.170067668f, 0.663316667f, 0.0720269084f, 0.232189819f, 0.562017322f, -0.183440894f, + -0.347347468f, -0.335952193f, -0.730238795f, -0.544150293f, 0.553840637f, 0.184011698f, 0.767817259f, + 0.745885015f, 0.976456225f, -0.986563206f, -0.0422216654f, -0.192295507f, -0.341191202f, -0.336682707f, + -0.0163732246f, -0.686039388f, -0.063584581f, 0.491957754f, -0.538300633f, -0.0856280401f, 0.450188905f, + -0.828274667f, 0.0836815909f, 0.892657995f, 0.265582919f, 0.899855137f, 0.576848447f, 0.499594331f, + 0.248813227f, 0.908041477f, 0.16196008f, 0.151925236f, -0.812848389f, 0.658065915f, 0.463295311f, + 0.264224946f, 0.95046401f, -0.955538034f, 0.276211649f, -0.40541935f, 0.602113187f, -0.496955395f, + 0.462563246f, 0.039277669f, -0.251371711f, 0.751291275f, -0.361828536f, 0.0308743026f, 0.455781162f, + -0.763784528f, 0.475852847f, -0.72977674f, 0.944320977f, -0.350237161f, -0.151783928f, -0.286080807f, + 0.347383112f, 0.159609988f, 0.63763988f, 0.407173306f, 0.177937254f, 0.584125519f, -0.0998558551f, + 0.132590353f, 0.0821683183f, -0.696435571f, 0.0971686766f, -0.321772039f, -0.318891376f, -0.0305383448f, + 0.203646198f, 0.775055528f, -0.860039294f, 0.827999055f, -0.792796493f, -0.117176279f, -0.886375427f, + 0.0285208263f, -0.122411013f, 0.688743353f, -0.0268455986f, -0.577509224f, -0.493763298f, 0.577431858f, + 0.10628932f, -0.696299672f, 0.150706381f, -0.192938849f, 0.777684212f, 0.225231871f, -0.465665311f, + -0.393166989f, -0.498132527f, 0.435424417f, 0.954996407f, -0.704997241f, 0.128173754f, 0.527111053f, + 0.136118278f, 0.662174702f, 0.630945027f, 0.222267464f, -0.608596683f, -0.123643547f, 0.686017871f, + -0.194440603f, 0.618669391f, 0.854756236f, -0.272111535f, 0.869400442f, 0.184399337f, -0.359803408f, + -0.00275021978f, 0.0618350655f, 0.85228461f, -0.86477375f, 0.977271795f, 0.465167642f, 0.146858737f, + -0.671030104f, 0.720858395f, -0.455291718f, -0.203506544f, -0.277454823f, 0.303929508f, 0.0320516117f, + 0.779917479f, 0.756985128f, 0.514720082f, -0.0365563296f, 0.615411758f, 0.926585078f, -0.780261815f, + -0.738232076f, -0.174118936f, 0.591012657f, -0.503982008f, 0.130876303f, -0.908502519f, 0.747487962f, + -0.881801844f, -0.974536121f, -0.0525671281f, 0.952378273f, 0.495896161f, 0.530910075f, -0.508264899f, + -0.354362905f, -0.984723985f, -0.140451208f, 0.990657508f, -0.177918494f, 0.128382728f, -0.623921752f, + -0.782555044f, 0.631610274f, 0.477959692f, -0.632726669f, -0.634041369f, 0.640478909f, 0.623303533f, + -0.144686416f, -0.401110858f, -0.95693171f, -0.721114993f, -0.596790671f, 0.801935077f, -0.102101669f, + -0.722236872f, -0.217927754f, -0.890089691f, -0.530988514f, 0.757338941f, 0.0794112384f, -0.546232402f, + -0.599697888f, -0.636467993f, -0.967827439f, -0.878135145f, 0.0160822552f, 0.123312809f, -0.545294106f, + -0.467987746f, 0.359215409f, 0.472572416f, 0.816371739f, 0.464979798f, 0.137316808f, 0.504776061f, + 0.187266096f, -0.368145406f, -0.185749441f, 0.0440745167f, 0.148235187f, 0.330388814f, -0.539717913f, + -0.767727375f, -0.716775358f, -0.757850528f, 0.204515532f, 0.213545367f, 0.35104546f, 0.0235495027f, + -0.533094943f, 0.205866009f, 0.472964495f, -0.795452952f, 0.615262747f, 0.935639679f, 0.517080426f, + -0.343403339f, -0.250604928f, 0.656790376f, -0.282597572f, 0.0413633958f, 0.123338088f, 0.867463112f, + 0.18068108f, -0.150076851f, 0.0422971882f, 0.776715636f, -0.28115055f, -0.19361724f, -0.661511064f, + 0.301309675f, -0.638001919f, 0.811051071f, -0.871183395f, 0.948539376f, -0.770727158f, -0.817951322f, + -0.160726562f, 0.0931762904f, -0.743706882f, 0.402068675f, -0.0809535086f, 0.283134878f, 0.89438951f, + -0.0672287643f, 0.599556744f, 0.423455745f, -0.244624004f, 0.261017472f, 0.0576169193f, -0.144681543f, + 0.824709713f, 0.242378533f, 0.217266783f, -0.890198052f, -0.108802192f, 0.559674263f, 0.280199915f, + -0.299601406f, 0.375562161f, -0.501240611f, -0.669160247f, -0.810304403f, 0.151934981f, -0.272159845f, + 0.490481198f, -0.410355181f, 0.778609931f, -0.917468429f, 0.427184999f, -0.321937352f, 0.00192129461f, + 0.280193716f, -0.0369988941f, 0.891356647f, 0.876215816f, 0.293067187f, 0.441812277f, 0.0170265213f, + -0.572607815f, 0.824422419f, 0.735766351f, 0.161908001f, 0.0726398006f, 0.067408435f, -0.134430915f, + 0.575253844f, -0.0138006778f, -0.499339193f, 0.241480708f, 0.764153302f, 0.84375453f, 0.512285829f, + 0.670702636f, 0.773791432f, -0.580197692f, -0.226294383f, -0.993002594f, -0.127372846f, -0.937829971f, + 0.62965554f, 0.342731684f, 0.325422913f, 0.819926798f, -0.951373279f, 0.574738324f, 0.956073105f, + -0.39607802f, -0.514887631f, -0.973141909f, 0.0514548384f, -0.892038286f, -0.0227125641f, 0.72127372f, + 0.896060407f, 0.0207078159f, -0.0543755628f, 0.361647815f, 0.580828786f, -0.841035783f, 0.666987181f, + -0.730169058f, 0.658967197f, 0.119749859f, 0.793828428f, -0.625015199f, -0.914164484f, 0.580243051f, + 0.426987857f, 0.346978456f, 0.726022363f, 0.252979517f, 0.200574651f, 0.151913673f, 0.342503846f, + 0.728244483f, -0.842705011f, 0.172636211f, 0.846609116f, -0.512583196f, 0.618494809f, -0.120143853f, + -0.312718391f, 0.0487228334f, 0.729016244f, 0.763647199f, -0.931607902f, -0.573061645f, 0.504196286f, + 0.682170212f, 0.377401352f, -0.244140163f, -0.653396249f, -0.686027169f, 0.485405505f, 0.191110075f, + 0.256261498f, 0.868250132f, -0.948295832f, -0.817871392f, -0.975933254f, -0.126255929f, 0.249300301f, + -0.752654076f, -0.523269534f, -0.729932725f, -0.761045992f, -0.158306301f, 0.099048458f, 0.294286191f, + -0.39989078f, -0.402161568f, -0.141518921f, -0.913335145f, -0.719596863f, -0.716900826f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..cbff825a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/output_ref_data.h @@ -0,0 +1,92 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_nhwc_f32_output_ref_data[630] = { + -1.61631596f, -2.83687901f, 1.13320053f, 2.45633602f, 2.29206085f, 1.14614165f, 0.752825081f, + -1.85067439f, 0.326140732f, -0.846411347f, 0.609416485f, 1.16106498f, 3.82436419f, 2.15799141f, + 0.267580181f, 0.0445064381f, 1.84948611f, 2.32883096f, 0.927589834f, 0.748189211f, -0.210221007f, + 1.11551011f, 1.00762916f, -0.418744653f, 0.0580557249f, -1.72039628f, 3.75218844f, -2.08637261f, + -2.09382176f, 2.0687151f, 2.4465518f, -0.363941014f, -0.543645918f, 0.813171029f, 3.61810827f, + 1.05705214f, -3.32504082f, 0.320072681f, 2.49391961f, -2.20342898f, -0.522432029f, 2.06587815f, + 1.35623062f, -0.266723961f, -1.48848498f, -0.59049058f, 0.647279561f, 0.673987269f, -1.1594497f, + -1.36311114f, -0.413906187f, -3.75890589f, 1.2996459f, 0.667339861f, -2.64620924f, -1.0087353f, + 2.01878309f, 0.671196461f, -1.75324667f, 0.911951065f, -0.625389755f, -0.112761587f, -1.94828272f, + -2.84726572f, 0.849574029f, 1.37444377f, -1.83026552f, 0.32405135f, 1.22429395f, 2.3233881f, + -0.810441136f, -0.874356985f, 1.04387343f, 1.52227139f, -1.75370395f, 1.76114547f, -0.42089504f, + -1.85879242f, -1.78095341f, -1.80031753f, -2.03959894f, -3.49059033f, -3.45138621f, -1.3561151f, + 2.84858036f, -1.45226109f, -1.76684475f, 0.01714167f, 2.57494712f, 1.23025775f, -1.06752133f, + -2.21496511f, -0.711613774f, -2.19947267f, -2.69045925f, 1.40373981f, -0.938657045f, -0.00383573771f, + -1.20759451f, 1.94694495f, -0.369449168f, 1.77577364f, -1.53873038f, -1.99563801f, 0.854668021f, + 1.61289072f, 0.305436492f, -0.406307131f, 1.33956277f, 2.38618112f, -0.621033549f, -0.338287741f, + -1.35158944f, 3.14535356f, 2.24361634f, 0.0831689611f, -0.952336073f, -1.08451879f, 2.40687776f, + -1.28239501f, -0.40172562f, -3.65745306f, -1.63881147f, -0.250924408f, 2.47928953f, 0.137436405f, + 1.0023967f, -0.659570932f, -0.215785772f, 4.32992172f, 2.74573421f, 0.47677514f, 2.31950712f, + 3.54705858f, 1.21291673f, 0.483119041f, 0.845033169f, 1.3097173f, -0.23139292f, -3.92182779f, + -1.13475919f, -1.43575704f, -3.08448696f, -1.36261535f, 1.18015277f, -1.80949497f, 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b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/weights_data.h new file mode 100644 index 00000000..d324ef1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_basic_nhwc_f32/weights_data.h @@ -0,0 +1,80 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_basic_nhwc_f32_weights_data[540] = { + 0.654108584f, 0.130909219f, 0.00868309569f, -0.423151731f, 0.484744132f, 0.179416969f, 0.0930115506f, + 0.125510558f, 0.113125563f, 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-0.129653767f, -0.324905246f, 0.531821489f, 0.0710003003f, 0.671080351f, 0.325725943f, 0.431728125f, + 0.0331248529f, -0.404683143f, -0.023475742f, 0.190936416f, -0.112107404f, -0.495105296f, -0.352897525f, + 0.131207094f, 0.192889348f, 0.57959336f, -0.538277149f, -0.147115558f, 0.288170516f, 0.41132623f, + 0.194620937f, -0.129776657f, 0.575938821f, -0.102809004f, -0.741457283f, -0.130591005f, -0.558178425f, + 0.703390777f, -0.364934832f, -0.315897524f, -0.583822131f, -0.703210413f, -0.314542055f, -0.0663317218f, + 0.21213977f, -0.281780779f, -0.313325167f, 0.578261375f, -0.183366239f, -0.711195052f, -0.628928721f, + -0.122639813f, 0.607919574f, -0.593453348f, 0.40699625f, -0.392705381f, -0.0582027771f, -0.675418317f, + -0.52631253f, 0.446176529f, 0.181487888f, -0.209887058f, -0.300907373f, 0.164665252f, -0.368421316f, + 0.738601625f, -0.712789237f, 0.0968990549f, -0.425145268f, -0.50365901f, 0.712693691f, 0.296326727f, + 0.537376881f, -0.307313591f, 0.638682306f, 0.396418631f, 0.0716193914f, -0.190504849f, -0.550039351f, + -0.557148278f, -0.640496194f, -0.423899889f, -0.704995275f, 0.10222207f, 0.596519291f, 0.682925165f, + 0.197077394f, -0.572160542f, 0.32556057f, 0.62416935f, -0.627715707f, 0.0772336274f, -0.37024653f, + -0.340459019f, 0.198027626f, 0.215473667f, 0.283572286f, -0.473671257f, 0.143663272f, -0.0674864873f, + 0.557315111f, 0.110365093f, -0.394203424f, 0.520702481f, -0.608966768f, 0.176137149f, 0.527758062f, + -0.554757833f, 0.607483745f, 0.749483168f, 0.217630327f, 0.0755309239f, -0.399748832f, -0.00998539384f, + -0.533108473f, -0.177804038f, -0.633755445f, 0.00198874599f, -0.120129295f, -0.209981114f, 0.445927203f, + 0.570706785f, 0.325309396f, 0.310128838f, 0.700438917f, 0.0444560461f, -0.45058164f, -0.538374782f, + -0.606204748f, 0.44789508f, -0.160081998f, -0.156964764f, -0.120575652f, 0.428270876f, -0.655076504f, + 0.477631301f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/biases_data.h new file mode 100644 index 00000000..4ad7baaf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/biases_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_f16_biases_data[12] = {(float16_t)0.183959961f, + (float16_t)0.431884766f, + (float16_t)-0.246337891f, + (float16_t)-0.304443359f, + (float16_t)-0.108459473f, + (float16_t)0.498779297f, + (float16_t)0.332763672f, + (float16_t)0.234863281f, + (float16_t)-0.354736328f, + (float16_t)0.028427124f, + (float16_t)-0.490478516f, + (float16_t)-0.479248047f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/config_data.h new file mode 100644 index 00000000..1d940ed0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K3_OPT_F16_INPUT_BATCHES 1 +#define CONV_K3_OPT_F16_INPUT_H 1 +#define CONV_K3_OPT_F16_INPUT_W 21 +#define CONV_K3_OPT_F16_IN_CH 8 +#define CONV_K3_OPT_F16_FILTER_H 1 +#define CONV_K3_OPT_F16_FILTER_W 3 +#define CONV_K3_OPT_F16_OUT_CH 12 +#define CONV_K3_OPT_F16_STRIDE_H 1 +#define CONV_K3_OPT_F16_STRIDE_W 1 +#define CONV_K3_OPT_F16_PADDING_H 0 +#define CONV_K3_OPT_F16_PADDING_W 0 +#define CONV_K3_OPT_F16_DILATION_H 1 +#define CONV_K3_OPT_F16_DILATION_W 1 +#define CONV_K3_OPT_F16_OUTPUT_H 1 +#define CONV_K3_OPT_F16_OUTPUT_W 19 +#define CONV_K3_OPT_F16_OUTPUT_C 12 +#define CONV_K3_OPT_F16_DST_SIZE 228 +#define CONV_K3_OPT_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K3_OPT_F16_USE_WRAPPER 0 +#define CONV_K3_OPT_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_K3_OPT_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/input_data.h new file mode 100644 index 00000000..69a73c30 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/input_data.h @@ -0,0 +1,44 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_f16_input_data[168] = { + (float16_t)0.77734375f, (float16_t)0.675292969f, (float16_t)0.626953125f, (float16_t)0.361328125f, + (float16_t)-0.809082031f, (float16_t)0.732421875f, (float16_t)-0.981933594f, (float16_t)0.805664062f, + (float16_t)0.921386719f, (float16_t)0.912597656f, (float16_t)-0.498779297f, (float16_t)-0.903320312f, + (float16_t)0.45703125f, (float16_t)0.709960938f, (float16_t)0.0594787598f, (float16_t)0.650390625f, + (float16_t)0.316162109f, (float16_t)-0.727050781f, (float16_t)-0.0827636719f, (float16_t)0.356689453f, + (float16_t)0.128051758f, (float16_t)0.600585938f, (float16_t)0.959472656f, (float16_t)-0.683105469f, + (float16_t)-0.697265625f, (float16_t)0.803222656f, (float16_t)-0.640625f, (float16_t)-0.100463867f, + (float16_t)0.278076172f, (float16_t)0.448486328f, (float16_t)0.359863281f, (float16_t)0.532714844f, + (float16_t)0.385009766f, (float16_t)-0.504882812f, (float16_t)-0.637207031f, (float16_t)0.333496094f, + (float16_t)0.0309448242f, (float16_t)-0.451660156f, (float16_t)-0.0604248047f, (float16_t)0.662109375f, + (float16_t)-0.695800781f, (float16_t)0.526855469f, (float16_t)-0.725097656f, (float16_t)0.0786132812f, + (float16_t)0.801757812f, (float16_t)-0.141357422f, (float16_t)-0.955566406f, (float16_t)0.966796875f, + (float16_t)-0.349365234f, (float16_t)0.140136719f, (float16_t)-0.799804688f, (float16_t)0.715820312f, + (float16_t)0.567382812f, (float16_t)-0.208374023f, (float16_t)0.731445312f, (float16_t)0.528320312f, + (float16_t)-0.656738281f, (float16_t)0.146362305f, (float16_t)-0.503417969f, (float16_t)-0.0952148438f, + (float16_t)0.943359375f, (float16_t)-0.0531311035f, (float16_t)0.9375f, (float16_t)-0.361572266f, + (float16_t)0.79296875f, (float16_t)0.0883789062f, (float16_t)0.761230469f, (float16_t)-0.197631836f, + (float16_t)-0.861328125f, (float16_t)0.571777344f, (float16_t)-0.886230469f, (float16_t)-0.938964844f, + (float16_t)-0.290039062f, (float16_t)0.360595703f, (float16_t)0.500488281f, (float16_t)0.0686645508f, + (float16_t)-0.574707031f, (float16_t)0.0815429688f, (float16_t)0.188476562f, (float16_t)0.712402344f, + (float16_t)0.8671875f, (float16_t)-0.119934082f, (float16_t)-0.0938110352f, (float16_t)0.967285156f, + (float16_t)0.391357422f, (float16_t)0.454345703f, (float16_t)0.935546875f, (float16_t)-0.611816406f, + (float16_t)-0.926757812f, (float16_t)-0.732910156f, (float16_t)-0.863769531f, (float16_t)-0.768554688f, + (float16_t)-0.996582031f, (float16_t)-0.440429688f, (float16_t)-0.518554688f, (float16_t)-0.0673828125f, + (float16_t)-0.784179688f, (float16_t)-0.369140625f, (float16_t)0.133056641f, (float16_t)0.408203125f, + (float16_t)0.0519104004f, (float16_t)-0.960449219f, (float16_t)-0.871582031f, (float16_t)0.796875f, + (float16_t)-0.182006836f, (float16_t)0.54296875f, (float16_t)0.361328125f, (float16_t)0.0783081055f, + (float16_t)-0.408203125f, (float16_t)0.548828125f, (float16_t)-0.482421875f, (float16_t)0.633789062f, + (float16_t)0.657714844f, (float16_t)0.187133789f, (float16_t)0.755859375f, (float16_t)-0.512207031f, + (float16_t)-0.563476562f, (float16_t)0.918457031f, (float16_t)-0.0825195312f, (float16_t)-0.528320312f, + (float16_t)-0.788085938f, (float16_t)-0.684570312f, (float16_t)0.896484375f, (float16_t)0.00155639648f, + (float16_t)-0.279052734f, (float16_t)0.915527344f, (float16_t)-0.0589599609f, (float16_t)-0.182250977f, + (float16_t)-0.0897216797f, (float16_t)-0.464599609f, (float16_t)0.81640625f, (float16_t)-0.312744141f, + (float16_t)0.471679688f, (float16_t)-0.20300293f, (float16_t)-0.377441406f, (float16_t)-0.569824219f, + (float16_t)0.228759766f, (float16_t)-0.408203125f, (float16_t)0.00922393799f, (float16_t)-0.565917969f, + (float16_t)-0.833496094f, (float16_t)-0.813964844f, (float16_t)-0.830078125f, (float16_t)-0.790527344f, + (float16_t)-0.735351562f, (float16_t)-0.174560547f, (float16_t)0.245727539f, (float16_t)-0.486572266f, + (float16_t)-0.543945312f, (float16_t)0.349365234f, (float16_t)-0.124145508f, (float16_t)-0.481201172f, + (float16_t)-0.817871094f, (float16_t)0.295898438f, (float16_t)0.977050781f, (float16_t)0.604492188f, + (float16_t)0.429443359f, (float16_t)-0.569335938f, (float16_t)0.790527344f, (float16_t)-0.102661133f, + (float16_t)-0.616699219f, (float16_t)-0.553222656f, (float16_t)0.557128906f, (float16_t)-0.601074219f, + (float16_t)0.904785156f, (float16_t)0.489990234f, (float16_t)-0.780273438f, (float16_t)-0.134521484f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/output_ref_data.h new file mode 100644 index 00000000..4997fc22 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/output_ref_data.h @@ -0,0 +1,59 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_f16_output_ref_data[228] = { + (float16_t)2.64648438f, (float16_t)-1.59960938f, (float16_t)1.33496094f, (float16_t)-0.903320312f, + (float16_t)1.1875f, (float16_t)2.68945312f, (float16_t)-0.231567383f, (float16_t)1.89160156f, + (float16_t)0.331787109f, (float16_t)-2.89257812f, (float16_t)2.27929688f, (float16_t)-1.85351562f, + (float16_t)-0.280273438f, (float16_t)0.926269531f, (float16_t)-2.79882812f, (float16_t)0.0446472168f, + (float16_t)0.844238281f, (float16_t)0.727050781f, (float16_t)0.468994141f, (float16_t)1.00292969f, + (float16_t)1.4296875f, (float16_t)-0.332519531f, (float16_t)-1.72851562f, (float16_t)-2.50390625f, + (float16_t)0.169677734f, (float16_t)-0.602050781f, (float16_t)0.831054688f, (float16_t)-1.234375f, + (float16_t)1.97265625f, (float16_t)-0.452392578f, (float16_t)-0.0040473938f, (float16_t)1.55175781f, + (float16_t)0.108886719f, (float16_t)-0.234130859f, (float16_t)-1.43554688f, (float16_t)-0.809082031f, + (float16_t)-0.21472168f, (float16_t)0.763183594f, (float16_t)-1.46972656f, (float16_t)-1.07324219f, + (float16_t)0.262207031f, (float16_t)0.579589844f, (float16_t)-0.762207031f, (float16_t)-1.92871094f, + (float16_t)0.6875f, (float16_t)1.14550781f, (float16_t)-1.42773438f, (float16_t)-2.43164062f, + (float16_t)2.29882812f, (float16_t)-1.17480469f, (float16_t)1.64648438f, (float16_t)-0.79296875f, + (float16_t)-0.399902344f, (float16_t)-1.58984375f, (float16_t)-1.2109375f, (float16_t)0.122436523f, + (float16_t)0.537109375f, (float16_t)0.121643066f, (float16_t)0.138793945f, (float16_t)0.475830078f, + (float16_t)1.6484375f, (float16_t)0.286132812f, (float16_t)-0.068359375f, (float16_t)-1.59472656f, + (float16_t)-0.817871094f, (float16_t)1.34277344f, (float16_t)-1.76171875f, (float16_t)-2.14257812f, + (float16_t)1.84472656f, (float16_t)-0.972167969f, (float16_t)-1.20800781f, (float16_t)-0.567382812f, + (float16_t)-1.75195312f, (float16_t)1.23632812f, (float16_t)2.53125f, (float16_t)2.18164062f, + (float16_t)-0.354492188f, (float16_t)1.84570312f, (float16_t)0.790527344f, (float16_t)-0.16027832f, + (float16_t)0.571289062f, (float16_t)-2.86914062f, (float16_t)-3.56640625f, (float16_t)-0.854492188f, + (float16_t)-1.90234375f, (float16_t)1.29980469f, (float16_t)-0.829589844f, (float16_t)0.754394531f, + (float16_t)0.896972656f, (float16_t)0.684570312f, (float16_t)0.80859375f, (float16_t)1.22851562f, + (float16_t)-0.379882812f, (float16_t)2.71289062f, (float16_t)-1.16210938f, (float16_t)0.913574219f, + (float16_t)2.21484375f, (float16_t)-0.770996094f, (float16_t)-2.26367188f, (float16_t)-2.0390625f, + (float16_t)1.47460938f, (float16_t)0.253662109f, (float16_t)-0.23059082f, (float16_t)2.2890625f, + (float16_t)-1.22363281f, (float16_t)-1.23535156f, (float16_t)0.979492188f, (float16_t)-1.00683594f, + (float16_t)0.505371094f, (float16_t)1.41796875f, (float16_t)-1.5546875f, (float16_t)-0.419189453f, + (float16_t)-2.27734375f, (float16_t)2.51367188f, (float16_t)1.30175781f, (float16_t)-0.6171875f, + (float16_t)-1.28027344f, (float16_t)-0.261474609f, (float16_t)0.201049805f, (float16_t)-1.92480469f, + (float16_t)-1.15332031f, (float16_t)1.27636719f, (float16_t)-0.421875f, (float16_t)-1.59082031f, + (float16_t)2.27148438f, (float16_t)-0.944335938f, (float16_t)2.87109375f, (float16_t)-1.43945312f, + (float16_t)-0.669921875f, (float16_t)1.47070312f, (float16_t)-2.08203125f, (float16_t)-0.47265625f, + (float16_t)0.249511719f, (float16_t)-1.2265625f, (float16_t)0.452148438f, (float16_t)0.673339844f, + (float16_t)-2.11132812f, (float16_t)-2.0625f, (float16_t)-0.133911133f, (float16_t)-0.484130859f, + (float16_t)0.372558594f, (float16_t)0.551757812f, (float16_t)-0.934570312f, (float16_t)-0.440429688f, + (float16_t)1.18359375f, (float16_t)-0.169921875f, (float16_t)2.36523438f, (float16_t)1.1796875f, + (float16_t)-1.61523438f, (float16_t)1.20703125f, (float16_t)-0.303955078f, (float16_t)0.524902344f, + (float16_t)-0.455078125f, (float16_t)-1.21289062f, (float16_t)-0.0223693848f, (float16_t)1.21191406f, + (float16_t)0.895996094f, (float16_t)-0.233764648f, (float16_t)-0.859375f, (float16_t)1.06542969f, + (float16_t)0.0872192383f, (float16_t)2.83789062f, (float16_t)2.03710938f, (float16_t)-0.00339126587f, + (float16_t)0.190185547f, (float16_t)-0.433837891f, (float16_t)1.05664062f, (float16_t)-0.753417969f, + (float16_t)0.762207031f, (float16_t)-0.184082031f, (float16_t)-2.9765625f, (float16_t)1.11914062f, + (float16_t)0.759765625f, (float16_t)0.724609375f, (float16_t)1.67871094f, (float16_t)0.0814819336f, + (float16_t)-0.981933594f, (float16_t)-0.854980469f, (float16_t)0.544433594f, (float16_t)0.548828125f, + (float16_t)-0.190185547f, (float16_t)-0.0255889893f, (float16_t)-1.36230469f, (float16_t)1.97265625f, + (float16_t)-2.53125f, (float16_t)1.21386719f, (float16_t)0.987792969f, (float16_t)-0.202270508f, + (float16_t)-2.890625f, (float16_t)1.15136719f, (float16_t)0.90625f, (float16_t)-0.322753906f, + (float16_t)-0.162597656f, (float16_t)1.48144531f, (float16_t)-0.560058594f, (float16_t)-0.346191406f, + (float16_t)-1.71679688f, (float16_t)-0.141357422f, (float16_t)1.89257812f, (float16_t)0.280273438f, + (float16_t)-1.13671875f, (float16_t)2.63867188f, (float16_t)0.345947266f, (float16_t)-0.0146484375f, + (float16_t)-0.224243164f, (float16_t)-0.523925781f, (float16_t)-1.57714844f, (float16_t)-0.528808594f, + (float16_t)-0.0589599609f, (float16_t)-0.370361328f, (float16_t)2.07617188f, (float16_t)-0.505859375f, + (float16_t)-0.0492858887f, (float16_t)0.0737915039f, (float16_t)-0.148193359f, (float16_t)2.09179688f, + (float16_t)0.888671875f, (float16_t)-0.362060547f, (float16_t)-1.6796875f, (float16_t)0.624511719f, + (float16_t)-1.52734375f, (float16_t)0.493652344f, (float16_t)0.445068359f, (float16_t)-2.28320312f, + (float16_t)0.759765625f, (float16_t)0.552734375f, (float16_t)-0.205078125f, (float16_t)-0.0966186523f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/weights_data.h new file mode 100644 index 00000000..a8247c8b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f16/weights_data.h @@ -0,0 +1,74 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_f16_weights_data[288] = { + (float16_t)0.220336914f, (float16_t)-0.0808105469f, (float16_t)0.440429688f, (float16_t)0.224121094f, + (float16_t)-0.145751953f, (float16_t)-0.0248413086f, (float16_t)-0.372314453f, (float16_t)0.373535156f, + (float16_t)-0.255859375f, (float16_t)0.0248260498f, (float16_t)0.451660156f, (float16_t)0.307128906f, + (float16_t)0.178344727f, (float16_t)0.104492188f, (float16_t)-0.366455078f, (float16_t)0.713867188f, + (float16_t)-0.330078125f, (float16_t)-0.637695312f, (float16_t)-0.658203125f, (float16_t)0.063293457f, + (float16_t)0.343017578f, (float16_t)0.5f, (float16_t)0.411132812f, (float16_t)-0.240234375f, + (float16_t)0.237548828f, (float16_t)-0.0759277344f, (float16_t)0.176757812f, (float16_t)-0.0282440186f, + (float16_t)0.708496094f, (float16_t)-0.593261719f, (float16_t)0.521972656f, (float16_t)0.19543457f, + (float16_t)0.639648438f, (float16_t)-0.615234375f, (float16_t)-0.164916992f, (float16_t)0.610839844f, + (float16_t)-0.575683594f, (float16_t)-0.377685547f, (float16_t)0.360595703f, (float16_t)-0.326416016f, + (float16_t)0.161254883f, (float16_t)-0.274902344f, (float16_t)0.143066406f, (float16_t)0.28125f, + (float16_t)0.114624023f, (float16_t)-0.147827148f, (float16_t)-0.287597656f, (float16_t)-0.397216797f, + (float16_t)0.332275391f, (float16_t)-0.533203125f, (float16_t)-0.607910156f, (float16_t)0.396240234f, + (float16_t)0.456787109f, (float16_t)-0.706054688f, (float16_t)-0.328125f, (float16_t)0.295898438f, + (float16_t)0.213867188f, (float16_t)0.647460938f, (float16_t)-0.679199219f, (float16_t)-0.662597656f, + (float16_t)0.709472656f, (float16_t)-0.18347168f, (float16_t)-0.200561523f, (float16_t)0.451660156f, + (float16_t)0.610839844f, (float16_t)-0.661132812f, (float16_t)0.694824219f, (float16_t)-0.679199219f, + (float16_t)-0.437011719f, (float16_t)0.252929688f, (float16_t)-0.0734863281f, (float16_t)0.518066406f, + (float16_t)-0.0102081299f, (float16_t)-0.0950317383f, (float16_t)-0.260986328f, (float16_t)-0.347412109f, + (float16_t)0.033203125f, (float16_t)0.299560547f, (float16_t)0.0927124023f, (float16_t)0.581542969f, + (float16_t)-0.483398438f, (float16_t)0.318359375f, (float16_t)0.684082031f, (float16_t)-0.312011719f, + (float16_t)0.732910156f, (float16_t)0.475830078f, (float16_t)-0.528320312f, (float16_t)-0.483642578f, + (float16_t)-0.0225372314f, (float16_t)0.585449219f, (float16_t)0.391845703f, (float16_t)-0.216552734f, + (float16_t)-0.398193359f, (float16_t)0.141357422f, (float16_t)-0.715332031f, (float16_t)-0.400634766f, + (float16_t)0.483886719f, (float16_t)0.528320312f, (float16_t)-0.580078125f, (float16_t)0.348632812f, + (float16_t)0.0958251953f, (float16_t)0.648925781f, (float16_t)0.257080078f, (float16_t)-0.705566406f, + (float16_t)0.0981445312f, (float16_t)0.0933837891f, (float16_t)-0.545410156f, (float16_t)-0.248901367f, + (float16_t)-0.563964844f, (float16_t)0.591796875f, (float16_t)0.137695312f, (float16_t)0.100158691f, + (float16_t)0.5546875f, (float16_t)-0.175170898f, (float16_t)0.379882812f, (float16_t)0.160644531f, + (float16_t)0.477294922f, (float16_t)0.0577697754f, (float16_t)0.240112305f, (float16_t)0.496826172f, + (float16_t)-0.718261719f, (float16_t)0.678222656f, (float16_t)-0.072265625f, (float16_t)0.639160156f, + (float16_t)-0.167480469f, (float16_t)0.0941772461f, (float16_t)-0.270019531f, (float16_t)0.0936279297f, + (float16_t)0.51171875f, (float16_t)-0.0662231445f, (float16_t)-0.00576400757f, (float16_t)-0.348388672f, + (float16_t)-0.0536804199f, (float16_t)0.717773438f, (float16_t)0.495361328f, (float16_t)-0.431884766f, + (float16_t)0.114868164f, (float16_t)-0.356689453f, (float16_t)-0.213623047f, (float16_t)-0.0308837891f, + (float16_t)0.164306641f, (float16_t)0.109802246f, (float16_t)-0.18762207f, (float16_t)-0.570800781f, + (float16_t)0.334960938f, (float16_t)-0.177856445f, (float16_t)0.560058594f, (float16_t)0.149658203f, + (float16_t)-0.224365234f, (float16_t)-0.12890625f, (float16_t)0.165893555f, (float16_t)0.0585021973f, + (float16_t)-0.184692383f, (float16_t)-0.450439453f, (float16_t)-0.197509766f, (float16_t)-0.248779297f, + (float16_t)-0.269042969f, (float16_t)0.0772705078f, (float16_t)0.325683594f, (float16_t)-0.579101562f, + (float16_t)-0.59375f, (float16_t)-0.228149414f, (float16_t)0.69140625f, (float16_t)0.441162109f, + (float16_t)-0.483642578f, (float16_t)0.0412597656f, (float16_t)-0.303466797f, (float16_t)0.216552734f, + (float16_t)0.748046875f, (float16_t)-0.604980469f, (float16_t)-0.011428833f, (float16_t)-0.659667969f, + (float16_t)0.161621094f, (float16_t)0.230834961f, (float16_t)0.260986328f, (float16_t)-0.436279297f, + (float16_t)0.604003906f, (float16_t)0.261230469f, (float16_t)-0.0484008789f, (float16_t)-0.115661621f, + (float16_t)-0.00176811218f, (float16_t)0.620117188f, (float16_t)-0.179077148f, (float16_t)0.237548828f, + (float16_t)0.678710938f, (float16_t)0.279541016f, (float16_t)-0.330810547f, (float16_t)0.163208008f, + (float16_t)-0.723144531f, (float16_t)0.560058594f, (float16_t)0.372314453f, (float16_t)-0.138183594f, + (float16_t)-0.310302734f, (float16_t)0.431884766f, (float16_t)-0.61328125f, (float16_t)-0.464599609f, + (float16_t)-0.276123047f, (float16_t)0.280029297f, (float16_t)0.3671875f, (float16_t)0.355957031f, + (float16_t)-0.689941406f, (float16_t)0.592773438f, (float16_t)-0.739746094f, (float16_t)-0.404296875f, + (float16_t)-0.1640625f, (float16_t)-0.0624389648f, (float16_t)0.256835938f, (float16_t)-0.225585938f, + (float16_t)-0.262939453f, (float16_t)0.0834960938f, (float16_t)0.221801758f, (float16_t)-0.651855469f, + (float16_t)0.480224609f, (float16_t)0.699707031f, (float16_t)0.606933594f, (float16_t)0.329101562f, + (float16_t)-0.377685547f, (float16_t)-0.479492188f, (float16_t)0.546386719f, (float16_t)0.0285186768f, + (float16_t)0.505859375f, (float16_t)0.148925781f, (float16_t)0.286132812f, (float16_t)-0.272949219f, + (float16_t)0.259521484f, (float16_t)0.164306641f, (float16_t)0.541015625f, (float16_t)0.0517883301f, + (float16_t)-0.370117188f, (float16_t)-0.711425781f, (float16_t)-0.666015625f, (float16_t)-0.480224609f, + (float16_t)-0.654785156f, (float16_t)0.412353516f, (float16_t)-0.440917969f, (float16_t)-0.0794677734f, + (float16_t)0.244262695f, (float16_t)-0.159790039f, (float16_t)0.0507507324f, (float16_t)0.567382812f, + (float16_t)0.417480469f, (float16_t)-0.296630859f, (float16_t)0.436523438f, (float16_t)-0.499511719f, + (float16_t)-0.363037109f, (float16_t)0.128295898f, (float16_t)-0.337646484f, (float16_t)0.552734375f, + (float16_t)0.705566406f, (float16_t)0.643066406f, (float16_t)0.687011719f, (float16_t)-0.0542297363f, + (float16_t)-0.0879516602f, (float16_t)-0.124511719f, (float16_t)-0.722167969f, (float16_t)0.321533203f, + (float16_t)-0.258544922f, (float16_t)-0.528808594f, (float16_t)-0.1953125f, (float16_t)0.0125961304f, + (float16_t)0.312988281f, (float16_t)-0.461914062f, (float16_t)-0.0364379883f, (float16_t)-0.521484375f, + (float16_t)0.0883178711f, (float16_t)-0.270263672f, (float16_t)-0.115112305f, (float16_t)0.681640625f, + (float16_t)0.131835938f, (float16_t)-0.652832031f, (float16_t)0.0512695312f, (float16_t)-0.639160156f, + (float16_t)-0.321289062f, (float16_t)0.428222656f, (float16_t)0.556640625f, (float16_t)0.189575195f, + (float16_t)0.269042969f, (float16_t)0.598144531f, (float16_t)-0.741699219f, (float16_t)0.121765137f, + (float16_t)-0.580078125f, (float16_t)0.606445312f, (float16_t)0.649902344f, (float16_t)-0.708496094f, + (float16_t)0.267089844f, (float16_t)-0.515625f, (float16_t)0.413818359f, (float16_t)-0.434082031f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/biases_data.h new file mode 100644 index 00000000..1ae98e6d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/biases_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_f32_biases_data[12] = {0.183923975f, + 0.431868553f, + -0.246395811f, + -0.304440081f, + -0.108465716f, + 0.498667091f, + 0.332763344f, + 0.234872952f, + -0.354654133f, + 0.0284294281f, + -0.490387261f, + -0.479224324f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/config_data.h new file mode 100644 index 00000000..600e1198 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K3_OPT_F32_INPUT_BATCHES 1 +#define CONV_K3_OPT_F32_INPUT_H 1 +#define CONV_K3_OPT_F32_INPUT_W 21 +#define CONV_K3_OPT_F32_IN_CH 8 +#define CONV_K3_OPT_F32_FILTER_H 1 +#define CONV_K3_OPT_F32_FILTER_W 3 +#define CONV_K3_OPT_F32_OUT_CH 12 +#define CONV_K3_OPT_F32_STRIDE_H 1 +#define CONV_K3_OPT_F32_STRIDE_W 1 +#define CONV_K3_OPT_F32_PADDING_H 0 +#define CONV_K3_OPT_F32_PADDING_W 0 +#define CONV_K3_OPT_F32_DILATION_H 1 +#define CONV_K3_OPT_F32_DILATION_W 1 +#define CONV_K3_OPT_F32_OUTPUT_H 1 +#define CONV_K3_OPT_F32_OUTPUT_W 19 +#define CONV_K3_OPT_F32_OUTPUT_C 12 +#define CONV_K3_OPT_F32_DST_SIZE 228 +#define CONV_K3_OPT_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K3_OPT_F32_USE_WRAPPER 0 +#define CONV_K3_OPT_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_K3_OPT_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/input_data.h new file mode 100644 index 00000000..3150e175 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/input_data.h @@ -0,0 +1,26 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_f32_input_data[168] = { + 0.77751869f, 0.675213873f, 0.626878679f, 0.36128068f, -0.808851004f, 0.732550681f, -0.981978476f, + 0.805829942f, 0.921422482f, 0.912646532f, -0.498748302f, -0.903496325f, 0.457143575f, 0.710043907f, + 0.059493959f, 0.650170445f, 0.316225231f, -0.727150977f, -0.0827454776f, 0.35666135f, 0.128053933f, + 0.600674987f, 0.959253907f, -0.682955861f, -0.697486997f, 0.803426325f, -0.64062202f, -0.100454837f, + 0.278045237f, 0.448464602f, 0.359864146f, 0.532761216f, 0.385044038f, -0.5050264f, -0.63736558f, + 0.333463728f, 0.0309454724f, -0.451759666f, -0.060421057f, 0.662023246f, -0.695755064f, 0.526689827f, + -0.72516942f, 0.0786289945f, 0.801855266f, -0.141395792f, -0.955642283f, 0.966746211f, -0.349435031f, + 0.140187368f, -0.799881935f, 0.715706646f, 0.567541957f, -0.208370417f, 0.73131609f, 0.528374135f, + -0.656745076f, 0.146382689f, -0.503574729f, -0.0951860771f, 0.943331838f, -0.0531366765f, 0.937440932f, + -0.361525148f, 0.792878509f, 0.0883869305f, 0.761425614f, -0.197638735f, -0.861335933f, 0.571838379f, + -0.886030793f, -0.939111471f, -0.290134609f, 0.360504359f, 0.500403464f, 0.0686551407f, -0.574489653f, + 0.0815545842f, 0.188533902f, 0.712229669f, 0.867365777f, -0.119938403f, -0.0937847197f, 0.967217803f, + 0.391331881f, 0.454265296f, 0.935386598f, -0.611876845f, -0.926813602f, -0.732839823f, -0.863634288f, + -0.768484175f, -0.996344209f, -0.440384299f, -0.518569648f, -0.0674039572f, -0.784031332f, -0.369250625f, + 0.133018374f, 0.408105105f, 0.0519015752f, -0.960690439f, -0.871389449f, 0.797077358f, -0.181967035f, + 0.542756319f, 0.361249208f, 0.0783110633f, -0.408207923f, 0.54887712f, -0.482520074f, 0.633669376f, + 0.657785475f, 0.187156662f, 0.755624115f, -0.512353718f, -0.563353062f, 0.918643236f, -0.0824967697f, + -0.528502822f, -0.788207114f, -0.684408605f, 0.896306038f, 0.00155611476f, -0.279124767f, 0.915507078f, + -0.0589675792f, -0.182287589f, -0.0896928608f, -0.464642942f, 0.816494346f, -0.31265536f, 0.471719712f, + -0.202997625f, -0.377546847f, -0.569714308f, 0.228770837f, -0.408281863f, 0.00922409818f, -0.565828204f, + -0.833292663f, -0.813934267f, -0.830301642f, -0.79065311f, -0.735304475f, -0.174582049f, 0.245690495f, + -0.48654452f, -0.544159591f, 0.349419117f, -0.124167264f, -0.48115471f, -0.817874789f, 0.29599914f, + 0.976961851f, 0.604596317f, 0.429504216f, -0.569385707f, 0.790581107f, -0.102685928f, -0.616873145f, + -0.553071797f, 0.557001948f, -0.601223528f, 0.904960692f, 0.490031868f, -0.780468166f, -0.134571701f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/output_ref_data.h new file mode 100644 index 00000000..28f7373e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/output_ref_data.h @@ -0,0 +1,35 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_f32_output_ref_data[228] = { + 2.64565873f, -1.5995822f, 1.33461237f, -0.903452575f, 1.18785071f, 2.69037151f, -0.231548578f, + 1.89189541f, 0.33166796f, -2.89304566f, 2.27981639f, -1.85320592f, -0.280379772f, 0.926190376f, + -2.7980392f, 0.0446617007f, 0.844408631f, 0.726993084f, 0.468880773f, 1.00250185f, 1.42960906f, + -0.332423657f, -1.72890234f, -2.50387669f, 0.16968143f, -0.602068186f, 0.830949664f, -1.23438168f, + 1.97218871f, -0.452345043f, -0.00404918194f, 1.55140829f, 0.108909339f, -0.234170333f, -1.43549824f, + -0.809165835f, -0.214693055f, 0.76310426f, -1.46979713f, -1.07286215f, 0.26229924f, 0.57934761f, + -0.761964202f, -1.92886341f, 0.687495887f, 1.14510059f, -1.42773128f, -2.430902f, 2.29806566f, + -1.17439461f, 1.64615476f, -0.792737961f, -0.399870574f, -1.58986366f, -1.21085978f, 0.122442484f, + 0.537024975f, 0.121625349f, 0.138816059f, 0.475826383f, 1.64797187f, 0.286204636f, -0.0683745742f, + -1.59465241f, -0.818004251f, 1.34276867f, -1.76203334f, -2.14273357f, 1.84517407f, -0.972397029f, + -1.20842814f, -0.567467093f, -1.75223744f, 1.2365346f, 2.53132272f, 2.18137336f, -0.354555249f, + 1.84589791f, 0.790673256f, -0.16025646f, 0.57149893f, -2.86999488f, -3.56659555f, -0.854296565f, + -1.90216637f, 1.30000877f, -0.82964164f, 0.754438102f, 0.896754682f, 0.684638679f, 0.80865705f, + 1.22835076f, -0.379946381f, 2.71267843f, -1.16165257f, 0.913435102f, 2.21512961f, -0.770926952f, + -2.26291585f, -2.03844237f, 1.47490704f, 0.253782213f, -0.230575472f, 2.28941107f, -1.22335911f, + -1.23540056f, 0.979624212f, -1.00658035f, 0.505417824f, 1.41836679f, -1.55494273f, -0.419176072f, + -2.27696753f, 2.51360011f, 1.30186462f, -0.617336035f, -1.28073835f, -0.261523455f, 0.201091409f, + -1.92527139f, -1.1537298f, 1.27610302f, -0.421838373f, -1.59078312f, 2.2720778f, -0.944283485f, + 2.87125945f, -1.43942213f, -0.669866562f, 1.47117662f, -2.08295584f, -0.472657979f, 0.249519676f, + -1.22607601f, 0.452175796f, 0.673269689f, -2.11226821f, -2.0623219f, -0.133895814f, -0.484054983f, + 0.372625947f, 0.551994264f, -0.934708953f, -0.440357655f, 1.18375039f, -0.169895828f, 2.3647294f, + 1.17963028f, -1.61559522f, 1.20669603f, -0.303888828f, 0.524926245f, -0.455121428f, -1.2132082f, + -0.0223691463f, 1.2116884f, 0.896158516f, -0.233760536f, -0.859266937f, 1.06497288f, 0.0872486085f, + 2.83822918f, 2.03750205f, -0.00339052081f, 0.190196753f, -0.433800638f, 1.05664873f, -0.753395677f, + 0.76239109f, -0.184046984f, -2.97625518f, 1.11888766f, 0.759525061f, 0.724775195f, 1.67898858f, + 0.0814682841f, -0.982036471f, -0.854947984f, 0.544533312f, 0.548969626f, -0.190152273f, -0.0255882144f, + -1.36269248f, 1.97295856f, -2.53095412f, 1.21380651f, 0.98781395f, -0.202248231f, -2.89058995f, + 1.15088487f, 0.906456292f, -0.322752237f, -0.162590012f, 1.48166919f, -0.560168386f, -0.346174389f, + -1.71714485f, -0.141401142f, 1.89260817f, 0.280210137f, -1.13679194f, 2.63794637f, 0.345943868f, + -0.0146485567f, -0.224251643f, -0.523984075f, -1.57707095f, -0.528921783f, -0.0589482039f, -0.370245963f, + 2.07604361f, -0.50574708f, -0.0492822528f, 0.0738035813f, -0.148199797f, 2.09119511f, 0.888641298f, + -0.362072051f, -1.67986155f, 0.62469244f, -1.52751601f, 0.493770361f, 0.444985211f, -2.2825141f, + 0.759800851f, 0.552512586f, -0.205122203f, -0.0966432691f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/weights_data.h new file mode 100644 index 00000000..0a1593a9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_f32/weights_data.h @@ -0,0 +1,44 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_f32_weights_data[288] = { + 0.220302686f, -0.080832988f, 0.440371573f, 0.224085212f, -0.145735368f, -0.024836218f, -0.372368753f, + 0.373456538f, -0.255944908f, 0.0248313844f, 0.45161292f, 0.307021707f, 0.178288788f, 0.104468323f, + -0.366337001f, 0.713951349f, -0.329962581f, -0.637923419f, -0.658036649f, 0.063302651f, 0.342909932f, + 0.500172853f, 0.411222368f, -0.24025254f, 0.23758173f, -0.0759226158f, 0.176705346f, -0.0282497536f, + 0.708438694f, -0.593154013f, 0.521744668f, 0.195403591f, 0.639433444f, -0.615037143f, -0.164921552f, + 0.610835612f, -0.575513363f, -0.377585858f, 0.360559016f, -0.326461136f, 0.161226779f, -0.274984658f, + 0.143079177f, 0.281183332f, 0.114608943f, -0.147822991f, -0.287490815f, -0.397098958f, 0.332157433f, + -0.533360302f, -0.607832849f, 0.39631173f, 0.45674476f, -0.706255317f, -0.328080982f, 0.295842499f, + 0.213907838f, 0.647433758f, -0.678974867f, -0.662742734f, 0.709445059f, -0.183447033f, -0.20056437f, + 0.45162344f, 0.610946834f, -0.660955548f, 0.694750309f, -0.678995013f, -0.437118798f, 0.252836853f, + -0.0734869987f, 0.517958462f, -0.0102112461f, -0.0950321108f, -0.260979146f, -0.3474406f, 0.0332047492f, + 0.299563199f, 0.0926828086f, 0.581695497f, -0.483298838f, 0.318396211f, 0.684225559f, -0.31198898f, + 0.732868195f, 0.475771189f, -0.528227568f, -0.483751446f, -0.0225425474f, 0.585355163f, 0.391860843f, + -0.216530904f, -0.398293644f, 0.141390085f, -0.715225458f, -0.400749236f, 0.483945012f, 0.528298914f, + -0.579972386f, 0.348614812f, 0.0958165824f, 0.649059832f, 0.257081807f, -0.705361962f, 0.0981617197f, + 0.0934063569f, -0.545495808f, -0.248888016f, -0.564123929f, 0.591943562f, 0.137652293f, 0.100183532f, + 0.554513276f, -0.175115839f, 0.379909843f, 0.160619318f, 0.477367222f, 0.0577549264f, 0.240142152f, + 0.496751368f, -0.718138337f, 0.678141236f, -0.0722689927f, 0.639080107f, -0.167482182f, 0.0941514149f, + -0.270045459f, 0.0936359614f, 0.511488676f, -0.0661960244f, -0.00576517265f, -0.348499477f, -0.053672865f, + 0.717903733f, 0.495426863f, -0.431995869f, 0.11488153f, -0.356634825f, -0.213633657f, -0.0308899786f, + 0.164306015f, 0.109815314f, -0.187605366f, -0.570649505f, 0.33493492f, -0.177812397f, 0.56006521f, + 0.149669617f, -0.224347249f, -0.128878564f, 0.165921941f, 0.0585012883f, -0.184639618f, -0.450410277f, + -0.197483882f, -0.248758495f, -0.269142777f, 0.0772954598f, 0.325630188f, -0.579186916f, -0.593937993f, + -0.228197902f, 0.691438019f, 0.441138238f, -0.483626395f, 0.0412720479f, -0.303392738f, 0.216511473f, + 0.747920632f, -0.604948461f, -0.011429145f, -0.659689426f, 0.161639422f, 0.230858713f, 0.261032999f, + -0.436263382f, 0.604131401f, 0.261109293f, -0.0484019518f, -0.11568211f, -0.00176854141f, 0.61993283f, + -0.179115444f, 0.237540856f, 0.678834438f, 0.279641241f, -0.330745339f, 0.163229838f, -0.722916007f, + 0.559883595f, 0.37234652f, -0.138195187f, -0.310201555f, 0.431868523f, -0.613144994f, -0.46453765f, + -0.276165068f, 0.28009218f, 0.367164463f, 0.355865568f, -0.690054655f, 0.593005002f, -0.739677668f, + -0.404361486f, -0.164068893f, -0.0624436103f, 0.25683406f, -0.225640178f, -0.263061047f, 0.0835252255f, + 0.22182858f, -0.651927829f, 0.480277032f, 0.699582875f, 0.607065499f, 0.329147279f, -0.377590775f, + -0.479442716f, 0.546163142f, 0.0285121333f, 0.505834579f, 0.148883745f, 0.286238253f, -0.272940397f, + 0.259575188f, 0.164301336f, 0.540848672f, 0.0517803319f, -0.370223761f, -0.711224198f, -0.665847778f, + -0.480261177f, -0.655012906f, 0.412415355f, -0.440830976f, -0.0794470161f, 0.244219139f, -0.159748495f, + 0.050746996f, 0.567156434f, 0.41758132f, -0.296516418f, 0.436584532f, -0.499597013f, -0.363056511f, + 0.128301486f, -0.337561667f, 0.552594304f, 0.705688298f, 0.642992377f, 0.6869331f, -0.0542186163f, + -0.0879600793f, -0.124516629f, -0.722177982f, 0.321579218f, -0.258593053f, -0.528588593f, -0.195283547f, + 0.0125947623f, 0.313089192f, -0.46192652f, -0.0364418551f, -0.521677375f, 0.0882946998f, -0.270157337f, + -0.115105763f, 0.681876957f, 0.131812587f, -0.652954936f, 0.0512635373f, -0.639015436f, -0.321332037f, + 0.428290695f, 0.556497455f, 0.189605191f, 0.269006342f, 0.598182619f, -0.741457999f, 0.121747471f, + -0.579959691f, 0.606203318f, 0.64989233f, -0.708614111f, 0.267191082f, -0.515722036f, 0.413835913f, + -0.434024006f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/biases_data.h new file mode 100644 index 00000000..7a4b7675 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_nhwc_tuned_f16_biases_data[16] = {(float16_t)0.0875244141f, + (float16_t)0.246704102f, + (float16_t)0.232055664f, + (float16_t)-0.0953979492f, + (float16_t)-0.487792969f, + (float16_t)-0.101745605f, + (float16_t)0.188598633f, + (float16_t)0.0686645508f, + (float16_t)0.32421875f, + (float16_t)-0.372314453f, + (float16_t)0.0589294434f, + (float16_t)0.437744141f, + (float16_t)-0.396972656f, + (float16_t)-0.387695312f, + (float16_t)0.161621094f, + (float16_t)0.394042969f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/config_data.h new file mode 100644 index 00000000..0d68ba4b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K3_OPT_NHWC_TUNED_F16_INPUT_BATCHES 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_INPUT_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_INPUT_W 21 +#define CONV_K3_OPT_NHWC_TUNED_F16_IN_CH 16 +#define CONV_K3_OPT_NHWC_TUNED_F16_FILTER_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_FILTER_W 3 +#define CONV_K3_OPT_NHWC_TUNED_F16_OUT_CH 16 +#define CONV_K3_OPT_NHWC_TUNED_F16_STRIDE_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_STRIDE_W 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_PADDING_H 0 +#define CONV_K3_OPT_NHWC_TUNED_F16_PADDING_W 0 +#define CONV_K3_OPT_NHWC_TUNED_F16_DILATION_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_DILATION_W 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_OUTPUT_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_OUTPUT_W 19 +#define CONV_K3_OPT_NHWC_TUNED_F16_OUTPUT_C 16 +#define CONV_K3_OPT_NHWC_TUNED_F16_DST_SIZE 304 +#define CONV_K3_OPT_NHWC_TUNED_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K3_OPT_NHWC_TUNED_F16_USE_WRAPPER 1 +#define CONV_K3_OPT_NHWC_TUNED_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_K3_OPT_NHWC_TUNED_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/input_data.h new file mode 100644 index 00000000..2a2a9635 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/input_data.h @@ -0,0 +1,86 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_nhwc_tuned_f16_input_data[336] = { + (float16_t)0.280517578f, (float16_t)-0.415771484f, (float16_t)-0.039642334f, (float16_t)-0.691894531f, + (float16_t)0.59765625f, (float16_t)-0.994140625f, (float16_t)-0.817871094f, (float16_t)0.583984375f, + (float16_t)0.195800781f, (float16_t)-0.127075195f, (float16_t)0.409423828f, (float16_t)-0.734375f, + (float16_t)-0.474121094f, (float16_t)-0.0639648438f, (float16_t)-0.895507812f, (float16_t)-0.290771484f, + (float16_t)-0.418212891f, (float16_t)-0.732421875f, (float16_t)0.692382812f, (float16_t)-0.124633789f, + (float16_t)-0.952148438f, (float16_t)-0.789550781f, (float16_t)-0.795410156f, (float16_t)-0.774902344f, + (float16_t)0.700195312f, (float16_t)0.465087891f, (float16_t)-0.693359375f, (float16_t)0.850585938f, + (float16_t)-0.392089844f, (float16_t)-0.286376953f, (float16_t)0.612792969f, (float16_t)-0.436767578f, + (float16_t)0.832519531f, (float16_t)0.233642578f, (float16_t)-0.705566406f, (float16_t)0.90234375f, + (float16_t)-0.881347656f, (float16_t)0.81640625f, (float16_t)0.9140625f, (float16_t)-0.0830078125f, + (float16_t)0.119934082f, (float16_t)0.145751953f, (float16_t)-0.858886719f, (float16_t)0.185546875f, + (float16_t)0.970214844f, (float16_t)-0.365722656f, (float16_t)-0.113098145f, (float16_t)-0.780273438f, + (float16_t)0.325683594f, (float16_t)-0.159179688f, (float16_t)-0.374511719f, (float16_t)0.0626831055f, + (float16_t)-0.779785156f, (float16_t)0.530761719f, (float16_t)-0.285888672f, (float16_t)0.808105469f, + (float16_t)-0.568847656f, (float16_t)-0.533203125f, (float16_t)-0.788085938f, (float16_t)-0.690917969f, + (float16_t)-0.118896484f, (float16_t)0.450683594f, (float16_t)0.698730469f, (float16_t)-0.212036133f, + (float16_t)0.0982055664f, (float16_t)-0.151000977f, (float16_t)-0.753417969f, (float16_t)-0.997558594f, + (float16_t)-0.134155273f, (float16_t)-0.384033203f, (float16_t)0.543457031f, (float16_t)0.265380859f, + (float16_t)-0.0581054688f, (float16_t)0.125366211f, (float16_t)0.397949219f, (float16_t)-0.197631836f, + (float16_t)0.481201172f, (float16_t)0.1953125f, (float16_t)-0.854980469f, (float16_t)-0.904785156f, + (float16_t)-0.89453125f, (float16_t)0.671386719f, (float16_t)-0.956054688f, (float16_t)-0.783691406f, + (float16_t)-0.979492188f, (float16_t)0.477539062f, (float16_t)-0.206420898f, (float16_t)-0.326171875f, + (float16_t)0.0569152832f, (float16_t)-0.165161133f, (float16_t)0.109436035f, (float16_t)-0.570800781f, + (float16_t)-0.806152344f, (float16_t)0.197509766f, (float16_t)0.0419921875f, (float16_t)0.860839844f, + (float16_t)0.160644531f, (float16_t)-0.367919922f, (float16_t)-0.801269531f, (float16_t)0.11138916f, + (float16_t)-0.197998047f, (float16_t)-0.552246094f, (float16_t)0.172973633f, (float16_t)0.181396484f, + (float16_t)-0.877441406f, (float16_t)-0.997070312f, (float16_t)0.412353516f, (float16_t)-0.0543518066f, + (float16_t)-0.35546875f, (float16_t)0.81640625f, (float16_t)-0.801757812f, (float16_t)0.0383605957f, + (float16_t)0.0955200195f, (float16_t)-0.299804688f, (float16_t)-0.770507812f, (float16_t)-0.325683594f, + (float16_t)0.561035156f, (float16_t)-0.364990234f, (float16_t)-0.331298828f, (float16_t)0.983398438f, + (float16_t)-0.0401916504f, (float16_t)0.369384766f, (float16_t)-0.879394531f, (float16_t)0.665527344f, + (float16_t)-0.126098633f, (float16_t)-0.0264587402f, (float16_t)0.361816406f, (float16_t)0.940429688f, + (float16_t)-0.628417969f, (float16_t)0.638183594f, (float16_t)-0.297119141f, (float16_t)0.713867188f, + (float16_t)0.874023438f, (float16_t)0.168823242f, (float16_t)-0.756835938f, (float16_t)0.933105469f, + (float16_t)-0.135253906f, (float16_t)-0.641113281f, (float16_t)-0.577148438f, (float16_t)0.908203125f, + (float16_t)-0.674804688f, (float16_t)-0.228027344f, (float16_t)0.706542969f, (float16_t)-0.185791016f, + (float16_t)-0.0863037109f, (float16_t)0.111450195f, (float16_t)0.0539550781f, (float16_t)0.376953125f, + (float16_t)0.269042969f, (float16_t)-0.296875f, (float16_t)0.529296875f, (float16_t)-0.997558594f, + (float16_t)-0.229370117f, (float16_t)0.76171875f, (float16_t)0.172119141f, (float16_t)-0.0506591797f, + (float16_t)0.85546875f, (float16_t)0.142456055f, (float16_t)0.330078125f, (float16_t)0.330566406f, + (float16_t)0.238647461f, (float16_t)0.8828125f, (float16_t)0.979980469f, (float16_t)0.289550781f, + (float16_t)0.7890625f, (float16_t)-0.447509766f, (float16_t)-0.552246094f, (float16_t)0.295898438f, + (float16_t)0.0220031738f, (float16_t)0.677246094f, (float16_t)-0.774414062f, (float16_t)0.0386047363f, + (float16_t)-0.734863281f, (float16_t)0.314453125f, (float16_t)-0.1015625f, (float16_t)-0.660644531f, + (float16_t)0.409667969f, (float16_t)0.150512695f, (float16_t)-0.531738281f, (float16_t)0.172607422f, + (float16_t)-0.629882812f, (float16_t)-0.572753906f, (float16_t)-0.217651367f, (float16_t)-0.453125f, + (float16_t)-0.429199219f, (float16_t)-0.754394531f, (float16_t)0.670898438f, (float16_t)-0.529785156f, + (float16_t)-0.746582031f, (float16_t)-0.0441894531f, (float16_t)-0.789550781f, (float16_t)-0.0349731445f, + (float16_t)-0.245483398f, (float16_t)-0.865722656f, (float16_t)0.0301818848f, (float16_t)-0.364501953f, + (float16_t)0.326416016f, (float16_t)0.500488281f, (float16_t)0.962402344f, (float16_t)0.684570312f, + (float16_t)0.611816406f, (float16_t)0.0974731445f, (float16_t)0.197265625f, (float16_t)0.0426025391f, + (float16_t)-0.971191406f, (float16_t)-0.664550781f, (float16_t)0.184814453f, (float16_t)-0.482421875f, + (float16_t)-0.665527344f, (float16_t)0.00298309326f, (float16_t)-0.00397491455f, (float16_t)-0.652832031f, + (float16_t)-0.510742188f, (float16_t)-0.66796875f, (float16_t)0.625976562f, (float16_t)-0.0794067383f, + (float16_t)-0.89453125f, (float16_t)0.254882812f, (float16_t)-0.153808594f, (float16_t)-0.833496094f, + (float16_t)-0.104309082f, (float16_t)0.776367188f, (float16_t)0.807128906f, (float16_t)0.576171875f, + (float16_t)0.215332031f, (float16_t)0.831054688f, (float16_t)0.720214844f, (float16_t)0.601074219f, + (float16_t)-0.620605469f, (float16_t)0.849609375f, (float16_t)-0.303466797f, (float16_t)0.632324219f, + (float16_t)0.463623047f, (float16_t)-0.179443359f, (float16_t)0.384033203f, (float16_t)0.246337891f, + (float16_t)-0.226806641f, (float16_t)0.972167969f, (float16_t)0.0115280151f, (float16_t)0.362304688f, + (float16_t)0.665527344f, (float16_t)-0.7421875f, (float16_t)0.329101562f, (float16_t)-0.96484375f, + (float16_t)0.388427734f, (float16_t)-0.788574219f, (float16_t)-0.796875f, (float16_t)0.0679931641f, + (float16_t)-0.030960083f, (float16_t)0.478759766f, (float16_t)0.367919922f, (float16_t)0.362548828f, + (float16_t)-0.231567383f, (float16_t)-0.416748047f, (float16_t)0.700683594f, (float16_t)0.487304688f, + (float16_t)-0.408691406f, (float16_t)-0.391113281f, (float16_t)0.512695312f, (float16_t)-0.413818359f, + (float16_t)-0.325195312f, (float16_t)-0.541992188f, (float16_t)0.228759766f, (float16_t)-0.299072266f, + (float16_t)0.162231445f, (float16_t)0.434570312f, (float16_t)-0.00235939026f, (float16_t)0.0736694336f, + (float16_t)-0.681640625f, (float16_t)-0.688964844f, (float16_t)0.612304688f, (float16_t)0.0856933594f, + (float16_t)0.746582031f, (float16_t)0.977050781f, (float16_t)-0.805175781f, (float16_t)-0.709472656f, + (float16_t)0.213256836f, (float16_t)-0.962890625f, (float16_t)-0.556640625f, (float16_t)-0.964355469f, + (float16_t)-0.832519531f, (float16_t)0.279296875f, (float16_t)-0.520507812f, (float16_t)0.391845703f, + (float16_t)0.655761719f, (float16_t)-0.407714844f, (float16_t)-0.952636719f, (float16_t)0.0115890503f, + (float16_t)0.844726562f, (float16_t)0.389160156f, (float16_t)0.374511719f, (float16_t)-0.151855469f, + (float16_t)-0.884277344f, (float16_t)0.690429688f, (float16_t)0.520996094f, (float16_t)0.0655517578f, + (float16_t)-0.115478516f, (float16_t)-0.0274047852f, (float16_t)0.654785156f, (float16_t)-0.380859375f, + (float16_t)-0.307861328f, (float16_t)-0.547851562f, (float16_t)-0.787597656f, (float16_t)-0.0916137695f, + (float16_t)0.0629882812f, (float16_t)-0.801269531f, (float16_t)0.384521484f, (float16_t)0.321533203f, + (float16_t)-0.990234375f, (float16_t)-0.0114440918f, (float16_t)-0.247680664f, (float16_t)-0.177856445f, + (float16_t)-0.0520019531f, (float16_t)0.215087891f, (float16_t)0.7109375f, (float16_t)0.70703125f, + (float16_t)0.556152344f, (float16_t)0.0563964844f, (float16_t)-0.166870117f, (float16_t)0.989257812f, + (float16_t)0.399414062f, (float16_t)-0.423339844f, (float16_t)0.472167969f, (float16_t)0.612304688f, + (float16_t)0.8515625f, (float16_t)0.542480469f, (float16_t)0.987792969f, (float16_t)-0.435546875f, + (float16_t)0.185913086f, (float16_t)-0.986816406f, (float16_t)0.466552734f, (float16_t)0.810058594f, + (float16_t)0.5546875f, (float16_t)-0.63671875f, (float16_t)-0.171264648f, (float16_t)0.0227661133f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/output_ref_data.h new file mode 100644 index 00000000..5984db42 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/output_ref_data.h @@ -0,0 +1,78 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_nhwc_tuned_f16_output_ref_data[304] = { + (float16_t)1.53027344f, (float16_t)1.64453125f, (float16_t)-2.2421875f, (float16_t)-1.72851562f, + (float16_t)-2.8515625f, (float16_t)-1.08300781f, (float16_t)1.08984375f, (float16_t)1.87304688f, + (float16_t)1.33203125f, (float16_t)-0.173217773f, (float16_t)-2.81445312f, (float16_t)0.916503906f, + (float16_t)-2.21484375f, (float16_t)-0.561523438f, (float16_t)-0.046875f, (float16_t)-1.5f, + (float16_t)0.697265625f, (float16_t)2.07421875f, (float16_t)-1.12695312f, (float16_t)1.9296875f, + (float16_t)-4.5234375f, (float16_t)-0.71484375f, (float16_t)1.14550781f, (float16_t)2.75976562f, + (float16_t)-1.41015625f, (float16_t)3.109375f, (float16_t)1.80566406f, (float16_t)-1.6953125f, + (float16_t)-0.351806641f, (float16_t)-0.0129241943f, (float16_t)1.06933594f, (float16_t)-0.413085938f, + (float16_t)4.0703125f, (float16_t)-0.249511719f, (float16_t)1.89453125f, (float16_t)2.54101562f, + (float16_t)0.0770263672f, (float16_t)-2.05078125f, (float16_t)-2.875f, (float16_t)0.429931641f, + (float16_t)-0.239379883f, (float16_t)1.70703125f, (float16_t)-1.23828125f, (float16_t)-2.13671875f, + (float16_t)0.99609375f, (float16_t)-0.121765137f, (float16_t)2.30664062f, (float16_t)1.109375f, + (float16_t)2.54492188f, (float16_t)-0.274169922f, (float16_t)0.291748047f, (float16_t)1.93847656f, + (float16_t)0.719238281f, (float16_t)-0.387939453f, (float16_t)1.97460938f, (float16_t)1.68261719f, + (float16_t)-3.109375f, (float16_t)-1.33398438f, (float16_t)1.67578125f, (float16_t)0.967773438f, + (float16_t)2.84570312f, (float16_t)-0.0130462646f, (float16_t)-1.33984375f, (float16_t)0.323242188f, + (float16_t)2.43945312f, (float16_t)1.70996094f, (float16_t)-2.93359375f, (float16_t)1.80566406f, + (float16_t)1.71289062f, (float16_t)-1.21191406f, (float16_t)-0.292236328f, (float16_t)-0.193725586f, + (float16_t)-0.689941406f, (float16_t)2.76367188f, (float16_t)1.18359375f, (float16_t)-2.5078125f, + (float16_t)0.67578125f, (float16_t)-0.547363281f, (float16_t)0.149291992f, (float16_t)2.69335938f, + (float16_t)-0.967773438f, (float16_t)0.547851562f, (float16_t)-0.687011719f, (float16_t)0.66796875f, + (float16_t)-0.0701904297f, (float16_t)-2.40820312f, (float16_t)0.516113281f, (float16_t)0.434570312f, + (float16_t)-0.447998047f, (float16_t)-0.303466797f, (float16_t)2.4375f, (float16_t)1.92675781f, + (float16_t)-0.44921875f, (float16_t)0.923339844f, (float16_t)0.525878906f, (float16_t)1.95410156f, + (float16_t)-1.37304688f, (float16_t)4.3984375f, (float16_t)1.67285156f, (float16_t)-3.63671875f, + (float16_t)0.981445312f, (float16_t)-2.66796875f, (float16_t)2.08203125f, (float16_t)0.164672852f, + (float16_t)1.09179688f, (float16_t)0.793457031f, (float16_t)0.460449219f, (float16_t)1.84765625f, + (float16_t)1.54882812f, (float16_t)2.98242188f, (float16_t)0.466552734f, (float16_t)-0.893066406f, + (float16_t)-0.0877075195f, (float16_t)-0.991210938f, (float16_t)2.21484375f, (float16_t)-4.1171875f, + (float16_t)2.32421875f, (float16_t)1.78027344f, (float16_t)-1.32519531f, (float16_t)-4.53125f, + (float16_t)2.86328125f, (float16_t)-0.170776367f, (float16_t)-3.80859375f, (float16_t)-0.997070312f, + (float16_t)1.73046875f, (float16_t)-3.83203125f, (float16_t)-1.76367188f, (float16_t)-0.295410156f, + (float16_t)2.25976562f, (float16_t)0.271972656f, (float16_t)0.6953125f, (float16_t)-1.57617188f, + (float16_t)-2.13671875f, (float16_t)-1.71679688f, (float16_t)-1.17382812f, (float16_t)-0.627441406f, + (float16_t)-0.911621094f, (float16_t)-0.89453125f, (float16_t)-0.0386047363f, (float16_t)-1.33300781f, + (float16_t)-1.50683594f, (float16_t)-1.08203125f, (float16_t)1.16894531f, (float16_t)-0.181030273f, + (float16_t)0.537597656f, (float16_t)0.319091797f, (float16_t)1.6171875f, (float16_t)-0.443115234f, + (float16_t)-0.496826172f, (float16_t)0.567382812f, (float16_t)-1.44726562f, (float16_t)-0.196044922f, + (float16_t)2.21679688f, (float16_t)-1.80175781f, (float16_t)-1.23828125f, (float16_t)0.794921875f, + (float16_t)-2.17382812f, (float16_t)-4.2578125f, (float16_t)-0.452636719f, (float16_t)0.68359375f, + (float16_t)-1.5f, (float16_t)-2.02734375f, (float16_t)-3.21679688f, (float16_t)-0.884277344f, + (float16_t)-2.76757812f, (float16_t)1.29003906f, (float16_t)1.12207031f, (float16_t)-0.254882812f, + (float16_t)-1.21191406f, (float16_t)1.65722656f, (float16_t)3.1328125f, (float16_t)1.63085938f, + (float16_t)0.952636719f, (float16_t)-0.310546875f, (float16_t)2.22851562f, (float16_t)0.832519531f, + (float16_t)0.660644531f, (float16_t)0.114013672f, (float16_t)-0.0408325195f, (float16_t)1.37109375f, + (float16_t)0.544921875f, (float16_t)-1.29199219f, (float16_t)-0.322265625f, (float16_t)1.72265625f, + (float16_t)-0.286132812f, (float16_t)0.837890625f, (float16_t)0.583984375f, (float16_t)2.09179688f, + (float16_t)1.17480469f, (float16_t)-0.557128906f, (float16_t)-0.372070312f, (float16_t)-1.09863281f, + (float16_t)3.85742188f, (float16_t)-0.557617188f, (float16_t)-0.731445312f, (float16_t)-0.778320312f, + (float16_t)-2.14453125f, (float16_t)-3.05273438f, (float16_t)1.27832031f, (float16_t)0.41015625f, + (float16_t)-2.34960938f, (float16_t)2.00195312f, (float16_t)1.23046875f, (float16_t)-0.852539062f, + (float16_t)1.31738281f, (float16_t)-1.42285156f, (float16_t)2.921875f, (float16_t)2.37304688f, + (float16_t)-1.36816406f, (float16_t)0.909667969f, (float16_t)4.26171875f, (float16_t)-1.91308594f, + (float16_t)0.321777344f, (float16_t)2.87695312f, (float16_t)-0.447509766f, (float16_t)-1.72363281f, + (float16_t)4.2890625f, (float16_t)-2.16601562f, (float16_t)0.609863281f, (float16_t)2.67578125f, + (float16_t)-2.19921875f, (float16_t)-0.37890625f, (float16_t)-1.61425781f, (float16_t)1.390625f, + (float16_t)-0.228881836f, (float16_t)-0.249267578f, (float16_t)0.0407409668f, (float16_t)0.436523438f, + (float16_t)0.155761719f, (float16_t)-1.09277344f, (float16_t)1.88085938f, (float16_t)-0.157714844f, + (float16_t)-1.63964844f, (float16_t)-0.707519531f, (float16_t)0.90234375f, (float16_t)1.85449219f, + (float16_t)1.19042969f, (float16_t)1.86328125f, (float16_t)0.309326172f, (float16_t)-0.122070312f, + (float16_t)4.7265625f, (float16_t)-1.22363281f, (float16_t)-1.68554688f, (float16_t)0.155639648f, + (float16_t)-2.59570312f, (float16_t)2.38867188f, (float16_t)-2.43359375f, (float16_t)1.34277344f, + (float16_t)1.96875f, (float16_t)1.06347656f, (float16_t)-0.0241851807f, (float16_t)0.746582031f, + (float16_t)-0.65625f, (float16_t)-1.0546875f, (float16_t)-0.977539062f, (float16_t)-1.51269531f, + (float16_t)1.08007812f, (float16_t)-1.78417969f, (float16_t)-1.37988281f, (float16_t)-0.943847656f, + (float16_t)-2.90234375f, (float16_t)1.48828125f, (float16_t)2.82617188f, (float16_t)0.482421875f, + (float16_t)-2.33398438f, (float16_t)0.232788086f, (float16_t)1.95800781f, (float16_t)0.7734375f, + (float16_t)1.33984375f, (float16_t)-1.59960938f, (float16_t)1.47558594f, (float16_t)2.22851562f, + (float16_t)-0.714355469f, (float16_t)-0.740722656f, (float16_t)-0.895996094f, (float16_t)1.2421875f, + (float16_t)-0.724609375f, (float16_t)3.203125f, (float16_t)-3.171875f, (float16_t)-0.713378906f, + (float16_t)1.07421875f, (float16_t)-2.47460938f, (float16_t)-1.49707031f, (float16_t)2.41992188f, + (float16_t)-0.552734375f, (float16_t)-0.382080078f, (float16_t)-4.3359375f, (float16_t)0.668457031f, + (float16_t)-1.88378906f, (float16_t)0.293212891f, (float16_t)-1.90625f, (float16_t)0.67578125f, + (float16_t)1.76464844f, (float16_t)-0.676757812f, (float16_t)1.32421875f, (float16_t)0.911621094f, + (float16_t)-1.24902344f, (float16_t)-1.17773438f, (float16_t)0.346679688f, (float16_t)2.80078125f, + (float16_t)-1.28417969f, (float16_t)2.79492188f, (float16_t)1.20117188f, (float16_t)2.63867188f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/weights_data.h new file mode 100644 index 00000000..a6073f80 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f16/weights_data.h @@ -0,0 +1,194 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k3_opt_nhwc_tuned_f16_weights_data[768] = { + (float16_t)0.284423828f, (float16_t)-0.375244141f, (float16_t)-0.629394531f, (float16_t)0.589355469f, + (float16_t)-0.512695312f, (float16_t)-0.118347168f, (float16_t)-0.136108398f, (float16_t)0.559082031f, + (float16_t)0.485839844f, (float16_t)0.192993164f, (float16_t)-0.735351562f, (float16_t)-0.457519531f, + (float16_t)-0.260986328f, (float16_t)-0.579589844f, (float16_t)0.329589844f, (float16_t)-0.704101562f, + (float16_t)-0.289306641f, (float16_t)0.427490234f, (float16_t)0.451171875f, (float16_t)-0.532714844f, + (float16_t)-0.494628906f, (float16_t)-0.555664062f, (float16_t)0.32421875f, (float16_t)0.00868988037f, + (float16_t)-0.294433594f, (float16_t)0.173461914f, (float16_t)-0.345703125f, (float16_t)-0.631835938f, + (float16_t)-0.0719604492f, (float16_t)-0.530761719f, (float16_t)0.622558594f, (float16_t)0.161132812f, + (float16_t)0.572265625f, (float16_t)0.62109375f, (float16_t)-0.145263672f, (float16_t)-0.614746094f, + (float16_t)-0.497558594f, (float16_t)-0.622070312f, (float16_t)-0.0501708984f, (float16_t)-0.413085938f, + (float16_t)-0.231079102f, (float16_t)-0.0364990234f, (float16_t)-0.560058594f, (float16_t)0.536132812f, + (float16_t)-0.223022461f, (float16_t)0.534179688f, (float16_t)-0.549316406f, (float16_t)0.0272521973f, + (float16_t)-0.0738525391f, (float16_t)-0.749511719f, (float16_t)-0.25390625f, (float16_t)-0.275390625f, + (float16_t)0.156982422f, (float16_t)-0.0451965332f, (float16_t)-0.206298828f, (float16_t)-0.304199219f, + (float16_t)0.247680664f, (float16_t)-0.551757812f, (float16_t)-0.163208008f, (float16_t)-0.568359375f, + (float16_t)-0.0726318359f, (float16_t)0.701660156f, (float16_t)-0.541015625f, (float16_t)0.460449219f, + (float16_t)0.624023438f, (float16_t)-0.505371094f, (float16_t)-0.0608215332f, (float16_t)0.5703125f, + (float16_t)-0.239135742f, (float16_t)0.305419922f, (float16_t)-0.549316406f, (float16_t)-0.136962891f, + (float16_t)0.319091797f, (float16_t)0.265625f, (float16_t)-0.504882812f, (float16_t)-0.21472168f, + (float16_t)0.64453125f, (float16_t)0.606445312f, (float16_t)-0.0361328125f, (float16_t)0.573730469f, + (float16_t)-0.690917969f, (float16_t)-0.181030273f, (float16_t)-0.216308594f, (float16_t)0.553710938f, + (float16_t)-0.20300293f, (float16_t)-0.227783203f, (float16_t)-0.415771484f, (float16_t)-0.133666992f, + (float16_t)-0.734375f, (float16_t)-0.277587891f, (float16_t)-0.10925293f, (float16_t)0.3359375f, + (float16_t)-0.430908203f, (float16_t)0.525878906f, (float16_t)0.104370117f, (float16_t)-0.720214844f, + (float16_t)-0.702636719f, (float16_t)-0.396484375f, (float16_t)-0.600097656f, (float16_t)0.508300781f, + (float16_t)-0.0425720215f, (float16_t)-0.22668457f, (float16_t)0.381835938f, (float16_t)-0.298828125f, + (float16_t)-0.634277344f, (float16_t)-0.645507812f, (float16_t)0.0702514648f, (float16_t)-0.223022461f, + (float16_t)0.198974609f, (float16_t)-0.638671875f, (float16_t)0.744628906f, (float16_t)-0.372070312f, + (float16_t)0.333007812f, (float16_t)-0.0818481445f, (float16_t)0.733886719f, (float16_t)0.449462891f, + (float16_t)0.597167969f, (float16_t)0.607421875f, (float16_t)-0.718261719f, (float16_t)0.403808594f, + (float16_t)-0.0577392578f, (float16_t)-0.541503906f, (float16_t)-0.248168945f, (float16_t)0.239257812f, + (float16_t)0.424560547f, (float16_t)0.650390625f, (float16_t)0.567871094f, (float16_t)0.307128906f, + (float16_t)-0.734375f, (float16_t)-0.0607910156f, (float16_t)-0.138671875f, (float16_t)-0.308837891f, + (float16_t)-0.465576172f, (float16_t)0.121520996f, (float16_t)-0.607910156f, (float16_t)-0.738769531f, + (float16_t)0.0344543457f, (float16_t)0.734863281f, (float16_t)0.301025391f, (float16_t)0.409912109f, + (float16_t)-0.325439453f, (float16_t)-0.236328125f, (float16_t)0.435546875f, (float16_t)-0.318847656f, + (float16_t)0.141601562f, (float16_t)0.393310547f, (float16_t)0.314208984f, (float16_t)-0.0988769531f, + (float16_t)-0.654785156f, (float16_t)0.225463867f, (float16_t)0.305175781f, (float16_t)-0.0860595703f, + (float16_t)0.417724609f, (float16_t)0.322509766f, (float16_t)0.00785827637f, (float16_t)0.167602539f, + (float16_t)0.732421875f, (float16_t)-0.748535156f, (float16_t)-0.626953125f, (float16_t)0.118408203f, + (float16_t)0.0249786377f, (float16_t)-0.494628906f, (float16_t)0.302001953f, (float16_t)-0.741699219f, + (float16_t)-0.587402344f, (float16_t)0.024887085f, (float16_t)0.694335938f, (float16_t)-0.745605469f, + (float16_t)-0.346191406f, (float16_t)-0.635253906f, (float16_t)0.379638672f, (float16_t)0.240112305f, + (float16_t)0.525390625f, (float16_t)0.354003906f, (float16_t)0.0381469727f, (float16_t)-0.524414062f, + (float16_t)-0.704589844f, (float16_t)-0.435302734f, (float16_t)-0.301269531f, (float16_t)-0.409667969f, + (float16_t)-0.326660156f, (float16_t)-0.426025391f, (float16_t)-0.524902344f, (float16_t)-0.300292969f, + 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(float16_t)0.169677734f, (float16_t)0.581542969f, + (float16_t)-0.0182189941f, (float16_t)-0.404785156f, (float16_t)0.392333984f, (float16_t)0.250976562f, + (float16_t)0.440429688f, (float16_t)-0.509765625f, (float16_t)0.478759766f, (float16_t)-0.383056641f, + (float16_t)0.298828125f, (float16_t)-0.565917969f, (float16_t)-0.140136719f, (float16_t)0.572265625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/biases_data.h new file mode 100644 index 00000000..90e51382 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_nhwc_tuned_f32_biases_data[16] = {0.0875532851f, + 0.246729478f, + 0.232106999f, + -0.0954281315f, + -0.487854511f, + -0.101723924f, + 0.188553303f, + 0.0686930865f, + 0.324132204f, + -0.372270167f, + 0.0589236729f, + 0.437671781f, + -0.397066295f, + -0.387584776f, + 0.161635935f, + 0.393978953f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/config_data.h new file mode 100644 index 00000000..bb452c84 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K3_OPT_NHWC_TUNED_F32_INPUT_BATCHES 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_INPUT_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_INPUT_W 21 +#define CONV_K3_OPT_NHWC_TUNED_F32_IN_CH 16 +#define CONV_K3_OPT_NHWC_TUNED_F32_FILTER_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_FILTER_W 3 +#define CONV_K3_OPT_NHWC_TUNED_F32_OUT_CH 16 +#define CONV_K3_OPT_NHWC_TUNED_F32_STRIDE_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_STRIDE_W 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_PADDING_H 0 +#define CONV_K3_OPT_NHWC_TUNED_F32_PADDING_W 0 +#define CONV_K3_OPT_NHWC_TUNED_F32_DILATION_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_DILATION_W 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_OUTPUT_H 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_OUTPUT_W 19 +#define CONV_K3_OPT_NHWC_TUNED_F32_OUTPUT_C 16 +#define CONV_K3_OPT_NHWC_TUNED_F32_DST_SIZE 304 +#define CONV_K3_OPT_NHWC_TUNED_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K3_OPT_NHWC_TUNED_F32_USE_WRAPPER 1 +#define CONV_K3_OPT_NHWC_TUNED_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_K3_OPT_NHWC_TUNED_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/input_data.h new file mode 100644 index 00000000..e0ea8964 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/input_data.h @@ -0,0 +1,50 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_nhwc_tuned_f32_input_data[336] = { + 0.280406028f, -0.41579169f, -0.039656993f, -0.692074478f, 0.597459197f, -0.993997693f, -0.818069637f, + 0.583810389f, 0.195784703f, -0.12701489f, 0.409405142f, -0.734450042f, -0.474081635f, -0.0639407933f, + -0.895422041f, -0.290811241f, -0.418187439f, -0.732289314f, 0.692164421f, -0.124611735f, -0.952301323f, + -0.789338112f, -0.795450032f, -0.77485013f, 0.699972272f, 0.465193748f, -0.693450868f, 0.850418389f, + -0.392020226f, -0.286314636f, 0.612553716f, -0.436750621f, 0.832545757f, 0.233689085f, -0.705543697f, + 0.902380884f, -0.881212473f, 0.816404164f, 0.914151251f, -0.0829929933f, 0.119955927f, 0.145805329f, + -0.858862519f, 0.185582623f, 0.970430553f, -0.365715265f, -0.113089003f, -0.780068517f, 0.325575083f, + -0.15921779f, -0.374494672f, 0.0626829788f, -0.779604614f, 0.530949116f, -0.285910249f, 0.807998061f, + -0.568689883f, -0.533398211f, -0.788327932f, -0.690888882f, -0.118908562f, 0.450739443f, 0.698689282f, + -0.212015495f, 0.0981829688f, -0.150975809f, -0.753403544f, -0.997470498f, -0.134189531f, -0.383950233f, + 0.543453872f, 0.26533258f, -0.0580971874f, 0.125423655f, 0.398024499f, -0.19767417f, 0.48129487f, + 0.195364952f, -0.855065584f, -0.904577672f, -0.894531965f, 0.67127192f, -0.956058502f, -0.783908129f, + -0.979694307f, 0.477655709f, -0.206433237f, -0.32616812f, 0.056912899f, -0.165213913f, 0.109458528f, + -0.57101357f, -0.806016207f, 0.197467729f, 0.0420074202f, 0.860893965f, 0.160629869f, -0.367848217f, + -0.801061511f, 0.111412019f, -0.198012248f, -0.552314401f, 0.172967032f, 0.181438431f, -0.877462149f, + -0.997139215f, 0.412385195f, -0.0543541647f, -0.355466962f, 0.816537857f, -0.801796257f, 0.0383559763f, + 0.0955217928f, -0.299699754f, -0.770522356f, -0.325796038f, 0.561138511f, -0.364995152f, -0.331250012f, + 0.983508646f, -0.0402031094f, 0.369402319f, -0.879274786f, 0.665408611f, -0.1260591f, -0.0264532473f, + 0.361897677f, 0.940212727f, -0.628535748f, 0.6381073f, -0.297128499f, 0.714109242f, 0.873823643f, + 0.168841437f, -0.756613493f, 0.933091223f, -0.135208622f, -0.641290128f, -0.577371538f, 0.908016086f, + -0.674692988f, -0.228026286f, 0.706638932f, -0.185739383f, -0.0863124356f, 0.111427084f, 0.053963352f, + 0.376973122f, 0.269149363f, -0.296800166f, 0.529381573f, -0.997408271f, -0.229377478f, 0.761639714f, + 0.172163531f, -0.0506489538f, 0.855342865f, 0.142505422f, 0.330196083f, 0.330458522f, 0.23870416f, + 0.882655025f, 0.979881287f, 0.289536923f, 0.789077282f, -0.447510034f, -0.552392602f, 0.295913815f, + 0.0220063031f, 0.677167416f, -0.774657309f, 0.0386053212f, -0.734855413f, 0.314427495f, -0.10159196f, + -0.66068548f, 0.409687281f, 0.150550157f, -0.531946719f, 0.172580227f, -0.629844308f, -0.572933793f, + -0.217710108f, -0.453121126f, -0.429204911f, -0.754528463f, 0.67088306f, -0.529907823f, -0.746765971f, + -0.0441869386f, -0.789765537f, -0.0349620655f, -0.245536894f, -0.865591824f, 0.0301746912f, -0.364556462f, + 0.32647115f, 0.500435889f, 0.962343991f, 0.684809685f, 0.611678183f, 0.0974743888f, 0.19728449f, + 0.0426000729f, -0.971266448f, -0.664598942f, 0.184859574f, -0.48239547f, -0.66575098f, 0.00298353401f, + -0.00397304306f, -0.652808964f, -0.510764837f, -0.667946398f, 0.626121283f, -0.079394497f, -0.894476116f, + 0.254887581f, -0.15376316f, -0.833403409f, -0.104323827f, 0.776602685f, 0.806988597f, 0.576261163f, + 0.215290248f, 0.830903709f, 0.720305622f, 0.600966394f, -0.62066853f, 0.849794865f, -0.303532213f, + 0.632482111f, 0.463562042f, -0.179456398f, 0.384073853f, 0.246347651f, -0.226817772f, 0.97205925f, + 0.0115265567f, 0.362367809f, 0.665385485f, -0.741967976f, 0.329207301f, -0.96499753f, 0.388395458f, + -0.788500428f, -0.796682f, 0.0679677352f, -0.0309567582f, 0.478658676f, 0.367963433f, 0.362484872f, + -0.231597915f, -0.416703314f, 0.700760603f, 0.487410069f, -0.408618927f, -0.391204566f, 0.512530148f, + -0.413817346f, -0.325140953f, -0.541831732f, 0.228739113f, -0.299026906f, 0.162170693f, 0.434500843f, + -0.00235892762f, 0.073690787f, -0.681735873f, -0.689128816f, 0.612333417f, 0.0856647193f, 0.746393323f, + 0.976857722f, -0.805099249f, -0.709574103f, 0.213207096f, -0.963124394f, -0.556797445f, -0.96454823f, + -0.832680702f, 0.279360592f, -0.520476937f, 0.391953021f, 0.655631721f, -0.407684743f, -0.95246768f, + 0.0115881572f, 0.844690859f, 0.389270097f, 0.374408931f, -0.151830137f, -0.884449959f, 0.690331757f, + 0.520866454f, 0.0655434653f, -0.115505062f, -0.0273975525f, 0.654735208f, -0.380845517f, -0.307825625f, + -0.54772377f, -0.787380695f, -0.0915990472f, 0.0629651546f, -0.801386893f, 0.384538054f, 0.321501553f, + -0.990357816f, -0.0114404839f, -0.247621119f, -0.177859098f, -0.0519905686f, 0.21512723f, 0.711142659f, + 0.707130075f, 0.556282341f, 0.0564090163f, -0.16685459f, 0.989264786f, 0.399315327f, -0.423393965f, + 0.472046345f, 0.612531662f, 0.851796269f, 0.542538941f, 0.987583637f, -0.435458481f, 0.18591772f, + -0.986596644f, 0.466664046f, 0.809869051f, 0.554588914f, -0.636808336f, -0.171290725f, 0.0227674991f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/output_ref_data.h new file mode 100644 index 00000000..32c3b71d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/output_ref_data.h @@ -0,0 +1,46 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_nhwc_tuned_f32_output_ref_data[304] = { + 1.53057206f, 1.64420342f, -2.2420454f, -1.72849f, -2.85152197f, -1.08262825f, 1.08991957f, + 1.87298608f, 1.332479f, -0.173223615f, -2.81368494f, 0.916721761f, -2.21557879f, -0.561570406f, + -0.0468804836f, -1.49969387f, 0.697296917f, 2.07326341f, -1.12738478f, 1.92959249f, -4.52422094f, + -0.715031028f, 1.14512789f, 2.76039028f, -1.40981078f, 3.11015129f, 1.80581081f, -1.69495189f, + -0.351881385f, -0.0129257143f, 1.06925738f, -0.413027048f, 4.07049656f, -0.249452963f, 1.89480793f, + 2.54139876f, 0.0770300329f, -2.05007124f, -2.87463522f, 0.429818928f, -0.239352942f, 1.70735955f, + -1.23829091f, -2.13575172f, 0.996034443f, -0.121737272f, 2.30623603f, 1.10974622f, 2.54407358f, + -0.274107993f, 0.291859895f, 1.93799531f, 0.719474554f, -0.387978554f, 1.97458112f, 1.68252468f, + -3.1097548f, -1.33439255f, 1.67622304f, 0.96768719f, 2.8464613f, -0.0130441487f, -1.33976865f, + 0.32336244f, 2.4393847f, 1.70987868f, -2.933213f, 1.80609345f, 1.71304965f, -1.2119838f, + -0.29214409f, -0.19370766f, -0.690047264f, 2.76454115f, 1.18392575f, -2.50697184f, 0.67582804f, + -0.547353148f, 0.149271354f, 2.6943078f, -0.967962265f, 0.547896743f, -0.687243402f, 0.667773902f, + -0.0702160895f, -2.40829277f, 0.516002893f, 0.43446821f, -0.448081076f, -0.303580612f, 2.4378655f, + 1.92667437f, -0.449100524f, 0.923428416f, 0.526028931f, 1.95419133f, -1.37298f, 4.39770174f, + 1.67284369f, -3.63654709f, 0.981563807f, -2.66742921f, 2.0823586f, 0.164726347f, 1.0913558f, + 0.793400705f, 0.460476995f, 1.84759009f, 1.54855943f, 2.98162413f, 0.466652036f, -0.892989635f, + -0.0876924768f, -0.991312861f, 2.21396637f, -4.11809778f, 2.32330894f, 1.78038633f, -1.32538855f, + -4.5319829f, 2.86237335f, -0.170790866f, -3.80804372f, -0.996909261f, 1.7302103f, -3.8311553f, + -1.76331711f, -0.295407414f, 2.26041293f, 0.272033811f, 0.695285797f, -1.57655668f, -2.13604093f, + -1.71631753f, -1.17412019f, -0.627679527f, -0.91179812f, -0.894711375f, -0.0385937504f, -1.33283913f, + -1.50670791f, -1.08212018f, 1.16937304f, -0.181074023f, 0.537581801f, 0.319099724f, 1.61745203f, + -0.443037331f, -0.496820331f, 0.567450464f, -1.44727528f, -0.196039334f, 2.21642733f, -1.80151486f, + -1.23852873f, 0.795082927f, -2.17383099f, -4.25875425f, -0.452663064f, 0.683641791f, -1.49988568f, + -2.02772832f, -3.21718693f, -0.884050071f, -2.76769161f, 1.29013216f, 1.12159443f, -0.254941881f, + -1.21143389f, 1.65718985f, 3.13228512f, 1.63077092f, 0.952710807f, -0.310522288f, 2.22773075f, + 0.832628787f, 0.660687685f, 0.113999367f, -0.0408466607f, 1.37154377f, 0.544787884f, -1.2922157f, + -0.322360188f, 1.72249877f, -0.286044717f, 0.837714255f, 0.584213197f, 2.09180784f, 1.17507362f, + -0.557310343f, -0.372133195f, -1.09853911f, 3.85797215f, -0.557415068f, -0.731344461f, -0.77835238f, + -2.14528942f, -3.05370069f, 1.27848673f, 0.410106301f, -2.34934187f, 2.00196123f, 1.2306366f, + -0.852355957f, 1.31740212f, -1.42324293f, 2.92106843f, 2.37391543f, -1.36835158f, 0.909829021f, + 4.26140738f, -1.9126811f, 0.321767718f, 2.87637258f, -0.447508007f, -1.72372139f, 4.28769398f, + -2.16622996f, 0.609700143f, 2.67536783f, -2.19854069f, -0.379021257f, -1.61406302f, 1.39077878f, + -0.228885055f, -0.249268487f, 0.0407510698f, 0.436530858f, 0.155799598f, -1.0927825f, 1.88128138f, + -0.157691419f, -1.64008951f, -0.70731771f, 0.902105987f, 1.85496461f, 1.19066024f, 1.86372888f, + 0.309209585f, -0.122076273f, 4.7283392f, -1.22338867f, -1.68507349f, 0.155621588f, -2.59583497f, + 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mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/weights_data.h new file mode 100644 index 00000000..07802120 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k3_opt_nhwc_tuned_f32/weights_data.h @@ -0,0 +1,112 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k3_opt_nhwc_tuned_f32_weights_data[768] = { + 0.284478098f, -0.375200838f, -0.629363418f, 0.589217603f, -0.51277566f, -0.118354216f, -0.136104956f, + 0.558842242f, 0.485905915f, 0.192969322f, -0.735422969f, -0.457439899f, 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-0.286446273f, 0.141594648f, -0.354312301f, -0.743910968f, + -0.699397147f, 0.113691181f, 0.215722978f, -0.747729003f, -0.528973043f, 0.643277287f, 0.455814511f, + 0.0748862103f, -0.711157858f, -0.698928237f, -0.665020049f, 0.321960628f, 0.420031846f, -0.105211258f, + -0.686552882f, 0.684011757f, -0.175289556f, 0.366741836f, 0.547900379f, 0.385799766f, -0.123218529f, + -0.641648173f, 0.0849070624f, 0.0478237942f, 0.00515118288f, 0.540436983f, 0.249739289f, -0.188739419f, + -0.302388906f, -0.34291476f, -0.352800369f, 0.643620789f, 0.340590447f, -0.741748035f, -0.280319303f, + 0.657361984f, -0.509726405f, -0.395189375f, -0.427070469f, 0.538810909f, 0.472506911f, -0.0918845534f, + -0.0242875982f, 0.337155819f, 0.322212398f, -0.346809298f, -0.428583205f, 0.25833717f, 0.48016879f, + -0.615723729f, -0.326501876f, 0.708859503f, -0.512741208f, 0.449292988f, -0.21408546f, -0.286059201f, + -0.0457699001f, 0.237246737f, -0.182449505f, 0.128990039f, 0.370978057f, -0.722340405f, 0.359355092f, + -0.518034637f, 0.25990817f, 0.171721622f, 0.563014686f, 0.0823568404f, 0.169712052f, 0.581750929f, + -0.0182254128f, -0.40484947f, 0.392348856f, 0.250896245f, 0.440399051f, -0.509922266f, 0.478727371f, + -0.382979244f, 0.29886049f, -0.566115618f, -0.14013052f, 0.572141409f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/biases_data.h new file mode 100644 index 00000000..755d241f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/biases_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_f16_biases_data[12] = {(float16_t)0.370361328f, + (float16_t)-0.0276947021f, + (float16_t)-0.333740234f, + (float16_t)-0.143188477f, + (float16_t)-0.0529785156f, + (float16_t)0.00524139404f, + (float16_t)0.107666016f, + (float16_t)-0.155151367f, + (float16_t)0.0354003906f, + (float16_t)0.436279297f, + (float16_t)-0.195922852f, + (float16_t)0.226806641f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/config_data.h new file mode 100644 index 00000000..e77b869b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K5_OPT_F16_INPUT_BATCHES 1 +#define CONV_K5_OPT_F16_INPUT_H 1 +#define CONV_K5_OPT_F16_INPUT_W 21 +#define CONV_K5_OPT_F16_IN_CH 8 +#define CONV_K5_OPT_F16_FILTER_H 1 +#define CONV_K5_OPT_F16_FILTER_W 5 +#define CONV_K5_OPT_F16_OUT_CH 12 +#define CONV_K5_OPT_F16_STRIDE_H 1 +#define CONV_K5_OPT_F16_STRIDE_W 1 +#define CONV_K5_OPT_F16_PADDING_H 0 +#define CONV_K5_OPT_F16_PADDING_W 0 +#define CONV_K5_OPT_F16_DILATION_H 1 +#define CONV_K5_OPT_F16_DILATION_W 1 +#define CONV_K5_OPT_F16_OUTPUT_H 1 +#define CONV_K5_OPT_F16_OUTPUT_W 17 +#define CONV_K5_OPT_F16_OUTPUT_C 12 +#define CONV_K5_OPT_F16_DST_SIZE 204 +#define CONV_K5_OPT_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K5_OPT_F16_USE_WRAPPER 0 +#define CONV_K5_OPT_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_K5_OPT_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/input_data.h new file mode 100644 index 00000000..b7afb04f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/input_data.h @@ -0,0 +1,44 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_f16_input_data[168] = { + (float16_t)-0.410644531f, (float16_t)0.580078125f, (float16_t)-0.180908203f, (float16_t)0.842773438f, + (float16_t)-0.482910156f, (float16_t)-0.264648438f, (float16_t)-0.51953125f, (float16_t)0.898925781f, + (float16_t)0.762207031f, (float16_t)0.293212891f, (float16_t)-0.031463623f, (float16_t)-0.454589844f, + (float16_t)-0.863769531f, (float16_t)0.342285156f, (float16_t)0.182250977f, (float16_t)0.768066406f, + (float16_t)0.302734375f, (float16_t)0.706054688f, (float16_t)-0.94140625f, (float16_t)0.0303955078f, + (float16_t)0.624511719f, (float16_t)0.594726562f, (float16_t)0.517578125f, (float16_t)-0.402587891f, + (float16_t)0.997070312f, (float16_t)-0.786621094f, (float16_t)-0.15222168f, (float16_t)-0.390625f, + (float16_t)-0.239990234f, (float16_t)0.094543457f, (float16_t)0.0810546875f, (float16_t)0.0789794922f, + (float16_t)0.964355469f, (float16_t)0.83203125f, (float16_t)0.246582031f, (float16_t)0.547363281f, + (float16_t)0.896972656f, (float16_t)0.932128906f, (float16_t)-0.141845703f, (float16_t)0.257568359f, + (float16_t)-0.715820312f, (float16_t)-0.230712891f, (float16_t)0.422607422f, (float16_t)-0.168823242f, + (float16_t)-0.836425781f, (float16_t)0.460449219f, (float16_t)0.293457031f, (float16_t)-0.954589844f, + (float16_t)-0.592285156f, (float16_t)0.0966796875f, (float16_t)0.724609375f, (float16_t)-0.11895752f, + (float16_t)0.662109375f, (float16_t)-0.992675781f, (float16_t)-0.0669555664f, (float16_t)0.110778809f, + (float16_t)-0.5234375f, (float16_t)-0.836914062f, (float16_t)-0.241210938f, (float16_t)-0.079284668f, + (float16_t)-0.328125f, (float16_t)0.606445312f, (float16_t)0.591308594f, (float16_t)-0.281982422f, + (float16_t)-0.46875f, (float16_t)-0.373779297f, (float16_t)0.0448608398f, (float16_t)-0.534667969f, + (float16_t)0.517089844f, (float16_t)-0.650878906f, (float16_t)0.270019531f, (float16_t)-0.445556641f, + (float16_t)0.421630859f, (float16_t)-0.989746094f, (float16_t)0.930175781f, (float16_t)0.704101562f, + (float16_t)0.320800781f, (float16_t)0.447265625f, (float16_t)0.560058594f, (float16_t)0.5078125f, + (float16_t)-0.962402344f, (float16_t)-0.899414062f, (float16_t)0.0294799805f, (float16_t)-0.395751953f, + (float16_t)-0.755371094f, (float16_t)0.276123047f, (float16_t)0.908203125f, (float16_t)0.159545898f, + (float16_t)-0.745605469f, (float16_t)-0.551757812f, (float16_t)-0.282958984f, (float16_t)-0.985351562f, + (float16_t)-0.00723266602f, (float16_t)0.494140625f, (float16_t)0.0303955078f, (float16_t)-0.9765625f, + (float16_t)-0.297607422f, (float16_t)0.805664062f, (float16_t)0.940917969f, (float16_t)0.26953125f, + (float16_t)0.999511719f, (float16_t)0.611328125f, (float16_t)0.651367188f, (float16_t)-0.443847656f, + (float16_t)-0.61328125f, (float16_t)0.471923828f, (float16_t)0.944824219f, (float16_t)-0.343994141f, + (float16_t)0.537109375f, (float16_t)-0.595703125f, (float16_t)0.741210938f, (float16_t)0.0952758789f, + (float16_t)0.0012254715f, (float16_t)0.314941406f, (float16_t)0.74609375f, (float16_t)0.9921875f, + (float16_t)-0.329589844f, (float16_t)-0.380615234f, (float16_t)-0.351806641f, (float16_t)0.799804688f, + (float16_t)-0.223632812f, (float16_t)-0.53125f, (float16_t)-0.362792969f, (float16_t)0.777832031f, + (float16_t)0.888671875f, (float16_t)0.544433594f, (float16_t)-0.923828125f, (float16_t)-0.9921875f, + (float16_t)-0.969238281f, (float16_t)-0.518066406f, (float16_t)-0.483398438f, (float16_t)-0.375976562f, + (float16_t)0.428222656f, (float16_t)0.52734375f, (float16_t)0.501953125f, (float16_t)-0.568847656f, + (float16_t)-0.158935547f, (float16_t)0.547851562f, (float16_t)0.702636719f, (float16_t)-0.733886719f, + (float16_t)0.90625f, (float16_t)-0.055267334f, (float16_t)-0.589355469f, (float16_t)0.678710938f, + (float16_t)-0.3984375f, (float16_t)-0.896972656f, (float16_t)-0.360351562f, (float16_t)0.886230469f, + (float16_t)0.292724609f, (float16_t)0.961425781f, (float16_t)0.865722656f, (float16_t)0.645996094f, + (float16_t)0.842285156f, (float16_t)-0.0720214844f, (float16_t)0.327880859f, (float16_t)0.418457031f, + (float16_t)-0.132324219f, (float16_t)-0.737304688f, (float16_t)-0.651367188f, (float16_t)0.377929688f, + (float16_t)0.947265625f, (float16_t)-0.418212891f, (float16_t)0.901367188f, (float16_t)-0.0431213379f, + (float16_t)0.767578125f, (float16_t)0.0434875488f, (float16_t)0.92578125f, (float16_t)-0.462890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/output_ref_data.h new file mode 100644 index 00000000..6da3a10f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/output_ref_data.h @@ -0,0 +1,53 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_f16_output_ref_data[204] = { + (float16_t)-1.57714844f, (float16_t)1.16699219f, (float16_t)-2.01367188f, (float16_t)1.24902344f, + (float16_t)2.91992188f, (float16_t)1.29199219f, (float16_t)1.45214844f, (float16_t)-1.35253906f, + (float16_t)2.36523438f, (float16_t)6.640625f, (float16_t)-2.1015625f, (float16_t)0.915039062f, + (float16_t)0.895507812f, (float16_t)-0.213745117f, (float16_t)-3.0f, (float16_t)0.950683594f, + (float16_t)-1.48828125f, (float16_t)0.942382812f, (float16_t)1.55175781f, (float16_t)0.771484375f, + (float16_t)0.606445312f, (float16_t)-1.43554688f, (float16_t)1.16308594f, (float16_t)-0.619140625f, + (float16_t)0.794433594f, (float16_t)2.34375f, (float16_t)-0.240234375f, (float16_t)0.712890625f, + (float16_t)-3.36523438f, (float16_t)0.880859375f, (float16_t)0.173950195f, (float16_t)3.1484375f, + (float16_t)2.33789062f, (float16_t)-0.588378906f, (float16_t)0.944824219f, (float16_t)1.95800781f, + (float16_t)1.25f, (float16_t)1.66503906f, (float16_t)-0.405761719f, (float16_t)1.88964844f, + (float16_t)-0.542480469f, (float16_t)0.608886719f, (float16_t)-1.55664062f, (float16_t)0.0614929199f, + (float16_t)1.20703125f, (float16_t)-1.18359375f, (float16_t)1.39746094f, (float16_t)0.877441406f, + (float16_t)0.504882812f, (float16_t)0.316162109f, (float16_t)0.497558594f, (float16_t)0.220214844f, + (float16_t)2.34765625f, (float16_t)-1.73828125f, (float16_t)-0.711425781f, (float16_t)0.642089844f, + (float16_t)-0.375244141f, (float16_t)-2.55859375f, (float16_t)-0.403564453f, (float16_t)1.21777344f, + (float16_t)0.482666016f, (float16_t)0.294921875f, (float16_t)-0.104370117f, (float16_t)0.00898742676f, + (float16_t)-1.61523438f, (float16_t)0.00392150879f, (float16_t)1.0859375f, (float16_t)0.343017578f, + (float16_t)0.365966797f, (float16_t)0.220336914f, (float16_t)-0.356689453f, (float16_t)0.0299072266f, + (float16_t)-0.592285156f, (float16_t)-1.83007812f, (float16_t)0.44921875f, (float16_t)-1.75878906f, + (float16_t)1.85742188f, (float16_t)-2.06445312f, (float16_t)-0.0749511719f, (float16_t)-1.59765625f, + (float16_t)-2.94335938f, (float16_t)-0.706542969f, (float16_t)-1.48925781f, (float16_t)2.24023438f, + (float16_t)0.627929688f, (float16_t)0.833496094f, (float16_t)0.426757812f, (float16_t)-0.885742188f, + (float16_t)-2.40820312f, (float16_t)-2.91796875f, (float16_t)0.203857422f, (float16_t)3.42382812f, + (float16_t)-0.022567749f, (float16_t)-2.02148438f, (float16_t)-0.528320312f, (float16_t)-0.0429382324f, + (float16_t)1.86328125f, (float16_t)0.621582031f, (float16_t)-0.428710938f, (float16_t)0.950683594f, + (float16_t)-1.70410156f, (float16_t)-0.209960938f, (float16_t)0.375244141f, (float16_t)2.11132812f, + (float16_t)-0.459472656f, (float16_t)-0.200561523f, (float16_t)-1.83007812f, (float16_t)3.2109375f, + (float16_t)-2.515625f, (float16_t)1.42285156f, (float16_t)-0.632324219f, (float16_t)1.18164062f, + (float16_t)-0.559082031f, (float16_t)-2.56445312f, (float16_t)-2.85742188f, (float16_t)-0.345703125f, + (float16_t)1.42871094f, (float16_t)-1.33105469f, (float16_t)0.764160156f, (float16_t)4.625f, + (float16_t)-4.8359375f, (float16_t)4.20703125f, (float16_t)0.340087891f, (float16_t)-0.147827148f, + (float16_t)-2.21875f, (float16_t)-2.0390625f, (float16_t)-1.95507812f, (float16_t)0.938964844f, + (float16_t)1.6171875f, (float16_t)-0.0582885742f, (float16_t)2.55273438f, (float16_t)1.29296875f, + (float16_t)0.253173828f, (float16_t)0.845214844f, (float16_t)1.98144531f, (float16_t)-2.0703125f, + (float16_t)-0.34765625f, (float16_t)-2.51953125f, (float16_t)-0.178222656f, (float16_t)0.736816406f, + (float16_t)0.536621094f, (float16_t)-2.03320312f, (float16_t)0.0206604004f, (float16_t)-2.06054688f, + (float16_t)2.32226562f, (float16_t)-1.29101562f, (float16_t)-0.163452148f, (float16_t)-2.9375f, + (float16_t)-1.26855469f, (float16_t)-0.751953125f, (float16_t)-0.620605469f, (float16_t)1.90820312f, + (float16_t)-1.19238281f, (float16_t)-5.35546875f, (float16_t)1.46582031f, (float16_t)-1.15625f, + (float16_t)1.48242188f, (float16_t)0.220092773f, (float16_t)0.276367188f, (float16_t)-1.89648438f, + (float16_t)-1.91699219f, (float16_t)0.158691406f, (float16_t)-0.307373047f, (float16_t)-0.760742188f, + (float16_t)-0.113769531f, (float16_t)0.547363281f, (float16_t)-1.08496094f, (float16_t)-0.54296875f, + (float16_t)1.11523438f, (float16_t)1.06347656f, (float16_t)-2.50390625f, (float16_t)1.66308594f, + (float16_t)0.448974609f, (float16_t)0.267089844f, (float16_t)-0.283691406f, (float16_t)-1.26757812f, + (float16_t)-0.306152344f, (float16_t)1.3671875f, (float16_t)3.05078125f, (float16_t)0.1328125f, + (float16_t)-0.914550781f, (float16_t)-1.47265625f, (float16_t)0.411376953f, (float16_t)-2.71484375f, + (float16_t)1.77148438f, (float16_t)-0.843261719f, (float16_t)2.015625f, (float16_t)0.312744141f, + (float16_t)-1.00976562f, (float16_t)-1.6015625f, (float16_t)-0.66796875f, (float16_t)1.00976562f, + (float16_t)-1.52929688f, (float16_t)0.672851562f, (float16_t)0.602539062f, (float16_t)-0.693847656f, + (float16_t)-0.958007812f, (float16_t)-0.631835938f, (float16_t)2.0f, (float16_t)1.20507812f, + (float16_t)2.11132812f, (float16_t)0.0494384766f, (float16_t)-0.0436096191f, (float16_t)-2.26757812f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/weights_data.h new file mode 100644 index 00000000..d2c80547 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f16/weights_data.h @@ -0,0 +1,122 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_f16_weights_data[480] = { + (float16_t)0.680175781f, (float16_t)0.641113281f, (float16_t)-0.263671875f, (float16_t)-0.559082031f, + (float16_t)-0.0724487305f, (float16_t)-0.285888672f, (float16_t)-0.732910156f, (float16_t)-0.654296875f, + (float16_t)0.080078125f, (float16_t)0.248168945f, (float16_t)-0.170288086f, (float16_t)0.559082031f, + (float16_t)0.678222656f, (float16_t)-0.314453125f, (float16_t)0.51953125f, (float16_t)-0.468017578f, + (float16_t)-0.164428711f, (float16_t)-0.471679688f, (float16_t)-0.455322266f, (float16_t)0.306884766f, + (float16_t)-0.489501953f, (float16_t)0.0233459473f, (float16_t)-0.638183594f, (float16_t)0.184204102f, + (float16_t)-0.340332031f, (float16_t)-0.0952148438f, (float16_t)0.253662109f, (float16_t)-0.256591797f, + (float16_t)-0.502441406f, (float16_t)0.591308594f, (float16_t)-0.4609375f, (float16_t)-0.472412109f, + (float16_t)-0.246704102f, (float16_t)-0.169555664f, (float16_t)-0.524414062f, (float16_t)-0.479980469f, + (float16_t)0.510253906f, (float16_t)0.499023438f, (float16_t)-0.0450134277f, (float16_t)0.528808594f, + (float16_t)-0.609375f, (float16_t)-0.566894531f, (float16_t)0.578613281f, (float16_t)0.306884766f, + (float16_t)-0.564453125f, (float16_t)0.699707031f, (float16_t)0.275390625f, (float16_t)0.662109375f, + (float16_t)0.64453125f, (float16_t)0.33984375f, (float16_t)-0.280273438f, (float16_t)0.163574219f, + (float16_t)-0.0110702515f, (float16_t)0.730957031f, (float16_t)-0.376953125f, (float16_t)-0.633300781f, + (float16_t)-0.0759277344f, (float16_t)0.749023438f, (float16_t)-0.271728516f, (float16_t)0.474609375f, + (float16_t)-0.294433594f, (float16_t)0.301513672f, (float16_t)0.424804688f, (float16_t)0.0920410156f, + (float16_t)-0.500488281f, (float16_t)-0.108642578f, (float16_t)0.172485352f, (float16_t)-0.0468139648f, + (float16_t)-0.236450195f, (float16_t)-0.515625f, (float16_t)0.350341797f, (float16_t)-0.119995117f, + (float16_t)-0.60546875f, (float16_t)0.240356445f, (float16_t)0.662597656f, (float16_t)0.231933594f, + (float16_t)0.626953125f, (float16_t)-0.634277344f, (float16_t)0.0279083252f, (float16_t)-0.326660156f, + (float16_t)-0.519042969f, (float16_t)-0.116088867f, (float16_t)-0.359863281f, (float16_t)0.280517578f, + (float16_t)0.360595703f, (float16_t)0.0346374512f, (float16_t)0.577148438f, (float16_t)-0.425292969f, + (float16_t)-0.376464844f, (float16_t)0.321289062f, (float16_t)0.7421875f, (float16_t)0.642578125f, + (float16_t)-0.339599609f, (float16_t)-0.0773925781f, (float16_t)-0.655761719f, (float16_t)-0.140991211f, + (float16_t)-0.330322266f, (float16_t)0.34375f, (float16_t)-0.273925781f, (float16_t)0.0209655762f, + (float16_t)-0.342285156f, (float16_t)-0.205200195f, (float16_t)0.288574219f, (float16_t)0.0783081055f, + (float16_t)-0.122436523f, (float16_t)-0.3046875f, (float16_t)0.646484375f, (float16_t)0.615722656f, + (float16_t)-0.346191406f, (float16_t)-0.212036133f, (float16_t)0.689453125f, (float16_t)-0.0998535156f, + (float16_t)-0.0520019531f, (float16_t)0.140380859f, (float16_t)-0.0821533203f, (float16_t)-0.0353088379f, + (float16_t)-0.506347656f, (float16_t)-0.413330078f, (float16_t)-0.341064453f, (float16_t)-0.396484375f, + (float16_t)0.416748047f, (float16_t)-0.0704345703f, (float16_t)-0.168701172f, (float16_t)0.284912109f, + (float16_t)-0.417236328f, (float16_t)0.719238281f, (float16_t)-0.536132812f, (float16_t)0.548339844f, + (float16_t)0.741210938f, (float16_t)0.0872802734f, (float16_t)-0.204345703f, (float16_t)0.0148849487f, + (float16_t)-0.0122451782f, (float16_t)-0.124389648f, (float16_t)-0.305175781f, (float16_t)0.154541016f, + (float16_t)-0.383789062f, (float16_t)-0.336914062f, (float16_t)0.50390625f, (float16_t)-0.246826172f, + (float16_t)-0.218139648f, (float16_t)0.0833129883f, (float16_t)0.538574219f, (float16_t)-0.489257812f, + (float16_t)0.385986328f, (float16_t)0.23425293f, (float16_t)0.0698852539f, (float16_t)-0.547363281f, + (float16_t)-0.473388672f, (float16_t)0.125976562f, (float16_t)-0.0555419922f, (float16_t)-0.2890625f, + (float16_t)-0.455566406f, (float16_t)0.352783203f, (float16_t)0.216430664f, (float16_t)0.285400391f, + (float16_t)0.0601806641f, (float16_t)-0.27734375f, (float16_t)0.501953125f, (float16_t)0.336914062f, + (float16_t)-0.137207031f, (float16_t)0.334716797f, (float16_t)0.0826416016f, (float16_t)0.390625f, + (float16_t)0.677246094f, (float16_t)0.446044922f, (float16_t)-0.741699219f, (float16_t)0.50390625f, + (float16_t)0.250488281f, (float16_t)0.296875f, (float16_t)0.135864258f, (float16_t)-0.540039062f, + (float16_t)-0.52734375f, (float16_t)0.00924682617f, (float16_t)-0.137817383f, (float16_t)-0.232666016f, + (float16_t)-0.50390625f, (float16_t)-0.276367188f, (float16_t)0.517089844f, (float16_t)-0.666015625f, + (float16_t)-0.393554688f, (float16_t)-0.674316406f, (float16_t)-0.274658203f, (float16_t)-0.702148438f, + (float16_t)0.666503906f, (float16_t)-0.628417969f, (float16_t)0.00986480713f, (float16_t)0.116088867f, + (float16_t)-0.384521484f, (float16_t)2.92062759e-05f, (float16_t)0.041015625f, (float16_t)0.671875f, + (float16_t)0.70703125f, (float16_t)0.302001953f, (float16_t)-0.327392578f, (float16_t)0.1171875f, + (float16_t)-0.075378418f, (float16_t)0.454833984f, (float16_t)0.236206055f, (float16_t)0.390869141f, + (float16_t)0.151245117f, (float16_t)0.716308594f, (float16_t)-0.343017578f, (float16_t)-0.0650634766f, + (float16_t)0.154418945f, (float16_t)-0.246704102f, (float16_t)-0.594726562f, (float16_t)0.250244141f, + (float16_t)0.693359375f, (float16_t)0.534667969f, (float16_t)-0.13684082f, (float16_t)0.516601562f, + (float16_t)0.361816406f, (float16_t)-0.451904297f, (float16_t)-0.0162506104f, (float16_t)0.221801758f, + (float16_t)-0.202148438f, (float16_t)0.583496094f, (float16_t)-0.127197266f, (float16_t)0.220214844f, + (float16_t)-0.397705078f, (float16_t)0.20703125f, (float16_t)-0.727539062f, (float16_t)0.728515625f, + (float16_t)0.18762207f, (float16_t)0.524902344f, (float16_t)0.366943359f, (float16_t)-0.529296875f, + (float16_t)0.146362305f, (float16_t)0.182739258f, (float16_t)0.724121094f, (float16_t)-0.305419922f, + (float16_t)0.48046875f, (float16_t)-0.121337891f, (float16_t)0.514648438f, (float16_t)0.237915039f, + (float16_t)-0.319824219f, (float16_t)0.311767578f, (float16_t)0.368164062f, (float16_t)0.200317383f, + (float16_t)-0.11529541f, (float16_t)0.537597656f, (float16_t)0.138183594f, (float16_t)-0.412841797f, + (float16_t)0.616210938f, (float16_t)0.683105469f, (float16_t)-0.287353516f, (float16_t)-0.0487976074f, + (float16_t)0.215942383f, (float16_t)-0.272460938f, (float16_t)-0.338867188f, (float16_t)0.284179688f, + (float16_t)0.319335938f, (float16_t)-0.0325927734f, (float16_t)-0.158325195f, (float16_t)0.472900391f, + (float16_t)0.518066406f, (float16_t)-0.517578125f, (float16_t)-0.473632812f, (float16_t)-0.487060547f, + (float16_t)0.571777344f, (float16_t)0.124328613f, (float16_t)0.682128906f, (float16_t)0.592285156f, + (float16_t)0.14453125f, (float16_t)-0.302734375f, (float16_t)-0.575195312f, (float16_t)0.0583190918f, + (float16_t)0.0489501953f, (float16_t)0.0502319336f, (float16_t)0.0222167969f, (float16_t)-0.027053833f, + (float16_t)0.734375f, (float16_t)-0.369628906f, (float16_t)0.347167969f, (float16_t)0.204833984f, + (float16_t)-0.699707031f, (float16_t)0.529785156f, (float16_t)-0.506347656f, (float16_t)-0.103393555f, + (float16_t)-0.253173828f, (float16_t)0.537597656f, (float16_t)-0.725585938f, (float16_t)0.294189453f, + (float16_t)-0.361083984f, (float16_t)0.306884766f, (float16_t)0.534667969f, (float16_t)-0.343994141f, + (float16_t)0.429199219f, (float16_t)-0.623535156f, (float16_t)0.561035156f, (float16_t)-0.125610352f, + (float16_t)0.312011719f, (float16_t)0.522949219f, (float16_t)0.659667969f, (float16_t)-0.552734375f, + (float16_t)0.336181641f, (float16_t)-0.201782227f, (float16_t)0.400390625f, (float16_t)0.745117188f, + (float16_t)-0.676757812f, (float16_t)0.557128906f, (float16_t)0.583984375f, (float16_t)0.389892578f, + (float16_t)-0.462890625f, (float16_t)0.207275391f, (float16_t)-0.177856445f, (float16_t)0.0304107666f, + (float16_t)0.371826172f, (float16_t)0.399169922f, (float16_t)0.227905273f, (float16_t)0.245239258f, + (float16_t)0.029006958f, (float16_t)-0.0872802734f, (float16_t)-0.02293396f, (float16_t)-0.549804688f, + (float16_t)0.373046875f, (float16_t)-0.309326172f, (float16_t)-0.101806641f, (float16_t)-0.132202148f, + (float16_t)0.512207031f, (float16_t)0.468994141f, (float16_t)-0.564941406f, (float16_t)0.481933594f, + (float16_t)-0.0870361328f, (float16_t)0.408935547f, (float16_t)0.10748291f, (float16_t)0.475830078f, + (float16_t)0.611328125f, (float16_t)-0.181640625f, (float16_t)-0.188598633f, (float16_t)-0.678710938f, + (float16_t)0.430664062f, (float16_t)-0.264160156f, (float16_t)0.670410156f, (float16_t)-0.00799560547f, + (float16_t)-0.406738281f, (float16_t)-0.0180664062f, (float16_t)-0.239746094f, (float16_t)0.359863281f, + (float16_t)0.524902344f, (float16_t)0.369140625f, (float16_t)0.708007812f, (float16_t)-0.732910156f, + (float16_t)-0.645996094f, (float16_t)0.715332031f, (float16_t)-0.0270843506f, (float16_t)0.645507812f, + (float16_t)-0.0534057617f, (float16_t)-0.696289062f, (float16_t)-0.244384766f, (float16_t)-0.182617188f, + (float16_t)0.720703125f, (float16_t)0.255615234f, (float16_t)0.729003906f, (float16_t)-0.470214844f, + (float16_t)0.196166992f, (float16_t)0.329101562f, (float16_t)-0.522460938f, (float16_t)-0.313232422f, + (float16_t)-0.494628906f, (float16_t)0.462646484f, (float16_t)-0.0138397217f, (float16_t)0.421875f, + (float16_t)-0.677734375f, (float16_t)-0.635253906f, (float16_t)-0.370361328f, (float16_t)0.255615234f, + (float16_t)0.3359375f, (float16_t)-0.297607422f, (float16_t)0.650390625f, (float16_t)-0.341796875f, + (float16_t)-0.357421875f, (float16_t)0.19909668f, (float16_t)-0.562011719f, (float16_t)-0.21081543f, + (float16_t)0.581542969f, (float16_t)0.173950195f, (float16_t)-0.662597656f, (float16_t)-0.692871094f, + (float16_t)-0.449707031f, (float16_t)0.489257812f, (float16_t)-0.676269531f, (float16_t)-0.351074219f, + (float16_t)0.746582031f, (float16_t)-0.20690918f, (float16_t)0.283935547f, (float16_t)-0.5703125f, + (float16_t)-0.703125f, (float16_t)-0.720703125f, (float16_t)0.522949219f, (float16_t)0.638671875f, + (float16_t)0.595214844f, (float16_t)0.705078125f, (float16_t)0.0138244629f, (float16_t)-0.0335998535f, + (float16_t)0.371826172f, (float16_t)0.541015625f, (float16_t)-0.162719727f, (float16_t)0.196289062f, + (float16_t)0.00445556641f, (float16_t)0.0411987305f, (float16_t)0.191894531f, (float16_t)0.326660156f, + (float16_t)0.121459961f, (float16_t)0.630371094f, (float16_t)0.299804688f, (float16_t)0.0381774902f, + (float16_t)-0.283203125f, (float16_t)0.468017578f, (float16_t)0.00332641602f, (float16_t)0.190307617f, + (float16_t)0.434082031f, (float16_t)0.443359375f, (float16_t)-0.71875f, (float16_t)-0.575683594f, + (float16_t)0.443359375f, (float16_t)0.261474609f, (float16_t)-0.104431152f, (float16_t)-0.425048828f, + (float16_t)0.6484375f, (float16_t)0.665039062f, (float16_t)0.361328125f, (float16_t)0.509765625f, + (float16_t)-0.521972656f, (float16_t)0.488037109f, (float16_t)0.257324219f, (float16_t)0.164794922f, + (float16_t)0.620117188f, (float16_t)-0.565429688f, (float16_t)-0.401367188f, (float16_t)-0.625488281f, + (float16_t)-0.678222656f, (float16_t)0.578125f, (float16_t)-0.562988281f, (float16_t)0.0560913086f, + (float16_t)-0.422119141f, (float16_t)-0.108215332f, (float16_t)0.431152344f, (float16_t)0.261230469f, + (float16_t)0.228027344f, (float16_t)-0.295166016f, (float16_t)0.463867188f, (float16_t)0.363769531f, + (float16_t)0.399169922f, (float16_t)-0.0310058594f, (float16_t)-0.309326172f, (float16_t)-0.110290527f, + (float16_t)0.4140625f, (float16_t)-0.608398438f, (float16_t)0.279541016f, (float16_t)-0.579589844f, + (float16_t)-0.106933594f, (float16_t)0.412597656f, (float16_t)0.685058594f, (float16_t)0.0848999023f, + (float16_t)-0.0105743408f, (float16_t)0.429931641f, (float16_t)-0.51171875f, (float16_t)-0.0896606445f, + (float16_t)-0.747558594f, (float16_t)0.119750977f, (float16_t)0.621582031f, (float16_t)-0.64453125f, + (float16_t)-0.625488281f, (float16_t)0.223632812f, (float16_t)0.019821167f, (float16_t)-0.0855102539f, + (float16_t)-0.525390625f, (float16_t)0.633789062f, (float16_t)0.718261719f, (float16_t)0.0284576416f, + (float16_t)-0.331787109f, (float16_t)0.298583984f, (float16_t)0.237304688f, (float16_t)0.264892578f, + (float16_t)-0.409179688f, (float16_t)-0.192382812f, (float16_t)0.565429688f, (float16_t)0.351318359f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/biases_data.h new file mode 100644 index 00000000..f4c983a8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/biases_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_f32_biases_data[12] = {0.370316595f, + -0.0276993774f, + -0.333786786f, + -0.143184528f, + -0.0529888347f, + 0.00524023687f, + 0.107681215f, + -0.155145317f, + 0.0354041979f, + 0.436348975f, + -0.195979178f, + 0.226857617f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/config_data.h new file mode 100644 index 00000000..f5143a57 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K5_OPT_F32_INPUT_BATCHES 1 +#define CONV_K5_OPT_F32_INPUT_H 1 +#define CONV_K5_OPT_F32_INPUT_W 21 +#define CONV_K5_OPT_F32_IN_CH 8 +#define CONV_K5_OPT_F32_FILTER_H 1 +#define CONV_K5_OPT_F32_FILTER_W 5 +#define CONV_K5_OPT_F32_OUT_CH 12 +#define CONV_K5_OPT_F32_STRIDE_H 1 +#define CONV_K5_OPT_F32_STRIDE_W 1 +#define CONV_K5_OPT_F32_PADDING_H 0 +#define CONV_K5_OPT_F32_PADDING_W 0 +#define CONV_K5_OPT_F32_DILATION_H 1 +#define CONV_K5_OPT_F32_DILATION_W 1 +#define CONV_K5_OPT_F32_OUTPUT_H 1 +#define CONV_K5_OPT_F32_OUTPUT_W 17 +#define CONV_K5_OPT_F32_OUTPUT_C 12 +#define CONV_K5_OPT_F32_DST_SIZE 204 +#define CONV_K5_OPT_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K5_OPT_F32_USE_WRAPPER 0 +#define CONV_K5_OPT_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_K5_OPT_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/input_data.h new file mode 100644 index 00000000..cbf78612 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/input_data.h @@ -0,0 +1,26 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_f32_input_data[168] = { + -0.410740495f, 0.580221832f, -0.18094629f, 0.842740476f, -0.482950032f, -0.264683366f, -0.519374728f, + 0.898863375f, 0.762317836f, 0.293113172f, -0.0314536653f, -0.454496056f, -0.863539994f, 0.342282146f, + 0.182253674f, 0.768074214f, 0.302691579f, 0.705897093f, -0.941241264f, 0.0304005332f, 0.624476492f, + 0.59469825f, 0.517573297f, -0.402538836f, 0.996828735f, -0.786692083f, -0.152202979f, -0.390577346f, + -0.239995509f, 0.0945436656f, 0.0810585693f, 0.07897643f, 0.964254558f, 0.832027495f, 0.24660556f, + 0.547376573f, 0.897122979f, 0.931929052f, -0.141790673f, 0.25756669f, -0.716044366f, -0.230699465f, + 0.422665179f, -0.168862015f, -0.836242974f, 0.460339099f, 0.293430418f, -0.954550982f, -0.59252423f, + 0.0966654792f, 0.724737942f, -0.11898312f, 0.662317157f, -0.992590666f, -0.0669297725f, 0.110779688f, + -0.523486316f, -0.836769462f, -0.241247728f, -0.0792861357f, -0.328031361f, 0.606531441f, 0.591443717f, + -0.281964272f, -0.468789935f, -0.373661131f, 0.0448636264f, -0.534749448f, 0.517219603f, -0.650942683f, + 0.270058215f, -0.445458651f, 0.421618819f, -0.989589155f, 0.930160999f, 0.703922153f, 0.320915788f, + 0.447347492f, 0.560242891f, 0.507736623f, -0.962266564f, -0.899599195f, 0.0294798613f, -0.395854801f, + -0.755361617f, 0.276021808f, 0.90837568f, 0.159533739f, -0.745436013f, -0.551963687f, -0.282925069f, + -0.98524189f, -0.00723368814f, 0.494025797f, 0.0303942524f, -0.976403773f, -0.297628164f, 0.805478871f, + 0.940944552f, 0.269550353f, 0.999717712f, 0.611142457f, 0.651604712f, -0.443744689f, -0.613443732f, + 0.471912295f, 0.944616973f, -0.343917549f, 0.536972225f, -0.595548809f, 0.741418004f, 0.0952845365f, + 0.00122548861f, 0.314859897f, 0.746153712f, 0.992270529f, -0.329478443f, -0.380513251f, -0.35170421f, + 0.799805701f, -0.223668039f, -0.531352937f, -0.36270079f, 0.777887106f, 0.888769567f, 0.544607699f, + -0.923733711f, -0.991953194f, -0.969219685f, -0.518154502f, -0.483434558f, -0.375947624f, 0.428111732f, + 0.527337611f, 0.501947582f, -0.568623662f, -0.158980697f, 0.547689021f, 0.702697814f, -0.734004915f, + 0.906086445f, -0.0552745499f, -0.589218736f, 0.678827345f, -0.398383558f, -0.89712739f, -0.360243499f, + 0.886261165f, 0.292725712f, 0.961310446f, 0.865724802f, 0.646161914f, 0.842383742f, -0.0720396712f, + 0.327837169f, 0.418554038f, -0.132363528f, -0.737274826f, -0.65138489f, 0.377925426f, 0.947374761f, + -0.41826722f, 0.9013834f, -0.0431175232f, 0.767751396f, 0.0434999615f, 0.925951064f, -0.462799996f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/output_ref_data.h new file mode 100644 index 00000000..670e086f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_f32_output_ref_data[204] = { + -1.57721555f, 1.16667235f, -2.01428175f, 1.24910486f, 2.91896367f, 1.29190481f, 1.45216227f, + -1.35281634f, 2.36612082f, 6.63958645f, -2.10102201f, 0.914849937f, 0.895696521f, -0.213770211f, + -3.00040174f, 0.950858951f, -1.48843992f, 0.94244355f, 1.55186462f, 0.771296978f, 0.60643518f, + -1.43602991f, 1.16351748f, -0.619269073f, 0.794355392f, 2.34385633f, -0.24027428f, 0.713033617f, + -3.36430788f, 0.880998909f, 0.173952967f, 3.14927197f, 2.33790398f, -0.588541567f, 0.944983184f, + 1.95838785f, 1.2498554f, 1.66467977f, -0.405858457f, 1.8900584f, -0.542338669f, 0.609052241f, + -1.55693316f, 0.0615023077f, 1.20726502f, -1.1831193f, 1.39713526f, 0.877670705f, 0.504775047f, + 0.316162407f, 0.497441053f, 0.220211938f, 2.34819412f, -1.73851001f, -0.711573124f, 0.642131329f, + -0.375343889f, -2.55835128f, -0.403536856f, 1.21770012f, 0.482653052f, 0.294935256f, -0.104386255f, + 0.00899036229f, -1.61543524f, 0.00392093277f, 1.08598566f, 0.343013793f, 0.365888536f, 0.220335245f, + -0.356774032f, 0.029910177f, -0.592403889f, -1.83037627f, 0.449258208f, -1.75918233f, 1.85780811f, + -2.06366849f, -0.0749584734f, -1.59771848f, -2.94285059f, -0.706623971f, -1.48951888f, 2.24047565f, + 0.628137827f, 0.833703399f, 0.426831186f, -0.8857705f, -2.40863109f, -2.91875029f, 0.203827083f, + 3.42368889f, -0.0225613788f, -2.02092791f, -0.52837956f, -0.0429491252f, 1.86366987f, 0.621758521f, + -0.428700596f, 0.950445294f, -1.70362604f, -0.209935188f, 0.375233442f, 2.11091948f, -0.459422886f, + -0.200594544f, -1.83012724f, 3.21063352f, -2.51621342f, 1.42318201f, -0.632438302f, 1.18119073f, + -0.559069633f, -2.56465125f, -2.85815048f, -0.345613658f, 1.42855525f, -1.33093691f, 0.7639907f, + 4.62599945f, -4.83592749f, 4.20838118f, 0.339969933f, -0.147884101f, -2.21921945f, -2.03947663f, + -1.95485997f, 0.938968062f, 1.61711836f, -0.0582990944f, 2.55365944f, 1.29281771f, 0.253264308f, + 0.844990671f, 1.98162484f, -2.06937599f, -0.347769678f, -2.51976919f, -0.178266257f, 0.737057209f, + 0.536435664f, -2.03358579f, 0.0206662565f, -2.06054139f, 2.32256961f, -1.29054666f, -0.163488641f, + -2.93733144f, -1.26884663f, -0.75209707f, -0.620821834f, 1.90825975f, -1.19279695f, -5.35524321f, + 1.46581149f, -1.15603685f, 1.48285711f, 0.220062256f, 0.276420832f, -1.89678228f, -1.91681468f, + 0.158657208f, -0.307393819f, -0.760721922f, -0.113754384f, 0.547512949f, -1.08517241f, -0.542759418f, + 1.11512089f, 1.06363297f, -2.50342631f, 1.66295052f, 0.448878706f, 0.266999304f, -0.283651322f, + -1.26758075f, -0.306163847f, 1.36752152f, 3.0512085f, 0.132826298f, -0.914603472f, -1.47221947f, + 0.411454141f, -2.71512604f, 1.77194619f, -0.84323895f, 2.01493049f, 0.312723756f, -1.00999343f, + -1.60151243f, -0.66783452f, 1.00981188f, -1.52909136f, 0.672811389f, 0.602673531f, -0.693986118f, + -0.958010614f, -0.631714284f, 1.99988818f, 1.20481431f, 2.11042953f, 0.049430877f, -0.0436158776f, + -2.26767612f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/weights_data.h new file mode 100644 index 00000000..b3035062 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_f32/weights_data.h @@ -0,0 +1,71 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_f32_weights_data[480] = { + 0.679981768f, 0.641121924f, -0.263633549f, -0.558839858f, -0.0724343359f, -0.285799801f, -0.733062506f, + -0.654347301f, 0.0800557211f, 0.248121679f, -0.170324102f, 0.559322953f, 0.678001523f, -0.314405859f, + 0.519523025f, -0.46805796f, -0.164382055f, -0.471603006f, -0.455431193f, 0.306952804f, -0.489380836f, + 0.0233432166f, -0.638344407f, 0.184253484f, -0.340249151f, -0.0952058285f, 0.253690749f, -0.256575376f, + -0.50255388f, 0.591351032f, -0.461042881f, -0.472427785f, -0.246661305f, -0.169603422f, -0.524462163f, + -0.479865104f, 0.510497451f, 0.499017715f, -0.0450229272f, 0.528676331f, -0.609449089f, -0.567042112f, + 0.578377366f, 0.306815833f, -0.564559937f, 0.699571729f, 0.275442779f, 0.661869764f, 0.644470692f, + 0.339806467f, -0.28039068f, 0.163619414f, -0.0110672824f, 0.731061578f, -0.376984745f, -0.633122087f, + -0.075907439f, 0.748843789f, -0.271657258f, 0.474711984f, -0.294424236f, 0.301608294f, 0.424884826f, + 0.0920652077f, -0.500406742f, -0.108664356f, 0.172431871f, -0.0468261801f, -0.236486033f, -0.515585363f, + 0.350368857f, -0.119999789f, -0.605398238f, 0.240297779f, 0.662773371f, 0.231950656f, 0.62708205f, + -0.634348392f, 0.0279046055f, -0.326685876f, -0.51916784f, -0.116093069f, -0.359946609f, 0.280632317f, + 0.360527098f, 0.0346502177f, 0.577379465f, -0.425261945f, -0.376440525f, 0.321271896f, 0.742188871f, + 0.642804623f, -0.339493334f, -0.0773649141f, -0.655655205f, -0.140949681f, -0.330372274f, 0.343728095f, + -0.273973823f, 0.0209582392f, -0.342368394f, -0.205208078f, 0.288641036f, 0.0782951936f, -0.122412466f, + -0.304722995f, 0.646333039f, 0.615837514f, -0.346235961f, -0.212012857f, 0.689635515f, -0.0998436585f, + -0.0519873574f, 0.140420452f, -0.0821794346f, -0.0353049375f, -0.506529093f, -0.413333416f, -0.340973198f, + -0.396471739f, 0.416731149f, -0.0704424679f, -0.168717325f, 0.284811556f, -0.417301536f, 0.719136894f, + -0.536050141f, 0.548295319f, 0.741449654f, 0.087292321f, -0.204311699f, 0.0148845399f, -0.012244557f, + -0.124393784f, -0.30523482f, 0.154512495f, -0.383819073f, -0.336918741f, 0.504057288f, -0.246808782f, + -0.218080103f, 0.0833174959f, 0.538644552f, -0.489336401f, 0.386019379f, 0.234293476f, 0.069884561f, + -0.547387004f, -0.473315597f, 0.125940606f, -0.0555304848f, -0.289020926f, -0.455687702f, 0.352678627f, + 0.216388673f, 0.285491526f, 0.0601843037f, -0.277323544f, 0.501725972f, 0.336819857f, -0.137185887f, + 0.334796041f, 0.0826622993f, 0.390611261f, 0.677383184f, 0.44595325f, -0.741476357f, 0.504029632f, + 0.250604838f, 0.296766341f, 0.135895982f, -0.540055752f, -0.527570784f, 0.00924638379f, -0.137780905f, + -0.232661098f, -0.503715813f, -0.276269972f, 0.517053843f, -0.666092336f, -0.393571407f, -0.674531221f, + -0.274618626f, -0.70233506f, 0.66664201f, -0.628611863f, 0.00986384228f, 0.11611288f, -0.384614587f, + 2.91768974e-05f, 0.0410274863f, 0.671640277f, 0.707089961f, 0.30197379f, -0.327286631f, 0.117188029f, + -0.0754022971f, 0.45478195f, 0.236245558f, 0.390941858f, 0.151276827f, 0.716382027f, -0.343022346f, + -0.0650433451f, 0.15447256f, -0.246720657f, -0.594766617f, 0.250288874f, 0.69344604f, 0.534739316f, + -0.136791557f, 0.51650399f, 0.361810178f, -0.451852739f, -0.0162485503f, 0.221844673f, -0.202167481f, + 0.583438575f, -0.127179578f, 0.220261231f, -0.397786856f, 0.207088947f, -0.727632761f, 0.728522658f, + 0.18759124f, 0.525014222f, 0.367007196f, -0.529400527f, 0.14639923f, 0.182775468f, 0.724013269f, + -0.305475712f, 0.480513036f, -0.121350139f, 0.514552295f, 0.237886339f, -0.319790334f, 0.311764956f, + 0.36807546f, 0.200265422f, -0.115295798f, 0.537372828f, 0.138167471f, -0.412866503f, 0.616023898f, + 0.683034241f, -0.287241191f, -0.0488079824f, 0.215976998f, -0.272390157f, -0.338832349f, 0.284233838f, + 0.319453061f, -0.0325776152f, -0.158319473f, 0.472912788f, 0.51797688f, -0.517481267f, -0.473670036f, + -0.486965269f, 0.571813583f, 0.124300011f, 0.681924939f, 0.592150986f, 0.144536063f, -0.30271709f, + -0.575071275f, 0.0583315603f, 0.0489369556f, 0.0502186567f, 0.0222133882f, -0.0270580444f, 0.734581888f, + -0.369568586f, 0.347083122f, 0.204810545f, -0.699495614f, 0.529759228f, -0.50624615f, -0.103370175f, + -0.253168225f, 0.53772527f, -0.72573936f, 0.294151694f, -0.361015052f, 0.306812644f, 0.534532845f, + -0.34389466f, 0.4292427f, -0.623544574f, 0.560949683f, -0.125559762f, 0.312076032f, 0.523044646f, + 0.659576178f, -0.552783906f, 0.336126685f, -0.201843232f, 0.4002994f, 0.744907916f, -0.676553607f, + 0.557171941f, 0.583747864f, 0.38999325f, -0.462854117f, 0.207272083f, -0.177854583f, 0.030403791f, + 0.37177825f, 0.399049252f, 0.227911323f, 0.245259658f, 0.0290129408f, -0.0872836635f, -0.0229362044f, + -0.549675822f, 0.372933298f, -0.309383065f, -0.101779565f, -0.132162258f, 0.512009382f, 0.468928784f, + -0.5651443f, 0.481999218f, -0.0870361924f, 0.408909589f, 0.107506618f, 0.475883007f, 0.611131191f, + -0.181675687f, -0.188649386f, -0.678631604f, 0.430597663f, -0.264195293f, 0.670612812f, -0.00799868442f, + -0.406635702f, -0.0180659629f, -0.239695489f, 0.359981477f, 0.52466011f, 0.369171798f, 0.7077685f, + -0.7329216f, -0.645847499f, 0.715113223f, -0.0270882249f, 0.645660102f, -0.0534159765f, -0.696338952f, + -0.244369239f, -0.182657465f, 0.720886171f, 0.255617023f, 0.729226947f, -0.47013697f, 0.196191043f, + 0.329100311f, -0.522418797f, -0.313273817f, -0.494518757f, 0.462601215f, -0.013842186f, 0.421903014f, + -0.677672267f, -0.635273218f, -0.370250911f, 0.255515039f, 0.335883528f, -0.297698557f, 0.650451422f, + -0.341776222f, -0.357324451f, 0.199116766f, -0.561805546f, -0.21078521f, 0.581587374f, 0.173971608f, + -0.662721455f, -0.692957819f, -0.449615359f, 0.489373803f, -0.676209748f, -0.351114601f, 0.746738553f, + -0.206870526f, 0.284044206f, -0.570473731f, -0.70318228f, -0.720571637f, 0.52282542f, 0.638471007f, + 0.595067561f, 0.70494467f, 0.0138282618f, -0.0335856006f, 0.371793628f, 0.541149318f, -0.162700847f, + 0.196323738f, 0.00445643719f, 0.0411920175f, 0.191920519f, 0.326667339f, 0.121461421f, 0.630492628f, + 0.299688011f, 0.038169995f, -0.283101916f, 0.468066633f, 0.00332579878f, 0.190327495f, 0.434075356f, + 0.443475932f, -0.718611777f, -0.575694501f, 0.443476379f, 0.261594266f, -0.104456209f, -0.425161093f, + 0.648444951f, 0.665129721f, 0.361284047f, 0.51000303f, -0.521793783f, 0.487971723f, 0.257330388f, + 0.164807975f, 0.620212078f, -0.565557122f, -0.401265949f, -0.625561893f, -0.678090096f, 0.578336298f, + -0.562908351f, 0.056103237f, -0.421998858f, -0.108207256f, 0.431144148f, 0.261203796f, 0.228018284f, + -0.295253068f, 0.463943779f, 0.36381793f, 0.399163365f, -0.0310040116f, -0.309400141f, -0.110308327f, + 0.41394949f, -0.608617008f, 0.279538125f, -0.579483509f, -0.106953859f, 0.412615359f, 0.68524915f, + 0.0848737285f, -0.0105760284f, 0.429870337f, -0.511632681f, -0.0896394625f, -0.747768223f, 0.119734898f, + 0.621583521f, -0.644577086f, -0.625466883f, 0.223650366f, 0.0198272709f, -0.0855244547f, -0.52520436f, + 0.633894682f, 0.718024731f, 0.0284633357f, -0.331760734f, 0.298484564f, 0.237262353f, 0.2649194f, + -0.409134656f, -0.192335844f, 0.565270305f, 0.351323694f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/biases_data.h new file mode 100644 index 00000000..1f7a365e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_nhwc_tuned_f16_biases_data[16] = {(float16_t)0.431396484f, + (float16_t)-0.0834960938f, + (float16_t)-0.477783203f, + (float16_t)0.107910156f, + (float16_t)-0.0681152344f, + (float16_t)0.445068359f, + (float16_t)0.128173828f, + (float16_t)0.408447266f, + (float16_t)0.306884766f, + (float16_t)-0.370117188f, + (float16_t)0.397216797f, + (float16_t)0.324951172f, + (float16_t)-0.352050781f, + (float16_t)-0.380126953f, + (float16_t)-0.241943359f, + (float16_t)0.0535888672f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/config_data.h new file mode 100644 index 00000000..dc7ecce3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K5_OPT_NHWC_TUNED_F16_INPUT_BATCHES 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_INPUT_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_INPUT_W 21 +#define CONV_K5_OPT_NHWC_TUNED_F16_IN_CH 16 +#define CONV_K5_OPT_NHWC_TUNED_F16_FILTER_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_FILTER_W 5 +#define CONV_K5_OPT_NHWC_TUNED_F16_OUT_CH 16 +#define CONV_K5_OPT_NHWC_TUNED_F16_STRIDE_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_STRIDE_W 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_PADDING_H 0 +#define CONV_K5_OPT_NHWC_TUNED_F16_PADDING_W 0 +#define CONV_K5_OPT_NHWC_TUNED_F16_DILATION_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_DILATION_W 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_OUTPUT_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_OUTPUT_W 17 +#define CONV_K5_OPT_NHWC_TUNED_F16_OUTPUT_C 16 +#define CONV_K5_OPT_NHWC_TUNED_F16_DST_SIZE 272 +#define CONV_K5_OPT_NHWC_TUNED_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K5_OPT_NHWC_TUNED_F16_USE_WRAPPER 1 +#define CONV_K5_OPT_NHWC_TUNED_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_K5_OPT_NHWC_TUNED_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/input_data.h new file mode 100644 index 00000000..08e7d248 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/input_data.h @@ -0,0 +1,86 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_nhwc_tuned_f16_input_data[336] = { + (float16_t)0.435546875f, (float16_t)0.13269043f, (float16_t)-0.794921875f, (float16_t)-0.369873047f, + (float16_t)-0.378417969f, (float16_t)-0.78515625f, (float16_t)0.489990234f, (float16_t)-0.650878906f, + (float16_t)-0.563476562f, (float16_t)0.514648438f, (float16_t)0.695800781f, (float16_t)-0.384277344f, + (float16_t)0.182495117f, (float16_t)-0.762207031f, (float16_t)0.236450195f, (float16_t)0.958007812f, + (float16_t)-0.0322570801f, (float16_t)-0.296142578f, (float16_t)-0.373535156f, (float16_t)-0.516113281f, + (float16_t)0.921875f, (float16_t)0.153320312f, (float16_t)-0.864257812f, (float16_t)0.384765625f, + (float16_t)-0.098449707f, (float16_t)0.685058594f, (float16_t)-0.603027344f, (float16_t)0.512695312f, + (float16_t)0.0802001953f, (float16_t)-0.851074219f, (float16_t)0.165771484f, (float16_t)0.263916016f, + (float16_t)0.610351562f, (float16_t)0.361816406f, (float16_t)0.219360352f, (float16_t)0.0300598145f, + (float16_t)-0.227661133f, (float16_t)-0.836425781f, (float16_t)0.520507812f, (float16_t)0.989746094f, + (float16_t)-0.572753906f, (float16_t)0.679199219f, (float16_t)0.110961914f, (float16_t)0.821289062f, + (float16_t)0.331542969f, (float16_t)-0.124694824f, (float16_t)0.637207031f, (float16_t)-0.836914062f, + (float16_t)0.794433594f, (float16_t)0.424072266f, (float16_t)-0.00509643555f, (float16_t)-0.0545043945f, + (float16_t)-0.841796875f, (float16_t)-0.203857422f, (float16_t)-0.302734375f, (float16_t)0.498535156f, + (float16_t)0.595214844f, (float16_t)0.813964844f, (float16_t)0.113525391f, (float16_t)-0.0324401855f, + (float16_t)0.714355469f, (float16_t)0.783203125f, (float16_t)-0.457763672f, (float16_t)0.686523438f, + (float16_t)-0.966308594f, (float16_t)-0.615722656f, (float16_t)-0.209716797f, (float16_t)-0.370605469f, + (float16_t)-0.9765625f, (float16_t)0.0169219971f, (float16_t)0.041809082f, (float16_t)0.824707031f, + (float16_t)0.407470703f, (float16_t)0.739746094f, (float16_t)0.868164062f, (float16_t)0.597167969f, + (float16_t)-0.00249671936f, (float16_t)0.802246094f, (float16_t)0.0518798828f, (float16_t)0.610839844f, + (float16_t)0.183349609f, (float16_t)0.581054688f, (float16_t)0.600585938f, (float16_t)-0.706054688f, + (float16_t)-0.404785156f, (float16_t)-0.653808594f, (float16_t)-0.330078125f, (float16_t)-0.785644531f, + (float16_t)0.691894531f, (float16_t)0.415039062f, (float16_t)0.587890625f, (float16_t)0.526855469f, + (float16_t)-0.00129890442f, (float16_t)-0.629882812f, (float16_t)-0.0203094482f, (float16_t)-0.657226562f, + (float16_t)0.0142669678f, (float16_t)0.880371094f, (float16_t)-0.562988281f, (float16_t)0.83984375f, + (float16_t)0.451171875f, (float16_t)-0.838867188f, (float16_t)-0.852050781f, (float16_t)0.274902344f, + (float16_t)0.851074219f, (float16_t)0.572753906f, (float16_t)-0.163574219f, (float16_t)-0.788085938f, + (float16_t)-0.970703125f, (float16_t)0.537597656f, (float16_t)0.722167969f, (float16_t)0.380859375f, + (float16_t)0.365966797f, (float16_t)-0.370361328f, (float16_t)0.768554688f, (float16_t)-0.732421875f, + (float16_t)-0.987304688f, (float16_t)0.653320312f, (float16_t)0.635253906f, (float16_t)-0.321533203f, + (float16_t)-0.0830078125f, (float16_t)-0.16027832f, (float16_t)0.529296875f, (float16_t)0.133789062f, + (float16_t)0.0622253418f, (float16_t)-0.360351562f, (float16_t)0.286865234f, (float16_t)-0.277832031f, + (float16_t)-0.993164062f, (float16_t)0.232543945f, (float16_t)-0.263671875f, (float16_t)0.299072266f, + (float16_t)-0.287841797f, (float16_t)-0.831054688f, (float16_t)0.185791016f, (float16_t)-0.395751953f, + (float16_t)0.52734375f, (float16_t)0.818847656f, (float16_t)0.517089844f, (float16_t)0.194580078f, + (float16_t)-0.0254364014f, (float16_t)-0.232299805f, (float16_t)-0.141113281f, (float16_t)-0.360107422f, + (float16_t)-0.290283203f, (float16_t)0.779296875f, (float16_t)0.350341797f, (float16_t)-0.275634766f, + (float16_t)0.943359375f, (float16_t)0.691894531f, (float16_t)0.825195312f, (float16_t)-0.764648438f, + (float16_t)0.881347656f, (float16_t)0.946777344f, (float16_t)0.880859375f, (float16_t)0.608886719f, + (float16_t)0.763671875f, (float16_t)0.703125f, (float16_t)-0.266601562f, (float16_t)-0.998535156f, + (float16_t)0.446044922f, (float16_t)0.256591797f, (float16_t)0.909667969f, (float16_t)-0.261230469f, + (float16_t)-0.846191406f, (float16_t)0.484375f, (float16_t)-0.274658203f, (float16_t)0.223754883f, + (float16_t)-0.125366211f, (float16_t)-0.214599609f, (float16_t)0.67578125f, (float16_t)-0.189941406f, + (float16_t)-0.488037109f, (float16_t)0.421142578f, (float16_t)-0.20300293f, (float16_t)0.895996094f, + (float16_t)-0.0676269531f, (float16_t)-0.779296875f, (float16_t)0.287353516f, (float16_t)-0.858398438f, + (float16_t)-0.711914062f, (float16_t)-0.0293579102f, (float16_t)-0.390625f, (float16_t)-0.623046875f, + (float16_t)0.0054473877f, (float16_t)-0.864257812f, (float16_t)0.419189453f, (float16_t)-0.260009766f, + (float16_t)-0.174438477f, (float16_t)-0.677246094f, (float16_t)0.341796875f, (float16_t)-0.409179688f, + (float16_t)0.192626953f, (float16_t)-0.789550781f, (float16_t)0.370117188f, (float16_t)0.529785156f, + (float16_t)0.711425781f, (float16_t)0.705078125f, (float16_t)0.848632812f, (float16_t)-0.575195312f, + (float16_t)-0.195678711f, (float16_t)0.592285156f, (float16_t)-0.609863281f, (float16_t)0.0872192383f, + (float16_t)0.329833984f, (float16_t)-0.327880859f, (float16_t)-0.988769531f, (float16_t)0.0744628906f, + (float16_t)0.328857422f, (float16_t)0.273193359f, (float16_t)0.158325195f, (float16_t)0.267333984f, + (float16_t)-0.227539062f, (float16_t)0.608886719f, (float16_t)0.86328125f, (float16_t)-0.508789062f, + (float16_t)0.94140625f, (float16_t)-0.282958984f, (float16_t)0.830566406f, (float16_t)0.033782959f, + (float16_t)-0.0723876953f, (float16_t)0.676757812f, (float16_t)0.000132799149f, (float16_t)0.657714844f, + (float16_t)0.811035156f, (float16_t)0.85546875f, (float16_t)0.7421875f, (float16_t)0.170288086f, + (float16_t)0.0361328125f, (float16_t)-0.450683594f, (float16_t)-0.16394043f, (float16_t)-0.583007812f, + (float16_t)0.11529541f, (float16_t)-0.387939453f, (float16_t)0.743652344f, (float16_t)0.574707031f, + (float16_t)0.435791016f, (float16_t)0.11126709f, (float16_t)0.794433594f, (float16_t)-0.413574219f, + (float16_t)0.340576172f, (float16_t)0.752929688f, (float16_t)0.3046875f, (float16_t)0.660644531f, + (float16_t)0.390380859f, (float16_t)-0.54296875f, (float16_t)0.440673828f, (float16_t)-0.382324219f, + (float16_t)0.963378906f, (float16_t)-0.463134766f, (float16_t)-0.415771484f, (float16_t)-0.450195312f, + (float16_t)-0.414306641f, (float16_t)-0.248901367f, (float16_t)0.623046875f, (float16_t)0.786621094f, + (float16_t)-0.610839844f, (float16_t)-0.516601562f, (float16_t)-0.618652344f, (float16_t)0.220703125f, + (float16_t)0.412109375f, (float16_t)-0.952148438f, (float16_t)-0.0754394531f, (float16_t)-0.207641602f, + (float16_t)-0.87890625f, (float16_t)-0.233642578f, (float16_t)-0.0712280273f, (float16_t)-0.842285156f, + (float16_t)0.565917969f, (float16_t)0.259277344f, (float16_t)-0.915039062f, (float16_t)-0.89453125f, + (float16_t)0.897949219f, (float16_t)-0.665527344f, (float16_t)0.0748901367f, (float16_t)0.948242188f, + (float16_t)0.717285156f, (float16_t)0.825195312f, (float16_t)-0.0581054688f, (float16_t)-0.349853516f, + (float16_t)-0.537109375f, (float16_t)0.338378906f, (float16_t)0.434570312f, (float16_t)-0.565917969f, + (float16_t)-0.0246429443f, (float16_t)0.0936889648f, (float16_t)-0.437011719f, (float16_t)-0.274658203f, + (float16_t)0.607910156f, (float16_t)-0.162231445f, (float16_t)0.811035156f, (float16_t)-0.90625f, + (float16_t)0.312011719f, (float16_t)0.728027344f, (float16_t)0.375732422f, (float16_t)-0.283691406f, + (float16_t)-0.65234375f, (float16_t)0.343017578f, (float16_t)0.335205078f, (float16_t)-0.895019531f, + (float16_t)0.514160156f, (float16_t)0.572265625f, (float16_t)-0.126464844f, (float16_t)0.459716797f, + (float16_t)-0.694335938f, (float16_t)-0.416015625f, (float16_t)-0.201049805f, (float16_t)-0.722167969f, + (float16_t)-0.993164062f, (float16_t)0.159301758f, (float16_t)-0.094543457f, (float16_t)-0.0506896973f, + (float16_t)0.638183594f, (float16_t)0.836425781f, (float16_t)0.239868164f, (float16_t)0.614257812f, + (float16_t)0.689453125f, (float16_t)-0.990722656f, (float16_t)-0.137695312f, (float16_t)0.53125f, + (float16_t)-0.234741211f, (float16_t)0.921386719f, (float16_t)-0.579101562f, (float16_t)0.927246094f, + (float16_t)0.812011719f, (float16_t)0.216796875f, (float16_t)0.192626953f, (float16_t)0.359619141f, + (float16_t)0.196289062f, (float16_t)0.0815429688f, (float16_t)0.654296875f, (float16_t)-0.717285156f, + (float16_t)0.174438477f, (float16_t)-0.94140625f, (float16_t)-0.813476562f, (float16_t)-0.76953125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/output_ref_data.h new file mode 100644 index 00000000..931ec599 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/output_ref_data.h @@ -0,0 +1,70 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_nhwc_tuned_f16_output_ref_data[272] = { + (float16_t)2.96679688f, (float16_t)-1.61035156f, (float16_t)0.800292969f, (float16_t)-1.7109375f, + (float16_t)1.10546875f, (float16_t)2.015625f, (float16_t)1.62890625f, (float16_t)-0.0372009277f, + (float16_t)0.0225219727f, (float16_t)-1.55859375f, (float16_t)-1.45507812f, (float16_t)0.14050293f, + (float16_t)0.112304688f, (float16_t)-2.79492188f, (float16_t)-2.27539062f, (float16_t)-1.41210938f, + (float16_t)1.44433594f, (float16_t)0.944824219f, (float16_t)1.67089844f, (float16_t)-0.263183594f, + (float16_t)-1.5390625f, (float16_t)-1.38183594f, (float16_t)2.27148438f, (float16_t)-0.829101562f, + (float16_t)1.80078125f, (float16_t)1.3125f, (float16_t)1.56835938f, (float16_t)2.5234375f, + (float16_t)0.503417969f, (float16_t)-1.37109375f, (float16_t)0.517578125f, (float16_t)-0.631347656f, + (float16_t)1.39355469f, (float16_t)-0.619628906f, (float16_t)3.32617188f, (float16_t)1.64648438f, + (float16_t)-0.107543945f, (float16_t)0.695800781f, (float16_t)-0.826660156f, (float16_t)-0.889160156f, + (float16_t)0.84375f, (float16_t)-1.78222656f, (float16_t)1.92089844f, (float16_t)1.38964844f, + (float16_t)0.392578125f, (float16_t)3.74609375f, (float16_t)2.21679688f, (float16_t)0.114196777f, + (float16_t)0.561035156f, (float16_t)-2.3203125f, (float16_t)-3.22851562f, (float16_t)-0.120361328f, + (float16_t)4.0390625f, (float16_t)3.53125f, (float16_t)0.251708984f, (float16_t)0.907714844f, + (float16_t)-0.872558594f, (float16_t)2.640625f, (float16_t)-1.68457031f, (float16_t)-2.60546875f, + (float16_t)-1.70898438f, (float16_t)3.17773438f, (float16_t)0.142333984f, (float16_t)-0.400634766f, + (float16_t)-0.796875f, (float16_t)-1.39550781f, (float16_t)3.81054688f, (float16_t)0.85546875f, + (float16_t)-0.779296875f, (float16_t)1.75390625f, (float16_t)-2.58398438f, (float16_t)-4.96484375f, + (float16_t)-3.16601562f, (float16_t)0.941894531f, (float16_t)-2.57226562f, (float16_t)0.139526367f, + (float16_t)0.771972656f, (float16_t)0.642089844f, (float16_t)1.9921875f, (float16_t)1.74121094f, + (float16_t)2.16601562f, (float16_t)1.41308594f, (float16_t)-2.1171875f, (float16_t)3.0390625f, + (float16_t)-0.0473022461f, (float16_t)2.65234375f, (float16_t)5.87890625f, (float16_t)-0.192504883f, + (float16_t)-0.151611328f, (float16_t)-2.36132812f, (float16_t)4.51171875f, (float16_t)-2.6171875f, + (float16_t)-2.51953125f, (float16_t)-1.24511719f, (float16_t)-2.109375f, (float16_t)0.782226562f, + (float16_t)3.63671875f, (float16_t)2.12304688f, (float16_t)-3.125f, (float16_t)1.1015625f, + (float16_t)-0.593261719f, (float16_t)4.75390625f, (float16_t)-1.06152344f, (float16_t)-0.76171875f, + (float16_t)1.48632812f, (float16_t)-0.622070312f, (float16_t)-2.11523438f, (float16_t)1.73925781f, + (float16_t)0.763671875f, (float16_t)-0.417236328f, (float16_t)-0.797363281f, (float16_t)3.72070312f, + (float16_t)1.66503906f, (float16_t)-1.37890625f, (float16_t)0.164306641f, (float16_t)-1.56738281f, + (float16_t)-0.643066406f, (float16_t)-1.76953125f, (float16_t)2.51367188f, (float16_t)-2.92382812f, + (float16_t)1.41992188f, (float16_t)1.49707031f, (float16_t)0.218505859f, (float16_t)2.91796875f, + (float16_t)-0.285888672f, (float16_t)-0.562011719f, (float16_t)3.90429688f, (float16_t)-5.12890625f, + (float16_t)-0.966796875f, (float16_t)-3.90625f, (float16_t)1.55664062f, (float16_t)-0.159545898f, + (float16_t)-0.364013672f, (float16_t)2.48632812f, (float16_t)2.53710938f, (float16_t)-1.23242188f, + (float16_t)-1.39746094f, (float16_t)-2.0234375f, (float16_t)2.63867188f, (float16_t)1.73046875f, + (float16_t)-0.0197143555f, (float16_t)-0.055847168f, (float16_t)1.79980469f, (float16_t)0.544433594f, + (float16_t)2.57617188f, (float16_t)2.5234375f, (float16_t)-2.34570312f, (float16_t)0.485595703f, + (float16_t)-1.41210938f, (float16_t)4.8828125f, (float16_t)-2.50390625f, (float16_t)0.107299805f, + (float16_t)-2.02148438f, (float16_t)-0.930175781f, (float16_t)-3.13867188f, (float16_t)-1.04394531f, + (float16_t)-1.10546875f, (float16_t)0.649414062f, (float16_t)-4.97265625f, (float16_t)4.96484375f, + (float16_t)2.10742188f, (float16_t)1.95800781f, (float16_t)-1.19628906f, (float16_t)-1.9375f, + (float16_t)-1.32910156f, (float16_t)-0.805664062f, (float16_t)2.15429688f, (float16_t)-4.8828125f, + (float16_t)2.02539062f, (float16_t)3.45507812f, (float16_t)-2.43554688f, (float16_t)2.05859375f, + (float16_t)0.033416748f, (float16_t)1.33300781f, (float16_t)-0.0370483398f, (float16_t)1.93554688f, + (float16_t)-2.2734375f, (float16_t)1.85351562f, (float16_t)-0.224243164f, (float16_t)-0.455810547f, + (float16_t)-1.60644531f, (float16_t)-0.973144531f, (float16_t)1.17285156f, (float16_t)-2.2421875f, + (float16_t)-1.57421875f, (float16_t)-1.50976562f, (float16_t)0.0876464844f, (float16_t)0.954101562f, + (float16_t)1.08203125f, (float16_t)-1.22167969f, (float16_t)3.45898438f, (float16_t)-2.72265625f, + (float16_t)-4.22265625f, (float16_t)-2.30078125f, (float16_t)0.274658203f, (float16_t)-1.16796875f, + (float16_t)-1.03027344f, (float16_t)1.17871094f, (float16_t)-1.35546875f, (float16_t)3.89257812f, + (float16_t)0.719726562f, (float16_t)-1.34082031f, (float16_t)2.67578125f, (float16_t)-1.21386719f, + (float16_t)-1.19824219f, (float16_t)1.50488281f, (float16_t)-0.2578125f, (float16_t)0.398925781f, + (float16_t)0.433105469f, (float16_t)1.82714844f, (float16_t)-1.12890625f, (float16_t)1.34667969f, + (float16_t)1.17871094f, (float16_t)3.30078125f, (float16_t)2.56445312f, (float16_t)-4.4921875f, + (float16_t)-4.2890625f, (float16_t)-0.748535156f, (float16_t)0.233276367f, (float16_t)-4.1953125f, + (float16_t)-4.9765625f, (float16_t)2.12695312f, (float16_t)-2.37304688f, (float16_t)1.24023438f, + (float16_t)1.83300781f, (float16_t)2.703125f, (float16_t)-3.68164062f, (float16_t)2.99414062f, + (float16_t)-0.258789062f, (float16_t)2.33398438f, (float16_t)4.625f, (float16_t)-0.274658203f, + (float16_t)-1.85742188f, (float16_t)0.662109375f, (float16_t)0.927246094f, (float16_t)2.63085938f, + (float16_t)-0.122802734f, (float16_t)-4.8046875f, (float16_t)-2.64648438f, (float16_t)3.29492188f, + (float16_t)-1.84960938f, (float16_t)-2.45507812f, (float16_t)-3.16992188f, (float16_t)1.71484375f, + (float16_t)-0.165405273f, (float16_t)-1.04199219f, (float16_t)2.75976562f, (float16_t)-0.719726562f, + (float16_t)5.3125f, (float16_t)0.18347168f, (float16_t)0.0758666992f, (float16_t)1.47949219f, + (float16_t)1.11914062f, (float16_t)-4.93359375f, (float16_t)0.743164062f, (float16_t)-2.3203125f, + (float16_t)-0.532226562f, (float16_t)-1.35351562f, (float16_t)2.80078125f, (float16_t)-2.078125f, + (float16_t)-4.703125f, (float16_t)1.03515625f, (float16_t)-1.66796875f, (float16_t)-1.70214844f, + (float16_t)2.36132812f, (float16_t)-4.984375f, (float16_t)4.75f, (float16_t)-1.29199219f, + (float16_t)1.85839844f, (float16_t)-0.483642578f, (float16_t)1.06640625f, (float16_t)2.25195312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/weights_data.h new file mode 100644 index 00000000..297311dc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f16/weights_data.h @@ -0,0 +1,322 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_k5_opt_nhwc_tuned_f16_weights_data[1280] = { + (float16_t)-0.139160156f, (float16_t)0.7421875f, (float16_t)-0.565429688f, (float16_t)-0.588867188f, + (float16_t)-0.342041016f, (float16_t)-0.411621094f, (float16_t)-0.0148468018f, (float16_t)-0.102172852f, + (float16_t)-0.33984375f, (float16_t)0.11907959f, (float16_t)0.295410156f, (float16_t)0.646484375f, + (float16_t)-0.205932617f, (float16_t)-0.493408203f, (float16_t)-0.299316406f, (float16_t)0.474609375f, + (float16_t)0.453857422f, (float16_t)-0.255371094f, (float16_t)-0.384277344f, (float16_t)0.0083770752f, + (float16_t)-0.500488281f, (float16_t)-0.443603516f, (float16_t)0.279785156f, (float16_t)-0.00518417358f, + (float16_t)0.157714844f, (float16_t)-0.4140625f, (float16_t)-0.185546875f, (float16_t)0.34375f, + (float16_t)-0.106750488f, (float16_t)-0.166259766f, (float16_t)0.537109375f, (float16_t)-0.0100402832f, + (float16_t)-0.686035156f, (float16_t)-0.534179688f, (float16_t)0.740234375f, (float16_t)-0.619140625f, + (float16_t)-0.0156860352f, (float16_t)0.388916016f, (float16_t)-0.226806641f, (float16_t)-0.152954102f, + (float16_t)-0.330078125f, (float16_t)0.578613281f, (float16_t)-0.493652344f, (float16_t)0.641601562f, + (float16_t)-0.0364379883f, (float16_t)0.0362243652f, (float16_t)0.450195312f, (float16_t)-0.499511719f, + (float16_t)-0.359863281f, (float16_t)-0.449951172f, (float16_t)0.0823974609f, (float16_t)-0.396972656f, + (float16_t)-0.281982422f, (float16_t)0.265380859f, (float16_t)-0.445800781f, (float16_t)-0.282470703f, + (float16_t)-0.442382812f, (float16_t)-0.293212891f, (float16_t)-0.263671875f, (float16_t)0.279052734f, + (float16_t)0.679199219f, (float16_t)0.490722656f, (float16_t)-0.476806641f, (float16_t)-0.489746094f, + (float16_t)0.441650391f, (float16_t)0.592285156f, (float16_t)0.14453125f, (float16_t)-0.357421875f, + (float16_t)-0.652832031f, (float16_t)0.265625f, (float16_t)0.353759766f, (float16_t)-0.0353393555f, + (float16_t)-0.346191406f, (float16_t)-0.568359375f, (float16_t)0.553222656f, (float16_t)-0.202392578f, + (float16_t)-0.6796875f, (float16_t)0.4921875f, (float16_t)-0.172973633f, (float16_t)0.0174255371f, + (float16_t)-0.149536133f, (float16_t)0.320556641f, (float16_t)0.559082031f, (float16_t)0.230102539f, + (float16_t)0.438720703f, (float16_t)0.28125f, (float16_t)-0.368408203f, (float16_t)0.407470703f, + (float16_t)-0.264160156f, (float16_t)-0.386474609f, (float16_t)0.719238281f, (float16_t)0.410400391f, + (float16_t)-0.74609375f, (float16_t)-0.322998047f, (float16_t)0.223144531f, (float16_t)-0.0841674805f, + (float16_t)-0.591796875f, (float16_t)-0.724609375f, (float16_t)0.607910156f, (float16_t)-0.356445312f, + (float16_t)-0.442138672f, (float16_t)0.41015625f, (float16_t)-0.0791015625f, (float16_t)0.0677490234f, + (float16_t)-0.00995635986f, (float16_t)0.215698242f, (float16_t)-0.716796875f, (float16_t)-0.408203125f, + (float16_t)0.0826416016f, (float16_t)-0.561523438f, (float16_t)0.617675781f, (float16_t)0.300292969f, + (float16_t)-0.581542969f, (float16_t)0.375488281f, (float16_t)0.608886719f, (float16_t)0.134643555f, + (float16_t)-0.258544922f, (float16_t)-0.457275391f, (float16_t)0.569824219f, (float16_t)0.0703735352f, + (float16_t)0.330810547f, (float16_t)-0.633789062f, (float16_t)0.383789062f, (float16_t)0.192993164f, + (float16_t)0.0363464355f, (float16_t)0.350585938f, (float16_t)-0.512207031f, (float16_t)0.131591797f, + (float16_t)-0.104675293f, (float16_t)-0.512695312f, (float16_t)-0.0420227051f, (float16_t)0.640136719f, + (float16_t)0.524414062f, (float16_t)-0.0281524658f, (float16_t)0.432861328f, (float16_t)0.606445312f, + (float16_t)-0.711425781f, (float16_t)0.537597656f, (float16_t)-0.436279297f, (float16_t)0.610351562f, + (float16_t)0.6796875f, (float16_t)0.41015625f, (float16_t)-0.445068359f, (float16_t)-0.710449219f, + (float16_t)0.453125f, (float16_t)0.283447266f, (float16_t)-0.668457031f, (float16_t)-0.15246582f, + (float16_t)0.549316406f, (float16_t)-0.538085938f, (float16_t)0.278320312f, (float16_t)-0.385253906f, + (float16_t)0.17980957f, (float16_t)0.172485352f, (float16_t)0.662597656f, (float16_t)-0.375732422f, + (float16_t)0.678710938f, (float16_t)-0.336669922f, (float16_t)0.337158203f, (float16_t)-0.377685547f, + (float16_t)0.391357422f, (float16_t)-0.450439453f, (float16_t)0.340332031f, (float16_t)-0.555175781f, + (float16_t)0.015838623f, (float16_t)0.145751953f, (float16_t)0.210693359f, (float16_t)0.353515625f, + (float16_t)-0.685546875f, (float16_t)-0.160888672f, (float16_t)0.285644531f, (float16_t)-0.445068359f, + (float16_t)0.620605469f, (float16_t)0.442626953f, (float16_t)0.6171875f, (float16_t)-0.249755859f, + (float16_t)0.20690918f, (float16_t)0.666992188f, (float16_t)0.226074219f, (float16_t)-0.401611328f, + (float16_t)-0.116271973f, (float16_t)0.734863281f, (float16_t)-0.595703125f, (float16_t)0.0378112793f, + (float16_t)0.363525391f, (float16_t)-0.116149902f, (float16_t)-0.732910156f, (float16_t)0.676757812f, + (float16_t)0.466552734f, (float16_t)-0.736328125f, (float16_t)-0.554199219f, (float16_t)-0.571289062f, + (float16_t)0.506835938f, (float16_t)0.0316467285f, (float16_t)0.387695312f, (float16_t)0.0646972656f, + (float16_t)-0.177001953f, (float16_t)0.224609375f, (float16_t)-0.502929688f, (float16_t)0.167358398f, + (float16_t)0.0319824219f, (float16_t)0.236694336f, (float16_t)-0.452392578f, (float16_t)0.226928711f, + (float16_t)0.473876953f, (float16_t)0.386474609f, (float16_t)-0.133422852f, (float16_t)0.563964844f, + (float16_t)-0.606933594f, (float16_t)0.537109375f, (float16_t)0.156982422f, (float16_t)-0.608398438f, + (float16_t)-0.11151123f, (float16_t)0.671875f, (float16_t)-0.344482422f, (float16_t)-0.0188903809f, + (float16_t)0.500976562f, (float16_t)0.234130859f, (float16_t)-0.372314453f, (float16_t)0.304931641f, + (float16_t)-0.448242188f, (float16_t)-0.728027344f, (float16_t)-0.23840332f, (float16_t)-0.3515625f, + (float16_t)-0.462646484f, (float16_t)-0.592773438f, (float16_t)-0.563964844f, (float16_t)0.104309082f, + (float16_t)0.0882568359f, (float16_t)-0.613769531f, (float16_t)0.198364258f, (float16_t)-0.745605469f, + (float16_t)-0.0478515625f, (float16_t)0.578125f, (float16_t)0.309326172f, (float16_t)-0.353271484f, + (float16_t)-0.524414062f, (float16_t)-0.0983886719f, (float16_t)-0.425048828f, (float16_t)-0.35546875f, + (float16_t)0.431152344f, (float16_t)0.631347656f, (float16_t)-0.415039062f, (float16_t)-0.0670166016f, + (float16_t)-0.657714844f, (float16_t)-0.6796875f, (float16_t)-0.600585938f, (float16_t)-0.65234375f, + (float16_t)0.702636719f, (float16_t)0.443359375f, (float16_t)-0.226074219f, (float16_t)0.358642578f, + (float16_t)-0.485351562f, (float16_t)0.596191406f, (float16_t)-0.669433594f, (float16_t)-0.348632812f, + (float16_t)-0.248413086f, (float16_t)0.72265625f, (float16_t)-0.502441406f, (float16_t)0.606445312f, + (float16_t)-0.257568359f, (float16_t)-0.293212891f, (float16_t)0.387939453f, (float16_t)-0.244628906f, + (float16_t)-0.483154297f, (float16_t)-0.383789062f, (float16_t)-0.264160156f, (float16_t)-0.261230469f, + (float16_t)0.603515625f, (float16_t)-0.280517578f, (float16_t)-0.0911254883f, (float16_t)0.546875f, + (float16_t)-0.32421875f, (float16_t)-0.0156402588f, (float16_t)-0.676269531f, (float16_t)-0.284667969f, + (float16_t)-0.684570312f, (float16_t)-0.246582031f, (float16_t)-0.464355469f, (float16_t)-0.293701172f, + (float16_t)-0.0574951172f, (float16_t)0.391601562f, (float16_t)-0.169311523f, (float16_t)-0.518066406f, + (float16_t)0.463623047f, (float16_t)0.216064453f, (float16_t)0.696289062f, (float16_t)0.36328125f, + (float16_t)0.0411071777f, (float16_t)0.365722656f, (float16_t)0.628417969f, (float16_t)0.107055664f, + (float16_t)0.0829467773f, (float16_t)0.0581665039f, (float16_t)-0.336425781f, (float16_t)-0.308105469f, + (float16_t)-0.390136719f, (float16_t)-0.369140625f, (float16_t)-0.642578125f, (float16_t)-0.602050781f, + (float16_t)0.674804688f, (float16_t)0.573242188f, (float16_t)-0.623046875f, (float16_t)-0.245483398f, + (float16_t)-0.597167969f, (float16_t)0.577636719f, (float16_t)0.528320312f, (float16_t)-0.120910645f, + (float16_t)0.266601562f, (float16_t)0.0418701172f, (float16_t)-0.6328125f, (float16_t)-0.549316406f, + (float16_t)0.109924316f, (float16_t)0.403808594f, (float16_t)-0.711914062f, (float16_t)-0.0280761719f, + (float16_t)0.385498047f, (float16_t)0.141601562f, 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(float16_t)0.706542969f, (float16_t)0.261962891f, (float16_t)0.0345458984f, (float16_t)-0.557617188f, + (float16_t)-0.186279297f, (float16_t)-0.727050781f, (float16_t)-0.322998047f, (float16_t)-0.647949219f, + (float16_t)0.51171875f, (float16_t)0.588378906f, (float16_t)-0.624511719f, (float16_t)0.289794922f, + (float16_t)-0.363769531f, (float16_t)-0.661621094f, (float16_t)0.301757812f, (float16_t)-0.000675678253f, + (float16_t)-0.416015625f, (float16_t)-0.128417969f, (float16_t)0.276611328f, (float16_t)0.198486328f, + (float16_t)-0.111999512f, (float16_t)0.390625f, (float16_t)0.677246094f, (float16_t)-0.698242188f, + (float16_t)0.124023438f, (float16_t)0.484375f, (float16_t)0.592773438f, (float16_t)0.262939453f, + (float16_t)-0.694824219f, (float16_t)-0.592773438f, (float16_t)-0.420654297f, (float16_t)-0.563476562f, + (float16_t)-0.591796875f, (float16_t)-0.724121094f, (float16_t)0.397705078f, (float16_t)0.455078125f, + (float16_t)-0.279296875f, (float16_t)0.704589844f, 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(float16_t)0.239013672f, (float16_t)-0.0653076172f, (float16_t)0.297607422f, (float16_t)0.0895385742f, + (float16_t)-0.528320312f, (float16_t)0.311035156f, (float16_t)-0.0784912109f, (float16_t)0.480712891f, + (float16_t)-0.342285156f, (float16_t)0.218994141f, (float16_t)0.671386719f, (float16_t)0.0678710938f, + (float16_t)0.411865234f, (float16_t)0.459716797f, (float16_t)0.224609375f, (float16_t)0.155883789f, + (float16_t)0.557128906f, (float16_t)-0.630371094f, (float16_t)-0.0310974121f, (float16_t)-0.658691406f, + (float16_t)-0.568847656f, (float16_t)0.625976562f, (float16_t)0.133178711f, (float16_t)-0.14465332f, + (float16_t)0.535644531f, (float16_t)0.257080078f, (float16_t)0.506835938f, (float16_t)0.625976562f, + (float16_t)-0.151733398f, (float16_t)0.588378906f, (float16_t)0.239746094f, (float16_t)0.294433594f, + (float16_t)0.65234375f, (float16_t)-0.333007812f, (float16_t)0.328857422f, (float16_t)-0.54296875f, + (float16_t)0.708496094f, (float16_t)0.546386719f, (float16_t)-0.608886719f, (float16_t)0.504394531f, + (float16_t)0.102416992f, (float16_t)-0.167358398f, (float16_t)-0.352050781f, (float16_t)0.694335938f, + (float16_t)0.686523438f, (float16_t)-0.193481445f, (float16_t)0.586914062f, (float16_t)0.405761719f, + (float16_t)-0.736328125f, (float16_t)0.603515625f, (float16_t)-0.284179688f, (float16_t)-0.696289062f, + (float16_t)0.0233001709f, (float16_t)-0.576171875f, (float16_t)0.480957031f, (float16_t)-0.251464844f, + (float16_t)-0.705078125f, (float16_t)0.735839844f, (float16_t)0.189941406f, (float16_t)-0.476318359f, + (float16_t)-0.223144531f, (float16_t)-0.581542969f, (float16_t)0.173828125f, (float16_t)-0.729980469f, + (float16_t)0.267578125f, (float16_t)0.0381164551f, (float16_t)-0.208984375f, (float16_t)0.520996094f, + (float16_t)-0.741210938f, (float16_t)-0.0614624023f, (float16_t)-0.546875f, (float16_t)-0.638183594f, + (float16_t)-0.0120391846f, (float16_t)-0.630859375f, (float16_t)-0.593261719f, (float16_t)-0.139892578f, + (float16_t)0.0437927246f, (float16_t)0.369873047f, (float16_t)0.218505859f, (float16_t)-0.431152344f, + (float16_t)-0.398193359f, (float16_t)-0.0540771484f, (float16_t)0.121398926f, (float16_t)0.214355469f, + (float16_t)-0.17565918f, (float16_t)-0.749511719f, (float16_t)0.147216797f, (float16_t)0.4140625f, + (float16_t)0.499267578f, (float16_t)0.109069824f, (float16_t)0.0287475586f, (float16_t)-0.0944213867f, + (float16_t)0.465332031f, (float16_t)-0.555664062f, (float16_t)-0.453125f, (float16_t)0.482910156f, + (float16_t)-0.166381836f, (float16_t)-0.431640625f, (float16_t)-0.094543457f, (float16_t)-0.532226562f, + (float16_t)0.498779297f, (float16_t)-0.416259766f, (float16_t)-0.134643555f, (float16_t)-0.717773438f, + (float16_t)-0.442382812f, (float16_t)0.213256836f, (float16_t)0.0957641602f, (float16_t)-0.0573730469f, + (float16_t)0.449462891f, (float16_t)-0.229003906f, (float16_t)-0.0444946289f, (float16_t)0.423095703f, + (float16_t)-0.187255859f, (float16_t)0.226928711f, (float16_t)-0.421875f, (float16_t)-0.238525391f, + (float16_t)-0.397949219f, (float16_t)0.668945312f, (float16_t)0.470703125f, (float16_t)0.592285156f, + (float16_t)0.740722656f, (float16_t)0.630859375f, (float16_t)0.537597656f, (float16_t)-0.491455078f, + (float16_t)-0.0541992188f, (float16_t)0.673828125f, (float16_t)-0.0861206055f, (float16_t)-0.540527344f, + (float16_t)0.0444946289f, (float16_t)0.500488281f, (float16_t)0.514648438f, (float16_t)-0.210205078f, + (float16_t)0.273193359f, (float16_t)0.297363281f, (float16_t)-0.20324707f, (float16_t)0.595703125f, + (float16_t)-0.336914062f, (float16_t)-0.137939453f, (float16_t)0.415283203f, (float16_t)0.00444412231f, + (float16_t)-0.702148438f, (float16_t)-0.428222656f, (float16_t)0.557128906f, (float16_t)-0.385986328f, + (float16_t)0.338378906f, (float16_t)0.0659790039f, (float16_t)-0.264404297f, (float16_t)0.213500977f, + (float16_t)0.3515625f, (float16_t)-0.463623047f, (float16_t)-0.318603516f, (float16_t)-0.484375f, + (float16_t)-0.2734375f, (float16_t)-0.729003906f, (float16_t)-0.150390625f, (float16_t)-0.254394531f, + (float16_t)0.203613281f, (float16_t)-0.659667969f, (float16_t)0.134887695f, (float16_t)-0.364013672f, + (float16_t)-0.0152130127f, (float16_t)-0.0034160614f, (float16_t)-0.119018555f, (float16_t)0.325439453f, + (float16_t)0.2890625f, (float16_t)-0.149414062f, (float16_t)-0.65625f, (float16_t)0.311279297f, + (float16_t)0.0688476562f, (float16_t)-0.0516357422f, (float16_t)0.131469727f, (float16_t)0.637695312f, + (float16_t)0.431884766f, (float16_t)0.006980896f, (float16_t)0.532714844f, (float16_t)-0.51953125f, + (float16_t)0.704101562f, (float16_t)-0.619628906f, (float16_t)0.528808594f, (float16_t)-0.709472656f, + (float16_t)-0.29296875f, (float16_t)0.62890625f, (float16_t)-0.490966797f, (float16_t)-0.462890625f, + (float16_t)-0.502441406f, (float16_t)-0.652832031f, (float16_t)0.106323242f, (float16_t)0.611328125f, + (float16_t)-0.483154297f, (float16_t)0.440917969f, (float16_t)0.508300781f, (float16_t)0.44140625f, + (float16_t)-0.302734375f, (float16_t)0.0591125488f, (float16_t)0.426757812f, (float16_t)0.00724029541f, + (float16_t)-0.250976562f, (float16_t)-0.521484375f, (float16_t)0.454589844f, (float16_t)-0.277099609f, + (float16_t)-0.486572266f, (float16_t)0.547851562f, (float16_t)-0.531738281f, (float16_t)-0.655761719f, + (float16_t)-0.0546875f, (float16_t)-0.471435547f, (float16_t)0.505859375f, (float16_t)-0.0535888672f, + (float16_t)0.329345703f, (float16_t)-0.643554688f, (float16_t)0.555664062f, (float16_t)-0.051940918f, + (float16_t)0.0617370605f, (float16_t)0.353271484f, (float16_t)0.233642578f, (float16_t)-0.299560547f, + (float16_t)0.743164062f, (float16_t)0.53515625f, (float16_t)-0.3203125f, (float16_t)-0.257080078f, + (float16_t)-0.0350341797f, (float16_t)-0.431396484f, (float16_t)-0.345458984f, (float16_t)-0.484863281f, + (float16_t)0.260009766f, (float16_t)0.118713379f, (float16_t)0.123474121f, (float16_t)-0.0199432373f, + (float16_t)0.147216797f, (float16_t)0.00428390503f, (float16_t)0.309082031f, (float16_t)-0.478759766f, + (float16_t)-0.155883789f, (float16_t)0.4921875f, (float16_t)-0.375f, (float16_t)-0.085144043f, + (float16_t)0.635253906f, (float16_t)-0.0817260742f, (float16_t)-0.306152344f, (float16_t)-0.353515625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/biases_data.h new file mode 100644 index 00000000..25d35302 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_nhwc_tuned_f32_biases_data[16] = {0.431395292f, + -0.0834989697f, + -0.477899998f, + 0.107916646f, + -0.0680970326f, + 0.445025265f, + 0.128233626f, + 0.408402383f, + 0.306879491f, + -0.370227635f, + 0.397327513f, + 0.325068325f, + -0.351979762f, + -0.380198926f, + -0.241995156f, + 0.05359561f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/config_data.h new file mode 100644 index 00000000..606b7cf3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_K5_OPT_NHWC_TUNED_F32_INPUT_BATCHES 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_INPUT_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_INPUT_W 21 +#define CONV_K5_OPT_NHWC_TUNED_F32_IN_CH 16 +#define CONV_K5_OPT_NHWC_TUNED_F32_FILTER_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_FILTER_W 5 +#define CONV_K5_OPT_NHWC_TUNED_F32_OUT_CH 16 +#define CONV_K5_OPT_NHWC_TUNED_F32_STRIDE_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_STRIDE_W 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_PADDING_H 0 +#define CONV_K5_OPT_NHWC_TUNED_F32_PADDING_W 0 +#define CONV_K5_OPT_NHWC_TUNED_F32_DILATION_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_DILATION_W 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_OUTPUT_H 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_OUTPUT_W 17 +#define CONV_K5_OPT_NHWC_TUNED_F32_OUTPUT_C 16 +#define CONV_K5_OPT_NHWC_TUNED_F32_DST_SIZE 272 +#define CONV_K5_OPT_NHWC_TUNED_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_K5_OPT_NHWC_TUNED_F32_USE_WRAPPER 1 +#define CONV_K5_OPT_NHWC_TUNED_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_K5_OPT_NHWC_TUNED_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/input_data.h new file mode 100644 index 00000000..f396d4e7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/input_data.h @@ -0,0 +1,50 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_nhwc_tuned_f32_input_data[336] = { + 0.435494006f, 0.132680461f, -0.794884384f, -0.369759738f, -0.378527284f, -0.785390615f, 0.489964247f, + -0.6509642f, -0.563253582f, 0.514658868f, 0.695590794f, -0.384232402f, 0.182469606f, -0.762175858f, + 0.236468762f, 0.957910419f, -0.0322582051f, -0.296129405f, -0.373568892f, -0.516255558f, 0.921832263f, + 0.153259784f, -0.864229023f, 0.384667158f, -0.0984411687f, 0.685056448f, -0.603055179f, 0.51252228f, + 0.0801861361f, -0.851055205f, 0.165712193f, 0.263856053f, 0.610153913f, 0.361765444f, 0.219340608f, + 0.0300564282f, -0.227628648f, -0.836427391f, 0.520481765f, 0.989691615f, -0.572687626f, 0.679396808f, + 0.110989057f, 0.82134831f, 0.331495047f, -0.124683924f, 0.637098193f, -0.83670634f, 0.794526994f, + 0.424157172f, -0.00509604858f, -0.0544955395f, -0.841826439f, -0.203863859f, -0.302709877f, 0.498449236f, + 0.595189154f, 0.813760102f, 0.113521367f, -0.0324528664f, 0.714221179f, 0.78312099f, -0.45767051f, + 0.686491609f, -0.966420293f, -0.615708649f, -0.209708527f, -0.370625556f, -0.976628542f, 0.0169221703f, + 0.0418218076f, 0.824694037f, 0.407472879f, 0.739908814f, 0.868062079f, 0.597308755f, -0.00249625207f, + 0.802273691f, 0.0518682636f, 0.611075521f, 0.183382973f, 0.581284344f, 0.600741684f, -0.70614177f, + -0.404757142f, -0.654005647f, -0.330067813f, -0.785530388f, 0.691774905f, 0.414932251f, 0.588046253f, + 0.526814699f, -0.00129921595f, -0.629872501f, -0.0203061197f, -0.657383263f, 0.0142654199f, 0.880375087f, + -0.562911093f, 0.840039551f, 0.451084316f, -0.838891506f, -0.852279544f, 0.274951994f, 0.851150274f, + 0.572604358f, -0.163597256f, -0.788126886f, -0.970914781f, 0.537715256f, 0.722407162f, 0.380907536f, + 0.365939051f, -0.370421261f, 0.768505931f, -0.732245624f, -0.98737669f, 0.653145611f, 0.63517487f, + -0.321608901f, -0.0830336735f, -0.160231709f, 0.529053688f, 0.133824423f, 0.0622359253f, -0.36044699f, + 0.286805362f, -0.277783215f, -0.993171215f, 0.232551545f, -0.263600886f, 0.299180329f, -0.287907034f, + -0.831274748f, 0.185798943f, -0.395829499f, 0.527202666f, 0.818833649f, 0.516930223f, 0.194634184f, + -0.0254293643f, -0.232338384f, -0.141129196f, -0.360064745f, -0.290200144f, 0.779232204f, 0.35026297f, + -0.275589228f, 0.943414271f, 0.691845f, 0.824964941f, -0.764711916f, 0.881557465f, 0.946840763f, + 0.881041706f, 0.60910219f, 0.763843954f, 0.703363419f, -0.266483426f, -0.998368323f, 0.446078449f, + 0.256550372f, 0.909602106f, -0.261242509f, -0.846415699f, 0.484417677f, -0.274554431f, 0.223784715f, + -0.125345886f, -0.214584932f, 0.675582111f, -0.189904392f, -0.488156199f, 0.421176374f, -0.203004614f, + 0.895916224f, -0.0676516742f, -0.779058218f, 0.287413538f, -0.858559012f, -0.711937249f, -0.0293534677f, + -0.39057982f, -0.623200357f, 0.00544587523f, -0.864455879f, 0.419265896f, -0.260115951f, -0.174477741f, + -0.677142024f, 0.341864556f, -0.409169495f, 0.192596197f, -0.789492965f, 0.370146364f, 0.529816926f, + 0.711319268f, 0.705010533f, 0.848596632f, -0.57497263f, -0.19566831f, 0.592286408f, -0.610037446f, + 0.0872359127f, 0.329724729f, -0.32792142f, -0.988648236f, 0.074460417f, 0.328960061f, 0.273174435f, + 0.158303231f, 0.267387092f, -0.227564692f, 0.609112263f, 0.863221347f, -0.508570611f, 0.941503406f, + -0.282903612f, 0.830573559f, 0.0337781608f, -0.0724056363f, 0.676562905f, 0.000132818008f, 0.657934844f, + 0.811071754f, 0.855508387f, 0.742142379f, 0.170307383f, 0.036144536f, -0.4507294f, -0.163968667f, + -0.582895577f, 0.115297988f, -0.387872577f, 0.74341166f, 0.574510753f, 0.435796022f, 0.111258328f, + 0.794281662f, -0.413476348f, 0.340663046f, 0.752978384f, 0.304688126f, 0.66065979f, 0.390411139f, + -0.542935729f, 0.440748453f, -0.382386386f, 0.963480711f, -0.463235915f, -0.41581139f, -0.450104535f, + -0.414262533f, -0.248926446f, 0.623212993f, 0.786439121f, -0.610958338f, -0.516473889f, -0.618593276f, + 0.220691711f, 0.41220963f, -0.95209825f, -0.0754586384f, -0.207674503f, -0.87914896f, -0.23369354f, + -0.0712141991f, -0.842508018f, 0.566036522f, 0.259191155f, -0.915151656f, -0.894538343f, 0.898032665f, + -0.665691197f, 0.0749177486f, 0.948349535f, 0.717401624f, 0.824958086f, -0.0581027977f, -0.349822164f, + -0.537192702f, 0.338286191f, 0.434601814f, -0.566108584f, -0.02463587f, 0.0937179998f, -0.437079459f, + -0.274638414f, 0.608091116f, -0.162172005f, 0.811117411f, -0.906367242f, 0.311998785f, 0.728069305f, + 0.37569353f, -0.283733964f, -0.652404428f, 0.343059868f, 0.33509478f, -0.894809902f, 0.514340997f, + 0.572088242f, -0.126464248f, 0.459610313f, -0.69417578f, -0.415969372f, -0.201019496f, -0.722317398f, + -0.992956936f, 0.15936102f, -0.0945617035f, -0.0506804287f, 0.638052702f, 0.836457372f, 0.239849791f, + 0.614494681f, 0.6894539f, -0.990945101f, -0.137706354f, 0.531257391f, -0.234690189f, 0.921188116f, + -0.579172909f, 0.927405f, 0.812170386f, 0.216816723f, 0.192607626f, 0.359706402f, 0.196260557f, + 0.0815173611f, 0.654196918f, -0.717423856f, 0.174425036f, -0.941318274f, -0.813306391f, -0.769537807f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/output_ref_data.h new file mode 100644 index 00000000..6e61f2cb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/output_ref_data.h @@ -0,0 +1,41 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_nhwc_tuned_f32_output_ref_data[272] = { + 2.96586013f, -1.61030555f, 0.800316215f, -1.71078348f, 1.10521197f, 2.01522732f, 1.62880898f, + -0.0372112393f, 0.0225284696f, -1.55828428f, -1.45488691f, 0.140548021f, 0.112293065f, -2.79486084f, + -2.27517676f, -1.41220665f, 1.44448781f, 0.944725275f, 1.67081833f, -0.263073504f, -1.53949857f, + -1.38182378f, 2.27184916f, -0.829191864f, 1.80098248f, 1.3120153f, 1.56806743f, 2.52391696f, + 0.503289223f, -1.37117493f, 0.517573774f, -0.631317914f, 1.39379358f, -0.619395733f, 3.3266542f, + 1.64601088f, -0.107546732f, 0.695907593f, -0.826681256f, -0.889250457f, 0.843729019f, -1.78248024f, + 1.92049444f, 1.38949287f, 0.39246127f, 3.7454114f, 2.21665525f, 0.114169717f, 0.561234474f, + -2.3198669f, -3.22918558f, -0.120379336f, 4.0403676f, 3.5316391f, 0.251827985f, 0.907670617f, + -0.872761369f, 2.64062285f, -1.6849581f, -2.60556674f, -1.70851946f, 3.17791176f, 0.142277777f, + -0.40062207f, -0.796772718f, -1.3956964f, 3.81011295f, 0.855402827f, -0.779289126f, 1.75428939f, + -2.58485293f, -4.96665621f, -3.16672826f, 0.941860735f, -2.57323647f, 0.139535785f, 0.771929502f, + 0.642117143f, 1.99254322f, 1.74160981f, 2.16577077f, 1.41349745f, -2.1164279f, 3.03979278f, + -0.0473150089f, 2.65290833f, 5.87908411f, -0.192494988f, -0.151567519f, -2.36049485f, 4.51201773f, + -2.61661839f, -2.5199964f, -1.24539161f, -2.10924602f, 0.782134414f, 3.63685942f, 2.12349343f, + -3.12566805f, 1.10165727f, -0.59318763f, 4.75552416f, -1.06200647f, -0.76170224f, 1.48622501f, + -0.62195313f, -2.11523604f, 1.73895562f, 0.763679385f, -0.417208195f, -0.797458351f, 3.72087264f, + 1.66523743f, -1.37880552f, 0.164304286f, -1.56749845f, -0.643105626f, -1.76931f, 2.5127871f, + -2.92408013f, 1.42032659f, 1.49675512f, 0.218547225f, 2.9186213f, -0.286001593f, -0.562008262f, + 3.90392804f, -5.12859869f, -0.96671164f, -3.90714121f, 1.5567143f, -0.159603029f, -0.363923192f, + 2.48679233f, 2.53627229f, -1.23216081f, -1.39757824f, -2.02304387f, 2.63959098f, 1.73040819f, + -0.019720763f, -0.0558576882f, 1.79992247f, 0.544436753f, 2.57691407f, 2.52386761f, -2.34583187f, + 0.485561669f, -1.41248524f, 4.88274193f, -2.50341105f, 0.107304096f, -2.0217793f, -0.930109084f, + -3.13963294f, -1.04417348f, -1.10560358f, 0.649549484f, -4.97436237f, 4.96458673f, 2.10752487f, + 1.95838165f, -1.19625401f, -1.93777752f, -1.32905173f, -0.80570823f, 2.15432525f, -4.8824029f, + 2.02474618f, 3.45602298f, -2.43536639f, 2.05770898f, 0.0334290266f, 1.33269703f, -0.0370338261f, + 1.93546653f, -2.27276516f, 1.85326648f, -0.224232584f, -0.455887258f, -1.60657787f, -0.973074734f, + 1.17267811f, -2.24226427f, -1.57459939f, -1.5095911f, 0.0876689255f, 0.953907847f, 1.08233178f, + -1.22129571f, 3.45874023f, -2.72300982f, -4.2237587f, -2.30025482f, 0.27470389f, -1.16806114f, + -1.03011477f, 1.1783855f, -1.35549462f, 3.89171576f, 0.719806135f, -1.34117424f, 2.67633772f, + -1.21372771f, -1.19825149f, 1.505059f, -0.257867038f, 0.39897418f, 0.432991773f, 1.82691503f, + -1.12909687f, 1.34697902f, 1.17900705f, 3.30021977f, 2.5636692f, -4.49089479f, -4.28864193f, + -0.748748243f, 0.23327519f, -4.19601965f, -4.97584581f, 2.1260066f, -2.37319422f, 1.23992825f, + 1.83268309f, 2.70370269f, -3.68187475f, 2.99367523f, -0.258822352f, 2.33442307f, 4.62367535f, + -0.274744332f, -1.85751462f, 0.662097037f, 0.927315831f, 2.63178706f, -0.122819528f, -4.80652046f, + -2.64660263f, 3.29442167f, -1.84931397f, -2.45599198f, -3.16947269f, 1.71529007f, -0.16535899f, + -1.04160404f, 2.76024985f, -0.719931781f, 5.31402826f, 0.183470249f, 0.07586357f, 1.47929037f, + 1.1192112f, -4.93400431f, 0.743201017f, -2.3195312f, -0.532141685f, -1.3532306f, 2.80009365f, + -2.07745385f, -4.70332623f, 1.03480971f, -1.66813374f, -1.70234179f, 2.36047626f, -4.98367977f, + 4.74888563f, -1.29237986f, 1.85825396f, -0.483627915f, 1.06687045f, 2.25210547f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/weights_data.h new file mode 100644 index 00000000..1185c71b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_k5_opt_nhwc_tuned_f32/weights_data.h @@ -0,0 +1,216 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_k5_opt_nhwc_tuned_f32_weights_data[1280] = { + -0.139129683f, 0.742426574f, -0.565499485f, -0.588739157f, -0.342155814f, -0.411696345f, + -0.0148457754f, -0.102153756f, -0.339889258f, 0.119073749f, 0.295338631f, 0.646286368f, + -0.205873713f, -0.493302077f, -0.299271345f, 0.474712402f, 0.453965813f, -0.25528872f, + -0.384336442f, 0.00837817229f, -0.500615239f, -0.443701237f, 0.27972582f, -0.00518416381f, + 0.157684833f, -0.414049804f, -0.185492411f, 0.343843848f, -0.106742762f, -0.166200966f, + 0.536883056f, -0.0100392373f, -0.686057031f, -0.534196079f, 0.740264297f, -0.619063497f, + -0.0156864747f, 0.388860047f, -0.226790056f, -0.152964532f, -0.330135435f, 0.578483999f, + -0.493567884f, 0.641471565f, -0.0364484563f, 0.0362207554f, 0.45020932f, -0.499565482f, + -0.359762937f, -0.449863613f, 0.082385093f, -0.396918237f, -0.282034665f, 0.265461445f, + -0.445825964f, -0.282388449f, -0.442355275f, -0.293310642f, -0.263655692f, 0.278956801f, + 0.679200292f, 0.490671128f, -0.476830959f, -0.489645422f, 0.441622764f, 0.592307031f, + 0.144535214f, -0.357343882f, -0.652949333f, 0.265742302f, 0.353766024f, -0.0353296883f, + -0.346164078f, -0.568538308f, 0.553156257f, -0.20241645f, -0.679879248f, 0.492072076f, + -0.172972962f, 0.0174297858f, -0.149508119f, 0.320530802f, 0.559166968f, 0.230128706f, + 0.438693285f, 0.281224757f, -0.368448555f, 0.407574683f, -0.264133185f, -0.386509538f, + 0.719250441f, 0.410439909f, -0.746290326f, -0.323027819f, 0.223149896f, -0.0841484442f, + -0.591943383f, -0.724641562f, 0.607951164f, -0.356516093f, -0.442022175f, 0.410259545f, + -0.0790885687f, 0.0677567944f, -0.00995511655f, 0.215744093f, -0.716654539f, -0.408132136f, + 0.0826217309f, -0.56162715f, 0.617471814f, 0.300381154f, -0.581521571f, 0.375399113f, + 0.609102547f, 0.134665743f, -0.258463085f, -0.45715639f, 0.569618821f, 0.0703868419f, + 0.330817401f, -0.633950412f, 0.383675635f, 0.192984343f, 0.036354769f, 0.350604445f, + -0.512136936f, 0.131645992f, -0.104700141f, -0.512889922f, -0.0420349017f, 0.6403175f, + 0.524649262f, -0.0281473156f, 0.432825178f, 0.606466413f, -0.711534977f, 0.537370563f, + -0.436366439f, 0.610525608f, 0.679858565f, 0.410266042f, -0.44498831f, -0.710416138f, + 0.453215659f, 0.283484817f, -0.668617785f, -0.152414009f, 0.549557805f, -0.537961662f, + 0.278355151f, -0.385357291f, 0.179861873f, 0.17251344f, 0.662801385f, -0.375659883f, + 0.678783178f, -0.336576074f, 0.337174714f, -0.377717555f, 0.391326487f, -0.450526774f, + 0.340210497f, -0.555089772f, 0.0158330351f, 0.145770684f, 0.210753486f, 0.353563637f, + -0.685366571f, -0.160905108f, 0.285665929f, -0.445180744f, 0.62056762f, 0.442600191f, + 0.617157817f, -0.249785364f, 0.206932411f, 0.66686666f, 0.226033539f, -0.401709259f, + -0.116301797f, 0.73480159f, -0.595643759f, 0.0378099605f, 0.363426775f, -0.11614956f, + -0.732913375f, 0.67695725f, 0.466462344f, -0.736495256f, -0.554380119f, -0.571119368f, + 0.506867707f, 0.0316491313f, 0.387699783f, 0.0646719486f, -0.177033156f, 0.224584326f, + -0.50281179f, 0.16740191f, 0.0319729708f, 0.236732319f, -0.452372015f, 0.226883292f, + 0.473800808f, 0.38648355f, -0.133453131f, 0.563796401f, -0.60710597f, 0.536943913f, + 0.15695627f, -0.608416617f, -0.111489542f, 0.672018468f, -0.344475538f, -0.0188910607f, + 0.501107275f, 0.234156534f, -0.37223658f, 0.304819822f, -0.448361933f, -0.728002429f, + -0.238439918f, -0.351583302f, -0.462695688f, -0.592882633f, -0.563798189f, 0.104323715f, + 0.0882803053f, -0.613684952f, 0.198310807f, -0.745572329f, -0.0478503965f, 0.578072131f, + 0.309321314f, -0.353306144f, -0.524233758f, -0.0984098911f, -0.425099909f, -0.355457991f, + 0.431239069f, 0.631299317f, -0.415106177f, -0.0670014098f, -0.65776968f, -0.679467678f, + -0.60074234f, 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-0.428174615f, + 0.557223499f, -0.386099756f, 0.338376641f, 0.065950796f, -0.264358878f, 0.2134424f, + 0.351595879f, -0.463668257f, -0.318602771f, -0.484445781f, -0.273377091f, -0.729199588f, + -0.150423184f, -0.254448235f, 0.203644827f, -0.659611404f, 0.134948179f, -0.363920957f, + -0.0152116474f, -0.00341531727f, -0.119048528f, 0.325466186f, 0.288950354f, -0.149397448f, + -0.656369567f, 0.31134066f, 0.0688341632f, -0.0516446792f, 0.131459072f, 0.637832165f, + 0.431803495f, 0.00697923079f, 0.532571435f, -0.51973927f, 0.703867257f, -0.619641423f, + 0.528962016f, -0.70929569f, -0.293058425f, 0.628746867f, -0.491043359f, -0.462887734f, + -0.502247751f, -0.652681649f, 0.106324017f, 0.61117655f, -0.483065754f, 0.440994531f, + 0.508458972f, 0.441347361f, -0.30263713f, 0.0591060258f, 0.426669747f, 0.00723920763f, + -0.25098899f, -0.521604657f, 0.45462966f, -0.277051806f, -0.486611933f, 0.547656119f, + -0.531911075f, -0.655550599f, -0.0546808802f, -0.471477151f, 0.505708933f, -0.0535778627f, + 0.32939145f, -0.643402994f, 0.555894017f, -0.0519523509f, 0.0617438033f, 0.353217423f, + 0.233685032f, -0.29961881f, 0.743068457f, 0.535057724f, -0.320252508f, -0.257092327f, + -0.035044048f, -0.431417525f, -0.345514715f, -0.484877646f, 0.260081679f, 0.118741676f, + 0.123459205f, -0.0199507028f, 0.14727053f, 0.00428542355f, 0.30919531f, -0.478638977f, + -0.155932754f, 0.492117047f, -0.375041842f, -0.0851647407f, 0.635186732f, -0.0817155018f, + -0.306057632f, -0.353471488f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/biases_data.h new file mode 100644 index 00000000..888aea66 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_2x2_f16_biases_data[5] = {(float16_t)-0.0123672485f, + (float16_t)-0.122680664f, + (float16_t)-0.391601562f, + (float16_t)0.495605469f, + (float16_t)-0.170288086f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/config_data.h new file mode 100644 index 00000000..81976d8d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_KERNEL_2X2_F16_INPUT_BATCHES 1 +#define CONV_KERNEL_2X2_F16_INPUT_H 6 +#define CONV_KERNEL_2X2_F16_INPUT_W 7 +#define CONV_KERNEL_2X2_F16_IN_CH 4 +#define CONV_KERNEL_2X2_F16_FILTER_H 2 +#define CONV_KERNEL_2X2_F16_FILTER_W 2 +#define CONV_KERNEL_2X2_F16_OUT_CH 5 +#define CONV_KERNEL_2X2_F16_STRIDE_H 1 +#define CONV_KERNEL_2X2_F16_STRIDE_W 1 +#define CONV_KERNEL_2X2_F16_PADDING_H 0 +#define CONV_KERNEL_2X2_F16_PADDING_W 0 +#define CONV_KERNEL_2X2_F16_DILATION_H 1 +#define CONV_KERNEL_2X2_F16_DILATION_W 1 +#define CONV_KERNEL_2X2_F16_OUTPUT_H 5 +#define CONV_KERNEL_2X2_F16_OUTPUT_W 6 +#define CONV_KERNEL_2X2_F16_OUTPUT_C 5 +#define CONV_KERNEL_2X2_F16_DST_SIZE 150 +#define CONV_KERNEL_2X2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_KERNEL_2X2_F16_USE_WRAPPER 0 +#define CONV_KERNEL_2X2_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_KERNEL_2X2_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/input_data.h new file mode 100644 index 00000000..f3520c1a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/input_data.h @@ -0,0 +1,44 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_2x2_f16_input_data[168] = { + (float16_t)0.744140625f, (float16_t)-0.502441406f, (float16_t)-0.99609375f, (float16_t)-0.883300781f, + (float16_t)0.118164062f, (float16_t)0.815917969f, (float16_t)0.222900391f, (float16_t)-0.9609375f, + (float16_t)-0.394775391f, (float16_t)0.439697266f, (float16_t)0.692871094f, (float16_t)0.904296875f, + (float16_t)-0.283691406f, (float16_t)0.125244141f, (float16_t)-0.383300781f, (float16_t)0.082824707f, + (float16_t)-0.409667969f, (float16_t)0.530761719f, (float16_t)-0.19519043f, (float16_t)0.110412598f, + (float16_t)0.919433594f, (float16_t)-0.0354309082f, (float16_t)0.116088867f, (float16_t)-0.337890625f, + (float16_t)-0.849609375f, (float16_t)-0.634277344f, (float16_t)0.579101562f, (float16_t)-0.487304688f, + (float16_t)-0.181030273f, (float16_t)0.103271484f, (float16_t)0.581054688f, (float16_t)0.584472656f, + (float16_t)0.893554688f, (float16_t)-0.450683594f, (float16_t)0.284423828f, (float16_t)0.511230469f, + (float16_t)0.735351562f, (float16_t)-0.0994873047f, (float16_t)-0.710449219f, (float16_t)0.26171875f, + (float16_t)-0.0532226562f, (float16_t)-0.669433594f, (float16_t)0.247924805f, (float16_t)-0.317382812f, + (float16_t)-0.890136719f, (float16_t)-0.646484375f, (float16_t)0.396240234f, (float16_t)-0.145996094f, + (float16_t)0.648925781f, (float16_t)-0.429931641f, (float16_t)0.189941406f, (float16_t)-0.591796875f, + (float16_t)0.806152344f, (float16_t)0.328613281f, (float16_t)0.0616455078f, (float16_t)0.957519531f, + (float16_t)-0.398193359f, (float16_t)0.231445312f, (float16_t)-0.117553711f, (float16_t)-0.455322266f, + (float16_t)-0.974609375f, (float16_t)-0.0852050781f, (float16_t)0.278320312f, (float16_t)-0.888183594f, + (float16_t)0.805175781f, (float16_t)-0.412353516f, (float16_t)0.463134766f, (float16_t)-0.598144531f, + (float16_t)0.219360352f, (float16_t)0.217529297f, (float16_t)-0.276855469f, (float16_t)-0.313232422f, + (float16_t)-0.169921875f, (float16_t)0.77734375f, (float16_t)0.403564453f, (float16_t)0.837890625f, + (float16_t)-0.708007812f, (float16_t)0.787597656f, (float16_t)0.475830078f, (float16_t)0.437255859f, + (float16_t)0.376953125f, (float16_t)-0.996582031f, (float16_t)-0.812011719f, (float16_t)0.114318848f, + (float16_t)0.177124023f, (float16_t)-0.437255859f, (float16_t)0.808105469f, (float16_t)0.249023438f, + (float16_t)0.84375f, (float16_t)-0.198242188f, (float16_t)0.788574219f, (float16_t)-0.201538086f, + (float16_t)0.447021484f, (float16_t)-0.159179688f, (float16_t)-0.336425781f, (float16_t)0.0569152832f, + (float16_t)0.916015625f, (float16_t)0.684082031f, (float16_t)0.597167969f, (float16_t)0.260253906f, + (float16_t)-0.288085938f, (float16_t)0.834472656f, (float16_t)-0.344726562f, (float16_t)-0.765136719f, + (float16_t)0.317626953f, (float16_t)-0.671875f, (float16_t)0.387939453f, (float16_t)-0.428710938f, + (float16_t)0.306396484f, (float16_t)0.409667969f, (float16_t)-0.762695312f, (float16_t)-0.894042969f, + (float16_t)-0.608398438f, (float16_t)-0.137573242f, (float16_t)0.290771484f, (float16_t)0.54296875f, + (float16_t)0.517089844f, (float16_t)0.460693359f, (float16_t)-0.862304688f, (float16_t)0.999023438f, + (float16_t)0.517089844f, (float16_t)-0.509277344f, (float16_t)-0.90625f, (float16_t)0.2734375f, + (float16_t)-0.0139770508f, (float16_t)0.470458984f, (float16_t)0.947753906f, (float16_t)-0.106994629f, + (float16_t)0.294677734f, (float16_t)0.515625f, (float16_t)-0.490722656f, (float16_t)0.55078125f, + (float16_t)-0.440185547f, (float16_t)-0.159301758f, (float16_t)-0.135375977f, (float16_t)-0.835449219f, + (float16_t)0.725585938f, (float16_t)-0.485595703f, (float16_t)0.111328125f, (float16_t)-0.953613281f, + (float16_t)-0.613769531f, (float16_t)0.701171875f, (float16_t)0.488769531f, (float16_t)0.434814453f, + (float16_t)0.606933594f, (float16_t)0.0819091797f, (float16_t)0.672851562f, (float16_t)0.916015625f, + (float16_t)-0.270507812f, (float16_t)-0.895507812f, (float16_t)-0.292480469f, (float16_t)0.867675781f, + (float16_t)0.131469727f, (float16_t)-0.292724609f, (float16_t)-0.297607422f, (float16_t)0.924316406f, + (float16_t)0.791503906f, (float16_t)-0.780761719f, (float16_t)-0.259277344f, (float16_t)0.325439453f, + (float16_t)-0.0213470459f, (float16_t)0.170654297f, (float16_t)-0.253662109f, (float16_t)-0.5234375f, + (float16_t)0.782226562f, (float16_t)0.120422363f, (float16_t)-0.825683594f, (float16_t)-0.17565918f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/output_ref_data.h new file mode 100644 index 00000000..fb767549 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_2x2_f16_output_ref_data[150] = { + (float16_t)0.447265625f, (float16_t)-1.69042969f, (float16_t)-0.424560547f, (float16_t)1.14746094f, + (float16_t)0.935546875f, (float16_t)-0.281982422f, (float16_t)0.723632812f, (float16_t)-1.53320312f, + (float16_t)0.518066406f, (float16_t)0.741210938f, (float16_t)-0.551757812f, (float16_t)0.104553223f, + (float16_t)-0.309570312f, (float16_t)-0.452392578f, (float16_t)0.355712891f, (float16_t)0.605957031f, + (float16_t)0.268554688f, (float16_t)-0.927734375f, (float16_t)-0.482910156f, (float16_t)-0.250244141f, + (float16_t)-0.032409668f, (float16_t)-0.113525391f, (float16_t)-1.24511719f, (float16_t)1.09375f, + (float16_t)-1.16210938f, (float16_t)-0.0243835449f, (float16_t)-0.948242188f, (float16_t)-1.36425781f, + (float16_t)-0.216918945f, (float16_t)-0.504394531f, (float16_t)0.815429688f, (float16_t)0.599609375f, + (float16_t)0.0737304688f, (float16_t)1.14941406f, (float16_t)-2.15429688f, (float16_t)0.254882812f, + (float16_t)-2.99414062f, (float16_t)-2.14257812f, (float16_t)-0.204345703f, (float16_t)-1.54785156f, + (float16_t)0.200683594f, (float16_t)-0.198974609f, (float16_t)-0.314208984f, (float16_t)-0.709472656f, + (float16_t)0.177124023f, (float16_t)-0.188232422f, (float16_t)-0.209228516f, (float16_t)-0.423095703f, + (float16_t)0.157104492f, (float16_t)-0.900878906f, (float16_t)0.870117188f, (float16_t)0.396240234f, + (float16_t)1.88378906f, (float16_t)2.53320312f, (float16_t)-0.351806641f, (float16_t)-0.028717041f, + (float16_t)-2.24609375f, (float16_t)-0.137939453f, (float16_t)2.16992188f, (float16_t)-0.524414062f, + (float16_t)0.0596313477f, (float16_t)-0.0689086914f, (float16_t)-1.54296875f, (float16_t)-0.0122299194f, + (float16_t)0.332275391f, (float16_t)0.955566406f, (float16_t)0.831542969f, (float16_t)0.268066406f, + (float16_t)1.6796875f, (float16_t)-0.510253906f, (float16_t)0.508300781f, (float16_t)-1.19726562f, + (float16_t)-1.64257812f, (float16_t)0.00984191895f, (float16_t)-0.108032227f, (float16_t)0.958984375f, + (float16_t)1.44433594f, (float16_t)-0.00542449951f, (float16_t)0.425537109f, (float16_t)1.65625f, + (float16_t)-0.281005859f, (float16_t)0.022354126f, (float16_t)-1.26074219f, (float16_t)0.359619141f, + (float16_t)-0.430664062f, (float16_t)-0.442626953f, (float16_t)1.05175781f, (float16_t)-0.759765625f, + (float16_t)-0.178100586f, (float16_t)-0.0108032227f, (float16_t)-0.0776367188f, (float16_t)-0.52734375f, + (float16_t)0.983398438f, (float16_t)3.00195312f, (float16_t)-0.865234375f, (float16_t)-0.813476562f, + (float16_t)-1.15234375f, (float16_t)0.616210938f, (float16_t)1.41015625f, (float16_t)0.401611328f, + (float16_t)0.518066406f, (float16_t)0.192993164f, (float16_t)-0.984863281f, (float16_t)-0.202392578f, + (float16_t)-0.58203125f, (float16_t)1.40039062f, (float16_t)-0.18737793f, (float16_t)0.193847656f, + (float16_t)1.82714844f, (float16_t)1.57128906f, (float16_t)-0.185180664f, (float16_t)2.046875f, + (float16_t)0.562011719f, (float16_t)1.53710938f, (float16_t)-0.431396484f, (float16_t)0.669921875f, + (float16_t)-0.885253906f, (float16_t)-1.56347656f, (float16_t)0.344726562f, (float16_t)-0.921875f, + (float16_t)-0.420410156f, (float16_t)-2.93945312f, (float16_t)-0.870117188f, (float16_t)0.489013672f, + (float16_t)0.58984375f, (float16_t)-0.577148438f, (float16_t)-2.5625f, (float16_t)1.21289062f, + (float16_t)-0.224609375f, (float16_t)1.90136719f, (float16_t)-0.944824219f, (float16_t)-0.633789062f, + (float16_t)0.219238281f, (float16_t)0.642089844f, (float16_t)0.142089844f, (float16_t)-0.666015625f, + (float16_t)-1.18847656f, (float16_t)-1.15332031f, (float16_t)0.800292969f, (float16_t)0.395019531f, + (float16_t)-0.408203125f, (float16_t)1.30273438f, (float16_t)0.0537719727f, (float16_t)-0.640625f, + (float16_t)1.24414062f, (float16_t)-0.262207031f, (float16_t)0.8125f, (float16_t)0.143920898f, + (float16_t)1.75878906f, (float16_t)-0.757324219f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/weights_data.h new file mode 100644 index 00000000..93d8e8d1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f16/weights_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_2x2_f16_weights_data[80] = { + (float16_t)0.324707031f, (float16_t)0.00717544556f, (float16_t)0.460693359f, (float16_t)-0.3046875f, + (float16_t)0.462402344f, (float16_t)0.511230469f, (float16_t)0.110351562f, (float16_t)-0.363525391f, + (float16_t)0.307617188f, (float16_t)0.0407409668f, (float16_t)0.709960938f, (float16_t)-0.382568359f, + (float16_t)-0.636230469f, (float16_t)0.132324219f, (float16_t)0.362060547f, (float16_t)-0.0908203125f, + (float16_t)-0.678222656f, (float16_t)0.691894531f, (float16_t)0.092590332f, (float16_t)-0.295410156f, + (float16_t)-0.149291992f, (float16_t)0.344970703f, (float16_t)0.695800781f, (float16_t)-0.530273438f, + (float16_t)0.237182617f, (float16_t)-0.240234375f, (float16_t)-0.372070312f, (float16_t)-0.0321350098f, + (float16_t)-0.7421875f, (float16_t)0.708496094f, (float16_t)-0.623046875f, (float16_t)-0.688476562f, + (float16_t)-0.216430664f, (float16_t)-0.292724609f, (float16_t)-0.100769043f, (float16_t)0.540527344f, + (float16_t)0.387207031f, (float16_t)-0.0954589844f, (float16_t)0.272460938f, (float16_t)-0.702148438f, + (float16_t)0.298583984f, (float16_t)0.701171875f, (float16_t)0.118469238f, (float16_t)0.535644531f, + (float16_t)-0.727050781f, (float16_t)0.000174403191f, (float16_t)-0.73828125f, (float16_t)0.22265625f, + (float16_t)-0.108337402f, (float16_t)0.212158203f, (float16_t)0.60546875f, (float16_t)-0.308105469f, + (float16_t)0.596191406f, (float16_t)0.165893555f, (float16_t)0.659179688f, (float16_t)-0.606933594f, + (float16_t)-0.59765625f, (float16_t)0.608886719f, (float16_t)0.245605469f, (float16_t)0.345214844f, + (float16_t)-0.274902344f, (float16_t)-0.00626373291f, (float16_t)-0.547363281f, (float16_t)0.228881836f, + (float16_t)-0.0545959473f, (float16_t)0.173828125f, (float16_t)-0.564941406f, (float16_t)0.375976562f, + (float16_t)-0.388671875f, (float16_t)0.658691406f, (float16_t)-0.658691406f, (float16_t)-0.156982422f, + (float16_t)0.703613281f, (float16_t)0.319824219f, (float16_t)0.382324219f, (float16_t)0.681152344f, + (float16_t)0.257568359f, (float16_t)0.316162109f, (float16_t)-0.436523438f, (float16_t)0.0356445312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/biases_data.h new file mode 100644 index 00000000..a3917665 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_2x2_f32_biases_data[5] = {-0.0123699661f, + -0.122685298f, + -0.391565561f, + 0.495593011f, + -0.170338348f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/config_data.h new file mode 100644 index 00000000..8205595a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_KERNEL_2X2_F32_INPUT_BATCHES 1 +#define CONV_KERNEL_2X2_F32_INPUT_H 6 +#define CONV_KERNEL_2X2_F32_INPUT_W 7 +#define CONV_KERNEL_2X2_F32_IN_CH 4 +#define CONV_KERNEL_2X2_F32_FILTER_H 2 +#define CONV_KERNEL_2X2_F32_FILTER_W 2 +#define CONV_KERNEL_2X2_F32_OUT_CH 5 +#define CONV_KERNEL_2X2_F32_STRIDE_H 1 +#define CONV_KERNEL_2X2_F32_STRIDE_W 1 +#define CONV_KERNEL_2X2_F32_PADDING_H 0 +#define CONV_KERNEL_2X2_F32_PADDING_W 0 +#define CONV_KERNEL_2X2_F32_DILATION_H 1 +#define CONV_KERNEL_2X2_F32_DILATION_W 1 +#define CONV_KERNEL_2X2_F32_OUTPUT_H 5 +#define CONV_KERNEL_2X2_F32_OUTPUT_W 6 +#define CONV_KERNEL_2X2_F32_OUTPUT_C 5 +#define CONV_KERNEL_2X2_F32_DST_SIZE 150 +#define CONV_KERNEL_2X2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_KERNEL_2X2_F32_USE_WRAPPER 0 +#define CONV_KERNEL_2X2_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_KERNEL_2X2_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/input_data.h new file mode 100644 index 00000000..fa46d684 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/input_data.h @@ -0,0 +1,26 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_2x2_f32_input_data[168] = { + 0.743952751f, -0.502276123f, -0.995964527f, -0.883170664f, 0.118143246f, 0.815825105f, 0.222845525f, + -0.961120665f, -0.394678414f, 0.43963176f, 0.692692399f, 0.904094517f, -0.283604324f, 0.125293747f, + -0.38323015f, 0.0828007832f, -0.409678668f, 0.530620635f, -0.195240676f, 0.110436231f, 0.919549525f, + -0.0354299545f, 0.116093114f, -0.337776929f, -0.84969914f, -0.634220541f, 0.579310894f, -0.487214804f, + -0.181015149f, 0.103266895f, 0.580860555f, 0.584486961f, 0.893350482f, -0.450776041f, 0.284316063f, + 0.511121571f, 0.735306144f, -0.0994715765f, -0.710413516f, 0.2617172f, -0.053231705f, -0.66954565f, + 0.247868031f, -0.317321539f, -0.890073895f, -0.646476865f, 0.396356016f, -0.146050289f, 0.649025083f, + -0.429903299f, 0.189930156f, -0.591964006f, 0.806165516f, 0.328566551f, 0.0616512857f, 0.957587898f, + -0.398236394f, 0.231469154f, -0.117557146f, -0.455350548f, -0.974625409f, -0.0851784796f, 0.278252393f, + -0.887981474f, 0.805012167f, -0.412402064f, 0.463215172f, -0.598383248f, 0.219305649f, 0.217573255f, + -0.276816189f, -0.313294232f, -0.1699325f, 0.777162373f, 0.403630316f, 0.838081479f, -0.708179176f, + 0.787811935f, 0.47573638f, 0.437312931f, 0.376975447f, -0.996574521f, -0.811847627f, 0.114299297f, + 0.177158058f, -0.437179148f, 0.807955384f, 0.249025747f, 0.843841314f, -0.198275089f, 0.788678348f, + -0.201526627f, 0.446957707f, -0.159148425f, -0.336429387f, 0.056919381f, 0.916023076f, 0.684181273f, + 0.597194612f, 0.260364681f, -0.288098663f, 0.834520638f, -0.344697773f, -0.764932752f, 0.317738354f, + -0.671872318f, 0.387843013f, -0.428691208f, 0.306325525f, 0.409618258f, -0.762543976f, -0.894207358f, + -0.60826087f, -0.13756004f, 0.290742576f, 0.543097377f, 0.51727277f, 0.460799873f, -0.862316012f, + 0.999090731f, 0.51706183f, -0.509501696f, -0.90630281f, 0.273443937f, -0.0139764333f, 0.470454782f, + 0.947809815f, -0.107007913f, 0.294572443f, 0.515668035f, -0.490734935f, 0.55080539f, -0.440157235f, + -0.159252122f, -0.135401636f, -0.835492194f, 0.725475907f, -0.485685766f, 0.111357577f, -0.953384757f, + -0.61359036f, 0.701298177f, 0.488678336f, 0.434890985f, 0.607123256f, 0.0819053426f, 0.672728956f, + 0.915813565f, -0.270424753f, -0.895369649f, -0.292576343f, 0.867777288f, 0.131417423f, -0.292702675f, + -0.297617525f, 0.924327433f, 0.791495442f, -0.780943274f, -0.259392738f, 0.325401932f, -0.0213534199f, + 0.17059578f, -0.253662556f, -0.523435771f, 0.782033443f, 0.120428324f, -0.825588465f, -0.175599039f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/output_ref_data.h new file mode 100644 index 00000000..03bac805 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_2x2_f32_output_ref_data[150] = { + 0.447265476f, -1.69089091f, -0.424597472f, 1.14774275f, 0.935591996f, -0.282099903f, 0.723504484f, + -1.53313351f, 0.517900467f, 0.741314113f, -0.551841438f, 0.104532063f, -0.309568882f, -0.452425897f, + 0.355657458f, 0.605986297f, 0.26850611f, -0.927529991f, -0.482790709f, -0.250355959f, -0.032403037f, + -0.113500223f, -1.24499536f, 1.09405375f, -1.16178024f, -0.0243794061f, -0.948187828f, -1.36395431f, + -0.216971517f, -0.504442692f, 0.81538254f, 0.599541187f, 0.0737250149f, 1.14930749f, -2.15502167f, + 0.254931241f, -2.99393249f, -2.14239979f, -0.204380274f, -1.54822254f, 0.200731575f, -0.198956951f, + -0.314317852f, -0.709391415f, 0.177106395f, -0.188227072f, -0.209219724f, -0.423113614f, 0.157103509f, + -0.900864661f, 0.870330513f, 0.396308959f, 1.88383722f, 2.53238225f, -0.351734102f, -0.0287215989f, + -2.24637175f, -0.137979478f, 2.17008114f, -0.524314582f, 0.0596248284f, -0.0689267665f, -1.54276347f, + -0.0122293234f, 0.332377017f, 0.95553261f, 0.831501961f, 0.268106699f, 1.67968941f, -0.510186911f, + 0.508347869f, -1.19715631f, -1.64293492f, 0.00984549522f, -0.108041681f, 0.959168494f, 1.44449198f, + -0.00542378426f, 0.425583243f, 1.65655839f, -0.281107932f, 0.0223527551f, -1.26107931f, 0.359591573f, + -0.430636168f, -0.442656785f, 1.05154252f, -0.759865522f, -0.1780743f, -0.0108037591f, -0.0776202828f, + -0.527562797f, 0.983610749f, 3.00238204f, -0.865131974f, -0.813632846f, -1.15274704f, 0.616374016f, + 1.40981388f, 0.401552081f, 0.518079758f, 0.192966655f, -0.984750867f, -0.202349961f, -0.582176566f, + 1.40087581f, -0.187366322f, 0.193893254f, 1.82759547f, 1.57095158f, -0.185241669f, 2.04637408f, + 0.562087297f, 1.53723717f, -0.431492269f, 0.669899762f, -0.885247707f, -1.5635941f, 0.344799459f, + -0.921845138f, -0.420486122f, -2.93912363f, -0.870146453f, 0.488994807f, 0.589607596f, -0.577131212f, + -2.5621748f, 1.21308625f, -0.224650681f, 1.90179276f, -0.944926023f, -0.633670866f, 0.21923089f, + 0.642181635f, 0.14203392f, -0.665801227f, -1.18842232f, -1.15336108f, 0.800116718f, 0.395076573f, + -0.408270985f, 1.30240822f, 0.0537707806f, -0.640800178f, 1.24406016f, -0.262199432f, 0.812691867f, + 0.143906415f, 1.7585721f, -0.757459641f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/weights_data.h new file mode 100644 index 00000000..04e274ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_2x2_f32/weights_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_2x2_f32_weights_data[80] = { + 0.324700266f, 0.00717509864f, 0.460612655f, -0.304620564f, 0.462498277f, 0.511044383f, 0.110327423f, + -0.36349833f, 0.307548523f, 0.0407385752f, 0.710043311f, -0.382601947f, -0.636200607f, 0.132319599f, + 0.362122893f, -0.0908258855f, -0.678001821f, 0.691841662f, 0.0925807506f, -0.295434654f, -0.149261966f, + 0.345028937f, 0.695780039f, -0.530510604f, 0.237181202f, -0.240189761f, -0.371980876f, -0.0321214795f, + -0.742051423f, 0.708266616f, -0.623093665f, -0.688462675f, -0.216447994f, -0.292794764f, -0.100766994f, + 0.540583611f, 0.387312412f, -0.0954580754f, 0.272500455f, -0.702226222f, 0.298522323f, 0.701377988f, + 0.118494019f, 0.535639822f, -0.72726357f, 0.00017435808f, -0.738107026f, 0.22266379f, -0.108316183f, + 0.21221216f, 0.605228424f, -0.308201551f, 0.596226871f, 0.165864244f, 0.659064472f, -0.606767774f, + -0.597450256f, 0.608981192f, 0.245616898f, 0.345190525f, -0.274864733f, -0.00626443466f, -0.547403514f, + 0.228924066f, -0.0545899756f, 0.173787355f, -0.56481415f, 0.375875562f, -0.388600647f, 0.658787787f, + -0.658756673f, -0.156990975f, 0.703674316f, 0.319778174f, 0.382388294f, 0.681097448f, 0.257676035f, + 0.316256702f, -0.436420828f, 0.0356505439f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/biases_data.h new file mode 100644 index 00000000..ab72cd84 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_3x3_pad1_f16_biases_data[4] = {(float16_t)-0.21862793f, + (float16_t)0.135620117f, + (float16_t)-0.454589844f, + (float16_t)-0.183227539f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/config_data.h new file mode 100644 index 00000000..1ba55d3f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_KERNEL_3X3_PAD1_F16_INPUT_BATCHES 1 +#define CONV_KERNEL_3X3_PAD1_F16_INPUT_H 3 +#define CONV_KERNEL_3X3_PAD1_F16_INPUT_W 6 +#define CONV_KERNEL_3X3_PAD1_F16_IN_CH 2 +#define CONV_KERNEL_3X3_PAD1_F16_FILTER_H 3 +#define CONV_KERNEL_3X3_PAD1_F16_FILTER_W 3 +#define CONV_KERNEL_3X3_PAD1_F16_OUT_CH 4 +#define CONV_KERNEL_3X3_PAD1_F16_STRIDE_H 1 +#define CONV_KERNEL_3X3_PAD1_F16_STRIDE_W 1 +#define CONV_KERNEL_3X3_PAD1_F16_PADDING_H 1 +#define CONV_KERNEL_3X3_PAD1_F16_PADDING_W 1 +#define CONV_KERNEL_3X3_PAD1_F16_DILATION_H 1 +#define CONV_KERNEL_3X3_PAD1_F16_DILATION_W 1 +#define CONV_KERNEL_3X3_PAD1_F16_OUTPUT_H 3 +#define CONV_KERNEL_3X3_PAD1_F16_OUTPUT_W 6 +#define CONV_KERNEL_3X3_PAD1_F16_OUTPUT_C 4 +#define CONV_KERNEL_3X3_PAD1_F16_DST_SIZE 72 +#define CONV_KERNEL_3X3_PAD1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_KERNEL_3X3_PAD1_F16_USE_WRAPPER 0 +#define CONV_KERNEL_3X3_PAD1_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define CONV_KERNEL_3X3_PAD1_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/input_data.h new file mode 100644 index 00000000..57299dc0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_3x3_pad1_f16_input_data[36] = { + (float16_t)0.51953125f, (float16_t)0.337402344f, (float16_t)-0.0354003906f, (float16_t)-0.638671875f, + (float16_t)-0.56640625f, (float16_t)-0.259033203f, (float16_t)0.320800781f, (float16_t)0.600097656f, + (float16_t)-0.416259766f, (float16_t)0.754882812f, (float16_t)0.216796875f, (float16_t)0.199951172f, + (float16_t)0.436767578f, (float16_t)0.556152344f, (float16_t)-0.797851562f, (float16_t)-0.515136719f, + (float16_t)0.346435547f, (float16_t)-0.433837891f, (float16_t)-0.551269531f, (float16_t)-0.884277344f, + (float16_t)0.341064453f, (float16_t)-0.260498047f, (float16_t)-0.619628906f, (float16_t)0.493164062f, + (float16_t)0.844238281f, (float16_t)-0.0481262207f, (float16_t)0.760742188f, (float16_t)-0.795410156f, + (float16_t)0.126953125f, (float16_t)0.335449219f, (float16_t)-0.438964844f, (float16_t)-0.495849609f, + (float16_t)0.521972656f, (float16_t)-0.681640625f, (float16_t)-0.222412109f, (float16_t)0.0363769531f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/output_ref_data.h new file mode 100644 index 00000000..8d743cae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_3x3_pad1_f16_output_ref_data[72] = { + (float16_t)0.767089844f, (float16_t)0.1015625f, (float16_t)-0.591308594f, (float16_t)-0.980957031f, + (float16_t)0.122802734f, (float16_t)-1.24511719f, (float16_t)-0.872558594f, (float16_t)0.0871582031f, + (float16_t)0.0575866699f, (float16_t)0.255615234f, (float16_t)-0.264160156f, (float16_t)-0.122924805f, + (float16_t)-2.04101562f, (float16_t)0.228393555f, (float16_t)-1.83984375f, (float16_t)0.163696289f, + (float16_t)-0.425048828f, (float16_t)0.962402344f, (float16_t)-0.583496094f, (float16_t)0.873046875f, + (float16_t)-0.420654297f, (float16_t)-0.278808594f, (float16_t)-1.06152344f, (float16_t)0.149902344f, + (float16_t)-0.590332031f, (float16_t)-0.101806641f, (float16_t)-0.47265625f, (float16_t)-1.15722656f, + (float16_t)-0.204711914f, (float16_t)0.960449219f, (float16_t)0.700683594f, (float16_t)-0.353027344f, + (float16_t)0.490722656f, (float16_t)1.11523438f, (float16_t)-1.13085938f, (float16_t)-0.8828125f, + (float16_t)-0.983398438f, (float16_t)0.398193359f, (float16_t)-1.10058594f, (float16_t)0.121887207f, + (float16_t)-0.630859375f, (float16_t)1.57617188f, (float16_t)-1.06445312f, (float16_t)0.199951172f, + (float16_t)-1.17773438f, (float16_t)0.880859375f, (float16_t)-1.10742188f, (float16_t)0.945800781f, + (float16_t)0.296386719f, (float16_t)0.631835938f, (float16_t)-1.05566406f, (float16_t)-1.35742188f, + (float16_t)1.55078125f, (float16_t)-1.01367188f, (float16_t)0.186401367f, (float16_t)-0.61328125f, + (float16_t)0.998535156f, (float16_t)-0.9453125f, (float16_t)-0.913085938f, (float16_t)-0.962890625f, + (float16_t)1.34375f, (float16_t)0.0763549805f, (float16_t)1.36523438f, (float16_t)-1.09472656f, + (float16_t)-0.230102539f, (float16_t)-0.478271484f, (float16_t)-1.60742188f, (float16_t)-0.636230469f, + (float16_t)0.677246094f, (float16_t)1.35351562f, (float16_t)-0.354492188f, (float16_t)-0.124023438f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/weights_data.h new file mode 100644 index 00000000..39b8a1ec --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f16/weights_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_kernel_3x3_pad1_f16_weights_data[72] = { + (float16_t)0.626464844f, (float16_t)-0.202758789f, (float16_t)-0.650390625f, (float16_t)-0.370117188f, + (float16_t)-0.413330078f, (float16_t)-0.658203125f, (float16_t)0.566894531f, (float16_t)-0.216186523f, + (float16_t)0.0550537109f, (float16_t)-0.604980469f, (float16_t)-0.326171875f, (float16_t)-0.639160156f, + (float16_t)-0.69140625f, (float16_t)-0.0350952148f, (float16_t)-0.286621094f, (float16_t)0.720214844f, + (float16_t)-0.533691406f, (float16_t)-0.0775756836f, (float16_t)0.598632812f, (float16_t)0.136352539f, + (float16_t)-0.673828125f, (float16_t)0.699707031f, (float16_t)-0.400878906f, (float16_t)0.674316406f, + (float16_t)-0.364501953f, (float16_t)-0.521484375f, (float16_t)-0.452392578f, (float16_t)0.573730469f, + (float16_t)0.455566406f, (float16_t)-0.618164062f, (float16_t)0.116699219f, (float16_t)-0.533203125f, + (float16_t)0.419677734f, (float16_t)-0.217651367f, (float16_t)0.261474609f, (float16_t)0.436523438f, + (float16_t)0.411376953f, (float16_t)0.192871094f, (float16_t)-0.382324219f, (float16_t)-0.597167969f, + (float16_t)0.467529297f, (float16_t)-0.339599609f, (float16_t)-0.11328125f, (float16_t)0.0107116699f, + (float16_t)-0.616699219f, (float16_t)-0.0876464844f, (float16_t)0.416015625f, (float16_t)-0.372558594f, + (float16_t)-0.475341797f, (float16_t)0.545898438f, (float16_t)0.493652344f, (float16_t)0.202148438f, + (float16_t)0.378417969f, (float16_t)0.0694580078f, (float16_t)0.08984375f, (float16_t)0.355224609f, + (float16_t)0.26171875f, (float16_t)0.270507812f, (float16_t)-0.0556335449f, (float16_t)0.660644531f, + (float16_t)0.397949219f, (float16_t)0.251953125f, (float16_t)-0.504882812f, (float16_t)0.0438537598f, + (float16_t)-0.490478516f, (float16_t)0.429931641f, (float16_t)-0.106079102f, (float16_t)-0.489501953f, + (float16_t)-0.460449219f, (float16_t)0.0662231445f, (float16_t)-0.0825195312f, (float16_t)0.377685547f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/biases_data.h new file mode 100644 index 00000000..024f949f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_3x3_pad1_f32_biases_data[4] = {-0.218663722f, 0.135642231f, -0.454595119f, -0.183234602f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/config_data.h new file mode 100644 index 00000000..af5d7eb0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_KERNEL_3X3_PAD1_F32_INPUT_BATCHES 1 +#define CONV_KERNEL_3X3_PAD1_F32_INPUT_H 3 +#define CONV_KERNEL_3X3_PAD1_F32_INPUT_W 6 +#define CONV_KERNEL_3X3_PAD1_F32_IN_CH 2 +#define CONV_KERNEL_3X3_PAD1_F32_FILTER_H 3 +#define CONV_KERNEL_3X3_PAD1_F32_FILTER_W 3 +#define CONV_KERNEL_3X3_PAD1_F32_OUT_CH 4 +#define CONV_KERNEL_3X3_PAD1_F32_STRIDE_H 1 +#define CONV_KERNEL_3X3_PAD1_F32_STRIDE_W 1 +#define CONV_KERNEL_3X3_PAD1_F32_PADDING_H 1 +#define CONV_KERNEL_3X3_PAD1_F32_PADDING_W 1 +#define CONV_KERNEL_3X3_PAD1_F32_DILATION_H 1 +#define CONV_KERNEL_3X3_PAD1_F32_DILATION_W 1 +#define CONV_KERNEL_3X3_PAD1_F32_OUTPUT_H 3 +#define CONV_KERNEL_3X3_PAD1_F32_OUTPUT_W 6 +#define CONV_KERNEL_3X3_PAD1_F32_OUTPUT_C 4 +#define CONV_KERNEL_3X3_PAD1_F32_DST_SIZE 72 +#define CONV_KERNEL_3X3_PAD1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_KERNEL_3X3_PAD1_F32_USE_WRAPPER 0 +#define CONV_KERNEL_3X3_PAD1_F32_OUT_ACTIVATION_MIN -1000.0f +#define CONV_KERNEL_3X3_PAD1_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/input_data.h new file mode 100644 index 00000000..c46bdf29 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_3x3_pad1_f32_input_data[36] = { + 0.519743741f, 0.33745262f, -0.0354107283f, -0.638779044f, -0.566468298f, -0.25891158f, + 0.320786655f, 0.600120127f, -0.41622141f, 0.754922092f, 0.216804564f, 0.199893579f, + 0.436652273f, 0.556187212f, -0.798088729f, -0.515138805f, 0.346428156f, -0.43392843f, + -0.551418364f, -0.884269834f, 0.340951979f, -0.260439068f, -0.619662702f, 0.493076563f, + 0.844430089f, -0.0481410623f, 0.760627329f, -0.795184493f, 0.126960054f, 0.335362315f, + -0.439080894f, -0.495832026f, 0.522058189f, -0.6815961f, -0.222437829f, 0.0363636762f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/output_ref_data.h new file mode 100644 index 00000000..5c826070 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_3x3_pad1_f32_output_ref_data[72] = { + 0.767159462f, 0.10157413f, -0.591239452f, -0.980964422f, 0.122782171f, -1.24553895f, -0.872438669f, + 0.0871350765f, 0.057574749f, 0.255716681f, -0.264068246f, -0.12291272f, -2.04091692f, 0.2284154f, + -1.83964813f, 0.163695276f, -0.425011396f, 0.962280452f, -0.583282769f, 0.873039603f, -0.420703888f, + -0.27872014f, -1.06104708f, 0.149930865f, -0.590193868f, -0.101791278f, -0.472696304f, -1.15705144f, + -0.204714969f, 0.960429132f, 0.700600028f, -0.353117526f, 0.490841597f, 1.11506581f, -1.1312449f, + -0.882700324f, -0.983424187f, 0.398280531f, -1.10095561f, 0.121896058f, -0.631027222f, 1.57585239f, + -1.06462181f, 0.19995296f, -1.1773299f, 0.881020427f, -1.10729527f, 0.945849895f, 0.296314031f, + 0.631596804f, -1.05574775f, -1.3575536f, 1.55065191f, -1.01377773f, 0.186423212f, -0.613486469f, + 0.998620749f, -0.945545256f, -0.913309038f, -0.962714553f, 1.34343398f, 0.0763404593f, 1.3654381f, + -1.09475684f, -0.230154216f, -0.478328407f, -1.60788095f, -0.636168838f, 0.677458405f, 1.35363245f, + -0.354560286f, -0.124051556f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/weights_data.h new file mode 100644 index 00000000..7c23f7b6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_kernel_3x3_pad1_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_kernel_3x3_pad1_f32_weights_data[72] = { + 0.626412094f, -0.202779427f, -0.650533736f, -0.370022088f, -0.413321674f, -0.658424139f, 0.566956103f, + -0.216154754f, 0.0550568514f, -0.604884148f, -0.326186895f, -0.639203072f, -0.691369236f, -0.0351002589f, + -0.286546558f, 0.720202446f, -0.533839643f, -0.0775903612f, 0.598503232f, 0.136348903f, -0.673928976f, + 0.699710369f, -0.400933295f, 0.674524307f, -0.364398777f, -0.521696746f, -0.452277511f, 0.573864341f, + 0.455580205f, -0.618179858f, 0.11669834f, -0.533042252f, 0.419776946f, -0.217698321f, 0.261589646f, + 0.436471552f, 0.411268711f, 0.192905203f, -0.382263988f, -0.597069085f, 0.467513829f, -0.339627415f, + -0.113302089f, 0.0107082967f, -0.616867781f, -0.0876604095f, 0.415961027f, -0.372616082f, -0.475232482f, + 0.545707822f, 0.493757993f, 0.20217219f, 0.378409266f, 0.0694604293f, 0.089834094f, 0.355125964f, + 0.261714697f, 0.270388693f, -0.0556451567f, 0.660448015f, 0.397937655f, 0.251931846f, -0.504722416f, + 0.0438600928f, -0.490400225f, 0.429894418f, -0.106086537f, -0.489605665f, -0.460384876f, 0.0661968738f, + -0.0825128853f, 0.377770841f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/biases_data.h new file mode 100644 index 00000000..7f0cf0fb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/biases_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_basic_f16_biases_data[7] = {(float16_t)-0.102294922f, + (float16_t)-0.425292969f, + (float16_t)0.0492248535f, + (float16_t)-0.12310791f, + (float16_t)0.0642089844f, + (float16_t)-0.0920410156f, + (float16_t)-0.278320312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/config_data.h new file mode 100644 index 00000000..7e4851f9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_BASIC_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_BASIC_F16_INPUT_H 5 +#define CONV_MATCH_1X1_BASIC_F16_INPUT_W 7 +#define CONV_MATCH_1X1_BASIC_F16_IN_CH 19 +#define CONV_MATCH_1X1_BASIC_F16_FILTER_H 1 +#define CONV_MATCH_1X1_BASIC_F16_FILTER_W 1 +#define CONV_MATCH_1X1_BASIC_F16_OUT_CH 7 +#define CONV_MATCH_1X1_BASIC_F16_STRIDE_H 1 +#define CONV_MATCH_1X1_BASIC_F16_STRIDE_W 1 +#define CONV_MATCH_1X1_BASIC_F16_PADDING_H 0 +#define CONV_MATCH_1X1_BASIC_F16_PADDING_W 0 +#define CONV_MATCH_1X1_BASIC_F16_DILATION_H 1 +#define CONV_MATCH_1X1_BASIC_F16_DILATION_W 1 +#define CONV_MATCH_1X1_BASIC_F16_OUTPUT_H 5 +#define CONV_MATCH_1X1_BASIC_F16_OUTPUT_W 7 +#define CONV_MATCH_1X1_BASIC_F16_OUTPUT_C 7 +#define CONV_MATCH_1X1_BASIC_F16_DST_SIZE 490 +#define CONV_MATCH_1X1_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_BASIC_F16_USE_WRAPPER 0 +#define CONV_MATCH_1X1_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 126.0f +#define CONV_MATCH_1X1_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/input_data.h new file mode 100644 index 00000000..11118459 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/input_data.h @@ -0,0 +1,335 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_basic_f16_input_data[1330] = { + (float16_t)0.587890625f, (float16_t)0.804199219f, (float16_t)-0.848144531f, (float16_t)0.98046875f, + (float16_t)-0.870117188f, (float16_t)0.626953125f, (float16_t)0.991699219f, (float16_t)0.673339844f, + (float16_t)-0.304443359f, (float16_t)-0.525878906f, (float16_t)-0.505371094f, (float16_t)-0.777832031f, + (float16_t)0.542480469f, (float16_t)-0.439453125f, (float16_t)0.483398438f, (float16_t)-0.720214844f, + (float16_t)-0.678710938f, (float16_t)0.666503906f, (float16_t)-0.538085938f, (float16_t)-0.618164062f, + (float16_t)0.546875f, (float16_t)-0.711914062f, (float16_t)-0.884765625f, (float16_t)0.147949219f, + (float16_t)-0.568847656f, (float16_t)-0.245849609f, (float16_t)-0.0993652344f, (float16_t)0.774902344f, + (float16_t)0.566894531f, (float16_t)-0.210693359f, (float16_t)-0.330810547f, (float16_t)-0.331298828f, + (float16_t)-0.0940551758f, (float16_t)-0.147216797f, (float16_t)0.528808594f, (float16_t)-0.287841797f, + (float16_t)-0.818359375f, (float16_t)-0.482666016f, (float16_t)-0.410888672f, (float16_t)-0.492431641f, + (float16_t)-0.741699219f, (float16_t)-0.768066406f, (float16_t)-0.121582031f, (float16_t)0.856933594f, + (float16_t)0.254394531f, (float16_t)-0.557128906f, (float16_t)-0.756835938f, (float16_t)-0.967285156f, + (float16_t)0.00177383423f, (float16_t)0.795410156f, (float16_t)0.219116211f, (float16_t)0.231323242f, + (float16_t)-0.243164062f, (float16_t)0.312988281f, (float16_t)-0.473388672f, (float16_t)0.654296875f, + (float16_t)0.583496094f, (float16_t)-0.596191406f, (float16_t)-0.639160156f, (float16_t)-0.0246124268f, + (float16_t)-0.529296875f, (float16_t)0.0621643066f, (float16_t)0.153930664f, (float16_t)-0.248291016f, + (float16_t)0.514160156f, (float16_t)0.458740234f, (float16_t)-0.668945312f, (float16_t)0.0838623047f, + (float16_t)-0.76953125f, (float16_t)0.500488281f, (float16_t)-0.587890625f, (float16_t)-0.481445312f, + (float16_t)0.371826172f, (float16_t)0.0897827148f, (float16_t)-0.882324219f, (float16_t)-0.589355469f, + (float16_t)-0.510742188f, (float16_t)-0.181152344f, (float16_t)0.0016374588f, (float16_t)0.629394531f, + (float16_t)0.867675781f, (float16_t)-0.01197052f, (float16_t)-0.790039062f, (float16_t)-0.333007812f, + (float16_t)0.452636719f, (float16_t)-0.607421875f, (float16_t)-0.398681641f, (float16_t)0.125732422f, + (float16_t)-0.325195312f, (float16_t)-0.284179688f, (float16_t)-0.0818481445f, (float16_t)-0.65625f, + (float16_t)-0.513671875f, (float16_t)0.335693359f, (float16_t)-0.109802246f, (float16_t)0.755371094f, + (float16_t)0.356933594f, (float16_t)0.790039062f, (float16_t)0.499511719f, (float16_t)-0.656738281f, + (float16_t)0.205444336f, (float16_t)-0.458251953f, (float16_t)0.786132812f, (float16_t)0.989746094f, + (float16_t)0.0408630371f, (float16_t)0.99609375f, (float16_t)0.0577392578f, (float16_t)-0.643066406f, + (float16_t)0.241943359f, (float16_t)0.636230469f, (float16_t)0.771484375f, (float16_t)-0.10760498f, + (float16_t)0.8515625f, (float16_t)-0.521972656f, (float16_t)0.723144531f, (float16_t)-0.791503906f, + (float16_t)0.771972656f, (float16_t)0.827148438f, (float16_t)-0.166625977f, (float16_t)-0.496337891f, + (float16_t)0.802734375f, (float16_t)-0.128295898f, (float16_t)-0.860351562f, (float16_t)-0.967285156f, + (float16_t)-0.0963134766f, (float16_t)-0.989746094f, (float16_t)0.827636719f, (float16_t)-0.451904297f, + (float16_t)0.575195312f, (float16_t)-0.530273438f, (float16_t)0.11529541f, (float16_t)0.793945312f, + (float16_t)0.879882812f, (float16_t)-0.6875f, (float16_t)-0.344970703f, (float16_t)0.549316406f, + (float16_t)-0.255615234f, (float16_t)-0.257568359f, (float16_t)-0.992675781f, (float16_t)0.00259017944f, + (float16_t)-0.871582031f, (float16_t)0.443603516f, (float16_t)-0.00172710419f, (float16_t)0.650878906f, + (float16_t)0.556152344f, (float16_t)0.559570312f, (float16_t)0.91796875f, (float16_t)0.659179688f, + (float16_t)0.439208984f, (float16_t)0.514160156f, (float16_t)0.933105469f, (float16_t)0.487304688f, + (float16_t)-0.377441406f, (float16_t)-0.741699219f, (float16_t)-0.506347656f, (float16_t)-0.0464477539f, + (float16_t)0.163574219f, (float16_t)0.979492188f, (float16_t)0.320556641f, (float16_t)-0.675292969f, + (float16_t)-0.583984375f, (float16_t)0.284667969f, (float16_t)0.0501403809f, (float16_t)0.160888672f, + (float16_t)0.0166625977f, (float16_t)-0.306152344f, (float16_t)-0.479736328f, (float16_t)0.631347656f, + (float16_t)-0.177978516f, (float16_t)-0.522949219f, (float16_t)-0.65234375f, (float16_t)-0.142578125f, + (float16_t)0.264404297f, (float16_t)-0.0529174805f, (float16_t)-0.893554688f, (float16_t)0.273193359f, + (float16_t)0.560546875f, (float16_t)-0.0285186768f, (float16_t)0.211791992f, (float16_t)-0.054107666f, + (float16_t)-0.308349609f, (float16_t)0.11151123f, (float16_t)0.354003906f, (float16_t)0.918457031f, + (float16_t)-0.87109375f, (float16_t)0.785644531f, (float16_t)-0.759277344f, (float16_t)-0.416992188f, + (float16_t)0.492919922f, (float16_t)-0.00881195068f, (float16_t)-0.897460938f, (float16_t)-0.546386719f, + (float16_t)0.744628906f, (float16_t)-0.475097656f, (float16_t)0.451171875f, (float16_t)-0.655273438f, + (float16_t)-0.17980957f, (float16_t)-0.944824219f, (float16_t)-0.432617188f, (float16_t)0.138183594f, + (float16_t)0.0672607422f, (float16_t)-0.338378906f, (float16_t)-0.043762207f, (float16_t)-0.148193359f, + (float16_t)0.647460938f, (float16_t)0.959960938f, (float16_t)-0.359130859f, (float16_t)-0.475585938f, + (float16_t)0.0298461914f, (float16_t)-0.487548828f, (float16_t)-0.0997924805f, (float16_t)0.379394531f, + (float16_t)0.198852539f, (float16_t)0.993652344f, (float16_t)-0.532226562f, (float16_t)0.364746094f, + (float16_t)0.120361328f, (float16_t)-0.439453125f, (float16_t)0.268554688f, (float16_t)-0.882324219f, + (float16_t)0.00309562683f, (float16_t)-0.512207031f, (float16_t)-0.927246094f, (float16_t)0.142333984f, + (float16_t)-0.912109375f, (float16_t)-0.0968017578f, (float16_t)0.0942382812f, (float16_t)-0.881835938f, + (float16_t)-0.0469055176f, (float16_t)0.173828125f, (float16_t)0.107666016f, (float16_t)-0.431884766f, + (float16_t)0.2890625f, (float16_t)-0.794433594f, (float16_t)0.578613281f, (float16_t)0.323242188f, + (float16_t)0.698730469f, (float16_t)0.528808594f, (float16_t)0.0848999023f, (float16_t)0.641601562f, + (float16_t)-0.524414062f, (float16_t)-0.345214844f, (float16_t)0.480224609f, (float16_t)-0.0712890625f, + (float16_t)-0.767578125f, (float16_t)-0.0265960693f, (float16_t)-0.410888672f, (float16_t)-0.813964844f, + (float16_t)0.65234375f, (float16_t)0.431640625f, (float16_t)-0.0414428711f, (float16_t)-0.398681641f, + (float16_t)0.448974609f, (float16_t)0.821289062f, (float16_t)-0.246337891f, (float16_t)0.756835938f, + (float16_t)-0.690917969f, (float16_t)0.298339844f, (float16_t)-0.527832031f, (float16_t)-0.560058594f, + (float16_t)0.247314453f, (float16_t)-0.11138916f, (float16_t)-0.146484375f, (float16_t)-0.863769531f, + (float16_t)-0.0825805664f, (float16_t)0.586914062f, (float16_t)0.0837402344f, (float16_t)-0.374755859f, + (float16_t)0.825195312f, (float16_t)0.918945312f, (float16_t)0.311767578f, (float16_t)0.122253418f, + (float16_t)0.155517578f, (float16_t)0.281982422f, (float16_t)-0.235839844f, (float16_t)-0.258056641f, + (float16_t)-0.211547852f, (float16_t)0.983398438f, (float16_t)0.039855957f, (float16_t)0.0673217773f, + (float16_t)0.297851562f, (float16_t)0.5390625f, (float16_t)0.696289062f, (float16_t)-0.703613281f, + (float16_t)-0.576171875f, (float16_t)-0.0753173828f, (float16_t)-0.869140625f, (float16_t)-0.342529297f, + (float16_t)0.0155944824f, (float16_t)0.704101562f, (float16_t)-0.586914062f, (float16_t)-0.653808594f, + (float16_t)-0.102539062f, (float16_t)-0.388916016f, (float16_t)0.515625f, (float16_t)-0.0581665039f, + (float16_t)0.483398438f, (float16_t)0.767578125f, (float16_t)-0.75f, (float16_t)-0.510742188f, + (float16_t)-0.149658203f, (float16_t)0.0671386719f, (float16_t)0.873535156f, (float16_t)0.576171875f, + (float16_t)-0.0138702393f, (float16_t)0.137939453f, (float16_t)0.227172852f, (float16_t)-0.396728516f, + (float16_t)-0.478271484f, (float16_t)-0.204345703f, (float16_t)0.548828125f, (float16_t)0.683105469f, + (float16_t)-0.434326172f, (float16_t)-0.408447266f, (float16_t)-0.61328125f, (float16_t)-0.181762695f, + (float16_t)-0.735351562f, (float16_t)0.532714844f, (float16_t)0.506347656f, (float16_t)0.0802612305f, + (float16_t)0.280517578f, (float16_t)0.912597656f, (float16_t)0.427490234f, (float16_t)-0.5703125f, + (float16_t)0.771484375f, (float16_t)0.930664062f, (float16_t)-0.733886719f, (float16_t)0.118469238f, + (float16_t)0.954589844f, (float16_t)-0.321533203f, (float16_t)0.123474121f, (float16_t)0.580078125f, + (float16_t)-0.946289062f, (float16_t)-0.241577148f, (float16_t)-0.444091797f, (float16_t)0.787109375f, + (float16_t)0.400634766f, (float16_t)-0.923828125f, (float16_t)-0.498046875f, (float16_t)-0.0537109375f, + (float16_t)0.900878906f, (float16_t)0.218505859f, (float16_t)-0.9765625f, (float16_t)-0.796386719f, + (float16_t)-0.579589844f, (float16_t)-0.253417969f, 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(float16_t)-0.500976562f, (float16_t)-0.341308594f, + (float16_t)-0.520507812f, (float16_t)0.782226562f, (float16_t)0.396240234f, (float16_t)-0.598632812f, + (float16_t)-0.0910644531f, (float16_t)-0.161499023f, (float16_t)0.319580078f, (float16_t)0.411376953f, + (float16_t)0.317382812f, (float16_t)-0.233886719f, (float16_t)0.916503906f, (float16_t)-0.598144531f, + (float16_t)0.827148438f, (float16_t)-0.575683594f, (float16_t)0.703125f, (float16_t)0.793457031f, + (float16_t)-0.0154571533f, (float16_t)0.916503906f, (float16_t)-0.624023438f, (float16_t)0.434326172f, + (float16_t)0.0311889648f, (float16_t)-0.747558594f, (float16_t)-0.271728516f, (float16_t)-0.143676758f, + (float16_t)0.219848633f, (float16_t)-0.00385475159f, (float16_t)-0.771484375f, (float16_t)0.631347656f, + (float16_t)-0.76953125f, (float16_t)-0.0662231445f, (float16_t)0.00392532349f, (float16_t)0.404296875f, + (float16_t)0.647460938f, (float16_t)-0.889648438f, (float16_t)0.308837891f, (float16_t)-0.357910156f, + (float16_t)-0.119750977f, (float16_t)-0.764648438f, (float16_t)-0.92578125f, (float16_t)-0.892089844f, + (float16_t)-0.587402344f, (float16_t)0.468505859f, (float16_t)-0.0673828125f, (float16_t)0.253662109f, + (float16_t)0.2734375f, (float16_t)0.909667969f, (float16_t)-0.994628906f, (float16_t)-0.770507812f, + (float16_t)-0.307128906f, (float16_t)-0.146972656f, (float16_t)0.548828125f, (float16_t)-0.772949219f, + (float16_t)0.719726562f, (float16_t)0.819824219f, (float16_t)0.272460938f, (float16_t)0.521484375f, + (float16_t)-0.538085938f, (float16_t)-0.86328125f, (float16_t)0.461914062f, (float16_t)0.170532227f, + (float16_t)0.739746094f, (float16_t)-0.583007812f, (float16_t)-0.145996094f, (float16_t)0.711914062f, + (float16_t)-0.045135498f, (float16_t)-0.321777344f, (float16_t)0.809570312f, (float16_t)0.63671875f, + (float16_t)-0.130126953f, (float16_t)0.802246094f, (float16_t)0.313720703f, (float16_t)0.717773438f, + (float16_t)0.0454711914f, (float16_t)0.656738281f, (float16_t)-0.7265625f, (float16_t)0.924804688f, + (float16_t)-0.7890625f, (float16_t)0.485351562f, (float16_t)0.948242188f, (float16_t)0.116516113f, + (float16_t)-0.795898438f, (float16_t)0.708496094f, (float16_t)0.118469238f, (float16_t)0.999023438f, + (float16_t)-0.986328125f, (float16_t)0.757324219f, (float16_t)-0.205078125f, (float16_t)-0.889648438f, + (float16_t)-0.0354003906f, (float16_t)-0.573730469f, (float16_t)-0.117248535f, (float16_t)-0.478515625f, + (float16_t)0.594238281f, (float16_t)-0.863769531f, (float16_t)-0.66796875f, (float16_t)-0.404541016f, + (float16_t)-0.304443359f, (float16_t)-0.455566406f, (float16_t)0.341308594f, (float16_t)-0.360839844f, + (float16_t)-0.725097656f, (float16_t)-0.357910156f, (float16_t)0.305175781f, (float16_t)0.849121094f, + (float16_t)0.637207031f, (float16_t)-0.0382995605f, (float16_t)-0.768554688f, (float16_t)-0.120239258f, + (float16_t)0.310546875f, (float16_t)0.470947266f, (float16_t)-0.282226562f, (float16_t)0.598144531f, + (float16_t)0.459228516f, (float16_t)0.55859375f, (float16_t)0.219726562f, (float16_t)0.704589844f, + (float16_t)0.758300781f, (float16_t)-0.926757812f, (float16_t)-0.819824219f, (float16_t)0.0491333008f, + (float16_t)0.0648193359f, (float16_t)0.670410156f, (float16_t)-0.993652344f, (float16_t)-0.176025391f, + (float16_t)0.932128906f, (float16_t)-0.0672607422f, (float16_t)0.294921875f, (float16_t)0.752929688f, + (float16_t)-0.450439453f, (float16_t)0.608886719f, (float16_t)-0.360107422f, (float16_t)-0.725097656f, + (float16_t)0.895507812f, (float16_t)0.2734375f, (float16_t)0.0550231934f, (float16_t)0.83984375f, + (float16_t)-0.191650391f, (float16_t)0.208862305f, (float16_t)0.083984375f, (float16_t)-0.807617188f, + (float16_t)0.472900391f, (float16_t)0.3046875f, (float16_t)0.984375f, (float16_t)-0.616210938f, + (float16_t)-0.778808594f, (float16_t)0.551269531f, (float16_t)0.927734375f, (float16_t)0.445556641f, + (float16_t)0.457275391f, (float16_t)-0.84765625f, (float16_t)-0.872558594f, (float16_t)-0.0542602539f, + (float16_t)-0.272949219f, (float16_t)-0.440185547f, (float16_t)-0.705566406f, (float16_t)-0.702148438f, + (float16_t)0.0500793457f, (float16_t)-0.58203125f, (float16_t)0.156616211f, (float16_t)-0.830078125f, + (float16_t)0.895019531f, (float16_t)0.159179688f, (float16_t)-0.173583984f, (float16_t)0.172729492f, + (float16_t)-0.961425781f, (float16_t)0.323486328f, (float16_t)-0.721679688f, (float16_t)-0.696289062f, + (float16_t)0.483886719f, (float16_t)0.395263672f, (float16_t)-0.0639648438f, (float16_t)0.28125f, + (float16_t)-0.329589844f, (float16_t)-0.743652344f, (float16_t)-0.594726562f, (float16_t)0.568359375f, + (float16_t)-0.896972656f, (float16_t)-0.317382812f, (float16_t)0.938476562f, (float16_t)-0.703125f, + (float16_t)-0.135253906f, (float16_t)-0.940429688f, (float16_t)0.0134429932f, (float16_t)0.597167969f, + (float16_t)0.677734375f, (float16_t)0.137451172f, (float16_t)0.498046875f, (float16_t)-0.0800170898f, + (float16_t)0.541015625f, (float16_t)0.958496094f, (float16_t)0.71875f, (float16_t)0.305908203f, + (float16_t)0.0601806641f, (float16_t)0.610351562f, (float16_t)-0.842773438f, (float16_t)-0.0116577148f, + (float16_t)-0.196655273f, (float16_t)-0.0411682129f, (float16_t)0.451660156f, (float16_t)-0.675292969f, + (float16_t)0.757324219f, (float16_t)0.563476562f, (float16_t)-0.891601562f, (float16_t)-0.811035156f, + (float16_t)-0.327392578f, (float16_t)0.348632812f, (float16_t)-0.446044922f, (float16_t)-0.734863281f, + (float16_t)0.921386719f, (float16_t)0.642578125f, (float16_t)0.53515625f, (float16_t)0.529296875f, + (float16_t)-0.907714844f, (float16_t)0.390625f, (float16_t)-0.901855469f, (float16_t)0.436523438f, + (float16_t)0.626953125f, (float16_t)0.0377807617f, (float16_t)-0.8125f, (float16_t)0.9609375f, + (float16_t)-0.283447266f, (float16_t)-0.135009766f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/output_ref_data.h new file mode 100644 index 00000000..063b7fc7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/output_ref_data.h @@ -0,0 +1,125 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_basic_f16_output_ref_data[490] = { + (float16_t)-0.516113281f, (float16_t)-0.924804688f, (float16_t)2.91601562f, (float16_t)2.36523438f, + (float16_t)-0.819335938f, (float16_t)-2.23242188f, (float16_t)-0.0641479492f, (float16_t)-1.00097656f, + (float16_t)-2.3359375f, (float16_t)-0.8359375f, (float16_t)-0.450927734f, (float16_t)0.079284668f, + (float16_t)-0.025680542f, (float16_t)-0.585449219f, (float16_t)-0.348144531f, (float16_t)1.29199219f, + (float16_t)-0.536621094f, (float16_t)-1.51464844f, (float16_t)0.39453125f, (float16_t)0.307617188f, + (float16_t)-0.0479431152f, (float16_t)-0.957519531f, (float16_t)-1.27148438f, (float16_t)-0.529296875f, + (float16_t)0.306640625f, (float16_t)-1.83398438f, (float16_t)-0.163330078f, (float16_t)1.14648438f, + (float16_t)0.807617188f, (float16_t)-1.16601562f, (float16_t)0.942871094f, (float16_t)-0.483642578f, + (float16_t)-1.0390625f, (float16_t)-0.568847656f, (float16_t)0.0199127197f, (float16_t)-1.52246094f, + (float16_t)-0.20715332f, (float16_t)-0.307617188f, (float16_t)-0.0908813477f, (float16_t)2.32421875f, + (float16_t)0.917480469f, (float16_t)0.0388183594f, (float16_t)1.1875f, (float16_t)1.90722656f, + (float16_t)1.69238281f, (float16_t)1.36621094f, (float16_t)-0.759765625f, (float16_t)-1.51855469f, + (float16_t)-0.510253906f, (float16_t)-1.45800781f, (float16_t)0.915527344f, (float16_t)-0.916503906f, + (float16_t)-1.54199219f, (float16_t)2.41601562f, (float16_t)1.43554688f, (float16_t)-0.242675781f, + (float16_t)-0.254638672f, (float16_t)-0.163208008f, (float16_t)-1.15625f, (float16_t)-1.125f, + (float16_t)-1.140625f, (float16_t)1.328125f, (float16_t)-0.406494141f, (float16_t)-0.920410156f, + (float16_t)-0.854980469f, (float16_t)0.62890625f, (float16_t)-0.532714844f, (float16_t)0.633300781f, + (float16_t)0.17956543f, (float16_t)0.830566406f, (float16_t)-0.0864257812f, (float16_t)0.657714844f, + (float16_t)-1.3828125f, (float16_t)-1.84765625f, (float16_t)1.03320312f, (float16_t)0.856933594f, + (float16_t)-0.727539062f, (float16_t)0.0931396484f, (float16_t)-2.28515625f, (float16_t)1.15332031f, + (float16_t)1.078125f, (float16_t)-0.765625f, (float16_t)0.154052734f, (float16_t)-0.570800781f, + (float16_t)-1.90625f, (float16_t)-1.68554688f, (float16_t)0.288085938f, (float16_t)-0.522949219f, + (float16_t)2.01367188f, (float16_t)-0.112487793f, (float16_t)-0.966308594f, (float16_t)-0.301513672f, + (float16_t)-0.324462891f, (float16_t)-0.406738281f, (float16_t)-0.402832031f, (float16_t)1.48046875f, + (float16_t)-2.08007812f, (float16_t)-0.501464844f, (float16_t)-0.506347656f, (float16_t)0.478271484f, + (float16_t)-0.334472656f, (float16_t)-0.24206543f, (float16_t)-0.334228516f, (float16_t)0.627441406f, + (float16_t)0.734375f, (float16_t)0.136962891f, (float16_t)-0.818847656f, (float16_t)0.427246094f, + (float16_t)-1.6171875f, (float16_t)-0.302490234f, (float16_t)1.20703125f, (float16_t)0.311035156f, + (float16_t)-0.567382812f, (float16_t)0.587890625f, (float16_t)0.714355469f, (float16_t)1.16210938f, + (float16_t)1.5390625f, (float16_t)-0.329101562f, (float16_t)-0.171264648f, (float16_t)-1.06152344f, + (float16_t)-0.707519531f, (float16_t)0.34375f, (float16_t)0.111206055f, (float16_t)-0.723144531f, + (float16_t)0.0805664062f, (float16_t)1.4453125f, (float16_t)0.799804688f, (float16_t)-1.02148438f, + (float16_t)0.0170288086f, (float16_t)0.184204102f, (float16_t)-0.175292969f, (float16_t)0.701660156f, + (float16_t)-0.651855469f, (float16_t)1.51660156f, (float16_t)-1.44238281f, (float16_t)0.375244141f, + (float16_t)0.140991211f, (float16_t)-1.359375f, (float16_t)-1.8671875f, (float16_t)1.03027344f, + (float16_t)-1.59765625f, (float16_t)0.811523438f, (float16_t)1.8828125f, (float16_t)1.35546875f, + (float16_t)-1.21191406f, (float16_t)-1.28222656f, (float16_t)-1.55761719f, (float16_t)-0.842285156f, + (float16_t)0.919921875f, (float16_t)0.770996094f, (float16_t)-1.62011719f, (float16_t)-0.676757812f, + (float16_t)-0.19152832f, (float16_t)0.044708252f, (float16_t)-0.625976562f, (float16_t)0.329101562f, + (float16_t)1.49414062f, (float16_t)-0.286132812f, (float16_t)-1.14550781f, (float16_t)0.889648438f, + (float16_t)0.864257812f, (float16_t)0.42578125f, (float16_t)0.702148438f, (float16_t)-0.503417969f, + (float16_t)0.928710938f, (float16_t)-0.683105469f, (float16_t)-0.460205078f, (float16_t)-1.30078125f, + (float16_t)-1.47851562f, (float16_t)-1.89550781f, (float16_t)1.14746094f, (float16_t)-0.205200195f, + (float16_t)1.05957031f, (float16_t)-1.45800781f, (float16_t)-0.426025391f, (float16_t)0.898925781f, + (float16_t)-0.00955963135f, (float16_t)0.789550781f, (float16_t)-0.325683594f, (float16_t)0.0249633789f, + (float16_t)-0.0905151367f, (float16_t)-0.837890625f, (float16_t)0.82421875f, (float16_t)0.196777344f, + (float16_t)-0.478027344f, (float16_t)-2.9375f, (float16_t)0.963378906f, (float16_t)-1.484375f, + (float16_t)-0.13293457f, (float16_t)-0.102294922f, (float16_t)0.241455078f, (float16_t)-1.50292969f, + (float16_t)-2.57226562f, (float16_t)0.0949707031f, (float16_t)2.68164062f, (float16_t)1.25097656f, + (float16_t)-0.435058594f, (float16_t)0.524414062f, (float16_t)-0.468017578f, (float16_t)-1.09570312f, + (float16_t)1.38085938f, (float16_t)0.739257812f, (float16_t)0.257080078f, (float16_t)2.82617188f, + (float16_t)-0.403320312f, (float16_t)-0.966796875f, (float16_t)-0.0873413086f, (float16_t)1.23730469f, + (float16_t)-1.13867188f, (float16_t)-1.81640625f, (float16_t)-0.704101562f, (float16_t)-1.44726562f, + (float16_t)1.28125f, (float16_t)0.551269531f, (float16_t)-0.301513672f, (float16_t)0.0841064453f, + (float16_t)0.716308594f, (float16_t)0.125488281f, (float16_t)-0.12286377f, (float16_t)-0.681640625f, + (float16_t)-2.03320312f, (float16_t)2.77929688f, (float16_t)-0.298828125f, (float16_t)-0.266113281f, + (float16_t)-0.0332336426f, (float16_t)-1.87890625f, (float16_t)0.0606079102f, (float16_t)-1.265625f, + (float16_t)1.23828125f, (float16_t)-0.354003906f, (float16_t)-0.206542969f, (float16_t)-1.19140625f, + (float16_t)-1.68945312f, (float16_t)1.77832031f, (float16_t)1.40722656f, (float16_t)-0.711914062f, + (float16_t)-1.23535156f, (float16_t)0.0491027832f, (float16_t)0.0510864258f, (float16_t)-1.54492188f, + (float16_t)-0.967285156f, (float16_t)0.0149765015f, (float16_t)-2.15234375f, (float16_t)1.734375f, + (float16_t)0.453857422f, (float16_t)1.08691406f, (float16_t)-1.99511719f, (float16_t)-0.672851562f, + (float16_t)0.926269531f, (float16_t)-0.297363281f, (float16_t)0.542480469f, (float16_t)-0.0705566406f, + (float16_t)-0.973632812f, (float16_t)-0.0596923828f, (float16_t)0.729003906f, (float16_t)-0.767089844f, + (float16_t)1.47363281f, (float16_t)-0.289550781f, (float16_t)-0.304931641f, (float16_t)1.0859375f, + (float16_t)0.55859375f, (float16_t)-0.0676879883f, (float16_t)0.850097656f, (float16_t)0.323486328f, + (float16_t)-0.16003418f, (float16_t)-0.133422852f, (float16_t)-0.0959472656f, (float16_t)0.132080078f, + (float16_t)-0.635742188f, (float16_t)-0.735351562f, (float16_t)-0.514648438f, (float16_t)0.677246094f, + (float16_t)-0.655761719f, (float16_t)-0.125366211f, (float16_t)-0.124450684f, (float16_t)-1.70800781f, + (float16_t)-1.47949219f, (float16_t)0.406982422f, (float16_t)1.8828125f, (float16_t)0.0373840332f, + (float16_t)-0.213867188f, (float16_t)0.69140625f, (float16_t)-0.592773438f, (float16_t)-0.373779297f, + (float16_t)0.446533203f, (float16_t)2.31054688f, (float16_t)1.77636719f, (float16_t)-0.766113281f, + (float16_t)-1.48339844f, (float16_t)1.33300781f, (float16_t)-1.66308594f, (float16_t)-0.245117188f, + (float16_t)-1.03125f, (float16_t)1.38769531f, (float16_t)-0.739746094f, (float16_t)-1.30078125f, + (float16_t)0.74609375f, (float16_t)0.396240234f, (float16_t)-0.138916016f, (float16_t)1.65136719f, + (float16_t)-1.15039062f, (float16_t)0.0560913086f, (float16_t)-0.177246094f, (float16_t)0.293212891f, + (float16_t)-0.962402344f, (float16_t)0.603515625f, (float16_t)-1.55664062f, (float16_t)0.256591797f, + (float16_t)1.30859375f, (float16_t)-1.8671875f, (float16_t)-0.368896484f, (float16_t)1.40332031f, + (float16_t)-0.583007812f, (float16_t)0.705078125f, (float16_t)-1.24804688f, (float16_t)-1.75976562f, + (float16_t)-0.853515625f, (float16_t)-0.448242188f, (float16_t)-0.474609375f, (float16_t)1.36035156f, + (float16_t)0.276611328f, (float16_t)-1.24707031f, (float16_t)1.24414062f, (float16_t)-1.20507812f, + (float16_t)-0.525878906f, (float16_t)1.62304688f, (float16_t)-1.61132812f, (float16_t)-0.131225586f, + (float16_t)-0.298583984f, (float16_t)1.61425781f, (float16_t)-0.7265625f, (float16_t)-3.203125f, + (float16_t)-1.07226562f, (float16_t)1.00976562f, (float16_t)-0.145141602f, (float16_t)-0.68359375f, + (float16_t)-0.0498657227f, (float16_t)-0.333496094f, (float16_t)0.912109375f, (float16_t)0.620117188f, + (float16_t)0.368408203f, (float16_t)-1.84277344f, (float16_t)-1.60058594f, (float16_t)0.484130859f, + (float16_t)-0.341308594f, (float16_t)-0.893554688f, (float16_t)-0.927734375f, (float16_t)2.125f, + (float16_t)-0.82421875f, (float16_t)-0.88671875f, (float16_t)-0.93359375f, (float16_t)-0.442382812f, + (float16_t)0.4921875f, (float16_t)-0.689941406f, (float16_t)-0.239624023f, (float16_t)-0.728515625f, + (float16_t)-0.377441406f, (float16_t)-0.78125f, (float16_t)-0.968261719f, (float16_t)-0.444580078f, + (float16_t)-1.40332031f, (float16_t)0.354736328f, (float16_t)3.51757812f, (float16_t)0.779785156f, + (float16_t)2.23632812f, (float16_t)0.0772094727f, (float16_t)-0.126220703f, (float16_t)0.29296875f, + (float16_t)1.89257812f, (float16_t)-1.55175781f, (float16_t)-2.3984375f, (float16_t)1.94238281f, + (float16_t)-1.36230469f, (float16_t)-2.12695312f, (float16_t)0.0562133789f, (float16_t)0.166381836f, + (float16_t)0.512695312f, (float16_t)0.157714844f, (float16_t)-0.5f, (float16_t)-0.0426940918f, + (float16_t)-0.0185699463f, (float16_t)-0.341064453f, (float16_t)-0.275146484f, (float16_t)-0.908691406f, + (float16_t)1.49804688f, (float16_t)-0.478759766f, (float16_t)-1.20117188f, (float16_t)-0.190063477f, + (float16_t)0.6953125f, (float16_t)1.46386719f, (float16_t)-0.813964844f, (float16_t)-1.04101562f, + (float16_t)-0.622070312f, (float16_t)2.02539062f, (float16_t)-0.348388672f, (float16_t)-0.0406188965f, + (float16_t)-0.220336914f, (float16_t)-0.151611328f, (float16_t)-1.43261719f, (float16_t)-1.1640625f, + (float16_t)-0.0935058594f, (float16_t)0.829101562f, (float16_t)-0.281738281f, (float16_t)-1.35742188f, + (float16_t)0.295410156f, (float16_t)0.0447387695f, (float16_t)-0.0715942383f, (float16_t)0.432373047f, + (float16_t)-0.298095703f, (float16_t)0.147827148f, (float16_t)0.387695312f, (float16_t)-0.275634766f, + (float16_t)1.49511719f, (float16_t)0.837402344f, (float16_t)-0.890625f, (float16_t)0.418701172f, + (float16_t)-0.0255584717f, (float16_t)1.14257812f, (float16_t)-1.58007812f, (float16_t)-0.489501953f, + (float16_t)1.56152344f, (float16_t)0.416503906f, (float16_t)0.537109375f, (float16_t)-0.862792969f, + (float16_t)-0.684082031f, (float16_t)0.599121094f, (float16_t)-1.95214844f, (float16_t)-0.316162109f, + (float16_t)-0.886230469f, (float16_t)-0.347167969f, (float16_t)-1.6953125f, (float16_t)0.604003906f, + (float16_t)0.301025391f, (float16_t)1.8359375f, (float16_t)-0.841308594f, (float16_t)-0.395996094f, + (float16_t)0.0816650391f, (float16_t)-1.13574219f, (float16_t)-0.602050781f, (float16_t)0.155273438f, + (float16_t)1.50488281f, (float16_t)0.910644531f, (float16_t)-0.0195922852f, (float16_t)-0.0191650391f, + (float16_t)-0.139282227f, (float16_t)0.633789062f, (float16_t)-0.479248047f, (float16_t)-0.593261719f, + (float16_t)-0.162841797f, (float16_t)-0.442626953f, (float16_t)0.971679688f, (float16_t)1.51660156f, + (float16_t)-2.65625f, (float16_t)-1.69335938f, (float16_t)0.987792969f, (float16_t)-0.586914062f, + (float16_t)1.82226562f, (float16_t)-1.296875f, (float16_t)-1.25878906f, (float16_t)1.32910156f, + (float16_t)0.161010742f, (float16_t)-0.271484375f, (float16_t)0.510253906f, (float16_t)-0.153198242f, + (float16_t)-0.656738281f, (float16_t)1.5234375f, (float16_t)-0.102844238f, (float16_t)1.54199219f, + (float16_t)-0.892578125f, (float16_t)1.25292969f, (float16_t)1.28417969f, (float16_t)1.47460938f, + (float16_t)0.0853881836f, (float16_t)-1.17285156f, (float16_t)-3.4296875f, (float16_t)-2.76757812f, + (float16_t)-1.00195312f, (float16_t)-0.674316406f, (float16_t)1.04394531f, (float16_t)1.79003906f, + (float16_t)-1.140625f, (float16_t)1.62207031f, (float16_t)0.0327148438f, (float16_t)-0.892089844f, + (float16_t)-0.220947266f, (float16_t)0.0192565918f, (float16_t)1.79492188f, (float16_t)-0.739746094f, + (float16_t)-0.887695312f, (float16_t)1.14355469f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/weights_data.h new file mode 100644 index 00000000..07697fef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f16/weights_data.h @@ -0,0 +1,36 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_basic_f16_weights_data[133] = { + (float16_t)0.340820312f, (float16_t)0.181152344f, (float16_t)-0.106628418f, (float16_t)0.453857422f, + (float16_t)-0.0520935059f, (float16_t)0.0467529297f, (float16_t)-0.700683594f, (float16_t)-0.73046875f, + (float16_t)-0.692382812f, (float16_t)-0.25f, (float16_t)0.0669555664f, (float16_t)-0.366455078f, + (float16_t)-0.696777344f, (float16_t)-0.431640625f, (float16_t)-0.0987548828f, (float16_t)-0.608886719f, + (float16_t)0.387451172f, (float16_t)-0.486816406f, (float16_t)0.728027344f, (float16_t)0.499755859f, + (float16_t)-0.573242188f, (float16_t)0.320800781f, (float16_t)0.0747070312f, (float16_t)0.0624694824f, + (float16_t)0.388916016f, (float16_t)0.180541992f, (float16_t)-0.296875f, (float16_t)-0.421142578f, + (float16_t)-0.422363281f, (float16_t)-0.0546569824f, (float16_t)-0.0102157593f, (float16_t)0.170166016f, + (float16_t)0.732910156f, (float16_t)0.590332031f, (float16_t)0.573242188f, (float16_t)-0.0765380859f, + (float16_t)-0.0102310181f, (float16_t)0.698242188f, (float16_t)0.259521484f, (float16_t)-0.11932373f, + (float16_t)-0.259765625f, (float16_t)0.488037109f, (float16_t)0.331787109f, (float16_t)-0.212890625f, + (float16_t)0.0803833008f, (float16_t)0.739746094f, (float16_t)-0.124572754f, (float16_t)-0.00654220581f, + (float16_t)-0.564941406f, (float16_t)-0.297363281f, (float16_t)0.498046875f, (float16_t)0.604003906f, + (float16_t)0.252929688f, (float16_t)-0.703613281f, (float16_t)-0.684570312f, (float16_t)0.311279297f, + (float16_t)-0.170532227f, (float16_t)0.541015625f, (float16_t)0.215209961f, (float16_t)-0.153320312f, + (float16_t)-0.0103607178f, (float16_t)-0.180541992f, (float16_t)-0.302734375f, (float16_t)0.440673828f, + (float16_t)0.30859375f, (float16_t)-0.0977783203f, (float16_t)-0.556640625f, (float16_t)-0.298095703f, + (float16_t)-0.725585938f, (float16_t)-0.270507812f, (float16_t)0.092590332f, (float16_t)-0.0565185547f, + (float16_t)-0.639648438f, (float16_t)0.672363281f, (float16_t)0.0693969727f, (float16_t)-0.729492188f, + (float16_t)-0.609375f, (float16_t)0.364257812f, (float16_t)0.479492188f, (float16_t)-0.250488281f, + (float16_t)-0.679199219f, (float16_t)-0.282958984f, (float16_t)0.169311523f, (float16_t)0.373291016f, + (float16_t)-0.0946655273f, (float16_t)0.6015625f, (float16_t)0.706054688f, (float16_t)0.706542969f, + (float16_t)-0.460693359f, (float16_t)0.0569763184f, (float16_t)0.705566406f, (float16_t)0.254394531f, + (float16_t)-0.520996094f, (float16_t)0.422851562f, (float16_t)0.600585938f, (float16_t)0.0268554688f, + (float16_t)-0.569824219f, (float16_t)0.551269531f, (float16_t)-0.594238281f, (float16_t)-0.606445312f, + (float16_t)0.356933594f, (float16_t)-0.479003906f, (float16_t)-0.735351562f, (float16_t)0.202514648f, + (float16_t)0.719726562f, (float16_t)-0.448730469f, (float16_t)0.590332031f, (float16_t)-0.000566005707f, + (float16_t)-0.552734375f, (float16_t)-0.089050293f, (float16_t)0.514160156f, (float16_t)0.666015625f, + (float16_t)0.723144531f, (float16_t)-0.71484375f, (float16_t)-0.627929688f, (float16_t)-0.131225586f, + (float16_t)0.245727539f, (float16_t)0.0992431641f, (float16_t)-0.293701172f, (float16_t)0.541503906f, + (float16_t)-0.368408203f, (float16_t)0.685058594f, (float16_t)0.437011719f, (float16_t)-0.152099609f, + (float16_t)-0.572753906f, (float16_t)0.287109375f, (float16_t)0.650390625f, (float16_t)-0.427734375f, + (float16_t)0.0933837891f, (float16_t)0.286376953f, (float16_t)0.375976562f, (float16_t)0.09765625f, + (float16_t)0.174560547f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/biases_data.h new file mode 100644 index 00000000..eb91e24c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_basic_f32_biases_data[7] = + {-0.102267995f, -0.425354481f, 0.0492131747f, -0.123081155f, 0.0641790107f, -0.09203659f, -0.278269589f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/config_data.h new file mode 100644 index 00000000..9802ae1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_BASIC_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_BASIC_F32_INPUT_H 5 +#define CONV_MATCH_1X1_BASIC_F32_INPUT_W 7 +#define CONV_MATCH_1X1_BASIC_F32_IN_CH 19 +#define CONV_MATCH_1X1_BASIC_F32_FILTER_H 1 +#define CONV_MATCH_1X1_BASIC_F32_FILTER_W 1 +#define CONV_MATCH_1X1_BASIC_F32_OUT_CH 7 +#define CONV_MATCH_1X1_BASIC_F32_STRIDE_H 1 +#define CONV_MATCH_1X1_BASIC_F32_STRIDE_W 1 +#define CONV_MATCH_1X1_BASIC_F32_PADDING_H 0 +#define CONV_MATCH_1X1_BASIC_F32_PADDING_W 0 +#define CONV_MATCH_1X1_BASIC_F32_DILATION_H 1 +#define CONV_MATCH_1X1_BASIC_F32_DILATION_W 1 +#define CONV_MATCH_1X1_BASIC_F32_OUTPUT_H 5 +#define CONV_MATCH_1X1_BASIC_F32_OUTPUT_W 7 +#define CONV_MATCH_1X1_BASIC_F32_OUTPUT_C 7 +#define CONV_MATCH_1X1_BASIC_F32_DST_SIZE 490 +#define CONV_MATCH_1X1_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_BASIC_F32_USE_WRAPPER 0 +#define CONV_MATCH_1X1_BASIC_F32_OUT_ACTIVATION_MIN -126.0f +#define CONV_MATCH_1X1_BASIC_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/input_data.h new file mode 100644 index 00000000..098d82df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/input_data.h @@ -0,0 +1,224 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_basic_f32_input_data[1330] = { + 0.587860763f, 0.804137707f, -0.848317325f, 0.980568945f, -0.87029922f, 0.626944065f, + 0.991464317f, 0.673384726f, -0.304487586f, -0.526103795f, -0.505579829f, -0.777708471f, + 0.542297363f, -0.439477116f, 0.483398944f, -0.720080435f, -0.678833365f, 0.666292429f, + -0.538164675f, -0.618218899f, 0.54703337f, -0.711737692f, -0.884575546f, 0.14794147f, + -0.56905818f, -0.245815054f, -0.0993825942f, 0.774995506f, 0.566842735f, -0.210734531f, + -0.330816656f, -0.331243396f, -0.0940535963f, -0.147253484f, 0.529018164f, -0.287900299f, + -0.81821698f, -0.482716203f, -0.410793722f, -0.492354631f, -0.741539121f, -0.768128395f, + -0.121587567f, 0.856922269f, 0.254340976f, -0.556897879f, -0.756635368f, -0.967051208f, + 0.00177351234f, 0.795271337f, 0.219069257f, 0.231356353f, -0.243197665f, 0.313106775f, + -0.473462045f, 0.654090047f, 0.583289504f, -0.596356332f, -0.639057815f, -0.0246111285f, + -0.52937746f, 0.062160641f, 0.153979927f, -0.248303026f, 0.513952017f, 0.458801478f, + -0.669073939f, 0.0838880688f, -0.769471407f, 0.500504613f, -0.587987065f, -0.481506109f, + 0.371740758f, 0.0897813663f, -0.882340729f, -0.589294016f, -0.510650039f, -0.181104392f, + 0.0016372205f, 0.629256427f, 0.867488027f, -0.0119709391f, -0.789994895f, -0.332990736f, + 0.452676237f, -0.60728842f, -0.39874962f, 0.125767127f, -0.325305432f, -0.284087747f, + -0.0818339363f, -0.656156182f, -0.513692856f, 0.33577168f, -0.10982167f, 0.755213201f, + 0.356822968f, 0.790125787f, 0.499471307f, -0.656734049f, 0.205415845f, -0.458238751f, + 0.78594321f, 0.989894867f, 0.0408529118f, 0.996216357f, 0.0577266738f, -0.642882526f, + 0.241898462f, 0.636363029f, 0.771518111f, -0.10758581f, 0.851543665f, -0.522006273f, + 0.723143756f, -0.791291595f, 0.771753669f, 0.82707113f, -0.166576952f, -0.496295244f, + 0.802674711f, -0.128285825f, -0.860413015f, -0.967415214f, -0.0963063315f, -0.989766777f, + 0.827400088f, -0.452023327f, 0.575337708f, -0.530056f, 0.115285635f, 0.794031262f, + 0.880031765f, -0.687671006f, -0.34495312f, 0.54925555f, -0.25557059f, -0.257534176f, + -0.992818296f, 0.00259066001f, -0.871814609f, 0.443662971f, -0.00172709592f, 0.651059687f, + 0.556337059f, 0.559472024f, 0.917838275f, 0.658963263f, 0.439245284f, 0.513938725f, + 0.933173001f, 0.487338096f, -0.37742421f, -0.741734207f, -0.506243527f, -0.0464348309f, + 0.163536698f, 0.97947675f, 0.320464939f, -0.675212383f, -0.583932936f, 0.284662783f, + 0.0501380228f, 0.160857603f, 0.0166668333f, -0.306104839f, -0.479645282f, 0.631348431f, + -0.177994832f, -0.5227108f, -0.652258694f, -0.142623857f, 0.264321774f, -0.0529289283f, + -0.893673539f, 0.273266822f, 0.560409367f, -0.0285185203f, 0.211765945f, -0.0541083328f, + -0.308302969f, 0.11152295f, 0.353919446f, 0.918519378f, -0.871104062f, 0.785543263f, + -0.759319425f, -0.416936278f, 0.493003547f, -0.00881408993f, -0.897271156f, -0.546181798f, + 0.744728863f, -0.475113988f, 0.451178312f, -0.655035675f, -0.179759145f, -0.944762111f, + -0.432612747f, 0.138188124f, 0.0672769696f, -0.338492423f, -0.0437637381f, -0.1481819f, + 0.647692323f, 0.960090995f, -0.359102666f, -0.475534081f, 0.0298432056f, -0.487588793f, + -0.0997967273f, 0.379273772f, 0.198871613f, 0.993588924f, -0.5323717f, 0.364722997f, + 0.120351523f, -0.439344436f, 0.268592447f, -0.882142127f, 0.00309595652f, -0.512432456f, + -0.927144647f, 0.142294466f, -0.912099421f, -0.0967731699f, 0.0942248031f, -0.881915092f, + -0.046908617f, 0.173774213f, 0.107638821f, -0.431904167f, 0.28912276f, -0.794665396f, + 0.578419864f, 0.323305368f, 0.698670328f, 0.528696716f, 0.0849133134f, 0.641498625f, + -0.524647713f, -0.345313281f, 0.480318844f, -0.0713143945f, -0.767737508f, -0.0265935436f, + -0.410936266f, -0.814067841f, 0.652336895f, 0.431589931f, -0.0414463654f, -0.398630649f, + 0.448967695f, 0.821398735f, -0.246353641f, 0.757008731f, -0.690764904f, 0.298221231f, + -0.527925491f, -0.560283959f, 0.24732621f, -0.111399792f, -0.146514207f, -0.863984585f, + -0.0825616047f, 0.586889863f, 0.0837468728f, -0.374635488f, 0.825133801f, 0.918793738f, + 0.311653823f, 0.122280188f, 0.155499414f, 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b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/output_ref_data.h @@ -0,0 +1,72 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_basic_f32_output_ref_data[490] = { + -0.516233742f, -0.924809635f, 2.91595459f, 2.36601639f, -0.819289505f, -2.23254442f, -0.064162612f, + -1.00051308f, -2.33565211f, -0.835707128f, -0.450866491f, 0.0792960599f, -0.0256829206f, -0.585421205f, + -0.348028034f, 1.2918098f, -0.536816478f, -1.51493168f, 0.394564033f, 0.307659268f, -0.0479308665f, + -0.957511127f, -1.27179527f, -0.529391468f, 0.306671917f, -1.83398473f, -0.163343698f, 1.14670551f, + 0.80748719f, -1.16611671f, 0.943014503f, -0.483580291f, -1.03945088f, -0.568647742f, 0.019913191f, + -1.52222025f, -0.207204178f, -0.307643414f, -0.0908994824f, 2.32425547f, 0.917614222f, 0.0388297439f, + 1.18724608f, 1.90752745f, 1.69226897f, 1.36628807f, -0.759545326f, -1.51858008f, -0.510105371f, + -1.4575454f, 0.915692627f, -0.916339755f, -1.54180253f, 2.41654134f, 1.43551362f, -0.242725492f, + -0.254755199f, -0.163220778f, -1.15641248f, -1.12463236f, -1.14044011f, 1.32802236f, -0.406415433f, + -0.920636296f, -0.854747713f, 0.628850222f, -0.532793462f, 0.63349396f, 0.179623634f, 0.830405295f, + -0.0864129215f, 0.657666206f, -1.38263321f, -1.84734488f, 1.0333339f, 0.856911361f, -0.727452695f, + 0.0931274965f, -2.28560758f, 1.15309024f, 1.07827091f, -0.765842676f, 0.154050112f, -0.570812702f, + -1.90599906f, -1.68517375f, 0.288041055f, -0.523046255f, 2.01295447f, -0.11248491f, -0.96635437f, + -0.301401287f, -0.324535549f, -0.406702578f, -0.402795702f, 1.48057675f, -2.07946515f, -0.501674712f, + -0.506206572f, 0.478294075f, -0.334447503f, -0.242078081f, -0.334263384f, 0.627276778f, 0.734387279f, + 0.136963189f, -0.819090724f, 0.427237064f, -1.61728239f, -0.302397698f, 1.20663714f, 0.311035156f, + -0.567178845f, 0.587943316f, 0.714545369f, 1.16209662f, 1.53941405f, -0.329051226f, -0.171272531f, + -1.06113005f, -0.707310081f, 0.34383142f, 0.111213543f, -0.723018169f, 0.0805932432f, 1.44545472f, + 0.799951732f, -1.02129602f, 0.0170253776f, 0.184151679f, -0.175239161f, 0.701534927f, -0.651910305f, + 1.51643276f, -1.4427321f, 0.375354201f, 0.140991613f, -1.35923183f, -1.86674619f, 1.03065026f, + -1.59783912f, 0.811304927f, 1.88295233f, 1.35567462f, -1.21183646f, -1.28221095f, -1.55785692f, + -0.842429996f, 0.920019388f, 0.77108252f, -1.62040555f, -0.676847517f, -0.191546991f, 0.0446970575f, + -0.626093805f, 0.328991771f, 1.4945358f, -0.286034793f, -1.14523232f, 0.889561117f, 0.864050031f, + 0.425886691f, 0.701917768f, -0.50350982f, 0.928478658f, -0.682891369f, -0.460098624f, -1.30093145f, + -1.478423f, -1.89566243f, 1.1477766f, -0.205199927f, 1.05991733f, -1.4583149f, -0.426063448f, + 0.899044394f, -0.00955583621f, 0.78976512f, -0.325626194f, 0.0249646008f, -0.0905409381f, -0.83770138f, + 0.824227095f, 0.196812585f, -0.478114992f, -2.93710351f, 0.963582635f, -1.4841603f, -0.132902667f, + -0.102324858f, 0.241420388f, -1.50291383f, -2.57185793f, 0.0949926227f, 2.6820147f, 1.25076878f, + -0.435033381f, 0.524169981f, -0.468020111f, -1.09528887f, 1.38103366f, 0.739367187f, 0.257013321f, + 2.82528663f, -0.403284699f, -0.966894329f, -0.0873474628f, 1.23687255f, -1.13821661f, -1.81677842f, + -0.704092443f, -1.44749618f, 1.28103733f, 0.55130291f, -0.301519841f, 0.0841244385f, 0.716485739f, + 0.125529721f, -0.122834526f, -0.681742847f, -2.03369522f, 2.77945614f, -0.298947155f, -0.26623112f, + -0.03322253f, -1.8790462f, 0.0606166534f, -1.2656579f, 1.2383045f, -0.35388267f, -0.206545725f, + -1.19181299f, -1.68903184f, 1.7780087f, 1.40676391f, -0.712029994f, -1.2355963f, 0.0490912758f, + 0.0510723852f, -1.54448712f, -0.967226505f, 0.0149730369f, -2.15287566f, 1.73397434f, 0.453958154f, + 1.08710015f, -1.99542463f, -0.672628045f, 0.926113248f, -0.297301561f, 0.542479336f, -0.0705344081f, + -0.973798215f, -0.0597033016f, 0.729024172f, -0.766875029f, 1.47397363f, -0.289546847f, -0.304999471f, + 1.08567691f, 0.558832645f, -0.0677001923f, 0.850114346f, 0.323565543f, -0.159989253f, -0.133482829f, + -0.0959452912f, 0.132078156f, -0.635609508f, -0.735325217f, -0.51443547f, 0.677355349f, -0.655879021f, + -0.125401065f, -0.124441005f, -1.70762408f, -1.47964942f, 0.406989723f, 1.8828249f, 0.0373801664f, + -0.213896185f, 0.691591918f, -0.592785299f, -0.373882145f, 0.446570992f, 2.31081915f, 1.77620399f, + -0.766241789f, -1.48309648f, 1.33280098f, -1.66273224f, -0.245102629f, -1.03140092f, 1.38789785f, + -0.739785492f, -1.30077791f, 0.745931149f, 0.396191448f, -0.138958231f, 1.65123773f, -1.14995289f, + 0.0561019331f, -0.17719081f, 0.293246329f, -0.962419629f, 0.603449225f, -1.55659008f, 0.256531239f, + 1.30819702f, -1.86760759f, -0.368899822f, 1.40307975f, -0.58306253f, 0.70483917f, -1.24822998f, + -1.75928688f, -0.853631675f, -0.448253065f, -0.474693984f, 1.36033094f, 0.276498854f, -1.24733257f, + 1.24458432f, -1.20510221f, -0.525864124f, 1.62327993f, -1.61170709f, -0.131195948f, -0.298506916f, + 1.61381853f, -0.726727247f, -3.2023437f, -1.07199669f, 1.0099448f, -0.145132557f, -0.683573604f, + -0.0498512834f, -0.333552301f, 0.912128568f, 0.620299339f, 0.368459851f, -1.84255278f, -1.60093975f, + 0.484042525f, -0.341360182f, -0.893747866f, -0.927634418f, 2.1257236f, -0.82444489f, -0.886797547f, + -0.933452368f, -0.44243747f, 0.492108792f, -0.690128446f, -0.239648283f, -0.728382587f, -0.377553344f, + -0.781458199f, -0.968365788f, -0.44453916f, -1.40359616f, 0.354713678f, 3.51798201f, 0.779955745f, + 2.23554921f, 0.0772126392f, -0.126277983f, 0.293043047f, 1.89279532f, -1.55183899f, -2.39798641f, + 1.94214487f, -1.36269617f, -2.12664819f, 0.056201119f, 0.166381463f, 0.512574434f, 0.157731161f, + -0.50011313f, -0.0426961929f, -0.018577544f, -0.341174781f, -0.27507332f, -0.908451557f, 1.49801338f, + -0.478855491f, -1.20095277f, -0.190100431f, 0.695448875f, 1.46347713f, -0.813764095f, -1.04132032f, + -0.622247875f, 2.02551913f, -0.348458201f, -0.0406169109f, -0.220323116f, -0.151555642f, -1.43239903f, + -1.16361201f, -0.0935178176f, 0.829340041f, -0.281624824f, -1.35775316f, 0.295393556f, 0.0447410457f, + -0.0715791434f, 0.432382613f, -0.298085988f, 0.147840992f, 0.387641847f, -0.275697857f, 1.495574f, + 0.837333858f, -0.890584707f, 0.418641001f, -0.0255548973f, 1.14231575f, -1.57961345f, -0.489386946f, + 1.56116986f, 0.416610301f, 0.536865294f, -0.862901807f, -0.68402338f, 0.598908901f, -1.95241702f, + -0.316056639f, -0.886055946f, -0.347200394f, -1.69569325f, 0.603829086f, 0.300922632f, 1.83595788f, + -0.841137826f, -0.396019161f, 0.0816448405f, -1.13575852f, -0.601850569f, 0.155330464f, 1.50455832f, + 0.910480499f, -0.0195964798f, -0.0191702824f, -0.139221415f, 0.633991957f, -0.479329377f, -0.593420684f, + -0.162841156f, -0.442606717f, 0.971812248f, 1.51628649f, -2.65686893f, -1.69360161f, 0.987759948f, + -0.586793303f, 1.82207978f, -1.29734921f, -1.25898135f, 1.32906449f, 0.160949811f, -0.271378666f, + 0.510036588f, -0.153155535f, -0.656657517f, 1.52376235f, -0.10286431f, 1.54215062f, -0.892699301f, + 1.25273359f, 1.28386486f, 1.47441137f, 0.0853619501f, -1.17264271f, -3.43034911f, -2.76755857f, + -1.00186229f, -0.674262464f, 1.04382503f, 1.78961265f, -1.14101601f, 1.62233043f, 0.0327007435f, + -0.89224869f, -0.220950097f, 0.0192603003f, 1.79470825f, -0.739877224f, -0.887508929f, 1.143224f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/weights_data.h new file mode 100644 index 00000000..8891153a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_basic_f32/weights_data.h @@ -0,0 +1,21 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_basic_f32_weights_data[133] = { + 0.34072575f, 0.181150734f, -0.106630467f, 0.453737706f, -0.0521004908f, 0.04674045f, -0.700722098f, + -0.730445981f, -0.692236662f, -0.250004947f, 0.0669563338f, -0.366381973f, -0.696938038f, -0.431622982f, + -0.0987391844f, -0.608824968f, 0.387370318f, -0.486878484f, 0.728011191f, 0.499676287f, -0.573054373f, + 0.320803553f, 0.0746887475f, 0.0624583252f, 0.388892531f, 0.180524081f, -0.296770841f, -0.421234995f, + -0.422364116f, -0.0546689071f, -0.0102139004f, 0.170195863f, 0.733022094f, 0.590243161f, 0.573403835f, + -0.0765257329f, -0.0102319177f, 0.698358536f, 0.259412229f, -0.119310565f, -0.25983429f, 0.488105655f, + 0.331753284f, -0.212876603f, 0.0803968087f, 0.739819586f, -0.1245979f, -0.00654293457f, -0.564939022f, + -0.297363192f, 0.498060793f, 0.604198694f, 0.253011853f, -0.7034989f, -0.684392393f, 0.311228782f, + -0.170516089f, 0.541189253f, 0.215156436f, -0.15336597f, -0.0103633944f, -0.180533111f, -0.302749366f, + 0.44070828f, 0.308604568f, -0.0977809653f, -0.556594372f, -0.298097253f, -0.725433826f, -0.270489186f, + 0.0926158354f, -0.0565268919f, -0.639663339f, 0.672264755f, 0.0694145411f, -0.729613483f, -0.609464705f, + 0.364144921f, 0.479434997f, -0.250547111f, -0.679231942f, -0.282856137f, 0.169357851f, 0.373241991f, + -0.0946535766f, 0.601671159f, 0.706104338f, 0.706562161f, -0.460640013f, 0.0569789447f, 0.705754042f, + 0.254297733f, -0.521156132f, 0.422972351f, 0.600666225f, 0.0268545821f, -0.5698722f, 0.551135421f, + -0.594477713f, -0.606223643f, 0.357001871f, -0.479055047f, -0.735268652f, 0.202459782f, 0.719798088f, + -0.448738068f, 0.590366781f, -0.000565844181f, -0.552702248f, -0.0890551284f, 0.514343679f, 0.665982664f, + 0.723259509f, -0.714841366f, -0.627774775f, -0.131213948f, 0.245685384f, 0.0992714688f, -0.293682873f, + 0.541407108f, -0.368365318f, 0.685207427f, 0.437021464f, -0.152066872f, -0.572531641f, 0.287164271f, + 0.650466263f, -0.427762806f, 0.093359299f, 0.286427349f, 0.376009822f, 0.0976257399f, 0.174577311f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/biases_data.h new file mode 100644 index 00000000..7cd1152c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_f16_biases_data[5] = {(float16_t)0.303710938f, + (float16_t)-0.0841674805f, + (float16_t)-0.291992188f, + (float16_t)0.243164062f, + (float16_t)0.109802246f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/config_data.h new file mode 100644 index 00000000..d5580d53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_F16_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_F16_INPUT_W 7 +#define CONV_MATCH_1X1_STRIDE_X_F16_IN_CH 9 +#define CONV_MATCH_1X1_STRIDE_X_F16_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F16_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_F16_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_F16_STRIDE_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F16_STRIDE_W 3 +#define CONV_MATCH_1X1_STRIDE_X_F16_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_F16_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_F16_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F16_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_F16_OUTPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_F16_OUTPUT_W 3 +#define CONV_MATCH_1X1_STRIDE_X_F16_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_F16_DST_SIZE 120 +#define CONV_MATCH_1X1_STRIDE_X_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_F16_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_F16_OUT_ACTIVATION_MIN (float16_t) - 126.0f +#define CONV_MATCH_1X1_STRIDE_X_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/input_data.h new file mode 100644 index 00000000..841d940d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/input_data.h @@ -0,0 +1,128 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_f16_input_data[504] = { + (float16_t)-0.0875244141f, (float16_t)-0.151733398f, (float16_t)-0.17590332f, (float16_t)-0.073425293f, + (float16_t)0.00529098511f, (float16_t)0.118774414f, (float16_t)0.596191406f, (float16_t)0.629394531f, + (float16_t)-0.0758666992f, (float16_t)-0.0301818848f, (float16_t)-0.314208984f, (float16_t)-0.308349609f, + (float16_t)-0.2109375f, (float16_t)0.0963745117f, (float16_t)0.382324219f, (float16_t)0.110839844f, + (float16_t)0.783691406f, (float16_t)-0.518066406f, (float16_t)0.974609375f, (float16_t)0.723632812f, + (float16_t)0.541992188f, (float16_t)0.410400391f, (float16_t)0.637695312f, (float16_t)0.567382812f, + (float16_t)0.0163269043f, (float16_t)-0.525878906f, (float16_t)0.341064453f, (float16_t)-0.730957031f, + (float16_t)0.676269531f, (float16_t)0.875976562f, (float16_t)-0.659667969f, (float16_t)0.942871094f, + (float16_t)0.549316406f, (float16_t)-0.48828125f, (float16_t)0.590820312f, (float16_t)-0.793457031f, + (float16_t)-0.896972656f, (float16_t)-0.254150391f, (float16_t)0.247680664f, (float16_t)-0.159423828f, + (float16_t)-0.153808594f, (float16_t)-0.360107422f, (float16_t)0.770507812f, (float16_t)0.963378906f, + (float16_t)0.541503906f, (float16_t)0.121887207f, (float16_t)-0.553222656f, (float16_t)0.552734375f, + (float16_t)0.598632812f, (float16_t)0.751464844f, (float16_t)0.620605469f, (float16_t)-0.807128906f, + (float16_t)-0.437011719f, (float16_t)0.141723633f, (float16_t)-0.909179688f, (float16_t)-0.104370117f, + (float16_t)0.908691406f, (float16_t)0.152587891f, (float16_t)0.122802734f, (float16_t)0.658203125f, + (float16_t)0.881347656f, (float16_t)0.124633789f, (float16_t)-0.78125f, (float16_t)0.156494141f, + (float16_t)-0.956542969f, (float16_t)-0.283691406f, (float16_t)-0.754394531f, (float16_t)0.108276367f, + (float16_t)0.0639648438f, (float16_t)-0.255615234f, (float16_t)0.416748047f, (float16_t)0.93359375f, + (float16_t)0.6875f, (float16_t)0.133911133f, (float16_t)-0.762207031f, (float16_t)0.820800781f, + (float16_t)0.693359375f, (float16_t)0.217407227f, (float16_t)0.964355469f, (float16_t)-0.7734375f, + (float16_t)0.371582031f, (float16_t)-0.404785156f, (float16_t)0.417480469f, (float16_t)0.381591797f, + (float16_t)-0.854492188f, (float16_t)-0.431396484f, (float16_t)-0.788574219f, (float16_t)-0.0569152832f, + (float16_t)-0.450927734f, (float16_t)0.972167969f, (float16_t)0.224731445f, (float16_t)0.0106048584f, + (float16_t)-0.403320312f, (float16_t)-0.528320312f, (float16_t)0.991210938f, (float16_t)-0.670898438f, + (float16_t)-0.814941406f, (float16_t)-0.805664062f, (float16_t)0.545410156f, (float16_t)0.533691406f, + (float16_t)-0.793945312f, (float16_t)0.807128906f, (float16_t)0.40625f, (float16_t)-0.00356674194f, + (float16_t)0.612792969f, (float16_t)-0.08203125f, (float16_t)0.474853516f, (float16_t)-0.993164062f, + (float16_t)-0.857421875f, (float16_t)-0.51953125f, (float16_t)0.494384766f, (float16_t)-0.239013672f, + (float16_t)0.0950317383f, (float16_t)0.087890625f, (float16_t)-0.831542969f, (float16_t)0.645019531f, + (float16_t)0.940429688f, (float16_t)-0.402099609f, (float16_t)-0.0413513184f, (float16_t)-0.0385742188f, + (float16_t)-0.732421875f, (float16_t)-0.462402344f, (float16_t)0.643554688f, (float16_t)-0.814941406f, + (float16_t)0.214477539f, (float16_t)0.756347656f, (float16_t)0.824707031f, (float16_t)0.467285156f, + (float16_t)-0.0191345215f, (float16_t)-0.193847656f, (float16_t)0.386230469f, (float16_t)-0.30078125f, + (float16_t)-0.348144531f, (float16_t)-0.181030273f, (float16_t)-0.887207031f, (float16_t)0.578613281f, + (float16_t)0.428222656f, (float16_t)0.964355469f, (float16_t)-0.689453125f, (float16_t)-0.770996094f, + (float16_t)0.697753906f, (float16_t)-0.336914062f, (float16_t)0.980957031f, (float16_t)0.888183594f, + (float16_t)0.463134766f, (float16_t)0.967285156f, (float16_t)0.684570312f, (float16_t)-0.59765625f, + (float16_t)-0.422363281f, (float16_t)-0.15222168f, (float16_t)0.28515625f, (float16_t)-0.937988281f, + (float16_t)-0.395751953f, (float16_t)-0.472412109f, (float16_t)-0.0673828125f, (float16_t)0.627929688f, + (float16_t)-0.102600098f, (float16_t)-0.146606445f, (float16_t)-0.991210938f, (float16_t)0.216796875f, + (float16_t)0.744140625f, (float16_t)-0.664550781f, (float16_t)0.528320312f, (float16_t)0.374023438f, + (float16_t)0.948242188f, (float16_t)-0.409667969f, (float16_t)-0.865722656f, (float16_t)-0.815429688f, + (float16_t)-0.513183594f, (float16_t)-0.0389099121f, (float16_t)0.6328125f, (float16_t)0.18762207f, + (float16_t)-0.562988281f, (float16_t)0.872070312f, (float16_t)-0.311035156f, (float16_t)0.0468139648f, + (float16_t)-0.297363281f, (float16_t)0.997558594f, (float16_t)-0.492675781f, (float16_t)0.106628418f, + (float16_t)0.24609375f, (float16_t)0.983398438f, (float16_t)0.134643555f, (float16_t)-0.125854492f, + (float16_t)-0.775390625f, (float16_t)-0.939453125f, (float16_t)0.411621094f, (float16_t)-0.9296875f, + (float16_t)0.111755371f, (float16_t)0.462890625f, (float16_t)0.876464844f, (float16_t)-0.792480469f, + (float16_t)0.0654907227f, (float16_t)-0.975585938f, (float16_t)0.10144043f, (float16_t)-0.427246094f, + (float16_t)-0.369384766f, (float16_t)0.338623047f, (float16_t)-0.369384766f, (float16_t)0.669433594f, + (float16_t)0.720703125f, (float16_t)-0.559570312f, (float16_t)0.851074219f, (float16_t)0.380859375f, + (float16_t)0.84765625f, (float16_t)-0.528808594f, (float16_t)-0.7890625f, (float16_t)0.895507812f, + (float16_t)-0.256347656f, (float16_t)-0.424804688f, (float16_t)-0.118347168f, (float16_t)0.546875f, + (float16_t)-0.465087891f, (float16_t)0.755371094f, (float16_t)-0.17565918f, (float16_t)0.120788574f, + (float16_t)-0.981445312f, (float16_t)0.682617188f, (float16_t)0.106384277f, (float16_t)-0.991699219f, + (float16_t)-0.162841797f, (float16_t)0.427001953f, (float16_t)0.0207061768f, (float16_t)0.735351562f, + (float16_t)-0.104125977f, (float16_t)0.937988281f, (float16_t)-0.668945312f, (float16_t)-0.022567749f, + (float16_t)-0.232055664f, (float16_t)-0.337646484f, (float16_t)-0.682128906f, (float16_t)-0.37890625f, + (float16_t)-0.365966797f, (float16_t)-0.684082031f, (float16_t)0.178833008f, (float16_t)0.157714844f, + (float16_t)-0.580078125f, (float16_t)-0.723144531f, (float16_t)-0.833007812f, (float16_t)-0.0325012207f, + (float16_t)-0.522949219f, (float16_t)-0.844726562f, (float16_t)0.0837402344f, (float16_t)-0.937011719f, + (float16_t)0.159301758f, (float16_t)-0.175537109f, (float16_t)0.0255279541f, (float16_t)-0.5703125f, + (float16_t)-0.420654297f, (float16_t)0.562988281f, (float16_t)-0.393066406f, (float16_t)-0.985839844f, + (float16_t)-0.592773438f, (float16_t)0.756347656f, (float16_t)0.808105469f, (float16_t)-0.538574219f, + (float16_t)-0.684570312f, (float16_t)0.453857422f, (float16_t)-0.232788086f, (float16_t)-0.804199219f, + (float16_t)-0.802734375f, (float16_t)0.531738281f, (float16_t)0.363525391f, (float16_t)-0.678222656f, + (float16_t)0.970703125f, (float16_t)0.507324219f, (float16_t)-0.169921875f, (float16_t)0.567871094f, + (float16_t)-0.234130859f, (float16_t)-0.709960938f, (float16_t)0.314453125f, (float16_t)-0.328613281f, + (float16_t)-0.96484375f, (float16_t)0.575683594f, (float16_t)0.602539062f, (float16_t)-0.224853516f, + (float16_t)-0.352294922f, (float16_t)0.838378906f, (float16_t)-0.740722656f, (float16_t)-0.574707031f, + (float16_t)-0.866210938f, (float16_t)-0.795898438f, (float16_t)-0.573242188f, (float16_t)-0.153808594f, + (float16_t)0.94140625f, (float16_t)0.622558594f, (float16_t)0.055480957f, (float16_t)0.0946655273f, + (float16_t)-0.798339844f, (float16_t)0.120605469f, (float16_t)0.616699219f, (float16_t)-0.13684082f, + (float16_t)-0.0798339844f, (float16_t)0.217407227f, (float16_t)-0.016998291f, (float16_t)-0.830566406f, + (float16_t)-0.0809936523f, (float16_t)-0.186767578f, (float16_t)-0.0811767578f, (float16_t)-0.616699219f, + (float16_t)0.905273438f, (float16_t)0.915527344f, (float16_t)-0.106567383f, (float16_t)-0.8671875f, + (float16_t)-0.508789062f, (float16_t)0.991699219f, (float16_t)-0.282470703f, (float16_t)-0.192260742f, + (float16_t)0.6796875f, (float16_t)0.284423828f, (float16_t)-0.541992188f, (float16_t)-0.212768555f, + (float16_t)0.480224609f, (float16_t)0.5546875f, (float16_t)0.178833008f, (float16_t)0.989746094f, + (float16_t)0.776367188f, (float16_t)0.399902344f, (float16_t)-0.448730469f, (float16_t)-0.365722656f, + (float16_t)0.498291016f, (float16_t)0.363769531f, (float16_t)0.799316406f, (float16_t)-0.110351562f, + (float16_t)-0.604003906f, (float16_t)0.29296875f, (float16_t)0.268310547f, (float16_t)0.931640625f, + (float16_t)-0.00901031494f, (float16_t)0.874511719f, (float16_t)0.113769531f, (float16_t)-0.307861328f, + (float16_t)0.582519531f, (float16_t)-0.512695312f, (float16_t)0.927734375f, (float16_t)0.109375f, + (float16_t)0.506347656f, (float16_t)0.275634766f, (float16_t)-0.708984375f, (float16_t)0.0651245117f, + (float16_t)-0.490722656f, (float16_t)0.373535156f, (float16_t)0.181884766f, (float16_t)-0.261230469f, + (float16_t)0.367431641f, (float16_t)0.859375f, (float16_t)0.0166168213f, (float16_t)0.889160156f, + (float16_t)-0.750976562f, (float16_t)0.563476562f, (float16_t)0.656738281f, (float16_t)-0.441894531f, + (float16_t)0.58984375f, (float16_t)-0.862304688f, (float16_t)0.50390625f, (float16_t)-0.599609375f, + (float16_t)-0.0733642578f, (float16_t)0.798339844f, (float16_t)-0.873046875f, (float16_t)0.624023438f, + (float16_t)0.736816406f, (float16_t)-0.717285156f, (float16_t)-0.120300293f, (float16_t)-0.701171875f, + (float16_t)-0.947265625f, (float16_t)-0.739746094f, (float16_t)0.0896606445f, (float16_t)0.425537109f, + (float16_t)-0.275634766f, (float16_t)0.380126953f, (float16_t)0.0668334961f, (float16_t)0.525390625f, + (float16_t)-0.30859375f, (float16_t)0.6015625f, (float16_t)0.454833984f, (float16_t)0.44140625f, + (float16_t)-0.122741699f, (float16_t)0.549804688f, (float16_t)0.0667724609f, (float16_t)-0.10546875f, + (float16_t)0.507324219f, (float16_t)0.100097656f, (float16_t)0.155273438f, (float16_t)0.0467834473f, + (float16_t)-0.355957031f, (float16_t)0.523925781f, (float16_t)-0.0675048828f, (float16_t)-0.6875f, + (float16_t)0.104064941f, (float16_t)-0.198730469f, (float16_t)0.13293457f, (float16_t)0.856933594f, + (float16_t)-0.946777344f, (float16_t)-0.973144531f, (float16_t)0.158203125f, (float16_t)0.741699219f, + (float16_t)-0.0268554688f, (float16_t)0.0848999023f, (float16_t)0.509277344f, (float16_t)0.256103516f, + (float16_t)-0.206420898f, (float16_t)0.406982422f, (float16_t)0.806640625f, (float16_t)-0.74609375f, + (float16_t)-0.674316406f, (float16_t)-0.658691406f, (float16_t)0.566894531f, (float16_t)-0.767578125f, + (float16_t)0.472900391f, (float16_t)-0.402832031f, (float16_t)0.613769531f, (float16_t)0.354492188f, + (float16_t)-0.484375f, (float16_t)-0.384765625f, (float16_t)0.373779297f, (float16_t)-0.7890625f, + (float16_t)0.637695312f, (float16_t)0.302978516f, (float16_t)0.829589844f, (float16_t)-0.524902344f, + (float16_t)-0.187744141f, (float16_t)0.612304688f, (float16_t)0.383544922f, (float16_t)-0.655273438f, + (float16_t)0.99609375f, (float16_t)0.425048828f, (float16_t)-0.934570312f, (float16_t)-0.913085938f, + (float16_t)0.873535156f, (float16_t)0.0208587646f, (float16_t)0.336914062f, (float16_t)-0.831542969f, + (float16_t)0.672851562f, (float16_t)0.891113281f, (float16_t)0.76171875f, (float16_t)0.745117188f, + (float16_t)0.857421875f, (float16_t)-0.927246094f, (float16_t)0.730957031f, (float16_t)-0.817382812f, + (float16_t)0.633789062f, (float16_t)0.967285156f, (float16_t)0.700683594f, (float16_t)-0.184570312f, + (float16_t)0.981933594f, (float16_t)0.692382812f, (float16_t)0.675292969f, (float16_t)-0.721679688f, + (float16_t)0.0874023438f, (float16_t)-0.0694580078f, (float16_t)0.609863281f, (float16_t)-0.0977783203f, + (float16_t)-0.204101562f, (float16_t)0.415527344f, (float16_t)-0.795898438f, (float16_t)-0.278076172f, + (float16_t)-0.372558594f, (float16_t)-0.183837891f, (float16_t)-0.313720703f, (float16_t)0.234985352f, + (float16_t)-0.486572266f, (float16_t)-0.290771484f, (float16_t)-0.907226562f, (float16_t)0.640136719f, + (float16_t)0.280029297f, (float16_t)0.00202941895f, (float16_t)0.449462891f, (float16_t)0.565917969f, + (float16_t)0.717285156f, (float16_t)0.953613281f, (float16_t)-0.469238281f, (float16_t)0.952636719f, + (float16_t)0.143798828f, (float16_t)-0.778320312f, (float16_t)-0.310546875f, (float16_t)-0.687011719f, + (float16_t)0.0941772461f, (float16_t)-0.756347656f, (float16_t)0.324707031f, (float16_t)0.235229492f, + (float16_t)0.984863281f, (float16_t)-0.123718262f, (float16_t)0.0983276367f, (float16_t)0.708496094f, + (float16_t)0.734863281f, (float16_t)0.766601562f, (float16_t)0.909179688f, (float16_t)0.924804688f, + (float16_t)0.557617188f, (float16_t)0.87109375f, (float16_t)0.480712891f, (float16_t)-0.448242188f, + (float16_t)-0.0120773315f, (float16_t)0.48046875f, (float16_t)-0.772460938f, (float16_t)-0.061126709f, + (float16_t)-0.804199219f, (float16_t)0.0959472656f, (float16_t)-0.254638672f, (float16_t)0.170654297f, + (float16_t)0.931640625f, (float16_t)0.0232849121f, (float16_t)0.452392578f, (float16_t)-0.209106445f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/output_ref_data.h new file mode 100644 index 00000000..82735c91 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_f16_output_ref_data[120] = { + (float16_t)0.285644531f, (float16_t)-0.241577148f, (float16_t)-0.696777344f, (float16_t)-0.18359375f, + (float16_t)-0.0107116699f, (float16_t)-0.118652344f, (float16_t)-0.908203125f, (float16_t)-0.220947266f, + (float16_t)-1.28027344f, (float16_t)0.429931641f, (float16_t)0.294677734f, (float16_t)-0.980957031f, + (float16_t)-0.5625f, (float16_t)-0.191162109f, (float16_t)0.385009766f, (float16_t)-0.0523681641f, + (float16_t)-0.349365234f, (float16_t)-0.00208854675f, (float16_t)0.852050781f, (float16_t)-1.546875f, + (float16_t)0.750976562f, (float16_t)0.754394531f, (float16_t)0.801269531f, (float16_t)0.52734375f, + (float16_t)-0.1640625f, (float16_t)-0.946777344f, (float16_t)-0.286621094f, (float16_t)-0.202880859f, + (float16_t)0.565917969f, (float16_t)-1.60742188f, (float16_t)0.950195312f, (float16_t)0.288330078f, + (float16_t)-0.241088867f, (float16_t)-0.527832031f, (float16_t)1.25878906f, (float16_t)0.602539062f, + (float16_t)-0.69140625f, (float16_t)-1.85546875f, (float16_t)-0.35546875f, (float16_t)0.911132812f, + (float16_t)0.326904297f, (float16_t)1.36328125f, (float16_t)-0.297607422f, (float16_t)0.354980469f, + (float16_t)0.861816406f, (float16_t)-0.303466797f, (float16_t)1.08105469f, (float16_t)-0.385742188f, + (float16_t)0.350585938f, (float16_t)-0.0439147949f, (float16_t)-0.7734375f, (float16_t)-0.0802612305f, + (float16_t)-1.00292969f, (float16_t)-0.934570312f, (float16_t)-0.743652344f, (float16_t)0.682617188f, + (float16_t)0.358154297f, (float16_t)-1.88671875f, (float16_t)-0.0699462891f, (float16_t)0.442382812f, + (float16_t)-0.270019531f, (float16_t)0.0843505859f, (float16_t)-0.590820312f, (float16_t)-0.0337524414f, + (float16_t)0.106079102f, (float16_t)0.0575256348f, (float16_t)-0.0229187012f, (float16_t)-0.567382812f, + (float16_t)0.455566406f, (float16_t)-1.74609375f, (float16_t)0.0082321167f, (float16_t)-0.784179688f, + (float16_t)-0.869628906f, (float16_t)0.459716797f, (float16_t)0.258300781f, (float16_t)-0.337646484f, + (float16_t)-0.0625f, (float16_t)0.0565490723f, (float16_t)-0.759765625f, (float16_t)0.539550781f, + (float16_t)-0.360839844f, (float16_t)-1.37792969f, (float16_t)0.434814453f, (float16_t)0.865234375f, + (float16_t)-0.0857543945f, (float16_t)0.116455078f, (float16_t)-0.110656738f, (float16_t)1.32714844f, + (float16_t)0.236328125f, (float16_t)-0.069152832f, (float16_t)0.169555664f, (float16_t)-0.893554688f, + (float16_t)-0.357421875f, (float16_t)0.108520508f, (float16_t)0.361572266f, (float16_t)0.165649414f, + (float16_t)0.628417969f, (float16_t)-0.703125f, (float16_t)0.0202026367f, (float16_t)0.0827636719f, + (float16_t)0.995605469f, (float16_t)0.6953125f, (float16_t)1.71289062f, (float16_t)0.373535156f, + (float16_t)2.05273438f, (float16_t)0.428955078f, (float16_t)-0.263183594f, (float16_t)0.995605469f, + (float16_t)-0.138916016f, (float16_t)1.36816406f, (float16_t)0.911132812f, (float16_t)0.995117188f, + (float16_t)0.259033203f, (float16_t)0.293212891f, (float16_t)1.75683594f, (float16_t)0.143676758f, + (float16_t)-1.02441406f, (float16_t)-0.265869141f, (float16_t)0.276855469f, (float16_t)-0.769042969f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/weights_data.h new file mode 100644 index 00000000..84caf48c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f16/weights_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_f16_weights_data[45] = { + (float16_t)0.222045898f, (float16_t)-0.327148438f, (float16_t)-0.161499023f, (float16_t)0.247070312f, + (float16_t)0.392822266f, (float16_t)-0.412597656f, (float16_t)0.187988281f, (float16_t)-0.251464844f, + (float16_t)-0.456787109f, (float16_t)0.136962891f, (float16_t)0.547851562f, (float16_t)-0.626953125f, + (float16_t)-0.329833984f, (float16_t)-0.0602111816f, (float16_t)-0.264404297f, (float16_t)0.293457031f, + (float16_t)-0.5234375f, (float16_t)0.13671875f, (float16_t)0.524414062f, (float16_t)0.233154297f, + (float16_t)0.349365234f, (float16_t)0.0117263794f, (float16_t)0.737304688f, (float16_t)0.532226562f, + (float16_t)0.137573242f, (float16_t)-0.562011719f, (float16_t)0.743652344f, (float16_t)0.0249328613f, + (float16_t)-0.665039062f, (float16_t)0.144897461f, (float16_t)0.313964844f, (float16_t)-0.347167969f, + (float16_t)-0.0253143311f, (float16_t)-0.162841797f, (float16_t)-0.53515625f, (float16_t)0.500488281f, + (float16_t)0.395507812f, (float16_t)0.553710938f, (float16_t)0.319335938f, (float16_t)0.735839844f, + (float16_t)0.298583984f, (float16_t)-0.489746094f, (float16_t)0.135253906f, (float16_t)0.0647583008f, + (float16_t)-0.57421875f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/biases_data.h new file mode 100644 index 00000000..1333b71b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_f32_biases_data[5] = {0.303631485f, + -0.0841947123f, + -0.291997075f, + 0.243136406f, + 0.109799214f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/config_data.h new file mode 100644 index 00000000..9703e5ce --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_F32_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_F32_INPUT_W 7 +#define CONV_MATCH_1X1_STRIDE_X_F32_IN_CH 9 +#define CONV_MATCH_1X1_STRIDE_X_F32_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F32_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_F32_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_F32_STRIDE_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F32_STRIDE_W 3 +#define CONV_MATCH_1X1_STRIDE_X_F32_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_F32_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_F32_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_F32_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_F32_OUTPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_F32_OUTPUT_W 3 +#define CONV_MATCH_1X1_STRIDE_X_F32_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_F32_DST_SIZE 120 +#define CONV_MATCH_1X1_STRIDE_X_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_F32_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_F32_OUT_ACTIVATION_MIN -126.0f +#define CONV_MATCH_1X1_STRIDE_X_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/input_data.h new file mode 100644 index 00000000..a13fb163 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/input_data.h @@ -0,0 +1,74 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_f32_input_data[504] = { + -0.0875188336f, -0.151742697f, -0.175933868f, -0.0734529048f, 0.00528997649f, 0.118779734f, 0.59612757f, + 0.629632711f, -0.0758415908f, -0.0301890653f, -0.314124286f, -0.308462441f, -0.210992262f, 0.0963844582f, + 0.38220951f, 0.110812806f, 0.783609211f, -0.518026114f, 0.974541366f, 0.72348243f, 0.542180955f, + 0.410285711f, 0.637744844f, 0.567493439f, 0.0163309928f, -0.526021242f, 0.341062486f, -0.730713308f, + 0.676445067f, 0.876165032f, -0.659733653f, 0.943097055f, 0.549446166f, -0.488288283f, 0.590819716f, + -0.793581069f, -0.897074878f, -0.254147768f, 0.24765642f, -0.159455955f, -0.153810963f, -0.360050619f, + 0.770483136f, 0.963480592f, 0.541529417f, 0.121913888f, -0.553418159f, 0.552898586f, 0.598470271f, + 0.751333833f, 0.62065649f, -0.807044506f, -0.437034726f, 0.141763449f, -0.909153283f, -0.104361489f, + 0.90888238f, 0.152574435f, 0.12278109f, 0.658249795f, 0.881390154f, 0.124645628f, -0.781172693f, + 0.156488359f, -0.956629932f, -0.283700705f, -0.754527271f, 0.108282097f, 0.0639557093f, -0.255600005f, + 0.416847914f, 0.933554292f, 0.687426627f, 0.133878708f, -0.762340069f, 0.821003914f, 0.693530262f, + 0.217411384f, 0.964271128f, -0.773587584f, 0.371559352f, -0.404896677f, 0.417549729f, 0.381653219f, + -0.854701102f, -0.431426883f, -0.788565457f, -0.0569223724f, -0.450907141f, 0.972104371f, 0.224694401f, + 0.0106055848f, -0.403405696f, -0.528417587f, 0.991015315f, -0.670981407f, -0.814807236f, -0.805732667f, + 0.545470536f, 0.533447862f, -0.793892324f, 0.807284951f, 0.406286806f, -0.00356587884f, 0.612566173f, + -0.0820403099f, 0.474963188f, -0.993213117f, -0.857277155f, -0.519584537f, 0.494426161f, -0.238953277f, + 0.0950564966f, 0.0879044011f, -0.831710517f, 0.644891679f, 0.940553844f, -0.402184635f, -0.041352056f, + -0.0385637693f, -0.732628524f, -0.462342113f, 0.643629074f, -0.814746916f, 0.214459479f, 0.756228149f, + 0.824677825f, 0.467220485f, -0.0191387795f, -0.193818375f, 0.38627249f, -0.300661355f, -0.348128676f, + -0.181060299f, -0.887328029f, 0.57873565f, 0.428132236f, 0.964581549f, -0.689392865f, -0.77093631f, + 0.697533309f, -0.336882621f, 0.980860233f, 0.888006985f, 0.463244379f, 0.967453063f, 0.684338272f, + -0.597894013f, -0.422421247f, -0.152221352f, 0.285235643f, -0.938147366f, -0.39582023f, -0.472488821f, + -0.0674056262f, 0.627885163f, -0.102588236f, -0.146564126f, -0.991201639f, 0.216805592f, 0.744059384f, + -0.664579034f, 0.528360307f, 0.374129772f, 0.948023379f, -0.409739345f, -0.865584016f, -0.815505385f, + -0.513104439f, -0.0389194638f, 0.632750034f, 0.187599063f, -0.563037515f, 0.871962845f, -0.310991257f, + 0.0468087383f, -0.297475278f, 0.997615576f, -0.492624074f, 0.1066055f, 0.246049196f, 0.983509719f, + 0.134633034f, -0.125846326f, -0.775474846f, -0.939656496f, 0.411572635f, -0.92989397f, 0.111750364f, + 0.462872803f, 0.876422644f, -0.792466938f, 0.0655096844f, -0.975806534f, 0.101424277f, -0.427347362f, + -0.369339317f, 0.338688701f, -0.369370788f, 0.669214487f, 0.720672429f, -0.559544504f, 0.851144791f, + 0.380768985f, 0.847774744f, -0.529007971f, -0.789223552f, 0.895582557f, -0.256419808f, -0.424904287f, + -0.118359469f, 0.54706955f, -0.464972198f, 0.755353272f, -0.17565757f, 0.120806225f, -0.981568933f, + 0.682616293f, 0.106371954f, -0.991464436f, -0.162898779f, 0.427083164f, 0.0207110774f, 0.735114455f, + -0.10414882f, 0.938154221f, -0.669141412f, -0.0225697272f, -0.232081324f, -0.337685674f, -0.681964397f, + -0.379014194f, -0.36591509f, -0.683843791f, 0.178773969f, 0.157762006f, -0.580108702f, -0.723116696f, + -0.832763731f, -0.0325005911f, -0.52288413f, -0.844770074f, 0.0837153196f, -0.937196374f, 0.15936099f, + -0.175504804f, 0.0255305823f, -0.570117593f, -0.420604497f, 0.563013673f, -0.393051028f, -0.985984921f, + -0.592844605f, 0.756118417f, 0.807913423f, -0.538557172f, -0.684769332f, 0.45375669f, -0.232772663f, + -0.804203808f, -0.802620232f, 0.531640828f, 0.363637209f, -0.678445816f, 0.970921159f, 0.507204711f, + -0.169971496f, 0.567800462f, -0.234112144f, -0.710119367f, 0.31438002f, -0.32861501f, -0.964973032f, + 0.575878978f, 0.602567613f, -0.224851117f, -0.352336705f, 0.838383317f, -0.74061048f, -0.574503005f, + -0.866323531f, -0.795709372f, -0.573095083f, -0.153794676f, 0.941340208f, 0.622572839f, 0.0554779023f, + 0.0946653187f, -0.798443675f, 0.12059918f, 0.616455495f, -0.136814013f, -0.0798280537f, 0.217357472f, + -0.0169974715f, -0.830440998f, -0.0810050815f, -0.18671672f, -0.0811902285f, -0.61665976f, 0.905265212f, + 0.915694594f, -0.106560826f, -0.867185295f, -0.508960187f, 0.991914511f, -0.282504916f, -0.19225949f, + 0.679683864f, 0.284365714f, -0.542030632f, -0.212743193f, 0.480231225f, 0.554675817f, 0.178843781f, + 0.989578605f, 0.776248217f, 0.399783492f, -0.448734373f, -0.365634978f, 0.498371035f, 0.363727957f, + 0.79946363f, -0.11033687f, -0.604117632f, 0.292889804f, 0.268202513f, 0.93184948f, -0.00901353639f, + 0.874617815f, 0.113762371f, -0.3077797f, 0.582604468f, -0.512490213f, 0.927644432f, 0.10939502f, + 0.506151497f, 0.275690705f, -0.709189594f, 0.0651238933f, -0.490824282f, 0.373480201f, 0.181868136f, + -0.261225641f, 0.367343098f, 0.859135449f, 0.0166098587f, 0.889062226f, -0.75088042f, 0.563537717f, + 0.656656623f, -0.44186452f, 0.58968097f, -0.862083972f, 0.503817976f, -0.5995031f, -0.0733588189f, + 0.798230231f, -0.872881413f, 0.62384814f, 0.736777842f, -0.717043102f, -0.120273836f, -0.701046526f, + -0.947450519f, -0.739936531f, 0.0896423087f, 0.425461918f, -0.275675774f, 0.380192101f, 0.0668063015f, + 0.525413871f, -0.308581352f, 0.601394832f, 0.454725981f, 0.441379488f, -0.12275707f, 0.549877405f, + 0.0667692274f, -0.105458207f, 0.507316649f, 0.100097977f, 0.155287564f, 0.0467827767f, -0.355900079f, + 0.523771822f, -0.0674877688f, -0.687308609f, 0.104075052f, -0.198762715f, 0.132965386f, 0.857097447f, + -0.946933329f, -0.973383486f, 0.158163294f, 0.741645634f, -0.0268487502f, 0.084876962f, 0.509428084f, + 0.256142348f, -0.206435978f, 0.407053977f, 0.806848288f, -0.745875239f, -0.674409688f, -0.658644795f, + 0.566763997f, -0.767530739f, 0.472862363f, -0.4028005f, 0.613550305f, 0.354431361f, -0.484302431f, + -0.384785563f, 0.373801976f, -0.789189577f, 0.637897849f, 0.303022474f, 0.829439759f, -0.525084496f, + -0.187693566f, 0.612084031f, 0.38362056f, -0.655427396f, 0.996012032f, 0.42507261f, -0.934609234f, + -0.912990749f, 0.873304009f, 0.0208544675f, 0.336832136f, -0.831652045f, 0.672789454f, 0.890901208f, + 0.761949241f, 0.745340824f, 0.857566893f, -0.927248955f, 0.73113966f, -0.817461848f, 0.633728147f, + 0.967234135f, 0.700740635f, -0.184585407f, 0.981696844f, 0.692595422f, 0.675300062f, -0.721778035f, + 0.0874170437f, -0.0694659799f, 0.609779656f, -0.0977730155f, -0.204157129f, 0.415441424f, -0.795863569f, + -0.277977616f, -0.372505099f, -0.183852047f, -0.313816607f, 0.234989285f, -0.486621737f, -0.290846348f, + -0.907432795f, 0.640235007f, 0.279938877f, 0.00202846993f, 0.44956097f, 0.565821946f, 0.717490494f, + 0.953545749f, -0.469203115f, 0.952760637f, 0.143808365f, -0.778133154f, -0.310485572f, -0.687143207f, + 0.0941761583f, -0.756338418f, 0.324709028f, 0.23523955f, 0.98498553f, -0.123699829f, 0.0983179435f, + 0.708491266f, 0.735074282f, 0.766691864f, 0.90939945f, 0.924738407f, 0.557795405f, 0.87093997f, + 0.48059985f, -0.448208153f, -0.0120744575f, 0.48048988f, -0.772301078f, -0.0611128025f, -0.804182231f, + 0.0959599316f, -0.25468713f, 0.170702294f, 0.931406438f, 0.0232896339f, 0.452291667f, -0.209088668f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/output_ref_data.h new file mode 100644 index 00000000..7ea74b3e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_f32_output_ref_data[120] = { + 0.285533339f, -0.241554722f, -0.696724415f, -0.183601052f, -0.0107110739f, -0.118667349f, -0.908316076f, + -0.220924139f, -1.28053916f, 0.430020541f, 0.294620842f, -0.980764687f, -0.56264466f, -0.191131815f, + 0.384983629f, -0.0523805395f, -0.349431187f, -0.00208791252f, 0.851954222f, -1.54641294f, 0.751107335f, + 0.754517853f, 0.801084161f, 0.527390063f, -0.164119259f, -0.946624458f, -0.286541969f, -0.202928126f, + 0.565685093f, -1.60740352f, 0.950215638f, 0.288287461f, -0.24108696f, -0.527805865f, 1.25916028f, + 0.602497339f, -0.691318989f, -1.85579598f, -0.355409771f, 0.911185682f, 0.32698372f, 1.36365998f, + -0.297686785f, 0.354879409f, 0.861878335f, -0.303450733f, 1.08081615f, -0.385702372f, 0.350545257f, + -0.0439085029f, -0.773455322f, -0.0802742466f, -1.0025363f, -0.93448323f, -0.743763506f, 0.68266964f, + 0.358212024f, -1.88644397f, -0.0699699f, 0.442498237f, -0.270031631f, 0.0843540803f, -0.590967059f, + -0.0337482281f, 0.106073573f, 0.0575160421f, -0.0229204949f, -0.56714195f, 0.455683112f, -1.74643528f, + 0.00823286362f, -0.78416872f, -0.86973089f, 0.459696114f, 0.258368105f, -0.337584317f, -0.0625044629f, + 0.0565463752f, -0.759543002f, 0.539524555f, -0.360721171f, -1.37783372f, 0.434746325f, 0.865410447f, + -0.0857616737f, 0.116481997f, -0.110636853f, 1.32700026f, 0.236323491f, -0.0691329092f, 0.169596002f, + -0.89346236f, -0.357395977f, 0.108545572f, 0.36146757f, 0.165589884f, 0.628371477f, -0.703287363f, + 0.0202073976f, 0.0827869698f, 0.99575901f, 0.695453405f, 1.71334565f, 0.373422652f, 2.05340457f, + 0.428970277f, -0.263151824f, 0.99550432f, -0.138960019f, 1.36809504f, 0.911134422f, 0.995138705f, + 0.25908339f, 0.293269902f, 1.75642753f, 0.143628895f, -1.0245775f, -0.265815824f, 0.276977211f, + -0.76910162f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/weights_data.h new file mode 100644 index 00000000..6a59dab9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_f32_weights_data[45] = { + 0.222057477f, -0.327172339f, -0.161502227f, 0.247066557f, 0.392818928f, -0.412566751f, 0.188048482f, + -0.251556426f, -0.456714004f, 0.136916339f, 0.548065186f, -0.626876831f, -0.329797834f, -0.0602032803f, + -0.26447621f, 0.293495029f, -0.523459494f, 0.136682391f, 0.5243119f, 0.233106762f, 0.349274009f, + 0.0117231142f, 0.737225175f, 0.532370925f, 0.137603089f, -0.56208837f, 0.74381566f, 0.0249254704f, + -0.665090203f, 0.144900948f, 0.313937187f, -0.347139508f, -0.0253168549f, -0.162800863f, -0.535329282f, + 0.500669241f, 0.395543963f, 0.553745508f, 0.319287956f, 0.735966265f, 0.298698783f, -0.489670128f, + 0.135255322f, 0.0647677109f, -0.574091196f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/biases_data.h new file mode 100644 index 00000000..2d71b6e3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_1_f16_biases_data[5] = {(float16_t)0.195556641f, + (float16_t)-0.267578125f, + (float16_t)-0.0159912109f, + (float16_t)0.376953125f, + (float16_t)-0.264404297f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/config_data.h new file mode 100644 index 00000000..e21e9aa1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_INPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_IN_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_STRIDE_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_STRIDE_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUTPUT_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUTPUT_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_DST_SIZE 40 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUT_ACTIVATION_MIN (float16_t) - 126.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/input_data.h new file mode 100644 index 00000000..d9332f51 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/input_data.h @@ -0,0 +1,42 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_1_f16_input_data[160] = { + (float16_t)-0.322265625f, (float16_t)0.204589844f, (float16_t)0.96875f, (float16_t)0.420166016f, + (float16_t)0.609863281f, (float16_t)0.152954102f, (float16_t)-0.199829102f, (float16_t)-0.779296875f, + (float16_t)-0.952636719f, (float16_t)-0.361083984f, (float16_t)0.07421875f, (float16_t)-0.604492188f, + (float16_t)-0.107177734f, (float16_t)0.776855469f, (float16_t)-0.822265625f, (float16_t)-0.28125f, + (float16_t)0.592773438f, (float16_t)-0.15637207f, (float16_t)0.951660156f, (float16_t)0.119140625f, + (float16_t)0.810546875f, (float16_t)0.394042969f, (float16_t)0.469970703f, (float16_t)-0.9296875f, + (float16_t)-0.936523438f, (float16_t)-0.997558594f, (float16_t)-0.525878906f, (float16_t)-0.153076172f, + (float16_t)0.78515625f, (float16_t)-0.40234375f, (float16_t)-0.247070312f, (float16_t)0.750976562f, + (float16_t)-0.657226562f, (float16_t)-0.667480469f, (float16_t)0.386962891f, (float16_t)0.679199219f, + (float16_t)-0.106201172f, (float16_t)0.260742188f, (float16_t)-0.3984375f, (float16_t)0.685546875f, + (float16_t)0.00112533569f, (float16_t)0.336181641f, (float16_t)0.700195312f, (float16_t)-0.718261719f, + (float16_t)-0.419921875f, (float16_t)-0.239013672f, (float16_t)-0.277832031f, (float16_t)0.292480469f, + (float16_t)-0.306152344f, (float16_t)-0.791015625f, (float16_t)-0.716308594f, (float16_t)-0.970703125f, + (float16_t)0.953613281f, (float16_t)-0.563964844f, (float16_t)-0.605957031f, (float16_t)0.15637207f, + (float16_t)0.524902344f, (float16_t)-0.495361328f, (float16_t)-0.979003906f, (float16_t)0.171142578f, + (float16_t)-0.751464844f, (float16_t)0.113342285f, (float16_t)0.311523438f, (float16_t)-0.160766602f, + (float16_t)-0.186035156f, (float16_t)-0.0694580078f, (float16_t)0.734375f, (float16_t)0.117492676f, + (float16_t)0.285400391f, (float16_t)0.8984375f, (float16_t)0.586425781f, (float16_t)0.403320312f, + (float16_t)0.49609375f, (float16_t)-0.207519531f, (float16_t)-0.277832031f, (float16_t)-0.0391845703f, + (float16_t)0.0903930664f, (float16_t)0.468994141f, (float16_t)-0.377929688f, (float16_t)0.863769531f, + (float16_t)0.69140625f, (float16_t)-0.641113281f, (float16_t)-0.682617188f, (float16_t)-0.637695312f, + (float16_t)-0.69921875f, (float16_t)0.423095703f, (float16_t)0.394042969f, (float16_t)-0.854003906f, + (float16_t)0.327880859f, (float16_t)0.555175781f, (float16_t)-0.425537109f, (float16_t)0.739746094f, + (float16_t)-0.642089844f, (float16_t)-0.647460938f, (float16_t)0.061340332f, (float16_t)0.368896484f, + (float16_t)-0.14440918f, (float16_t)-0.670410156f, (float16_t)-0.178955078f, (float16_t)0.0441589355f, + (float16_t)0.338378906f, (float16_t)-0.148925781f, (float16_t)-0.180541992f, (float16_t)-0.770507812f, + (float16_t)-0.801269531f, (float16_t)-0.00964355469f, (float16_t)-0.772460938f, (float16_t)-0.235473633f, + (float16_t)0.397705078f, (float16_t)0.932128906f, (float16_t)-0.57421875f, (float16_t)0.48828125f, + (float16_t)-0.00664520264f, (float16_t)0.510253906f, (float16_t)0.280029297f, (float16_t)-0.705078125f, + (float16_t)-0.384521484f, (float16_t)-0.423583984f, (float16_t)-0.603515625f, (float16_t)0.344238281f, + (float16_t)0.116333008f, (float16_t)0.087097168f, (float16_t)0.841308594f, (float16_t)0.00859069824f, + (float16_t)-0.244995117f, (float16_t)-0.338867188f, (float16_t)-0.287353516f, (float16_t)0.787597656f, + (float16_t)-0.487304688f, (float16_t)0.463134766f, (float16_t)-0.91796875f, (float16_t)-0.0342102051f, + (float16_t)0.401611328f, (float16_t)-0.459472656f, (float16_t)-0.333007812f, (float16_t)-0.0603637695f, + (float16_t)0.283935547f, (float16_t)0.83984375f, (float16_t)0.75f, (float16_t)-0.252197266f, + (float16_t)0.396484375f, (float16_t)0.762207031f, (float16_t)0.382568359f, (float16_t)0.128540039f, + (float16_t)-0.824707031f, (float16_t)-0.440917969f, (float16_t)-0.3984375f, (float16_t)-0.174682617f, + (float16_t)0.316162109f, (float16_t)0.892089844f, (float16_t)0.0961914062f, (float16_t)0.207275391f, + (float16_t)-0.527832031f, (float16_t)-0.632324219f, (float16_t)-0.869628906f, (float16_t)0.222900391f, + (float16_t)-0.767089844f, (float16_t)-0.129882812f, (float16_t)-0.895019531f, (float16_t)-0.769042969f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/output_ref_data.h new file mode 100644 index 00000000..ba852016 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/output_ref_data.h @@ -0,0 +1,12 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_1_f16_output_ref_data[40] = { + (float16_t)-0.058380127f, (float16_t)-0.142944336f, (float16_t)0.695800781f, (float16_t)0.9140625f, + (float16_t)-0.416259766f, (float16_t)0.262695312f, (float16_t)-0.152587891f, (float16_t)0.288818359f, + (float16_t)0.533203125f, (float16_t)0.0343017578f, (float16_t)0.185913086f, (float16_t)-0.255859375f, + (float16_t)-0.324462891f, (float16_t)0.663085938f, (float16_t)-1.49316406f, (float16_t)0.375732422f, + (float16_t)-0.68359375f, (float16_t)-0.129882812f, (float16_t)0.370117188f, (float16_t)-0.693847656f, + (float16_t)0.843261719f, (float16_t)-0.555175781f, (float16_t)-0.433837891f, (float16_t)0.206054688f, + (float16_t)-0.228393555f, (float16_t)-0.182495117f, (float16_t)-0.251953125f, (float16_t)-1.09570312f, + (float16_t)-0.470947266f, (float16_t)-0.494873047f, (float16_t)0.208496094f, (float16_t)-0.154174805f, + (float16_t)0.342773438f, (float16_t)1.00878906f, (float16_t)-1.02734375f, (float16_t)-0.122741699f, + (float16_t)-0.402832031f, (float16_t)-0.622070312f, (float16_t)-0.0786132812f, (float16_t)-0.709960938f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/weights_data.h new file mode 100644 index 00000000..fd92605f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f16/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_1_f16_weights_data[25] = { + (float16_t)0.448242188f, (float16_t)-0.421875f, (float16_t)0.0200653076f, (float16_t)-0.205810547f, + (float16_t)0.0718383789f, (float16_t)0.067565918f, (float16_t)0.351318359f, (float16_t)0.0458374023f, + (float16_t)0.274169922f, (float16_t)-0.139282227f, (float16_t)0.476806641f, (float16_t)-0.30078125f, + (float16_t)0.489990234f, (float16_t)0.55859375f, (float16_t)0.356933594f, (float16_t)0.65625f, + (float16_t)0.0199127197f, (float16_t)0.657226562f, (float16_t)0.24987793f, (float16_t)0.00491714478f, + (float16_t)-0.200561523f, (float16_t)-0.659667969f, (float16_t)-0.643066406f, (float16_t)0.428466797f, + (float16_t)0.592285156f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/biases_data.h new file mode 100644 index 00000000..96b716b2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_1_f32_biases_data[5] = {0.195574954f, + -0.267665327f, + -0.0159980971f, + 0.376846254f, + -0.264294446f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/config_data.h new file mode 100644 index 00000000..fd32bfa2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_INPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_IN_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_STRIDE_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_STRIDE_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUTPUT_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUTPUT_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_DST_SIZE 40 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUT_ACTIVATION_MIN -126.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_1_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/input_data.h new file mode 100644 index 00000000..019990f9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/input_data.h @@ -0,0 +1,25 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_1_f32_input_data[160] = { + -0.322182357f, 0.204597235f, 0.968657613f, 0.420096993f, 0.609701097f, 0.152928278f, -0.199818015f, + -0.779420078f, -0.952586114f, -0.361081451f, 0.0742151812f, -0.60432899f, -0.107164614f, 0.776700795f, + -0.822207987f, -0.281232983f, 0.592538118f, -0.156341523f, 0.951883316f, 0.119147807f, 0.81054306f, + 0.394060731f, 0.469860554f, -0.92961508f, -0.936371446f, -0.997799695f, -0.525686383f, -0.153073177f, + 0.785334647f, -0.402246803f, -0.247091651f, 0.750734687f, -0.657362998f, -0.667346597f, 0.386910737f, + 0.679348171f, -0.10617999f, 0.260655344f, -0.398423761f, 0.685528994f, 0.00112541125f, 0.336088926f, + 0.700413585f, -0.718261242f, -0.419906735f, -0.239005685f, -0.277953416f, 0.292409062f, -0.306188583f, + -0.791129053f, -0.716528654f, -0.970573306f, 0.953594089f, -0.56403023f, -0.606041074f, 0.156417266f, + 0.524787068f, -0.495370924f, -0.97921896f, 0.171158358f, -0.751469076f, 0.11336723f, 0.311482042f, + -0.160749227f, -0.186033949f, -0.0694837347f, 0.73444289f, 0.117522366f, 0.285390854f, 0.898425043f, + 0.586338162f, 0.40319857f, 0.496080101f, -0.207519665f, -0.27778092f, -0.0391941331f, 0.090374656f, + 0.469080716f, -0.377811939f, 0.863532543f, 0.69124645f, -0.641279876f, -0.682813823f, -0.63782829f, + -0.699076295f, 0.423018277f, 0.393979281f, -0.854200542f, 0.327796727f, 0.555261493f, -0.425587416f, + 0.739543796f, -0.64216888f, -0.6472857f, 0.0613327287f, 0.368889302f, -0.144406766f, -0.670478344f, + -0.179001704f, 0.0441540107f, 0.338436365f, -0.148893639f, -0.180587485f, -0.770366788f, -0.801253498f, + -0.00964248925f, -0.772661328f, -0.235520765f, 0.397692055f, 0.932155907f, -0.574258089f, 0.488352597f, + -0.00664483942f, 0.510487378f, 0.280085117f, -0.705070734f, -0.384408653f, -0.423595518f, -0.603481591f, + 0.344163686f, 0.116311565f, 0.0870716125f, 0.841164887f, 0.00858941115f, -0.244991273f, -0.338845134f, + -0.287414074f, 0.787387729f, -0.487272471f, 0.463135302f, -0.91795063f, -0.0342244618f, 0.401556939f, + -0.459576815f, -0.332962781f, -0.0603729673f, 0.284045309f, 0.839969754f, 0.750077069f, -0.252187252f, + 0.396553606f, 0.762040198f, 0.382689446f, 0.128520131f, -0.824624896f, -0.441002101f, -0.398455828f, + -0.17465122f, 0.316178054f, 0.892241597f, 0.0962048173f, 0.207278356f, -0.527851939f, -0.632283032f, + -0.869726002f, 0.222957045f, -0.767125666f, -0.129833013f, -0.895040154f, -0.769102156f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/output_ref_data.h new file mode 100644 index 00000000..e57bb101 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/output_ref_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_1_f32_output_ref_data[40] = { + -0.0583851375f, -0.142915621f, 0.695625901f, 0.914068878f, -0.416248262f, 0.262781501f, -0.152549177f, + 0.288846046f, 0.53309691f, 0.0342865959f, 0.185972542f, -0.255767792f, -0.32438308f, 0.663017929f, + -1.49288797f, 0.375612378f, -0.68364948f, -0.12992312f, 0.370209575f, -0.694039524f, 0.843354523f, + -0.55505234f, -0.433827162f, 0.20603779f, -0.228373468f, -0.18252404f, -0.251929551f, -1.09541965f, + -0.471039504f, -0.494911402f, 0.208486497f, -0.154182225f, 0.342686892f, 1.00858414f, -1.02718127f, + -0.122733593f, -0.40292415f, -0.622122049f, -0.0786070153f, -0.709819853f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/weights_data.h new file mode 100644 index 00000000..b153b54b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_1_f32/weights_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_1_f32_weights_data[25] = { + 0.44824791f, -0.421958596f, 0.0200718399f, -0.205797642f, 0.0718405545f, 0.0675898269f, 0.351408273f, + 0.0458321646f, 0.274079114f, -0.139259517f, 0.476755112f, -0.3006832f, 0.489875197f, 0.558427215f, + 0.356945544f, 0.656010389f, 0.0199089684f, 0.657138348f, 0.249854788f, 0.0049187094f, -0.200615197f, + -0.65950501f, -0.642854571f, 0.428559899f, 0.592115819f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/biases_data.h new file mode 100644 index 00000000..c60e7064 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_2_f16_biases_data[5] = {(float16_t)-0.44140625f, + (float16_t)0.308837891f, + (float16_t)0.486572266f, + (float16_t)-0.199829102f, + (float16_t)0.0531921387f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/config_data.h new file mode 100644 index 00000000..844e534f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_INPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_IN_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_STRIDE_H 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_STRIDE_W 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUTPUT_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUTPUT_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_DST_SIZE 40 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUT_ACTIVATION_MIN (float16_t) - 126.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/input_data.h new file mode 100644 index 00000000..86ee36e6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/input_data.h @@ -0,0 +1,42 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_2_f16_input_data[160] = { + (float16_t)-0.362792969f, (float16_t)-0.584960938f, (float16_t)-0.291259766f, (float16_t)-0.312744141f, + (float16_t)0.322998047f, (float16_t)0.340087891f, (float16_t)0.633300781f, (float16_t)0.791015625f, + (float16_t)-0.695800781f, (float16_t)0.361572266f, (float16_t)0.63671875f, (float16_t)-0.267333984f, + (float16_t)0.786132812f, (float16_t)-0.3046875f, (float16_t)0.882324219f, (float16_t)-0.775390625f, + (float16_t)0.940429688f, (float16_t)0.00344276428f, (float16_t)0.534179688f, (float16_t)-0.23449707f, + (float16_t)0.495361328f, (float16_t)-0.0974121094f, (float16_t)-0.139038086f, (float16_t)0.896972656f, + (float16_t)0.999023438f, (float16_t)-0.409179688f, (float16_t)0.232177734f, (float16_t)0.0605163574f, + (float16_t)-0.137573242f, (float16_t)-0.468994141f, (float16_t)0.892089844f, (float16_t)0.811035156f, + (float16_t)-0.317626953f, (float16_t)0.589355469f, (float16_t)-0.56640625f, (float16_t)0.0958862305f, + (float16_t)0.234863281f, (float16_t)0.583496094f, (float16_t)-0.903808594f, (float16_t)0.239868164f, + (float16_t)-0.00765228271f, (float16_t)0.833007812f, (float16_t)-0.0303649902f, (float16_t)0.719726562f, + (float16_t)0.315185547f, (float16_t)-0.279296875f, (float16_t)-0.124450684f, (float16_t)-0.155761719f, + (float16_t)0.998535156f, (float16_t)0.192260742f, (float16_t)0.135864258f, (float16_t)-0.705566406f, + (float16_t)-0.633300781f, (float16_t)-0.445068359f, (float16_t)0.966796875f, (float16_t)0.0508422852f, + (float16_t)0.615722656f, (float16_t)-0.46875f, (float16_t)0.391113281f, (float16_t)0.176269531f, + (float16_t)-0.429443359f, (float16_t)0.165405273f, (float16_t)0.627929688f, (float16_t)0.825683594f, + (float16_t)0.911132812f, (float16_t)0.612304688f, (float16_t)-0.0700683594f, (float16_t)-0.805664062f, + (float16_t)0.935058594f, (float16_t)0.748535156f, (float16_t)0.780761719f, (float16_t)0.373535156f, + (float16_t)0.217041016f, (float16_t)0.768554688f, (float16_t)0.422851562f, (float16_t)-0.1328125f, + (float16_t)-0.493896484f, (float16_t)0.582519531f, (float16_t)-0.753417969f, (float16_t)-0.909179688f, + (float16_t)-0.896972656f, (float16_t)-0.396240234f, (float16_t)0.322265625f, (float16_t)-0.905273438f, + (float16_t)-0.262451172f, (float16_t)-0.232421875f, (float16_t)0.861816406f, (float16_t)-0.310791016f, + (float16_t)0.906738281f, (float16_t)-0.438720703f, (float16_t)-0.520507812f, (float16_t)0.733886719f, + (float16_t)-0.344482422f, (float16_t)-0.814941406f, (float16_t)-0.603027344f, (float16_t)-0.604980469f, + (float16_t)-0.65625f, (float16_t)0.876464844f, (float16_t)-0.289306641f, (float16_t)0.694824219f, + (float16_t)-0.87109375f, (float16_t)0.90625f, (float16_t)0.995605469f, (float16_t)-0.0748901367f, + (float16_t)0.613769531f, (float16_t)-0.0324707031f, (float16_t)-0.142456055f, (float16_t)0.99609375f, + (float16_t)0.0613098145f, (float16_t)-0.3828125f, (float16_t)-0.84765625f, (float16_t)-0.69140625f, + (float16_t)0.0493164062f, (float16_t)-0.9609375f, (float16_t)-0.196655273f, (float16_t)0.376953125f, + (float16_t)-0.366699219f, (float16_t)-0.8671875f, (float16_t)0.810058594f, (float16_t)-0.111206055f, + (float16_t)-0.0630493164f, (float16_t)-0.892089844f, (float16_t)-0.0605163574f, (float16_t)-0.204711914f, + (float16_t)0.274414062f, (float16_t)-0.736328125f, (float16_t)0.623535156f, (float16_t)0.612792969f, + (float16_t)-0.821289062f, (float16_t)-0.476806641f, (float16_t)-0.971679688f, (float16_t)0.572265625f, + (float16_t)-0.674316406f, (float16_t)-0.319091797f, (float16_t)-0.0626831055f, (float16_t)0.588378906f, + (float16_t)-0.0263671875f, (float16_t)-0.172241211f, (float16_t)0.357421875f, (float16_t)0.0631103516f, + (float16_t)0.828125f, (float16_t)0.270751953f, (float16_t)-0.84375f, (float16_t)0.991699219f, + (float16_t)-0.924316406f, (float16_t)-0.0132446289f, (float16_t)0.460449219f, (float16_t)0.182373047f, + (float16_t)0.893554688f, (float16_t)-0.345458984f, (float16_t)-0.750488281f, (float16_t)0.930175781f, + (float16_t)-0.91015625f, (float16_t)0.3828125f, (float16_t)0.331542969f, (float16_t)-0.684082031f, + (float16_t)-0.457763672f, (float16_t)-0.997558594f, (float16_t)0.775878906f, (float16_t)-0.96484375f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/output_ref_data.h new file mode 100644 index 00000000..ef0f2638 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/output_ref_data.h @@ -0,0 +1,12 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_2_f16_output_ref_data[40] = { + (float16_t)0.525878906f, (float16_t)0.941894531f, (float16_t)-0.115966797f, (float16_t)-0.289794922f, + (float16_t)0.291259766f, (float16_t)-1.06933594f, (float16_t)-1.03027344f, (float16_t)0.600585938f, + (float16_t)-0.202636719f, (float16_t)0.220214844f, (float16_t)-0.966796875f, (float16_t)-0.288574219f, + (float16_t)0.724609375f, (float16_t)0.310058594f, (float16_t)0.0264434814f, (float16_t)-0.458251953f, + (float16_t)-0.0889282227f, (float16_t)0.319335938f, (float16_t)0.314941406f, (float16_t)0.270019531f, + (float16_t)0.28125f, (float16_t)0.144042969f, (float16_t)-0.431396484f, (float16_t)-0.0537414551f, + (float16_t)0.802246094f, (float16_t)-0.0825805664f, (float16_t)0.350097656f, (float16_t)-0.0405883789f, + (float16_t)0.392089844f, (float16_t)0.516601562f, (float16_t)-1.16894531f, (float16_t)-0.0856323242f, + (float16_t)1.67382812f, (float16_t)-0.180541992f, (float16_t)-1.03417969f, (float16_t)0.0111923218f, + (float16_t)-0.46875f, (float16_t)0.72265625f, (float16_t)0.221801758f, (float16_t)-0.49609375f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/weights_data.h new file mode 100644 index 00000000..ed1dc992 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f16/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_2_f16_weights_data[25] = { + (float16_t)-0.366455078f, (float16_t)-0.558105469f, (float16_t)-0.736816406f, (float16_t)-0.559570312f, + (float16_t)0.366943359f, (float16_t)0.58203125f, (float16_t)-0.398193359f, (float16_t)-0.622070312f, + (float16_t)-0.646484375f, (float16_t)0.705566406f, (float16_t)0.488769531f, (float16_t)0.0489501953f, + (float16_t)0.258056641f, (float16_t)0.693847656f, (float16_t)-0.323486328f, (float16_t)-0.15234375f, + (float16_t)-0.5f, (float16_t)0.609375f, (float16_t)0.514648438f, (float16_t)-0.307861328f, + (float16_t)-0.396728516f, (float16_t)0.227539062f, (float16_t)0.0682983398f, (float16_t)-0.570800781f, + (float16_t)0.212646484f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/biases_data.h new file mode 100644 index 00000000..6693ae1d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_2_f32_biases_data[5] = {-0.441401422f, + 0.308796972f, + 0.486553043f, + -0.199850842f, + 0.0531912446f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/config_data.h new file mode 100644 index 00000000..615541af --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_INPUT_H 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_INPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_IN_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUT_CH 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_STRIDE_H 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_STRIDE_W 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUTPUT_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUTPUT_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUTPUT_C 5 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_DST_SIZE 40 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUT_ACTIVATION_MIN -126.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_2_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/input_data.h new file mode 100644 index 00000000..8f655491 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/input_data.h @@ -0,0 +1,25 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_2_f32_input_data[160] = { + -0.362904996f, -0.585045278f, -0.291200489f, -0.312622905f, 0.323076665f, 0.340103596f, 0.633466125f, + 0.791204512f, -0.695647538f, 0.361625999f, 0.636863351f, -0.267286628f, 0.786181867f, -0.304649413f, + 0.882156134f, -0.775494039f, 0.940246224f, 0.00344371144f, 0.534200966f, -0.234518722f, 0.495318502f, + -0.0974321216f, -0.139064178f, 0.896914184f, 0.998822391f, -0.409237206f, 0.232188255f, 0.0605136305f, + -0.137593418f, -0.46905157f, 0.891863763f, 0.81109345f, -0.317665607f, 0.589357853f, -0.566177189f, + 0.0958771855f, 0.234883994f, 0.583479047f, -0.903730631f, 0.23992008f, -0.00765348272f, 0.833045363f, + -0.0303667579f, 0.719684005f, 0.315094709f, -0.279340476f, -0.124420539f, -0.155723095f, 0.998707652f, + 0.192299739f, 0.135838985f, -0.705543697f, -0.633309305f, -0.445106417f, 0.966731787f, 0.0508478917f, + 0.615489721f, -0.468807071f, 0.39110443f, 0.176292628f, -0.429417253f, 0.165439218f, 0.627939641f, + 0.825680971f, 0.911265492f, 0.612071574f, -0.0700721815f, -0.80580765f, 0.935293674f, 0.748390913f, + 0.780940473f, 0.373464584f, 0.217034578f, 0.768338084f, 0.422922701f, -0.132805988f, -0.494003415f, + 0.582759976f, -0.753293037f, -0.909090161f, -0.896973133f, -0.396260262f, 0.322342068f, -0.905389309f, + -0.262445122f, -0.232480392f, 0.861636043f, -0.310800344f, 0.906872332f, -0.438671142f, -0.520701468f, + 0.733719587f, -0.344427407f, -0.814877212f, -0.603120446f, -0.604759514f, -0.6563434f, 0.876637995f, + -0.289251179f, 0.694973588f, -0.871090353f, 0.906348765f, 0.995701671f, -0.0748858079f, 0.613675714f, + -0.0324786752f, -0.142464578f, 0.996211708f, 0.0613062494f, -0.382721782f, -0.847854972f, -0.691642761f, + 0.0493023023f, -0.960798144f, -0.196642041f, 0.377031714f, -0.366631925f, -0.86711973f, 0.810101867f, + -0.111218505f, -0.0630641729f, -0.891921163f, -0.0605205819f, -0.204722196f, 0.274380833f, -0.736150742f, + 0.623338401f, 0.612672627f, -0.821317196f, -0.476837069f, -0.971491635f, 0.572270215f, -0.674082398f, + -0.319080144f, -0.0626821294f, 0.588418186f, -0.0263725445f, -0.172191516f, 0.357372582f, 0.0631027669f, + 0.827948451f, 0.270744443f, -0.843600035f, 0.99162823f, -0.924384594f, -0.0132454671f, 0.460372329f, + 0.182425544f, 0.893344104f, -0.345539182f, -0.750711739f, 0.930351019f, -0.910129726f, 0.382851809f, + 0.331616521f, -0.683962643f, -0.457778275f, -0.997607052f, 0.776040435f, -0.964745939f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/output_ref_data.h new file mode 100644 index 00000000..ec989dcd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/output_ref_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_2_f32_output_ref_data[40] = { + 0.526081204f, 0.941881001f, -0.115997136f, -0.289875776f, 0.29138121f, -1.06957507f, -1.03016365f, + 0.600766063f, -0.202610835f, 0.220186472f, -0.9669047f, -0.288496763f, 0.724667907f, 0.310060501f, + 0.0264416672f, -0.458304167f, -0.0889400244f, 0.319305182f, 0.314869583f, 0.27004841f, 0.281369746f, + 0.144019723f, -0.431378692f, -0.0537411608f, 0.802107334f, -0.0825718939f, 0.350165337f, -0.0405767113f, + 0.392045885f, 0.516630054f, -1.16854143f, -0.0856162235f, 1.67389882f, -0.180538014f, -1.03418946f, + 0.0111893499f, -0.468854189f, 0.722861707f, 0.221804097f, -0.496101558f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/weights_data.h new file mode 100644 index 00000000..eae3afff --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_2_f32/weights_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_2_f32_weights_data[25] = { + -0.366346449f, -0.557985246f, -0.736750603f, -0.559801877f, 0.366903603f, 0.582133353f, -0.39830178f, + -0.622239649f, -0.646508694f, 0.705741644f, 0.488891304f, 0.0489415936f, 0.257951677f, 0.69369632f, + -0.323499113f, -0.152394354f, -0.500148892f, 0.609586537f, 0.514793277f, -0.307949483f, -0.396775723f, + 0.227595299f, 0.068294771f, -0.571040511f, 0.212701097f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/biases_data.h new file mode 100644 index 00000000..f1995a12 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/biases_data.h @@ -0,0 +1,16 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_f16_biases_data[15] = {(float16_t)0.180419922f, + (float16_t)0.421630859f, + (float16_t)-0.178344727f, + (float16_t)0.217529297f, + (float16_t)0.432128906f, + (float16_t)0.414794922f, + (float16_t)0.0681152344f, + (float16_t)0.0812988281f, + (float16_t)0.405761719f, + (float16_t)0.155639648f, + (float16_t)-0.0745849609f, + (float16_t)0.161987305f, + (float16_t)-0.274169922f, + (float16_t)0.143798828f, + (float16_t)-0.445800781f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/config_data.h new file mode 100644 index 00000000..b1279155 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_INPUT_BATCHES 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_INPUT_H 6 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_INPUT_W 7 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_IN_CH 23 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUT_CH 15 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_STRIDE_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_STRIDE_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUTPUT_H 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUTPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUTPUT_C 15 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_DST_SIZE 540 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUT_ACTIVATION_MIN (float16_t) - 6.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/input_data.h new file mode 100644 index 00000000..8c69be1c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/input_data.h @@ -0,0 +1,727 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_f16_input_data[2898] = { + (float16_t)0.625f, (float16_t)-0.751464844f, (float16_t)-0.728515625f, (float16_t)0.961914062f, + (float16_t)0.0640869141f, (float16_t)0.838867188f, (float16_t)0.529296875f, (float16_t)0.929199219f, + (float16_t)-0.320800781f, (float16_t)0.431640625f, (float16_t)-0.483154297f, (float16_t)0.495117188f, + (float16_t)0.230712891f, (float16_t)0.277587891f, (float16_t)0.0977783203f, (float16_t)0.903808594f, + (float16_t)0.820800781f, (float16_t)-0.580566406f, (float16_t)0.0103225708f, (float16_t)0.0046043396f, + (float16_t)-0.0213165283f, (float16_t)-0.69921875f, (float16_t)-0.566894531f, (float16_t)-0.846679688f, + (float16_t)-0.173339844f, (float16_t)0.164428711f, (float16_t)-0.236083984f, (float16_t)-0.740234375f, + (float16_t)-0.0648803711f, (float16_t)-0.876464844f, (float16_t)-0.133544922f, (float16_t)0.065246582f, + (float16_t)-0.810058594f, (float16_t)0.601074219f, (float16_t)-0.7578125f, (float16_t)0.407470703f, + (float16_t)0.895019531f, (float16_t)-0.873046875f, (float16_t)0.678710938f, (float16_t)0.587402344f, + (float16_t)-0.918945312f, (float16_t)0.178833008f, (float16_t)0.831054688f, (float16_t)-0.298583984f, + (float16_t)-0.760742188f, (float16_t)0.157714844f, (float16_t)-0.103210449f, (float16_t)-0.553710938f, + (float16_t)-0.596679688f, (float16_t)-0.96875f, (float16_t)-0.624511719f, (float16_t)-0.635253906f, + (float16_t)-0.145385742f, (float16_t)-0.135498047f, (float16_t)0.387695312f, (float16_t)0.607421875f, + (float16_t)0.386962891f, (float16_t)-0.136108398f, (float16_t)-0.52734375f, (float16_t)0.311523438f, + (float16_t)-0.521972656f, (float16_t)-0.537109375f, (float16_t)-0.772949219f, (float16_t)0.842285156f, + (float16_t)0.382568359f, (float16_t)0.456542969f, (float16_t)-0.225708008f, (float16_t)0.0651855469f, + (float16_t)-0.922363281f, (float16_t)0.683105469f, (float16_t)0.670410156f, (float16_t)0.614746094f, + (float16_t)-0.422119141f, (float16_t)-0.0897216797f, (float16_t)-0.223388672f, (float16_t)0.017364502f, + (float16_t)0.82421875f, (float16_t)0.465576172f, (float16_t)0.472900391f, (float16_t)-0.174926758f, + (float16_t)-0.0325622559f, (float16_t)-0.655761719f, (float16_t)-0.523925781f, (float16_t)-0.493408203f, + (float16_t)0.536621094f, (float16_t)0.0560913086f, (float16_t)-0.475585938f, (float16_t)-0.934082031f, + (float16_t)0.970214844f, (float16_t)0.703125f, (float16_t)0.106872559f, (float16_t)-0.885742188f, + (float16_t)-0.9140625f, (float16_t)0.107177734f, (float16_t)-0.309082031f, (float16_t)0.241088867f, + (float16_t)0.680175781f, (float16_t)-0.714355469f, (float16_t)-0.154785156f, (float16_t)0.456542969f, + (float16_t)-0.271728516f, (float16_t)0.865234375f, (float16_t)0.625f, (float16_t)-0.895996094f, + (float16_t)-0.595703125f, (float16_t)-0.418212891f, (float16_t)-0.969238281f, (float16_t)-0.555664062f, + (float16_t)0.242675781f, (float16_t)-0.991699219f, (float16_t)0.181152344f, (float16_t)-0.815917969f, + (float16_t)-0.389892578f, (float16_t)-0.698730469f, (float16_t)-0.540039062f, (float16_t)0.413818359f, + (float16_t)-0.211181641f, (float16_t)-0.374267578f, (float16_t)-0.0156021118f, (float16_t)-0.0737915039f, + (float16_t)0.272216797f, (float16_t)-0.900878906f, (float16_t)0.190185547f, (float16_t)0.866699219f, + (float16_t)0.151733398f, (float16_t)0.879394531f, (float16_t)0.261962891f, (float16_t)0.991699219f, + (float16_t)0.0100326538f, (float16_t)-0.391357422f, (float16_t)-0.782226562f, (float16_t)0.837402344f, + (float16_t)0.760253906f, (float16_t)0.0499267578f, (float16_t)0.581542969f, (float16_t)-0.40234375f, + (float16_t)-0.239013672f, (float16_t)0.534667969f, (float16_t)-0.0687866211f, (float16_t)-0.831542969f, + (float16_t)-0.15637207f, (float16_t)-0.606933594f, (float16_t)0.519042969f, (float16_t)0.52734375f, + (float16_t)0.553222656f, (float16_t)0.102844238f, (float16_t)0.428466797f, (float16_t)-0.982421875f, + (float16_t)0.198974609f, (float16_t)0.44921875f, (float16_t)-0.378173828f, (float16_t)-0.376464844f, + (float16_t)0.34765625f, (float16_t)0.504882812f, (float16_t)-0.123352051f, (float16_t)-0.514648438f, + (float16_t)-0.303955078f, (float16_t)0.13293457f, (float16_t)0.565917969f, (float16_t)0.708984375f, + (float16_t)0.801757812f, (float16_t)0.627441406f, (float16_t)0.829101562f, (float16_t)0.912597656f, + (float16_t)-0.784667969f, (float16_t)-0.672363281f, (float16_t)-0.0816040039f, (float16_t)-0.692382812f, + (float16_t)-0.667480469f, (float16_t)-0.974121094f, (float16_t)-0.360839844f, (float16_t)0.350830078f, + (float16_t)0.281005859f, (float16_t)-0.0610046387f, (float16_t)0.822753906f, (float16_t)-0.390136719f, + (float16_t)0.915039062f, (float16_t)0.163574219f, (float16_t)0.628417969f, (float16_t)-0.121154785f, + (float16_t)-0.258789062f, (float16_t)0.538085938f, (float16_t)0.0661621094f, (float16_t)0.854980469f, + (float16_t)0.463623047f, (float16_t)0.413818359f, (float16_t)-0.0423583984f, (float16_t)0.670410156f, + (float16_t)0.243652344f, (float16_t)0.519042969f, (float16_t)0.174560547f, (float16_t)-0.228149414f, + (float16_t)0.432861328f, (float16_t)0.489013672f, (float16_t)0.698730469f, (float16_t)-0.818359375f, + (float16_t)-0.390380859f, (float16_t)-0.234985352f, (float16_t)-0.16784668f, (float16_t)-0.0289916992f, + (float16_t)0.878417969f, (float16_t)-0.9453125f, (float16_t)0.0581665039f, (float16_t)0.581054688f, + (float16_t)-0.559570312f, (float16_t)0.746582031f, (float16_t)-0.150024414f, (float16_t)-0.00738143921f, + (float16_t)0.0639648438f, (float16_t)0.274902344f, (float16_t)0.8125f, (float16_t)0.345458984f, + (float16_t)0.530273438f, (float16_t)0.986816406f, (float16_t)-0.124694824f, (float16_t)-0.767089844f, + (float16_t)-0.212158203f, (float16_t)-0.262451172f, (float16_t)0.919921875f, (float16_t)0.822753906f, + (float16_t)-0.298828125f, (float16_t)0.444091797f, (float16_t)-0.73828125f, (float16_t)-0.9921875f, + (float16_t)0.286376953f, (float16_t)-0.114379883f, (float16_t)0.625f, (float16_t)0.780761719f, + (float16_t)-0.0314331055f, (float16_t)0.359863281f, (float16_t)-0.215332031f, (float16_t)0.738769531f, + (float16_t)0.466064453f, (float16_t)-0.0662841797f, (float16_t)0.927246094f, (float16_t)-0.175537109f, + (float16_t)0.709472656f, (float16_t)0.611816406f, (float16_t)-0.0214691162f, (float16_t)-0.0245056152f, + (float16_t)-0.169433594f, (float16_t)0.218017578f, (float16_t)-0.98046875f, (float16_t)0.330322266f, + (float16_t)0.267822266f, (float16_t)0.617675781f, (float16_t)0.622070312f, 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(float16_t)-0.115112305f, (float16_t)-0.523925781f, (float16_t)0.314697266f, + (float16_t)-0.427734375f, (float16_t)0.903808594f, (float16_t)0.845703125f, (float16_t)-0.158447266f, + (float16_t)-0.478271484f, (float16_t)0.721191406f, (float16_t)0.385742188f, (float16_t)-0.843261719f, + (float16_t)-0.944824219f, (float16_t)-0.248291016f, (float16_t)-0.129516602f, (float16_t)-0.846679688f, + (float16_t)0.992675781f, (float16_t)0.434082031f, (float16_t)0.436035156f, (float16_t)-0.903320312f, + (float16_t)0.8125f, (float16_t)0.377929688f, (float16_t)0.830078125f, (float16_t)-0.944335938f, + (float16_t)0.494873047f, (float16_t)0.867675781f, (float16_t)-0.733398438f, (float16_t)0.608886719f, + (float16_t)-0.378173828f, (float16_t)0.981933594f, (float16_t)-0.331787109f, (float16_t)-0.854003906f, + (float16_t)0.885742188f, (float16_t)0.277832031f, (float16_t)-0.425048828f, (float16_t)0.947753906f, + (float16_t)0.114440918f, (float16_t)0.481689453f, (float16_t)0.642578125f, (float16_t)0.91015625f, + (float16_t)0.632324219f, (float16_t)0.645996094f, (float16_t)0.38671875f, (float16_t)-0.419921875f, + (float16_t)0.5546875f, (float16_t)0.0836181641f, (float16_t)0.181518555f, (float16_t)-0.270751953f, + (float16_t)-0.0133514404f, (float16_t)-0.950683594f, (float16_t)-0.797851562f, (float16_t)-0.615722656f, + (float16_t)0.0932617188f, (float16_t)0.175537109f, (float16_t)0.791992188f, (float16_t)-0.0132217407f, + (float16_t)-0.831054688f, (float16_t)-0.992675781f, (float16_t)0.188232422f, (float16_t)-0.306884766f, + (float16_t)0.70703125f, (float16_t)-0.770019531f, (float16_t)-0.741699219f, (float16_t)-0.133544922f, + (float16_t)-0.807617188f, (float16_t)0.325439453f, (float16_t)0.518554688f, (float16_t)-0.406494141f, + (float16_t)-0.311767578f, (float16_t)-0.763671875f, (float16_t)0.28125f, (float16_t)0.613769531f, + (float16_t)-0.681152344f, (float16_t)0.554199219f, (float16_t)0.351074219f, (float16_t)-0.0642089844f, + (float16_t)-0.534667969f, (float16_t)0.125366211f, (float16_t)0.352539062f, (float16_t)-0.677246094f, + (float16_t)0.820800781f, (float16_t)-0.841796875f, (float16_t)-0.0656738281f, (float16_t)0.931640625f, + (float16_t)0.489990234f, (float16_t)0.739746094f, (float16_t)-0.261962891f, (float16_t)-0.684570312f, + (float16_t)0.7890625f, (float16_t)-0.897460938f, (float16_t)-0.322509766f, (float16_t)0.631835938f, + (float16_t)0.58984375f, (float16_t)0.135131836f, (float16_t)-0.220214844f, (float16_t)-0.0631713867f, + (float16_t)0.928710938f, (float16_t)-0.317871094f, (float16_t)-0.677734375f, (float16_t)-0.100646973f, + (float16_t)0.159545898f, (float16_t)0.841308594f, (float16_t)0.177856445f, (float16_t)-0.119384766f, + (float16_t)0.254150391f, (float16_t)0.634765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/output_ref_data.h new file mode 100644 index 00000000..cdfe3f93 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/output_ref_data.h @@ -0,0 +1,182 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_f16_output_ref_data[540] = { + (float16_t)2.54296875f, (float16_t)0.801757812f, (float16_t)-1.12792969f, + (float16_t)-1.42578125f, (float16_t)-0.937011719f, (float16_t)1.44433594f, + (float16_t)-0.267089844f, (float16_t)0.00969696045f, (float16_t)0.209716797f, + (float16_t)-2.1640625f, (float16_t)0.21875f, (float16_t)2.37890625f, + (float16_t)1.36132812f, (float16_t)0.0590820312f, (float16_t)-0.213745117f, + (float16_t)-1.33691406f, (float16_t)0.185791016f, (float16_t)-0.573730469f, + (float16_t)0.959960938f, (float16_t)1.13671875f, (float16_t)-0.00445556641f, + (float16_t)1.21777344f, (float16_t)1.73339844f, (float16_t)-0.142944336f, + (float16_t)0.618164062f, (float16_t)-0.0430908203f, (float16_t)-0.479248047f, + (float16_t)0.780761719f, (float16_t)0.173217773f, (float16_t)0.51953125f, + (float16_t)-0.723144531f, (float16_t)0.682128906f, (float16_t)1.95800781f, + (float16_t)0.123474121f, (float16_t)-0.755371094f, (float16_t)-0.108032227f, + (float16_t)1.1484375f, (float16_t)1.50683594f, (float16_t)-1.09082031f, + (float16_t)-0.350830078f, (float16_t)-0.10168457f, (float16_t)1.125f, + (float16_t)-0.276123047f, (float16_t)-0.98046875f, (float16_t)-0.592285156f, + (float16_t)2.48632812f, (float16_t)0.887695312f, (float16_t)-0.46484375f, + (float16_t)-1.85546875f, (float16_t)-0.351074219f, (float16_t)-0.157714844f, + (float16_t)0.0662231445f, (float16_t)0.824707031f, (float16_t)1.56542969f, + (float16_t)-1.24023438f, (float16_t)-0.17578125f, (float16_t)0.322265625f, + (float16_t)-2.89648438f, (float16_t)0.565429688f, (float16_t)0.432617188f, + (float16_t)1.29101562f, (float16_t)-2.34570312f, (float16_t)1.97949219f, + (float16_t)-0.8671875f, (float16_t)0.0954589844f, (float16_t)0.663574219f, + (float16_t)1.61328125f, (float16_t)-1.99316406f, (float16_t)0.159179688f, + (float16_t)0.562011719f, (float16_t)-1.01660156f, (float16_t)-0.0651855469f, + (float16_t)1.15917969f, (float16_t)-0.340820312f, (float16_t)-2.15820312f, + (float16_t)1.06152344f, (float16_t)0.200195312f, (float16_t)-0.00124549866f, + (float16_t)-1.234375f, (float16_t)-0.493408203f, (float16_t)2.2109375f, + (float16_t)-0.0646362305f, (float16_t)0.404296875f, (float16_t)0.0242156982f, + (float16_t)-1.08398438f, (float16_t)1.40820312f, (float16_t)0.543945312f, + (float16_t)0.807617188f, (float16_t)1.2890625f, (float16_t)-0.764160156f, + (float16_t)-2.18164062f, (float16_t)3.15625f, (float16_t)-0.789550781f, + (float16_t)2.40429688f, (float16_t)-0.696289062f, (float16_t)0.541503906f, + (float16_t)-0.678222656f, (float16_t)-0.444335938f, (float16_t)-0.284667969f, + (float16_t)0.215820312f, (float16_t)0.230712891f, (float16_t)-0.670410156f, + (float16_t)-0.561523438f, (float16_t)0.762695312f, (float16_t)0.550292969f, + (float16_t)-0.092956543f, (float16_t)-0.647460938f, (float16_t)0.634277344f, + (float16_t)-0.598632812f, (float16_t)0.869628906f, (float16_t)0.529296875f, + (float16_t)-1.27734375f, (float16_t)-0.426025391f, (float16_t)1.90625f, + (float16_t)-0.430419922f, (float16_t)-1.51171875f, (float16_t)-0.0875854492f, + (float16_t)-3.21289062f, (float16_t)-0.218383789f, (float16_t)-0.708007812f, + (float16_t)0.760742188f, (float16_t)-2.5546875f, (float16_t)0.393066406f, + (float16_t)-0.590820312f, (float16_t)-0.291015625f, (float16_t)-1.30761719f, + (float16_t)0.580078125f, (float16_t)2.9375f, (float16_t)1.37890625f, + (float16_t)-1.58984375f, (float16_t)-0.62109375f, (float16_t)0.285888672f, + (float16_t)-0.44140625f, (float16_t)1.33984375f, (float16_t)0.387939453f, + (float16_t)-2.18554688f, (float16_t)0.200439453f, (float16_t)0.475341797f, + (float16_t)1.53808594f, (float16_t)1.83496094f, (float16_t)1.01269531f, + (float16_t)0.685546875f, (float16_t)0.147705078f, (float16_t)-0.347412109f, + (float16_t)1.38085938f, (float16_t)-0.184814453f, (float16_t)-4.58203125f, + (float16_t)-1.20410156f, (float16_t)0.923339844f, (float16_t)-1.07324219f, + (float16_t)-1.67382812f, (float16_t)0.362792969f, (float16_t)1.80078125f, + (float16_t)-1.26464844f, (float16_t)0.99609375f, (float16_t)-1.66894531f, + (float16_t)1.13476562f, (float16_t)0.246948242f, (float16_t)1.296875f, + (float16_t)0.15625f, (float16_t)-0.229614258f, (float16_t)-0.353027344f, + (float16_t)-0.991699219f, (float16_t)1.68261719f, (float16_t)1.87890625f, + (float16_t)0.856933594f, (float16_t)0.229003906f, (float16_t)1.29492188f, + (float16_t)1.72753906f, (float16_t)-0.297607422f, (float16_t)1.16894531f, + (float16_t)-1.34472656f, (float16_t)-1.64453125f, (float16_t)0.0877685547f, + (float16_t)2.19335938f, (float16_t)-0.3359375f, (float16_t)0.412597656f, + (float16_t)-0.452636719f, (float16_t)0.705566406f, (float16_t)-1.44140625f, + (float16_t)0.264404297f, (float16_t)1.46484375f, (float16_t)2.796875f, + (float16_t)-2.23046875f, (float16_t)0.134521484f, (float16_t)0.645507812f, + (float16_t)0.547851562f, (float16_t)-0.851074219f, (float16_t)-0.276367188f, + (float16_t)3.22265625f, (float16_t)-0.717285156f, (float16_t)0.0347290039f, + (float16_t)-0.102844238f, (float16_t)0.751464844f, (float16_t)0.947265625f, + (float16_t)-2.359375f, (float16_t)0.977050781f, (float16_t)1.77441406f, + (float16_t)-0.200683594f, (float16_t)-1.18066406f, (float16_t)1.56347656f, + (float16_t)1.75f, (float16_t)-1.18066406f, (float16_t)-0.701171875f, + (float16_t)1.13378906f, (float16_t)-1.05273438f, (float16_t)-0.497558594f, + (float16_t)-0.586914062f, (float16_t)0.505371094f, (float16_t)-1.68261719f, + (float16_t)1.95996094f, (float16_t)2.5859375f, (float16_t)-1.99414062f, + (float16_t)0.381835938f, (float16_t)-1.97460938f, (float16_t)1.33300781f, + (float16_t)0.647949219f, (float16_t)1.08300781f, (float16_t)-0.179077148f, + (float16_t)-0.383544922f, (float16_t)-0.387451172f, (float16_t)2.00390625f, + (float16_t)-0.760253906f, (float16_t)-0.164794922f, (float16_t)-1.06054688f, + (float16_t)0.982910156f, (float16_t)0.160522461f, (float16_t)0.116943359f, + (float16_t)0.279785156f, (float16_t)0.477050781f, (float16_t)0.909179688f, + (float16_t)0.96875f, (float16_t)-0.447509766f, (float16_t)0.441162109f, + (float16_t)0.255126953f, (float16_t)0.210327148f, (float16_t)-0.00634002686f, + (float16_t)0.0772705078f, (float16_t)1.34082031f, (float16_t)-1.70605469f, + (float16_t)0.387207031f, (float16_t)2.3671875f, (float16_t)1.41113281f, + (float16_t)0.190795898f, (float16_t)-2.06835938f, (float16_t)2.42578125f, + (float16_t)-0.833984375f, (float16_t)-0.20300293f, (float16_t)-1.78222656f, + (float16_t)3.5f, (float16_t)-0.266113281f, (float16_t)-0.247314453f, + (float16_t)0.338134766f, (float16_t)0.6640625f, (float16_t)0.500976562f, + (float16_t)-1.1015625f, (float16_t)1.84375f, (float16_t)0.516601562f, + (float16_t)-0.396484375f, (float16_t)0.638671875f, (float16_t)1.41796875f, + (float16_t)-0.720703125f, (float16_t)1.19140625f, (float16_t)-1.40332031f, + (float16_t)0.501464844f, (float16_t)1.90917969f, (float16_t)-0.507324219f, + (float16_t)-0.644042969f, (float16_t)0.136962891f, (float16_t)0.0340881348f, + (float16_t)1.26074219f, (float16_t)0.276611328f, (float16_t)0.54296875f, + (float16_t)-1.17480469f, (float16_t)-0.916992188f, (float16_t)-0.301269531f, + (float16_t)1.46289062f, (float16_t)0.857910156f, (float16_t)0.606445312f, + (float16_t)-0.0254211426f, (float16_t)-0.312011719f, (float16_t)2.1015625f, + (float16_t)-0.0997314453f, (float16_t)1.58691406f, (float16_t)-1.3203125f, + (float16_t)-0.0589904785f, (float16_t)1.14355469f, (float16_t)-0.861816406f, + (float16_t)-0.741210938f, (float16_t)0.341796875f, (float16_t)-2.18554688f, + (float16_t)-1.71386719f, (float16_t)0.910644531f, (float16_t)0.287597656f, + (float16_t)-1.17382812f, (float16_t)0.770019531f, (float16_t)3.10351562f, + (float16_t)-1.80957031f, (float16_t)-0.300048828f, (float16_t)2.16015625f, + (float16_t)-0.343994141f, (float16_t)0.322265625f, (float16_t)0.947265625f, + (float16_t)-0.0938720703f, (float16_t)1.09179688f, (float16_t)-0.684570312f, + (float16_t)-1.23925781f, (float16_t)-0.436035156f, (float16_t)0.257324219f, + (float16_t)1.63574219f, (float16_t)-0.638671875f, (float16_t)-1.58300781f, + (float16_t)0.0712280273f, (float16_t)1.15722656f, (float16_t)3.0625f, + (float16_t)1.90332031f, (float16_t)0.775390625f, (float16_t)0.673339844f, + (float16_t)-0.950683594f, (float16_t)-0.154296875f, (float16_t)-0.141235352f, + (float16_t)-0.875488281f, (float16_t)1.68359375f, (float16_t)0.522949219f, + (float16_t)0.815429688f, (float16_t)-0.965332031f, (float16_t)0.419921875f, + (float16_t)-0.802734375f, (float16_t)0.564941406f, (float16_t)0.388671875f, + (float16_t)2.17773438f, (float16_t)2.51171875f, (float16_t)-2.96875f, + (float16_t)1.55566406f, (float16_t)-0.264648438f, (float16_t)-0.291015625f, + (float16_t)-2.64648438f, (float16_t)-0.618164062f, (float16_t)2.05859375f, + (float16_t)0.613769531f, (float16_t)-1.89648438f, (float16_t)0.186889648f, + (float16_t)-0.521972656f, (float16_t)2.08984375f, (float16_t)0.14050293f, + (float16_t)0.424560547f, (float16_t)0.355224609f, (float16_t)1.76074219f, + (float16_t)1.37304688f, (float16_t)-0.200805664f, (float16_t)-2.24414062f, + (float16_t)0.841796875f, (float16_t)2.41210938f, (float16_t)0.994140625f, + (float16_t)0.299804688f, (float16_t)-0.235107422f, (float16_t)0.51171875f, + (float16_t)0.223266602f, (float16_t)1.2109375f, (float16_t)2.76367188f, + (float16_t)0.565429688f, (float16_t)0.466552734f, (float16_t)1.9921875f, + (float16_t)-1.63671875f, (float16_t)0.519042969f, (float16_t)0.294433594f, + (float16_t)2.00390625f, (float16_t)-2.58203125f, (float16_t)1.06445312f, + (float16_t)-0.539550781f, (float16_t)-0.750976562f, (float16_t)-1.42480469f, + (float16_t)0.701171875f, (float16_t)0.608398438f, (float16_t)-2.5234375f, + (float16_t)-1.41699219f, (float16_t)0.336914062f, (float16_t)0.96875f, + (float16_t)-0.0675048828f, (float16_t)-0.2421875f, (float16_t)1.93164062f, + (float16_t)0.19909668f, (float16_t)-3.41015625f, (float16_t)0.221557617f, + (float16_t)1.93847656f, (float16_t)-2.046875f, (float16_t)-0.731445312f, + (float16_t)-0.388671875f, (float16_t)-0.514648438f, (float16_t)-1.41796875f, + (float16_t)1.08691406f, (float16_t)1.875f, (float16_t)-0.250244141f, + (float16_t)-0.225341797f, (float16_t)0.553222656f, (float16_t)-0.798339844f, + (float16_t)-0.0326843262f, (float16_t)0.302001953f, (float16_t)1.69433594f, + (float16_t)0.566894531f, (float16_t)-0.0420227051f, (float16_t)-0.947265625f, + (float16_t)-2.02734375f, (float16_t)1.50292969f, (float16_t)0.726074219f, + (float16_t)1.43164062f, (float16_t)-1.33691406f, (float16_t)-0.193359375f, + (float16_t)0.78125f, (float16_t)1.31542969f, (float16_t)1.28320312f, + (float16_t)-2.91015625f, (float16_t)-1.16210938f, (float16_t)-0.0640258789f, + (float16_t)1.52734375f, (float16_t)-0.373046875f, (float16_t)-0.127319336f, + (float16_t)-1.05078125f, (float16_t)-0.595703125f, (float16_t)-0.412353516f, + (float16_t)1.93945312f, (float16_t)-0.93359375f, (float16_t)2.12109375f, + (float16_t)-0.973144531f, (float16_t)0.0759277344f, (float16_t)2.58789062f, + (float16_t)0.626953125f, (float16_t)0.0899658203f, (float16_t)-1.15332031f, + (float16_t)2.15429688f, (float16_t)0.437988281f, (float16_t)0.214355469f, + (float16_t)1.34082031f, (float16_t)1.68554688f, (float16_t)-0.661621094f, + (float16_t)0.280273438f, (float16_t)2.04882812f, (float16_t)0.82421875f, + (float16_t)0.543945312f, (float16_t)0.867675781f, (float16_t)2.22851562f, + (float16_t)-0.380615234f, (float16_t)0.339355469f, (float16_t)0.82421875f, + (float16_t)0.0589294434f, (float16_t)-0.102905273f, (float16_t)-0.906738281f, + (float16_t)-1.41601562f, (float16_t)-0.623535156f, (float16_t)-2.7421875f, + (float16_t)0.299316406f, (float16_t)1.71777344f, (float16_t)0.338623047f, + (float16_t)-0.548339844f, (float16_t)1.20019531f, (float16_t)-0.18737793f, + (float16_t)-0.167358398f, (float16_t)0.525390625f, (float16_t)-0.461914062f, + (float16_t)-1.68359375f, (float16_t)1.9921875f, (float16_t)-0.249755859f, + (float16_t)-2.046875f, (float16_t)-1.70605469f, (float16_t)0.00846862793f, + (float16_t)-0.340087891f, (float16_t)-0.749023438f, (float16_t)0.993652344f, + (float16_t)-0.783691406f, (float16_t)1.38476562f, (float16_t)-0.226196289f, + (float16_t)0.258300781f, (float16_t)0.459960938f, (float16_t)0.749023438f, + (float16_t)1.66699219f, (float16_t)1.06152344f, (float16_t)-0.617675781f, + (float16_t)-1.13769531f, (float16_t)-0.332763672f, (float16_t)-1.46386719f, + (float16_t)0.42578125f, (float16_t)-0.967773438f, (float16_t)0.843261719f, + (float16_t)-3.25f, (float16_t)-0.571289062f, (float16_t)-0.317871094f, + (float16_t)1.1796875f, (float16_t)0.831542969f, (float16_t)1.35742188f, + (float16_t)0.345458984f, (float16_t)-1.09570312f, (float16_t)1.02050781f, + (float16_t)-0.999511719f, (float16_t)0.260742188f, (float16_t)-1.43945312f, + (float16_t)-0.142822266f, (float16_t)0.200317383f, (float16_t)1.12695312f, + (float16_t)-0.0969238281f, (float16_t)0.0934448242f, (float16_t)0.962890625f, + (float16_t)2.10742188f, (float16_t)0.210327148f, (float16_t)0.250488281f, + (float16_t)1.23144531f, (float16_t)-1.35058594f, (float16_t)0.725585938f, + (float16_t)0.520507812f, (float16_t)1.00585938f, (float16_t)-3.2109375f, + (float16_t)0.330322266f, (float16_t)1.58886719f, (float16_t)-1.96386719f, + (float16_t)1.56933594f, (float16_t)0.729003906f, (float16_t)0.754882812f, + (float16_t)-1.4453125f, (float16_t)-2.20507812f, (float16_t)1.41894531f, + (float16_t)0.404541016f, (float16_t)-0.474609375f, (float16_t)1.19433594f, + (float16_t)0.916992188f, (float16_t)-0.765625f, (float16_t)-1.609375f, + (float16_t)0.965332031f, (float16_t)1.23925781f, (float16_t)0.0201721191f, + (float16_t)-0.845214844f, (float16_t)1.72460938f, (float16_t)-0.786132812f, + (float16_t)2.8984375f, (float16_t)2.91601562f, (float16_t)1.68261719f, + (float16_t)-1.79980469f, (float16_t)0.109924316f, (float16_t)-0.112243652f, + (float16_t)1.17382812f, (float16_t)-0.376220703f, (float16_t)-1.93945312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/weights_data.h new file mode 100644 index 00000000..f2c498fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f16/weights_data.h @@ -0,0 +1,89 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1x1_stride_x_y_f16_weights_data[345] = { + (float16_t)-0.135864258f, (float16_t)-0.493408203f, (float16_t)-0.68359375f, (float16_t)0.184692383f, + (float16_t)0.461181641f, (float16_t)0.666992188f, (float16_t)0.15222168f, (float16_t)0.089050293f, + (float16_t)-0.0572814941f, (float16_t)0.213012695f, (float16_t)-0.425048828f, (float16_t)0.280029297f, + (float16_t)-0.637695312f, (float16_t)0.232055664f, (float16_t)0.690429688f, (float16_t)0.34765625f, + (float16_t)0.232666016f, (float16_t)-0.300292969f, (float16_t)-0.0845947266f, (float16_t)0.0530395508f, + (float16_t)0.0893554688f, (float16_t)0.139282227f, (float16_t)0.651855469f, (float16_t)-0.108154297f, + (float16_t)-0.703613281f, (float16_t)0.196044922f, (float16_t)0.522460938f, (float16_t)0.0648193359f, + (float16_t)0.0997924805f, (float16_t)-0.399902344f, (float16_t)-0.328369141f, (float16_t)-0.614746094f, + (float16_t)-0.313964844f, (float16_t)0.5703125f, (float16_t)0.135864258f, (float16_t)-0.347900391f, + (float16_t)0.263183594f, (float16_t)-0.482666016f, (float16_t)0.572753906f, (float16_t)-0.57421875f, + (float16_t)0.294189453f, (float16_t)-0.60546875f, (float16_t)-0.361816406f, (float16_t)0.461181641f, + (float16_t)-0.603027344f, (float16_t)0.159057617f, (float16_t)-0.00506591797f, (float16_t)0.70703125f, + (float16_t)-0.589355469f, (float16_t)-0.578613281f, (float16_t)0.459716797f, (float16_t)-0.245239258f, + (float16_t)-0.604003906f, (float16_t)-0.538574219f, (float16_t)0.668457031f, (float16_t)0.0458984375f, + (float16_t)-0.362060547f, (float16_t)-0.31640625f, (float16_t)0.394287109f, (float16_t)-0.727050781f, + (float16_t)-0.584960938f, (float16_t)0.431640625f, (float16_t)0.00955200195f, (float16_t)-0.731445312f, + (float16_t)-0.239257812f, (float16_t)-0.413574219f, (float16_t)-0.301513672f, (float16_t)-0.258789062f, + (float16_t)-0.0838012695f, (float16_t)0.306152344f, (float16_t)0.68359375f, (float16_t)0.17199707f, + (float16_t)0.439208984f, (float16_t)-0.673339844f, (float16_t)-0.385742188f, (float16_t)-0.530761719f, + (float16_t)-0.417480469f, (float16_t)-0.490234375f, (float16_t)0.649902344f, (float16_t)0.478027344f, + (float16_t)0.0731811523f, (float16_t)-0.594726562f, (float16_t)0.0903320312f, (float16_t)-0.385742188f, + (float16_t)-0.412353516f, (float16_t)0.171142578f, (float16_t)0.512207031f, (float16_t)-0.738769531f, + (float16_t)0.712890625f, (float16_t)0.508789062f, (float16_t)-0.230957031f, (float16_t)0.515136719f, + (float16_t)0.165527344f, (float16_t)0.660644531f, (float16_t)-0.00302124023f, (float16_t)0.296630859f, + (float16_t)0.221435547f, (float16_t)-0.210693359f, (float16_t)-0.297851562f, (float16_t)-0.51953125f, + (float16_t)0.409423828f, (float16_t)-0.690429688f, (float16_t)-0.0377807617f, (float16_t)0.471923828f, + (float16_t)-0.573730469f, (float16_t)0.371337891f, (float16_t)0.30078125f, (float16_t)-0.10144043f, + (float16_t)0.0543212891f, (float16_t)0.623535156f, (float16_t)0.454101562f, (float16_t)-0.193481445f, + (float16_t)-0.473632812f, (float16_t)0.358398438f, (float16_t)-0.638671875f, (float16_t)-0.458984375f, + (float16_t)0.125244141f, (float16_t)0.381103516f, (float16_t)0.622558594f, (float16_t)-0.491210938f, + (float16_t)0.487304688f, (float16_t)0.423339844f, (float16_t)-0.259033203f, (float16_t)0.546386719f, + (float16_t)0.43359375f, (float16_t)0.309326172f, (float16_t)0.0817871094f, (float16_t)0.73046875f, + (float16_t)-0.17980957f, (float16_t)-0.19934082f, (float16_t)0.708984375f, (float16_t)-0.494140625f, + (float16_t)-0.339599609f, (float16_t)0.189941406f, (float16_t)0.748535156f, (float16_t)-0.441894531f, + (float16_t)-0.389160156f, (float16_t)-0.0129394531f, (float16_t)-0.698242188f, (float16_t)-0.48046875f, + (float16_t)-0.392089844f, (float16_t)-0.566894531f, (float16_t)0.691894531f, (float16_t)-0.44140625f, + (float16_t)-0.311767578f, (float16_t)-0.547851562f, (float16_t)-0.371582031f, (float16_t)0.455810547f, + (float16_t)-0.102172852f, (float16_t)0.607421875f, (float16_t)0.516601562f, (float16_t)0.413085938f, + (float16_t)0.412109375f, (float16_t)-0.719238281f, (float16_t)0.198852539f, (float16_t)-0.659179688f, + (float16_t)0.30859375f, (float16_t)0.296386719f, (float16_t)0.385253906f, (float16_t)0.242797852f, + (float16_t)-0.595703125f, (float16_t)-0.530761719f, (float16_t)-0.469238281f, (float16_t)-0.553222656f, + (float16_t)-0.609375f, (float16_t)-0.0152511597f, (float16_t)-0.543945312f, (float16_t)0.616210938f, + (float16_t)0.488769531f, (float16_t)0.626464844f, (float16_t)-0.132324219f, (float16_t)0.0577392578f, + (float16_t)0.286865234f, (float16_t)-0.619628906f, (float16_t)0.300537109f, (float16_t)0.4765625f, + (float16_t)-0.596191406f, (float16_t)0.150756836f, (float16_t)0.0991210938f, (float16_t)-0.3203125f, + (float16_t)-0.377685547f, (float16_t)-0.698730469f, (float16_t)-0.73046875f, (float16_t)0.218017578f, + (float16_t)0.357421875f, (float16_t)-0.316650391f, (float16_t)0.311523438f, (float16_t)-0.0193023682f, + (float16_t)-0.141113281f, (float16_t)0.478759766f, (float16_t)-0.221069336f, (float16_t)0.0163574219f, + (float16_t)-0.414550781f, (float16_t)-0.715332031f, (float16_t)-0.276855469f, (float16_t)0.408203125f, + (float16_t)-0.524902344f, (float16_t)0.00827026367f, (float16_t)0.280517578f, (float16_t)-0.680664062f, + (float16_t)-0.10949707f, (float16_t)-0.262939453f, (float16_t)0.0373840332f, (float16_t)0.116821289f, + (float16_t)-0.172241211f, (float16_t)0.265380859f, (float16_t)0.0662231445f, (float16_t)-0.116394043f, + (float16_t)0.513671875f, (float16_t)-0.589355469f, (float16_t)-0.283691406f, (float16_t)-0.684570312f, + (float16_t)0.635253906f, (float16_t)-0.749023438f, (float16_t)-0.690429688f, (float16_t)-0.622070312f, + (float16_t)0.0510559082f, (float16_t)0.320800781f, (float16_t)-0.745117188f, (float16_t)0.703613281f, + (float16_t)-0.389404297f, (float16_t)-0.457519531f, (float16_t)0.105834961f, (float16_t)-0.490966797f, + (float16_t)0.673339844f, (float16_t)-0.529785156f, (float16_t)-0.234863281f, (float16_t)-0.735839844f, + (float16_t)0.138183594f, (float16_t)0.688476562f, (float16_t)-0.153320312f, (float16_t)-0.104919434f, + (float16_t)0.125610352f, (float16_t)-0.102905273f, (float16_t)0.7421875f, (float16_t)-0.401611328f, + (float16_t)-0.224609375f, (float16_t)-0.431152344f, (float16_t)0.513671875f, (float16_t)0.111022949f, + (float16_t)0.181152344f, (float16_t)-0.193481445f, (float16_t)-0.205566406f, (float16_t)0.475585938f, + (float16_t)0.547851562f, (float16_t)0.520019531f, (float16_t)0.737792969f, (float16_t)0.357910156f, + (float16_t)0.0654907227f, (float16_t)0.413330078f, (float16_t)0.440429688f, (float16_t)-0.701171875f, + (float16_t)-0.212524414f, (float16_t)0.322753906f, (float16_t)0.024887085f, (float16_t)-0.0883789062f, + (float16_t)-0.657714844f, (float16_t)-0.197998047f, (float16_t)0.212524414f, (float16_t)0.287353516f, + (float16_t)0.715332031f, (float16_t)-0.622558594f, (float16_t)0.727539062f, (float16_t)-0.395019531f, + (float16_t)-0.714355469f, (float16_t)-0.674804688f, (float16_t)0.543457031f, (float16_t)0.584472656f, + (float16_t)0.383789062f, (float16_t)0.491699219f, (float16_t)-0.663574219f, (float16_t)-0.198486328f, + (float16_t)-0.155273438f, (float16_t)0.406005859f, (float16_t)-0.220092773f, (float16_t)0.109680176f, + (float16_t)-0.241088867f, (float16_t)-0.0711669922f, (float16_t)-0.698242188f, (float16_t)0.471923828f, + (float16_t)-0.666015625f, (float16_t)-0.585449219f, (float16_t)-0.592285156f, (float16_t)0.197265625f, + (float16_t)0.23425293f, (float16_t)0.729980469f, (float16_t)-0.687011719f, (float16_t)0.468994141f, + (float16_t)0.733398438f, (float16_t)-0.288330078f, (float16_t)0.525878906f, (float16_t)0.53515625f, + (float16_t)-0.307861328f, (float16_t)0.352539062f, (float16_t)0.635253906f, (float16_t)-0.388916016f, + (float16_t)-0.694335938f, (float16_t)-0.3828125f, (float16_t)-0.310058594f, (float16_t)-0.166870117f, + (float16_t)-0.0198059082f, (float16_t)-0.339111328f, (float16_t)-0.114807129f, (float16_t)0.253173828f, + (float16_t)0.126953125f, (float16_t)-0.38671875f, (float16_t)0.563964844f, (float16_t)-0.696289062f, + (float16_t)-0.521484375f, (float16_t)-0.295654297f, (float16_t)0.366699219f, (float16_t)0.0463867188f, + (float16_t)0.569824219f, (float16_t)-0.450195312f, (float16_t)0.0198822021f, (float16_t)-0.504882812f, + (float16_t)0.369384766f, (float16_t)-0.418212891f, (float16_t)0.501464844f, (float16_t)-0.623046875f, + (float16_t)0.0787353516f, (float16_t)0.645507812f, (float16_t)0.676757812f, (float16_t)-0.0389099121f, + (float16_t)-0.354003906f, (float16_t)-0.607910156f, (float16_t)0.152954102f, (float16_t)-0.314208984f, + (float16_t)-0.374023438f, (float16_t)0.715332031f, (float16_t)0.570800781f, (float16_t)-0.387451172f, + (float16_t)0.378662109f, (float16_t)0.226074219f, (float16_t)-0.323974609f, (float16_t)-0.0454101562f, + (float16_t)-0.680175781f, (float16_t)0.654296875f, (float16_t)0.646972656f, (float16_t)0.720703125f, + (float16_t)-0.669921875f, (float16_t)-0.653320312f, (float16_t)-0.344726562f, (float16_t)0.531738281f, + (float16_t)-0.244140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/biases_data.h new file mode 100644 index 00000000..6d627b8a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/biases_data.h @@ -0,0 +1,16 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_f32_biases_data[15] = {0.180467397f, + 0.421718329f, + -0.178369731f, + 0.217498109f, + 0.432220548f, + 0.414732635f, + 0.0681066737f, + 0.0813253447f, + 0.405736446f, + 0.155580238f, + -0.0746081769f, + 0.161945522f, + -0.274216086f, + 0.143802434f, + -0.445686072f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/config_data.h new file mode 100644 index 00000000..4ce48c10 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_INPUT_BATCHES 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_INPUT_H 6 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_INPUT_W 7 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_IN_CH 23 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_FILTER_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_FILTER_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUT_CH 15 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_STRIDE_H 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_STRIDE_W 2 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_PADDING_H 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_PADDING_W 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_DILATION_H 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_DILATION_W 1 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUTPUT_H 3 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUTPUT_W 4 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUTPUT_C 15 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_DST_SIZE 540 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_USE_WRAPPER 0 +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUT_ACTIVATION_MIN -6.0f +#define CONV_MATCH_1X1_STRIDE_X_Y_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/input_data.h new file mode 100644 index 00000000..6f23800f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/input_data.h @@ -0,0 +1,485 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_f32_input_data[2898] = { + 0.625222266f, -0.75126636f, -0.728627443f, 0.961970508f, 0.0641111657f, 0.838844895f, + 0.52947855f, 0.929204226f, -0.320694089f, 0.431760937f, -0.483128369f, 0.495025992f, + 0.230715424f, 0.277545244f, 0.097798124f, 0.903852642f, 0.820985675f, -0.580496609f, + 0.0103249634f, 0.00460595312f, -0.0213213954f, -0.698985577f, -0.567088366f, -0.846568286f, + -0.173358262f, 0.164378256f, -0.236042514f, -0.740296483f, -0.0648643896f, -0.87645638f, + -0.133484513f, 0.0652198344f, -0.810009897f, 0.600996494f, -0.757569194f, 0.40739733f, + 0.895150363f, -0.873027027f, 0.678825915f, 0.587587118f, -0.919064105f, 0.178830072f, + 0.830958426f, -0.298577577f, -0.7607795f, 0.157669067f, -0.103186838f, -0.553929448f, + -0.596794665f, -0.968796074f, -0.624621749f, -0.635418296f, -0.145390034f, -0.135538042f, + 0.387627512f, 0.607540846f, 0.387083322f, -0.136111692f, -0.527310073f, 0.311410844f, + -0.522070765f, -0.536969841f, -0.773032188f, 0.842107058f, 0.382465661f, 0.45657593f, + -0.225702554f, 0.0651700348f, -0.922486365f, 0.683307707f, 0.670221865f, 0.614715576f, + -0.42223525f, -0.0896953046f, -0.22338298f, 0.0173719805f, 0.824408531f, 0.465559274f, + 0.473006248f, -0.174958989f, -0.0325749218f, -0.655851245f, -0.523922086f, -0.493300408f, + 0.536497056f, 0.0560843274f, -0.475694925f, -0.934113503f, 0.970437765f, 0.703199446f, + 0.106896251f, -0.885520458f, -0.914049625f, 0.10717658f, -0.309148401f, 0.241075635f, + 0.680158436f, -0.714172184f, -0.154747903f, 0.456601292f, -0.271713555f, 0.865212083f, + 0.625214577f, -0.895792603f, -0.595558286f, -0.418171525f, -0.96930939f, -0.555587351f, + 0.242677242f, -0.991839707f, 0.181099266f, -0.816046715f, -0.390007555f, -0.698708475f, + -0.539943874f, 0.413707018f, -0.211126566f, -0.374152452f, -0.0156014198f, -0.0738095492f, + 0.272273153f, -0.900737405f, 0.190177217f, 0.866779864f, 0.151781246f, 0.879470468f, + 0.261853069f, 0.991749763f, 0.0100360541f, -0.391246587f, -0.782086253f, 0.83731252f, + 0.760298789f, 0.0499374047f, 0.581421316f, -0.402337193f, -0.239031985f, 0.534609556f, + -0.0687888488f, -0.831582129f, -0.156398714f, -0.606924295f, 0.51923281f, 0.527397633f, + 0.553251505f, 0.102843709f, 0.428387344f, -0.982561052f, 0.198966965f, 0.449179739f, + -0.378088266f, -0.376540095f, 0.347649395f, 0.504823446f, -0.123374023f, -0.514552176f, + -0.304029822f, 0.132965997f, 0.565769553f, 0.708837032f, 0.801829875f, 0.627222776f, + 0.829181671f, 0.912801504f, -0.78448391f, -0.672540605f, -0.0815817788f, -0.692313015f, + -0.667669237f, -0.973905146f, -0.360937744f, 0.350945532f, 0.28099075f, -0.0610034354f, + 0.822859704f, -0.390147448f, 0.915131569f, 0.163524404f, 0.62835592f, -0.121157803f, + -0.258789569f, 0.538288474f, 0.0661848336f, 0.8549968f, 0.463743299f, 0.413782448f, + -0.0423600189f, 0.670458734f, 0.243698031f, 0.518936336f, 0.174595267f, -0.228175551f, + 0.432882249f, 0.48912707f, 0.698679984f, -0.818125963f, -0.390451252f, -0.235040709f, 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0.489962995f, 0.739874721f, -0.262005299f, -0.68456316f, + 0.789223135f, -0.897518992f, -0.322495401f, 0.631709099f, 0.590016186f, 0.135143518f, + -0.220183462f, -0.0631463155f, 0.928643286f, -0.317939579f, -0.677516341f, -0.100674823f, + 0.159541383f, 0.841542304f, 0.177847788f, -0.119366907f, 0.254268497f, 0.634873211f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/output_ref_data.h new file mode 100644 index 00000000..3db1ed4f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/output_ref_data.h @@ -0,0 +1,80 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_f32_output_ref_data[540] = { + 2.54349971f, 0.801926851f, -1.12773788f, -1.42553735f, -0.93704623f, 1.44426119f, -0.267024636f, + 0.00969683845f, 0.209671348f, -2.16446829f, 0.218736693f, 2.37828207f, 1.36135209f, 0.0590952821f, + -0.213784665f, -1.3367337f, 0.185811877f, -0.573775589f, 0.960179687f, 1.13665986f, -0.00445495872f, + 1.21800268f, 1.73374069f, -0.142979071f, 0.618035674f, -0.0431001894f, -0.479308963f, 0.780975878f, + 0.173243999f, 0.519361615f, -0.722923994f, 0.682313323f, 1.95754933f, 0.123489425f, -0.755221784f, + -0.10805472f, 1.14799356f, 1.50635111f, -1.09040463f, -0.350759566f, -0.101711191f, 1.12542951f, + -0.276195019f, -0.980503857f, -0.592158914f, 2.48698258f, 0.88768357f, -0.464742452f, -1.85584509f, + -0.351101965f, -0.157692298f, 0.0662100762f, 0.824706316f, 1.56571662f, -1.24007726f, -0.175792143f, + 0.322361857f, -2.89727354f, 0.565613627f, 0.432497203f, 1.29137492f, -2.34559917f, 1.97985101f, + -0.867093861f, 0.095446147f, 0.663359821f, 1.61312628f, -1.99278009f, 0.15917477f, 0.562000692f, + -1.01660085f, -0.0652121976f, 1.1591543f, -0.340863138f, -2.15813065f, 1.06107938f, 0.200178206f, + -0.00124597363f, -1.23410451f, -0.493515939f, 2.20998001f, -0.064634338f, 0.404322714f, 0.0242195893f, + -1.08426499f, 1.4086318f, 0.543801844f, 0.807762206f, 1.28942013f, -0.764100134f, -2.18077397f, + 3.15630627f, -0.789517581f, 2.40390444f, -0.696370482f, 0.541684926f, -0.67842859f, -0.444444418f, + -0.284776062f, 0.215812102f, 0.230683431f, -0.670484304f, -0.561648071f, 0.762669802f, 0.55038774f, + -0.0929731578f, -0.647659063f, 0.634190083f, -0.598543882f, 0.86955303f, 0.529132247f, -1.27761173f, + -0.42606622f, 1.90621555f, -0.430398673f, -1.51143444f, -0.0875560716f, -3.21214962f, -0.218387261f, + -0.708029389f, 0.760778427f, -2.55382705f, 0.393032163f, -0.590899646f, -0.290972084f, -1.30767322f, + 0.580113471f, 2.93672013f, 1.37921035f, -1.58997619f, -0.62132597f, 0.285859346f, -0.441511065f, + 1.33949602f, 0.388023108f, -2.1856842f, 0.200400874f, 0.475238025f, 1.53839362f, 1.83491039f, + 1.01259387f, 0.685334563f, 0.147697583f, -0.347344905f, 1.38064909f, -0.184858099f, -4.58130789f, + -1.20458388f, 0.923559129f, -1.07335842f, -1.6737802f, 0.362738132f, 1.80123973f, -1.26495445f, + 0.995877385f, -1.66886973f, 1.13474107f, 0.24691081f, 1.29680657f, 0.156261086f, -0.229628906f, + -0.352973431f, -0.991723537f, 1.68248081f, 1.87928617f, 0.856957197f, 0.22898832f, 1.29476643f, + 1.72789574f, -0.297525197f, 1.16872418f, -1.34445679f, -1.64417446f, 0.0877791941f, 2.19418573f, + -0.335874498f, 0.41264537f, -0.452703804f, 0.705678105f, -1.44141281f, 0.264489621f, 1.46446204f, + 2.79712558f, -2.23100853f, 0.13452664f, 0.645489573f, 0.547999263f, -0.850975692f, -0.276371986f, + 3.22273326f, -0.717286527f, 0.0347238816f, -0.102852277f, 0.751298487f, 0.947441161f, -2.35948253f, + 0.977134883f, 1.77424061f, -0.200642645f, -1.18053603f, 1.56301641f, 1.74955583f, -1.18040085f, + -0.701156914f, 1.13375509f, -1.05309451f, -0.49759531f, -0.586675584f, 0.505387127f, -1.68225873f, + 1.96008492f, 2.58603644f, -1.99428892f, 0.381879091f, -1.9747479f, 1.33281946f, 0.64808017f, + 1.08286369f, -0.179063082f, -0.383629054f, -0.387361318f, 2.00458884f, -0.76042825f, -0.16484946f, + -1.06024849f, 0.983011901f, 0.160539359f, 0.116964146f, 0.279742569f, 0.477114737f, 0.908978343f, + 0.968628943f, -0.447603822f, 0.441236585f, 0.255232632f, 0.210335046f, -0.00633946946f, 0.0772559121f, + 1.34084737f, -1.70650136f, 0.38717556f, 2.3679173f, 1.41064787f, 0.190821961f, -2.06816769f, + 2.42592192f, -0.833882749f, -0.202976331f, -1.78256762f, 3.499964f, -0.266012132f, -0.247293845f, + 0.338138491f, 0.664116919f, 0.500956655f, -1.10148621f, 1.84339404f, 0.516570568f, -0.39638868f, + 0.638579607f, 1.41787016f, -0.720695198f, 1.1913873f, -1.40302551f, 0.501383066f, 1.90926611f, + -0.507402301f, -0.644095778f, 0.136967227f, 0.0340853594f, 1.26048911f, 0.276707381f, 0.542914569f, + -1.17477953f, -0.916847348f, -0.301330417f, 1.46302009f, 0.857930005f, 0.606620073f, -0.0254199896f, + -0.312072247f, 2.10155582f, -0.099745743f, 1.58724487f, -1.31982994f, -0.058986444f, 1.14364457f, + -0.861797094f, -0.741184652f, 0.341723531f, -2.18643904f, -1.71368611f, 0.910574496f, 0.287501305f, + -1.17365813f, 0.77008456f, 3.10408211f, -1.80966926f, -0.300167143f, 2.16063643f, -0.343997002f, + 0.322150618f, 0.947352707f, -0.0938506424f, 1.09209251f, -0.68448019f, -1.2389431f, -0.435957909f, + 0.257287711f, 1.63534927f, -0.638717413f, -1.58267689f, 0.0712197572f, 1.15751827f, 3.06291795f, + 1.90377772f, 0.775589943f, 0.673122466f, -0.950894415f, -0.154304862f, -0.141251102f, -0.875479519f, + 1.68336666f, 0.5229882f, 0.815596998f, -0.965511799f, 0.420034289f, -0.802559197f, 0.565031767f, + 0.388617724f, 2.17780638f, 2.51169276f, -2.96872306f, 1.55578685f, -0.264559835f, -0.290935457f, + -2.64580584f, -0.618397951f, 2.05923176f, 0.613666475f, -1.89676607f, 0.186916471f, -0.52176863f, + 2.08998656f, 0.140486121f, 0.424439549f, 0.355265796f, 1.76058125f, 1.37331438f, -0.200773597f, + -2.24456859f, 0.841615558f, 2.41273069f, 0.993923306f, 0.299723595f, -0.235105023f, 0.511606932f, + 0.223308817f, 1.21088147f, 2.7635231f, 0.565185785f, 0.466437995f, 1.99191725f, -1.6371026f, + 0.518874168f, 0.294535637f, 2.00297451f, -2.58125687f, 1.0639993f, -0.539545536f, -0.750878572f, + -1.42519736f, 0.701090515f, 0.608428776f, -2.52411985f, -1.41700828f, 0.336986393f, 0.968701482f, + -0.0675135627f, -0.242134631f, 1.93148422f, 0.199125156f, -3.41104031f, 0.221548915f, 1.93895829f, + -2.04755521f, -0.731374264f, -0.38857612f, -0.514795005f, -1.41751671f, 1.08732784f, 1.87452722f, + -0.250251234f, -0.225377038f, 0.553447902f, -0.798542261f, -0.0326715223f, 0.302057147f, 1.69423509f, + 0.567124486f, -0.0420341007f, -0.947120845f, -2.02814746f, 1.50278223f, 0.725970924f, 1.43123937f, + -1.3365469f, -0.193402693f, 0.781017721f, 1.31563485f, 1.28312397f, -2.91086864f, -1.16192412f, + -0.0640362352f, 1.52730179f, -0.373028874f, -0.127323955f, -1.0508666f, -0.595894456f, -0.412307024f, + 1.9398917f, -0.933675587f, 2.12027264f, -0.973383367f, 0.075946793f, 2.58860636f, 0.627015412f, + 0.0899454579f, -1.15376937f, 2.15473509f, 0.438015699f, 0.214335173f, 1.34099305f, 1.68522179f, + -0.661596358f, 0.280201495f, 2.04887199f, 0.824217021f, 0.543756723f, 0.867749333f, 2.22790241f, + -0.380518317f, 0.339333892f, 0.824216723f, 0.0589336678f, -0.102928802f, -0.906572819f, -1.41561973f, + -0.62351352f, -2.74144673f, 0.299401373f, 1.71785676f, 0.338505477f, -0.548204899f, 1.20047319f, + -0.187361032f, -0.167405829f, 0.525512874f, -0.461897671f, -1.68357527f, 1.99224496f, -0.249814138f, + -2.04708743f, -1.70576942f, 0.00846759602f, -0.339979142f, -0.748933911f, 0.993453681f, -0.783498466f, + 1.38507378f, -0.226154849f, 0.258338869f, 0.459875494f, 0.749253154f, 1.66720116f, 1.06192112f, + -0.617478967f, -1.1373688f, -0.332685262f, -1.46362638f, 0.425878704f, -0.967990816f, 0.84318769f, + -3.25023103f, -0.571147025f, -0.31793052f, 1.18013239f, 0.831504464f, 1.35708034f, 0.34540692f, + -1.09596443f, 1.02013123f, -0.999538898f, 0.260748327f, -1.43901742f, -0.142786711f, 0.200312793f, + 1.12726104f, -0.0969251543f, 0.093418099f, 0.96311903f, 2.10749125f, 0.210299551f, 0.250599682f, + 1.23113239f, -1.35050309f, 0.725561798f, 0.520297885f, 1.00574017f, -3.21154976f, 0.330352902f, + 1.58907616f, -1.96399271f, 1.56946683f, 0.729027808f, 0.754942238f, -1.44561315f, -2.20556879f, + 1.41906345f, 0.404460937f, -0.474630088f, 1.19470191f, 0.917202294f, -0.765649498f, -1.60973859f, + 0.965431213f, 1.23938668f, 0.0201775786f, -0.84538734f, 1.72430658f, -0.786312163f, 2.89864707f, + 2.9150455f, 1.6825949f, -1.79947078f, 0.109905198f, -0.112243779f, 1.17400789f, -0.376204133f, + -1.93963301f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/weights_data.h new file mode 100644 index 00000000..b597a7e3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1x1_stride_x_y_f32/weights_data.h @@ -0,0 +1,52 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1x1_stride_x_y_f32_weights_data[345] = { + -0.135907605f, -0.493431449f, -0.683647752f, 0.184694216f, 0.461065799f, 0.667229235f, 0.152196199f, + 0.0890431479f, -0.0572949722f, 0.213054836f, -0.425017178f, 0.280056268f, -0.637708366f, 0.232002005f, + 0.690441251f, 0.347592503f, 0.232611299f, -0.300350279f, -0.084616214f, 0.0530292392f, 0.0893255174f, + 0.139340162f, 0.652033329f, -0.108177789f, -0.703834653f, 0.196078658f, 0.522632837f, 0.0648150966f, + 0.0997857302f, -0.399959356f, -0.328310817f, -0.614875019f, -0.313920766f, 0.570145786f, 0.135809436f, + -0.348012656f, 0.263082415f, -0.482604444f, 0.572910309f, -0.574078739f, 0.294252634f, -0.605356932f, + -0.361732662f, 0.461131155f, -0.602995038f, 0.159104437f, -0.00506777409f, 0.707207561f, -0.589480877f, + -0.57879293f, 0.459788918f, -0.245181724f, -0.604063213f, -0.538631558f, 0.668341517f, 0.0458945371f, + -0.361952007f, -0.316458583f, 0.394352376f, -0.726969302f, -0.585017979f, 0.43171978f, 0.00955432188f, + -0.731580317f, -0.23925221f, -0.413645387f, -0.301421374f, -0.258778811f, -0.0838189125f, 0.306091875f, + 0.683743656f, 0.17203474f, 0.439150155f, -0.67343235f, -0.385809213f, -0.53094101f, -0.417361289f, + -0.490126729f, 0.650108933f, 0.477989793f, 0.0731587484f, -0.594510376f, 0.0903600827f, -0.38563028f, + -0.412374496f, 0.171164274f, 0.512318254f, -0.738531291f, 0.713055611f, 0.508844674f, -0.230970502f, + 0.515098631f, 0.165540114f, 0.660751104f, -0.00302084745f, 0.29655847f, 0.221460253f, -0.210632443f, + -0.297880709f, -0.519639492f, 0.409340024f, -0.690226674f, -0.037793152f, 0.471953928f, -0.573953986f, + 0.371306509f, 0.300709009f, -0.101444237f, 0.0543101467f, 0.623359621f, 0.454169154f, -0.193481177f, + -0.473514736f, 0.358310878f, -0.638507962f, -0.458895147f, 0.125205368f, 0.381189466f, 0.622548461f, + -0.491189629f, 0.487402409f, 0.423301101f, -0.258999407f, 0.546321154f, 0.433477521f, 0.309413135f, + 0.0817849934f, 0.73024714f, -0.179779366f, -0.199374869f, 0.709130943f, -0.494237453f, -0.339632124f, + 0.189944878f, 0.748497784f, -0.441840053f, -0.389126003f, -0.0129404832f, -0.697998106f, -0.480509847f, + -0.392000467f, -0.566859245f, 0.69191283f, -0.441509694f, -0.311679751f, -0.547959089f, -0.371641457f, + 0.455778897f, -0.102177665f, 0.607435584f, 0.516714454f, 0.413024604f, 0.412002832f, -0.719397843f, + 0.198838502f, -0.659178793f, 0.30848977f, 0.296345919f, 0.385292172f, 0.242800459f, -0.595490158f, + -0.530666292f, -0.469261885f, -0.553228915f, -0.609139681f, -0.0152502758f, -0.543776214f, 0.616100669f, + 0.488831848f, 0.62646389f, -0.132330894f, 0.0577248447f, 0.28687945f, -0.61939472f, 0.300464839f, + 0.476658881f, -0.596205771f, 0.150743112f, 0.09913975f, -0.320197552f, -0.377615571f, -0.698586702f, + -0.73031193f, 0.218072712f, 0.357483774f, -0.316531718f, 0.311624229f, -0.0193019379f, -0.141159996f, + 0.478847921f, -0.221081942f, 0.0163500793f, -0.414459646f, -0.715518713f, -0.276852548f, 0.408085316f, + -0.525026977f, 0.00827391353f, 0.280612558f, -0.680592537f, -0.109476872f, -0.262855381f, 0.0373809598f, + 0.116804883f, -0.172193706f, 0.265326619f, 0.0662249327f, -0.116394311f, 0.51361829f, -0.589321136f, + -0.283812821f, -0.68475157f, 0.635448992f, -0.749201179f, -0.690281093f, -0.622041762f, 0.051069364f, + 0.320775717f, -0.745238245f, 0.703806043f, -0.389415801f, -0.457592458f, 0.105857246f, -0.490959585f, + 0.673288107f, -0.529804528f, -0.234877005f, -0.735687315f, 0.138167679f, 0.688341141f, -0.153369188f, + -0.104899853f, 0.125621811f, -0.102875598f, 0.742194057f, -0.401609033f, -0.224568754f, -0.43116501f, + 0.513450027f, 0.111011982f, 0.181127623f, -0.19346799f, -0.205536798f, 0.47561878f, 0.547667861f, + 0.520190358f, 0.737570405f, 0.358006895f, 0.0655088946f, 0.413300753f, 0.440364093f, -0.701078475f, + -0.212509096f, 0.322642028f, 0.0248846728f, -0.0883628353f, -0.657730281f, -0.198050007f, 0.212566763f, + 0.287246168f, 0.715312958f, -0.622550249f, 0.727570176f, -0.394908488f, -0.71437943f, -0.674753249f, + 0.543345928f, 0.584389806f, 0.383746117f, 0.491695106f, -0.663730741f, -0.198469937f, -0.155273378f, + 0.405940324f, -0.220105395f, 0.109685898f, -0.241097063f, -0.0711442083f, -0.698091507f, 0.471921951f, + -0.666221619f, -0.585212946f, -0.592063308f, 0.197322011f, 0.234247729f, 0.730210304f, -0.686965227f, + 0.469043672f, 0.733307719f, -0.288421929f, 0.52586627f, 0.535006702f, -0.307839721f, 0.352422595f, + 0.635276675f, -0.388963997f, -0.694169164f, -0.382811159f, -0.30997023f, -0.16685082f, -0.0198132116f, + -0.339063197f, -0.114805065f, 0.253212661f, 0.126909524f, -0.386725634f, 0.563763082f, -0.696172893f, + -0.521723032f, -0.295748442f, 0.366609722f, 0.0463900417f, 0.5697034f, -0.45007509f, 0.0198758841f, + -0.504894376f, 0.369370192f, -0.41828388f, 0.501559913f, -0.623172462f, 0.0787555873f, 0.645576239f, + 0.676838696f, -0.0389172323f, -0.353890538f, -0.607718647f, 0.152995244f, -0.314305335f, -0.374018431f, + 0.715567827f, 0.570694327f, -0.387510389f, 0.378749013f, 0.226033777f, -0.323896348f, -0.0454162508f, + -0.680172741f, 0.654268503f, 0.647064447f, 0.720689893f, -0.670054495f, -0.653197527f, -0.34460637f, + 0.531605065f, -0.244083241f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/biases_data.h new file mode 100644 index 00000000..f218c067 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/biases_data.h @@ -0,0 +1,4 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_1_f16_biases_data[3] = {(float16_t)-0.00494003296f, + (float16_t)0.149414062f, + (float16_t)-0.0446472168f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/config_data.h new file mode 100644 index 00000000..a688230b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_1_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_1_F16_INPUT_H 1 +#define CONV_MATCH_1XN_1_F16_INPUT_W 2 +#define CONV_MATCH_1XN_1_F16_IN_CH 4 +#define CONV_MATCH_1XN_1_F16_FILTER_H 1 +#define CONV_MATCH_1XN_1_F16_FILTER_W 3 +#define CONV_MATCH_1XN_1_F16_OUT_CH 3 +#define CONV_MATCH_1XN_1_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_1_F16_STRIDE_W 1 +#define CONV_MATCH_1XN_1_F16_PADDING_H 0 +#define CONV_MATCH_1XN_1_F16_PADDING_W 1 +#define CONV_MATCH_1XN_1_F16_DILATION_H 1 +#define CONV_MATCH_1XN_1_F16_DILATION_W 1 +#define CONV_MATCH_1XN_1_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_1_F16_OUTPUT_W 2 +#define CONV_MATCH_1XN_1_F16_OUTPUT_C 3 +#define CONV_MATCH_1XN_1_F16_DST_SIZE 12 +#define CONV_MATCH_1XN_1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_1_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_1_F16_OUT_ACTIVATION_MIN (float16_t) - 127.0f +#define CONV_MATCH_1XN_1_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/input_data.h new file mode 100644 index 00000000..e41e8fbd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/input_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_1_f16_input_data[16] = {(float16_t)-0.772460938f, + (float16_t)-0.660644531f, + (float16_t)-0.756347656f, + (float16_t)0.496337891f, + (float16_t)-0.337402344f, + (float16_t)-0.813476562f, + (float16_t)-0.566894531f, + (float16_t)0.141601562f, + (float16_t)0.918945312f, + (float16_t)0.880371094f, + (float16_t)-0.7265625f, + (float16_t)-0.499023438f, + (float16_t)-0.0616760254f, + (float16_t)0.733886719f, + (float16_t)-0.407226562f, + (float16_t)0.798828125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/output_ref_data.h new file mode 100644 index 00000000..61d0f648 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_1_f16_output_ref_data[12] = {(float16_t)0.584472656f, + (float16_t)-1.04199219f, + (float16_t)0.259033203f, + (float16_t)-1.3515625f, + (float16_t)-0.613769531f, + (float16_t)0.114562988f, + (float16_t)0.402832031f, + (float16_t)0.319824219f, + (float16_t)0.587890625f, + (float16_t)1.48828125f, + (float16_t)0.475830078f, + (float16_t)0.235717773f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/weights_data.h new file mode 100644 index 00000000..57fa92bc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_1_f16_weights_data[36] = { + (float16_t)0.635742188f, (float16_t)0.483398438f, (float16_t)0.278564453f, (float16_t)-0.432617188f, + (float16_t)-0.708007812f, (float16_t)0.544921875f, (float16_t)-0.1796875f, (float16_t)-0.0590820312f, + (float16_t)0.749023438f, (float16_t)-0.113586426f, (float16_t)-0.724609375f, (float16_t)0.317138672f, + (float16_t)0.133056641f, (float16_t)0.126953125f, (float16_t)0.0525512695f, (float16_t)0.581054688f, + (float16_t)0.719238281f, (float16_t)0.702636719f, (float16_t)0.00292015076f, (float16_t)-0.0622558594f, + (float16_t)-0.0631103516f, (float16_t)-0.435302734f, (float16_t)0.744628906f, (float16_t)-0.650390625f, + (float16_t)-0.362060547f, (float16_t)0.204833984f, (float16_t)-0.193725586f, (float16_t)-0.71484375f, + (float16_t)0.0934448242f, (float16_t)-0.218505859f, (float16_t)-0.119384766f, (float16_t)0.0661621094f, + (float16_t)0.234619141f, (float16_t)0.241943359f, (float16_t)-0.58984375f, (float16_t)0.353027344f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/biases_data.h new file mode 100644 index 00000000..19163c1e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_1_f32_biases_data[3] = {-0.00493812934f, 0.149408609f, -0.0446372218f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/config_data.h new file mode 100644 index 00000000..506e8d8d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_1_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_1_F32_INPUT_H 1 +#define CONV_MATCH_1XN_1_F32_INPUT_W 2 +#define CONV_MATCH_1XN_1_F32_IN_CH 4 +#define CONV_MATCH_1XN_1_F32_FILTER_H 1 +#define CONV_MATCH_1XN_1_F32_FILTER_W 3 +#define CONV_MATCH_1XN_1_F32_OUT_CH 3 +#define CONV_MATCH_1XN_1_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_1_F32_STRIDE_W 1 +#define CONV_MATCH_1XN_1_F32_PADDING_H 0 +#define CONV_MATCH_1XN_1_F32_PADDING_W 1 +#define CONV_MATCH_1XN_1_F32_DILATION_H 1 +#define CONV_MATCH_1XN_1_F32_DILATION_W 1 +#define CONV_MATCH_1XN_1_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_1_F32_OUTPUT_W 2 +#define CONV_MATCH_1XN_1_F32_OUTPUT_C 3 +#define CONV_MATCH_1XN_1_F32_DST_SIZE 12 +#define CONV_MATCH_1XN_1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_1_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_1_F32_OUT_ACTIVATION_MIN -127.0f +#define CONV_MATCH_1XN_1_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/input_data.h new file mode 100644 index 00000000..3fa3b737 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/input_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_1_f32_input_data[16] = {-0.772372007f, + -0.660661757f, + -0.75625217f, + 0.496417463f, + -0.337415308f, + -0.813707113f, + -0.566869438f, + 0.141572952f, + 0.918903887f, + 0.880469799f, + -0.72632128f, + -0.499031752f, + -0.0616773069f, + 0.733968556f, + -0.407162637f, + 0.798722029f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/output_ref_data.h new file mode 100644 index 00000000..49bda034 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_1_f32_output_ref_data[12] = {0.58425808f, + -1.04209054f, + 0.258911282f, + -1.3516531f, + -0.613562644f, + 0.114563726f, + 0.402786672f, + 0.319794923f, + 0.587856352f, + 1.48796213f, + 0.475916415f, + 0.235712066f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/weights_data.h new file mode 100644 index 00000000..4c8846de --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_1_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_1_f32_weights_data[36] = { + 0.635910034f, 0.483288199f, 0.278591424f, -0.432631463f, -0.708175302f, 0.544756472f, + -0.179712936f, -0.0590889081f, 0.748864055f, -0.113614656f, -0.724829435f, 0.317088664f, + 0.133011013f, 0.126940265f, 0.0525435098f, 0.580860376f, 0.719177425f, 0.702778161f, + 0.00291988556f, -0.0622540154f, -0.0631180778f, -0.435289621f, 0.744505405f, -0.650362372f, + -0.362156689f, 0.204839125f, -0.193666294f, -0.714990497f, 0.0934626088f, -0.218494788f, + -0.119359694f, 0.0661668405f, 0.234634027f, 0.241988495f, -0.589841604f, 0.353079677f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/biases_data.h new file mode 100644 index 00000000..8c4871b8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/biases_data.h @@ -0,0 +1,4 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_2_f16_biases_data[3] = {(float16_t)-0.106994629f, + (float16_t)-0.0610656738f, + (float16_t)0.0238647461f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/config_data.h new file mode 100644 index 00000000..f11a9e66 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_2_F16_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_2_F16_INPUT_H 1 +#define CONV_MATCH_1XN_2_F16_INPUT_W 296 +#define CONV_MATCH_1XN_2_F16_IN_CH 4 +#define CONV_MATCH_1XN_2_F16_FILTER_H 1 +#define CONV_MATCH_1XN_2_F16_FILTER_W 48 +#define CONV_MATCH_1XN_2_F16_OUT_CH 3 +#define CONV_MATCH_1XN_2_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_2_F16_STRIDE_W 2 +#define CONV_MATCH_1XN_2_F16_PADDING_H 0 +#define CONV_MATCH_1XN_2_F16_PADDING_W 0 +#define CONV_MATCH_1XN_2_F16_DILATION_H 1 +#define CONV_MATCH_1XN_2_F16_DILATION_W 1 +#define CONV_MATCH_1XN_2_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_2_F16_OUTPUT_W 125 +#define CONV_MATCH_1XN_2_F16_OUTPUT_C 3 +#define CONV_MATCH_1XN_2_F16_DST_SIZE 375 +#define CONV_MATCH_1XN_2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_2_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_2_F16_OUT_ACTIVATION_MIN (float16_t) - 111.0f +#define CONV_MATCH_1XN_2_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/input_data.h new file mode 100644 index 00000000..8eab3b92 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/input_data.h @@ -0,0 +1,397 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_2_f16_input_data[1184] = { + (float16_t)-0.893554688f, (float16_t)0.672851562f, (float16_t)0.676757812f, + (float16_t)0.412597656f, (float16_t)0.441650391f, (float16_t)0.337402344f, + (float16_t)-0.119812012f, (float16_t)-0.00247192383f, (float16_t)0.0983276367f, + (float16_t)0.0246582031f, (float16_t)0.592773438f, (float16_t)0.516601562f, + (float16_t)0.643554688f, (float16_t)-0.233520508f, (float16_t)0.518554688f, + (float16_t)-0.505859375f, (float16_t)-0.639160156f, (float16_t)0.0354003906f, + (float16_t)0.177734375f, (float16_t)-0.340576172f, (float16_t)0.968261719f, + (float16_t)0.551269531f, (float16_t)0.923828125f, (float16_t)0.575195312f, + (float16_t)0.15246582f, (float16_t)0.195800781f, (float16_t)0.298583984f, + (float16_t)-0.999023438f, (float16_t)-0.0161590576f, (float16_t)-0.885742188f, + (float16_t)0.720214844f, (float16_t)0.950195312f, (float16_t)-0.609375f, + (float16_t)-0.758300781f, (float16_t)0.778808594f, (float16_t)0.469726562f, + (float16_t)-0.0654296875f, (float16_t)0.914550781f, (float16_t)0.0269012451f, + (float16_t)0.78515625f, (float16_t)0.609863281f, (float16_t)-0.991210938f, + (float16_t)0.0520935059f, (float16_t)-0.807617188f, (float16_t)-0.8984375f, + (float16_t)0.902832031f, (float16_t)0.303466797f, (float16_t)-0.202148438f, + (float16_t)-0.931152344f, (float16_t)-0.661132812f, (float16_t)-0.545410156f, + (float16_t)0.697265625f, (float16_t)0.583007812f, (float16_t)0.353759766f, + (float16_t)-0.607910156f, (float16_t)-0.760253906f, (float16_t)-0.143310547f, + (float16_t)0.787597656f, (float16_t)0.452880859f, (float16_t)-0.512207031f, + (float16_t)-0.446044922f, (float16_t)-0.481933594f, (float16_t)0.0923461914f, + (float16_t)-0.0601196289f, (float16_t)-0.245727539f, (float16_t)-0.69140625f, + (float16_t)-0.161254883f, (float16_t)-0.431640625f, (float16_t)-0.21105957f, + (float16_t)0.545410156f, (float16_t)0.700195312f, (float16_t)0.561523438f, + (float16_t)0.128173828f, (float16_t)0.619140625f, (float16_t)0.263183594f, + (float16_t)0.0161590576f, (float16_t)0.88671875f, (float16_t)-0.0673217773f, + (float16_t)0.657226562f, (float16_t)0.340820312f, (float16_t)-0.116333008f, + (float16_t)-0.841796875f, (float16_t)0.250976562f, (float16_t)-0.355712891f, + (float16_t)-0.649902344f, (float16_t)0.0240325928f, (float16_t)0.481445312f, + (float16_t)-0.995605469f, (float16_t)0.850097656f, (float16_t)0.5703125f, + (float16_t)0.145507812f, (float16_t)0.859863281f, (float16_t)0.953613281f, + (float16_t)0.623046875f, (float16_t)0.318359375f, (float16_t)0.290527344f, + (float16_t)0.89453125f, (float16_t)0.672851562f, (float16_t)0.467041016f, + (float16_t)-0.212768555f, (float16_t)-0.434814453f, (float16_t)0.0579833984f, + (float16_t)-0.560546875f, (float16_t)-0.622558594f, (float16_t)-0.741210938f, + (float16_t)-0.567382812f, (float16_t)0.100769043f, (float16_t)0.547363281f, + (float16_t)-0.453613281f, (float16_t)0.456298828f, (float16_t)-0.610351562f, + (float16_t)-0.206298828f, (float16_t)0.389892578f, (float16_t)0.574707031f, + (float16_t)-0.329589844f, (float16_t)0.393066406f, (float16_t)0.959960938f, + (float16_t)0.404052734f, (float16_t)0.421386719f, (float16_t)-0.622070312f, + (float16_t)0.512695312f, (float16_t)0.706054688f, (float16_t)0.797363281f, + (float16_t)0.0776367188f, (float16_t)0.0841064453f, (float16_t)0.55078125f, + (float16_t)-0.926757812f, (float16_t)-0.904785156f, (float16_t)0.432128906f, + (float16_t)0.89453125f, (float16_t)0.432861328f, (float16_t)-0.63671875f, + (float16_t)-0.222900391f, (float16_t)-0.382080078f, (float16_t)0.592285156f, + (float16_t)-0.873046875f, (float16_t)-0.569335938f, (float16_t)-0.786132812f, + (float16_t)-0.0564575195f, (float16_t)0.228637695f, (float16_t)0.415527344f, + (float16_t)0.0355834961f, (float16_t)-0.8046875f, (float16_t)-0.147949219f, + (float16_t)0.57421875f, (float16_t)0.482421875f, (float16_t)0.939453125f, + (float16_t)-0.34375f, (float16_t)-0.727539062f, (float16_t)0.815429688f, + (float16_t)-0.577636719f, (float16_t)-0.544921875f, (float16_t)0.0397644043f, + (float16_t)-0.375f, (float16_t)0.859375f, (float16_t)0.271484375f, + (float16_t)-0.126464844f, (float16_t)-0.00924682617f, (float16_t)0.581054688f, + (float16_t)0.686035156f, (float16_t)-0.290771484f, (float16_t)0.907226562f, + (float16_t)0.311035156f, (float16_t)0.063659668f, (float16_t)-0.157470703f, + (float16_t)-0.367675781f, (float16_t)-0.854003906f, (float16_t)-0.793457031f, + (float16_t)-0.266845703f, (float16_t)-0.986816406f, (float16_t)0.94921875f, + (float16_t)0.411621094f, (float16_t)0.905761719f, (float16_t)-0.837402344f, + (float16_t)0.344726562f, (float16_t)0.549804688f, (float16_t)-0.301757812f, + (float16_t)-0.841796875f, (float16_t)-0.217895508f, (float16_t)-0.592285156f, + (float16_t)0.235351562f, (float16_t)-0.838378906f, (float16_t)-0.98828125f, + (float16_t)0.312255859f, (float16_t)-0.549316406f, (float16_t)0.75390625f, + (float16_t)0.0351867676f, (float16_t)-0.973144531f, (float16_t)-0.0742797852f, + (float16_t)-0.7421875f, (float16_t)-0.859863281f, (float16_t)-0.922363281f, + (float16_t)-0.362548828f, (float16_t)-0.20703125f, (float16_t)0.592773438f, + (float16_t)-0.875488281f, (float16_t)0.966308594f, (float16_t)0.566894531f, + (float16_t)-0.930175781f, (float16_t)0.327148438f, (float16_t)-0.604492188f, + (float16_t)0.0134811401f, (float16_t)0.735351562f, (float16_t)-0.48046875f, + (float16_t)0.566894531f, (float16_t)0.610839844f, (float16_t)-0.425292969f, + (float16_t)0.760253906f, (float16_t)0.132446289f, (float16_t)-0.421875f, + (float16_t)0.645507812f, (float16_t)0.245727539f, (float16_t)0.162719727f, + (float16_t)-0.505859375f, (float16_t)-0.213623047f, (float16_t)0.949707031f, + (float16_t)-0.715332031f, (float16_t)0.918945312f, (float16_t)-0.582519531f, + (float16_t)-0.0664672852f, (float16_t)0.203735352f, (float16_t)-0.694824219f, + (float16_t)-0.69921875f, (float16_t)0.0886230469f, (float16_t)-0.385009766f, + (float16_t)-0.645019531f, (float16_t)0.0561828613f, (float16_t)-0.525390625f, + (float16_t)-0.418457031f, (float16_t)-0.915039062f, (float16_t)-0.35546875f, + (float16_t)-0.651367188f, (float16_t)0.693359375f, (float16_t)-0.0195007324f, + (float16_t)0.22644043f, (float16_t)-0.168212891f, (float16_t)0.452148438f, + (float16_t)-0.10760498f, (float16_t)-0.44921875f, (float16_t)-0.481689453f, + (float16_t)-0.0163726807f, (float16_t)0.930175781f, (float16_t)0.8828125f, + (float16_t)0.905761719f, (float16_t)0.76953125f, (float16_t)0.034362793f, + (float16_t)0.498046875f, (float16_t)0.442871094f, (float16_t)0.9453125f, + (float16_t)-0.812011719f, (float16_t)-0.160400391f, (float16_t)-0.722167969f, + (float16_t)-0.323974609f, (float16_t)0.768554688f, (float16_t)0.278320312f, + (float16_t)0.222412109f, (float16_t)0.122741699f, (float16_t)-0.634765625f, + (float16_t)0.0446777344f, (float16_t)-0.969238281f, (float16_t)0.403076172f, + (float16_t)0.767089844f, (float16_t)0.447509766f, (float16_t)-0.924316406f, + (float16_t)0.320068359f, (float16_t)0.479980469f, (float16_t)0.340820312f, + (float16_t)0.00171852112f, (float16_t)-0.279541016f, (float16_t)0.69921875f, + (float16_t)0.669921875f, (float16_t)-0.608398438f, (float16_t)-0.713378906f, + (float16_t)-0.260253906f, (float16_t)0.927246094f, (float16_t)-0.911621094f, + (float16_t)-0.956054688f, (float16_t)0.246704102f, (float16_t)0.217773438f, + (float16_t)-0.8515625f, (float16_t)0.342773438f, (float16_t)-0.5f, + (float16_t)0.567871094f, (float16_t)0.00941467285f, (float16_t)0.374755859f, + (float16_t)-0.260986328f, (float16_t)0.19934082f, (float16_t)0.881835938f, + (float16_t)0.740234375f, (float16_t)-0.0863647461f, (float16_t)0.278320312f, + (float16_t)0.815917969f, (float16_t)0.514160156f, (float16_t)-0.371337891f, + (float16_t)-0.509765625f, (float16_t)-0.00666427612f, (float16_t)-0.0321960449f, + (float16_t)-0.507324219f, (float16_t)0.0913696289f, (float16_t)0.85546875f, + (float16_t)-0.267578125f, (float16_t)0.237670898f, (float16_t)0.281005859f, + (float16_t)-0.489990234f, (float16_t)-0.723144531f, (float16_t)0.554199219f, + (float16_t)0.91796875f, (float16_t)0.28515625f, (float16_t)0.483642578f, + (float16_t)0.346435547f, (float16_t)-0.181640625f, (float16_t)0.0827636719f, + (float16_t)-0.380615234f, (float16_t)0.387939453f, (float16_t)-0.207641602f, + (float16_t)0.319335938f, (float16_t)0.596679688f, (float16_t)0.947753906f, + (float16_t)0.661621094f, (float16_t)-0.0749511719f, (float16_t)0.6015625f, + (float16_t)0.849121094f, (float16_t)0.340820312f, (float16_t)0.201171875f, + (float16_t)-0.415039062f, (float16_t)-0.454101562f, (float16_t)-0.728027344f, + (float16_t)0.00942230225f, (float16_t)0.417236328f, (float16_t)-0.645019531f, + (float16_t)-0.471191406f, (float16_t)0.266357422f, (float16_t)0.243286133f, + (float16_t)-0.494384766f, (float16_t)0.251220703f, (float16_t)-0.447998047f, + (float16_t)-0.965332031f, (float16_t)-0.0720214844f, (float16_t)-0.83203125f, + (float16_t)-0.611328125f, (float16_t)-0.880859375f, (float16_t)-0.525878906f, + (float16_t)0.418701172f, (float16_t)0.308105469f, (float16_t)-0.116333008f, + (float16_t)-0.292724609f, (float16_t)0.176391602f, (float16_t)-0.83984375f, + (float16_t)-0.404785156f, (float16_t)-0.265625f, (float16_t)0.114196777f, + (float16_t)0.788085938f, (float16_t)0.836425781f, (float16_t)0.55859375f, + (float16_t)0.324707031f, (float16_t)0.913085938f, (float16_t)0.57421875f, + (float16_t)0.868652344f, (float16_t)-0.751953125f, (float16_t)-0.628417969f, + (float16_t)-0.475830078f, (float16_t)-0.0705566406f, (float16_t)-0.383300781f, + (float16_t)0.532714844f, (float16_t)-0.939453125f, (float16_t)0.506835938f, + (float16_t)-0.305175781f, (float16_t)-0.190307617f, (float16_t)0.0534362793f, + (float16_t)0.649902344f, (float16_t)0.401123047f, (float16_t)0.503417969f, + (float16_t)-0.407958984f, (float16_t)-0.402099609f, (float16_t)0.0400085449f, + (float16_t)0.907714844f, (float16_t)0.628417969f, (float16_t)-0.134033203f, + (float16_t)0.267089844f, (float16_t)0.182861328f, (float16_t)0.314208984f, + (float16_t)-0.240234375f, (float16_t)0.372070312f, (float16_t)0.315673828f, + (float16_t)0.997558594f, (float16_t)0.87109375f, (float16_t)0.434814453f, + (float16_t)0.364013672f, (float16_t)-0.307617188f, (float16_t)0.765136719f, + (float16_t)0.0262298584f, (float16_t)-0.603515625f, (float16_t)0.385742188f, + (float16_t)-0.273193359f, (float16_t)0.913085938f, (float16_t)-0.671386719f, + (float16_t)0.748535156f, (float16_t)0.55078125f, (float16_t)-0.811523438f, + (float16_t)-0.0968017578f, (float16_t)0.479492188f, (float16_t)0.0889282227f, + (float16_t)-0.469970703f, (float16_t)-0.599121094f, (float16_t)-0.727539062f, + (float16_t)0.173339844f, (float16_t)-0.249755859f, (float16_t)0.895507812f, + (float16_t)0.270996094f, (float16_t)0.178955078f, (float16_t)0.565429688f, + (float16_t)0.947265625f, (float16_t)-0.897949219f, 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(float16_t)0.556152344f, + (float16_t)-0.250244141f, (float16_t)0.433105469f, (float16_t)-0.913085938f, + (float16_t)0.859863281f, (float16_t)0.709960938f, (float16_t)0.982910156f, + (float16_t)-0.588867188f, (float16_t)0.164916992f, (float16_t)0.182983398f, + (float16_t)-0.500488281f, (float16_t)-0.230102539f, (float16_t)0.764160156f, + (float16_t)-0.186157227f, (float16_t)-0.110412598f, (float16_t)0.746582031f, + (float16_t)0.450195312f, (float16_t)0.502441406f, (float16_t)0.0944824219f, + (float16_t)0.552246094f, (float16_t)0.640625f, (float16_t)0.763183594f, + (float16_t)-0.00256919861f, (float16_t)0.372314453f, (float16_t)-0.475585938f, + (float16_t)0.95703125f, (float16_t)-0.291259766f, (float16_t)-0.580566406f, + (float16_t)0.415527344f, (float16_t)0.379150391f, (float16_t)-0.9921875f, + (float16_t)0.0629882812f, (float16_t)-0.333007812f, (float16_t)0.422119141f, + (float16_t)0.116699219f, (float16_t)0.0666503906f, (float16_t)-0.443115234f, + (float16_t)0.0236663818f, (float16_t)-0.893066406f, (float16_t)-0.58984375f, + (float16_t)-0.209838867f, (float16_t)0.997070312f, (float16_t)0.0624389648f, + (float16_t)-0.852539062f, (float16_t)0.723144531f, (float16_t)0.673828125f, + (float16_t)0.028717041f, (float16_t)-0.469970703f, (float16_t)-0.574707031f, + (float16_t)0.431884766f, (float16_t)0.495849609f, (float16_t)-0.443847656f, + (float16_t)0.229980469f, (float16_t)0.158081055f, (float16_t)0.311035156f, + (float16_t)0.271728516f, (float16_t)-0.774902344f, (float16_t)-0.145385742f, + (float16_t)0.256347656f, (float16_t)-0.970703125f, (float16_t)-0.805175781f, + (float16_t)-0.416503906f, (float16_t)-0.13269043f, (float16_t)0.861816406f, + (float16_t)-0.883300781f, (float16_t)0.569824219f, (float16_t)-0.366699219f, + (float16_t)0.973632812f, (float16_t)-0.669921875f, (float16_t)0.489501953f, + (float16_t)-0.483886719f, (float16_t)-0.591796875f, (float16_t)-0.241088867f, + (float16_t)0.521484375f, (float16_t)0.285644531f, (float16_t)-0.749511719f, + (float16_t)-0.358398438f, (float16_t)-0.856445312f, (float16_t)-0.042175293f, + (float16_t)0.500976562f, (float16_t)-0.405517578f, (float16_t)0.203613281f, + (float16_t)-0.850097656f, (float16_t)0.735839844f, (float16_t)-0.424072266f, + (float16_t)0.0361022949f, (float16_t)-0.301269531f, (float16_t)0.875f, + (float16_t)-0.327148438f, (float16_t)0.358642578f, (float16_t)-0.367919922f, + (float16_t)-0.469970703f, (float16_t)0.467041016f, (float16_t)0.337402344f, + (float16_t)-0.918457031f, (float16_t)-0.140258789f, (float16_t)-0.258789062f, + (float16_t)-0.0444335938f, (float16_t)-0.304443359f, (float16_t)-0.2734375f, + (float16_t)0.939453125f, (float16_t)-0.796386719f, (float16_t)-0.181640625f, + (float16_t)-0.425537109f, (float16_t)-0.0153045654f, (float16_t)0.9140625f, + (float16_t)0.053894043f, (float16_t)0.907714844f, (float16_t)0.967285156f, + (float16_t)-0.74609375f, (float16_t)0.041809082f, (float16_t)0.982421875f, + (float16_t)-0.725585938f, (float16_t)-0.578125f, (float16_t)0.373291016f, + (float16_t)-0.255371094f, (float16_t)0.807617188f, (float16_t)-0.447509766f, + (float16_t)0.15637207f, (float16_t)-0.361816406f, (float16_t)0.736328125f, + (float16_t)-0.902832031f, (float16_t)-0.833984375f, (float16_t)0.8671875f, + (float16_t)0.395996094f, (float16_t)-0.858398438f, (float16_t)-0.199462891f, + (float16_t)0.148925781f, (float16_t)0.481445312f, (float16_t)-0.194580078f, + (float16_t)-0.69140625f, (float16_t)-0.122436523f, (float16_t)0.370849609f, + (float16_t)-0.596679688f, (float16_t)0.555664062f, (float16_t)-0.329589844f, + (float16_t)0.797363281f, (float16_t)0.165771484f, (float16_t)0.458496094f, + (float16_t)0.741210938f, (float16_t)0.502441406f, (float16_t)-0.3984375f, + (float16_t)0.181884766f, (float16_t)0.630371094f, (float16_t)-0.10369873f, + (float16_t)-0.151855469f, (float16_t)0.0456542969f, (float16_t)0.138305664f, + (float16_t)0.139526367f, (float16_t)0.875f, (float16_t)0.894042969f, + (float16_t)-0.647949219f, (float16_t)-0.297363281f, (float16_t)-0.629882812f, + (float16_t)-0.463623047f, (float16_t)-0.992675781f, (float16_t)0.96875f, + (float16_t)0.0142745972f, (float16_t)0.0526733398f, (float16_t)-0.699707031f, + (float16_t)0.274414062f, (float16_t)-0.851074219f, (float16_t)-0.171508789f, + (float16_t)-0.708007812f, (float16_t)0.490966797f, (float16_t)-0.826660156f, + (float16_t)0.278564453f, (float16_t)-0.528808594f, (float16_t)0.542480469f, + (float16_t)-0.849609375f, (float16_t)0.165649414f, (float16_t)0.745117188f, + (float16_t)0.036315918f, (float16_t)0.952148438f, (float16_t)-0.288574219f, + (float16_t)-0.669433594f, (float16_t)-0.161132812f, (float16_t)0.424804688f, + (float16_t)0.737792969f, (float16_t)0.278564453f, (float16_t)0.0581054688f, + (float16_t)-0.0445861816f, (float16_t)-0.437255859f, (float16_t)0.520507812f, + (float16_t)0.423583984f, (float16_t)-0.724121094f, (float16_t)0.439941406f, + (float16_t)-0.990234375f, (float16_t)0.663574219f, (float16_t)0.889160156f, + (float16_t)0.165405273f, (float16_t)0.335205078f, (float16_t)0.736816406f, + (float16_t)0.40625f, (float16_t)-0.482177734f, (float16_t)0.59765625f, + (float16_t)-0.298583984f, (float16_t)0.21496582f, (float16_t)-0.0897216797f, + (float16_t)-0.317871094f, (float16_t)0.038269043f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/output_ref_data.h new file mode 100644 index 00000000..4d050dde --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/output_ref_data.h @@ -0,0 +1,96 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_2_f16_output_ref_data[375] = { + (float16_t)0.0933227539f, (float16_t)1.89257812f, (float16_t)-1.51074219f, (float16_t)5.3984375f, + (float16_t)-2.73046875f, (float16_t)0.18347168f, (float16_t)-2.046875f, (float16_t)2.81445312f, + (float16_t)1.38378906f, (float16_t)7.484375f, (float16_t)1.37890625f, (float16_t)0.39453125f, + (float16_t)6.265625f, (float16_t)0.228759766f, (float16_t)-1.61230469f, (float16_t)-0.733398438f, + (float16_t)1.171875f, (float16_t)-5.359375f, (float16_t)-2.78710938f, (float16_t)0.535644531f, + (float16_t)-0.750488281f, (float16_t)2.32421875f, (float16_t)-1.14550781f, (float16_t)0.875f, + (float16_t)1.32226562f, (float16_t)-1.41699219f, (float16_t)-1.99804688f, (float16_t)-2.21484375f, + (float16_t)2.703125f, (float16_t)-0.903808594f, (float16_t)3.32421875f, (float16_t)-2.02734375f, + (float16_t)3.7734375f, (float16_t)2.88085938f, (float16_t)2.40625f, (float16_t)4.66015625f, + (float16_t)6.14453125f, (float16_t)-1.86132812f, (float16_t)1.44628906f, (float16_t)-0.501464844f, + (float16_t)-4.59375f, (float16_t)2.32226562f, (float16_t)-0.673828125f, (float16_t)-1.92285156f, + (float16_t)6.1171875f, (float16_t)-1.38671875f, (float16_t)1.20507812f, (float16_t)0.470947266f, + (float16_t)-3.99414062f, (float16_t)-1.76855469f, (float16_t)-1.04882812f, (float16_t)-0.428710938f, + (float16_t)-3.96875f, (float16_t)7.6484375f, (float16_t)2.43359375f, (float16_t)5.875f, + (float16_t)3.12695312f, (float16_t)5.234375f, (float16_t)-5.203125f, (float16_t)-0.443115234f, + (float16_t)-1.95800781f, (float16_t)5.5546875f, (float16_t)4.41015625f, (float16_t)-2.76757812f, + (float16_t)1.65136719f, (float16_t)-5.8125f, (float16_t)-4.1640625f, (float16_t)-5.21484375f, + (float16_t)1.08203125f, (float16_t)-3.50195312f, (float16_t)0.500976562f, (float16_t)3.64257812f, + (float16_t)-1.0703125f, (float16_t)2.36523438f, (float16_t)0.877441406f, (float16_t)3.54296875f, + (float16_t)8.53125f, (float16_t)-1.29101562f, (float16_t)3.79492188f, (float16_t)4.29296875f, + (float16_t)-6.21484375f, (float16_t)0.101135254f, (float16_t)4.3203125f, (float16_t)1.94140625f, + (float16_t)2.22460938f, (float16_t)0.604492188f, (float16_t)5.328125f, (float16_t)-0.57421875f, + (float16_t)4.54296875f, (float16_t)5.796875f, (float16_t)-0.726074219f, (float16_t)-4.62890625f, + (float16_t)-1.29394531f, (float16_t)-2.54296875f, (float16_t)-7.79296875f, (float16_t)1.67871094f, + (float16_t)-1.2578125f, (float16_t)1.56835938f, (float16_t)1.86621094f, (float16_t)-2.92773438f, + (float16_t)1.71484375f, (float16_t)-0.126098633f, (float16_t)-1.75390625f, (float16_t)-0.353759766f, + (float16_t)3.21679688f, (float16_t)5.39453125f, (float16_t)0.357666016f, (float16_t)0.8828125f, + (float16_t)5.1015625f, (float16_t)-4.6484375f, (float16_t)1.74511719f, (float16_t)-2.77929688f, + (float16_t)-0.552246094f, (float16_t)-2.52734375f, (float16_t)0.0123519897f, (float16_t)-4.40625f, + (float16_t)0.851074219f, (float16_t)-0.685058594f, (float16_t)-3.00195312f, (float16_t)0.575195312f, + (float16_t)0.498779297f, (float16_t)1.64453125f, (float16_t)-0.459960938f, (float16_t)-2.19921875f, + (float16_t)0.278564453f, (float16_t)-3.89453125f, (float16_t)-1.83984375f, (float16_t)1.08886719f, + (float16_t)2.69921875f, (float16_t)8.5390625f, (float16_t)0.933105469f, (float16_t)-8.296875f, + (float16_t)2.11132812f, (float16_t)1.46582031f, (float16_t)0.635253906f, (float16_t)-1.69628906f, + (float16_t)0.110778809f, (float16_t)3.29101562f, (float16_t)6.59375f, (float16_t)-1.11621094f, + (float16_t)-4.41015625f, (float16_t)-3.0703125f, (float16_t)-3.47070312f, (float16_t)-1.38476562f, + (float16_t)-1.796875f, (float16_t)2.94921875f, (float16_t)2.29296875f, (float16_t)-1.74707031f, + (float16_t)-7.453125f, (float16_t)2.73828125f, (float16_t)-3.23828125f, (float16_t)2.73046875f, + (float16_t)0.920898438f, (float16_t)3.4140625f, (float16_t)-3.63085938f, (float16_t)-5.78515625f, + (float16_t)-0.746582031f, (float16_t)-4.80859375f, (float16_t)-1.82714844f, (float16_t)-3.703125f, + (float16_t)0.698730469f, (float16_t)-3.04296875f, (float16_t)-0.131958008f, (float16_t)-0.514648438f, + (float16_t)1.796875f, (float16_t)0.868652344f, (float16_t)1.96679688f, (float16_t)-2.04101562f, + (float16_t)-2.65039062f, (float16_t)-2.484375f, (float16_t)-6.921875f, (float16_t)0.408691406f, + (float16_t)1.9375f, (float16_t)0.684082031f, (float16_t)-5.53515625f, (float16_t)-1.46972656f, + (float16_t)-8.953125f, (float16_t)4.1015625f, (float16_t)-4.68359375f, (float16_t)1.24023438f, + (float16_t)-1.49609375f, (float16_t)0.605957031f, (float16_t)-3.97070312f, (float16_t)-5.9375f, + (float16_t)0.740722656f, (float16_t)-0.391113281f, (float16_t)2.92773438f, (float16_t)-1.49707031f, + (float16_t)-1.58496094f, (float16_t)-8.046875f, (float16_t)-3.33789062f, (float16_t)-3.10742188f, + (float16_t)3.67382812f, (float16_t)4.3125f, (float16_t)-2.67382812f, (float16_t)-2.44921875f, + (float16_t)-5.54296875f, (float16_t)-5.48046875f, (float16_t)0.49609375f, (float16_t)-1.71972656f, + (float16_t)2.84960938f, (float16_t)0.925292969f, (float16_t)-2.41601562f, (float16_t)4.3203125f, + (float16_t)3.05078125f, (float16_t)-6.37890625f, (float16_t)3.57617188f, (float16_t)-0.744628906f, + (float16_t)-1.28027344f, (float16_t)-6.09765625f, (float16_t)-0.723144531f, (float16_t)-1.70898438f, + (float16_t)5.22265625f, (float16_t)-5.296875f, (float16_t)-4.94921875f, (float16_t)-3.14453125f, + (float16_t)-1.48632812f, (float16_t)-4.03515625f, (float16_t)1.69921875f, (float16_t)-6.47265625f, + (float16_t)2.96289062f, (float16_t)8.265625f, (float16_t)-3.91210938f, (float16_t)0.181396484f, + (float16_t)-2.8828125f, (float16_t)0.0417175293f, (float16_t)1.85449219f, (float16_t)-1.98828125f, + (float16_t)-0.151489258f, (float16_t)-5.140625f, (float16_t)-2.38671875f, (float16_t)8.59375f, + (float16_t)-6.4921875f, (float16_t)1.00683594f, (float16_t)3.54101562f, (float16_t)-4.734375f, + (float16_t)-0.716308594f, (float16_t)-2.66796875f, (float16_t)10.25f, (float16_t)2.73046875f, + (float16_t)-2.72851562f, (float16_t)0.210693359f, (float16_t)-1.72851562f, (float16_t)3.41992188f, + (float16_t)-2.44140625f, (float16_t)-3.77539062f, (float16_t)-1.33886719f, (float16_t)-5.25f, + (float16_t)2.03515625f, (float16_t)1.14550781f, (float16_t)-8.640625f, (float16_t)2.83789062f, + (float16_t)2.74414062f, (float16_t)-6.0078125f, (float16_t)3.30273438f, (float16_t)2.40429688f, + (float16_t)0.341064453f, (float16_t)0.751464844f, (float16_t)5.5625f, (float16_t)3.15429688f, + (float16_t)-3.91796875f, (float16_t)-2.26953125f, (float16_t)3.4921875f, (float16_t)-6.05078125f, + (float16_t)-5.0f, (float16_t)-0.596191406f, (float16_t)8.28125f, (float16_t)-2.82617188f, + (float16_t)-4.34375f, (float16_t)4.47265625f, (float16_t)1.03710938f, (float16_t)-0.565917969f, + (float16_t)1.70703125f, (float16_t)3.10351562f, (float16_t)-4.47265625f, (float16_t)0.357177734f, + (float16_t)1.48828125f, (float16_t)0.168701172f, (float16_t)-1.12402344f, (float16_t)-8.34375f, + (float16_t)-0.874511719f, (float16_t)0.979492188f, (float16_t)-6.88671875f, (float16_t)1.60058594f, + (float16_t)4.55859375f, (float16_t)-2.5390625f, (float16_t)4.8046875f, (float16_t)-2.58398438f, + (float16_t)2.87890625f, (float16_t)2.7265625f, (float16_t)-1.81640625f, (float16_t)-2.56640625f, + (float16_t)-0.774902344f, (float16_t)-6.24609375f, (float16_t)-1.9453125f, (float16_t)-0.826660156f, + (float16_t)-0.419433594f, (float16_t)-3.85351562f, (float16_t)1.50683594f, (float16_t)1.94921875f, + (float16_t)1.62402344f, (float16_t)1.63378906f, (float16_t)2.38085938f, (float16_t)3.04492188f, + (float16_t)2.03710938f, (float16_t)-0.137817383f, (float16_t)3.14257812f, (float16_t)5.265625f, + (float16_t)-1.07714844f, (float16_t)5.90234375f, (float16_t)4.15625f, (float16_t)-0.655273438f, + (float16_t)0.776855469f, (float16_t)3.0625f, (float16_t)1.00390625f, (float16_t)1.41992188f, + (float16_t)-2.58984375f, (float16_t)8.03125f, (float16_t)-0.0822753906f, (float16_t)-2.35742188f, + (float16_t)-3.44726562f, (float16_t)-0.6484375f, (float16_t)-2.46679688f, (float16_t)3.35546875f, + (float16_t)-6.6796875f, (float16_t)0.0241241455f, (float16_t)2.2734375f, (float16_t)-3.29296875f, + (float16_t)-2.70117188f, (float16_t)-2.30078125f, (float16_t)1.00976562f, (float16_t)-6.4140625f, + (float16_t)-1.30566406f, (float16_t)-3.3359375f, (float16_t)1.03417969f, (float16_t)6.9609375f, + (float16_t)-4.48046875f, (float16_t)0.636230469f, (float16_t)-4.90234375f, (float16_t)-3.9765625f, + (float16_t)4.91015625f, (float16_t)1.60253906f, (float16_t)-1.08398438f, (float16_t)-2.52734375f, + (float16_t)0.875f, (float16_t)2.13671875f, (float16_t)-2.18945312f, (float16_t)-2.04296875f, + (float16_t)4.31640625f, (float16_t)6.7109375f, (float16_t)1.79882812f, (float16_t)2.61132812f, + (float16_t)2.87695312f, (float16_t)1.6328125f, (float16_t)1.61816406f, (float16_t)-0.455078125f, + (float16_t)-1.43261719f, (float16_t)-0.111938477f, (float16_t)-0.316650391f, (float16_t)-2.04492188f, + (float16_t)0.989746094f, (float16_t)-1.26660156f, (float16_t)1.54882812f, (float16_t)-0.881347656f, + (float16_t)4.17578125f, (float16_t)-2.15625f, (float16_t)0.642089844f, (float16_t)-5.85546875f, + (float16_t)-0.8359375f, (float16_t)4.92578125f, (float16_t)0.190429688f, (float16_t)0.931152344f, + (float16_t)-1.03710938f, (float16_t)8.34375f, (float16_t)3.36914062f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/weights_data.h new file mode 100644 index 00000000..290c784b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f16/weights_data.h @@ -0,0 +1,146 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_2_f16_weights_data[576] = { + (float16_t)0.701660156f, (float16_t)0.538574219f, (float16_t)0.596679688f, (float16_t)-0.0848388672f, + (float16_t)0.177856445f, (float16_t)-0.665527344f, (float16_t)0.288085938f, (float16_t)0.470947266f, + (float16_t)0.562988281f, (float16_t)0.0494995117f, (float16_t)0.0268554688f, (float16_t)0.645507812f, + (float16_t)0.420654297f, (float16_t)0.562988281f, (float16_t)-0.235351562f, (float16_t)0.188110352f, + (float16_t)-0.273925781f, (float16_t)-0.0221557617f, (float16_t)0.602539062f, (float16_t)0.320556641f, + (float16_t)0.143432617f, (float16_t)-0.367675781f, (float16_t)0.4140625f, (float16_t)-0.370117188f, + (float16_t)-0.0285644531f, (float16_t)-0.208129883f, (float16_t)-0.505859375f, (float16_t)-0.476806641f, + (float16_t)0.0777587891f, (float16_t)0.1484375f, (float16_t)-0.324707031f, (float16_t)0.272216797f, + (float16_t)0.343505859f, (float16_t)0.368164062f, (float16_t)0.321044922f, (float16_t)-0.176757812f, + (float16_t)-0.719726562f, (float16_t)-0.58984375f, (float16_t)0.396484375f, (float16_t)0.583007812f, + (float16_t)0.216308594f, (float16_t)0.535644531f, (float16_t)0.568359375f, (float16_t)-0.229248047f, + (float16_t)-0.620605469f, (float16_t)0.504882812f, (float16_t)0.262939453f, (float16_t)-0.34375f, + (float16_t)0.0827636719f, (float16_t)-0.295654297f, (float16_t)-0.396728516f, (float16_t)-0.550292969f, + (float16_t)0.145019531f, (float16_t)0.517089844f, (float16_t)0.541503906f, (float16_t)-0.194580078f, + (float16_t)-0.143432617f, (float16_t)-0.60546875f, (float16_t)-0.169433594f, (float16_t)0.474609375f, + (float16_t)-0.579101562f, (float16_t)0.211425781f, (float16_t)0.296875f, (float16_t)0.525878906f, + (float16_t)0.162841797f, (float16_t)0.590332031f, (float16_t)0.666992188f, (float16_t)0.109558105f, + (float16_t)0.302001953f, (float16_t)-0.701660156f, (float16_t)-0.19519043f, (float16_t)-0.131103516f, + (float16_t)-0.0187530518f, (float16_t)-0.682128906f, (float16_t)-0.712890625f, (float16_t)0.405273438f, + (float16_t)-0.139892578f, (float16_t)0.708496094f, (float16_t)0.114196777f, (float16_t)-0.00503158569f, + (float16_t)0.420898438f, (float16_t)-0.622070312f, (float16_t)-0.698242188f, (float16_t)-0.0642700195f, + (float16_t)-0.591308594f, (float16_t)0.0199737549f, (float16_t)0.390136719f, (float16_t)0.604492188f, + (float16_t)0.502441406f, (float16_t)0.482910156f, (float16_t)0.350585938f, (float16_t)0.180297852f, + (float16_t)0.0574645996f, (float16_t)0.155029297f, (float16_t)-0.161743164f, (float16_t)-0.222412109f, + (float16_t)-0.408691406f, (float16_t)-0.0413818359f, (float16_t)0.457763672f, (float16_t)-0.0727539062f, + (float16_t)0.0876464844f, (float16_t)-0.670898438f, (float16_t)0.0106201172f, (float16_t)0.234619141f, + (float16_t)-0.73828125f, (float16_t)0.717773438f, (float16_t)0.522949219f, (float16_t)-0.713867188f, + (float16_t)-0.282470703f, (float16_t)0.273193359f, (float16_t)-0.414306641f, (float16_t)-0.668457031f, + (float16_t)-0.350341797f, (float16_t)0.250488281f, (float16_t)0.684570312f, (float16_t)-0.5234375f, + (float16_t)-0.457275391f, (float16_t)0.342529297f, (float16_t)-0.5625f, (float16_t)-0.658203125f, + (float16_t)-0.712402344f, (float16_t)-0.203857422f, (float16_t)0.7109375f, (float16_t)-0.489013672f, + (float16_t)-0.702636719f, (float16_t)-0.518066406f, (float16_t)-0.203735352f, (float16_t)-0.694824219f, + (float16_t)-0.501953125f, (float16_t)0.0530395508f, (float16_t)-0.212036133f, (float16_t)-0.0936889648f, + (float16_t)-0.419189453f, (float16_t)-0.351318359f, (float16_t)-0.402099609f, (float16_t)-0.0455627441f, + (float16_t)-0.553222656f, (float16_t)-0.620605469f, (float16_t)-0.251953125f, (float16_t)0.580566406f, + (float16_t)-0.135620117f, (float16_t)-0.536132812f, (float16_t)0.552734375f, (float16_t)-0.604492188f, + (float16_t)0.398925781f, (float16_t)-0.741699219f, (float16_t)0.479980469f, (float16_t)0.188842773f, + (float16_t)0.146118164f, (float16_t)0.181884766f, (float16_t)-0.619628906f, (float16_t)0.720214844f, + (float16_t)0.500976562f, (float16_t)0.000414848328f, (float16_t)-0.091003418f, (float16_t)0.263916016f, + (float16_t)-0.541015625f, (float16_t)0.533691406f, (float16_t)-0.147705078f, (float16_t)0.155883789f, + (float16_t)0.0406494141f, (float16_t)-0.459716797f, (float16_t)0.440185547f, (float16_t)0.644042969f, + (float16_t)-0.237548828f, (float16_t)0.334960938f, (float16_t)0.249267578f, (float16_t)-0.141601562f, + (float16_t)-0.423583984f, (float16_t)-0.0579223633f, (float16_t)0.500488281f, (float16_t)0.400878906f, + (float16_t)0.671875f, (float16_t)0.540039062f, (float16_t)-0.607910156f, (float16_t)-0.131103516f, + (float16_t)-0.577636719f, (float16_t)0.16809082f, (float16_t)0.603027344f, (float16_t)-0.52734375f, + (float16_t)0.726074219f, (float16_t)-0.142089844f, (float16_t)0.564941406f, (float16_t)-0.370361328f, + (float16_t)0.24621582f, (float16_t)-0.0374145508f, (float16_t)0.13684082f, (float16_t)0.036529541f, + (float16_t)0.102844238f, (float16_t)-0.6015625f, (float16_t)0.1796875f, (float16_t)0.748046875f, + (float16_t)-0.51953125f, (float16_t)0.0353088379f, (float16_t)-0.219726562f, (float16_t)0.60546875f, + (float16_t)-0.0361938477f, (float16_t)0.393310547f, (float16_t)-0.432373047f, (float16_t)0.19152832f, + (float16_t)0.0888671875f, (float16_t)-0.475097656f, (float16_t)0.729003906f, (float16_t)0.620605469f, + (float16_t)-0.664550781f, (float16_t)-0.681152344f, (float16_t)0.141967773f, (float16_t)0.609863281f, + (float16_t)-0.178710938f, (float16_t)0.58203125f, (float16_t)-0.611816406f, (float16_t)-0.296875f, + (float16_t)-0.321777344f, (float16_t)-0.661132812f, (float16_t)-0.740234375f, (float16_t)0.213256836f, + (float16_t)0.6171875f, (float16_t)0.182128906f, (float16_t)0.218139648f, (float16_t)0.560058594f, + (float16_t)-0.115722656f, (float16_t)0.145263672f, (float16_t)-0.516113281f, (float16_t)0.43359375f, + (float16_t)0.421875f, (float16_t)0.32421875f, (float16_t)0.525878906f, (float16_t)0.0510253906f, + (float16_t)0.319824219f, (float16_t)0.699707031f, (float16_t)0.405029297f, (float16_t)-0.323242188f, + (float16_t)-0.4140625f, (float16_t)0.366210938f, (float16_t)-0.457275391f, (float16_t)-0.392333984f, + (float16_t)0.191894531f, (float16_t)0.505859375f, (float16_t)0.744628906f, (float16_t)-0.278808594f, + (float16_t)0.364746094f, (float16_t)-0.433837891f, (float16_t)0.503417969f, (float16_t)-0.551757812f, + (float16_t)0.708984375f, (float16_t)0.559082031f, (float16_t)-0.0747680664f, (float16_t)0.369384766f, + (float16_t)-0.280273438f, (float16_t)-0.341308594f, (float16_t)0.694824219f, (float16_t)-0.549804688f, + (float16_t)0.206420898f, (float16_t)0.246948242f, (float16_t)-0.736816406f, (float16_t)0.0621032715f, + (float16_t)0.604980469f, (float16_t)-0.231201172f, (float16_t)0.314208984f, (float16_t)-0.312744141f, + (float16_t)-0.4609375f, (float16_t)-0.587890625f, (float16_t)0.433105469f, (float16_t)0.365966797f, + (float16_t)0.471435547f, (float16_t)0.15612793f, (float16_t)0.715332031f, (float16_t)-0.143676758f, + (float16_t)0.23840332f, (float16_t)0.349609375f, (float16_t)0.324707031f, (float16_t)-0.701660156f, + (float16_t)-0.181152344f, (float16_t)0.602539062f, (float16_t)0.229248047f, (float16_t)-0.414550781f, + (float16_t)-0.687988281f, (float16_t)0.241821289f, (float16_t)0.453613281f, (float16_t)0.720214844f, + (float16_t)0.137695312f, (float16_t)0.696777344f, (float16_t)-0.528320312f, (float16_t)0.696777344f, + (float16_t)-0.257568359f, (float16_t)0.081237793f, (float16_t)-0.0194396973f, (float16_t)-0.389160156f, + (float16_t)-0.290039062f, (float16_t)0.58984375f, (float16_t)0.0541687012f, (float16_t)0.709472656f, + (float16_t)0.130004883f, (float16_t)0.215332031f, (float16_t)0.438232422f, (float16_t)-0.0673828125f, + (float16_t)-0.555664062f, (float16_t)-0.380859375f, (float16_t)0.14831543f, (float16_t)0.271240234f, + (float16_t)-0.515625f, (float16_t)0.447509766f, (float16_t)-0.145385742f, (float16_t)0.103637695f, + (float16_t)-0.744140625f, (float16_t)-0.395507812f, (float16_t)0.437744141f, (float16_t)-0.233032227f, + (float16_t)0.353027344f, (float16_t)0.0367126465f, (float16_t)0.584960938f, (float16_t)0.573242188f, + (float16_t)0.251953125f, (float16_t)-0.00652313232f, (float16_t)0.270507812f, (float16_t)-0.134765625f, + (float16_t)-0.344726562f, (float16_t)-0.513671875f, (float16_t)-0.401611328f, (float16_t)0.223754883f, + (float16_t)0.487304688f, (float16_t)0.635742188f, (float16_t)-0.244628906f, (float16_t)0.270996094f, + (float16_t)-0.327636719f, (float16_t)0.0397644043f, (float16_t)0.3125f, (float16_t)0.582519531f, + (float16_t)0.490722656f, (float16_t)0.0244598389f, (float16_t)0.512695312f, (float16_t)-0.415771484f, + (float16_t)-0.613769531f, (float16_t)-0.618164062f, (float16_t)0.0172729492f, (float16_t)0.135742188f, + (float16_t)-0.0780639648f, (float16_t)-0.557128906f, (float16_t)0.339111328f, (float16_t)-0.249267578f, + (float16_t)-0.504394531f, (float16_t)-0.593261719f, (float16_t)-0.673339844f, (float16_t)-0.352539062f, + (float16_t)0.169921875f, (float16_t)-0.263427734f, (float16_t)-0.397705078f, (float16_t)0.442871094f, + (float16_t)-0.213256836f, (float16_t)0.021987915f, (float16_t)0.338378906f, (float16_t)0.0596008301f, + (float16_t)0.071472168f, (float16_t)0.352050781f, (float16_t)0.365722656f, (float16_t)0.121520996f, + (float16_t)0.458007812f, (float16_t)-0.232666016f, (float16_t)-0.740722656f, (float16_t)0.157348633f, + (float16_t)0.0325927734f, (float16_t)0.298339844f, (float16_t)-0.73046875f, (float16_t)0.583496094f, + (float16_t)-0.271240234f, (float16_t)-0.299804688f, (float16_t)0.697265625f, (float16_t)0.126953125f, + (float16_t)0.416015625f, (float16_t)-0.467285156f, (float16_t)-0.140380859f, (float16_t)0.288085938f, + (float16_t)-0.694335938f, (float16_t)0.563476562f, (float16_t)-0.210693359f, (float16_t)0.684570312f, + (float16_t)-0.467285156f, (float16_t)-0.508300781f, 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(float16_t)0.458251953f, (float16_t)-0.341796875f, (float16_t)0.309570312f, (float16_t)0.0433044434f, + (float16_t)0.0950317383f, (float16_t)-0.535644531f, (float16_t)-0.379150391f, (float16_t)0.630371094f, + (float16_t)-0.708496094f, (float16_t)0.126708984f, (float16_t)0.671386719f, (float16_t)-0.243652344f, + (float16_t)0.210571289f, (float16_t)0.140625f, (float16_t)-0.204589844f, (float16_t)-0.174072266f, + (float16_t)-0.121643066f, (float16_t)-0.336425781f, (float16_t)-0.372558594f, (float16_t)0.325439453f, + (float16_t)-0.667480469f, (float16_t)-0.7109375f, (float16_t)-0.178588867f, (float16_t)-0.649414062f, + (float16_t)-0.630371094f, (float16_t)-0.678710938f, (float16_t)0.481201172f, (float16_t)0.741699219f, + (float16_t)0.692382812f, (float16_t)0.576171875f, (float16_t)-0.0717773438f, (float16_t)0.042388916f, + (float16_t)0.33203125f, (float16_t)-0.738769531f, (float16_t)-0.657226562f, (float16_t)0.502929688f, + (float16_t)0.0487976074f, (float16_t)-0.369628906f, (float16_t)-0.0439453125f, (float16_t)0.662597656f, + (float16_t)-0.699707031f, (float16_t)0.230224609f, (float16_t)0.285888672f, (float16_t)0.300537109f, + (float16_t)-0.332275391f, (float16_t)-0.404541016f, (float16_t)-0.169311523f, (float16_t)-0.036895752f, + (float16_t)-0.320068359f, (float16_t)0.362792969f, (float16_t)0.391113281f, (float16_t)0.0471801758f, + (float16_t)0.473876953f, (float16_t)-0.652832031f, (float16_t)0.317382812f, (float16_t)0.321044922f, + (float16_t)-0.114196777f, (float16_t)0.277099609f, (float16_t)0.157958984f, (float16_t)-0.343505859f, + (float16_t)-0.161865234f, (float16_t)-0.6171875f, (float16_t)0.6171875f, (float16_t)-0.544433594f, + (float16_t)0.596679688f, (float16_t)-0.601074219f, (float16_t)-0.360839844f, (float16_t)0.61328125f, + (float16_t)-0.702148438f, (float16_t)0.556152344f, (float16_t)-0.708496094f, (float16_t)0.231323242f, + (float16_t)0.218017578f, (float16_t)0.0377197266f, (float16_t)0.248535156f, (float16_t)0.425537109f, + (float16_t)0.104919434f, (float16_t)-0.493896484f, (float16_t)-0.7109375f, (float16_t)-0.536132812f, + (float16_t)-0.544921875f, (float16_t)0.0841064453f, (float16_t)0.669921875f, (float16_t)-0.685058594f, + (float16_t)0.156494141f, (float16_t)-0.145263672f, (float16_t)-0.291503906f, (float16_t)0.236328125f, + (float16_t)-0.0175933838f, (float16_t)-0.118774414f, (float16_t)0.510253906f, (float16_t)0.502929688f, + (float16_t)-0.228759766f, (float16_t)0.0916137695f, (float16_t)-0.193603516f, (float16_t)0.244995117f, + (float16_t)-0.0590209961f, (float16_t)0.665527344f, (float16_t)0.443603516f, (float16_t)-0.586425781f, + (float16_t)-0.323486328f, (float16_t)0.669921875f, (float16_t)-0.0750732422f, (float16_t)-0.244995117f, + (float16_t)0.108581543f, (float16_t)0.00728988647f, (float16_t)0.578125f, (float16_t)0.733886719f, + (float16_t)-0.190917969f, (float16_t)-0.314697266f, (float16_t)0.584472656f, (float16_t)-0.551269531f, + (float16_t)0.451416016f, (float16_t)0.714355469f, (float16_t)-0.533691406f, (float16_t)0.546386719f, + (float16_t)0.0585021973f, (float16_t)-0.0814819336f, (float16_t)-0.269287109f, (float16_t)0.0846557617f, + (float16_t)0.278076172f, (float16_t)-0.440185547f, (float16_t)0.52734375f, (float16_t)0.307617188f, + (float16_t)0.110290527f, (float16_t)0.622070312f, (float16_t)0.714355469f, (float16_t)0.401367188f, + (float16_t)0.0263366699f, (float16_t)0.00364494324f, (float16_t)0.454345703f, (float16_t)-0.262695312f, + (float16_t)0.0949707031f, (float16_t)-0.0804443359f, (float16_t)0.253173828f, (float16_t)-0.292236328f, + (float16_t)0.606445312f, (float16_t)-0.273193359f, (float16_t)0.606445312f, (float16_t)-0.397460938f, + (float16_t)-0.232299805f, (float16_t)-0.0819091797f, (float16_t)-0.437011719f, (float16_t)0.163574219f, + (float16_t)0.326904297f, (float16_t)0.290771484f, (float16_t)0.0665893555f, (float16_t)0.255859375f, + (float16_t)0.443359375f, (float16_t)0.00122642517f, (float16_t)0.157226562f, (float16_t)-0.473388672f, + (float16_t)0.697265625f, (float16_t)-0.0159301758f, (float16_t)0.160400391f, (float16_t)0.0836181641f, + (float16_t)-0.440673828f, (float16_t)-0.641113281f, (float16_t)0.165649414f, (float16_t)0.189819336f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/biases_data.h new file mode 100644 index 00000000..12c7a5db --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_2_f32_biases_data[3] = {-0.106994495f, -0.0610736087f, 0.0238682404f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/config_data.h new file mode 100644 index 00000000..24747029 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_2_F32_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_2_F32_INPUT_H 1 +#define CONV_MATCH_1XN_2_F32_INPUT_W 296 +#define CONV_MATCH_1XN_2_F32_IN_CH 4 +#define CONV_MATCH_1XN_2_F32_FILTER_H 1 +#define CONV_MATCH_1XN_2_F32_FILTER_W 48 +#define CONV_MATCH_1XN_2_F32_OUT_CH 3 +#define CONV_MATCH_1XN_2_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_2_F32_STRIDE_W 2 +#define CONV_MATCH_1XN_2_F32_PADDING_H 0 +#define CONV_MATCH_1XN_2_F32_PADDING_W 0 +#define CONV_MATCH_1XN_2_F32_DILATION_H 1 +#define CONV_MATCH_1XN_2_F32_DILATION_W 1 +#define CONV_MATCH_1XN_2_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_2_F32_OUTPUT_W 125 +#define CONV_MATCH_1XN_2_F32_OUTPUT_C 3 +#define CONV_MATCH_1XN_2_F32_DST_SIZE 375 +#define CONV_MATCH_1XN_2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_2_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_2_F32_OUT_ACTIVATION_MIN -111.0f +#define CONV_MATCH_1XN_2_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/input_data.h new file mode 100644 index 00000000..fdd8e84c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/input_data.h @@ -0,0 +1,172 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_2_f32_input_data[1184] = { + -0.893706203f, 0.673080385f, 0.676588356f, 0.412517875f, 0.441716343f, 0.337378263f, -0.119810037f, + -0.00247202697f, 0.0983046368f, 0.0246573575f, 0.592704535f, 0.516795397f, 0.643347919f, -0.233566791f, + 0.518489718f, -0.505815625f, -0.639206052f, 0.0353997722f, 0.177712858f, -0.340581894f, 0.968215764f, + 0.551274598f, 0.923829675f, 0.575390518f, 0.152497455f, 0.195828602f, 0.29859218f, -0.999108434f, + -0.0161558948f, -0.885506988f, 0.720362723f, 0.950430453f, -0.609484315f, -0.758070707f, 0.778659284f, + 0.469662607f, -0.0654469356f, 0.914493442f, 0.0269080475f, 0.785191059f, 0.610010445f, -0.99137044f, + 0.0520838164f, -0.807425857f, -0.898426354f, 0.90268594f, 0.303493291f, -0.202098623f, -0.931313038f, + -0.6612553f, -0.545315742f, 0.697369456f, 0.582883f, 0.353794724f, -0.608013093f, -0.760449409f, + -0.143360704f, 0.787396252f, 0.452953875f, -0.512272239f, -0.446043164f, -0.481925756f, 0.0923756808f, + -0.0601243004f, -0.245779842f, -0.691457391f, -0.161242858f, -0.431611687f, -0.21111469f, 0.545638144f, + 0.700029612f, 0.561458707f, 0.128143966f, 0.619297922f, 0.263274848f, 0.0161629729f, 0.886868238f, + -0.0673054159f, 0.657109201f, 0.340843499f, -0.116304651f, -0.84183079f, 0.251007318f, -0.355673432f, + -0.650051475f, 0.0240289662f, 0.481502831f, -0.995627284f, 0.850335062f, 0.570096433f, 0.145476192f, + 0.859712601f, 0.95385164f, 0.623288929f, 0.318316966f, 0.290510952f, 0.894598842f, 0.673009157f, + 0.46693185f, -0.212715104f, -0.434716612f, 0.0579908453f, -0.560690701f, -0.622757494f, -0.741120875f, + -0.567353785f, 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-0.317829907f, + 0.0382778011f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/output_ref_data.h new file mode 100644 index 00000000..ea0fd517 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_2_f32_output_ref_data[375] = { + 0.0933451205f, 1.89267111f, -1.51041174f, 5.39732122f, -2.73022485f, 0.183502108f, -2.04665685f, + 2.81352687f, 1.38371098f, 7.48330593f, 1.37847805f, 0.394649833f, 6.2644062f, 0.228747934f, + -1.61245143f, -0.733298957f, 1.17171061f, -5.36011076f, -2.78763604f, 0.535860717f, -0.750548542f, + 2.32419658f, -1.14597952f, 0.875089705f, 1.32187688f, -1.41655266f, -1.99781024f, -2.21496677f, + 2.70291924f, -0.903917849f, 3.32356834f, -2.02690029f, 3.7736485f, 2.88019919f, 2.40527511f, + 4.66112232f, 6.14376068f, -1.86127198f, 1.44588649f, -0.50133276f, -4.59292507f, 2.32174468f, + -0.67375654f, -1.92307222f, 6.11575699f, -1.38640988f, 1.20499325f, 0.470963508f, -3.99499536f, + -1.7681042f, -1.0487963f, -0.428676486f, -3.96864557f, 7.64863777f, 2.43299031f, 5.87631273f, + 3.12683082f, 5.23439169f, -5.2037034f, -0.443144917f, -1.95806003f, 5.55571508f, 4.4083457f, + -2.76715159f, 1.65154052f, -5.81103849f, -4.1652112f, -5.21306515f, 1.08177245f, -3.502074f, + 0.500859678f, 3.64283156f, -1.07063258f, 2.3646729f, 0.877394259f, 3.54273391f, 8.52971363f, + -1.290676f, 3.79532218f, 4.29364014f, -6.21305943f, 0.101128235f, 4.31937647f, 1.94140899f, + 2.2250185f, 0.604323566f, 5.32842112f, -0.574310541f, 4.54147482f, 5.79681635f, -0.726278424f, + -4.62752867f, -1.29354584f, -2.5434978f, -7.79145002f, 1.67845893f, -1.25777864f, 1.56833589f, + 1.86576891f, -2.9283464f, 1.7144686f, -0.12614879f, -1.75387847f, -0.353773236f, 3.21641231f, + 5.39313889f, 0.357681036f, 0.882664859f, 5.1025629f, -4.64834881f, 1.74545348f, -2.77944541f, + -0.552103341f, -2.52697277f, 0.0123554617f, -4.40770769f, 0.850961626f, -0.684880376f, -3.00156689f, + 0.575172007f, 0.498885155f, 1.64457762f, -0.460028857f, -2.19851708f, 0.278627336f, -3.89472961f, + -1.84001648f, 1.08853507f, 2.6990056f, 8.53779507f, 0.933171511f, -8.29390717f, 2.11104417f, + 1.46548259f, 0.635098875f, -1.69639254f, 0.110771947f, 3.29120302f, 6.59269667f, -1.11622894f, + -4.40834761f, -3.07015157f, -3.47010088f, -1.38460898f, -1.79650664f, 2.94877076f, 2.29240131f, + -1.74687922f, -7.45273209f, 2.73842835f, -3.23844433f, 2.73088288f, 0.920934618f, 3.41352963f, + -3.6304369f, -5.78573942f, -0.746362567f, -4.80910254f, -1.8270483f, -3.70404696f, 0.698727906f, + -3.04294038f, -0.131975204f, -0.514477789f, 1.79668295f, 0.868617833f, 1.96685207f, -2.04176474f, + -2.65094876f, -2.48411274f, -6.92311335f, 0.408658862f, 1.93781865f, 0.684318006f, -5.53521299f, + -1.46970356f, -8.95703125f, 4.09997606f, -4.6831193f, 1.24043763f, -1.49609172f, 0.606198549f, + -3.97150826f, -5.93623447f, 0.740533292f, -0.391124636f, 2.92683792f, -1.49749112f, -1.5847466f, + -8.04336739f, -3.33825421f, -3.10829091f, 3.67415714f, 4.31286097f, -2.67352128f, -2.44913745f, + -5.54150867f, -5.48156738f, 0.496131897f, -1.71961963f, 2.84993052f, 0.925204396f, -2.41666579f, + 4.32210398f, 3.05019259f, -6.37808275f, 3.57659173f, -0.744693518f, -1.28022838f, -6.09704208f, + -0.722920656f, -1.70890653f, 5.2225256f, -5.29611588f, -4.95081472f, -3.14514136f, -1.48632586f, + -4.03356934f, 1.69874036f, -6.47457409f, 2.96220732f, 8.26317787f, -3.91166401f, 0.18135196f, + -2.88352084f, 0.0417131335f, 1.85403693f, -1.98819268f, -0.151540101f, -5.14138317f, -2.3866725f, + 8.5961132f, -6.4925766f, 1.00727439f, 3.54073548f, -4.73503447f, -0.716503561f, -2.66754365f, + 10.2467852f, 2.73046613f, -2.72755504f, 0.21073693f, -1.72879398f, 3.42001224f, -2.44228315f, + -3.77624035f, -1.33892298f, -5.25134897f, 2.03532171f, 1.14568532f, -8.64057446f, 2.83778644f, + 2.74415874f, -6.00672102f, 3.30209517f, 2.40395045f, 0.341127545f, 0.751417339f, 5.56438971f, + 3.15510249f, -3.91797996f, -2.27016854f, 3.49262261f, -6.05036354f, -4.99949789f, -0.596206665f, + 8.28081322f, -2.8254776f, -4.34235334f, 4.47438955f, 1.03758109f, -0.566112578f, 1.70715606f, + 3.10389924f, -4.47260046f, 0.357114464f, 1.48871481f, 0.168714821f, -1.12406743f, -8.33999157f, + -0.874598384f, 0.979636192f, -6.8853879f, 1.60099757f, 4.55705404f, -2.53865671f, 4.80660677f, + -2.58355141f, 2.87969828f, 2.72658372f, -1.81642818f, -2.56632447f, -0.775116265f, -6.24510717f, + -1.94566047f, -0.826492369f, -0.419386953f, -3.85437799f, 1.50681531f, 1.94956779f, 1.62401748f, + 1.63340104f, 2.38129592f, 3.04403853f, 2.03737283f, -0.137833625f, 3.14174438f, 5.26534224f, + -1.07724702f, 5.90124226f, 4.15797615f, -0.655226767f, 0.776980639f, 3.06187153f, 1.00362337f, + 1.41960955f, -2.59061241f, 8.03071499f, -0.0822925419f, -2.35649061f, -3.44732881f, -0.648401856f, + -2.4667964f, 3.35504079f, -6.68097305f, 0.0241251811f, 2.27346134f, -3.29210734f, -2.70025492f, + -2.30061245f, 1.00975204f, -6.4135499f, -1.30599463f, -3.3361969f, 1.03405678f, 6.96243715f, + -4.4822731f, 0.636146843f, -4.9037776f, -3.97581029f, 4.90840864f, 1.60270584f, -1.08370662f, + -2.52718735f, 0.874828517f, 2.13657713f, -2.19003463f, -2.04251599f, 4.3183279f, 6.71138096f, + 1.79858077f, 2.61184096f, 2.87739182f, 1.63303256f, 1.61860049f, -0.455149412f, -1.43236315f, + -0.111924276f, -0.316756546f, -2.04434991f, 0.989919662f, -1.26614153f, 1.54917192f, -0.881280661f, + 4.17656469f, -2.15560699f, 0.642270029f, -5.85557556f, -0.835892916f, 4.92766523f, 0.190437496f, + 0.931277931f, -1.0369693f, 8.3412714f, 3.36947179f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/weights_data.h new file mode 100644 index 00000000..99335698 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_2_f32/weights_data.h @@ -0,0 +1,85 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_2_f32_weights_data[576] = { + 0.701666832f, 0.53845495f, 0.596538842f, -0.0848537311f, 0.177900672f, -0.665292978f, 0.288098335f, + 0.470949084f, 0.563118517f, 0.0495072976f, 0.0268498678f, 0.645308614f, 0.420618743f, 0.563076138f, + -0.235307649f, 0.188101441f, -0.273961484f, -0.0221586991f, 0.602675796f, 0.320675969f, 0.143450603f, + -0.367698044f, 0.414044857f, -0.370159745f, -0.0285606701f, -0.208176345f, -0.505952656f, -0.476832062f, + 0.0777794048f, 0.148414955f, -0.324775159f, 0.272106081f, 0.343464196f, 0.368134946f, 0.321019262f, + -0.176753476f, -0.719884872f, -0.589885712f, 0.396480143f, 0.582803667f, 0.21630761f, 0.535691679f, + 0.568210423f, -0.22926192f, -0.620614052f, 0.505098403f, 0.262929857f, -0.343653291f, 0.0827896893f, + -0.29575479f, -0.396675378f, -0.55011785f, 0.145062596f, 0.517234981f, 0.541395426f, -0.194634557f, + -0.143386662f, -0.605655253f, -0.169462979f, 0.474727303f, -0.579217434f, 0.211480334f, 0.296974361f, + 0.525828242f, 0.162816674f, 0.590167582f, 0.667046309f, 0.109561786f, 0.301990569f, -0.701657414f, + -0.195181742f, -0.131076112f, -0.0187521558f, -0.682319462f, -0.713097572f, 0.405224532f, -0.139936492f, + 0.708454907f, 0.114182644f, -0.0050328006f, 0.421002299f, -0.622217953f, -0.698408484f, -0.064267315f, + -0.591322541f, 0.0199728087f, 0.390054464f, 0.604304314f, 0.502360702f, 0.483000517f, 0.350635976f, + 0.180318832f, 0.0574526936f, 0.155048072f, -0.161776483f, -0.222457856f, -0.408594161f, -0.041378174f, + 0.457832903f, -0.0727832466f, 0.0876175091f, -0.67086792f, 0.0106173605f, 0.234576866f, -0.738401175f, + 0.717645943f, 0.522805154f, -0.714105546f, -0.282553136f, 0.273108095f, -0.414350271f, -0.668393314f, + -0.350325316f, 0.250582039f, 0.684769332f, -0.523539186f, -0.457159668f, 0.342621893f, -0.562665641f, + -0.658349276f, -0.712306261f, -0.203853577f, 0.710754871f, -0.489101321f, -0.702493072f, -0.518014133f, + -0.203686267f, -0.694746673f, -0.501895607f, 0.0530523174f, -0.211976677f, -0.0936717615f, -0.419141352f, + -0.351330042f, -0.402207494f, -0.0455636047f, -0.55329144f, -0.620838583f, -0.252047509f, 0.580714047f, + -0.135588139f, -0.536072612f, 0.552716136f, -0.604685962f, 0.398977846f, -0.741528332f, 0.47995618f, + 0.188823745f, 0.14613609f, 0.181909785f, -0.619641244f, 0.720131636f, 0.501171231f, 0.000414787268f, + -0.0909955278f, 0.264014125f, -0.54123646f, 0.533649147f, -0.147665501f, 0.15591605f, 0.0406628065f, + -0.459800452f, 0.440306187f, 0.644204676f, -0.237573341f, 0.33493042f, 0.249272361f, -0.141567722f, + -0.423555285f, -0.0579232201f, 0.500518978f, 0.400824755f, 0.672055364f, 0.53988272f, -0.607884943f, + -0.131134659f, -0.577466667f, 0.168125287f, 0.603046179f, -0.527561963f, 0.726163983f, -0.142086804f, + 0.565103948f, -0.370412499f, 0.246272206f, -0.0374196507f, 0.136835083f, 0.0365280695f, 0.10286241f, + -0.601595044f, 0.17968452f, 0.747849286f, -0.519296527f, 0.035298042f, -0.219698295f, 0.605584621f, + -0.0361919217f, 0.393244028f, -0.432280898f, 0.191541418f, 0.0888872445f, -0.474993974f, 0.728982329f, + 0.620734215f, -0.664376676f, -0.68110013f, 0.142028257f, 0.609717011f, -0.178751975f, 0.581965446f, + -0.611657917f, -0.296873301f, -0.321736515f, -0.661047399f, -0.740082622f, 0.213219121f, 0.616979361f, + 0.18207252f, 0.218135908f, 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0.218044728f, 0.0377119929f, + 0.248547435f, 0.425433308f, 0.104908764f, -0.493913978f, -0.710884273f, -0.536376119f, -0.544732153f, + 0.0841252133f, 0.669902802f, -0.685217559f, 0.156454578f, -0.145292312f, -0.291529834f, 0.236381292f, + -0.0175992511f, -0.118761174f, 0.510124326f, 0.503029346f, -0.228722855f, 0.0916119367f, -0.193616703f, + 0.244934335f, -0.0590242147f, 0.6653229f, 0.443614006f, -0.586620033f, -0.323405951f, 0.669949114f, + -0.0750855654f, -0.245040834f, 0.108567372f, 0.00729056448f, 0.578241706f, 0.733878314f, -0.190928981f, + -0.314590454f, 0.584703743f, -0.551432014f, 0.451346368f, 0.714574575f, -0.533606708f, 0.546449602f, + 0.0585021041f, -0.0814522803f, -0.269208938f, 0.0846684277f, 0.277959108f, -0.440300465f, 0.527267635f, + 0.307554513f, 0.110301539f, 0.622292757f, 0.714410305f, 0.401450902f, 0.0263352487f, 0.00364455651f, + 0.45433706f, -0.262633443f, 0.0949674174f, -0.080437243f, 0.253250778f, -0.292115808f, 0.606414557f, + -0.27316907f, 0.606420219f, -0.39737317f, -0.232300878f, -0.0819091424f, -0.437087297f, 0.163582638f, + 0.326858163f, 0.290728152f, 0.0665816814f, 0.255883157f, 0.44323799f, 0.00122633122f, 0.157179356f, + -0.473448008f, 0.697154641f, -0.0159363039f, 0.160358593f, 0.0836284757f, -0.440638691f, -0.641156256f, + 0.165701032f, 0.189818099f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/biases_data.h new file mode 100644 index 00000000..f1091c35 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_3_f16_biases_data[1] = {(float16_t)-0.134643555f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/config_data.h new file mode 100644 index 00000000..02679d48 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_3_F16_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_3_F16_INPUT_H 1 +#define CONV_MATCH_1XN_3_F16_INPUT_W 296 +#define CONV_MATCH_1XN_3_F16_IN_CH 4 +#define CONV_MATCH_1XN_3_F16_FILTER_H 1 +#define CONV_MATCH_1XN_3_F16_FILTER_W 48 +#define CONV_MATCH_1XN_3_F16_OUT_CH 1 +#define CONV_MATCH_1XN_3_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_3_F16_STRIDE_W 2 +#define CONV_MATCH_1XN_3_F16_PADDING_H 0 +#define CONV_MATCH_1XN_3_F16_PADDING_W 23 +#define CONV_MATCH_1XN_3_F16_DILATION_H 1 +#define CONV_MATCH_1XN_3_F16_DILATION_W 1 +#define CONV_MATCH_1XN_3_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_3_F16_OUTPUT_W 148 +#define CONV_MATCH_1XN_3_F16_OUTPUT_C 1 +#define CONV_MATCH_1XN_3_F16_DST_SIZE 148 +#define CONV_MATCH_1XN_3_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_3_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_3_F16_OUT_ACTIVATION_MIN (float16_t) - 111.0f +#define CONV_MATCH_1XN_3_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/input_data.h new file mode 100644 index 00000000..106023f8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/input_data.h @@ -0,0 +1,593 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_3_f16_input_data[1184] = {(float16_t)-0.0437011719f, (float16_t)-0.465576172f, + (float16_t)-0.769042969f, (float16_t)0.696289062f, + (float16_t)-0.587402344f, (float16_t)-0.0384216309f, + (float16_t)0.567382812f, (float16_t)0.172607422f, + (float16_t)-0.287353516f, (float16_t)-0.664550781f, + (float16_t)-0.346679688f, (float16_t)0.579589844f, + (float16_t)-0.97265625f, (float16_t)0.533203125f, + (float16_t)-0.662109375f, (float16_t)-0.577636719f, + (float16_t)-0.637695312f, (float16_t)-0.19921875f, + (float16_t)-0.513183594f, (float16_t)-0.0829467773f, + (float16_t)-0.194580078f, (float16_t)-0.844726562f, + (float16_t)0.905273438f, (float16_t)-0.23828125f, + (float16_t)0.25390625f, (float16_t)0.552734375f, + (float16_t)-0.226074219f, (float16_t)0.869140625f, + (float16_t)0.668457031f, (float16_t)0.269287109f, + (float16_t)0.848632812f, (float16_t)0.346435547f, + (float16_t)0.309326172f, (float16_t)0.417236328f, + (float16_t)-0.331054688f, (float16_t)-0.705566406f, + (float16_t)-0.22644043f, (float16_t)-0.497314453f, + (float16_t)0.798339844f, (float16_t)0.727539062f, + (float16_t)-0.983886719f, (float16_t)-0.904296875f, + (float16_t)0.28125f, (float16_t)-0.101745605f, + (float16_t)-0.743652344f, (float16_t)-0.175537109f, + (float16_t)-0.381591797f, (float16_t)0.0285186768f, + (float16_t)0.60546875f, (float16_t)-0.720214844f, + (float16_t)0.147705078f, (float16_t)0.411865234f, + (float16_t)0.661621094f, (float16_t)-0.757324219f, + (float16_t)-0.132446289f, (float16_t)0.0320739746f, + (float16_t)0.105529785f, (float16_t)0.380126953f, + (float16_t)-0.0702514648f, (float16_t)-0.852539062f, + (float16_t)0.19152832f, (float16_t)-0.850097656f, + (float16_t)0.25390625f, (float16_t)-0.276855469f, + (float16_t)0.21484375f, (float16_t)-0.500488281f, + (float16_t)-0.381103516f, (float16_t)-0.00551605225f, + (float16_t)0.787597656f, (float16_t)-0.480224609f, + (float16_t)0.151123047f, (float16_t)-0.253417969f, + (float16_t)0.571289062f, (float16_t)0.432617188f, + (float16_t)-0.398681641f, (float16_t)0.923339844f, + (float16_t)0.78125f, (float16_t)0.631835938f, + (float16_t)-0.0955200195f, (float16_t)0.0907592773f, + (float16_t)0.916992188f, (float16_t)0.458007812f, + (float16_t)0.222290039f, (float16_t)0.111022949f, + (float16_t)0.597167969f, (float16_t)-0.890136719f, + (float16_t)0.267089844f, (float16_t)-0.4921875f, + (float16_t)-0.0608215332f, (float16_t)0.568359375f, + (float16_t)-0.7109375f, (float16_t)0.207763672f, + (float16_t)0.53515625f, (float16_t)0.537597656f, + (float16_t)0.931152344f, (float16_t)0.507324219f, + (float16_t)-0.668945312f, (float16_t)0.842285156f, + (float16_t)-0.22253418f, (float16_t)0.572265625f, + (float16_t)0.468994141f, (float16_t)-0.110229492f, + (float16_t)-0.346435547f, (float16_t)0.828125f, + (float16_t)0.146606445f, (float16_t)0.486816406f, + (float16_t)0.121398926f, (float16_t)0.00537872314f, + (float16_t)0.333251953f, (float16_t)-0.692871094f, + (float16_t)-0.923339844f, (float16_t)0.596191406f, + (float16_t)0.234863281f, (float16_t)-0.62109375f, + (float16_t)0.985351562f, (float16_t)-0.846191406f, + (float16_t)0.0518493652f, (float16_t)0.406982422f, + (float16_t)-0.434570312f, (float16_t)-0.148681641f, + (float16_t)0.417236328f, (float16_t)-0.219238281f, + (float16_t)0.994140625f, (float16_t)-0.576171875f, + (float16_t)0.577148438f, (float16_t)0.415039062f, + (float16_t)0.7421875f, (float16_t)0.838867188f, + (float16_t)0.373046875f, (float16_t)-0.6015625f, + (float16_t)0.745605469f, (float16_t)-0.421875f, + (float16_t)0.592285156f, (float16_t)-0.254882812f, + (float16_t)0.274902344f, (float16_t)-0.35546875f, + (float16_t)0.301269531f, (float16_t)-0.943359375f, + (float16_t)-0.493408203f, (float16_t)0.724121094f, + (float16_t)0.125610352f, (float16_t)0.235717773f, + (float16_t)0.0878295898f, (float16_t)-0.302734375f, + (float16_t)-0.678222656f, (float16_t)0.0536193848f, + (float16_t)-0.973144531f, (float16_t)0.161621094f, + (float16_t)-0.69921875f, (float16_t)0.412597656f, + (float16_t)0.529296875f, (float16_t)0.874511719f, + (float16_t)0.689453125f, (float16_t)0.20300293f, + (float16_t)0.148071289f, (float16_t)0.276123047f, + (float16_t)0.777832031f, (float16_t)0.641113281f, + (float16_t)0.0158233643f, (float16_t)-0.165771484f, + (float16_t)0.54296875f, (float16_t)-0.48046875f, + (float16_t)-0.355712891f, (float16_t)0.112976074f, + (float16_t)-0.945800781f, (float16_t)-0.681152344f, + (float16_t)-0.365966797f, (float16_t)-0.724609375f, + (float16_t)0.383300781f, (float16_t)0.0314025879f, + (float16_t)0.226928711f, (float16_t)-0.0573425293f, + (float16_t)0.102600098f, (float16_t)-0.532714844f, + (float16_t)-0.799804688f, (float16_t)0.654785156f, + (float16_t)0.166259766f, (float16_t)-0.710449219f, + (float16_t)-0.924804688f, (float16_t)-0.375732422f, + (float16_t)0.497558594f, (float16_t)-0.617675781f, + (float16_t)0.102111816f, (float16_t)0.807617188f, + (float16_t)0.245117188f, (float16_t)-0.825683594f, + (float16_t)-0.945800781f, (float16_t)0.22265625f, + (float16_t)0.69921875f, (float16_t)-0.0138397217f, + (float16_t)-0.463378906f, (float16_t)-0.762207031f, + (float16_t)0.750488281f, (float16_t)0.00900268555f, + (float16_t)-0.356445312f, (float16_t)0.824707031f, + (float16_t)-0.489746094f, (float16_t)-0.0817871094f, + (float16_t)0.618652344f, (float16_t)0.342529297f, + (float16_t)-0.553710938f, (float16_t)-0.0952148438f, + (float16_t)0.147094727f, (float16_t)0.776367188f, + (float16_t)0.8671875f, 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(float16_t)0.922363281f, + (float16_t)-0.706054688f, (float16_t)-0.645996094f, + (float16_t)0.199707031f, (float16_t)-0.354736328f, + (float16_t)0.499755859f, (float16_t)-0.116577148f, + (float16_t)-0.0234832764f, (float16_t)-0.76953125f, + (float16_t)-0.275634766f, (float16_t)0.798828125f, + (float16_t)-0.695800781f, (float16_t)0.6953125f, + (float16_t)0.390136719f, (float16_t)0.229370117f, + (float16_t)-0.868164062f, (float16_t)-0.615234375f, + (float16_t)-0.519042969f, (float16_t)0.4921875f, + (float16_t)-0.622070312f, (float16_t)-0.237304688f, + (float16_t)-0.285644531f, (float16_t)0.0687866211f, + (float16_t)-0.20715332f, (float16_t)-0.03515625f, + (float16_t)-0.362548828f, (float16_t)-0.205444336f, + (float16_t)0.278808594f, (float16_t)-0.65625f, + (float16_t)-0.643066406f, (float16_t)-0.747558594f, + (float16_t)-0.309570312f, (float16_t)-0.671875f, + (float16_t)0.344970703f, (float16_t)0.6328125f, + (float16_t)0.738769531f, (float16_t)-0.301025391f, + (float16_t)-0.721679688f, (float16_t)-0.829589844f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/output_ref_data.h new file mode 100644 index 00000000..1380d7b1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/output_ref_data.h @@ -0,0 +1,39 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_3_f16_output_ref_data[148] = { + (float16_t)-1.14160156f, (float16_t)-0.989746094f, (float16_t)-0.530761719f, (float16_t)0.549804688f, + (float16_t)0.962402344f, (float16_t)1.99902344f, (float16_t)-3.77929688f, (float16_t)-2.33203125f, + (float16_t)6.734375f, (float16_t)0.690429688f, (float16_t)-3.77539062f, (float16_t)0.037689209f, + (float16_t)-3.20898438f, (float16_t)-7.328125f, (float16_t)2.69140625f, (float16_t)4.4296875f, + (float16_t)-4.21875f, (float16_t)-0.420898438f, (float16_t)2.74023438f, (float16_t)-1.11328125f, + (float16_t)0.484619141f, (float16_t)4.95703125f, (float16_t)-4.30859375f, (float16_t)-4.796875f, + (float16_t)4.83203125f, (float16_t)0.705566406f, (float16_t)-5.96875f, (float16_t)1.3359375f, + (float16_t)2.75976562f, (float16_t)0.9921875f, (float16_t)0.630371094f, (float16_t)3.69335938f, + (float16_t)-2.7578125f, (float16_t)-3.60546875f, (float16_t)7.66796875f, (float16_t)3.63867188f, + (float16_t)-3.91601562f, (float16_t)-0.392578125f, (float16_t)-2.53125f, (float16_t)-0.818359375f, + (float16_t)3.11132812f, (float16_t)-4.359375f, (float16_t)-2.82421875f, (float16_t)3.15625f, + (float16_t)0.854003906f, (float16_t)4.16015625f, (float16_t)-5.41796875f, (float16_t)-6.0390625f, + (float16_t)-7.2421875f, (float16_t)-5.08984375f, (float16_t)3.28320312f, (float16_t)-0.888183594f, + (float16_t)-2.33203125f, (float16_t)1.55175781f, (float16_t)3.23828125f, (float16_t)0.364501953f, + (float16_t)3.73046875f, (float16_t)0.0509643555f, (float16_t)-0.272949219f, (float16_t)-2.71484375f, + (float16_t)1.2890625f, (float16_t)0.216186523f, (float16_t)-0.964355469f, (float16_t)5.26171875f, + (float16_t)-5.86328125f, (float16_t)0.0420837402f, (float16_t)0.914550781f, (float16_t)-6.16015625f, + (float16_t)-1.34277344f, (float16_t)-1.26855469f, (float16_t)-2.75390625f, (float16_t)-1.88867188f, + (float16_t)1.76171875f, (float16_t)2.80664062f, (float16_t)-2.71679688f, (float16_t)-2.81640625f, + (float16_t)-1.05957031f, (float16_t)-1.63476562f, (float16_t)1.70703125f, (float16_t)-2.84960938f, + (float16_t)3.98632812f, (float16_t)1.88085938f, (float16_t)-1.37402344f, (float16_t)3.31054688f, + (float16_t)-0.790039062f, (float16_t)-2.515625f, (float16_t)-4.5234375f, (float16_t)0.572265625f, + (float16_t)-5.953125f, (float16_t)2.5390625f, (float16_t)-3.23828125f, (float16_t)-1.07714844f, + (float16_t)0.266357422f, (float16_t)-0.255371094f, (float16_t)-0.912597656f, (float16_t)1.31054688f, + (float16_t)1.44238281f, (float16_t)-1.29394531f, (float16_t)-5.7109375f, (float16_t)6.08203125f, + (float16_t)-7.078125f, (float16_t)2.16210938f, (float16_t)-1.36132812f, (float16_t)8.359375f, + (float16_t)-2.95117188f, (float16_t)1.93066406f, (float16_t)5.4296875f, (float16_t)-7.625f, + (float16_t)-2.84570312f, (float16_t)-0.829101562f, (float16_t)1.93359375f, (float16_t)1.79785156f, + (float16_t)-0.205078125f, (float16_t)0.553222656f, (float16_t)-2.43945312f, (float16_t)1.76464844f, + (float16_t)5.5625f, (float16_t)1.62011719f, (float16_t)1.51953125f, (float16_t)5.95703125f, + (float16_t)0.501953125f, (float16_t)-3.90039062f, (float16_t)-1.05371094f, (float16_t)-1.62109375f, + (float16_t)-8.3125f, (float16_t)-1.66796875f, (float16_t)-4.2734375f, (float16_t)-8.7734375f, + (float16_t)-1.12695312f, (float16_t)-5.9765625f, (float16_t)-0.158081055f, (float16_t)0.683105469f, + (float16_t)1.09863281f, (float16_t)-0.082824707f, (float16_t)3.43359375f, (float16_t)2.06445312f, + (float16_t)-4.4375f, (float16_t)-7.33203125f, (float16_t)4.3671875f, (float16_t)-4.09765625f, + (float16_t)-3.9453125f, (float16_t)1.765625f, (float16_t)-1.18261719f, (float16_t)-1.52441406f, + (float16_t)-2.84765625f, (float16_t)-0.939453125f, (float16_t)-2.55859375f, (float16_t)-1.57714844f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/weights_data.h new file mode 100644 index 00000000..b90f14cf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f16/weights_data.h @@ -0,0 +1,50 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_3_f16_weights_data[192] = { + (float16_t)0.182739258f, (float16_t)0.333496094f, (float16_t)0.676269531f, (float16_t)-0.418457031f, + (float16_t)0.311523438f, (float16_t)-0.715820312f, (float16_t)0.447509766f, (float16_t)0.0919189453f, + (float16_t)-0.363525391f, (float16_t)0.0407104492f, (float16_t)0.442138672f, (float16_t)-0.423339844f, + (float16_t)0.158447266f, (float16_t)0.532714844f, (float16_t)0.403320312f, (float16_t)-0.0950317383f, + (float16_t)-0.327392578f, (float16_t)0.250488281f, (float16_t)0.408447266f, (float16_t)0.696777344f, + (float16_t)0.291748047f, (float16_t)0.118469238f, (float16_t)-0.647949219f, (float16_t)0.0560913086f, + (float16_t)0.0335083008f, (float16_t)-0.203125f, (float16_t)0.167480469f, (float16_t)0.455322266f, + (float16_t)0.209350586f, (float16_t)0.576660156f, (float16_t)0.165527344f, (float16_t)-0.378173828f, + (float16_t)-0.101745605f, (float16_t)0.462158203f, (float16_t)-0.590332031f, (float16_t)-0.718261719f, + (float16_t)-0.408935547f, (float16_t)-0.523925781f, (float16_t)0.00344657898f, (float16_t)0.343261719f, + (float16_t)-0.180908203f, (float16_t)0.297119141f, (float16_t)-0.510253906f, (float16_t)-0.205444336f, + (float16_t)0.695800781f, (float16_t)0.667480469f, (float16_t)-0.460693359f, (float16_t)0.195922852f, + (float16_t)-0.548339844f, (float16_t)0.119750977f, (float16_t)-0.614746094f, (float16_t)-0.272705078f, + (float16_t)0.469970703f, (float16_t)0.406005859f, (float16_t)0.58203125f, (float16_t)0.468994141f, + (float16_t)0.208984375f, (float16_t)0.337402344f, (float16_t)0.00387191772f, (float16_t)-0.313232422f, + (float16_t)0.714355469f, (float16_t)-0.496826172f, (float16_t)0.6328125f, (float16_t)0.0705566406f, + (float16_t)-0.65625f, (float16_t)-0.702636719f, (float16_t)-0.132080078f, (float16_t)-0.528808594f, + (float16_t)-0.350097656f, (float16_t)-0.411132812f, (float16_t)-0.556640625f, (float16_t)-0.414794922f, + (float16_t)0.0864868164f, (float16_t)0.651855469f, (float16_t)-0.549316406f, (float16_t)0.476806641f, + (float16_t)0.456542969f, (float16_t)-0.132202148f, (float16_t)-0.218261719f, (float16_t)0.546386719f, + (float16_t)-0.0251312256f, (float16_t)-0.13671875f, (float16_t)-0.359130859f, (float16_t)-0.27734375f, + (float16_t)0.388916016f, (float16_t)-0.122680664f, (float16_t)-0.647460938f, (float16_t)-0.645507812f, + (float16_t)0.0548400879f, (float16_t)-0.61328125f, (float16_t)-0.620117188f, (float16_t)0.0430297852f, + (float16_t)0.571777344f, (float16_t)-0.440917969f, (float16_t)0.369140625f, (float16_t)0.496337891f, + (float16_t)-0.135375977f, (float16_t)0.473876953f, (float16_t)0.746582031f, (float16_t)0.397460938f, + (float16_t)0.748535156f, (float16_t)-0.0830078125f, (float16_t)-0.011390686f, (float16_t)0.706054688f, + (float16_t)-0.00724029541f, (float16_t)0.68359375f, (float16_t)-0.188354492f, (float16_t)0.358886719f, + (float16_t)0.422119141f, (float16_t)-0.733886719f, (float16_t)-0.742675781f, (float16_t)0.0519104004f, + (float16_t)0.478515625f, (float16_t)-0.333984375f, (float16_t)-0.408935547f, (float16_t)0.327880859f, + (float16_t)0.372802734f, (float16_t)-0.198120117f, (float16_t)-0.418701172f, (float16_t)-0.215209961f, + (float16_t)-0.274658203f, (float16_t)-0.143554688f, (float16_t)0.0793457031f, (float16_t)0.227539062f, + (float16_t)-0.134277344f, (float16_t)0.472412109f, (float16_t)-0.453369141f, (float16_t)-0.220703125f, + (float16_t)-0.170532227f, (float16_t)0.145629883f, (float16_t)-0.0657958984f, (float16_t)0.476318359f, + (float16_t)0.411376953f, (float16_t)0.312988281f, (float16_t)0.449707031f, (float16_t)-0.0392456055f, + (float16_t)0.203613281f, (float16_t)-0.110229492f, (float16_t)-0.540039062f, (float16_t)-0.219116211f, + (float16_t)0.632324219f, (float16_t)-0.286132812f, (float16_t)0.550292969f, (float16_t)0.076171875f, + (float16_t)-0.0254058838f, (float16_t)0.252685547f, (float16_t)-0.426025391f, (float16_t)-0.495117188f, + (float16_t)-0.594238281f, (float16_t)-0.714355469f, (float16_t)-0.547851562f, (float16_t)0.310058594f, + (float16_t)0.00868225098f, (float16_t)0.496582031f, (float16_t)-0.163085938f, (float16_t)0.293701172f, + (float16_t)-0.255859375f, (float16_t)-0.113037109f, (float16_t)0.197265625f, (float16_t)-0.625f, + (float16_t)0.044921875f, (float16_t)0.437255859f, (float16_t)0.263183594f, (float16_t)-0.584960938f, + (float16_t)-0.0390930176f, (float16_t)0.546386719f, (float16_t)-0.040802002f, (float16_t)-0.466064453f, + (float16_t)0.571777344f, (float16_t)-0.243774414f, (float16_t)-0.357910156f, (float16_t)-0.297363281f, + (float16_t)-0.370117188f, (float16_t)0.269042969f, (float16_t)-0.113525391f, (float16_t)-0.673339844f, + (float16_t)0.429931641f, (float16_t)0.6953125f, (float16_t)0.585449219f, (float16_t)0.525390625f, + (float16_t)0.0480957031f, (float16_t)-0.236206055f, (float16_t)0.712402344f, (float16_t)-0.231689453f, + (float16_t)-0.464111328f, (float16_t)-0.145385742f, (float16_t)0.212646484f, (float16_t)-0.62890625f, + (float16_t)0.060760498f, (float16_t)-0.032409668f, (float16_t)-0.0782470703f, (float16_t)0.189819336f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/biases_data.h new file mode 100644 index 00000000..6f1be896 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_3_f32_biases_data[1] = {-0.134621412f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/config_data.h new file mode 100644 index 00000000..032baa96 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_3_F32_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_3_F32_INPUT_H 1 +#define CONV_MATCH_1XN_3_F32_INPUT_W 296 +#define CONV_MATCH_1XN_3_F32_IN_CH 4 +#define CONV_MATCH_1XN_3_F32_FILTER_H 1 +#define CONV_MATCH_1XN_3_F32_FILTER_W 48 +#define CONV_MATCH_1XN_3_F32_OUT_CH 1 +#define CONV_MATCH_1XN_3_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_3_F32_STRIDE_W 2 +#define CONV_MATCH_1XN_3_F32_PADDING_H 0 +#define CONV_MATCH_1XN_3_F32_PADDING_W 23 +#define CONV_MATCH_1XN_3_F32_DILATION_H 1 +#define CONV_MATCH_1XN_3_F32_DILATION_W 1 +#define CONV_MATCH_1XN_3_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_3_F32_OUTPUT_W 148 +#define CONV_MATCH_1XN_3_F32_OUTPUT_C 1 +#define CONV_MATCH_1XN_3_F32_DST_SIZE 148 +#define CONV_MATCH_1XN_3_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_3_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_3_F32_OUT_ACTIVATION_MIN -111.0f +#define CONV_MATCH_1XN_3_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/input_data.h new file mode 100644 index 00000000..d0696823 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/input_data.h @@ -0,0 +1,172 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_3_f32_input_data[1184] = { + -0.0437010452f, -0.465568602f, -0.768970191f, 0.696111023f, -0.587604225f, -0.0384270363f, 0.567143738f, + 0.172636583f, -0.287386477f, -0.66455245f, -0.346643329f, 0.579801083f, -0.972821832f, 0.53316462f, + -0.662204921f, -0.57777077f, -0.637535274f, -0.199181587f, -0.513106942f, -0.08294788f, -0.19453001f, + -0.844731629f, 0.905333221f, -0.238264263f, 0.253891826f, 0.552946866f, -0.226035178f, 0.869196594f, + 0.668687999f, 0.269349903f, 0.848389745f, 0.346502513f, 0.309438169f, 0.417169183f, -0.331052691f, 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0.447571456f, -0.92957437f, + -0.243220225f, -0.481144726f, 0.773159564f, -0.840866923f, 0.109682888f, 0.199975505f, 0.873260081f, + 0.221203908f, 0.743968308f, 0.398002297f, 0.293853402f, -0.280614823f, -0.247465596f, 0.0665291473f, + 0.348141938f, -0.561112761f, 0.775958121f, 0.127401173f, -0.304789633f, -0.666780531f, 0.969139457f, + 0.988399088f, 0.950893164f, -0.495096803f, -0.189604312f, 0.150781378f, -0.155776724f, 0.763561845f, + -0.146643683f, 0.832184553f, -0.338693142f, 0.39925912f, 0.0516246073f, -0.462191522f, -0.757395387f, + -0.975562215f, -0.52102375f, 0.177807912f, -0.0807667375f, 0.000216736691f, -0.766132593f, -0.234531075f, + 0.699355841f, -0.8681494f, -0.520588338f, 0.76153636f, 0.598043382f, -0.872432649f, 0.175844669f, + 0.995131075f, -0.0898691118f, 0.356811434f, -0.852747321f, 0.544441521f, -0.531923473f, 0.147896454f, + 0.408909529f, -0.843085766f, -0.223614514f, 0.840169191f, -0.643297374f, 0.510580182f, 0.761447608f, + 0.611490905f, -0.551575065f, -0.427089393f, -0.0835716128f, -0.292276353f, 0.440269619f, -0.131476507f, + -0.522136748f, -0.329405338f, -0.976702392f, -0.317578465f, 0.666182995f, 0.846072018f, -0.144289508f, + -0.0618835241f, -0.914166749f, 0.806411803f, -0.875568151f, 0.730988324f, 0.811567068f, 0.82192415f, + 0.204846844f, 0.187520832f, -0.995350957f, 0.96178627f, 0.568092883f, -0.618817806f, 0.178127274f, + 0.787067592f, -0.053863097f, 0.0420487858f, 0.983805239f, 0.460697055f, 0.656352043f, -0.861952424f, + 0.700633645f, 0.310804814f, -0.0595116206f, 0.671879053f, -0.924644828f, 0.519252896f, 0.826301396f, + 0.813669741f, -0.701786995f, 0.855247736f, -0.723501384f, 0.101120763f, -0.792615175f, 0.321310014f, + -0.0232113805f, 0.735598862f, 0.766831577f, 0.263987511f, 0.668146253f, -0.717884779f, -0.623157144f, + -0.397368222f, -0.828040183f, -0.965568304f, 0.795116186f, 0.938384593f, -0.730904102f, 0.0105820196f, + -0.0246836208f, 0.795323133f, -0.572853208f, -0.784428775f, 0.010700901f, -0.0307019483f, -0.273074985f, + -0.291606903f, -0.180482835f, -0.0728036016f, -0.629587233f, -0.532637954f, -0.197595298f, 0.586760461f, + -0.358663112f, 0.569781065f, -0.674676776f, 0.757953405f, -0.321147233f, 0.495152265f, 0.973578751f, + -0.099541299f, 0.049299527f, -0.934519112f, 0.762994647f, 0.912154615f, 0.0546397604f, -0.267960817f, + 0.617890179f, -0.932570338f, 0.0930228606f, 0.682503581f, -0.40138635f, 0.337619752f, 0.140297398f, + 0.775080562f, 0.225799531f, 0.238379061f, -0.173594564f, 0.922408581f, -0.706047475f, -0.646134198f, + 0.199680671f, -0.35468033f, 0.499850065f, -0.11657799f, -0.0234891586f, -0.769448817f, -0.275575161f, + 0.798980594f, -0.695612311f, 0.695419073f, 0.390214443f, 0.229338124f, -0.868227899f, -0.615218103f, + -0.519184887f, 0.492201835f, -0.622231543f, -0.237303019f, -0.285593241f, 0.0687626526f, -0.207151577f, + -0.0351590216f, -0.362614214f, -0.205456749f, 0.278863043f, -0.656075954f, -0.64302206f, -0.747797251f, + -0.309690326f, -0.671908557f, 0.345083028f, 0.633007884f, 0.73866576f, -0.301006228f, -0.721621454f, + -0.829408705f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/output_ref_data.h new file mode 100644 index 00000000..827c0ea7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_3_f32_output_ref_data[148] = { + -1.14182246f, -0.989565551f, -0.530925393f, 0.549719512f, 0.96216464f, 1.99914205f, -3.77958679f, + -2.33141518f, 6.7362442f, 0.69064188f, -3.77452302f, 0.0376905799f, -3.20939517f, -7.32686663f, + 2.69136262f, 4.43096256f, -4.2174387f, -0.420984149f, 2.74078035f, -1.11371827f, 0.484686881f, + 4.95648432f, -4.30738068f, -4.79494619f, 4.83049059f, 0.705552161f, -5.96688938f, 1.33611584f, + 2.75931311f, 0.992147386f, 0.630409598f, 3.69385815f, -2.75687265f, -3.60565853f, 7.66674852f, + 3.63884544f, -3.91523457f, -0.392564863f, -2.53065276f, -0.818504989f, 3.11179447f, -4.35826206f, + -2.82350969f, 3.15570331f, 0.854231596f, 4.15928221f, -5.41977692f, -6.03957272f, -7.24266338f, + -5.08944082f, 3.28335071f, -0.888394535f, -2.33162785f, 1.55129385f, 3.23765397f, 0.364442855f, + 3.73075676f, 0.0509654582f, -0.273040265f, -2.71462369f, 1.28926551f, 0.216138467f, -0.964176893f, + 5.26092577f, -5.86199379f, 0.0420946516f, 0.914454699f, -6.15921926f, -1.34275091f, -1.26859474f, + -2.75378132f, -1.88903069f, 1.76144576f, 2.80600095f, -2.71772814f, -2.81614685f, -1.05988276f, + -1.6350888f, 1.70695496f, -2.84876227f, 3.98632789f, 1.88061941f, -1.37448955f, 3.31100392f, + -0.789836943f, -2.51524997f, -4.52168608f, 0.572277904f, -5.95384645f, 2.53815818f, -3.23739552f, + -1.07684386f, 0.266326457f, -0.25546965f, -0.91262573f, 1.31024444f, 1.44233155f, -1.29430985f, + -5.71055412f, 6.08011293f, -7.07657003f, 2.16144633f, -1.36148441f, 8.3619442f, -2.95200729f, + 1.93093109f, 5.42910385f, -7.62542772f, -2.84509873f, -0.829273641f, 1.93313932f, 1.79812717f, + -0.205077887f, 0.553213179f, -2.44005275f, 1.76428294f, 5.56278992f, 1.61977708f, 1.51985431f, + 5.95836973f, 0.501983941f, -3.90082788f, -1.05329514f, -1.62080455f, -8.31315994f, -1.66834557f, + -4.27518082f, -8.77351952f, -1.12691855f, -5.9769845f, -0.158029035f, 0.682903409f, 1.09833634f, + -0.0828448161f, 3.43314672f, 2.06435633f, -4.43650579f, -7.33391094f, 4.36859179f, -4.0976944f, + -3.94559336f, 1.76592195f, -1.18268383f, -1.52419269f, -2.84770966f, -0.939637542f, -2.55881619f, + -1.57729113f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/weights_data.h new file mode 100644 index 00000000..1d60f685 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_3_f32/weights_data.h @@ -0,0 +1,30 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_3_f32_weights_data[192] = { + 0.182756573f, 0.333428562f, 0.676282406f, -0.418556452f, 0.311451524f, -0.715636194f, 0.447622001f, + 0.091941826f, -0.363596857f, 0.0407138132f, 0.442114085f, -0.423394084f, 0.158428505f, 0.532632411f, + 0.403323084f, -0.0950352624f, -0.327407211f, 0.250397176f, 0.408543617f, 0.696647346f, 0.291799515f, + 0.118485361f, -0.647831738f, 0.0561012551f, 0.0335039236f, -0.203144044f, 0.167494148f, 0.455420136f, + 0.209409967f, 0.576441348f, 0.165548041f, -0.378157884f, -0.101721875f, 0.462260723f, -0.59033078f, + -0.718467772f, -0.408994287f, -0.523852825f, 0.00344648142f, 0.343285859f, -0.180931166f, 0.297096044f, + -0.510095119f, -0.205444068f, 0.695804179f, 0.667457879f, -0.460736513f, 0.195939258f, -0.548466265f, + 0.119755782f, -0.614953339f, -0.272601157f, 0.469934613f, 0.406102002f, 0.582227767f, 0.469071239f, + 0.209034145f, 0.337427616f, 0.00387212262f, -0.31332022f, 0.714520395f, -0.4968898f, 0.632874966f, + 0.0705419928f, -0.656194687f, -0.702453256f, -0.132029533f, -0.528837681f, -0.350157201f, -0.411177754f, + -0.556658387f, -0.414684057f, 0.0864703432f, 0.651694834f, -0.549079418f, 0.476774126f, 0.456469506f, + -0.132168338f, -0.218322575f, 0.546471298f, -0.0251329448f, -0.13670744f, -0.359135747f, -0.277459532f, + 0.388851613f, -0.122698195f, -0.647360265f, -0.645561159f, 0.0548538566f, -0.613162816f, -0.620010912f, + 0.0430206433f, 0.571640193f, -0.440883189f, 0.36912477f, 0.496365011f, -0.135361999f, 0.473790675f, + 0.746628702f, 0.397507459f, 0.748392045f, -0.0830219314f, -0.011387771f, 0.706198692f, -0.00724087888f, + 0.683739781f, -0.188372418f, 0.358977258f, 0.422031701f, -0.73364532f, -0.742512167f, 0.0519189797f, + 0.478507698f, -0.334103495f, -0.408864081f, 0.327950895f, 0.372808516f, -0.198101357f, -0.418819219f, + -0.215219215f, -0.274761021f, -0.14349775f, 0.079353638f, 0.227561891f, -0.134237766f, 0.472324669f, + -0.453412473f, -0.220735252f, -0.170570239f, 0.145594403f, -0.0658231303f, 0.476298928f, 0.411390483f, + 0.31298694f, 0.449715346f, -0.039245192f, 0.203625813f, -0.110235065f, -0.54011333f, -0.219162479f, + 0.632256806f, -0.286224782f, 0.550439775f, 0.0761804059f, -0.025413489f, 0.252749443f, -0.425993055f, + -0.495016247f, -0.594283998f, -0.714184582f, -0.547902763f, 0.309989959f, 0.00868606474f, 0.496514469f, + -0.163048521f, 0.293645173f, -0.255839169f, -0.113064878f, 0.197275162f, -0.625132978f, 0.0449224822f, + 0.437318951f, 0.263188839f, -0.58510083f, -0.0391044579f, 0.546423495f, -0.0408064425f, -0.466062397f, + 0.571607411f, -0.243758529f, -0.357865155f, -0.297353327f, -0.370138049f, 0.269099355f, -0.113513798f, + -0.673197448f, 0.429844916f, 0.695311189f, 0.585427344f, 0.525629997f, 0.0481073894f, -0.236255944f, + 0.712381423f, -0.231689423f, -0.464131296f, -0.145421728f, 0.212649927f, -0.628772259f, 0.060767699f, + -0.0324105993f, -0.0782677084f, 0.189828381f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/biases_data.h new file mode 100644 index 00000000..4b52aa22 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_4_f16_biases_data[4] = {(float16_t)-0.257568359f, + (float16_t)0.485839844f, + (float16_t)0.165771484f, + (float16_t)0.314697266f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/config_data.h new file mode 100644 index 00000000..6abbbeb8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_4_F16_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_4_F16_INPUT_H 1 +#define CONV_MATCH_1XN_4_F16_INPUT_W 16 +#define CONV_MATCH_1XN_4_F16_IN_CH 4 +#define CONV_MATCH_1XN_4_F16_FILTER_H 1 +#define CONV_MATCH_1XN_4_F16_FILTER_W 3 +#define CONV_MATCH_1XN_4_F16_OUT_CH 4 +#define CONV_MATCH_1XN_4_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_4_F16_STRIDE_W 2 +#define CONV_MATCH_1XN_4_F16_PADDING_H 0 +#define CONV_MATCH_1XN_4_F16_PADDING_W 0 +#define CONV_MATCH_1XN_4_F16_DILATION_H 1 +#define CONV_MATCH_1XN_4_F16_DILATION_W 1 +#define CONV_MATCH_1XN_4_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_4_F16_OUTPUT_W 7 +#define CONV_MATCH_1XN_4_F16_OUTPUT_C 4 +#define CONV_MATCH_1XN_4_F16_DST_SIZE 28 +#define CONV_MATCH_1XN_4_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_4_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_4_F16_OUT_ACTIVATION_MIN (float16_t) - 88.0f +#define CONV_MATCH_1XN_4_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/input_data.h new file mode 100644 index 00000000..63f4a42d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/input_data.h @@ -0,0 +1,18 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_4_f16_input_data[64] = { + (float16_t)0.314697266f, (float16_t)-0.870117188f, (float16_t)0.801269531f, (float16_t)0.0375976562f, + (float16_t)-0.0430297852f, (float16_t)-0.665527344f, (float16_t)-0.180664062f, (float16_t)-0.467285156f, + (float16_t)-0.666992188f, (float16_t)0.349609375f, (float16_t)-0.393310547f, (float16_t)-0.310791016f, + (float16_t)-0.755859375f, (float16_t)-0.940917969f, (float16_t)0.970214844f, (float16_t)0.646972656f, + (float16_t)-0.979980469f, (float16_t)0.575683594f, (float16_t)-0.871582031f, (float16_t)-0.693847656f, + (float16_t)-0.0450744629f, (float16_t)-0.955078125f, (float16_t)0.259765625f, (float16_t)0.338378906f, + (float16_t)0.750976562f, (float16_t)0.252685547f, (float16_t)0.343261719f, (float16_t)-0.898925781f, + (float16_t)0.804199219f, (float16_t)-0.364013672f, (float16_t)0.139892578f, (float16_t)-0.7109375f, + (float16_t)0.142700195f, (float16_t)-0.10546875f, (float16_t)-0.639160156f, (float16_t)-0.782714844f, + (float16_t)-0.765136719f, (float16_t)-0.764160156f, (float16_t)-0.209960938f, (float16_t)-0.135864258f, + (float16_t)-0.225341797f, (float16_t)-0.866210938f, (float16_t)0.640625f, (float16_t)0.480712891f, + (float16_t)0.480712891f, (float16_t)0.525878906f, (float16_t)-0.121948242f, (float16_t)-0.9375f, + (float16_t)-0.437011719f, (float16_t)-0.49609375f, (float16_t)0.846191406f, (float16_t)0.443359375f, + (float16_t)-0.741210938f, (float16_t)-0.234741211f, (float16_t)-0.901855469f, (float16_t)0.373779297f, + (float16_t)-0.949707031f, (float16_t)-0.577636719f, (float16_t)-0.208251953f, (float16_t)0.102783203f, + (float16_t)-0.724121094f, (float16_t)0.321044922f, (float16_t)0.493652344f, (float16_t)-0.037322998f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/output_ref_data.h new file mode 100644 index 00000000..2b7c3e68 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/output_ref_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_4_f16_output_ref_data[28] = { + (float16_t)-1.02246094f, (float16_t)-0.752441406f, (float16_t)0.0682983398f, (float16_t)0.339355469f, + (float16_t)0.134033203f, (float16_t)0.469482422f, (float16_t)-0.613769531f, (float16_t)2.09570312f, + (float16_t)-0.0251464844f, (float16_t)1.65527344f, (float16_t)1.12695312f, (float16_t)-0.392822266f, + (float16_t)-0.538085938f, (float16_t)0.263183594f, (float16_t)2.37304688f, (float16_t)1.0546875f, + (float16_t)-0.253173828f, (float16_t)0.897460938f, (float16_t)-0.338134766f, (float16_t)0.983398438f, + (float16_t)-0.719726562f, (float16_t)-0.0426940918f, (float16_t)-0.3984375f, (float16_t)-0.985351562f, + (float16_t)0.305175781f, (float16_t)-0.384033203f, (float16_t)-1.76660156f, (float16_t)0.270263672f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/weights_data.h new file mode 100644 index 00000000..724a1c00 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f16/weights_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_4_f16_weights_data[48] = { + (float16_t)-0.717773438f, (float16_t)0.682617188f, (float16_t)0.404052734f, (float16_t)0.181152344f, + (float16_t)-0.214233398f, (float16_t)-0.208129883f, (float16_t)0.189208984f, (float16_t)-0.0822143555f, + (float16_t)0.447998047f, (float16_t)-0.64453125f, (float16_t)-0.722167969f, (float16_t)0.602539062f, + (float16_t)-0.101745605f, (float16_t)0.609863281f, (float16_t)-0.735351562f, (float16_t)-0.158203125f, + (float16_t)-0.705078125f, (float16_t)0.72265625f, (float16_t)-0.284667969f, (float16_t)-0.552246094f, + (float16_t)0.0755615234f, (float16_t)0.154418945f, (float16_t)0.423339844f, (float16_t)-0.719238281f, + (float16_t)0.602050781f, (float16_t)0.334472656f, (float16_t)-0.0123977661f, (float16_t)-0.264892578f, + (float16_t)0.514160156f, (float16_t)-0.119567871f, (float16_t)0.141723633f, (float16_t)-0.446533203f, + (float16_t)0.657226562f, (float16_t)0.0280456543f, (float16_t)0.0500793457f, (float16_t)-0.745117188f, + (float16_t)0.692871094f, (float16_t)-0.12902832f, (float16_t)-0.373779297f, (float16_t)-0.736328125f, + (float16_t)-0.5078125f, (float16_t)0.501953125f, (float16_t)0.575683594f, (float16_t)0.429931641f, + (float16_t)-0.646484375f, (float16_t)-0.348144531f, (float16_t)-0.681640625f, (float16_t)-0.197998047f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/biases_data.h new file mode 100644 index 00000000..e781a842 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_4_f32_biases_data[4] = {-0.257575452f, 0.485877573f, 0.165795356f, 0.314711511f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/config_data.h new file mode 100644 index 00000000..b86be126 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_4_F32_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_4_F32_INPUT_H 1 +#define CONV_MATCH_1XN_4_F32_INPUT_W 16 +#define CONV_MATCH_1XN_4_F32_IN_CH 4 +#define CONV_MATCH_1XN_4_F32_FILTER_H 1 +#define CONV_MATCH_1XN_4_F32_FILTER_W 3 +#define CONV_MATCH_1XN_4_F32_OUT_CH 4 +#define CONV_MATCH_1XN_4_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_4_F32_STRIDE_W 2 +#define CONV_MATCH_1XN_4_F32_PADDING_H 0 +#define CONV_MATCH_1XN_4_F32_PADDING_W 0 +#define CONV_MATCH_1XN_4_F32_DILATION_H 1 +#define CONV_MATCH_1XN_4_F32_DILATION_W 1 +#define CONV_MATCH_1XN_4_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_4_F32_OUTPUT_W 7 +#define CONV_MATCH_1XN_4_F32_OUTPUT_C 4 +#define CONV_MATCH_1XN_4_F32_DST_SIZE 28 +#define CONV_MATCH_1XN_4_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_4_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_4_F32_OUT_ACTIVATION_MIN -88.0f +#define CONV_MATCH_1XN_4_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/input_data.h new file mode 100644 index 00000000..aebd0222 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/input_data.h @@ -0,0 +1,12 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_4_f32_input_data[64] = { + 0.314629048f, -0.870243609f, 0.801388025f, 0.0375991575f, -0.043018464f, -0.665401578f, -0.180719212f, + -0.467207611f, -0.666779101f, 0.349497527f, -0.393315077f, -0.310842484f, -0.755912364f, -0.940955639f, + 0.970157266f, 0.646841943f, -0.98020798f, 0.575457871f, -0.871656358f, -0.693746269f, -0.0450785495f, + -0.955072641f, 0.259701371f, 0.338498324f, 0.750929296f, 0.252777159f, 0.343155622f, -0.898901582f, + 0.804136157f, -0.363921255f, 0.139881611f, -0.711160183f, 0.142750099f, -0.10544654f, -0.638967514f, + -0.782684743f, -0.765093923f, -0.764355242f, -0.209964365f, -0.135919258f, -0.225341946f, -0.866127849f, + 0.640427828f, 0.480704546f, 0.480592102f, 0.526114643f, -0.121918581f, -0.937693536f, -0.437128901f, + -0.49606666f, 0.845991552f, 0.443288445f, -0.7414186f, -0.234755501f, -0.901731431f, 0.373823255f, + -0.949609637f, -0.577653408f, -0.208249778f, 0.102802649f, -0.724129498f, 0.32100144f, 0.493566155f, + -0.0373189747f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/output_ref_data.h new file mode 100644 index 00000000..6eb3d4fc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_4_f32_output_ref_data[28] = { + -1.02239561f, -0.752499044f, 0.0683080852f, 0.339460313f, 0.133991569f, 0.469497502f, -0.613664389f, + 2.096627f, -0.025151521f, 1.65514565f, 1.12700295f, -0.392932773f, -0.538251996f, 0.263261497f, + 2.37287664f, 1.05428421f, -0.253236651f, 0.897251725f, -0.338049382f, 0.983451664f, -0.719726562f, + -0.0426883101f, -0.398386329f, -0.9853459f, 0.305171311f, -0.383927047f, -1.76636624f, 0.2702353f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/weights_data.h new file mode 100644 index 00000000..9c1a2f16 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_4_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_4_f32_weights_data[48] = { + -0.717900455f, 0.68277818f, 0.404053867f, 0.181187615f, -0.214247838f, -0.208082572f, 0.189195782f, + -0.0822011977f, 0.448058933f, -0.644434392f, -0.722182393f, 0.602653563f, -0.101753637f, 0.609860659f, + -0.735192895f, -0.158258438f, -0.705227435f, 0.722789347f, -0.284630179f, -0.552307725f, 0.0755650625f, + 0.15441671f, 0.423248291f, -0.719027579f, 0.602094769f, 0.334478199f, -0.0123973461f, -0.264854282f, + 0.514332592f, -0.119574241f, 0.141710848f, -0.446418196f, 0.657076538f, 0.0280468669f, 0.0500915982f, + -0.745304465f, 0.692713737f, -0.129067913f, -0.373673677f, -0.736365259f, -0.508052528f, 0.502171636f, + 0.575887501f, 0.430047899f, -0.646413863f, -0.348253101f, -0.681558251f, -0.197983429f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/biases_data.h new file mode 100644 index 00000000..69c4999b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_5_f16_biases_data[1] = {(float16_t)-0.107055664f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/config_data.h new file mode 100644 index 00000000..0fc6e885 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_5_F16_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_5_F16_INPUT_H 1 +#define CONV_MATCH_1XN_5_F16_INPUT_W 17 +#define CONV_MATCH_1XN_5_F16_IN_CH 4 +#define CONV_MATCH_1XN_5_F16_FILTER_H 1 +#define CONV_MATCH_1XN_5_F16_FILTER_W 3 +#define CONV_MATCH_1XN_5_F16_OUT_CH 1 +#define CONV_MATCH_1XN_5_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_5_F16_STRIDE_W 3 +#define CONV_MATCH_1XN_5_F16_PADDING_H 0 +#define CONV_MATCH_1XN_5_F16_PADDING_W 0 +#define CONV_MATCH_1XN_5_F16_DILATION_H 1 +#define CONV_MATCH_1XN_5_F16_DILATION_W 1 +#define CONV_MATCH_1XN_5_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_5_F16_OUTPUT_W 5 +#define CONV_MATCH_1XN_5_F16_OUTPUT_C 1 +#define CONV_MATCH_1XN_5_F16_DST_SIZE 5 +#define CONV_MATCH_1XN_5_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_5_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_5_F16_OUT_ACTIVATION_MIN (float16_t) - 88.0f +#define CONV_MATCH_1XN_5_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/input_data.h new file mode 100644 index 00000000..e1fcf85b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/input_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_5_f16_input_data[68] = { + (float16_t)-0.536132812f, (float16_t)0.67578125f, (float16_t)-0.851074219f, (float16_t)-0.766601562f, + (float16_t)0.568847656f, (float16_t)0.700683594f, (float16_t)0.792480469f, (float16_t)-0.0495910645f, + (float16_t)0.765136719f, (float16_t)-0.0102081299f, (float16_t)0.907226562f, (float16_t)0.487548828f, + (float16_t)0.721679688f, (float16_t)-0.569335938f, (float16_t)0.801757812f, (float16_t)-0.00796508789f, + (float16_t)-0.109008789f, (float16_t)0.814453125f, (float16_t)0.668457031f, (float16_t)-0.478027344f, + (float16_t)0.244750977f, (float16_t)-0.773925781f, (float16_t)0.396240234f, (float16_t)0.966796875f, + (float16_t)0.674804688f, (float16_t)0.444580078f, (float16_t)0.181518555f, (float16_t)-0.961914062f, + (float16_t)0.57421875f, (float16_t)-0.916503906f, (float16_t)-0.724121094f, (float16_t)-0.924804688f, + (float16_t)0.264160156f, (float16_t)-0.809570312f, (float16_t)0.776367188f, (float16_t)-0.495605469f, + (float16_t)0.402832031f, (float16_t)0.276611328f, (float16_t)0.580078125f, (float16_t)-0.901367188f, + (float16_t)-0.0233306885f, (float16_t)-0.604003906f, (float16_t)0.901367188f, (float16_t)-0.0601501465f, + (float16_t)-0.0358276367f, (float16_t)0.96484375f, (float16_t)-0.028717041f, (float16_t)0.181518555f, + (float16_t)0.952636719f, (float16_t)-0.177856445f, (float16_t)0.856933594f, (float16_t)0.0654907227f, + (float16_t)0.298339844f, (float16_t)0.324707031f, (float16_t)0.447753906f, (float16_t)-0.359130859f, + (float16_t)-0.0861206055f, (float16_t)0.697265625f, (float16_t)-0.0390319824f, (float16_t)0.5390625f, + (float16_t)-0.773925781f, (float16_t)0.904785156f, (float16_t)0.168579102f, (float16_t)-0.690917969f, + (float16_t)-0.00895690918f, (float16_t)-0.720703125f, (float16_t)-0.291992188f, (float16_t)0.0115432739f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/output_ref_data.h new file mode 100644 index 00000000..8443e01f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_5_f16_output_ref_data[5] = {(float16_t)1.24023438f, + (float16_t)0.444824219f, + (float16_t)0.380859375f, + (float16_t)-1.73242188f, + (float16_t)-0.606445312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/weights_data.h new file mode 100644 index 00000000..7dff8352 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f16/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_5_f16_weights_data[12] = {(float16_t)-0.118530273f, + (float16_t)-0.400146484f, + (float16_t)-0.725097656f, + (float16_t)0.0318908691f, + (float16_t)0.593261719f, + (float16_t)0.521484375f, + (float16_t)-0.467529297f, + (float16_t)-0.16796875f, + (float16_t)-0.258789062f, + (float16_t)-0.351074219f, + (float16_t)0.685546875f, + (float16_t)0.396728516f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/biases_data.h new file mode 100644 index 00000000..c9d2137b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_5_f32_biases_data[1] = {-0.1070273f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/config_data.h new file mode 100644 index 00000000..6da54b22 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_5_F32_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_5_F32_INPUT_H 1 +#define CONV_MATCH_1XN_5_F32_INPUT_W 17 +#define CONV_MATCH_1XN_5_F32_IN_CH 4 +#define CONV_MATCH_1XN_5_F32_FILTER_H 1 +#define CONV_MATCH_1XN_5_F32_FILTER_W 3 +#define CONV_MATCH_1XN_5_F32_OUT_CH 1 +#define CONV_MATCH_1XN_5_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_5_F32_STRIDE_W 3 +#define CONV_MATCH_1XN_5_F32_PADDING_H 0 +#define CONV_MATCH_1XN_5_F32_PADDING_W 0 +#define CONV_MATCH_1XN_5_F32_DILATION_H 1 +#define CONV_MATCH_1XN_5_F32_DILATION_W 1 +#define CONV_MATCH_1XN_5_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_5_F32_OUTPUT_W 5 +#define CONV_MATCH_1XN_5_F32_OUTPUT_C 1 +#define CONV_MATCH_1XN_5_F32_DST_SIZE 5 +#define CONV_MATCH_1XN_5_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_5_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_5_F32_OUT_ACTIVATION_MIN -88.0f +#define CONV_MATCH_1XN_5_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/input_data.h new file mode 100644 index 00000000..0f3b42b5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/input_data.h @@ -0,0 +1,12 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_5_f32_input_data[68] = { + -0.536353827f, 0.675934196f, -0.851110399f, -0.766643584f, 0.569043338f, 0.700899422f, 0.792390585f, + -0.0496046916f, 0.765182853f, -0.0102048367f, 0.906987607f, 0.487517416f, 0.721537471f, -0.569412351f, + 0.801871657f, -0.00796704087f, -0.10901355f, 0.81452179f, 0.668697298f, -0.477907449f, 0.244789779f, + -0.773878157f, 0.396285266f, 0.966622531f, 0.674677432f, 0.444594622f, 0.181522503f, -0.962004483f, + 0.574338019f, -0.916271627f, -0.724093437f, -0.924772799f, 0.264096946f, -0.809461474f, 0.776189685f, + -0.495568246f, 0.402878284f, 0.276714712f, 0.580079556f, -0.901420057f, -0.0233254228f, -0.60403198f, + 0.90142411f, -0.0601540357f, -0.0358229391f, 0.964993596f, -0.028721055f, 0.181520268f, 0.952641487f, + -0.177886546f, 0.856908202f, 0.0655174553f, 0.298324734f, 0.324679613f, 0.447831333f, -0.359164029f, + -0.0860972032f, 0.697497368f, -0.039027229f, 0.538870156f, -0.773942471f, 0.904703677f, 0.168563291f, + -0.69104743f, -0.00895893667f, -0.72082895f, -0.292066187f, 0.0115447706f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/output_ref_data.h new file mode 100644 index 00000000..671db2fa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_5_f32_output_ref_data[5] = {1.23991716f, + 0.444789171f, + 0.380858958f, + -1.73225701f, + -0.60636425f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/weights_data.h new file mode 100644 index 00000000..d5fb44b5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_5_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_5_f32_weights_data[12] = {-0.118545964f, + -0.400148779f, + -0.724927545f, + 0.0318921469f, + 0.593055844f, + 0.521471143f, + -0.467611074f, + -0.168010309f, + -0.25878042f, + -0.351125062f, + 0.685330093f, + 0.396624357f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/biases_data.h new file mode 100644 index 00000000..98c88863 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_6_generic_f16_biases_data[16] = {(float16_t)-0.431396484f, + (float16_t)0.0745849609f, + (float16_t)-0.064453125f, + (float16_t)0.319091797f, + (float16_t)0.391845703f, + (float16_t)-0.0865478516f, + (float16_t)-0.368408203f, + (float16_t)0.213623047f, + (float16_t)0.46484375f, + (float16_t)-0.365234375f, + (float16_t)0.392578125f, + (float16_t)-0.158691406f, + (float16_t)0.0581054688f, + (float16_t)-0.256835938f, + (float16_t)-0.00414276123f, + (float16_t)0.0168457031f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/config_data.h new file mode 100644 index 00000000..6d70ce37 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_6_GENERIC_F16_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_INPUT_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_INPUT_W 4 +#define CONV_MATCH_1XN_6_GENERIC_F16_IN_CH 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_FILTER_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_FILTER_W 8 +#define CONV_MATCH_1XN_6_GENERIC_F16_OUT_CH 16 +#define CONV_MATCH_1XN_6_GENERIC_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_STRIDE_W 4 +#define CONV_MATCH_1XN_6_GENERIC_F16_PADDING_H 0 +#define CONV_MATCH_1XN_6_GENERIC_F16_PADDING_W 2 +#define CONV_MATCH_1XN_6_GENERIC_F16_DILATION_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_DILATION_W 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_OUTPUT_W 1 +#define CONV_MATCH_1XN_6_GENERIC_F16_OUTPUT_C 16 +#define CONV_MATCH_1XN_6_GENERIC_F16_DST_SIZE 16 +#define CONV_MATCH_1XN_6_GENERIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_6_GENERIC_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_6_GENERIC_F16_OUT_ACTIVATION_MIN (float16_t) - 125.0f +#define CONV_MATCH_1XN_6_GENERIC_F16_OUT_ACTIVATION_MAX (float16_t)126.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/input_data.h new file mode 100644 index 00000000..4f15445a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/input_data.h @@ -0,0 +1,5 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_6_generic_f16_input_data[4] = {(float16_t)-0.189208984f, + (float16_t)-0.953125f, + (float16_t)0.709960938f, + (float16_t)0.130249023f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/output_ref_data.h new file mode 100644 index 00000000..428bd8c8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_6_generic_f16_output_ref_data[16] = {(float16_t)-0.504394531f, + (float16_t)0.577636719f, + (float16_t)-0.241699219f, + (float16_t)0.858886719f, + (float16_t)0.856933594f, + (float16_t)0.934082031f, + (float16_t)-0.128173828f, + (float16_t)0.256835938f, + (float16_t)1.02148438f, + (float16_t)-0.301025391f, + (float16_t)0.557128906f, + (float16_t)-0.458984375f, + (float16_t)0.0454406738f, + (float16_t)-0.444091797f, + (float16_t)0.172973633f, + (float16_t)0.416992188f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/weights_data.h new file mode 100644 index 00000000..be8aa874 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f16/weights_data.h @@ -0,0 +1,34 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_6_generic_f16_weights_data[128] = { + (float16_t)0.230957031f, (float16_t)0.625976562f, (float16_t)-0.671386719f, (float16_t)0.584960938f, + (float16_t)0.408203125f, (float16_t)0.517089844f, (float16_t)-0.485839844f, (float16_t)0.661621094f, + (float16_t)-0.473632812f, (float16_t)-0.633300781f, (float16_t)0.504394531f, (float16_t)-0.439453125f, + (float16_t)0.34375f, (float16_t)-0.496826172f, (float16_t)-0.236206055f, (float16_t)-0.115783691f, + (float16_t)0.700195312f, (float16_t)0.395019531f, (float16_t)0.693359375f, (float16_t)-0.342773438f, + (float16_t)-0.552734375f, (float16_t)0.152709961f, (float16_t)0.200561523f, (float16_t)-0.391845703f, + (float16_t)0.228637695f, (float16_t)-0.207519531f, (float16_t)0.294189453f, (float16_t)-0.183837891f, + (float16_t)0.720214844f, (float16_t)-0.702636719f, (float16_t)0.215698242f, (float16_t)0.494873047f, + (float16_t)-0.3515625f, (float16_t)-0.390625f, (float16_t)0.642578125f, (float16_t)-0.0520935059f, + (float16_t)0.648925781f, (float16_t)0.583984375f, (float16_t)0.69921875f, (float16_t)-0.384033203f, + (float16_t)-0.410644531f, (float16_t)0.164672852f, (float16_t)0.0231933594f, (float16_t)-0.541015625f, + (float16_t)0.717773438f, (float16_t)-0.00423431396f, (float16_t)0.746582031f, (float16_t)0.670410156f, + (float16_t)-0.709472656f, (float16_t)0.691894531f, (float16_t)-0.333740234f, (float16_t)0.166015625f, + (float16_t)0.605957031f, (float16_t)-0.73046875f, (float16_t)-0.712890625f, (float16_t)-0.542480469f, + (float16_t)-0.515625f, (float16_t)0.5390625f, (float16_t)0.12286377f, (float16_t)-0.179321289f, + (float16_t)-0.196899414f, (float16_t)0.270996094f, (float16_t)0.368164062f, (float16_t)0.526855469f, + (float16_t)0.420654297f, (float16_t)0.245483398f, (float16_t)-0.349365234f, (float16_t)-0.374511719f, + (float16_t)0.304199219f, (float16_t)-0.631835938f, (float16_t)-0.00846862793f, (float16_t)-0.511230469f, + (float16_t)0.159423828f, (float16_t)-0.487304688f, (float16_t)-0.386962891f, (float16_t)0.025390625f, + (float16_t)-0.110778809f, (float16_t)0.719238281f, (float16_t)0.123474121f, (float16_t)0.449951172f, + (float16_t)-0.033996582f, (float16_t)0.650878906f, (float16_t)-0.164550781f, (float16_t)0.0994873047f, + (float16_t)0.237060547f, (float16_t)0.458251953f, (float16_t)-0.544433594f, (float16_t)-0.423583984f, + (float16_t)-0.535644531f, (float16_t)0.680664062f, (float16_t)-0.348144531f, (float16_t)-0.0172271729f, + (float16_t)-0.546386719f, (float16_t)0.0422058105f, (float16_t)0.521484375f, (float16_t)0.232177734f, + (float16_t)0.327148438f, (float16_t)0.598144531f, (float16_t)-0.0378723145f, (float16_t)0.437744141f, + (float16_t)0.586425781f, (float16_t)-0.147338867f, (float16_t)-0.602050781f, (float16_t)-0.0114822388f, + (float16_t)-0.421875f, (float16_t)-0.514160156f, (float16_t)0.737792969f, (float16_t)-0.387695312f, + (float16_t)-0.603027344f, (float16_t)0.0838012695f, (float16_t)-0.108947754f, (float16_t)-0.478271484f, + (float16_t)-0.421142578f, (float16_t)0.13269043f, (float16_t)-0.697265625f, (float16_t)-0.505371094f, + (float16_t)-0.583984375f, (float16_t)-0.166625977f, (float16_t)-0.630371094f, (float16_t)-0.296142578f, + (float16_t)-0.543457031f, (float16_t)0.564453125f, (float16_t)-0.1953125f, (float16_t)-0.440673828f, + (float16_t)-0.00570678711f, (float16_t)-0.403808594f, (float16_t)-0.21875f, (float16_t)0.532714844f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/biases_data.h new file mode 100644 index 00000000..96acfce5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_6_generic_f32_biases_data[16] = {-0.431391329f, + 0.0745714307f, + -0.0644479319f, + 0.319185734f, + 0.391906619f, + -0.0865182951f, + -0.3684057f, + 0.213608131f, + 0.464909434f, + -0.365116388f, + 0.392644644f, + -0.158683047f, + 0.0581084006f, + -0.256927758f, + -0.00414332561f, + 0.0168471076f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/config_data.h new file mode 100644 index 00000000..41012fc9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_6_GENERIC_F32_INPUT_BATCHES 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_INPUT_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_INPUT_W 4 +#define CONV_MATCH_1XN_6_GENERIC_F32_IN_CH 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_FILTER_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_FILTER_W 8 +#define CONV_MATCH_1XN_6_GENERIC_F32_OUT_CH 16 +#define CONV_MATCH_1XN_6_GENERIC_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_STRIDE_W 4 +#define CONV_MATCH_1XN_6_GENERIC_F32_PADDING_H 0 +#define CONV_MATCH_1XN_6_GENERIC_F32_PADDING_W 2 +#define CONV_MATCH_1XN_6_GENERIC_F32_DILATION_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_DILATION_W 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_OUTPUT_W 1 +#define CONV_MATCH_1XN_6_GENERIC_F32_OUTPUT_C 16 +#define CONV_MATCH_1XN_6_GENERIC_F32_DST_SIZE 16 +#define CONV_MATCH_1XN_6_GENERIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_6_GENERIC_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_6_GENERIC_F32_OUT_ACTIVATION_MIN -125.0f +#define CONV_MATCH_1XN_6_GENERIC_F32_OUT_ACTIVATION_MAX 126.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/input_data.h new file mode 100644 index 00000000..f74731e5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/input_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_6_generic_f32_input_data[4] = {-0.18924658f, -0.953050971f, 0.710085094f, 0.130218863f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/output_ref_data.h new file mode 100644 index 00000000..ebfe5814 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_6_generic_f32_output_ref_data[16] = {-0.50431788f, + 0.577440083f, + -0.241667777f, + 0.858727872f, + 0.856838584f, + 0.933862209f, + -0.128223822f, + 0.256718338f, + 1.02174926f, + -0.301061541f, + 0.556995392f, + -0.458968997f, + 0.0454323106f, + -0.444143474f, + 0.173000634f, + 0.417106777f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/weights_data.h new file mode 100644 index 00000000..b73f76f5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_6_generic_f32/weights_data.h @@ -0,0 +1,21 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_6_generic_f32_weights_data[128] = { + 0.230899081f, 0.625765264f, -0.671313524f, 0.584721446f, 0.40831849f, 0.517268479f, -0.485787332f, + 0.661414802f, -0.47367698f, -0.633081913f, 0.504183233f, -0.439530224f, 0.343727857f, -0.49675706f, + -0.236175328f, -0.115773924f, 0.700331032f, 0.395123988f, 0.693145931f, -0.342713237f, -0.552819848f, + 0.152678385f, 0.200519711f, -0.391768903f, 0.228655055f, -0.207463473f, 0.294182509f, -0.183845237f, + 0.720335603f, -0.70265013f, 0.215718165f, 0.494970679f, -0.35163942f, -0.390505105f, 0.642695129f, + -0.0520894378f, 0.649045706f, 0.583926618f, 0.699077845f, -0.384082377f, -0.410754621f, 0.164732516f, + 0.0231998097f, -0.541039586f, 0.717778862f, -0.0042337859f, 0.746436238f, 0.670611501f, -0.709459841f, + 0.692089558f, -0.333629608f, 0.165967733f, 0.606081247f, -0.730692267f, -0.712793589f, -0.542469442f, + -0.515788257f, 0.539184391f, 0.122890987f, -0.179284275f, -0.196877778f, 0.271080732f, 0.368217289f, + 0.5269472f, 0.420755416f, 0.245497122f, -0.349275768f, -0.374610513f, 0.304196358f, -0.631917953f, + -0.00846775807f, -0.511211097f, 0.159420758f, -0.487285525f, -0.387037098f, 0.0253917798f, -0.110781766f, + 0.71935457f, 0.123495981f, 0.449963748f, -0.0339915007f, 0.650854647f, -0.164598033f, 0.09951552f, + 0.2371099f, 0.4582766f, -0.544424534f, -0.423703343f, -0.535402894f, 0.680539012f, -0.348050177f, + -0.0172223877f, -0.546501994f, 0.0422046371f, 0.521539986f, 0.232132807f, 0.32713905f, 0.59829706f, + -0.0378656015f, 0.43779695f, 0.586666167f, -0.147306934f, -0.602048576f, -0.0114826383f, -0.421865046f, + -0.514125586f, 0.737703145f, -0.387735546f, -0.602817178f, 0.0837960467f, -0.108953863f, -0.478388399f, + -0.421137959f, 0.132632941f, -0.697360575f, -0.505139649f, -0.583812475f, -0.166615143f, -0.630598247f, + -0.296185613f, -0.543280959f, 0.564314663f, -0.195342928f, -0.440600574f, -0.0057049212f, -0.403721362f, + -0.218782097f, 0.532729447f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/biases_data.h new file mode 100644 index 00000000..9f13ed7b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/biases_data.h @@ -0,0 +1,16 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_7_f16_biases_data[15] = {(float16_t)0.251220703f, + (float16_t)-0.373046875f, + (float16_t)0.316894531f, + (float16_t)0.34765625f, + (float16_t)-0.454589844f, + (float16_t)-0.225585938f, + (float16_t)0.411621094f, + (float16_t)0.447998047f, + (float16_t)-0.339111328f, + (float16_t)-0.01953125f, + (float16_t)0.497070312f, + (float16_t)0.0176544189f, + (float16_t)0.393798828f, + (float16_t)-0.154174805f, + (float16_t)0.335205078f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/config_data.h new file mode 100644 index 00000000..2f3da6ae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_7_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_7_F16_INPUT_H 1 +#define CONV_MATCH_1XN_7_F16_INPUT_W 148 +#define CONV_MATCH_1XN_7_F16_IN_CH 20 +#define CONV_MATCH_1XN_7_F16_FILTER_H 1 +#define CONV_MATCH_1XN_7_F16_FILTER_W 32 +#define CONV_MATCH_1XN_7_F16_OUT_CH 15 +#define CONV_MATCH_1XN_7_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_7_F16_STRIDE_W 1 +#define CONV_MATCH_1XN_7_F16_PADDING_H 0 +#define CONV_MATCH_1XN_7_F16_PADDING_W 15 +#define CONV_MATCH_1XN_7_F16_DILATION_H 1 +#define CONV_MATCH_1XN_7_F16_DILATION_W 1 +#define CONV_MATCH_1XN_7_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_7_F16_OUTPUT_W 147 +#define CONV_MATCH_1XN_7_F16_OUTPUT_C 15 +#define CONV_MATCH_1XN_7_F16_DST_SIZE 4410 +#define CONV_MATCH_1XN_7_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_7_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_7_F16_OUT_ACTIVATION_MIN (float16_t) - 127.0f +#define CONV_MATCH_1XN_7_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/input_data.h new file mode 100644 index 00000000..195abecd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/input_data.h @@ -0,0 +1,5921 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_7_f16_input_data[5920] = {(float16_t)-0.211303711f, + (float16_t)-0.406005859f, + (float16_t)0.963867188f, + (float16_t)-0.794433594f, + (float16_t)0.204223633f, + (float16_t)0.128540039f, + (float16_t)0.147216797f, + (float16_t)0.529785156f, + (float16_t)0.776367188f, + (float16_t)-0.391845703f, + (float16_t)0.666503906f, + (float16_t)0.763671875f, + (float16_t)-0.758789062f, + (float16_t)0.994628906f, + (float16_t)-0.196655273f, + (float16_t)-0.959472656f, + (float16_t)0.20715332f, + (float16_t)-0.62890625f, + (float16_t)-0.530761719f, + (float16_t)-0.505859375f, + (float16_t)0.734375f, + (float16_t)0.970703125f, + (float16_t)0.940429688f, + (float16_t)0.0365905762f, + (float16_t)0.548828125f, + (float16_t)-0.411621094f, + (float16_t)-0.0538330078f, + (float16_t)-0.922363281f, + (float16_t)0.416259766f, + (float16_t)0.91796875f, + (float16_t)0.729980469f, + (float16_t)0.521484375f, + (float16_t)0.406005859f, + (float16_t)-0.939453125f, + (float16_t)-0.63671875f, + (float16_t)-0.765625f, + (float16_t)0.104187012f, + (float16_t)0.671386719f, + (float16_t)-0.794433594f, + (float16_t)-0.532226562f, + (float16_t)-0.17590332f, + (float16_t)0.944824219f, + (float16_t)-0.628417969f, + (float16_t)0.529296875f, + (float16_t)-0.46875f, + (float16_t)0.139526367f, + (float16_t)-0.765136719f, + (float16_t)-0.051574707f, + (float16_t)-0.77734375f, + (float16_t)-0.555175781f, + (float16_t)-0.188354492f, + (float16_t)0.905761719f, + (float16_t)0.0911254883f, + (float16_t)-0.306640625f, + (float16_t)0.270507812f, + (float16_t)0.220092773f, + (float16_t)0.864257812f, + (float16_t)-0.829101562f, + (float16_t)0.307617188f, + (float16_t)0.837890625f, + (float16_t)0.458007812f, + (float16_t)0.183349609f, + (float16_t)-0.75390625f, + (float16_t)0.723632812f, + (float16_t)0.907714844f, + (float16_t)0.434326172f, + (float16_t)-0.760253906f, + (float16_t)0.434082031f, + (float16_t)-0.947753906f, + (float16_t)0.469238281f, + (float16_t)0.113586426f, + (float16_t)-0.891601562f, + (float16_t)-0.430908203f, + (float16_t)0.994628906f, + (float16_t)-0.7265625f, + (float16_t)-0.955078125f, + (float16_t)0.611816406f, + (float16_t)-0.557617188f, + (float16_t)0.700195312f, + (float16_t)-0.309326172f, + (float16_t)0.548339844f, + (float16_t)0.829101562f, + (float16_t)-0.558105469f, + (float16_t)0.456787109f, + (float16_t)-0.909667969f, + (float16_t)-0.935546875f, + (float16_t)-0.811035156f, + (float16_t)-0.302246094f, + (float16_t)-0.827148438f, + (float16_t)0.529296875f, + (float16_t)-0.887695312f, + (float16_t)-0.276611328f, + (float16_t)0.397949219f, + (float16_t)-0.200683594f, + (float16_t)-0.616210938f, + (float16_t)0.440185547f, + (float16_t)0.177490234f, + (float16_t)-0.815917969f, + (float16_t)-0.978027344f, + (float16_t)0.22253418f, + (float16_t)-0.936523438f, + (float16_t)0.793457031f, + (float16_t)0.0253295898f, + (float16_t)-0.662109375f, + (float16_t)-0.999511719f, + 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(float16_t)0.288818359f, + (float16_t)0.568359375f, + (float16_t)0.215209961f, + (float16_t)0.0542602539f, + (float16_t)-0.186889648f, + (float16_t)0.833007812f, + (float16_t)0.353759766f, + (float16_t)-0.114135742f, + (float16_t)-0.770996094f, + (float16_t)0.495117188f, + (float16_t)-0.831542969f, + (float16_t)-0.800292969f, + (float16_t)-0.811035156f, + (float16_t)-0.0226898193f, + (float16_t)-0.101318359f, + (float16_t)-0.375244141f, + (float16_t)-0.916503906f, + (float16_t)-0.00898742676f, + (float16_t)0.445556641f, + (float16_t)0.76953125f, + (float16_t)-0.372314453f, + (float16_t)0.94140625f, + (float16_t)0.184082031f, + (float16_t)-0.0836791992f, + (float16_t)0.482177734f, + (float16_t)0.0836181641f, + (float16_t)0.701171875f, + (float16_t)-0.0859375f, + (float16_t)0.0991210938f, + (float16_t)-0.337890625f, + (float16_t)-0.448486328f, + (float16_t)0.93359375f, + (float16_t)0.117370605f, + (float16_t)0.02684021f, + (float16_t)-0.205444336f, + (float16_t)-0.684082031f, + (float16_t)-0.995605469f, + (float16_t)0.00659179688f, + (float16_t)-0.0118179321f, + (float16_t)-0.729003906f, + (float16_t)-0.211181641f, + (float16_t)0.793457031f, + (float16_t)0.852050781f, + (float16_t)-0.00477981567f, + (float16_t)0.601074219f, + (float16_t)0.846679688f, + (float16_t)-0.524902344f, + (float16_t)0.378417969f, + (float16_t)0.309814453f, + (float16_t)-0.717285156f, + (float16_t)0.0798950195f, + (float16_t)-0.0733032227f, + (float16_t)-0.6796875f, + (float16_t)-0.0776367188f, + (float16_t)-0.215820312f, + (float16_t)-0.354003906f, + (float16_t)-0.499511719f, + (float16_t)-0.934082031f, + (float16_t)-0.0550537109f, + (float16_t)0.385253906f, + (float16_t)-0.407470703f, + (float16_t)-0.414306641f, + (float16_t)-0.749511719f, + (float16_t)0.235595703f, + (float16_t)-0.880859375f, + (float16_t)-0.0316467285f, + (float16_t)0.452880859f, + (float16_t)-0.548339844f, + (float16_t)0.0107421875f, + (float16_t)-0.166748047f, + (float16_t)-0.996582031f, + (float16_t)0.740722656f, + (float16_t)-0.537597656f, + (float16_t)0.111999512f, + (float16_t)-0.378173828f, + (float16_t)0.955566406f, + (float16_t)0.716308594f, + (float16_t)0.863769531f, + (float16_t)-0.139648438f, + (float16_t)-0.385498047f, + (float16_t)0.511230469f, + (float16_t)-0.635742188f, + (float16_t)0.332519531f, + (float16_t)0.581542969f, + (float16_t)-0.220825195f, + (float16_t)0.230102539f, + (float16_t)0.761230469f, + (float16_t)-0.622558594f, + (float16_t)-0.528320312f, + (float16_t)-0.285644531f, + (float16_t)0.362792969f, + (float16_t)-0.806152344f, + (float16_t)-0.9375f, + (float16_t)0.194458008f, + (float16_t)0.871582031f, + (float16_t)0.934570312f, + (float16_t)0.806152344f, + (float16_t)0.288574219f, + (float16_t)0.487792969f, + (float16_t)0.615722656f, + (float16_t)0.938476562f, + (float16_t)-0.223754883f, + (float16_t)0.200317383f, + (float16_t)-0.618652344f, + (float16_t)0.312988281f, + (float16_t)0.474609375f, + (float16_t)-0.461425781f, + (float16_t)0.953613281f, + (float16_t)-0.97265625f, + (float16_t)0.2109375f, + (float16_t)-0.634765625f, + (float16_t)0.607421875f, + (float16_t)-0.680664062f, + (float16_t)-0.442138672f, + (float16_t)0.941894531f, + (float16_t)-0.126220703f, + (float16_t)0.516113281f, + (float16_t)0.71875f, + (float16_t)0.468505859f, + (float16_t)0.8828125f, + (float16_t)0.909667969f, + (float16_t)-0.514160156f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/output_ref_data.h new file mode 100644 index 00000000..0fc2fd2a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/output_ref_data.h @@ -0,0 +1,4411 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_7_f16_output_ref_data[4410] = {(float16_t)2.609375f, + (float16_t)-4.3203125f, + (float16_t)-7.89453125f, + (float16_t)-1.29785156f, + (float16_t)-1.55566406f, + (float16_t)-2.63867188f, + (float16_t)-1.875f, + (float16_t)0.579589844f, + (float16_t)-3.90039062f, + (float16_t)0.122131348f, + (float16_t)3.88671875f, + (float16_t)2.38867188f, + (float16_t)-0.141479492f, + (float16_t)-3.484375f, + (float16_t)-0.782714844f, + (float16_t)5.45703125f, + (float16_t)-0.0786132812f, + (float16_t)3.02148438f, + (float16_t)1.37597656f, + (float16_t)2.58984375f, + (float16_t)-8.1796875f, + (float16_t)-1.99609375f, + (float16_t)-4.92578125f, + (float16_t)-3.22460938f, + (float16_t)-6.6171875f, + (float16_t)6.5859375f, + (float16_t)2.44726562f, + (float16_t)-4.8671875f, + (float16_t)-6.0859375f, + (float16_t)0.232299805f, + (float16_t)2.51367188f, + (float16_t)-1.19238281f, + (float16_t)0.390380859f, + (float16_t)-4.734375f, + (float16_t)-2.79101562f, + (float16_t)5.46484375f, + (float16_t)6.3125f, + (float16_t)1.58496094f, + (float16_t)13.3671875f, + (float16_t)2.34179688f, + (float16_t)-1.36621094f, + (float16_t)8.4609375f, + (float16_t)-6.46875f, + (float16_t)0.185791016f, + (float16_t)-5.6484375f, + (float16_t)9.421875f, + (float16_t)3.40039062f, + (float16_t)2.59765625f, + (float16_t)2.0859375f, + (float16_t)-0.20715332f, + (float16_t)0.678222656f, + (float16_t)5.421875f, + (float16_t)2.30078125f, + (float16_t)8.4375f, + (float16_t)-6.58984375f, + (float16_t)9.8203125f, + (float16_t)-0.522949219f, + (float16_t)3.45117188f, + (float16_t)2.390625f, + (float16_t)4.20703125f, + (float16_t)12.90625f, + (float16_t)6.2109375f, + (float16_t)-5.1875f, + (float16_t)3.18164062f, + (float16_t)8.140625f, + (float16_t)-4.9921875f, + (float16_t)2.2734375f, + (float16_t)5.91796875f, + (float16_t)-6.9296875f, + (float16_t)2.24414062f, + (float16_t)-3.45703125f, + (float16_t)2.34570312f, + (float16_t)-5.0f, + (float16_t)-2.890625f, + (float16_t)0.53125f, + (float16_t)3.75976562f, + (float16_t)-8.9921875f, + (float16_t)3.13867188f, + (float16_t)-5.5625f, + (float16_t)2.71484375f, + (float16_t)-8.9765625f, + (float16_t)-7.46875f, + (float16_t)-0.159790039f, + (float16_t)-2.04101562f, + (float16_t)-3.10546875f, + (float16_t)8.2265625f, + (float16_t)-1.83105469f, + (float16_t)5.578125f, + (float16_t)4.81640625f, + (float16_t)-5.203125f, + (float16_t)4.91015625f, + (float16_t)-5.94140625f, + (float16_t)2.0859375f, + (float16_t)1.13574219f, + (float16_t)-2.02148438f, + (float16_t)-3.57421875f, + (float16_t)-1.5859375f, + (float16_t)-1.54492188f, + (float16_t)-1.52441406f, + (float16_t)-2.82226562f, + (float16_t)-3.83007812f, + (float16_t)4.43359375f, + (float16_t)6.36328125f, + (float16_t)-4.921875f, + (float16_t)3.33984375f, + (float16_t)-6.0546875f, + (float16_t)-5.5078125f, + (float16_t)1.67285156f, + (float16_t)2.79296875f, + (float16_t)-2.36132812f, + (float16_t)-0.802246094f, + (float16_t)2.92382812f, + (float16_t)3.97851562f, + (float16_t)9.1015625f, + (float16_t)-1.38476562f, + (float16_t)-8.453125f, + (float16_t)-1.26074219f, + (float16_t)2.85546875f, + (float16_t)-1.8828125f, + (float16_t)-4.28125f, + (float16_t)-5.66796875f, + (float16_t)-6.5703125f, + (float16_t)1.52929688f, + (float16_t)0.938964844f, + (float16_t)5.59375f, + (float16_t)-0.552246094f, + (float16_t)-4.66796875f, + (float16_t)-4.52734375f, + (float16_t)-6.6171875f, + (float16_t)0.53125f, + (float16_t)-0.137084961f, + (float16_t)2.140625f, + (float16_t)-1.22851562f, + (float16_t)2.9296875f, + (float16_t)3.91796875f, + (float16_t)8.4296875f, + (float16_t)7.05078125f, + (float16_t)-5.96484375f, + (float16_t)-6.80078125f, + (float16_t)-5.25390625f, + (float16_t)0.256591797f, + (float16_t)3.10546875f, + (float16_t)-3.20898438f, + (float16_t)7.36328125f, + (float16_t)5.88671875f, + (float16_t)4.4453125f, + (float16_t)4.87890625f, + (float16_t)-9.109375f, + (float16_t)-0.10748291f, + (float16_t)1.5625f, + (float16_t)-2.25f, + (float16_t)1.02050781f, + (float16_t)2.1484375f, + (float16_t)4.95703125f, + (float16_t)-2.35546875f, + (float16_t)2.65234375f, + (float16_t)0.879882812f, + (float16_t)-3.55273438f, + (float16_t)-5.0f, + (float16_t)4.9140625f, + (float16_t)1.80761719f, + (float16_t)-8.453125f, + (float16_t)4.02734375f, + (float16_t)3.296875f, + (float16_t)12.140625f, + (float16_t)-2.38085938f, + (float16_t)-0.149536133f, + (float16_t)5.0703125f, + (float16_t)0.895507812f, + (float16_t)11.5f, + (float16_t)5.97265625f, + (float16_t)2.76757812f, + (float16_t)6.734375f, + (float16_t)-3.296875f, + (float16_t)-0.270996094f, + (float16_t)12.984375f, + (float16_t)-3.33007812f, + (float16_t)7.9453125f, + (float16_t)-0.464599609f, + (float16_t)-2.11914062f, + (float16_t)16.046875f, + (float16_t)-7.2421875f, + (float16_t)-0.5703125f, + (float16_t)-11.2890625f, + (float16_t)-3.984375f, + (float16_t)10.15625f, + (float16_t)-6.7734375f, + (float16_t)-3.06054688f, + (float16_t)4.99609375f, + (float16_t)5.3046875f, + (float16_t)0.0869750977f, + (float16_t)11.0703125f, + (float16_t)2.7109375f, + (float16_t)-3.26367188f, + (float16_t)2.96679688f, + (float16_t)-5.0390625f, + (float16_t)0.738769531f, + (float16_t)2.22460938f, + (float16_t)-1.73632812f, + (float16_t)-7.16015625f, + (float16_t)2.43554688f, + (float16_t)3.62109375f, + (float16_t)5.86328125f, + (float16_t)-2.16796875f, + (float16_t)1.09765625f, + (float16_t)-4.03125f, + (float16_t)-2.62695312f, + (float16_t)-0.12097168f, + (float16_t)9.6953125f, + (float16_t)1.28613281f, + (float16_t)5.05078125f, + (float16_t)4.4375f, + (float16_t)-4.87890625f, + (float16_t)7.2578125f, + (float16_t)5.10546875f, + (float16_t)-9.2109375f, + (float16_t)9.5390625f, + (float16_t)3.00195312f, + (float16_t)-8.0859375f, + (float16_t)5.03125f, + (float16_t)0.283691406f, + (float16_t)10.3125f, + (float16_t)-0.0540466309f, + (float16_t)2.48828125f, + (float16_t)6.90625f, + (float16_t)-4.36328125f, + (float16_t)-1.78515625f, + (float16_t)-4.078125f, + (float16_t)-14.5703125f, + (float16_t)-6.04296875f, + (float16_t)3.71875f, + (float16_t)5.05859375f, + (float16_t)0.797851562f, + (float16_t)3.0078125f, + (float16_t)0.7578125f, + (float16_t)-6.66015625f, + (float16_t)1.74707031f, + (float16_t)5.71484375f, + (float16_t)2.00976562f, + (float16_t)2.51953125f, + (float16_t)-0.830566406f, + (float16_t)-9.09375f, + (float16_t)7.67578125f, + (float16_t)1.04589844f, + (float16_t)1.72070312f, + (float16_t)-14.5234375f, + (float16_t)6.11328125f, + (float16_t)7.80859375f, + (float16_t)-2.56640625f, + (float16_t)-1.71875f, + (float16_t)-7.0703125f, + (float16_t)-11.2890625f, + (float16_t)19.34375f, + (float16_t)-4.58203125f, + (float16_t)-4.625f, + (float16_t)-1.46875f, + (float16_t)-3.40234375f, + (float16_t)6.69921875f, + (float16_t)0.686523438f, + (float16_t)11.9765625f, + (float16_t)-1.08789062f, + (float16_t)-11.1953125f, + (float16_t)-11.515625f, + (float16_t)8.703125f, + (float16_t)-5.3828125f, + (float16_t)-0.235595703f, + (float16_t)-15.390625f, + (float16_t)2.1796875f, + (float16_t)-16.46875f, + (float16_t)-1.60644531f, + (float16_t)2.1640625f, + (float16_t)1.73339844f, + (float16_t)-2.56054688f, + 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(float16_t)-5.94140625f, + (float16_t)-9.015625f, + (float16_t)-1.30664062f, + (float16_t)2.16210938f, + (float16_t)2.55273438f, + (float16_t)0.890625f, + (float16_t)-1.23242188f, + (float16_t)-6.1875f, + (float16_t)-1.51269531f, + (float16_t)8.7734375f, + (float16_t)1.62402344f, + (float16_t)-11.6875f, + (float16_t)5.73046875f, + (float16_t)1.27246094f, + (float16_t)-2.74609375f, + (float16_t)1.50488281f, + (float16_t)10.0f, + (float16_t)3.34570312f, + (float16_t)-5.94921875f, + (float16_t)5.9609375f, + (float16_t)-0.347412109f, + (float16_t)-1.84375f, + (float16_t)-1.578125f, + (float16_t)-5.640625f, + (float16_t)-1.93359375f, + (float16_t)-5.12890625f, + (float16_t)-9.6015625f, + (float16_t)0.9140625f, + (float16_t)-0.277832031f, + (float16_t)-3.1015625f, + (float16_t)-4.94140625f, + (float16_t)5.78515625f, + (float16_t)2.70703125f, + (float16_t)-3.64453125f, + (float16_t)-3.328125f, + (float16_t)7.02734375f, + (float16_t)2.72851562f, + (float16_t)-0.0654907227f, + (float16_t)-1.40820312f, + (float16_t)1.48046875f, + (float16_t)-5.04296875f, + (float16_t)-0.802246094f, + (float16_t)-3.140625f, + (float16_t)4.77734375f, + (float16_t)-1.21191406f, + (float16_t)-3.13671875f, + (float16_t)-3.87304688f, + (float16_t)3.41601562f, + (float16_t)-4.38671875f, + (float16_t)8.3984375f, + (float16_t)2.0546875f, + (float16_t)-4.12890625f, + (float16_t)0.333251953f, + (float16_t)-15.8671875f, + (float16_t)5.67578125f, + (float16_t)-5.60546875f, + (float16_t)-2.3203125f, + (float16_t)-6.05078125f, + (float16_t)4.62890625f, + (float16_t)-3.61914062f, + (float16_t)5.43359375f, + (float16_t)1.59375f, + (float16_t)3.86523438f, + (float16_t)7.94921875f, + (float16_t)-2.9140625f, + (float16_t)2.71484375f, + (float16_t)-2.671875f, + (float16_t)-2.296875f, + (float16_t)7.12890625f, + (float16_t)-5.13671875f, + (float16_t)-1.01269531f, + (float16_t)6.4765625f, + (float16_t)7.6640625f, + (float16_t)-4.140625f, + (float16_t)-0.530273438f, + (float16_t)3.58984375f, + (float16_t)7.32421875f, + (float16_t)-0.532226562f, + (float16_t)7.29296875f, + (float16_t)3.40234375f, + (float16_t)-0.361572266f, + (float16_t)-6.27734375f, + (float16_t)3.63476562f, + (float16_t)10.953125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/weights_data.h new file mode 100644 index 00000000..beed4293 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f16/weights_data.h @@ -0,0 +1,9601 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_7_f16_weights_data[9600] = {(float16_t)-0.0836791992f, + (float16_t)0.490234375f, + (float16_t)0.625f, + (float16_t)-0.0721435547f, + (float16_t)-0.564941406f, + (float16_t)-0.047668457f, + (float16_t)0.473632812f, + (float16_t)0.138671875f, + (float16_t)0.339355469f, + (float16_t)-0.310058594f, + (float16_t)-0.489501953f, + (float16_t)0.341308594f, + (float16_t)-0.603515625f, + (float16_t)-0.0173950195f, + (float16_t)0.402587891f, + (float16_t)-0.444824219f, + (float16_t)-0.350830078f, + (float16_t)-0.373291016f, + (float16_t)-0.732910156f, + (float16_t)0.4609375f, + (float16_t)0.0430297852f, + (float16_t)0.321044922f, + (float16_t)0.445800781f, + (float16_t)0.269042969f, + (float16_t)-0.446533203f, + (float16_t)-0.625976562f, + (float16_t)0.188232422f, + (float16_t)0.73046875f, + (float16_t)0.709472656f, + (float16_t)-0.479736328f, + (float16_t)0.412841797f, + (float16_t)0.674316406f, + (float16_t)-0.168334961f, + (float16_t)-0.234008789f, + (float16_t)-0.350097656f, + (float16_t)-0.460449219f, + (float16_t)-0.218383789f, + (float16_t)0.633300781f, + (float16_t)0.370849609f, + (float16_t)0.451904297f, + (float16_t)-0.380859375f, + (float16_t)-0.210571289f, + (float16_t)0.474853516f, + (float16_t)0.0184631348f, + (float16_t)-0.0140838623f, + (float16_t)0.681640625f, + (float16_t)0.159057617f, + (float16_t)-0.100097656f, + (float16_t)0.431152344f, + (float16_t)0.0845947266f, + (float16_t)0.530761719f, + (float16_t)0.0375671387f, + (float16_t)-0.359863281f, + (float16_t)-0.547851562f, + (float16_t)0.361328125f, + (float16_t)-0.6875f, + (float16_t)-0.0803833008f, + (float16_t)0.0473632812f, + (float16_t)-0.113525391f, + (float16_t)0.256591797f, + (float16_t)0.631835938f, + (float16_t)-0.3359375f, + (float16_t)-0.641601562f, + (float16_t)-0.710449219f, + 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(float16_t)-0.360595703f, + (float16_t)-0.103881836f, + (float16_t)0.333496094f, + (float16_t)-0.181884766f, + (float16_t)0.174072266f, + (float16_t)0.392822266f, + (float16_t)0.318115234f, + (float16_t)-0.40234375f, + (float16_t)0.487304688f, + (float16_t)-0.554199219f, + (float16_t)-0.0444335938f, + (float16_t)0.657714844f, + (float16_t)0.0985717773f, + (float16_t)-0.17578125f, + (float16_t)0.0165100098f, + (float16_t)0.529296875f, + (float16_t)0.743652344f, + (float16_t)-0.444824219f, + (float16_t)-0.438232422f, + (float16_t)0.197143555f, + (float16_t)-0.7265625f, + (float16_t)0.747558594f, + (float16_t)-0.414550781f, + (float16_t)-0.0829467773f, + (float16_t)0.125732422f, + (float16_t)0.727050781f, + (float16_t)0.0964355469f, + (float16_t)0.534667969f, + (float16_t)-0.0170440674f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/biases_data.h new file mode 100644 index 00000000..1a002da4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/biases_data.h @@ -0,0 +1,16 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_7_f32_biases_data[15] = {0.251110435f, + -0.373097807f, + 0.316800952f, + 0.347644895f, + -0.454703659f, + -0.225570187f, + 0.411710918f, + 0.448044121f, + -0.339217305f, + -0.0195359122f, + 0.497004777f, + 0.0176586304f, + 0.393892139f, + -0.154123113f, + 0.33527866f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/config_data.h new file mode 100644 index 00000000..c1536703 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_7_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_7_F32_INPUT_H 1 +#define CONV_MATCH_1XN_7_F32_INPUT_W 148 +#define CONV_MATCH_1XN_7_F32_IN_CH 20 +#define CONV_MATCH_1XN_7_F32_FILTER_H 1 +#define CONV_MATCH_1XN_7_F32_FILTER_W 32 +#define CONV_MATCH_1XN_7_F32_OUT_CH 15 +#define CONV_MATCH_1XN_7_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_7_F32_STRIDE_W 1 +#define CONV_MATCH_1XN_7_F32_PADDING_H 0 +#define CONV_MATCH_1XN_7_F32_PADDING_W 15 +#define CONV_MATCH_1XN_7_F32_DILATION_H 1 +#define CONV_MATCH_1XN_7_F32_DILATION_W 1 +#define CONV_MATCH_1XN_7_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_7_F32_OUTPUT_W 147 +#define CONV_MATCH_1XN_7_F32_OUTPUT_C 15 +#define CONV_MATCH_1XN_7_F32_DST_SIZE 4410 +#define CONV_MATCH_1XN_7_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_7_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_7_F32_OUT_ACTIVATION_MIN -127.0f +#define CONV_MATCH_1XN_7_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/input_data.h new file mode 100644 index 00000000..c5b38cf4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/input_data.h @@ -0,0 +1,989 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_7_f32_input_data[5920] = { + -0.211253032f, -0.406069696f, 0.963790476f, -0.794291794f, 0.204191312f, 0.12855576f, + 0.147161961f, 0.529721141f, 0.776371181f, -0.391925752f, 0.666611731f, 0.763689578f, + -0.758641243f, 0.994792819f, -0.196630627f, -0.959647119f, 0.207129195f, -0.628717005f, + -0.5305354f, -0.505750418f, 0.73432678f, 0.970879138f, 0.940273702f, 0.0365753956f, + 0.549002409f, -0.411578834f, -0.0538411625f, -0.922402382f, 0.416159809f, 0.918081343f, + 0.730057478f, 0.521687448f, 0.406006515f, -0.939264417f, -0.636704206f, -0.765625834f, + 0.104182936f, 0.671457887f, -0.794336021f, -0.532331228f, -0.17588526f, 0.944852531f, + -0.628439963f, 0.529115856f, -0.468828321f, 0.139528468f, -0.765114009f, -0.0515788198f, + -0.777299047f, -0.55530411f, -0.188396916f, 0.90595597f, 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conv_match_1xn_7_f32_output_ref_data[4410] = { + 2.60919762f, -4.32011747f, -7.89538288f, -1.29746628f, -1.55601084f, -2.63832355f, -1.87511563f, + 0.579476297f, -3.9005518f, 0.122137174f, 3.88731241f, 2.38795686f, -0.141491562f, -3.48526955f, + -0.782859564f, 5.45693541f, -0.0786401033f, 3.02109051f, 1.37591958f, 2.5890882f, -8.17950058f, + -1.99655652f, -4.9250989f, -3.22504282f, -6.61589432f, 6.58406115f, 2.44678402f, -4.86858988f, + -6.08648443f, 0.232331783f, 2.51290083f, -1.19239318f, 0.390331537f, -4.73439789f, -2.79196525f, + 5.4659071f, 6.31061268f, 1.58510971f, 13.3633728f, 2.34127879f, -1.3661015f, 8.46429825f, + -6.46751738f, 0.185739592f, -5.65025997f, 9.42195702f, 3.40106988f, 2.59777737f, 2.08506393f, + -0.207131416f, 0.678015709f, 5.42277718f, 2.30141115f, 8.43764591f, -6.58916473f, 9.81666088f, + -0.52304256f, 3.45029187f, 2.39066601f, 4.20665979f, 12.9074612f, 6.20946503f, -5.18911076f, + 3.18218708f, 8.14308548f, -4.99271727f, 2.27320242f, 5.91939878f, -6.92863226f, 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-3.61887074f, 5.43289423f, 1.59385991f, 3.8651123f, + 7.95028782f, -2.91496158f, 2.71475863f, -2.6722939f, -2.29754233f, 7.1281023f, -5.13843679f, + -1.01235843f, 6.47483444f, 7.66537952f, -4.14047813f, -0.530156314f, 3.59049249f, 7.32430887f, + -0.531999707f, 7.29106426f, 3.40197778f, -0.361559421f, -6.27616882f, 3.6341095f, 10.953371f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/weights_data.h new file mode 100644 index 00000000..481729f0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_7_f32/weights_data.h @@ -0,0 +1,1602 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_7_f32_weights_data[9600] = { + -0.0836702138f, 0.490157634f, 0.625073254f, -0.0721315816f, -0.564722538f, -0.047660429f, + 0.473650217f, 0.138713017f, 0.339340925f, -0.310140759f, -0.489566922f, 0.341241986f, + -0.603294671f, -0.0173918772f, 0.402582407f, -0.4447667f, -0.350899994f, -0.373229891f, + -0.733120918f, 0.460987151f, 0.0430305824f, 0.320975751f, 0.445758134f, 0.268992007f, + -0.446485847f, -0.625875115f, 0.188290998f, 0.730656922f, 0.70956099f, -0.479812145f, + 0.412738174f, 0.674178958f, -0.168342173f, -0.234025374f, -0.350188971f, -0.460557401f, + -0.218379363f, 0.633166969f, 0.370746195f, 0.451873183f, -0.380765051f, -0.210534215f, + 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0.534652948f, -0.0170389749f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/biases_data.h new file mode 100644 index 00000000..9846dc21 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_8_f16_biases_data[10] = {(float16_t)-0.399169922f, + (float16_t)-0.357910156f, + (float16_t)0.0983276367f, + (float16_t)-0.309814453f, + (float16_t)0.216796875f, + (float16_t)0.220825195f, + (float16_t)-0.415039062f, + (float16_t)0.0900878906f, + (float16_t)-0.283691406f, + (float16_t)0.209594727f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/config_data.h new file mode 100644 index 00000000..dadc8f25 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_8_F16_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_8_F16_INPUT_H 1 +#define CONV_MATCH_1XN_8_F16_INPUT_W 148 +#define CONV_MATCH_1XN_8_F16_IN_CH 12 +#define CONV_MATCH_1XN_8_F16_FILTER_H 1 +#define CONV_MATCH_1XN_8_F16_FILTER_W 48 +#define CONV_MATCH_1XN_8_F16_OUT_CH 10 +#define CONV_MATCH_1XN_8_F16_STRIDE_H 1 +#define CONV_MATCH_1XN_8_F16_STRIDE_W 2 +#define CONV_MATCH_1XN_8_F16_PADDING_H 0 +#define CONV_MATCH_1XN_8_F16_PADDING_W 23 +#define CONV_MATCH_1XN_8_F16_DILATION_H 1 +#define CONV_MATCH_1XN_8_F16_DILATION_W 1 +#define CONV_MATCH_1XN_8_F16_OUTPUT_H 1 +#define CONV_MATCH_1XN_8_F16_OUTPUT_W 74 +#define CONV_MATCH_1XN_8_F16_OUTPUT_C 10 +#define CONV_MATCH_1XN_8_F16_DST_SIZE 1480 +#define CONV_MATCH_1XN_8_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_8_F16_USE_WRAPPER 0 +#define CONV_MATCH_1XN_8_F16_OUT_ACTIVATION_MIN (float16_t) - 127.0f +#define CONV_MATCH_1XN_8_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/input_data.h new file mode 100644 index 00000000..b307ff41 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/input_data.h @@ -0,0 +1,3553 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_8_f16_input_data[3552] = {(float16_t)-0.203613281f, + (float16_t)0.696777344f, + (float16_t)0.918457031f, + (float16_t)0.271240234f, + (float16_t)0.845214844f, + (float16_t)-0.643066406f, + (float16_t)-0.502929688f, + (float16_t)0.0935668945f, + (float16_t)0.400390625f, + (float16_t)0.336181641f, + (float16_t)-0.47265625f, + (float16_t)-0.347900391f, + (float16_t)-0.4609375f, + (float16_t)-0.767578125f, + (float16_t)0.756347656f, + (float16_t)0.260498047f, + (float16_t)-0.514160156f, + (float16_t)-0.00121116638f, + (float16_t)0.722167969f, + (float16_t)0.388671875f, + (float16_t)-0.5859375f, + (float16_t)-0.986328125f, + (float16_t)0.924316406f, + (float16_t)0.218505859f, + (float16_t)-0.353515625f, + (float16_t)0.401855469f, + (float16_t)-0.581542969f, + (float16_t)-0.97265625f, + (float16_t)-0.219482422f, + (float16_t)-0.94921875f, + (float16_t)-0.546386719f, + (float16_t)0.452880859f, + (float16_t)0.19909668f, + (float16_t)-0.728027344f, + (float16_t)0.352294922f, + (float16_t)-0.904785156f, + (float16_t)-0.646484375f, + (float16_t)-0.0679931641f, + (float16_t)-0.627929688f, + (float16_t)-0.160766602f, + (float16_t)0.108032227f, + (float16_t)0.541992188f, + (float16_t)0.553222656f, + (float16_t)0.298339844f, + (float16_t)0.384521484f, + (float16_t)0.0573425293f, + (float16_t)0.153442383f, + (float16_t)-0.805664062f, + (float16_t)-0.024017334f, + (float16_t)-0.209838867f, + (float16_t)0.306396484f, + (float16_t)0.521484375f, + (float16_t)-0.473388672f, + (float16_t)-0.265625f, + (float16_t)0.817871094f, + (float16_t)0.66015625f, + (float16_t)-0.392578125f, + (float16_t)0.774414062f, + (float16_t)-0.504882812f, + (float16_t)0.387695312f, + (float16_t)-0.410400391f, + (float16_t)-0.871582031f, + (float16_t)-0.643554688f, + (float16_t)-0.750488281f, + (float16_t)0.277099609f, + (float16_t)-0.0269775391f, + (float16_t)0.340576172f, + (float16_t)-0.213867188f, + (float16_t)0.91796875f, + (float16_t)-0.9140625f, + (float16_t)-0.231689453f, + (float16_t)0.383789062f, + (float16_t)-0.271728516f, + (float16_t)0.239624023f, + (float16_t)0.764160156f, + (float16_t)-0.0664672852f, + (float16_t)-0.591796875f, + (float16_t)-0.561035156f, + (float16_t)0.955078125f, + (float16_t)0.237670898f, + (float16_t)0.848144531f, + (float16_t)0.575195312f, + (float16_t)-0.392089844f, + (float16_t)-0.0985717773f, + (float16_t)-0.236572266f, + (float16_t)-0.088684082f, + (float16_t)-0.322509766f, + (float16_t)0.0233154297f, + 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(float16_t)-0.920898438f, + (float16_t)0.244262695f, + (float16_t)0.990722656f, + (float16_t)0.502929688f, + (float16_t)0.0828857422f, + (float16_t)0.649902344f, + (float16_t)0.6484375f, + (float16_t)0.477783203f, + (float16_t)0.0548095703f, + (float16_t)0.10333252f, + (float16_t)-0.0151672363f, + (float16_t)-0.267333984f, + (float16_t)0.0607299805f, + (float16_t)-0.642089844f, + (float16_t)0.386962891f, + (float16_t)-0.611816406f, + (float16_t)-0.164794922f, + (float16_t)-0.950195312f, + (float16_t)0.645996094f, + (float16_t)0.46875f, + (float16_t)-0.567382812f, + (float16_t)0.889648438f, + (float16_t)0.603515625f, + (float16_t)-0.235229492f, + (float16_t)-0.556152344f, + (float16_t)0.778320312f, + (float16_t)0.44921875f, + (float16_t)-0.149780273f, + (float16_t)-0.0347595215f, + (float16_t)0.467773438f, + (float16_t)-0.597167969f, + (float16_t)-0.854980469f, + (float16_t)0.451171875f, + (float16_t)-0.409179688f, + (float16_t)0.177490234f, + (float16_t)0.966796875f, + (float16_t)-0.93359375f, + (float16_t)0.273193359f, + (float16_t)0.0866088867f, + (float16_t)-0.277832031f, + (float16_t)-0.0504455566f, + (float16_t)0.70703125f, + (float16_t)0.994140625f, + (float16_t)0.305419922f, + (float16_t)0.60546875f, + (float16_t)-0.710449219f, + (float16_t)0.690917969f, + (float16_t)-0.566894531f, + (float16_t)-0.180786133f, + (float16_t)0.321777344f, + (float16_t)-0.631347656f, + (float16_t)-0.953613281f, + (float16_t)0.0233612061f, + (float16_t)-0.837890625f, + (float16_t)0.0836181641f, + (float16_t)0.0355529785f, + (float16_t)0.545898438f, + (float16_t)0.0490112305f, + (float16_t)0.932617188f, + (float16_t)-0.343261719f, + (float16_t)-0.211181641f, + (float16_t)-0.400390625f, + (float16_t)0.0191955566f, + (float16_t)0.184570312f, + (float16_t)-0.835449219f, + (float16_t)0.571289062f, + (float16_t)0.0608520508f, + (float16_t)0.416748047f, + (float16_t)0.862304688f, + (float16_t)-0.431396484f, + (float16_t)0.512207031f, + (float16_t)-0.657714844f, + (float16_t)-0.234130859f, + (float16_t)-0.619628906f, + (float16_t)0.859375f, + (float16_t)0.479492188f, + (float16_t)-0.133666992f, + (float16_t)0.861328125f, + (float16_t)0.850097656f, + (float16_t)-0.0450744629f, + (float16_t)-0.0954589844f, + (float16_t)-0.386474609f, + (float16_t)-0.887695312f, + (float16_t)0.180297852f, + (float16_t)0.0287628174f, + (float16_t)0.734863281f, + (float16_t)0.833007812f, + (float16_t)-0.631835938f, + (float16_t)0.549804688f, + (float16_t)-0.637695312f, + (float16_t)0.590820312f, + (float16_t)-0.640136719f, + (float16_t)-0.803710938f, + (float16_t)-0.760742188f, + (float16_t)0.208984375f, + (float16_t)-0.794921875f, + (float16_t)0.960449219f, + (float16_t)-0.293701172f, + (float16_t)-0.0245819092f, + (float16_t)-0.878417969f, + (float16_t)-0.59375f, + (float16_t)0.698242188f, + (float16_t)-0.260498047f, + (float16_t)-0.336181641f, + (float16_t)0.860351562f, + (float16_t)-0.861328125f, + (float16_t)-0.975585938f, + (float16_t)0.0814208984f, + (float16_t)0.497314453f, + (float16_t)0.865722656f, + (float16_t)-0.831542969f, + (float16_t)-0.765136719f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/output_ref_data.h new file mode 100644 index 00000000..3be9c2be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/output_ref_data.h @@ -0,0 +1,372 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_8_f16_output_ref_data[1480] = { + (float16_t)0.383300781f, (float16_t)-0.03515625f, (float16_t)-0.332763672f, (float16_t)2.57421875f, + (float16_t)0.420410156f, (float16_t)-2.38085938f, (float16_t)0.273193359f, (float16_t)-2.81445312f, + (float16_t)0.738769531f, (float16_t)-1.29980469f, (float16_t)-7.98828125f, (float16_t)-10.2578125f, + (float16_t)-0.480712891f, (float16_t)2.76757812f, (float16_t)3.54882812f, (float16_t)2.84960938f, + (float16_t)-5.7734375f, (float16_t)-9.859375f, (float16_t)0.418701172f, (float16_t)-5.51953125f, + (float16_t)1.42871094f, (float16_t)0.452880859f, (float16_t)3.01757812f, (float16_t)1.45996094f, + (float16_t)-9.5078125f, (float16_t)4.44140625f, (float16_t)-5.0859375f, (float16_t)-2.14453125f, + (float16_t)-5.65625f, (float16_t)3.00976562f, (float16_t)-1.11523438f, (float16_t)7.3359375f, + (float16_t)3.04882812f, (float16_t)-1.13378906f, (float16_t)-4.05078125f, (float16_t)2.83984375f, + (float16_t)2.08789062f, (float16_t)2.12109375f, (float16_t)-5.515625f, (float16_t)1.42285156f, + (float16_t)0.173339844f, (float16_t)1.33203125f, (float16_t)1.31640625f, (float16_t)-3.26757812f, + (float16_t)-1.1015625f, (float16_t)-0.345703125f, (float16_t)5.20703125f, (float16_t)3.25f, + (float16_t)-2.88867188f, (float16_t)-2.73828125f, (float16_t)-2.39648438f, (float16_t)11.25f, + (float16_t)4.54296875f, (float16_t)9.96875f, (float16_t)-0.183837891f, (float16_t)7.71875f, + (float16_t)-1.16601562f, (float16_t)-0.203735352f, (float16_t)-4.078125f, (float16_t)-2.3828125f, + (float16_t)-1.82519531f, (float16_t)-0.688964844f, (float16_t)-2.11523438f, (float16_t)1.05078125f, + (float16_t)8.5546875f, (float16_t)-1.25878906f, (float16_t)3.10742188f, (float16_t)4.26171875f, + (float16_t)-6.36328125f, (float16_t)-2.26171875f, (float16_t)0.471191406f, (float16_t)-2.96289062f, + (float16_t)1.18359375f, (float16_t)8.78125f, (float16_t)0.916992188f, (float16_t)1.34570312f, + (float16_t)2.28515625f, (float16_t)5.1875f, (float16_t)2.359375f, (float16_t)2.078125f, + (float16_t)1.69433594f, (float16_t)10.2890625f, (float16_t)4.78515625f, (float16_t)6.51953125f, + (float16_t)0.756347656f, (float16_t)-1.88085938f, (float16_t)0.367919922f, (float16_t)2.41210938f, + (float16_t)-1.75292969f, (float16_t)-10.1640625f, (float16_t)-1.63574219f, (float16_t)3.734375f, + (float16_t)1.60058594f, (float16_t)4.5390625f, (float16_t)-1.546875f, (float16_t)0.673339844f, + (float16_t)6.73046875f, (float16_t)2.55664062f, (float16_t)5.4140625f, (float16_t)1.19335938f, + (float16_t)-1.60839844f, (float16_t)12.3984375f, (float16_t)-3.22265625f, (float16_t)4.35546875f, + (float16_t)7.40234375f, (float16_t)0.665039062f, (float16_t)-5.92578125f, (float16_t)5.55078125f, + (float16_t)-3.15039062f, (float16_t)-2.27539062f, (float16_t)-0.538574219f, (float16_t)-2.18359375f, + (float16_t)10.9453125f, (float16_t)-6.65234375f, (float16_t)-0.537109375f, (float16_t)-7.51953125f, + (float16_t)0.913085938f, (float16_t)-8.1796875f, (float16_t)-10.3515625f, (float16_t)-2.39453125f, + (float16_t)-6.08203125f, (float16_t)-2.16210938f, (float16_t)-4.046875f, (float16_t)4.50390625f, + (float16_t)3.15234375f, (float16_t)-1.11132812f, (float16_t)4.40234375f, (float16_t)-12.4609375f, + (float16_t)0.0869140625f, (float16_t)-8.375f, (float16_t)-11.109375f, (float16_t)2.92382812f, + (float16_t)1.41113281f, (float16_t)13.515625f, (float16_t)-1.93359375f, (float16_t)4.140625f, + (float16_t)-9.890625f, (float16_t)7.09375f, (float16_t)1.50683594f, (float16_t)5.4375f, + (float16_t)6.11328125f, (float16_t)-5.15625f, (float16_t)3.07226562f, (float16_t)0.493896484f, + (float16_t)-0.896484375f, (float16_t)1.63085938f, (float16_t)-1.38769531f, (float16_t)6.5859375f, + (float16_t)-7.76171875f, (float16_t)0.498046875f, (float16_t)3.91015625f, (float16_t)2.4375f, + (float16_t)-4.69140625f, (float16_t)8.9921875f, (float16_t)-17.46875f, (float16_t)-3.65234375f, + (float16_t)3.16992188f, (float16_t)3.88476562f, (float16_t)4.56640625f, (float16_t)1.24511719f, + (float16_t)2.83398438f, (float16_t)9.7578125f, (float16_t)-1.19238281f, (float16_t)1.09472656f, + (float16_t)-10.734375f, (float16_t)2.38085938f, (float16_t)-4.34765625f, (float16_t)-11.1015625f, + (float16_t)0.724609375f, (float16_t)-6.24609375f, (float16_t)2.10742188f, (float16_t)-6.96484375f, + (float16_t)0.17175293f, (float16_t)5.32421875f, (float16_t)6.83984375f, (float16_t)-0.701660156f, + (float16_t)0.483154297f, (float16_t)5.22265625f, (float16_t)6.1875f, (float16_t)-6.66796875f, + (float16_t)-3.45898438f, (float16_t)-1.8671875f, (float16_t)-4.41796875f, (float16_t)1.36914062f, + (float16_t)-0.786621094f, (float16_t)2.0f, (float16_t)-0.494873047f, (float16_t)4.42578125f, + (float16_t)12.1796875f, (float16_t)-7.33203125f, (float16_t)-1.33300781f, (float16_t)0.00174999237f, + (float16_t)1.57714844f, (float16_t)2.48242188f, (float16_t)-4.18359375f, (float16_t)-1.54589844f, + (float16_t)-2.40625f, (float16_t)-7.03515625f, (float16_t)-11.2265625f, (float16_t)4.99609375f, + (float16_t)0.614257812f, (float16_t)-0.0928955078f, (float16_t)-11.5f, (float16_t)1.46875f, + (float16_t)6.91796875f, (float16_t)6.48046875f, (float16_t)-5.19140625f, (float16_t)-1.18652344f, + (float16_t)-0.412597656f, (float16_t)-7.98046875f, (float16_t)-10.25f, (float16_t)-10.3125f, + (float16_t)-4.1328125f, (float16_t)5.33984375f, (float16_t)-0.0165557861f, (float16_t)-2.95898438f, + (float16_t)-2.75390625f, (float16_t)3.453125f, (float16_t)1.69238281f, (float16_t)-1.44140625f, + (float16_t)-1.6015625f, (float16_t)1.07324219f, (float16_t)6.421875f, (float16_t)-3.390625f, + (float16_t)2.453125f, (float16_t)-1.37890625f, (float16_t)-3.78320312f, (float16_t)2.37890625f, + (float16_t)0.526855469f, (float16_t)-6.3671875f, (float16_t)-8.7265625f, (float16_t)-5.15234375f, + (float16_t)-0.845214844f, (float16_t)-3.92773438f, (float16_t)2.63476562f, (float16_t)6.6796875f, + (float16_t)6.2421875f, (float16_t)3.07421875f, (float16_t)8.6875f, (float16_t)-4.6328125f, + (float16_t)-0.506347656f, (float16_t)0.342529297f, (float16_t)8.0f, (float16_t)4.0234375f, + (float16_t)0.0471801758f, (float16_t)7.83984375f, (float16_t)-4.6015625f, (float16_t)-3.453125f, + (float16_t)2.203125f, (float16_t)-2.67382812f, (float16_t)-2.65625f, (float16_t)4.8828125f, + (float16_t)2.88085938f, (float16_t)14.0703125f, (float16_t)-3.9375f, (float16_t)4.44921875f, + (float16_t)12.46875f, (float16_t)-2.78125f, (float16_t)-8.9375f, (float16_t)0.678222656f, + (float16_t)-11.328125f, (float16_t)-2.26757812f, (float16_t)7.78125f, (float16_t)4.5625f, + (float16_t)4.27734375f, (float16_t)-3.1796875f, (float16_t)5.109375f, (float16_t)-4.546875f, + (float16_t)-1.421875f, (float16_t)-8.1953125f, (float16_t)-3.28125f, (float16_t)-3.9375f, + (float16_t)-2.62109375f, (float16_t)3.65039062f, (float16_t)13.1484375f, (float16_t)2.25976562f, + (float16_t)-0.5390625f, (float16_t)3.67382812f, (float16_t)9.4609375f, (float16_t)-11.484375f, + (float16_t)-4.2421875f, (float16_t)5.3359375f, (float16_t)1.72949219f, (float16_t)8.15625f, + (float16_t)-2.45898438f, (float16_t)6.765625f, (float16_t)3.77929688f, (float16_t)-0.450683594f, + (float16_t)4.76953125f, (float16_t)-0.437255859f, (float16_t)4.71875f, (float16_t)10.1484375f, + (float16_t)8.109375f, (float16_t)7.234375f, (float16_t)-5.36328125f, (float16_t)3.72460938f, + (float16_t)-7.76953125f, (float16_t)-2.70703125f, (float16_t)-0.779785156f, (float16_t)-1.33300781f, + 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(float16_t)-6.0f, (float16_t)0.59375f, + (float16_t)0.00392532349f, (float16_t)4.1328125f, (float16_t)0.220458984f, (float16_t)-1.53808594f, + (float16_t)-0.958007812f, (float16_t)5.08984375f, (float16_t)-6.64453125f, (float16_t)-7.484375f, + (float16_t)8.8203125f, (float16_t)-3.32421875f, (float16_t)-4.57421875f, (float16_t)-5.03515625f, + (float16_t)-3.54296875f, (float16_t)-1.44433594f, (float16_t)0.689453125f, (float16_t)-9.2734375f, + (float16_t)-3.87890625f, (float16_t)0.462890625f, (float16_t)-3.3828125f, (float16_t)0.728027344f, + (float16_t)-4.16796875f, (float16_t)6.5625f, (float16_t)-5.2734375f, (float16_t)0.888183594f, + (float16_t)0.724609375f, (float16_t)2.83984375f, (float16_t)3.171875f, (float16_t)7.90625f, + (float16_t)2.31835938f, (float16_t)-2.81835938f, (float16_t)-6.6640625f, (float16_t)3.67578125f, + (float16_t)0.274169922f, (float16_t)7.24609375f, (float16_t)0.850097656f, (float16_t)-7.390625f, + (float16_t)1.29296875f, (float16_t)-3.34570312f, (float16_t)0.518066406f, (float16_t)1.51367188f, + (float16_t)0.475830078f, (float16_t)-7.10546875f, (float16_t)-7.91015625f, (float16_t)-6.62109375f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/weights_data.h new file mode 100644 index 00000000..1a71a366 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f16/weights_data.h @@ -0,0 +1,1442 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_1xn_8_f16_weights_data[5760] = { + (float16_t)0.673828125f, (float16_t)0.382080078f, (float16_t)-0.527832031f, (float16_t)-0.177246094f, + (float16_t)0.701171875f, (float16_t)-0.185913086f, (float16_t)-0.262695312f, (float16_t)0.487304688f, + (float16_t)0.485107422f, (float16_t)-0.580566406f, (float16_t)0.202514648f, (float16_t)0.600585938f, + (float16_t)0.0953979492f, (float16_t)0.434570312f, (float16_t)-0.226928711f, (float16_t)-0.215576172f, + (float16_t)0.709960938f, (float16_t)-0.535644531f, (float16_t)0.481445312f, (float16_t)0.0304260254f, + (float16_t)0.260742188f, (float16_t)-0.628417969f, (float16_t)-0.130004883f, (float16_t)0.0822143555f, + (float16_t)-0.282226562f, (float16_t)-0.00626373291f, (float16_t)0.338378906f, (float16_t)0.200195312f, + (float16_t)-0.156616211f, (float16_t)-0.189086914f, (float16_t)-0.7421875f, (float16_t)0.643554688f, + (float16_t)0.597167969f, (float16_t)0.221069336f, (float16_t)-0.388916016f, (float16_t)-0.447998047f, + (float16_t)-0.731445312f, (float16_t)0.282958984f, (float16_t)0.357421875f, (float16_t)0.575683594f, + (float16_t)-0.376464844f, (float16_t)-0.131469727f, (float16_t)0.182128906f, (float16_t)-0.0807495117f, + (float16_t)-0.404052734f, (float16_t)0.437744141f, (float16_t)0.671386719f, (float16_t)0.253173828f, + (float16_t)0.40625f, (float16_t)-0.696289062f, (float16_t)0.559570312f, (float16_t)0.172485352f, + (float16_t)0.00315856934f, (float16_t)-0.436279297f, (float16_t)0.634277344f, (float16_t)-0.0874633789f, + (float16_t)-0.596191406f, (float16_t)0.356445312f, (float16_t)-0.314697266f, (float16_t)0.730957031f, + (float16_t)-0.561035156f, (float16_t)0.635253906f, (float16_t)0.144165039f, (float16_t)0.275390625f, + (float16_t)-0.589355469f, (float16_t)0.159301758f, (float16_t)0.672363281f, (float16_t)0.543457031f, + (float16_t)-0.692382812f, (float16_t)-0.547363281f, (float16_t)0.0268859863f, (float16_t)0.473632812f, + (float16_t)0.541992188f, (float16_t)-0.334716797f, (float16_t)-0.352783203f, (float16_t)0.747558594f, + (float16_t)0.232177734f, (float16_t)0.513671875f, (float16_t)0.243408203f, (float16_t)0.568359375f, + (float16_t)0.206542969f, (float16_t)-0.244995117f, (float16_t)-0.481933594f, (float16_t)-0.0336303711f, + (float16_t)0.686035156f, (float16_t)-0.432128906f, (float16_t)-0.516113281f, (float16_t)-0.659667969f, + (float16_t)-0.688476562f, (float16_t)-0.00858306885f, (float16_t)0.0314941406f, (float16_t)0.221191406f, + (float16_t)0.101623535f, (float16_t)-0.373291016f, (float16_t)-0.560546875f, (float16_t)-0.461425781f, + (float16_t)-0.634765625f, (float16_t)-0.577148438f, (float16_t)0.194213867f, (float16_t)-0.239990234f, + (float16_t)-0.427490234f, (float16_t)0.299072266f, (float16_t)-0.547363281f, (float16_t)-0.515136719f, + (float16_t)0.181030273f, (float16_t)-0.0500488281f, (float16_t)-0.520996094f, (float16_t)-0.208007812f, + (float16_t)-0.394287109f, (float16_t)-0.169555664f, (float16_t)-0.141479492f, (float16_t)-0.182495117f, + (float16_t)-0.528320312f, (float16_t)0.65625f, (float16_t)-0.105651855f, (float16_t)0.0166778564f, + (float16_t)0.346191406f, (float16_t)0.436279297f, (float16_t)-0.562988281f, (float16_t)0.271240234f, + (float16_t)-0.410400391f, (float16_t)-0.411865234f, (float16_t)0.464111328f, (float16_t)0.011756897f, + (float16_t)0.517089844f, (float16_t)0.280761719f, (float16_t)-0.612792969f, (float16_t)-0.368408203f, + (float16_t)0.281738281f, (float16_t)0.33984375f, (float16_t)0.224853516f, (float16_t)-0.0157470703f, + (float16_t)0.609863281f, (float16_t)-0.323242188f, (float16_t)-0.509765625f, (float16_t)-0.287109375f, + (float16_t)-0.127807617f, (float16_t)-0.253662109f, (float16_t)-0.630371094f, (float16_t)0.00436401367f, + (float16_t)-0.0489196777f, (float16_t)0.569824219f, (float16_t)0.580566406f, (float16_t)-0.231933594f, + (float16_t)-0.206176758f, (float16_t)0.363525391f, (float16_t)-0.340820312f, (float16_t)0.343994141f, + (float16_t)0.466796875f, (float16_t)0.309082031f, (float16_t)-0.530761719f, (float16_t)-0.101074219f, + (float16_t)-0.695800781f, (float16_t)0.53125f, (float16_t)-0.112243652f, (float16_t)0.148193359f, + (float16_t)-0.6640625f, (float16_t)-0.690917969f, (float16_t)0.654785156f, (float16_t)-0.23046875f, + (float16_t)-0.275634766f, (float16_t)-0.737304688f, (float16_t)-0.255615234f, (float16_t)0.0344238281f, + (float16_t)0.297851562f, (float16_t)0.7421875f, (float16_t)-0.392089844f, (float16_t)-0.4375f, + (float16_t)0.437988281f, (float16_t)-0.313964844f, (float16_t)-0.476318359f, (float16_t)0.432373047f, + (float16_t)0.108581543f, (float16_t)0.0113983154f, (float16_t)-0.21105957f, (float16_t)-0.352294922f, + (float16_t)-0.2734375f, (float16_t)-0.456298828f, (float16_t)-0.197875977f, (float16_t)0.167358398f, + (float16_t)-0.230102539f, (float16_t)0.0888671875f, (float16_t)-0.563476562f, (float16_t)0.672363281f, + (float16_t)-0.145019531f, (float16_t)-0.623535156f, (float16_t)-0.271240234f, (float16_t)-0.140136719f, + (float16_t)-0.388183594f, (float16_t)-0.511230469f, (float16_t)-0.387207031f, (float16_t)0.540039062f, + (float16_t)-0.653320312f, (float16_t)-0.315185547f, (float16_t)0.584472656f, (float16_t)-0.408691406f, + (float16_t)0.135253906f, (float16_t)0.137817383f, (float16_t)-0.540039062f, (float16_t)-0.347167969f, + (float16_t)-0.293457031f, (float16_t)-0.484375f, (float16_t)-0.063659668f, (float16_t)-0.609863281f, + (float16_t)0.00762557983f, (float16_t)0.224243164f, (float16_t)-0.122558594f, (float16_t)0.388671875f, + (float16_t)0.447021484f, (float16_t)-0.576171875f, (float16_t)-0.126098633f, (float16_t)0.292236328f, + (float16_t)0.206542969f, (float16_t)-0.631347656f, (float16_t)0.302734375f, (float16_t)-0.20715332f, + (float16_t)0.591796875f, (float16_t)-0.718261719f, (float16_t)0.247924805f, (float16_t)0.705566406f, + (float16_t)0.653808594f, (float16_t)0.497558594f, (float16_t)0.385742188f, (float16_t)-0.0449523926f, + (float16_t)0.0333557129f, (float16_t)0.589355469f, (float16_t)-0.0866088867f, (float16_t)-0.0993652344f, + (float16_t)-0.366699219f, (float16_t)-0.631347656f, (float16_t)-0.275634766f, (float16_t)0.394775391f, + (float16_t)0.417236328f, (float16_t)-0.495605469f, (float16_t)0.623046875f, (float16_t)0.309082031f, + (float16_t)-0.569824219f, (float16_t)0.490722656f, (float16_t)0.715332031f, (float16_t)-0.0771484375f, + (float16_t)0.66796875f, (float16_t)-0.579589844f, (float16_t)-0.517578125f, (float16_t)0.177856445f, + (float16_t)0.290283203f, (float16_t)-0.494873047f, (float16_t)0.200195312f, (float16_t)0.394042969f, + (float16_t)0.369384766f, (float16_t)-0.443359375f, (float16_t)-0.667480469f, (float16_t)-0.178710938f, + (float16_t)-0.359375f, (float16_t)-0.474853516f, (float16_t)0.188842773f, (float16_t)0.570800781f, + (float16_t)-0.453125f, (float16_t)0.628417969f, (float16_t)-0.0362243652f, (float16_t)0.0385437012f, + (float16_t)-0.527832031f, (float16_t)-0.222045898f, (float16_t)0.0614624023f, (float16_t)-0.395996094f, + (float16_t)-0.732910156f, (float16_t)0.481689453f, (float16_t)-0.573730469f, (float16_t)-0.546386719f, + (float16_t)0.24230957f, (float16_t)-0.196044922f, (float16_t)0.389160156f, (float16_t)0.0547485352f, + (float16_t)-0.281494141f, (float16_t)-0.504394531f, (float16_t)-0.446777344f, (float16_t)-0.389648438f, + (float16_t)-0.291748047f, (float16_t)-0.368408203f, (float16_t)-0.586425781f, (float16_t)0.588867188f, + (float16_t)0.743652344f, (float16_t)0.122802734f, (float16_t)0.609375f, (float16_t)-0.686523438f, + (float16_t)-0.467529297f, (float16_t)0.251953125f, (float16_t)0.501953125f, (float16_t)0.172851562f, + (float16_t)-0.144287109f, (float16_t)0.313476562f, (float16_t)-0.639648438f, (float16_t)0.473876953f, + (float16_t)-0.444580078f, (float16_t)-0.0108566284f, (float16_t)-0.71875f, (float16_t)0.425048828f, + (float16_t)-0.632324219f, (float16_t)-0.304199219f, (float16_t)-0.538085938f, (float16_t)0.725585938f, + (float16_t)0.611328125f, (float16_t)-0.35546875f, (float16_t)0.569824219f, (float16_t)-0.342285156f, + (float16_t)0.741210938f, (float16_t)0.186645508f, (float16_t)-0.211669922f, (float16_t)0.048828125f, + (float16_t)0.505859375f, (float16_t)-0.280273438f, (float16_t)0.548339844f, (float16_t)0.680664062f, + (float16_t)0.459960938f, (float16_t)-0.572265625f, (float16_t)-0.69921875f, (float16_t)-0.293212891f, + (float16_t)-0.489501953f, (float16_t)0.0690307617f, (float16_t)0.397460938f, (float16_t)0.305419922f, + (float16_t)-0.388427734f, (float16_t)0.557128906f, (float16_t)-0.357421875f, (float16_t)0.629882812f, + (float16_t)0.669921875f, (float16_t)-0.149414062f, (float16_t)0.557617188f, (float16_t)-0.370605469f, + (float16_t)-0.459472656f, (float16_t)0.352050781f, (float16_t)0.383056641f, (float16_t)0.172729492f, + (float16_t)0.500488281f, (float16_t)0.719726562f, (float16_t)-0.0717163086f, (float16_t)0.108886719f, + (float16_t)-0.413574219f, 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(float16_t)0.309570312f, (float16_t)-0.648925781f, + (float16_t)0.161499023f, (float16_t)-0.600097656f, (float16_t)0.053527832f, (float16_t)-0.00480270386f, + (float16_t)-0.0922241211f, (float16_t)0.563964844f, (float16_t)-0.636230469f, (float16_t)-0.0381469727f, + (float16_t)-0.270996094f, (float16_t)-0.303710938f, (float16_t)-0.666015625f, (float16_t)-0.720214844f, + (float16_t)-0.705566406f, (float16_t)0.353515625f, (float16_t)0.0297851562f, (float16_t)-0.689941406f, + (float16_t)0.284179688f, (float16_t)0.60546875f, (float16_t)-0.600585938f, (float16_t)-0.416503906f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/biases_data.h new file mode 100644 index 00000000..8f232e3d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/biases_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_8_f32_biases_data[10] = {-0.39919129f, + -0.357987851f, + 0.098305136f, + -0.309873283f, + 0.216796428f, + 0.220862448f, + -0.415014416f, + 0.0901012793f, + -0.283576518f, + 0.209595889f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/config_data.h new file mode 100644 index 00000000..fd73d32d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_1XN_8_F32_INPUT_BATCHES 2 +#define CONV_MATCH_1XN_8_F32_INPUT_H 1 +#define CONV_MATCH_1XN_8_F32_INPUT_W 148 +#define CONV_MATCH_1XN_8_F32_IN_CH 12 +#define CONV_MATCH_1XN_8_F32_FILTER_H 1 +#define CONV_MATCH_1XN_8_F32_FILTER_W 48 +#define CONV_MATCH_1XN_8_F32_OUT_CH 10 +#define CONV_MATCH_1XN_8_F32_STRIDE_H 1 +#define CONV_MATCH_1XN_8_F32_STRIDE_W 2 +#define CONV_MATCH_1XN_8_F32_PADDING_H 0 +#define CONV_MATCH_1XN_8_F32_PADDING_W 23 +#define CONV_MATCH_1XN_8_F32_DILATION_H 1 +#define CONV_MATCH_1XN_8_F32_DILATION_W 1 +#define CONV_MATCH_1XN_8_F32_OUTPUT_H 1 +#define CONV_MATCH_1XN_8_F32_OUTPUT_W 74 +#define CONV_MATCH_1XN_8_F32_OUTPUT_C 10 +#define CONV_MATCH_1XN_8_F32_DST_SIZE 1480 +#define CONV_MATCH_1XN_8_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_1XN_8_F32_USE_WRAPPER 0 +#define CONV_MATCH_1XN_8_F32_OUT_ACTIVATION_MIN -127.0f +#define CONV_MATCH_1XN_8_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/input_data.h new file mode 100644 index 00000000..39dbec65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/input_data.h @@ -0,0 +1,594 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_8_f32_input_data[3552] = { + -0.203588784f, 0.696570218f, 0.918287575f, 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a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/output_ref_data.h new file mode 100644 index 00000000..b8ccb631 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/output_ref_data.h @@ -0,0 +1,214 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_8_f32_output_ref_data[1480] = { + 0.383195132f, -0.0351648033f, -0.332664818f, 2.57332039f, 0.42042321f, -2.38116145f, 0.273114413f, + -2.81423092f, 0.738911152f, -1.30027306f, -7.98696852f, -10.2544718f, -0.480646461f, 2.76802111f, + 3.54880691f, 2.84996223f, -5.77265978f, -9.85669327f, 0.418734044f, -5.51996565f, 1.42894745f, + 0.452819377f, 3.01709819f, 1.46020079f, -9.50510502f, 4.44267702f, -5.08760643f, -2.14460063f, + -5.6576786f, 3.00927854f, -1.11512625f, 7.33522129f, 3.0486207f, -1.13349402f, -4.04925823f, + 2.83971286f, 2.08829236f, 2.12097239f, -5.51714945f, 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1.51388574f, 0.475949854f, + -7.10632038f, -7.90911198f, -6.6194582f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/weights_data.h new file mode 100644 index 00000000..1e9697fa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_1xn_8_f32/weights_data.h @@ -0,0 +1,962 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_1xn_8_f32_weights_data[5760] = { + 0.674063504f, 0.382126272f, -0.52796936f, -0.17730239f, 0.700967431f, -0.185899377f, + -0.262643427f, 0.48726207f, 0.485181928f, -0.580671489f, 0.202513874f, 0.600714564f, + 0.0953856632f, 0.434568048f, -0.226959512f, -0.215541556f, 0.709913552f, -0.535849035f, + 0.481478035f, 0.0304219108f, 0.260710806f, -0.62822181f, -0.129978403f, 0.0822338834f, + -0.282117307f, -0.00626267819f, 0.338266283f, 0.200233877f, -0.156568468f, -0.189098179f, + -0.742239475f, 0.643337786f, 0.597014368f, 0.221046016f, -0.388976842f, -0.448015153f, + -0.731523275f, 0.283073574f, 0.357427269f, 0.57556206f, -0.376437306f, -0.131503761f, + 0.182088688f, -0.0807198137f, -0.404079974f, 0.437637955f, 0.67159903f, 0.253093958f, + 0.406319916f, -0.696182668f, 0.559540451f, 0.17254363f, 0.003157984f, -0.436364591f, + 0.634491086f, -0.0874843746f, -0.595975876f, 0.356366456f, -0.314722151f, 0.731182396f, + -0.561017632f, 0.635396659f, 0.144207478f, 0.275334388f, 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-0.409931004f, -0.516248524f, + -0.0808414817f, 0.247643232f, -0.559595883f, -0.00244432222f, 0.582695603f, 0.398803771f, + 0.33006525f, -0.27015847f, 0.301650047f, 0.00281507545f, 0.577329516f, 0.472936243f, + 0.00446606847f, -0.258804619f, -0.287734479f, -0.621442795f, 0.735912323f, 0.72074163f, + -0.0222364347f, -0.135087848f, -0.503816009f, -0.381890476f, 0.322547674f, -0.668789208f, + 0.667401612f, -0.150247246f, 0.670686603f, -0.498737961f, -0.712126255f, -0.504486501f, + -0.412577778f, 0.131915882f, -0.18248032f, 0.0598979257f, -0.0151031073f, -0.617207289f, + -0.351691455f, 0.470601678f, 0.331952542f, 0.53608346f, 0.605251372f, 0.52770251f, + 0.0867495164f, 0.438208342f, 0.309455633f, -0.648857772f, 0.161462039f, -0.600035667f, + 0.0535300747f, -0.00480367243f, -0.0922122225f, 0.563961506f, -0.636245787f, -0.0381609462f, + -0.270916611f, -0.30381155f, -0.666158259f, -0.720069528f, -0.705691993f, 0.353606611f, + 0.0297834221f, -0.690057814f, 0.284284979f, 0.605549514f, -0.600373268f, -0.416536331f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/biases_data.h new file mode 100644 index 00000000..c8a19e9d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/biases_data.h @@ -0,0 +1,3 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_5x5_input_f16_biases_data[2] = {(float16_t)-0.427001953f, + (float16_t)-0.215209961f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/config_data.h new file mode 100644 index 00000000..b93455bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_INPUT_BATCHES 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_INPUT_H 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_INPUT_W 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_IN_CH 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_FILTER_H 3 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_FILTER_W 3 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUT_CH 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_STRIDE_H 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_STRIDE_W 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_PADDING_H 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_PADDING_W 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_DILATION_H 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_DILATION_W 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUTPUT_H 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUTPUT_W 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUTPUT_C 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_DST_SIZE 50 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_USE_WRAPPER 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/input_data.h new file mode 100644 index 00000000..b63cb2f4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/input_data.h @@ -0,0 +1,15 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_5x5_input_f16_input_data[50] = { + (float16_t)-0.182373047f, (float16_t)-0.859863281f, (float16_t)-0.237060547f, (float16_t)-0.7109375f, + (float16_t)-0.145263672f, (float16_t)-0.260986328f, (float16_t)0.466308594f, (float16_t)-0.244506836f, + (float16_t)0.623535156f, (float16_t)0.0370483398f, (float16_t)0.326904297f, (float16_t)0.0897827148f, + (float16_t)-0.638183594f, (float16_t)-0.58203125f, (float16_t)-0.709472656f, (float16_t)0.241333008f, + (float16_t)0.475341797f, (float16_t)0.0068397522f, (float16_t)0.0837402344f, (float16_t)0.207519531f, + (float16_t)0.987304688f, (float16_t)-0.154663086f, (float16_t)0.453369141f, (float16_t)-0.515136719f, + (float16_t)-0.722167969f, (float16_t)-0.366699219f, (float16_t)-0.395019531f, (float16_t)-0.15246582f, + (float16_t)-0.0135955811f, (float16_t)-0.497314453f, (float16_t)0.834472656f, (float16_t)0.295654297f, + (float16_t)0.715820312f, (float16_t)0.385009766f, (float16_t)0.611816406f, (float16_t)-0.374511719f, + (float16_t)0.605957031f, (float16_t)0.857910156f, (float16_t)-0.703613281f, (float16_t)0.483642578f, + (float16_t)0.635253906f, (float16_t)0.7734375f, (float16_t)0.812011719f, (float16_t)0.99609375f, + (float16_t)-0.726074219f, (float16_t)0.257080078f, (float16_t)-0.193969727f, (float16_t)-0.812011719f, + (float16_t)-0.966308594f, (float16_t)-0.00365638733f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/output_ref_data.h new file mode 100644 index 00000000..f388e97a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/output_ref_data.h @@ -0,0 +1,15 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_5x5_input_f16_output_ref_data[50] = { + (float16_t)-1.58105469f, (float16_t)1.06054688f, (float16_t)-0.901855469f, (float16_t)0.887207031f, + (float16_t)0.354248047f, (float16_t)-0.318847656f, (float16_t)0.0877685547f, (float16_t)-0.17578125f, + (float16_t)-0.93359375f, (float16_t)-0.459716797f, (float16_t)-1.1796875f, (float16_t)-0.942871094f, + (float16_t)0.573242188f, (float16_t)0.202880859f, (float16_t)-0.571289062f, (float16_t)0.938964844f, + (float16_t)-0.459228516f, (float16_t)-0.7890625f, (float16_t)0.333496094f, (float16_t)-0.138305664f, + (float16_t)-1.3984375f, (float16_t)-0.0801391602f, (float16_t)-1.48339844f, (float16_t)-1.22753906f, + (float16_t)0.10369873f, (float16_t)-0.240112305f, (float16_t)-0.763671875f, (float16_t)-0.28515625f, + (float16_t)-0.321777344f, (float16_t)-0.676269531f, (float16_t)-0.703125f, (float16_t)-0.0346069336f, + (float16_t)-0.00333023071f, (float16_t)-1.93554688f, (float16_t)0.227539062f, (float16_t)0.0508422852f, + (float16_t)-1.25390625f, (float16_t)-1.17382812f, (float16_t)-0.337402344f, (float16_t)-0.907714844f, + (float16_t)-1.8203125f, (float16_t)-0.266357422f, (float16_t)-1.73046875f, (float16_t)-0.546386719f, + (float16_t)-0.438720703f, (float16_t)0.846679688f, (float16_t)0.063659668f, (float16_t)0.970214844f, + (float16_t)-0.616699219f, (float16_t)-0.555664062f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/weights_data.h new file mode 100644 index 00000000..00e921d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_5x5_input_f16_weights_data[36] = { + (float16_t)0.141235352f, (float16_t)0.127319336f, (float16_t)-0.622558594f, (float16_t)0.234863281f, + (float16_t)0.00730895996f, (float16_t)0.507324219f, (float16_t)0.160522461f, (float16_t)-0.16027832f, + (float16_t)-0.231445312f, (float16_t)-0.328125f, (float16_t)0.313720703f, (float16_t)0.30078125f, + (float16_t)0.315429688f, (float16_t)-0.196899414f, (float16_t)-0.734863281f, (float16_t)0.448486328f, + (float16_t)0.411621094f, (float16_t)0.715820312f, (float16_t)0.536621094f, (float16_t)0.165649414f, + (float16_t)0.0269622803f, (float16_t)0.649902344f, (float16_t)-0.707519531f, (float16_t)-0.478271484f, + (float16_t)-0.102783203f, (float16_t)0.630371094f, (float16_t)-0.198852539f, (float16_t)-0.682617188f, + (float16_t)-0.181152344f, (float16_t)-0.548828125f, (float16_t)0.465087891f, (float16_t)-0.299072266f, + (float16_t)0.158935547f, (float16_t)-0.568359375f, (float16_t)-0.597167969f, (float16_t)0.525390625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/biases_data.h new file mode 100644 index 00000000..ec901d08 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_5x5_input_f32_biases_data[2] = {-0.426923871f, -0.215216115f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/config_data.h new file mode 100644 index 00000000..0c97622b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_INPUT_BATCHES 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_INPUT_H 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_INPUT_W 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_IN_CH 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_FILTER_H 3 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_FILTER_W 3 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUT_CH 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_STRIDE_H 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_STRIDE_W 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_PADDING_H 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_PADDING_W 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_DILATION_H 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_DILATION_W 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUTPUT_H 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUTPUT_W 5 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUTPUT_C 2 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_DST_SIZE 50 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_USE_WRAPPER 1 +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_2X2_DILATION_5X5_INPUT_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/input_data.h new file mode 100644 index 00000000..f8395afc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/input_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_5x5_input_f32_input_data[50] = { + -0.182342231f, -0.859862924f, -0.23708804f, -0.710847616f, -0.145324707f, -0.260974139f, 0.466389805f, + -0.244535416f, 0.62364012f, 0.0370431654f, 0.326902866f, 0.0897700861f, -0.63826251f, -0.581804395f, + -0.709524989f, 0.241371036f, 0.475459605f, 0.00683904439f, 0.0837632269f, 0.207572028f, 0.987224936f, + -0.154655099f, 0.453383684f, -0.51535207f, -0.722179234f, -0.366605461f, -0.395115465f, -0.152444765f, + -0.0135980081f, -0.497388065f, 0.83470571f, 0.295706213f, 0.715868533f, 0.385049433f, 0.611893058f, + -0.374533445f, 0.60590297f, 0.857988596f, -0.703454912f, 0.483544439f, 0.635164022f, 0.773284078f, + 0.811901629f, 0.996291876f, -0.725963771f, 0.257009029f, -0.19400084f, -0.812013686f, -0.966310978f, + -0.00365635473f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/output_ref_data.h new file mode 100644 index 00000000..3f704418 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_5x5_input_f32_output_ref_data[50] = { + -1.58130085f, 1.06068707f, -0.902095556f, 0.88738662f, 0.354132712f, -0.318893433f, 0.0877587795f, + -0.175821587f, -0.933485091f, -0.459823251f, -1.17927516f, -0.942782462f, 0.573099971f, 0.202928856f, + -0.571134448f, 0.938778222f, -0.459137499f, -0.788910329f, 0.333582044f, -0.138358951f, -1.39832354f, + -0.0801358372f, -1.48387372f, -1.22760975f, 0.10371232f, -0.240082577f, -0.763445675f, -0.285224736f, + -0.321838409f, -0.676159561f, -0.702905118f, -0.0346186906f, -0.00333020091f, -1.93585742f, 0.227503061f, + 0.0508420616f, -1.25398016f, -1.17368186f, -0.337441921f, -0.907757342f, -1.82011628f, -0.266405165f, + -1.73029542f, -0.546215236f, -0.43881011f, 0.846551955f, 0.0636688471f, 0.970117152f, -0.616854846f, + -0.55570209f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/weights_data.h new file mode 100644 index 00000000..2156a4aa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_5x5_input_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_5x5_input_f32_weights_data[36] = { + 0.141256884f, 0.127260759f, -0.622336447f, 0.234911382f, 0.00730929896f, 0.507325709f, + 0.160522178f, -0.160239592f, -0.231428936f, -0.328158557f, 0.313724428f, 0.300762266f, + 0.315347582f, -0.196947128f, -0.734998405f, 0.448394924f, 0.411568493f, 0.715982199f, + 0.536443472f, 0.165618911f, 0.0269646421f, 0.649884284f, -0.707545102f, -0.478182644f, + -0.102786765f, 0.63021332f, -0.198864654f, -0.68249172f, -0.18113929f, -0.54865402f, + 0.465083063f, -0.299187869f, 0.158914521f, -0.568121314f, -0.596973002f, 0.52531898f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/biases_data.h new file mode 100644 index 00000000..eabc1f0d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_f16_biases_data[2] = {(float16_t)0.281982422f, (float16_t)0.0621337891f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/config_data.h new file mode 100644 index 00000000..9f9f0935 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X2_DILATION_F16_INPUT_BATCHES 1 +#define CONV_MATCH_2X2_DILATION_F16_INPUT_H 10 +#define CONV_MATCH_2X2_DILATION_F16_INPUT_W 10 +#define CONV_MATCH_2X2_DILATION_F16_IN_CH 2 +#define CONV_MATCH_2X2_DILATION_F16_FILTER_H 3 +#define CONV_MATCH_2X2_DILATION_F16_FILTER_W 3 +#define CONV_MATCH_2X2_DILATION_F16_OUT_CH 2 +#define CONV_MATCH_2X2_DILATION_F16_STRIDE_H 1 +#define CONV_MATCH_2X2_DILATION_F16_STRIDE_W 1 +#define CONV_MATCH_2X2_DILATION_F16_PADDING_H 0 +#define CONV_MATCH_2X2_DILATION_F16_PADDING_W 0 +#define CONV_MATCH_2X2_DILATION_F16_DILATION_H 2 +#define CONV_MATCH_2X2_DILATION_F16_DILATION_W 2 +#define CONV_MATCH_2X2_DILATION_F16_OUTPUT_H 6 +#define CONV_MATCH_2X2_DILATION_F16_OUTPUT_W 6 +#define CONV_MATCH_2X2_DILATION_F16_OUTPUT_C 2 +#define CONV_MATCH_2X2_DILATION_F16_DST_SIZE 72 +#define CONV_MATCH_2X2_DILATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X2_DILATION_F16_USE_WRAPPER 1 +#define CONV_MATCH_2X2_DILATION_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_2X2_DILATION_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/input_data.h new file mode 100644 index 00000000..dbab2e8b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/input_data.h @@ -0,0 +1,52 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_f16_input_data[200] = { + (float16_t)-0.541503906f, (float16_t)0.213989258f, (float16_t)-0.5859375f, (float16_t)0.669921875f, + (float16_t)0.874023438f, (float16_t)-0.75390625f, (float16_t)-0.443847656f, (float16_t)0.0326538086f, + (float16_t)0.207885742f, (float16_t)0.857910156f, (float16_t)-0.376464844f, (float16_t)-0.436767578f, + (float16_t)-0.807617188f, (float16_t)0.57421875f, (float16_t)0.678222656f, (float16_t)-0.463867188f, + (float16_t)0.0392456055f, (float16_t)-0.379882812f, (float16_t)0.0107345581f, (float16_t)0.431640625f, + (float16_t)0.748535156f, (float16_t)0.589355469f, (float16_t)-0.912597656f, (float16_t)0.112854004f, + (float16_t)-0.454833984f, (float16_t)-0.4453125f, (float16_t)-0.843261719f, (float16_t)0.174682617f, + (float16_t)0.278320312f, (float16_t)0.567871094f, (float16_t)-0.254638672f, (float16_t)0.692382812f, + (float16_t)0.609863281f, (float16_t)0.267578125f, (float16_t)-0.0224761963f, (float16_t)0.633300781f, + (float16_t)0.0170288086f, (float16_t)-0.856445312f, (float16_t)0.673339844f, (float16_t)-0.772460938f, + (float16_t)0.608398438f, (float16_t)0.711425781f, (float16_t)-0.474853516f, (float16_t)-0.640625f, + (float16_t)0.587890625f, (float16_t)0.805175781f, (float16_t)-0.336669922f, (float16_t)-0.883789062f, + (float16_t)0.838378906f, (float16_t)-0.529785156f, (float16_t)0.721191406f, (float16_t)-0.762207031f, + (float16_t)-0.162231445f, (float16_t)-0.389404297f, (float16_t)0.772460938f, (float16_t)0.237426758f, + (float16_t)-0.0264434814f, (float16_t)-0.508789062f, (float16_t)-0.953613281f, (float16_t)-0.8828125f, + (float16_t)-0.435302734f, (float16_t)-0.0292358398f, (float16_t)0.326171875f, (float16_t)0.916015625f, + (float16_t)-0.355957031f, (float16_t)0.711425781f, (float16_t)-0.654785156f, (float16_t)0.365722656f, + (float16_t)-0.0802612305f, (float16_t)0.425292969f, (float16_t)0.241088867f, (float16_t)-0.798339844f, + (float16_t)-0.114196777f, (float16_t)0.3046875f, (float16_t)0.857910156f, (float16_t)-0.781738281f, + (float16_t)-0.995117188f, (float16_t)0.700195312f, (float16_t)0.609863281f, (float16_t)-0.902832031f, + (float16_t)0.0808105469f, (float16_t)-0.777832031f, (float16_t)0.679199219f, (float16_t)-0.720214844f, + (float16_t)-0.766601562f, (float16_t)-0.307861328f, (float16_t)-0.86328125f, (float16_t)-0.0626831055f, + (float16_t)-0.469970703f, (float16_t)-0.794921875f, (float16_t)0.810058594f, (float16_t)0.62890625f, + (float16_t)0.701660156f, (float16_t)0.100036621f, (float16_t)0.763671875f, (float16_t)0.440429688f, + (float16_t)-0.447265625f, (float16_t)-0.201538086f, (float16_t)0.744628906f, (float16_t)-0.799316406f, + (float16_t)-0.666503906f, (float16_t)0.579101562f, (float16_t)0.0251312256f, (float16_t)0.827636719f, + (float16_t)-0.204345703f, (float16_t)-0.885253906f, (float16_t)-0.907714844f, (float16_t)-0.00381088257f, + (float16_t)0.19519043f, (float16_t)-0.332275391f, (float16_t)-0.0664672852f, (float16_t)-0.997070312f, + (float16_t)-0.691894531f, (float16_t)0.540039062f, (float16_t)0.489501953f, (float16_t)-0.196777344f, + (float16_t)-0.583496094f, (float16_t)-0.0155410767f, (float16_t)-0.518066406f, (float16_t)0.600585938f, + (float16_t)0.0501098633f, (float16_t)0.147094727f, (float16_t)-0.227416992f, (float16_t)-0.134521484f, + (float16_t)-0.399414062f, (float16_t)0.0497436523f, (float16_t)0.317138672f, (float16_t)0.951660156f, + (float16_t)-0.50390625f, (float16_t)0.900878906f, (float16_t)0.732421875f, (float16_t)-0.534667969f, + (float16_t)0.999023438f, (float16_t)0.543457031f, (float16_t)-0.209960938f, (float16_t)-0.988769531f, + (float16_t)-0.693847656f, (float16_t)-0.874023438f, (float16_t)0.514160156f, (float16_t)-0.171142578f, + (float16_t)-0.401611328f, (float16_t)-0.959960938f, (float16_t)-0.0748901367f, (float16_t)-0.274902344f, + (float16_t)0.256835938f, (float16_t)0.7265625f, (float16_t)0.243041992f, (float16_t)0.515625f, + (float16_t)-0.509277344f, (float16_t)-0.69921875f, (float16_t)0.9453125f, (float16_t)-0.00528335571f, + (float16_t)-0.889160156f, (float16_t)0.865722656f, (float16_t)-0.288818359f, (float16_t)-0.747070312f, + (float16_t)-0.654296875f, (float16_t)-0.595214844f, (float16_t)-0.78515625f, (float16_t)-0.783203125f, + (float16_t)-0.905273438f, (float16_t)-0.225830078f, (float16_t)0.611816406f, (float16_t)0.556640625f, + (float16_t)0.207763672f, (float16_t)-0.903320312f, (float16_t)0.189697266f, (float16_t)-0.243652344f, + (float16_t)-0.671875f, (float16_t)-0.366699219f, (float16_t)-0.0126342773f, (float16_t)0.411865234f, + (float16_t)0.233642578f, (float16_t)-0.198852539f, (float16_t)0.17956543f, (float16_t)0.233154297f, + (float16_t)0.489013672f, (float16_t)0.52734375f, (float16_t)0.976074219f, (float16_t)-0.952636719f, + (float16_t)0.299804688f, (float16_t)0.239990234f, (float16_t)0.362548828f, (float16_t)-0.458007812f, + (float16_t)-0.348388672f, (float16_t)-0.791015625f, (float16_t)-0.932128906f, (float16_t)-0.339599609f, + (float16_t)-0.65625f, (float16_t)-0.422851562f, (float16_t)0.0960083008f, (float16_t)0.561035156f, + (float16_t)-0.965332031f, (float16_t)-0.748046875f, (float16_t)0.703125f, (float16_t)0.0762329102f, + (float16_t)0.916992188f, (float16_t)-0.331542969f, (float16_t)-0.326660156f, (float16_t)-0.351074219f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/output_ref_data.h new file mode 100644 index 00000000..858e0ee9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_f16_output_ref_data[72] = { + (float16_t)1.11523438f, (float16_t)1.52539062f, (float16_t)1.68164062f, (float16_t)-0.346191406f, + (float16_t)-0.764648438f, (float16_t)0.0346679688f, (float16_t)-0.674316406f, (float16_t)0.278808594f, + (float16_t)0.315429688f, (float16_t)1.60839844f, (float16_t)0.854492188f, (float16_t)-0.366210938f, + (float16_t)-0.175537109f, (float16_t)-0.045715332f, (float16_t)1.32324219f, (float16_t)-1.00976562f, + (float16_t)1.00488281f, (float16_t)-0.332763672f, (float16_t)0.10760498f, (float16_t)-1.05664062f, + (float16_t)1.5546875f, (float16_t)0.684570312f, (float16_t)0.547363281f, (float16_t)1.140625f, + (float16_t)0.492919922f, (float16_t)0.210449219f, (float16_t)-0.344482422f, (float16_t)0.637695312f, + (float16_t)-0.55078125f, (float16_t)0.96484375f, (float16_t)-0.88671875f, (float16_t)-0.805664062f, + (float16_t)-1.88085938f, (float16_t)0.323486328f, (float16_t)-0.401855469f, (float16_t)1.4375f, + (float16_t)0.692871094f, (float16_t)-0.314941406f, (float16_t)1.58007812f, (float16_t)0.180175781f, + (float16_t)0.671386719f, (float16_t)-0.772949219f, (float16_t)0.141601562f, (float16_t)-1.12695312f, + (float16_t)0.820800781f, (float16_t)1.58398438f, (float16_t)0.153442383f, (float16_t)-0.637207031f, + (float16_t)-0.815429688f, (float16_t)-0.209350586f, (float16_t)-0.243164062f, (float16_t)-0.695800781f, + (float16_t)1.48046875f, (float16_t)-0.100219727f, (float16_t)1.84082031f, (float16_t)-0.8671875f, + (float16_t)0.607910156f, (float16_t)-0.396240234f, (float16_t)-0.498291016f, (float16_t)0.948242188f, + (float16_t)1.45214844f, (float16_t)-0.290771484f, (float16_t)1.19433594f, (float16_t)1.12597656f, + (float16_t)0.290039062f, (float16_t)-0.76171875f, (float16_t)1.24511719f, (float16_t)-0.0526428223f, + (float16_t)0.889648438f, (float16_t)-0.451904297f, (float16_t)-0.870605469f, (float16_t)-0.736328125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/weights_data.h new file mode 100644 index 00000000..8b1d83e4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x2_dilation_f16_weights_data[36] = { + (float16_t)-0.693847656f, (float16_t)0.565917969f, (float16_t)-0.272705078f, (float16_t)0.105712891f, + (float16_t)0.455810547f, (float16_t)0.0195770264f, (float16_t)-0.0917358398f, (float16_t)0.327148438f, + (float16_t)0.056854248f, (float16_t)0.218383789f, (float16_t)-0.712890625f, (float16_t)-0.0975952148f, + (float16_t)0.725097656f, (float16_t)-0.561035156f, (float16_t)-0.420898438f, (float16_t)0.0367736816f, + (float16_t)0.335693359f, (float16_t)-0.0668334961f, (float16_t)0.408203125f, (float16_t)0.606933594f, + (float16_t)0.437988281f, (float16_t)-0.00477218628f, (float16_t)-0.520019531f, (float16_t)-0.217529297f, + (float16_t)0.644042969f, (float16_t)-0.302490234f, (float16_t)0.146972656f, (float16_t)0.611816406f, + (float16_t)0.638671875f, (float16_t)0.101135254f, (float16_t)-0.420410156f, (float16_t)-0.477539062f, + (float16_t)0.188232422f, (float16_t)0.387939453f, (float16_t)-0.239257812f, (float16_t)-0.0489807129f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/biases_data.h new file mode 100644 index 00000000..91942506 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_f32_biases_data[2] = {0.2820265f, 0.062143486f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/config_data.h new file mode 100644 index 00000000..4eadf9f1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X2_DILATION_F32_INPUT_BATCHES 1 +#define CONV_MATCH_2X2_DILATION_F32_INPUT_H 10 +#define CONV_MATCH_2X2_DILATION_F32_INPUT_W 10 +#define CONV_MATCH_2X2_DILATION_F32_IN_CH 2 +#define CONV_MATCH_2X2_DILATION_F32_FILTER_H 3 +#define CONV_MATCH_2X2_DILATION_F32_FILTER_W 3 +#define CONV_MATCH_2X2_DILATION_F32_OUT_CH 2 +#define CONV_MATCH_2X2_DILATION_F32_STRIDE_H 1 +#define CONV_MATCH_2X2_DILATION_F32_STRIDE_W 1 +#define CONV_MATCH_2X2_DILATION_F32_PADDING_H 0 +#define CONV_MATCH_2X2_DILATION_F32_PADDING_W 0 +#define CONV_MATCH_2X2_DILATION_F32_DILATION_H 2 +#define CONV_MATCH_2X2_DILATION_F32_DILATION_W 2 +#define CONV_MATCH_2X2_DILATION_F32_OUTPUT_H 6 +#define CONV_MATCH_2X2_DILATION_F32_OUTPUT_W 6 +#define CONV_MATCH_2X2_DILATION_F32_OUTPUT_C 2 +#define CONV_MATCH_2X2_DILATION_F32_DST_SIZE 72 +#define CONV_MATCH_2X2_DILATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X2_DILATION_F32_USE_WRAPPER 1 +#define CONV_MATCH_2X2_DILATION_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_2X2_DILATION_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/input_data.h new file mode 100644 index 00000000..dbaef5fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/input_data.h @@ -0,0 +1,31 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_f32_input_data[200] = { + -0.541517735f, 0.213983491f, -0.585812807f, 0.670042217f, 0.874153078f, -0.754032075f, -0.443923652f, + 0.0326652564f, 0.207898378f, 0.857989669f, -0.376378804f, -0.436669379f, -0.807651281f, 0.574337959f, + 0.678223312f, -0.463969707f, 0.0392410606f, -0.37984544f, 0.0107343979f, 0.431638509f, 0.748708189f, + 0.589195907f, -0.912690878f, 0.112870917f, -0.454734921f, -0.44528234f, -0.843327284f, 0.174668476f, + 0.278279424f, 0.567996621f, -0.254584014f, 0.69254005f, 0.609917939f, 0.267700046f, -0.0224783048f, + 0.633471906f, 0.0170307066f, -0.856425762f, 0.673239768f, -0.772530377f, 0.608475506f, 0.711627781f, + -0.474749565f, -0.640770078f, 0.588098943f, 0.805176675f, -0.336569995f, -0.883771598f, 0.838333726f, + -0.529907525f, 0.721029341f, -0.762043655f, -0.16228047f, -0.389479399f, 0.772506297f, 0.237457529f, + -0.0264509246f, -0.508749247f, -0.953845739f, -0.882720768f, -0.4352732f, -0.0292302165f, 0.326273143f, + 0.915939212f, -0.356061846f, 0.711197317f, -0.654712081f, 0.365747094f, -0.0802422985f, 0.42534247f, + 0.241095692f, -0.798099279f, -0.114194162f, 0.304633588f, 0.85808295f, -0.781692982f, -0.995001793f, + 0.700320065f, 0.609957397f, -0.903038681f, 0.0808316171f, -0.77788347f, 0.679130077f, -0.72000885f, + -0.766783237f, -0.307915211f, -0.863482654f, -0.0626548082f, -0.470001578f, -0.794933975f, 0.809838176f, + 0.628749132f, 0.701880872f, 0.100034431f, 0.763777554f, 0.4405303f, -0.447338998f, -0.201517805f, + 0.744777799f, -0.799497724f, -0.666707575f, 0.57904917f, 0.0251291171f, 0.827699423f, -0.204340711f, + -0.885303438f, -0.907475114f, -0.00381053775f, 0.195131779f, -0.332238019f, -0.0664751977f, -0.997011185f, + -0.691885769f, 0.539820611f, 0.48941797f, -0.196776643f, -0.583486736f, -0.0155413803f, -0.51787138f, + 0.60074389f, 0.0501026101f, 0.147033855f, -0.227425605f, -0.134560749f, -0.39932707f, 0.049734544f, + 0.317046881f, 0.951684654f, -0.50406462f, 0.900670588f, 0.732474864f, -0.53455323f, 0.999079645f, + 0.543334782f, -0.210018605f, -0.988551676f, -0.693616509f, -0.874032557f, 0.514271319f, -0.171175539f, + -0.401613235f, -0.960151255f, -0.0748702064f, -0.274909168f, 0.256864309f, 0.726503015f, 0.242990062f, + 0.51560849f, -0.509258807f, -0.699310243f, 0.945252538f, -0.0052828812f, -0.889099598f, 0.865644813f, + -0.288877219f, -0.746866405f, -0.654523015f, -0.595407069f, -0.78528595f, -0.783085704f, -0.905407727f, + -0.225827664f, 0.61186105f, 0.556546271f, 0.20772624f, -0.903194964f, 0.189735234f, -0.243677005f, + -0.671871006f, -0.366624415f, -0.0126331262f, 0.411926299f, 0.233701676f, -0.198905632f, 0.179509908f, + 0.233125374f, 0.48902908f, 0.52710253f, 0.975848675f, -0.95259738f, 0.299901843f, 0.239959702f, + 0.362600595f, -0.458071053f, -0.348284811f, -0.790950239f, -0.932312131f, -0.339636147f, -0.656252205f, + -0.422773957f, 0.0960137025f, 0.560895443f, -0.965421081f, -0.748060226f, 0.70308733f, 0.0762265995f, + 0.91690284f, -0.331496477f, -0.326603413f, -0.351040125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/output_ref_data.h new file mode 100644 index 00000000..12683ae5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_f32_output_ref_data[72] = { + 1.11482525f, 1.52513576f, 1.6815263f, -0.346117526f, -0.76455009f, 0.0346783213f, -0.674215794f, + 0.278848052f, 0.315338284f, 1.60820591f, 0.854323983f, -0.366321385f, -0.175498635f, -0.0457152016f, + 1.32350075f, -1.0099045f, 1.00466406f, -0.332739145f, 0.107614189f, -1.05673492f, 1.55505121f, + 0.684530318f, 0.547577977f, 1.14055324f, 0.493014753f, 0.210413858f, -0.344509989f, 0.637520254f, + -0.550763369f, 0.965036929f, -0.886806965f, -0.805439115f, -1.88109422f, 0.323599845f, -0.401830107f, + 1.43705654f, 0.692815125f, -0.31498161f, 1.58027482f, 0.180160359f, 0.671218812f, -0.773065746f, + 0.141578168f, -1.12728846f, 0.820750475f, 1.58409739f, 0.153446883f, -0.637399137f, -0.815573454f, + -0.209307358f, -0.243146569f, -0.695885122f, 1.48026621f, -0.100191548f, 1.8412348f, -0.867177546f, + 0.607990146f, -0.396361947f, -0.498193115f, 0.948406935f, 1.45234132f, -0.290675968f, 1.19457924f, + 1.12627959f, 0.289945751f, -0.761896133f, 1.24533176f, -0.0526381023f, 0.889420629f, -0.451839298f, + -0.870573401f, -0.736324668f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/weights_data.h new file mode 100644 index 00000000..70110ab0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x2_dilation_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x2_dilation_f32_weights_data[36] = { + -0.694041014f, 0.565695584f, -0.272629261f, 0.105692625f, 0.455832601f, 0.0195779055f, + -0.0917643085f, 0.327247083f, 0.0568397343f, 0.218342856f, -0.712919772f, -0.097588174f, + 0.724977195f, -0.561267674f, -0.420939296f, 0.0367809162f, 0.335618496f, -0.0668462813f, + 0.408239812f, 0.606798649f, 0.438070297f, -0.0047716382f, -0.520215213f, -0.217524856f, + 0.644187272f, -0.30250147f, 0.147002578f, 0.611670971f, 0.638630509f, 0.101147428f, + -0.420463562f, -0.477454424f, 0.188222334f, 0.388018996f, -0.239275888f, -0.0489899702f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/biases_data.h new file mode 100644 index 00000000..9096d418 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x3_dilation_f16_biases_data[2] = {(float16_t)0.287353516f, (float16_t)0.393554688f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/config_data.h new file mode 100644 index 00000000..a4a6b54e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X3_DILATION_F16_INPUT_BATCHES 1 +#define CONV_MATCH_2X3_DILATION_F16_INPUT_H 3 +#define CONV_MATCH_2X3_DILATION_F16_INPUT_W 3 +#define CONV_MATCH_2X3_DILATION_F16_IN_CH 2 +#define CONV_MATCH_2X3_DILATION_F16_FILTER_H 3 +#define CONV_MATCH_2X3_DILATION_F16_FILTER_W 3 +#define CONV_MATCH_2X3_DILATION_F16_OUT_CH 2 +#define CONV_MATCH_2X3_DILATION_F16_STRIDE_H 1 +#define CONV_MATCH_2X3_DILATION_F16_STRIDE_W 1 +#define CONV_MATCH_2X3_DILATION_F16_PADDING_H 2 +#define CONV_MATCH_2X3_DILATION_F16_PADDING_W 2 +#define CONV_MATCH_2X3_DILATION_F16_DILATION_H 2 +#define CONV_MATCH_2X3_DILATION_F16_DILATION_W 2 +#define CONV_MATCH_2X3_DILATION_F16_OUTPUT_H 3 +#define CONV_MATCH_2X3_DILATION_F16_OUTPUT_W 3 +#define CONV_MATCH_2X3_DILATION_F16_OUTPUT_C 2 +#define CONV_MATCH_2X3_DILATION_F16_DST_SIZE 18 +#define CONV_MATCH_2X3_DILATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X3_DILATION_F16_USE_WRAPPER 1 +#define CONV_MATCH_2X3_DILATION_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_2X3_DILATION_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/input_data.h new file mode 100644 index 00000000..acd6818a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/input_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x3_dilation_f16_input_data[18] = {(float16_t)-0.0858764648f, + (float16_t)-0.205932617f, + (float16_t)0.825195312f, + (float16_t)-0.834960938f, + (float16_t)0.769042969f, + (float16_t)-0.8515625f, + (float16_t)-0.974609375f, + (float16_t)-0.616210938f, + (float16_t)-0.969238281f, + (float16_t)-0.407958984f, + (float16_t)-0.810546875f, + (float16_t)0.0664672852f, + (float16_t)0.562011719f, + (float16_t)0.0182647705f, + (float16_t)-0.654785156f, + (float16_t)0.0728149414f, + (float16_t)-0.689941406f, + (float16_t)-0.669433594f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/output_ref_data.h new file mode 100644 index 00000000..02d986b8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x3_dilation_f16_output_ref_data[18] = {(float16_t)-0.021270752f, + (float16_t)0.895019531f, + (float16_t)-0.392822266f, + (float16_t)0.238525391f, + (float16_t)0.122924805f, + (float16_t)0.21887207f, + (float16_t)-0.0266265869f, + (float16_t)0.565429688f, + (float16_t)0.145751953f, + (float16_t)0.467773438f, + (float16_t)-0.129882812f, + (float16_t)0.0974731445f, + (float16_t)1.01464844f, + (float16_t)0.640625f, + (float16_t)0.789550781f, + (float16_t)0.109436035f, + (float16_t)0.4453125f, + (float16_t)0.203979492f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/weights_data.h new file mode 100644 index 00000000..00c07797 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_2x3_dilation_f16_weights_data[36] = { + (float16_t)-0.360839844f, (float16_t)0.174072266f, (float16_t)-0.269287109f, (float16_t)-0.704101562f, + (float16_t)0.400878906f, (float16_t)-0.310302734f, (float16_t)0.200561523f, (float16_t)0.608886719f, + (float16_t)-0.134521484f, (float16_t)0.666503906f, (float16_t)0.033416748f, (float16_t)-0.109680176f, + (float16_t)0.205932617f, (float16_t)0.546875f, (float16_t)-0.0606689453f, (float16_t)-0.719238281f, + (float16_t)0.565917969f, (float16_t)-0.202514648f, (float16_t)-0.456542969f, (float16_t)0.694335938f, + (float16_t)-0.61328125f, (float16_t)-0.302734375f, (float16_t)-0.532714844f, (float16_t)-0.07421875f, + (float16_t)-0.038482666f, (float16_t)0.488525391f, (float16_t)0.0210723877f, (float16_t)-0.232177734f, + (float16_t)-0.109619141f, (float16_t)-0.590820312f, (float16_t)-0.399169922f, (float16_t)-0.525878906f, + (float16_t)0.510253906f, (float16_t)-0.439941406f, (float16_t)0.303710938f, (float16_t)0.0485534668f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/biases_data.h new file mode 100644 index 00000000..0f6f9231 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x3_dilation_f32_biases_data[2] = {0.287262499f, 0.393460512f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/config_data.h new file mode 100644 index 00000000..ae4af7a7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_2X3_DILATION_F32_INPUT_BATCHES 1 +#define CONV_MATCH_2X3_DILATION_F32_INPUT_H 3 +#define CONV_MATCH_2X3_DILATION_F32_INPUT_W 3 +#define CONV_MATCH_2X3_DILATION_F32_IN_CH 2 +#define CONV_MATCH_2X3_DILATION_F32_FILTER_H 3 +#define CONV_MATCH_2X3_DILATION_F32_FILTER_W 3 +#define CONV_MATCH_2X3_DILATION_F32_OUT_CH 2 +#define CONV_MATCH_2X3_DILATION_F32_STRIDE_H 1 +#define CONV_MATCH_2X3_DILATION_F32_STRIDE_W 1 +#define CONV_MATCH_2X3_DILATION_F32_PADDING_H 2 +#define CONV_MATCH_2X3_DILATION_F32_PADDING_W 2 +#define CONV_MATCH_2X3_DILATION_F32_DILATION_H 2 +#define CONV_MATCH_2X3_DILATION_F32_DILATION_W 2 +#define CONV_MATCH_2X3_DILATION_F32_OUTPUT_H 3 +#define CONV_MATCH_2X3_DILATION_F32_OUTPUT_W 3 +#define CONV_MATCH_2X3_DILATION_F32_OUTPUT_C 2 +#define CONV_MATCH_2X3_DILATION_F32_DST_SIZE 18 +#define CONV_MATCH_2X3_DILATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_2X3_DILATION_F32_USE_WRAPPER 1 +#define CONV_MATCH_2X3_DILATION_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_2X3_DILATION_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/input_data.h new file mode 100644 index 00000000..1dee0b37 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/input_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x3_dilation_f32_input_data[18] = {-0.085891515f, + -0.205923274f, + 0.82519269f, + -0.835019946f, + 0.768882513f, + -0.851318657f, + -0.97453773f, + -0.616146564f, + -0.969060481f, + -0.407987177f, + -0.810574174f, + 0.066462025f, + 0.561819494f, + 0.0182682853f, + -0.654772341f, + 0.0727946982f, + -0.689716816f, + -0.669412196f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/output_ref_data.h new file mode 100644 index 00000000..ad6bca53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x3_dilation_f32_output_ref_data[18] = {-0.0212760568f, + 0.895027399f, + -0.392920017f, + 0.238579467f, + 0.122933418f, + 0.21883209f, + -0.0266275108f, + 0.565565467f, + 0.145748004f, + 0.467788756f, + -0.129874647f, + 0.0974559188f, + 1.01468992f, + 0.640737832f, + 0.789777696f, + 0.109416783f, + 0.445390701f, + 0.204014003f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/weights_data.h new file mode 100644 index 00000000..652e2bd9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_2x3_dilation_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_2x3_dilation_f32_weights_data[36] = { + -0.360802114f, 0.174127877f, -0.269314945f, -0.704336822f, 0.400805771f, -0.310279071f, + 0.200528085f, 0.608742952f, -0.134553894f, 0.666455686f, 0.033426702f, -0.109666698f, + 0.205888033f, 0.547101438f, -0.0606643558f, -0.719382584f, 0.565779984f, -0.202465549f, + -0.45650056f, 0.694469154f, -0.613297403f, -0.302681476f, -0.532837152f, -0.0742028803f, + -0.0384844728f, 0.488515079f, 0.0210690387f, -0.232226476f, -0.109588735f, -0.590981364f, + -0.399056733f, -0.525972784f, 0.510329723f, -0.440018594f, 0.303691089f, 0.0485663153f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/biases_data.h new file mode 100644 index 00000000..9093d068 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x2_dilation_f16_biases_data[2] = {(float16_t)0.191650391f, (float16_t)0.199951172f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/config_data.h new file mode 100644 index 00000000..77ac3abc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_3X2_DILATION_F16_INPUT_BATCHES 1 +#define CONV_MATCH_3X2_DILATION_F16_INPUT_H 3 +#define CONV_MATCH_3X2_DILATION_F16_INPUT_W 3 +#define CONV_MATCH_3X2_DILATION_F16_IN_CH 2 +#define CONV_MATCH_3X2_DILATION_F16_FILTER_H 3 +#define CONV_MATCH_3X2_DILATION_F16_FILTER_W 3 +#define CONV_MATCH_3X2_DILATION_F16_OUT_CH 2 +#define CONV_MATCH_3X2_DILATION_F16_STRIDE_H 1 +#define CONV_MATCH_3X2_DILATION_F16_STRIDE_W 1 +#define CONV_MATCH_3X2_DILATION_F16_PADDING_H 2 +#define CONV_MATCH_3X2_DILATION_F16_PADDING_W 3 +#define CONV_MATCH_3X2_DILATION_F16_DILATION_H 2 +#define CONV_MATCH_3X2_DILATION_F16_DILATION_W 3 +#define CONV_MATCH_3X2_DILATION_F16_OUTPUT_H 3 +#define CONV_MATCH_3X2_DILATION_F16_OUTPUT_W 3 +#define CONV_MATCH_3X2_DILATION_F16_OUTPUT_C 2 +#define CONV_MATCH_3X2_DILATION_F16_DST_SIZE 18 +#define CONV_MATCH_3X2_DILATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_3X2_DILATION_F16_USE_WRAPPER 1 +#define CONV_MATCH_3X2_DILATION_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_3X2_DILATION_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/input_data.h new file mode 100644 index 00000000..731e443d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/input_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x2_dilation_f16_input_data[18] = {(float16_t)0.341552734f, + (float16_t)0.720214844f, + (float16_t)0.056854248f, + (float16_t)0.110534668f, + (float16_t)-0.288574219f, + (float16_t)0.498291016f, + (float16_t)-0.357910156f, + (float16_t)0.259521484f, + (float16_t)-0.0869750977f, + (float16_t)-0.480224609f, + (float16_t)-0.221069336f, + (float16_t)0.491210938f, + (float16_t)0.622558594f, + (float16_t)0.145507812f, + (float16_t)-0.2421875f, + (float16_t)0.790039062f, + (float16_t)0.848144531f, + (float16_t)0.400146484f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/output_ref_data.h new file mode 100644 index 00000000..b6db1f3c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x2_dilation_f16_output_ref_data[18] = {(float16_t)0.623046875f, + (float16_t)0.275390625f, + (float16_t)0.51953125f, + (float16_t)0.446289062f, + (float16_t)0.584960938f, + (float16_t)-0.0404663086f, + (float16_t)0.224243164f, + (float16_t)-0.119262695f, + (float16_t)0.0186157227f, + (float16_t)0.265380859f, + (float16_t)0.322021484f, + (float16_t)-0.0842895508f, + (float16_t)-0.0490722656f, + (float16_t)0.906738281f, + (float16_t)0.359619141f, + (float16_t)-0.130615234f, + (float16_t)0.169189453f, + (float16_t)0.92578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/weights_data.h new file mode 100644 index 00000000..14240ab5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x2_dilation_f16_weights_data[36] = { + (float16_t)0.437011719f, (float16_t)-0.33984375f, (float16_t)0.0352478027f, (float16_t)-0.548339844f, + (float16_t)0.633300781f, (float16_t)0.674316406f, (float16_t)0.687988281f, (float16_t)-0.584960938f, + (float16_t)0.150634766f, (float16_t)0.333007812f, (float16_t)-0.468994141f, (float16_t)0.198608398f, + (float16_t)-0.427490234f, (float16_t)0.431884766f, (float16_t)0.131469727f, (float16_t)0.397705078f, + (float16_t)-0.0729980469f, (float16_t)0.269775391f, (float16_t)0.65625f, (float16_t)0.23034668f, + (float16_t)-0.0434265137f, (float16_t)0.448242188f, (float16_t)-0.591308594f, (float16_t)0.618652344f, + (float16_t)-0.425537109f, (float16_t)0.73828125f, (float16_t)0.701660156f, (float16_t)-0.262939453f, + (float16_t)0.498291016f, (float16_t)-0.218505859f, (float16_t)0.0222625732f, (float16_t)0.258544922f, + (float16_t)-0.0269775391f, (float16_t)0.289794922f, (float16_t)0.374023438f, (float16_t)0.729980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/biases_data.h new file mode 100644 index 00000000..e690cd16 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x2_dilation_f32_biases_data[2] = {0.191691801f, 0.200007051f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/config_data.h new file mode 100644 index 00000000..c333f76f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_3X2_DILATION_F32_INPUT_BATCHES 1 +#define CONV_MATCH_3X2_DILATION_F32_INPUT_H 3 +#define CONV_MATCH_3X2_DILATION_F32_INPUT_W 3 +#define CONV_MATCH_3X2_DILATION_F32_IN_CH 2 +#define CONV_MATCH_3X2_DILATION_F32_FILTER_H 3 +#define CONV_MATCH_3X2_DILATION_F32_FILTER_W 3 +#define CONV_MATCH_3X2_DILATION_F32_OUT_CH 2 +#define CONV_MATCH_3X2_DILATION_F32_STRIDE_H 1 +#define CONV_MATCH_3X2_DILATION_F32_STRIDE_W 1 +#define CONV_MATCH_3X2_DILATION_F32_PADDING_H 2 +#define CONV_MATCH_3X2_DILATION_F32_PADDING_W 3 +#define CONV_MATCH_3X2_DILATION_F32_DILATION_H 2 +#define CONV_MATCH_3X2_DILATION_F32_DILATION_W 3 +#define CONV_MATCH_3X2_DILATION_F32_OUTPUT_H 3 +#define CONV_MATCH_3X2_DILATION_F32_OUTPUT_W 3 +#define CONV_MATCH_3X2_DILATION_F32_OUTPUT_C 2 +#define CONV_MATCH_3X2_DILATION_F32_DST_SIZE 18 +#define CONV_MATCH_3X2_DILATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_3X2_DILATION_F32_USE_WRAPPER 1 +#define CONV_MATCH_3X2_DILATION_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_3X2_DILATION_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/input_data.h new file mode 100644 index 00000000..f521c9a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/input_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x2_dilation_f32_input_data[18] = {0.341523707f, + 0.720427692f, + 0.0568604283f, + 0.110556446f, + -0.288536698f, + 0.498339117f, + -0.357840091f, + 0.259489924f, + -0.087001875f, + -0.480307192f, + -0.221107766f, + 0.491179079f, + 0.622759879f, + 0.145519033f, + -0.242240444f, + 0.789908111f, + 0.848077059f, + 0.400026858f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/output_ref_data.h new file mode 100644 index 00000000..bb5b7e3c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x2_dilation_f32_output_ref_data[18] = {0.622805476f, + 0.275501579f, + 0.519290149f, + 0.446186811f, + 0.584739029f, + -0.0404671133f, + 0.224211037f, + -0.119279772f, + 0.0186193883f, + 0.265282333f, + 0.321972311f, + -0.0842841864f, + -0.0490814894f, + 0.906658292f, + 0.359655082f, + -0.130595356f, + 0.169216871f, + 0.925633788f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/weights_data.h new file mode 100644 index 00000000..cfdbc9dc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x2_dilation_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x2_dilation_f32_weights_data[36] = { + 0.436891347f, -0.339942783f, 0.035235323f, -0.54840076f, 0.633230984f, 0.674287319f, + 0.687817514f, -0.585109353f, 0.150637329f, 0.333050758f, -0.469108403f, 0.19861874f, + -0.427597493f, 0.431881428f, 0.131465971f, 0.39758867f, -0.0730281547f, 0.269685835f, + 0.656407773f, 0.230381951f, -0.0434221737f, 0.44813329f, -0.591178298f, 0.618666053f, + -0.425584912f, 0.738426328f, 0.701558173f, -0.262982041f, 0.498188734f, -0.218497038f, + 0.0222663041f, 0.258521169f, -0.0269767921f, 0.289689869f, 0.374015927f, 0.730135322f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/biases_data.h new file mode 100644 index 00000000..3dbae2af --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/biases_data.h @@ -0,0 +1,3 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x3_dilation_5x5_input_f16_biases_data[2] = {(float16_t)-0.297119141f, + (float16_t)-0.199584961f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/config_data.h new file mode 100644 index 00000000..4ece34c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_INPUT_BATCHES 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_INPUT_H 11 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_INPUT_W 9 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_IN_CH 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_FILTER_H 3 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_FILTER_W 3 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUT_CH 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_STRIDE_H 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_STRIDE_W 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_PADDING_H 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_PADDING_W 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_DILATION_H 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_DILATION_W 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUTPUT_H 11 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUTPUT_W 9 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUTPUT_C 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_DST_SIZE 198 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_USE_WRAPPER 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/input_data.h new file mode 100644 index 00000000..f6b36ccf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/input_data.h @@ -0,0 +1,52 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x3_dilation_5x5_input_f16_input_data[198] = { + (float16_t)-0.831542969f, (float16_t)-0.0718383789f, (float16_t)0.665039062f, (float16_t)0.881347656f, + (float16_t)-0.84375f, (float16_t)-0.932128906f, (float16_t)0.343505859f, (float16_t)-0.177124023f, + (float16_t)-0.833496094f, (float16_t)-0.0290222168f, (float16_t)-0.357666016f, (float16_t)-0.284912109f, + (float16_t)-0.094909668f, (float16_t)0.421875f, (float16_t)0.218017578f, (float16_t)0.571289062f, + (float16_t)-0.589355469f, (float16_t)0.633300781f, (float16_t)0.71875f, (float16_t)-0.830566406f, + (float16_t)0.929199219f, (float16_t)-0.733398438f, (float16_t)0.354736328f, (float16_t)0.23059082f, + (float16_t)-0.405029297f, (float16_t)0.677734375f, (float16_t)0.611816406f, (float16_t)-0.47265625f, + (float16_t)0.478271484f, (float16_t)-0.219116211f, (float16_t)0.723144531f, (float16_t)0.177856445f, + (float16_t)-0.981933594f, (float16_t)0.374511719f, (float16_t)0.956054688f, (float16_t)-0.961425781f, + (float16_t)-0.236816406f, (float16_t)-0.987304688f, (float16_t)-0.946777344f, (float16_t)-0.805175781f, + (float16_t)-0.597167969f, (float16_t)-0.691894531f, (float16_t)-0.861328125f, (float16_t)-0.440185547f, + (float16_t)-0.848144531f, (float16_t)-0.521484375f, (float16_t)-0.646972656f, (float16_t)0.166503906f, + (float16_t)0.0136795044f, (float16_t)0.549316406f, (float16_t)-0.876464844f, (float16_t)-0.23425293f, + (float16_t)-0.952148438f, (float16_t)-0.448486328f, (float16_t)-0.643066406f, (float16_t)0.473144531f, + (float16_t)-0.494140625f, (float16_t)-0.834472656f, (float16_t)0.233520508f, (float16_t)-0.298583984f, + (float16_t)-0.94921875f, (float16_t)0.874023438f, (float16_t)0.693847656f, (float16_t)-0.422607422f, + (float16_t)0.266113281f, (float16_t)-0.598144531f, (float16_t)-0.775390625f, (float16_t)-0.308837891f, + (float16_t)-0.527832031f, (float16_t)0.594726562f, (float16_t)0.464111328f, (float16_t)-0.222412109f, + (float16_t)-0.497558594f, (float16_t)-0.0101089478f, (float16_t)0.415771484f, (float16_t)0.623046875f, + (float16_t)-0.0827636719f, (float16_t)-0.754394531f, (float16_t)0.59765625f, (float16_t)0.522460938f, + (float16_t)-0.234619141f, (float16_t)0.577636719f, (float16_t)-0.594726562f, (float16_t)-0.838378906f, + (float16_t)-0.308105469f, (float16_t)0.37109375f, (float16_t)-0.140991211f, (float16_t)-0.591308594f, + (float16_t)-0.29296875f, (float16_t)-0.253662109f, (float16_t)-0.715820312f, (float16_t)-0.589355469f, + (float16_t)-0.354003906f, (float16_t)0.0298156738f, (float16_t)0.986816406f, (float16_t)-0.117736816f, + (float16_t)0.82421875f, (float16_t)0.213134766f, (float16_t)0.342529297f, (float16_t)-0.0491027832f, + (float16_t)-0.0405273438f, (float16_t)-0.164794922f, (float16_t)0.0106735229f, (float16_t)0.316650391f, + (float16_t)-0.372802734f, (float16_t)-0.315917969f, (float16_t)-0.280029297f, (float16_t)-0.0252532959f, + (float16_t)0.721191406f, (float16_t)-0.962890625f, (float16_t)0.5625f, (float16_t)-0.351318359f, + (float16_t)-0.338867188f, (float16_t)-0.833984375f, (float16_t)0.704589844f, (float16_t)0.0835571289f, + (float16_t)-0.308105469f, (float16_t)0.188232422f, (float16_t)0.973632812f, (float16_t)-0.94921875f, + (float16_t)0.393798828f, (float16_t)-0.13684082f, (float16_t)0.0440673828f, (float16_t)0.518554688f, + (float16_t)-0.935546875f, (float16_t)-0.986328125f, (float16_t)0.427734375f, (float16_t)0.766113281f, + (float16_t)-0.489013672f, (float16_t)0.21875f, (float16_t)0.18359375f, (float16_t)-0.258544922f, + (float16_t)0.924804688f, (float16_t)-0.0971069336f, (float16_t)-0.708007812f, (float16_t)-0.721191406f, + (float16_t)-0.595214844f, (float16_t)0.69921875f, (float16_t)-0.767089844f, (float16_t)-0.698730469f, + (float16_t)-0.3046875f, (float16_t)0.662597656f, (float16_t)0.166625977f, (float16_t)-0.406982422f, + (float16_t)0.18359375f, (float16_t)0.0715942383f, (float16_t)0.250488281f, (float16_t)0.391357422f, + (float16_t)0.0751342773f, (float16_t)0.098449707f, (float16_t)-0.84765625f, (float16_t)0.414306641f, + (float16_t)0.366943359f, (float16_t)0.683105469f, (float16_t)-0.584472656f, (float16_t)-0.884765625f, + (float16_t)-0.462890625f, (float16_t)0.0979614258f, (float16_t)0.59375f, (float16_t)0.923828125f, + (float16_t)0.697753906f, (float16_t)-0.836425781f, (float16_t)-0.928710938f, (float16_t)-0.527832031f, + (float16_t)0.41796875f, (float16_t)-0.587890625f, (float16_t)-0.407226562f, (float16_t)0.0220489502f, + (float16_t)0.505371094f, (float16_t)-0.543457031f, (float16_t)-0.751953125f, (float16_t)0.148071289f, + (float16_t)-0.517578125f, (float16_t)0.132080078f, (float16_t)0.754394531f, (float16_t)-0.130859375f, + (float16_t)-0.496337891f, (float16_t)-0.986816406f, (float16_t)0.0333557129f, (float16_t)-0.0811157227f, + (float16_t)-0.360839844f, (float16_t)0.0202484131f, (float16_t)0.464355469f, (float16_t)0.0176849365f, + (float16_t)-0.00395965576f, (float16_t)-0.560546875f, (float16_t)-0.380371094f, (float16_t)-0.0170898438f, + (float16_t)0.333740234f, (float16_t)-0.0231323242f, (float16_t)-0.442871094f, (float16_t)0.438964844f, + (float16_t)-0.0750732422f, (float16_t)-0.0223236084f, (float16_t)0.182983398f, (float16_t)0.287841797f, + (float16_t)-0.509277344f, (float16_t)-0.252929688f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/output_ref_data.h new file mode 100644 index 00000000..f4e12834 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/output_ref_data.h @@ -0,0 +1,52 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x3_dilation_5x5_input_f16_output_ref_data[198] = { + (float16_t)-2.16796875f, (float16_t)-1.16796875f, (float16_t)-1.16796875f, (float16_t)0.562011719f, + (float16_t)-0.913574219f, (float16_t)-0.415039062f, (float16_t)-0.130126953f, (float16_t)-1.34863281f, + (float16_t)-0.357421875f, (float16_t)1.06835938f, (float16_t)-0.0838623047f, (float16_t)0.245605469f, + (float16_t)-0.336181641f, (float16_t)-0.0542297363f, (float16_t)-0.516601562f, (float16_t)0.495849609f, + (float16_t)-1.06933594f, (float16_t)0.2890625f, (float16_t)0.860351562f, (float16_t)0.264404297f, + (float16_t)0.62109375f, (float16_t)0.220092773f, (float16_t)-0.720703125f, (float16_t)-0.225952148f, + (float16_t)-0.419677734f, (float16_t)0.078125f, (float16_t)-0.162719727f, (float16_t)-0.23840332f, + (float16_t)-0.103149414f, (float16_t)-0.924316406f, (float16_t)-0.472167969f, (float16_t)0.292236328f, + (float16_t)-0.526855469f, (float16_t)-0.0559997559f, (float16_t)0.624023438f, (float16_t)-1.31640625f, + (float16_t)-0.172241211f, (float16_t)0.241455078f, (float16_t)-0.66796875f, (float16_t)-2.22460938f, + (float16_t)0.377197266f, (float16_t)0.389648438f, (float16_t)-0.418701172f, (float16_t)1.20117188f, + (float16_t)-0.0286712646f, (float16_t)0.201293945f, (float16_t)-2.04882812f, (float16_t)0.363525391f, + (float16_t)-0.631347656f, (float16_t)-0.787109375f, (float16_t)-1.01660156f, (float16_t)-1.04101562f, + (float16_t)-0.970703125f, (float16_t)-0.157226562f, (float16_t)-0.513671875f, (float16_t)-0.167480469f, + (float16_t)1.6171875f, (float16_t)-0.908691406f, (float16_t)-1.26953125f, (float16_t)-1.14550781f, + (float16_t)-3.24414062f, (float16_t)-0.0104675293f, (float16_t)-0.0386047363f, (float16_t)-0.952148438f, + (float16_t)1.515625f, (float16_t)-0.00164985657f, (float16_t)-1.609375f, (float16_t)-0.333251953f, + (float16_t)-1.76855469f, (float16_t)0.731445312f, (float16_t)0.635253906f, (float16_t)0.912109375f, + (float16_t)-0.926757812f, (float16_t)0.104980469f, (float16_t)0.846679688f, (float16_t)1.62304688f, + (float16_t)0.621582031f, (float16_t)0.754882812f, (float16_t)-0.332763672f, (float16_t)-2.21484375f, + (float16_t)-0.290283203f, (float16_t)-0.0595703125f, (float16_t)-0.358886719f, (float16_t)-1.50292969f, + (float16_t)-1.12207031f, (float16_t)-1.46191406f, (float16_t)0.422851562f, (float16_t)0.963378906f, + (float16_t)-0.368164062f, (float16_t)1.37890625f, (float16_t)-0.727050781f, (float16_t)0.395263672f, + (float16_t)1.12109375f, (float16_t)1.21484375f, (float16_t)-0.418701172f, (float16_t)0.850097656f, + (float16_t)-1.32324219f, (float16_t)-0.825683594f, (float16_t)0.158569336f, (float16_t)-0.118713379f, + (float16_t)1.7734375f, (float16_t)-0.445068359f, (float16_t)-1.48242188f, (float16_t)0.0955810547f, + (float16_t)-1.00683594f, (float16_t)-0.126220703f, (float16_t)-0.030166626f, (float16_t)-1.55175781f, + (float16_t)0.0974731445f, (float16_t)-0.396484375f, (float16_t)-0.389648438f, (float16_t)-0.574707031f, + (float16_t)1.12890625f, (float16_t)-0.177124023f, (float16_t)-0.320556641f, (float16_t)0.916992188f, + (float16_t)-1.0625f, (float16_t)-0.0322265625f, (float16_t)1.66601562f, (float16_t)1.42675781f, + (float16_t)-0.571289062f, (float16_t)-0.725585938f, (float16_t)0.138793945f, (float16_t)-1.08886719f, + (float16_t)-0.401611328f, (float16_t)-0.47265625f, (float16_t)-1.36425781f, (float16_t)0.958007812f, + (float16_t)-1.78125f, (float16_t)-0.0193328857f, (float16_t)-0.297607422f, (float16_t)-3.06835938f, + (float16_t)0.505859375f, (float16_t)-1.11816406f, (float16_t)-0.756347656f, (float16_t)-0.923828125f, + (float16_t)-0.759765625f, (float16_t)0.616210938f, (float16_t)-0.963378906f, (float16_t)-0.0391845703f, + (float16_t)-1.10058594f, (float16_t)0.221435547f, (float16_t)-0.0815429688f, (float16_t)1.03710938f, + (float16_t)0.729003906f, (float16_t)0.504394531f, (float16_t)-0.505859375f, (float16_t)-1.13476562f, + (float16_t)-0.142700195f, (float16_t)0.570800781f, (float16_t)-2.22851562f, (float16_t)-1.12109375f, + (float16_t)-1.14648438f, (float16_t)-1.40332031f, (float16_t)1.453125f, (float16_t)0.990722656f, + (float16_t)-0.0242919922f, (float16_t)0.0141220093f, (float16_t)-1.63671875f, (float16_t)0.227661133f, + (float16_t)0.560546875f, (float16_t)1.30859375f, (float16_t)-1.02148438f, (float16_t)-1.14550781f, + (float16_t)-1.00097656f, (float16_t)-0.182617188f, (float16_t)0.399902344f, (float16_t)1.28710938f, + (float16_t)1.20117188f, (float16_t)-1.29882812f, (float16_t)-0.0975341797f, (float16_t)1.83886719f, + (float16_t)-2.04296875f, (float16_t)-0.770996094f, (float16_t)0.147583008f, (float16_t)0.286132812f, + (float16_t)-0.0206756592f, (float16_t)-0.164916992f, (float16_t)0.0229492188f, (float16_t)-1.19628906f, + (float16_t)-0.84765625f, (float16_t)-0.668457031f, (float16_t)0.305664062f, (float16_t)-0.833007812f, + (float16_t)-0.402099609f, (float16_t)-0.225463867f, (float16_t)-0.188232422f, (float16_t)0.648925781f, + (float16_t)-0.178100586f, (float16_t)-0.400390625f, (float16_t)-0.13671875f, (float16_t)0.459228516f, + (float16_t)-0.958496094f, (float16_t)0.142211914f, (float16_t)-0.947265625f, (float16_t)-1.86132812f, + (float16_t)0.505859375f, (float16_t)-0.320556641f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/weights_data.h new file mode 100644 index 00000000..e099ba0c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_3x3_dilation_5x5_input_f16_weights_data[36] = { + (float16_t)-0.37109375f, (float16_t)0.479980469f, (float16_t)0.271728516f, (float16_t)-0.637207031f, + (float16_t)-0.730957031f, (float16_t)-0.0499572754f, (float16_t)0.0319519043f, (float16_t)0.105407715f, + (float16_t)0.502441406f, (float16_t)-0.483154297f, (float16_t)0.0795288086f, (float16_t)0.541992188f, + (float16_t)-0.0484008789f, (float16_t)-0.191650391f, (float16_t)-0.1171875f, (float16_t)0.485107422f, + (float16_t)0.281005859f, (float16_t)0.4296875f, (float16_t)0.4921875f, (float16_t)0.578613281f, + (float16_t)-0.634765625f, (float16_t)-0.543457031f, (float16_t)-0.266845703f, (float16_t)-0.440185547f, + (float16_t)-0.518066406f, (float16_t)-0.551269531f, (float16_t)-0.0376281738f, (float16_t)0.125732422f, + (float16_t)0.741210938f, (float16_t)0.469970703f, (float16_t)0.240844727f, (float16_t)0.2421875f, + (float16_t)-0.470458984f, (float16_t)0.0317077637f, (float16_t)-0.184326172f, (float16_t)0.168823242f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/biases_data.h new file mode 100644 index 00000000..e6bdbdda --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x3_dilation_5x5_input_f32_biases_data[2] = {-0.297023505f, -0.199539572f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/config_data.h new file mode 100644 index 00000000..0760d0ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_INPUT_BATCHES 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_INPUT_H 11 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_INPUT_W 9 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_IN_CH 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_FILTER_H 3 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_FILTER_W 3 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUT_CH 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_STRIDE_H 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_STRIDE_W 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_PADDING_H 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_PADDING_W 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_DILATION_H 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_DILATION_W 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUTPUT_H 11 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUTPUT_W 9 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUTPUT_C 2 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_DST_SIZE 198 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_USE_WRAPPER 1 +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_3X3_DILATION_5X5_INPUT_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/input_data.h new file mode 100644 index 00000000..959c9f28 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/input_data.h @@ -0,0 +1,31 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x3_dilation_5x5_input_f32_input_data[198] = { + -0.831315458f, -0.0718627423f, 0.665105641f, 0.881496429f, -0.843836248f, -0.932146072f, 0.343446404f, + -0.177073255f, -0.833319843f, -0.0290234294f, -0.357670814f, -0.284796923f, -0.0949086696f, 0.421874493f, + 0.218045428f, 0.571351826f, -0.589492798f, 0.633294106f, 0.718523085f, -0.830456674f, 0.929229796f, + -0.733189046f, 0.354720056f, 0.230537236f, -0.405116796f, 0.67776227f, 0.611905634f, -0.472593695f, + 0.478169113f, -0.219151393f, 0.723149955f, 0.177906156f, -0.982176125f, 0.374489218f, 0.955940127f, + -0.961640239f, -0.236865565f, -0.987419248f, -0.946784675f, -0.805255711f, -0.597119391f, -0.691995382f, + -0.861489236f, -0.440083444f, -0.848155737f, -0.521370232f, -0.647056758f, 0.166457236f, 0.0136776837f, + 0.549535871f, -0.876280844f, -0.234304771f, -0.952138245f, -0.448529989f, -0.642959416f, 0.473204762f, + -0.494103819f, -0.834533632f, 0.233566269f, -0.298670799f, -0.949082911f, 0.873842657f, 0.693915665f, + -0.422713161f, 0.266140968f, -0.598160923f, -0.775470257f, -0.308869123f, -0.527855933f, 0.594599247f, + 0.464121521f, -0.22246784f, -0.497548044f, -0.0101090027f, 0.41588217f, 0.622841895f, -0.0827784762f, + -0.754452646f, 0.597565889f, 0.522351384f, -0.234635606f, 0.577736437f, -0.594597876f, -0.838239789f, + -0.308102071f, 0.371023059f, -0.141037598f, -0.591437221f, -0.293023318f, -0.253635168f, -0.71605742f, + -0.589456499f, -0.353901863f, 0.0298130065f, 0.98689729f, -0.117716402f, 0.824329019f, 0.213144898f, + 0.342572331f, -0.0490925498f, -0.0405313633f, -0.164748162f, 0.0106770052f, 0.316732824f, -0.372914344f, + -0.316035628f, -0.280064344f, -0.0252541061f, 0.720991671f, -0.963060796f, 0.562648833f, -0.351329863f, + -0.338765323f, -0.833894014f, 0.704590023f, 0.0835799724f, -0.308052897f, 0.188285857f, 0.973853827f, + -0.949363887f, 0.393835127f, -0.136815563f, 0.0440728031f, 0.518574178f, -0.935736001f, -0.986358047f, + 0.427840203f, 0.766099691f, -0.489085525f, 0.218709707f, 0.183639526f, -0.258473188f, 0.924793661f, + -0.0971333832f, -0.708010614f, -0.721143544f, -0.595414042f, 0.699150324f, -0.766883016f, -0.698902726f, + -0.304801822f, 0.662781179f, 0.166589156f, -0.406934321f, 0.183609948f, 0.0716114715f, 0.250566959f, + 0.391468227f, 0.075120829f, 0.0984465778f, -0.84750694f, 0.414217889f, 0.366946876f, 0.68304497f, + -0.584396482f, -0.884920657f, -0.462837368f, 0.0979757532f, 0.593901217f, 0.923845172f, 0.697925448f, + -0.836278558f, -0.928581893f, -0.528010249f, 0.417991579f, -0.587970138f, -0.407257229f, 0.0220553968f, + 0.505239427f, -0.543606043f, -0.751970172f, 0.148127064f, -0.517592847f, 0.13202776f, 0.754435241f, + -0.13090843f, -0.496445388f, -0.986765206f, 0.0333542302f, -0.0811198726f, -0.360906541f, 0.0202485584f, + 0.464425921f, 0.0176825617f, -0.00396065507f, -0.560434163f, -0.380379319f, -0.0170917232f, 0.33385846f, + -0.0231318753f, -0.442811906f, 0.438945264f, -0.0750938803f, -0.0223236214f, 0.183014393f, 0.287901551f, + -0.509382844f, -0.252871007f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/output_ref_data.h new file mode 100644 index 00000000..a1acdffd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/output_ref_data.h @@ -0,0 +1,31 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x3_dilation_5x5_input_f32_output_ref_data[198] = { + -2.16853023f, -1.16762304f, -1.16833413f, 0.562084913f, -0.913798928f, -0.414942861f, -0.13018088f, + -1.34836817f, -0.357483298f, 1.06847894f, -0.0838673711f, 0.245581537f, -0.33613655f, -0.0542375743f, + -0.516563058f, 0.495794386f, -1.06957173f, 0.289171427f, 0.860430479f, 0.26436916f, 0.621278882f, + 0.220055014f, -0.720935941f, -0.225922227f, -0.419774294f, 0.078152597f, -0.162730321f, -0.238432482f, + -0.103140265f, -0.924165726f, -0.472089529f, 0.29235357f, -0.527073562f, -0.0559993535f, 0.624038577f, + -1.3161732f, -0.172275245f, 0.241511673f, -0.667941689f, -2.22376537f, 0.3771905f, 0.389526457f, + -0.418820947f, 1.20127344f, -0.028673321f, 0.201331824f, -2.04886794f, 0.363480777f, -0.631218076f, + -0.787041664f, -1.01663518f, -1.04101253f, -0.970904469f, -0.157173365f, -0.513737619f, -0.167528093f, + 1.61729324f, -0.908887744f, -1.26948202f, -1.14513624f, -3.24405789f, -0.0104676485f, -0.0386023521f, + -0.952316284f, 1.51578248f, -0.0016503036f, -1.6091578f, -0.333151639f, -1.76814651f, 0.731313229f, + 0.635013461f, 0.911991835f, -0.926945567f, 0.104988962f, 0.846581936f, 1.62263525f, 0.621519923f, + 0.754986525f, -0.33265242f, -2.21436834f, -0.290353924f, -0.0595552921f, -0.358834326f, -1.50291634f, + -1.12236571f, -1.46187341f, 0.422962457f, 0.963274598f, -0.368160963f, 1.37857699f, -0.727193832f, + 0.395169348f, 1.12094903f, 1.21500254f, -0.418711752f, 0.850164294f, -1.32367277f, -0.825674415f, + 0.158600509f, -0.118737429f, 1.77324128f, -0.445047557f, -1.48212266f, 0.0955524743f, -1.00636053f, + -0.126174629f, -0.0301633179f, -1.55168498f, 0.0975007117f, -0.396520793f, -0.389582068f, -0.574817181f, + 1.12851596f, -0.177099675f, -0.320550859f, 0.916966081f, -1.06295109f, -0.0322405249f, 1.66567039f, + 1.42709434f, -0.571416259f, -0.725588083f, 0.13876161f, -1.0891279f, -0.401534259f, -0.472763538f, + -1.364537f, 0.957924843f, -1.7816627f, -0.0193302631f, -0.297690749f, -3.06815481f, 0.506049037f, + -1.11839795f, -0.756328583f, -0.924041867f, -0.759986162f, 0.616439104f, -0.963449001f, -0.0391902924f, + -1.10033441f, 0.221475035f, -0.0815279037f, 1.03727174f, 0.728842258f, 0.504402637f, -0.505767405f, + -1.13514423f, -0.142693311f, 0.570658684f, -2.22876692f, -1.12099719f, -1.14639318f, -1.40292966f, + 1.45334315f, 0.990787745f, -0.0242897272f, 0.0141210854f, -1.63715756f, 0.227704257f, 0.560470819f, + 1.30815101f, -1.02185452f, -1.14589846f, -1.00086164f, -0.182588547f, 0.399977595f, 1.28706932f, + 1.20113385f, -1.29865849f, -0.0975249708f, 1.83882916f, -2.04374003f, -0.770924568f, 0.14753139f, + 0.286084056f, -0.0206814408f, -0.16494146f, 0.0229489505f, -1.19613802f, -0.847759008f, -0.668687701f, + 0.30562529f, -0.832918525f, -0.402006924f, -0.225504979f, -0.188280806f, 0.64868784f, -0.178147823f, + -0.400294781f, -0.136719555f, 0.459179729f, -0.958261847f, 0.142164141f, -0.947184205f, -1.86168814f, + 0.505853653f, -0.320640832f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/weights_data.h new file mode 100644 index 00000000..fb4b1bdc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_3x3_dilation_5x5_input_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_3x3_dilation_5x5_input_f32_weights_data[36] = { + -0.371149361f, 0.479958326f, 0.271703094f, -0.6371333f, -0.730973721f, -0.0499467142f, + 0.0319488607f, 0.105410568f, 0.502214432f, -0.483223885f, 0.0795553848f, 0.542108774f, + -0.0484078676f, -0.191634044f, -0.117210187f, 0.485006094f, 0.280887395f, 0.429790288f, + 0.492260218f, 0.578532755f, -0.63498193f, -0.543655634f, -0.266876131f, -0.440290242f, + -0.518124282f, -0.551095963f, -0.0376293361f, 0.125766277f, 0.741278052f, 0.470038027f, + 0.240800425f, 0.24220027f, -0.470558971f, 0.0317214616f, -0.184296504f, 0.168859348f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/biases_data.h new file mode 100644 index 00000000..abccfe34 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_basic_f16_biases_data[1] = {(float16_t)-0.321044922f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/config_data.h new file mode 100644 index 00000000..e296bc54 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_BASIC_F16_INPUT_BATCHES 1 +#define CONV_MATCH_BASIC_F16_INPUT_H 8 +#define CONV_MATCH_BASIC_F16_INPUT_W 5 +#define CONV_MATCH_BASIC_F16_IN_CH 1 +#define CONV_MATCH_BASIC_F16_FILTER_H 4 +#define CONV_MATCH_BASIC_F16_FILTER_W 2 +#define CONV_MATCH_BASIC_F16_OUT_CH 1 +#define CONV_MATCH_BASIC_F16_STRIDE_H 1 +#define CONV_MATCH_BASIC_F16_STRIDE_W 1 +#define CONV_MATCH_BASIC_F16_PADDING_H 0 +#define CONV_MATCH_BASIC_F16_PADDING_W 0 +#define CONV_MATCH_BASIC_F16_DILATION_H 1 +#define CONV_MATCH_BASIC_F16_DILATION_W 1 +#define CONV_MATCH_BASIC_F16_OUTPUT_H 5 +#define CONV_MATCH_BASIC_F16_OUTPUT_W 4 +#define CONV_MATCH_BASIC_F16_OUTPUT_C 1 +#define CONV_MATCH_BASIC_F16_DST_SIZE 20 +#define CONV_MATCH_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_BASIC_F16_USE_WRAPPER 1 +#define CONV_MATCH_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define CONV_MATCH_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/input_data.h new file mode 100644 index 00000000..ae97b05f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/input_data.h @@ -0,0 +1,12 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_basic_f16_input_data[40] = { + (float16_t)-0.783691406f, (float16_t)-0.0888671875f, (float16_t)0.941894531f, (float16_t)-0.019241333f, + (float16_t)-0.333984375f, (float16_t)0.089050293f, (float16_t)-0.729980469f, (float16_t)0.0271911621f, + (float16_t)0.58203125f, (float16_t)-0.134155273f, (float16_t)0.145385742f, (float16_t)-0.833007812f, + (float16_t)0.646484375f, (float16_t)-0.228027344f, (float16_t)-0.700683594f, (float16_t)0.794921875f, + (float16_t)-0.693847656f, (float16_t)-0.794433594f, (float16_t)-0.670410156f, (float16_t)0.184204102f, + (float16_t)0.38671875f, (float16_t)0.210205078f, (float16_t)0.104003906f, (float16_t)-0.244750977f, + (float16_t)-0.330078125f, (float16_t)0.235961914f, (float16_t)-0.219604492f, (float16_t)-0.358154297f, + (float16_t)0.826171875f, (float16_t)0.319824219f, (float16_t)0.194824219f, (float16_t)0.440429688f, + (float16_t)-0.328613281f, (float16_t)-0.776855469f, (float16_t)0.0719604492f, (float16_t)0.956054688f, + (float16_t)-0.336669922f, (float16_t)-0.387451172f, (float16_t)-0.701660156f, (float16_t)-0.885253906f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/output_ref_data.h new file mode 100644 index 00000000..4f278510 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_basic_f16_output_ref_data[20] = { + (float16_t)-0.00499343872f, (float16_t)-0.224975586f, (float16_t)0.308837891f, (float16_t)0.536621094f, + (float16_t)-0.476318359f, (float16_t)-0.690429688f, (float16_t)0.00262069702f, (float16_t)-0.419921875f, + (float16_t)0.41015625f, (float16_t)-0.475585938f, (float16_t)-1.53027344f, (float16_t)-1.11816406f, + (float16_t)-0.494140625f, (float16_t)0.429931641f, (float16_t)1.04785156f, (float16_t)0.0798339844f, + (float16_t)-0.815429688f, (float16_t)-0.209838867f, (float16_t)0.323730469f, (float16_t)1.61328125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/weights_data.h new file mode 100644 index 00000000..3d442823 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f16/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_basic_f16_weights_data[8] = {(float16_t)-0.441894531f, + (float16_t)-0.397705078f, + (float16_t)0.734863281f, + (float16_t)0.155883789f, + (float16_t)-0.0211334229f, + (float16_t)-0.0182495117f, + (float16_t)-0.618164062f, + (float16_t)-0.665527344f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/biases_data.h new file mode 100644 index 00000000..cf03d5d0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_basic_f32_biases_data[1] = {-0.320975542f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/config_data.h new file mode 100644 index 00000000..4a60b3f0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_BASIC_F32_INPUT_BATCHES 1 +#define CONV_MATCH_BASIC_F32_INPUT_H 8 +#define CONV_MATCH_BASIC_F32_INPUT_W 5 +#define CONV_MATCH_BASIC_F32_IN_CH 1 +#define CONV_MATCH_BASIC_F32_FILTER_H 4 +#define CONV_MATCH_BASIC_F32_FILTER_W 2 +#define CONV_MATCH_BASIC_F32_OUT_CH 1 +#define CONV_MATCH_BASIC_F32_STRIDE_H 1 +#define CONV_MATCH_BASIC_F32_STRIDE_W 1 +#define CONV_MATCH_BASIC_F32_PADDING_H 0 +#define CONV_MATCH_BASIC_F32_PADDING_W 0 +#define CONV_MATCH_BASIC_F32_DILATION_H 1 +#define CONV_MATCH_BASIC_F32_DILATION_W 1 +#define CONV_MATCH_BASIC_F32_OUTPUT_H 5 +#define CONV_MATCH_BASIC_F32_OUTPUT_W 4 +#define CONV_MATCH_BASIC_F32_OUTPUT_C 1 +#define CONV_MATCH_BASIC_F32_DST_SIZE 20 +#define CONV_MATCH_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_BASIC_F32_USE_WRAPPER 1 +#define CONV_MATCH_BASIC_F32_OUT_ACTIVATION_MIN -128.0f +#define CONV_MATCH_BASIC_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/input_data.h new file mode 100644 index 00000000..f6dffe3b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_basic_f32_input_data[40] = { + -0.783658803f, -0.088872008f, 0.942093074f, -0.019248385f, -0.333902717f, 0.0890364349f, -0.730103135f, + 0.0271892007f, 0.582116365f, -0.134131178f, 0.145358145f, -0.833161414f, 0.646511197f, -0.228057981f, + -0.700719893f, 0.795049191f, -0.694013f, -0.794212401f, -0.670477629f, 0.184179485f, 0.386734784f, + 0.210218132f, 0.103995994f, -0.24475643f, -0.330013424f, 0.235982478f, -0.219628528f, -0.358224809f, + 0.826354504f, 0.319785178f, 0.194825456f, 0.440426081f, -0.32865423f, -0.777046442f, 0.071956791f, + 0.956278741f, -0.336623043f, -0.387365222f, -0.7017501f, -0.885346711f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/output_ref_data.h new file mode 100644 index 00000000..1cf13512 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_basic_f32_output_ref_data[20] = { + -0.00499166688f, -0.224996641f, 0.3087264f, 0.536379814f, -0.476252228f, -0.690370083f, 0.00262115477f, + -0.419964701f, 0.410247922f, -0.475666225f, -1.53044915f, -1.11779416f, -0.494068772f, 0.429922462f, + 1.04768193f, 0.07986065f, -0.815418422f, -0.209852561f, 0.323722899f, 1.61347878f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/weights_data.h new file mode 100644 index 00000000..5446bd1b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_basic_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_basic_f32_weights_data[8] = {-0.441934943f, + -0.39759472f, + 0.734636545f, + 0.155938148f, + -0.02113037f, + -0.0182458982f, + -0.618008554f, + -0.665666223f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/biases_data.h new file mode 100644 index 00000000..e0b92953 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_2_f16_biases_data[4] = {(float16_t)0.244262695f, + (float16_t)-0.163696289f, + (float16_t)0.0114822388f, + (float16_t)-0.374267578f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/config_data.h new file mode 100644 index 00000000..9405cb4c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_2_F16_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_2_F16_INPUT_H 3 +#define CONV_MATCH_CONV_2_F16_INPUT_W 6 +#define CONV_MATCH_CONV_2_F16_IN_CH 2 +#define CONV_MATCH_CONV_2_F16_FILTER_H 3 +#define CONV_MATCH_CONV_2_F16_FILTER_W 3 +#define CONV_MATCH_CONV_2_F16_OUT_CH 4 +#define CONV_MATCH_CONV_2_F16_STRIDE_H 1 +#define CONV_MATCH_CONV_2_F16_STRIDE_W 1 +#define CONV_MATCH_CONV_2_F16_PADDING_H 1 +#define CONV_MATCH_CONV_2_F16_PADDING_W 1 +#define CONV_MATCH_CONV_2_F16_DILATION_H 1 +#define CONV_MATCH_CONV_2_F16_DILATION_W 1 +#define CONV_MATCH_CONV_2_F16_OUTPUT_H 3 +#define CONV_MATCH_CONV_2_F16_OUTPUT_W 6 +#define CONV_MATCH_CONV_2_F16_OUTPUT_C 4 +#define CONV_MATCH_CONV_2_F16_DST_SIZE 72 +#define CONV_MATCH_CONV_2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_2_F16_USE_WRAPPER 1 +#define CONV_MATCH_CONV_2_F16_OUT_ACTIVATION_MIN (float16_t) - 101.0f +#define CONV_MATCH_CONV_2_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/input_data.h new file mode 100644 index 00000000..972856ee --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_2_f16_input_data[36] = { + (float16_t)0.898925781f, (float16_t)0.156616211f, (float16_t)0.270263672f, (float16_t)0.960449219f, + (float16_t)-0.966796875f, (float16_t)-0.124816895f, (float16_t)0.0723266602f, (float16_t)0.564453125f, + (float16_t)-0.301513672f, (float16_t)0.420898438f, (float16_t)0.322265625f, (float16_t)-0.176147461f, + (float16_t)-0.428466797f, (float16_t)0.891601562f, (float16_t)-0.94140625f, (float16_t)0.378417969f, + (float16_t)0.696777344f, (float16_t)-0.959472656f, (float16_t)-0.59765625f, (float16_t)-0.697753906f, + (float16_t)0.51171875f, (float16_t)0.579589844f, (float16_t)0.722167969f, (float16_t)-0.569335938f, + (float16_t)-0.0676269531f, (float16_t)0.892089844f, (float16_t)0.579101562f, (float16_t)0.896484375f, + (float16_t)0.783203125f, (float16_t)0.106140137f, (float16_t)-0.431152344f, (float16_t)-0.415771484f, + (float16_t)-0.716308594f, (float16_t)0.263427734f, (float16_t)0.989746094f, (float16_t)-0.619140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/output_ref_data.h new file mode 100644 index 00000000..c5cb9acc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_2_f16_output_ref_data[72] = { + (float16_t)1.20507812f, (float16_t)-0.293212891f, (float16_t)-1.18457031f, (float16_t)-1.24121094f, + (float16_t)1.33398438f, (float16_t)-1.87011719f, (float16_t)-1.07519531f, (float16_t)-1.17675781f, + (float16_t)-0.328125f, (float16_t)-0.157226562f, (float16_t)2.078125f, (float16_t)-0.190429688f, + (float16_t)0.132324219f, (float16_t)0.00646972656f, (float16_t)0.944335938f, (float16_t)1.10253906f, + (float16_t)-0.355957031f, (float16_t)-0.905761719f, (float16_t)0.526367188f, (float16_t)-0.405761719f, + (float16_t)0.385253906f, (float16_t)0.506835938f, (float16_t)0.176025391f, (float16_t)-0.765625f, + (float16_t)0.168212891f, (float16_t)0.313476562f, (float16_t)0.0249023438f, (float16_t)-1.22265625f, + (float16_t)-1.73925781f, (float16_t)1.06054688f, (float16_t)0.516113281f, (float16_t)-1.17480469f, + (float16_t)1.109375f, (float16_t)-0.189453125f, (float16_t)-0.946777344f, (float16_t)-0.795898438f, + (float16_t)-0.611816406f, (float16_t)0.95703125f, (float16_t)-0.723144531f, (float16_t)-0.163574219f, + (float16_t)1.39550781f, (float16_t)-1.88183594f, (float16_t)-0.759765625f, (float16_t)0.704589844f, + (float16_t)0.595703125f, (float16_t)-0.333496094f, (float16_t)0.178588867f, (float16_t)-1.421875f, + (float16_t)-0.660644531f, (float16_t)0.00270652771f, (float16_t)-0.44921875f, (float16_t)-0.703613281f, + (float16_t)1.11132812f, (float16_t)-0.676269531f, (float16_t)-0.788085938f, (float16_t)1.75488281f, + (float16_t)1.41601562f, (float16_t)1.00878906f, (float16_t)-1.40136719f, (float16_t)-0.801269531f, + (float16_t)-1.43945312f, (float16_t)-1.71289062f, (float16_t)0.831054688f, (float16_t)-0.927246094f, + (float16_t)0.740722656f, (float16_t)0.427246094f, (float16_t)0.889648438f, (float16_t)0.945800781f, + (float16_t)1.39355469f, (float16_t)-0.141235352f, (float16_t)-0.0387878418f, (float16_t)-1.32714844f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/weights_data.h new file mode 100644 index 00000000..70282f1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f16/weights_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_2_f16_weights_data[72] = { + (float16_t)-0.487060547f, (float16_t)0.418945312f, (float16_t)0.526855469f, (float16_t)-0.255371094f, + (float16_t)0.502441406f, (float16_t)-0.4453125f, (float16_t)-0.576660156f, (float16_t)-0.63671875f, + (float16_t)0.747070312f, (float16_t)0.573730469f, (float16_t)-0.400390625f, (float16_t)-0.0439453125f, + (float16_t)-0.218383789f, (float16_t)0.359863281f, (float16_t)0.0712890625f, (float16_t)0.0933837891f, + (float16_t)-0.552246094f, (float16_t)-0.588867188f, (float16_t)-0.293212891f, (float16_t)0.475830078f, + (float16_t)0.636230469f, (float16_t)-0.409179688f, (float16_t)-0.708496094f, (float16_t)-0.575683594f, + (float16_t)-0.220458984f, (float16_t)-0.372558594f, (float16_t)-0.651855469f, (float16_t)0.339599609f, + (float16_t)0.0306091309f, (float16_t)-0.0111083984f, (float16_t)0.716308594f, (float16_t)0.28125f, + (float16_t)0.740722656f, (float16_t)0.0593261719f, (float16_t)-0.507324219f, (float16_t)0.508300781f, + (float16_t)0.323974609f, (float16_t)-0.666992188f, (float16_t)0.221801758f, (float16_t)0.638183594f, + (float16_t)0.372802734f, (float16_t)-0.449462891f, (float16_t)-0.648925781f, (float16_t)0.572753906f, + (float16_t)-0.691894531f, (float16_t)-0.715332031f, (float16_t)-0.452880859f, (float16_t)0.489990234f, + (float16_t)-0.571289062f, (float16_t)-0.363525391f, (float16_t)0.0463562012f, (float16_t)-0.517089844f, + (float16_t)0.362792969f, (float16_t)0.0317687988f, (float16_t)-0.277099609f, (float16_t)0.473388672f, + (float16_t)-0.569824219f, (float16_t)-0.0614318848f, (float16_t)0.617675781f, (float16_t)-0.319335938f, + (float16_t)-0.235717773f, (float16_t)0.110595703f, (float16_t)-0.617675781f, (float16_t)0.4765625f, + (float16_t)0.262207031f, (float16_t)-0.485351562f, (float16_t)0.183959961f, (float16_t)-0.528808594f, + (float16_t)-0.479248047f, (float16_t)-0.583984375f, (float16_t)-0.258789062f, (float16_t)0.214477539f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/biases_data.h new file mode 100644 index 00000000..8a9188f2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_2_f32_biases_data[4] = {0.244270504f, -0.163718268f, 0.0114850989f, -0.374288023f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/config_data.h new file mode 100644 index 00000000..7e8f79d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_2_F32_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_2_F32_INPUT_H 3 +#define CONV_MATCH_CONV_2_F32_INPUT_W 6 +#define CONV_MATCH_CONV_2_F32_IN_CH 2 +#define CONV_MATCH_CONV_2_F32_FILTER_H 3 +#define CONV_MATCH_CONV_2_F32_FILTER_W 3 +#define CONV_MATCH_CONV_2_F32_OUT_CH 4 +#define CONV_MATCH_CONV_2_F32_STRIDE_H 1 +#define CONV_MATCH_CONV_2_F32_STRIDE_W 1 +#define CONV_MATCH_CONV_2_F32_PADDING_H 1 +#define CONV_MATCH_CONV_2_F32_PADDING_W 1 +#define CONV_MATCH_CONV_2_F32_DILATION_H 1 +#define CONV_MATCH_CONV_2_F32_DILATION_W 1 +#define CONV_MATCH_CONV_2_F32_OUTPUT_H 3 +#define CONV_MATCH_CONV_2_F32_OUTPUT_W 6 +#define CONV_MATCH_CONV_2_F32_OUTPUT_C 4 +#define CONV_MATCH_CONV_2_F32_DST_SIZE 72 +#define CONV_MATCH_CONV_2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_2_F32_USE_WRAPPER 1 +#define CONV_MATCH_CONV_2_F32_OUT_ACTIVATION_MIN -101.0f +#define CONV_MATCH_CONV_2_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/input_data.h new file mode 100644 index 00000000..2f9835c0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_2_f32_input_data[36] = { + 0.898710191f, 0.156637326f, 0.270155728f, 0.960566282f, -0.966915011f, -0.124805301f, + 0.0723087341f, 0.564348996f, -0.301548243f, 0.420870602f, 0.322309792f, -0.176183134f, + -0.428563416f, 0.891554296f, -0.941320837f, 0.378481358f, 0.69684571f, -0.959614277f, + -0.597878456f, -0.697938859f, 0.511557221f, 0.579622507f, 0.722339451f, -0.569128752f, + -0.0676373616f, 0.891852558f, 0.578868687f, 0.896574497f, 0.783436835f, 0.10616532f, + -0.431157768f, -0.415710419f, -0.716524363f, 0.263522476f, 0.989850223f, -0.619254649f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/output_ref_data.h new file mode 100644 index 00000000..5a41cfbd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_2_f32_output_ref_data[72] = { + 1.20518899f, -0.293124497f, -1.18423247f, -1.24095893f, 1.3343389f, -1.8702774f, -1.07562411f, + -1.17705274f, -0.328124881f, -0.157228619f, 2.07804346f, -0.190484807f, 0.13230814f, 0.00647124648f, + 0.944447339f, 1.10261035f, -0.356028974f, -0.90577054f, 0.526421547f, -0.405863762f, 0.385172665f, + 0.506627798f, 0.176000461f, -0.765778303f, 0.168228358f, 0.313590705f, 0.0248981174f, -1.2227807f, + -1.73951554f, 1.06022263f, 0.516070902f, -1.17475247f, 1.10980713f, -0.189438015f, -0.946870863f, + -0.795966804f, -0.611832142f, 0.957267463f, -0.723044097f, -0.163589016f, 1.39504504f, -1.88156974f, + -0.759549499f, 0.70466882f, 0.595567584f, -0.333480656f, 0.178572237f, -1.42220283f, -0.660860419f, + 0.00270602107f, -0.449237049f, -0.703804016f, 1.11091423f, -0.676466703f, -0.788255632f, 1.75468755f, + 1.41645956f, 1.00878572f, -1.40185106f, -0.801044345f, -1.43980932f, -1.71259499f, 0.831129253f, + -0.927344978f, 0.740796804f, 0.427169263f, 0.889489055f, 0.945774853f, 1.39373541f, -0.141264841f, + -0.0387858152f, -1.32701969f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/weights_data.h new file mode 100644 index 00000000..42d601c9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_2_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_2_f32_weights_data[72] = { + -0.48715049f, 0.419054121f, 0.52688545f, -0.255338013f, 0.502451003f, -0.445225656f, -0.576684594f, + -0.63678056f, 0.747243643f, 0.573584914f, -0.400481611f, -0.043931134f, -0.218325764f, 0.359797418f, + 0.0713110566f, 0.0933938771f, -0.552433789f, -0.588701069f, -0.293188363f, 0.475721747f, 0.636185229f, + -0.409141928f, -0.708519757f, -0.575493038f, -0.220415115f, -0.372509599f, -0.651791334f, 0.339587897f, + 0.0306151919f, -0.0111075416f, 0.716169059f, 0.281358063f, 0.740488648f, 0.0593188815f, -0.507435262f, + 0.50827533f, 0.323861063f, -0.66722703f, 0.221819133f, 0.638266444f, 0.372731358f, -0.449353606f, + -0.6491009f, 0.572795451f, -0.692100823f, -0.715146184f, -0.452806741f, 0.490012437f, -0.571233749f, + -0.363427192f, 0.0463418402f, -0.516888916f, 0.362675786f, 0.0317778476f, -0.277021557f, 0.473289698f, + -0.569961071f, -0.061445348f, 0.617792785f, -0.319367886f, -0.235747546f, 0.110565245f, -0.617794454f, + 0.476613134f, 0.262090296f, -0.485350698f, 0.183947027f, -0.528648496f, -0.479265779f, -0.58420676f, + -0.258742452f, 0.214527726f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/biases_data.h new file mode 100644 index 00000000..410b8250 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_3_f16_biases_data[1] = {(float16_t)0.356445312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/config_data.h new file mode 100644 index 00000000..f313d357 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_3_F16_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_3_F16_INPUT_H 49 +#define CONV_MATCH_CONV_3_F16_INPUT_W 10 +#define CONV_MATCH_CONV_3_F16_IN_CH 3 +#define CONV_MATCH_CONV_3_F16_FILTER_H 10 +#define CONV_MATCH_CONV_3_F16_FILTER_W 4 +#define CONV_MATCH_CONV_3_F16_OUT_CH 1 +#define CONV_MATCH_CONV_3_F16_STRIDE_H 2 +#define CONV_MATCH_CONV_3_F16_STRIDE_W 1 +#define CONV_MATCH_CONV_3_F16_PADDING_H 4 +#define CONV_MATCH_CONV_3_F16_PADDING_W 1 +#define CONV_MATCH_CONV_3_F16_DILATION_H 1 +#define CONV_MATCH_CONV_3_F16_DILATION_W 1 +#define CONV_MATCH_CONV_3_F16_OUTPUT_H 24 +#define CONV_MATCH_CONV_3_F16_OUTPUT_W 9 +#define CONV_MATCH_CONV_3_F16_OUTPUT_C 1 +#define CONV_MATCH_CONV_3_F16_DST_SIZE 216 +#define CONV_MATCH_CONV_3_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_3_F16_USE_WRAPPER 1 +#define CONV_MATCH_CONV_3_F16_OUT_ACTIVATION_MIN (float16_t) - 127.0f +#define CONV_MATCH_CONV_3_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/input_data.h new file mode 100644 index 00000000..3fcce9e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/input_data.h @@ -0,0 +1,736 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_3_f16_input_data[1470] = {(float16_t)0.317626953f, (float16_t)-0.760742188f, + (float16_t)0.688476562f, (float16_t)-0.767578125f, + (float16_t)-0.337890625f, (float16_t)0.680664062f, + (float16_t)0.938476562f, (float16_t)-0.931640625f, + (float16_t)-0.700195312f, (float16_t)0.965332031f, + (float16_t)0.551757812f, (float16_t)-0.337402344f, + (float16_t)-0.376953125f, (float16_t)0.370605469f, + (float16_t)-0.0556030273f, (float16_t)-0.407470703f, + (float16_t)-0.826171875f, (float16_t)-0.317138672f, + (float16_t)-0.383789062f, (float16_t)0.592285156f, + (float16_t)-0.249633789f, (float16_t)-0.000327825546f, + (float16_t)0.322998047f, (float16_t)-0.412353516f, + (float16_t)0.258300781f, (float16_t)-0.0731201172f, + (float16_t)0.889648438f, (float16_t)-0.441650391f, + (float16_t)0.176635742f, (float16_t)0.838867188f, + (float16_t)-0.673828125f, (float16_t)-0.465576172f, + (float16_t)0.451416016f, (float16_t)-0.432617188f, + (float16_t)-0.255371094f, (float16_t)0.174316406f, + (float16_t)-0.018737793f, (float16_t)0.253173828f, + (float16_t)0.445800781f, (float16_t)-0.71875f, + (float16_t)0.290527344f, (float16_t)0.933105469f, + (float16_t)-0.747070312f, (float16_t)0.165161133f, + (float16_t)0.939453125f, (float16_t)0.098815918f, + (float16_t)0.708496094f, (float16_t)0.313232422f, + (float16_t)-0.0979003906f, (float16_t)0.25f, + (float16_t)0.95703125f, (float16_t)0.809570312f, + (float16_t)0.366943359f, (float16_t)-0.639160156f, + (float16_t)0.596191406f, (float16_t)0.944824219f, + (float16_t)0.447753906f, (float16_t)0.640625f, + (float16_t)0.828613281f, (float16_t)0.594726562f, + (float16_t)0.979003906f, (float16_t)-0.427001953f, + (float16_t)0.144897461f, (float16_t)-0.319580078f, + (float16_t)0.641113281f, (float16_t)-0.380371094f, + (float16_t)-0.0220336914f, (float16_t)-0.7578125f, + (float16_t)0.561035156f, (float16_t)0.386474609f, + (float16_t)-0.667480469f, (float16_t)-0.702636719f, + (float16_t)-0.512695312f, (float16_t)0.802734375f, + (float16_t)0.969238281f, (float16_t)0.770019531f, + (float16_t)0.381835938f, (float16_t)-0.411376953f, + (float16_t)0.207641602f, (float16_t)0.252197266f, + (float16_t)0.934570312f, (float16_t)0.927246094f, + (float16_t)0.180419922f, (float16_t)-0.5234375f, + (float16_t)-0.618652344f, (float16_t)-0.784179688f, + (float16_t)-0.626953125f, (float16_t)-0.0301361084f, + (float16_t)0.912597656f, (float16_t)-0.392333984f, + (float16_t)0.230712891f, (float16_t)-0.262939453f, + (float16_t)-0.583496094f, (float16_t)0.422851562f, + (float16_t)-0.482421875f, (float16_t)-0.169677734f, + (float16_t)0.876953125f, (float16_t)0.821289062f, + (float16_t)0.679199219f, (float16_t)-0.00499725342f, + (float16_t)0.807128906f, (float16_t)0.248168945f, + (float16_t)0.890625f, (float16_t)0.390380859f, + (float16_t)0.602539062f, (float16_t)0.00699996948f, + (float16_t)0.956542969f, (float16_t)0.972167969f, + (float16_t)0.829101562f, (float16_t)0.900878906f, + (float16_t)-0.749023438f, (float16_t)-0.18737793f, + (float16_t)0.449462891f, (float16_t)-0.0150985718f, + (float16_t)-0.846679688f, (float16_t)-0.639648438f, + (float16_t)0.0370788574f, (float16_t)0.0520629883f, + (float16_t)0.300048828f, (float16_t)0.568847656f, + (float16_t)-0.843261719f, (float16_t)0.733886719f, + (float16_t)-0.644042969f, (float16_t)-0.509277344f, + (float16_t)0.376220703f, (float16_t)-0.350830078f, + (float16_t)0.0231170654f, (float16_t)0.982421875f, + (float16_t)0.88671875f, (float16_t)0.285400391f, + (float16_t)-0.745117188f, (float16_t)0.472900391f, + (float16_t)0.186889648f, (float16_t)0.150756836f, + (float16_t)-0.599121094f, (float16_t)0.326416016f, + (float16_t)0.196777344f, (float16_t)-0.369140625f, + (float16_t)0.927246094f, (float16_t)-0.337158203f, + (float16_t)0.0966796875f, (float16_t)0.51171875f, + (float16_t)-0.849609375f, (float16_t)-0.567382812f, + (float16_t)0.534667969f, (float16_t)0.358154297f, + (float16_t)-0.352539062f, (float16_t)0.409912109f, + (float16_t)-0.856445312f, (float16_t)0.598632812f, + (float16_t)0.973144531f, (float16_t)-0.180175781f, + (float16_t)-0.923339844f, (float16_t)-0.202270508f, + (float16_t)-0.654785156f, (float16_t)0.970703125f, + (float16_t)-0.102539062f, (float16_t)-0.844238281f, + (float16_t)0.979492188f, (float16_t)0.877929688f, + (float16_t)0.488769531f, (float16_t)-0.9140625f, + (float16_t)-0.327636719f, (float16_t)0.801269531f, + (float16_t)-0.290039062f, (float16_t)-0.488525391f, + (float16_t)0.617675781f, (float16_t)-0.449951172f, + (float16_t)0.16784668f, (float16_t)-0.583496094f, + (float16_t)-0.758300781f, (float16_t)0.606933594f, + (float16_t)0.291259766f, (float16_t)-0.780761719f, + (float16_t)0.0973510742f, (float16_t)-0.653808594f, + (float16_t)-0.893066406f, (float16_t)0.39453125f, + (float16_t)0.561035156f, (float16_t)0.131835938f, + (float16_t)-0.739257812f, (float16_t)0.385742188f, + (float16_t)-0.279785156f, (float16_t)-0.990234375f, + (float16_t)-0.148803711f, (float16_t)-0.762695312f, + (float16_t)0.888183594f, (float16_t)-0.732421875f, + (float16_t)0.397460938f, (float16_t)0.999511719f, + (float16_t)-0.919921875f, (float16_t)0.170410156f, + (float16_t)-0.993164062f, (float16_t)-0.359375f, + (float16_t)-0.587890625f, (float16_t)0.0341796875f, + (float16_t)0.392578125f, (float16_t)-0.987304688f, + (float16_t)-0.990722656f, (float16_t)-0.267333984f, + (float16_t)-0.818359375f, (float16_t)-0.245361328f, + (float16_t)0.137817383f, (float16_t)-0.234741211f, + (float16_t)-0.57421875f, (float16_t)0.384765625f, + (float16_t)0.571289062f, (float16_t)-0.2734375f, + (float16_t)0.803710938f, (float16_t)-0.826171875f, + (float16_t)-0.920410156f, (float16_t)0.10369873f, + (float16_t)0.137939453f, (float16_t)-0.625976562f, + (float16_t)-0.124633789f, (float16_t)0.314697266f, + (float16_t)0.975097656f, (float16_t)-0.0537109375f, + (float16_t)0.600585938f, (float16_t)-0.440673828f, + (float16_t)-0.729492188f, (float16_t)-0.927734375f, + (float16_t)-0.457275391f, (float16_t)-0.924804688f, + (float16_t)-0.828125f, (float16_t)-0.488037109f, + (float16_t)0.616210938f, (float16_t)0.818847656f, + (float16_t)0.299560547f, (float16_t)-0.987792969f, + (float16_t)-0.180786133f, (float16_t)-0.670898438f, + (float16_t)0.838378906f, (float16_t)-0.927734375f, + (float16_t)0.283447266f, (float16_t)-0.468505859f, + (float16_t)0.0701904297f, (float16_t)0.465087891f, + (float16_t)0.303955078f, (float16_t)-0.787109375f, + (float16_t)0.286865234f, (float16_t)-0.483642578f, + (float16_t)-0.501953125f, (float16_t)0.948242188f, + (float16_t)0.332275391f, (float16_t)-0.530273438f, + (float16_t)0.781738281f, (float16_t)0.355712891f, + (float16_t)0.0903320312f, (float16_t)0.525390625f, + (float16_t)-0.643554688f, (float16_t)0.4375f, + (float16_t)-0.473632812f, (float16_t)-0.389160156f, + (float16_t)0.606933594f, (float16_t)0.00394058228f, + (float16_t)0.712402344f, (float16_t)-0.264892578f, + (float16_t)0.65234375f, (float16_t)-0.725585938f, + (float16_t)-0.591796875f, (float16_t)-0.0726928711f, + (float16_t)0.440673828f, (float16_t)0.942382812f, + 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(float16_t)-0.0860595703f, + (float16_t)-0.958496094f, (float16_t)0.967285156f, + (float16_t)-0.135986328f, (float16_t)-0.360351562f, + (float16_t)-0.421386719f, (float16_t)0.507324219f, + (float16_t)-0.161987305f, (float16_t)-0.746582031f, + (float16_t)-0.6171875f, (float16_t)-0.111022949f, + (float16_t)0.224365234f, (float16_t)0.511230469f, + (float16_t)0.314208984f, (float16_t)-0.478515625f, + (float16_t)0.237426758f, (float16_t)0.0432128906f, + (float16_t)-0.904785156f, (float16_t)-0.158325195f, + (float16_t)-0.560058594f, (float16_t)-0.369628906f, + (float16_t)-0.0308380127f, (float16_t)0.923339844f, + (float16_t)0.213256836f, (float16_t)0.0404663086f, + (float16_t)0.810546875f, (float16_t)0.780273438f, + (float16_t)-0.927246094f, (float16_t)-0.798339844f, + (float16_t)-0.817871094f, (float16_t)0.59765625f, + (float16_t)-0.293701172f, (float16_t)-0.642578125f, + (float16_t)0.819335938f, (float16_t)-0.408935547f, + (float16_t)0.900390625f, (float16_t)0.760253906f, + (float16_t)-0.92578125f, (float16_t)-0.729492188f, + (float16_t)-0.517578125f, (float16_t)-0.449462891f, + (float16_t)0.56640625f, (float16_t)-0.990234375f, + (float16_t)0.0761108398f, (float16_t)0.420654297f, + (float16_t)0.779296875f, (float16_t)0.909667969f, + (float16_t)-0.361328125f, (float16_t)0.845703125f, + (float16_t)0.0358581543f, (float16_t)0.943847656f, + (float16_t)-0.398193359f, (float16_t)-0.318603516f, + (float16_t)0.867675781f, (float16_t)0.0012178421f, + (float16_t)0.724121094f, (float16_t)-0.798339844f, + (float16_t)0.595214844f, (float16_t)-0.638671875f, + (float16_t)0.782714844f, (float16_t)-0.547363281f, + (float16_t)0.0178375244f, (float16_t)0.882324219f, + (float16_t)0.377685547f, (float16_t)-0.895996094f, + (float16_t)-0.6796875f, (float16_t)0.589355469f, + (float16_t)-0.131469727f, (float16_t)-0.270996094f, + (float16_t)-0.663574219f, (float16_t)0.279052734f, + (float16_t)0.20324707f, (float16_t)0.809570312f, + (float16_t)-0.90234375f, (float16_t)-0.413330078f, + (float16_t)-0.23059082f, (float16_t)-0.517578125f, + (float16_t)0.390625f, (float16_t)-0.221923828f, + (float16_t)0.181518555f, (float16_t)0.400878906f, + (float16_t)0.216064453f, (float16_t)0.115112305f, + (float16_t)-0.813964844f, (float16_t)-0.719238281f, + (float16_t)0.973144531f, (float16_t)0.677734375f, + (float16_t)-0.869140625f, (float16_t)-0.393798828f, + (float16_t)-0.411865234f, (float16_t)0.311035156f, + (float16_t)-0.139892578f, (float16_t)0.109375f, + (float16_t)0.79296875f, (float16_t)-0.717773438f, + (float16_t)-0.770507812f, (float16_t)-0.145507812f, + (float16_t)0.557617188f, (float16_t)0.778808594f, + (float16_t)0.431152344f, (float16_t)-0.420654297f, + (float16_t)0.397216797f, (float16_t)0.478515625f, + (float16_t)-0.656738281f, (float16_t)-0.0268707275f, + (float16_t)-0.723144531f, (float16_t)0.412109375f, + (float16_t)-0.00896453857f, (float16_t)0.254394531f, + (float16_t)0.843261719f, (float16_t)0.893554688f, + (float16_t)0.723632812f, (float16_t)-0.0759277344f, + (float16_t)-0.312988281f, (float16_t)0.282226562f, + (float16_t)0.174438477f, (float16_t)-0.235107422f, + (float16_t)0.909667969f, (float16_t)0.468994141f, + (float16_t)0.699707031f, (float16_t)-0.458496094f, + (float16_t)0.0516357422f, (float16_t)0.581054688f, + (float16_t)0.958984375f, (float16_t)-0.450439453f, + (float16_t)0.0918579102f, (float16_t)0.797363281f, + (float16_t)0.0897827148f, (float16_t)0.498535156f, + (float16_t)-0.892578125f, (float16_t)0.725585938f, + (float16_t)0.687988281f, (float16_t)-0.507324219f, + (float16_t)0.969238281f, (float16_t)-0.174560547f, + (float16_t)-0.611816406f, (float16_t)0.781738281f, + (float16_t)-0.883300781f, (float16_t)-0.996582031f, + (float16_t)-0.6640625f, (float16_t)-0.960449219f, + (float16_t)-0.772949219f, (float16_t)-0.169433594f, + (float16_t)0.380615234f, (float16_t)-0.823730469f, + (float16_t)-0.497558594f, (float16_t)0.939453125f, + (float16_t)0.765136719f, (float16_t)0.299316406f, + (float16_t)0.318115234f, (float16_t)0.365478516f, + (float16_t)-0.961914062f, (float16_t)0.197265625f, + (float16_t)0.732421875f, (float16_t)-0.392578125f, + (float16_t)-0.5859375f, (float16_t)-0.875f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/output_ref_data.h new file mode 100644 index 00000000..ea02fb59 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_3_f16_output_ref_data[216] = { + (float16_t)-3.734375f, (float16_t)2.84570312f, (float16_t)-2.41210938f, (float16_t)1.19335938f, + (float16_t)1.31933594f, (float16_t)0.502441406f, (float16_t)5.05078125f, (float16_t)1.07617188f, + (float16_t)1.640625f, (float16_t)-0.8203125f, (float16_t)2.00390625f, (float16_t)1.75878906f, + (float16_t)-1.34667969f, (float16_t)3.78710938f, (float16_t)-0.8359375f, (float16_t)-1.27246094f, + (float16_t)-1.734375f, (float16_t)2.31835938f, (float16_t)-1.10742188f, (float16_t)1.25585938f, + (float16_t)1.78613281f, (float16_t)3.15625f, (float16_t)2.80273438f, (float16_t)-0.324462891f, + (float16_t)6.62109375f, (float16_t)-2.58007812f, (float16_t)3.85351562f, (float16_t)2.36328125f, + (float16_t)-2.41601562f, (float16_t)-5.7421875f, (float16_t)1.97460938f, (float16_t)3.50195312f, + (float16_t)8.0f, (float16_t)1.58203125f, (float16_t)-0.845703125f, (float16_t)-2.02929688f, + (float16_t)0.717285156f, (float16_t)0.501953125f, (float16_t)1.47265625f, (float16_t)-0.336181641f, + (float16_t)6.01171875f, (float16_t)-0.263183594f, (float16_t)1.5078125f, (float16_t)2.15820312f, + (float16_t)-1.55273438f, (float16_t)2.46289062f, (float16_t)-4.99609375f, (float16_t)-1.21191406f, + (float16_t)1.30664062f, (float16_t)0.0776977539f, (float16_t)-4.7109375f, (float16_t)-1.83105469f, + (float16_t)-1.99023438f, (float16_t)3.03515625f, (float16_t)-2.18945312f, (float16_t)2.50976562f, + (float16_t)-1.7109375f, (float16_t)-0.032409668f, (float16_t)-1.32617188f, (float16_t)-3.01171875f, + (float16_t)0.130859375f, (float16_t)0.286621094f, (float16_t)2.94335938f, (float16_t)0.407226562f, + (float16_t)0.890625f, (float16_t)0.92578125f, (float16_t)-4.22265625f, (float16_t)-1.98144531f, + (float16_t)3.47070312f, (float16_t)4.4921875f, (float16_t)-7.046875f, (float16_t)0.21862793f, + (float16_t)1.13183594f, (float16_t)0.780761719f, (float16_t)0.585449219f, (float16_t)-4.2890625f, + (float16_t)3.35742188f, (float16_t)-0.462890625f, (float16_t)1.01953125f, (float16_t)1.20605469f, + (float16_t)0.818359375f, (float16_t)-1.86914062f, (float16_t)3.17578125f, (float16_t)-2.18164062f, + (float16_t)1.32910156f, (float16_t)-1.27148438f, (float16_t)-1.41113281f, (float16_t)-1.24414062f, + (float16_t)-3.54296875f, (float16_t)-0.181396484f, (float16_t)2.63085938f, (float16_t)-1.33691406f, + (float16_t)0.0509033203f, (float16_t)0.0702514648f, (float16_t)3.05859375f, (float16_t)1.73730469f, + (float16_t)-3.04882812f, (float16_t)-1.94140625f, (float16_t)-0.573730469f, (float16_t)-2.35351562f, + (float16_t)3.609375f, (float16_t)-2.92578125f, (float16_t)0.756835938f, (float16_t)0.326416016f, + (float16_t)-4.33203125f, (float16_t)0.20715332f, (float16_t)2.98828125f, (float16_t)-2.11132812f, + (float16_t)-2.96484375f, (float16_t)-1.40136719f, (float16_t)-1.04589844f, (float16_t)0.595703125f, + (float16_t)-2.796875f, (float16_t)3.57617188f, (float16_t)0.481689453f, (float16_t)6.0703125f, + (float16_t)-0.998535156f, (float16_t)1.16210938f, (float16_t)-2.5546875f, (float16_t)0.541015625f, + (float16_t)-2.62109375f, (float16_t)2.82617188f, (float16_t)1.91308594f, (float16_t)1.04199219f, + (float16_t)5.05859375f, (float16_t)-4.203125f, (float16_t)3.21679688f, (float16_t)3.43164062f, + (float16_t)1.09863281f, (float16_t)-1.42089844f, (float16_t)4.3359375f, (float16_t)-1.7578125f, + (float16_t)3.953125f, (float16_t)-0.96484375f, (float16_t)1.75097656f, (float16_t)-1.05761719f, + (float16_t)2.13085938f, (float16_t)1.19824219f, (float16_t)-0.460205078f, (float16_t)4.26953125f, + (float16_t)1.50488281f, (float16_t)2.95117188f, (float16_t)0.856445312f, (float16_t)-4.453125f, + (float16_t)-0.256103516f, (float16_t)3.39257812f, (float16_t)-5.015625f, (float16_t)1.88085938f, + (float16_t)-2.12695312f, (float16_t)0.338134766f, (float16_t)0.881347656f, (float16_t)-1.99023438f, + (float16_t)-1.36914062f, (float16_t)0.597167969f, (float16_t)-0.814453125f, (float16_t)-0.978515625f, + (float16_t)-0.662109375f, (float16_t)0.648925781f, (float16_t)2.7890625f, (float16_t)0.0523376465f, + (float16_t)-0.985351562f, (float16_t)2.125f, (float16_t)-0.243041992f, (float16_t)-4.6796875f, + (float16_t)3.25585938f, (float16_t)-5.08984375f, (float16_t)-2.765625f, (float16_t)1.33691406f, + (float16_t)-0.856445312f, (float16_t)4.69140625f, (float16_t)4.23828125f, (float16_t)-0.198852539f, + (float16_t)-3.84570312f, (float16_t)3.86523438f, (float16_t)-3.97070312f, (float16_t)-2.70703125f, + (float16_t)3.38085938f, (float16_t)4.12890625f, (float16_t)0.671875f, (float16_t)5.42578125f, + (float16_t)0.341308594f, (float16_t)0.920410156f, (float16_t)-1.49121094f, (float16_t)-1.41699219f, + (float16_t)0.8515625f, (float16_t)-4.30078125f, (float16_t)0.244628906f, (float16_t)3.02929688f, + (float16_t)-1.61816406f, (float16_t)2.73242188f, (float16_t)-0.9921875f, (float16_t)-1.71386719f, + (float16_t)1.29296875f, (float16_t)-1.53808594f, (float16_t)-2.0625f, (float16_t)0.965332031f, + (float16_t)0.889648438f, (float16_t)-5.6796875f, (float16_t)1.58105469f, (float16_t)0.353027344f, + (float16_t)0.130004883f, (float16_t)-4.1328125f, (float16_t)-1.17285156f, (float16_t)-1.17480469f, + (float16_t)0.0328369141f, (float16_t)-3.91601562f, (float16_t)0.151000977f, (float16_t)0.289550781f, + (float16_t)-1.27832031f, (float16_t)-0.0539855957f, (float16_t)-1.64355469f, (float16_t)3.390625f, + (float16_t)-0.716308594f, (float16_t)0.816894531f, (float16_t)0.303955078f, (float16_t)4.12890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/weights_data.h new file mode 100644 index 00000000..48dc8644 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f16/weights_data.h @@ -0,0 +1,32 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_3_f16_weights_data[120] = { + (float16_t)-0.46484375f, (float16_t)-0.676757812f, (float16_t)-0.133178711f, (float16_t)0.602539062f, + (float16_t)0.663574219f, (float16_t)0.360107422f, (float16_t)0.658203125f, (float16_t)0.700683594f, + (float16_t)-0.114746094f, (float16_t)0.497802734f, (float16_t)-0.258789062f, (float16_t)-0.687011719f, + (float16_t)0.518554688f, (float16_t)0.707519531f, (float16_t)0.12286377f, (float16_t)0.0404052734f, + (float16_t)0.6328125f, (float16_t)0.5234375f, (float16_t)-0.58984375f, (float16_t)0.452392578f, + (float16_t)-0.561035156f, (float16_t)-0.0182342529f, (float16_t)-0.516113281f, (float16_t)0.225708008f, + (float16_t)0.248901367f, (float16_t)0.216674805f, (float16_t)0.172851562f, (float16_t)-0.58203125f, + (float16_t)0.018371582f, (float16_t)-0.508789062f, (float16_t)0.284423828f, (float16_t)0.743652344f, + (float16_t)0.122009277f, (float16_t)0.268310547f, (float16_t)0.525390625f, (float16_t)-0.58203125f, + (float16_t)-0.323486328f, (float16_t)0.485107422f, (float16_t)0.559082031f, (float16_t)-0.309814453f, + (float16_t)0.522460938f, (float16_t)-0.160888672f, (float16_t)-0.609375f, (float16_t)-0.315185547f, + (float16_t)0.369628906f, (float16_t)0.516113281f, (float16_t)-0.692382812f, (float16_t)0.109191895f, + (float16_t)0.629394531f, (float16_t)-0.296142578f, (float16_t)-0.358154297f, (float16_t)-0.260253906f, + (float16_t)0.616699219f, (float16_t)-0.176391602f, (float16_t)-0.263427734f, (float16_t)-0.0188140869f, + (float16_t)-0.584472656f, (float16_t)0.477294922f, (float16_t)0.299316406f, (float16_t)-0.296875f, + (float16_t)0.306152344f, (float16_t)0.399414062f, (float16_t)-0.276367188f, (float16_t)-0.5625f, + (float16_t)-0.480957031f, (float16_t)0.180175781f, (float16_t)0.285644531f, (float16_t)0.0057220459f, + (float16_t)-0.272216797f, (float16_t)0.503417969f, (float16_t)0.673828125f, (float16_t)-0.302490234f, + (float16_t)0.41796875f, (float16_t)0.0178985596f, (float16_t)0.655273438f, (float16_t)-0.700195312f, + (float16_t)0.468994141f, (float16_t)-0.410644531f, (float16_t)0.180908203f, (float16_t)-0.494873047f, + (float16_t)-0.384765625f, (float16_t)-0.153808594f, (float16_t)0.263671875f, (float16_t)-0.397949219f, + (float16_t)0.189453125f, (float16_t)0.178710938f, (float16_t)0.0995483398f, (float16_t)-0.0241851807f, + (float16_t)-0.657714844f, (float16_t)0.319335938f, (float16_t)-0.240234375f, (float16_t)0.282714844f, + (float16_t)-0.160888672f, (float16_t)-0.176757812f, (float16_t)-0.55859375f, (float16_t)0.404052734f, + (float16_t)0.230712891f, (float16_t)0.166870117f, (float16_t)-0.297363281f, (float16_t)0.0197143555f, + (float16_t)-0.655761719f, (float16_t)0.364501953f, (float16_t)0.719726562f, (float16_t)0.290527344f, + (float16_t)0.45703125f, (float16_t)0.368652344f, (float16_t)-0.0954589844f, (float16_t)-0.0089263916f, + (float16_t)-0.0690307617f, (float16_t)0.351318359f, (float16_t)-0.530273438f, (float16_t)0.352294922f, + (float16_t)-0.687988281f, (float16_t)0.0958862305f, (float16_t)0.163208008f, (float16_t)0.737792969f, + (float16_t)-0.104003906f, (float16_t)-0.106262207f, (float16_t)-0.0287322998f, (float16_t)-0.545898438f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/biases_data.h new file mode 100644 index 00000000..85f9f720 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_3_f32_biases_data[1] = {0.35655278f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/config_data.h new file mode 100644 index 00000000..f959afed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_3_F32_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_3_F32_INPUT_H 49 +#define CONV_MATCH_CONV_3_F32_INPUT_W 10 +#define CONV_MATCH_CONV_3_F32_IN_CH 3 +#define CONV_MATCH_CONV_3_F32_FILTER_H 10 +#define CONV_MATCH_CONV_3_F32_FILTER_W 4 +#define CONV_MATCH_CONV_3_F32_OUT_CH 1 +#define CONV_MATCH_CONV_3_F32_STRIDE_H 2 +#define CONV_MATCH_CONV_3_F32_STRIDE_W 1 +#define CONV_MATCH_CONV_3_F32_PADDING_H 4 +#define CONV_MATCH_CONV_3_F32_PADDING_W 1 +#define CONV_MATCH_CONV_3_F32_DILATION_H 1 +#define CONV_MATCH_CONV_3_F32_DILATION_W 1 +#define CONV_MATCH_CONV_3_F32_OUTPUT_H 24 +#define CONV_MATCH_CONV_3_F32_OUTPUT_W 9 +#define CONV_MATCH_CONV_3_F32_OUTPUT_C 1 +#define CONV_MATCH_CONV_3_F32_DST_SIZE 216 +#define CONV_MATCH_CONV_3_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_3_F32_USE_WRAPPER 1 +#define CONV_MATCH_CONV_3_F32_OUT_ACTIVATION_MIN -127.0f +#define CONV_MATCH_CONV_3_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/input_data.h new file mode 100644 index 00000000..e65e81bd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/input_data.h @@ -0,0 +1,247 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_3_f32_input_data[1470] = { + 0.317512989f, -0.760831296f, 0.688553512f, -0.767379165f, -0.337966174f, 0.680894554f, + 0.938278317f, -0.931703985f, -0.700426877f, 0.96541816f, 0.551515102f, -0.337438494f, + -0.376897365f, 0.370546073f, -0.0556015f, -0.407456696f, -0.8261832f, -0.317097306f, + -0.38367033f, 0.592517018f, -0.249665141f, -0.000327737827f, 0.322948307f, -0.412290335f, + 0.258276731f, -0.0730977505f, 0.889811575f, -0.441619843f, 0.17669338f, 0.839096844f, + -0.673997343f, -0.465551853f, 0.451346636f, -0.432635814f, -0.255325615f, 0.17432797f, + -0.0187359154f, 0.253291368f, 0.445914686f, -0.718678296f, 0.290576458f, 0.93316263f, + -0.747311652f, 0.165207401f, 0.939226568f, 0.0987948626f, 0.708347559f, 0.313319176f, + -0.0979002714f, 0.250081062f, 0.957177699f, 0.809455097f, 0.366899788f, -0.639183998f, + 0.596415162f, 0.944936812f, 0.447775453f, 0.640742481f, 0.828530848f, 0.594578028f, + 0.979093254f, -0.426986158f, 0.144888595f, -0.319615871f, 0.64126873f, -0.38037914f, + -0.0220300667f, -0.757941127f, 0.561232924f, 0.386552364f, -0.667338967f, -0.702739775f, + -0.512601912f, 0.80275929f, 0.969368517f, 0.770052671f, 0.381838799f, -0.411440551f, + 0.20767346f, 0.252297908f, 0.93447113f, 0.927004218f, 0.180461556f, -0.523219705f, + -0.618521154f, -0.784213364f, -0.627164423f, -0.0301413257f, 0.912566066f, -0.392406613f, + 0.230698809f, -0.262997031f, -0.583567142f, 0.422770709f, -0.482499629f, -0.169673577f, + 0.876740277f, 0.821070075f, 0.678974688f, -0.00499889813f, 0.806979537f, 0.248163596f, + 0.890641868f, 0.390391558f, 0.602453709f, 0.00699947681f, 0.956520438f, 0.972323477f, + 0.829124987f, 0.900986075f, -0.749101102f, -0.187338501f, 0.449344546f, -0.0150993615f, + -0.846820831f, -0.639861822f, 0.03709279f, 0.0520717055f, 0.300156772f, 0.568995655f, + -0.843266845f, 0.733677328f, -0.644236088f, -0.509068131f, 0.37615633f, -0.350904047f, + 0.0231159013f, 0.982265413f, 0.886584818f, 0.285473436f, -0.74527508f, 0.47280696f, + 0.18686828f, 0.150775284f, -0.599294364f, 0.326375157f, 0.196807832f, -0.369097263f, + 0.927098811f, -0.337237895f, 0.0967008024f, 0.511589527f, -0.849574566f, -0.567550838f, + 0.534874916f, 0.358087242f, -0.352606028f, 0.410030752f, -0.856586039f, 0.59863168f, + 0.973083854f, -0.180166677f, -0.92350781f, -0.202218086f, -0.654912233f, 0.970886052f, + -0.102540307f, -0.844396353f, 0.979680717f, 0.878167093f, 0.488825858f, -0.913852513f, + -0.327623695f, 0.801178277f, -0.290010333f, -0.488620281f, 0.617781699f, -0.449876696f, + 0.167792305f, -0.583537042f, -0.758170903f, 0.606884599f, 0.291291684f, -0.780918777f, + 0.0973712653f, -0.653762162f, -0.892944932f, 0.394550383f, 0.560912728f, 0.131823942f, + -0.739060998f, 0.385746419f, -0.279754668f, -0.99039638f, -0.148825437f, -0.762732267f, + 0.888114214f, -0.732295096f, 0.397538215f, 0.999645591f, -0.919903815f, 0.170377672f, + -0.993054032f, -0.359360039f, -0.587817013f, 0.034174718f, 0.392656654f, -0.987540722f, + -0.990839422f, -0.267391026f, -0.818185925f, -0.245354027f, 0.137785673f, -0.234766975f, + -0.574458599f, 0.384824902f, 0.571182311f, -0.273378223f, 0.803781867f, -0.826211631f, + -0.920582533f, 0.103695571f, 0.137917772f, -0.625924468f, -0.124645323f, 0.314804822f, + 0.97498101f, -0.0537247546f, 0.600383699f, -0.440778285f, -0.729277551f, -0.9277969f, + -0.457245529f, -0.924739659f, -0.828061163f, -0.488091022f, 0.616011441f, 0.818833828f, + 0.29968071f, -0.98769629f, -0.180744946f, -0.670837462f, 0.83845377f, -0.927615702f, + 0.283362001f, -0.468603343f, 0.0701648369f, 0.465103626f, 0.303954571f, -0.787322581f, + 0.286971092f, -0.483747125f, -0.502028465f, 0.948038578f, 0.332360685f, -0.530240178f, + 0.78182441f, 0.355645895f, 0.0903302431f, 0.525490761f, -0.643776178f, 0.437613249f, + -0.473551869f, -0.389137119f, 0.607068717f, 0.00394011755f, 0.712536454f, -0.264791429f, + 0.652099669f, -0.725342095f, -0.591753662f, -0.0726736784f, 0.440593809f, 0.942394912f, + -0.414273381f, -0.158057809f, 0.34201178f, -0.892390966f, 0.839130163f, 0.876563847f, + -0.417119563f, 0.555398822f, -0.275703728f, 0.20627676f, -0.71542275f, 0.94068861f, + 0.971900344f, -0.582204163f, -0.0220964029f, 0.121751584f, 0.163463861f, -0.65366894f, + -0.591427743f, 0.830325842f, -0.995003819f, 0.972378314f, -0.448801696f, -0.440282136f, + 0.0562942475f, -0.480499476f, 0.419845909f, -0.677414715f, -0.786627293f, 0.412171662f, + -0.544032812f, 0.192586884f, -0.677436233f, -0.568352401f, 0.0418304689f, -0.363867551f, + -0.106193416f, 0.368959665f, 0.310526609f, -0.121366702f, -0.790584743f, 0.955703676f, + 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-0.174523264f, -0.612009227f, 0.781618357f, -0.883112073f, -0.99647516f, + -0.66392684f, -0.960345745f, -0.772895277f, -0.16944769f, 0.380706996f, -0.823584735f, + -0.497619331f, 0.939562857f, 0.765029848f, 0.299419522f, 0.318150759f, 0.365501463f, + -0.961999118f, 0.197239965f, 0.732420206f, -0.392537862f, -0.585880697f, -0.874764442f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/output_ref_data.h new file mode 100644 index 00000000..aa28d8bb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/output_ref_data.h @@ -0,0 +1,33 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_3_f32_output_ref_data[216] = { + -3.73514891f, 2.84634137f, -2.41124558f, 1.19308817f, 1.31942868f, 0.502381504f, 5.05157948f, + 1.07581794f, 1.64076722f, -0.820147634f, 2.00347137f, 1.7586385f, -1.34636831f, 3.78705621f, + -0.836081326f, -1.27200866f, -1.73388934f, 2.31809783f, -1.10725462f, 1.2561928f, 1.78636956f, + 3.15530348f, 2.80225945f, -0.324577957f, 6.61940861f, -2.58022356f, 3.85360789f, 2.36385632f, + -2.41599774f, -5.74233294f, 1.97487736f, 3.50183964f, 7.99837065f, 1.58232749f, -0.845944047f, + -2.03015351f, 0.717094719f, 0.502037704f, 1.47248685f, -0.336146921f, 6.01073027f, -0.263256073f, + 1.50741494f, 2.15885282f, -1.55293572f, 2.46385908f, -4.99621296f, -1.21233153f, 1.30692518f, + 0.0776910186f, -4.71142626f, -1.83067584f, -1.99033809f, 3.03476167f, -2.19006062f, 2.510396f, + -1.7107259f, -0.0324212536f, -1.32594979f, -3.01128864f, 0.130874738f, 0.286499739f, 2.94251585f, + 0.407193303f, 0.890716076f, 0.925676644f, -4.22274685f, -1.98152614f, 3.47003579f, 4.49114323f, + -7.04666758f, 0.21862562f, 1.13135469f, 0.780725062f, 0.585522056f, -4.28922749f, 3.35699701f, + -0.462942839f, 1.01946211f, 1.20614707f, 0.818408966f, -1.8694346f, 3.1759069f, -2.18211889f, + 1.32866681f, -1.27182615f, -1.41150069f, -1.24391556f, -3.54239821f, -0.181381077f, 2.63098025f, + -1.33699262f, 0.0508883856f, 0.0702539235f, 3.05835438f, 1.73688257f, -3.04825616f, -1.94138944f, + -0.573944271f, -2.35430074f, 3.60946894f, -2.92670727f, 0.756637573f, 0.326451302f, -4.3321166f, + 0.207193196f, 2.98864579f, -2.11112523f, -2.96476412f, -1.40162885f, -1.04577792f, 0.595812798f, + -2.79740238f, 3.57682681f, 0.481780171f, 6.07101488f, -0.998535216f, 1.1622678f, -2.5537219f, + 0.540974915f, -2.62164998f, 2.82590103f, 1.91275835f, 1.04245174f, 5.05946827f, -4.20230389f, + 3.21597457f, 3.43220639f, 1.0982939f, -1.42135835f, 4.33744764f, -1.75785279f, 3.95331383f, + -0.964775681f, 1.75133598f, -1.05740881f, 2.12995172f, 1.19814074f, -0.460207939f, 4.27014494f, + 1.50497365f, 2.95167875f, 0.856571615f, -4.45375681f, -0.255989343f, 3.39291215f, -5.01486826f, + 1.88082063f, -2.12743282f, 0.338048041f, 0.881498098f, -1.99043572f, -1.36945605f, 0.597101569f, + -0.814335883f, -0.978602946f, -0.661949992f, 0.64878267f, 2.78903699f, 0.0523309186f, -0.985192657f, + 2.12588334f, -0.243100107f, -4.67833281f, 3.25592351f, -5.08967686f, -2.76549554f, 1.33681762f, + -0.856337726f, 4.69041443f, 4.2385211f, -0.198856339f, -3.84618449f, 3.86455345f, -3.97160459f, + -2.70627689f, 3.37994552f, 4.12933922f, 0.672062397f, 5.42710447f, 0.341326803f, 0.920369208f, + -1.49134672f, -1.41658044f, 0.851633787f, -4.30114126f, 0.244634643f, 3.02979589f, -1.61772799f, + 2.73218322f, -0.992073059f, -1.71343529f, 1.29248869f, -1.53776705f, -2.06278658f, 0.965268731f, + 0.889564514f, -5.67936707f, 1.58111572f, 0.353019446f, 0.130015224f, -4.13092089f, -1.17258155f, + -1.17524397f, 0.0328244753f, -3.91517162f, 0.151028946f, 0.289660484f, -1.27876234f, -0.0539719686f, + -1.64384735f, 3.3897171f, -0.716267586f, 0.816681206f, 0.304070175f, 4.13008833f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/weights_data.h new file mode 100644 index 00000000..303c114b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_3_f32/weights_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_3_f32_weights_data[120] = { + -0.464810789f, -0.676830173f, -0.133156076f, 0.602405131f, 0.663744569f, 0.360075772f, 0.658043325f, + 0.700451255f, -0.1147587f, 0.497698098f, -0.258715153f, -0.687113106f, 0.518617988f, 0.707553566f, + 0.122878753f, 0.0404001214f, 0.632996678f, 0.523572385f, -0.589995503f, 0.452397645f, -0.560964048f, + -0.0182278566f, -0.51635623f, 0.225748062f, 0.248884588f, 0.216677591f, 0.172827721f, -0.581900835f, + 0.0183667094f, -0.508992553f, 0.284429997f, 0.743810534f, 0.122001909f, 0.268413752f, 0.52523911f, + -0.581973553f, -0.323599637f, 0.485075474f, 0.55916971f, -0.309934348f, 0.522597253f, -0.160839096f, + -0.609471381f, -0.315153837f, 0.369548619f, 0.516355634f, -0.692556202f, 0.109182283f, 0.629528582f, + -0.296233088f, -0.358191371f, -0.26021719f, 0.616513371f, -0.17635791f, -0.263374269f, -0.0188082661f, + -0.584469676f, 0.47727716f, 0.2992374f, -0.296984345f, 0.306178093f, 0.399311185f, -0.276486725f, + -0.56253773f, -0.481050223f, 0.180174246f, 0.285559267f, 0.00572095765f, -0.272211343f, 0.503553748f, + 0.673896194f, -0.302496463f, 0.41798082f, 0.0178987775f, 0.655277669f, -0.700189888f, 0.468942225f, + -0.410753489f, 0.180890784f, -0.494830489f, -0.3847377f, -0.153838277f, 0.263560712f, -0.398051113f, + 0.18950507f, 0.178658038f, 0.0995297506f, -0.0241790842f, -0.657507658f, 0.319240838f, -0.240286633f, + 0.282768816f, -0.160946399f, -0.176770821f, -0.558572173f, 0.404166222f, 0.230699494f, 0.166898087f, + -0.29743579f, 0.0197153166f, -0.655805707f, 0.364549637f, 0.719698012f, 0.290546954f, 0.456923664f, + 0.368703276f, -0.0954442173f, -0.00892897602f, -0.0690145418f, 0.351326019f, -0.530437469f, 0.352408469f, + -0.688148856f, 0.0959104896f, 0.163240954f, 0.737692297f, -0.104004912f, -0.106237054f, -0.028736515f, + -0.546015859f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/biases_data.h new file mode 100644 index 00000000..1b85c768 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/biases_data.h @@ -0,0 +1,4 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_4_f16_biases_data[3] = {(float16_t)-0.434570312f, + (float16_t)0.0111694336f, + (float16_t)-0.11920166f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/config_data.h new file mode 100644 index 00000000..b81ee2d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_4_F16_INPUT_BATCHES 3 +#define CONV_MATCH_CONV_4_F16_INPUT_H 5 +#define CONV_MATCH_CONV_4_F16_INPUT_W 5 +#define CONV_MATCH_CONV_4_F16_IN_CH 3 +#define CONV_MATCH_CONV_4_F16_FILTER_H 3 +#define CONV_MATCH_CONV_4_F16_FILTER_W 2 +#define CONV_MATCH_CONV_4_F16_OUT_CH 3 +#define CONV_MATCH_CONV_4_F16_STRIDE_H 2 +#define CONV_MATCH_CONV_4_F16_STRIDE_W 2 +#define CONV_MATCH_CONV_4_F16_PADDING_H 0 +#define CONV_MATCH_CONV_4_F16_PADDING_W 0 +#define CONV_MATCH_CONV_4_F16_DILATION_H 1 +#define CONV_MATCH_CONV_4_F16_DILATION_W 1 +#define CONV_MATCH_CONV_4_F16_OUTPUT_H 2 +#define CONV_MATCH_CONV_4_F16_OUTPUT_W 2 +#define CONV_MATCH_CONV_4_F16_OUTPUT_C 3 +#define CONV_MATCH_CONV_4_F16_DST_SIZE 36 +#define CONV_MATCH_CONV_4_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_4_F16_USE_WRAPPER 1 +#define CONV_MATCH_CONV_4_F16_OUT_ACTIVATION_MIN (float16_t) - 109.0f +#define CONV_MATCH_CONV_4_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/input_data.h new file mode 100644 index 00000000..bd8d8344 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/input_data.h @@ -0,0 +1,59 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_4_f16_input_data[225] = { + (float16_t)0.864257812f, (float16_t)0.712402344f, (float16_t)-0.326171875f, (float16_t)-0.724121094f, + (float16_t)0.0527648926f, (float16_t)0.193359375f, (float16_t)-0.66796875f, (float16_t)0.530761719f, + (float16_t)-0.739257812f, (float16_t)0.486328125f, (float16_t)-0.674316406f, (float16_t)-0.512695312f, + (float16_t)-0.123413086f, (float16_t)-0.0120773315f, (float16_t)-0.674804688f, (float16_t)-0.283691406f, + (float16_t)-0.0726928711f, (float16_t)-0.0251922607f, (float16_t)-0.545898438f, (float16_t)-0.933105469f, + (float16_t)-0.875976562f, (float16_t)0.394775391f, (float16_t)-0.591796875f, (float16_t)-0.108459473f, + (float16_t)0.756347656f, (float16_t)0.290527344f, (float16_t)-0.741210938f, (float16_t)-0.437744141f, + (float16_t)0.9921875f, (float16_t)-0.551757812f, (float16_t)-0.662597656f, (float16_t)-0.0572509766f, + (float16_t)-0.68359375f, (float16_t)0.854980469f, (float16_t)-0.312011719f, (float16_t)-0.852050781f, + (float16_t)0.757324219f, (float16_t)0.393066406f, (float16_t)0.557128906f, (float16_t)-0.420410156f, + (float16_t)-0.732421875f, (float16_t)-0.141479492f, (float16_t)-0.368164062f, (float16_t)0.272949219f, + (float16_t)0.118530273f, (float16_t)-0.653808594f, (float16_t)0.651367188f, (float16_t)-0.231323242f, + (float16_t)-0.619140625f, (float16_t)-0.803222656f, (float16_t)0.796875f, (float16_t)-0.375244141f, + (float16_t)0.0243682861f, (float16_t)-0.750488281f, (float16_t)0.350097656f, (float16_t)0.690429688f, + (float16_t)0.0913696289f, (float16_t)-0.069519043f, (float16_t)0.781738281f, (float16_t)0.174560547f, + (float16_t)0.401611328f, (float16_t)0.788085938f, (float16_t)0.303955078f, (float16_t)0.76953125f, + (float16_t)-0.423828125f, (float16_t)0.6640625f, (float16_t)-0.521972656f, (float16_t)0.200317383f, + (float16_t)0.96875f, (float16_t)0.0551147461f, (float16_t)-0.263183594f, (float16_t)-0.813964844f, + (float16_t)-0.761230469f, (float16_t)-0.0381774902f, (float16_t)0.973632812f, (float16_t)0.301269531f, + (float16_t)-0.547363281f, (float16_t)0.838867188f, (float16_t)-0.426513672f, (float16_t)-0.529785156f, + (float16_t)0.466796875f, (float16_t)-0.424560547f, (float16_t)-0.0899658203f, (float16_t)-0.507324219f, + (float16_t)0.308837891f, (float16_t)-0.583984375f, (float16_t)-0.899902344f, (float16_t)0.324951172f, + (float16_t)0.17956543f, (float16_t)-0.63671875f, (float16_t)0.949707031f, (float16_t)-0.614746094f, + (float16_t)0.416992188f, (float16_t)0.802734375f, (float16_t)-0.905761719f, (float16_t)0.349609375f, + (float16_t)-0.940917969f, (float16_t)0.467529297f, (float16_t)-0.533203125f, (float16_t)0.439453125f, + (float16_t)0.693847656f, (float16_t)-0.6171875f, (float16_t)-0.9609375f, (float16_t)-0.60546875f, + (float16_t)-0.211181641f, (float16_t)0.778320312f, (float16_t)-0.0176391602f, (float16_t)0.790039062f, + (float16_t)0.904785156f, (float16_t)0.751464844f, (float16_t)0.832519531f, (float16_t)0.936035156f, + (float16_t)-0.134887695f, (float16_t)-0.0463867188f, (float16_t)-0.134033203f, (float16_t)-0.281982422f, + (float16_t)0.548339844f, (float16_t)-0.68359375f, (float16_t)0.0756225586f, (float16_t)0.294433594f, + (float16_t)0.741699219f, (float16_t)0.41015625f, (float16_t)0.860351562f, (float16_t)-0.701171875f, + (float16_t)0.100463867f, (float16_t)-0.229125977f, (float16_t)0.891601562f, (float16_t)0.625488281f, + (float16_t)-0.667480469f, (float16_t)0.380371094f, (float16_t)-0.236694336f, (float16_t)0.974121094f, + (float16_t)0.333740234f, (float16_t)0.670898438f, (float16_t)0.463867188f, (float16_t)0.432373047f, + (float16_t)-0.920898438f, (float16_t)0.7734375f, (float16_t)0.490234375f, (float16_t)0.806152344f, + (float16_t)0.0217590332f, (float16_t)-0.126098633f, (float16_t)-0.282226562f, (float16_t)0.793945312f, + (float16_t)0.0204162598f, (float16_t)0.88671875f, (float16_t)0.072265625f, (float16_t)0.321289062f, + (float16_t)-0.0781860352f, (float16_t)0.389160156f, (float16_t)0.0441894531f, (float16_t)-0.150024414f, + (float16_t)-0.266357422f, (float16_t)0.00440597534f, (float16_t)-0.595214844f, (float16_t)0.0419006348f, + (float16_t)-0.0272979736f, (float16_t)0.799804688f, (float16_t)-0.601074219f, (float16_t)0.820800781f, + (float16_t)-0.445068359f, (float16_t)-0.411376953f, (float16_t)0.273193359f, (float16_t)0.973632812f, + (float16_t)0.18347168f, (float16_t)-0.615234375f, (float16_t)-0.0295410156f, (float16_t)0.0686645508f, + (float16_t)-0.223266602f, (float16_t)0.562988281f, (float16_t)0.655761719f, (float16_t)0.455566406f, + (float16_t)0.21472168f, (float16_t)-0.662597656f, (float16_t)0.496582031f, (float16_t)-0.751953125f, + (float16_t)0.772949219f, (float16_t)0.816894531f, (float16_t)0.0312042236f, (float16_t)0.622070312f, + (float16_t)0.418701172f, (float16_t)0.984863281f, (float16_t)0.413818359f, (float16_t)-0.262695312f, + (float16_t)0.204956055f, (float16_t)0.863769531f, (float16_t)-0.415527344f, (float16_t)-0.114196777f, + (float16_t)-0.938964844f, (float16_t)0.45703125f, (float16_t)0.94140625f, (float16_t)-0.722167969f, + (float16_t)0.334960938f, (float16_t)0.446777344f, (float16_t)0.8203125f, (float16_t)-0.389404297f, + (float16_t)-0.140136719f, (float16_t)0.399902344f, (float16_t)-0.458496094f, (float16_t)0.434326172f, + (float16_t)0.313964844f, (float16_t)-0.866210938f, (float16_t)-0.770019531f, (float16_t)0.461425781f, + (float16_t)0.565429688f, (float16_t)0.446044922f, (float16_t)0.360107422f, (float16_t)0.631347656f, + (float16_t)0.580078125f, (float16_t)0.709960938f, (float16_t)-0.3046875f, (float16_t)-0.679199219f, + (float16_t)0.821777344f, (float16_t)0.943359375f, (float16_t)0.757324219f, (float16_t)0.355712891f, + (float16_t)0.583007812f, (float16_t)0.77734375f, (float16_t)0.415771484f, (float16_t)0.961914062f, + (float16_t)-0.342529297f, (float16_t)0.0645751953f, (float16_t)0.468261719f, (float16_t)-0.078125f, + (float16_t)-0.427246094f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/output_ref_data.h new file mode 100644 index 00000000..e995815f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_4_f16_output_ref_data[36] = { + (float16_t)-0.290283203f, (float16_t)1.1015625f, (float16_t)0.364990234f, (float16_t)-0.339599609f, + (float16_t)-1.47949219f, (float16_t)-1.50976562f, (float16_t)-0.431152344f, (float16_t)-0.66796875f, + (float16_t)-1.30371094f, (float16_t)-1.48242188f, (float16_t)0.296386719f, (float16_t)0.631347656f, + (float16_t)-0.246826172f, (float16_t)-0.556152344f, (float16_t)2.3046875f, (float16_t)1.05761719f, + (float16_t)-0.784667969f, (float16_t)-1.67285156f, (float16_t)-0.295654297f, (float16_t)-0.373046875f, + (float16_t)3.93554688f, (float16_t)-1.40332031f, (float16_t)0.6875f, (float16_t)1.91113281f, + (float16_t)-0.214477539f, (float16_t)0.4453125f, (float16_t)-1.76953125f, (float16_t)-1.20703125f, + (float16_t)-0.503417969f, (float16_t)-0.240600586f, (float16_t)-0.494628906f, (float16_t)-0.15234375f, + (float16_t)1.87890625f, (float16_t)-2.48242188f, (float16_t)-0.421386719f, (float16_t)-0.907226562f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/weights_data.h new file mode 100644 index 00000000..a448cd12 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f16/weights_data.h @@ -0,0 +1,16 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_4_f16_weights_data[54] = { + (float16_t)-0.187988281f, (float16_t)-0.124023438f, (float16_t)0.274902344f, (float16_t)0.238525391f, + (float16_t)-0.637695312f, (float16_t)0.547851562f, (float16_t)-0.561035156f, (float16_t)-0.415039062f, + (float16_t)-0.665527344f, (float16_t)-0.665527344f, (float16_t)-0.41796875f, (float16_t)-0.693847656f, + (float16_t)0.629394531f, (float16_t)-0.643554688f, (float16_t)-0.420898438f, (float16_t)-0.390136719f, + (float16_t)0.108520508f, (float16_t)0.612792969f, (float16_t)0.477294922f, (float16_t)-0.432128906f, + (float16_t)-0.322998047f, (float16_t)-0.697265625f, (float16_t)0.363769531f, (float16_t)-0.0137863159f, + (float16_t)-0.173461914f, (float16_t)0.146972656f, (float16_t)-0.158935547f, (float16_t)-0.384033203f, + (float16_t)-0.0868530273f, (float16_t)-0.219116211f, (float16_t)-0.311035156f, (float16_t)0.556152344f, + (float16_t)0.0446166992f, (float16_t)-0.298828125f, (float16_t)0.642089844f, (float16_t)-0.17175293f, + (float16_t)0.670898438f, (float16_t)-0.268066406f, (float16_t)0.713378906f, (float16_t)0.53515625f, + (float16_t)0.463867188f, (float16_t)0.5f, (float16_t)0.288574219f, (float16_t)0.0639648438f, + (float16_t)-0.196044922f, (float16_t)0.158081055f, (float16_t)-0.347900391f, (float16_t)-0.236206055f, + (float16_t)0.0267333984f, (float16_t)-0.682128906f, (float16_t)0.746582031f, (float16_t)0.677734375f, + (float16_t)0.236816406f, (float16_t)-0.250976562f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/biases_data.h new file mode 100644 index 00000000..687ceecb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_4_f32_biases_data[3] = {-0.434648991f, 0.0111679761f, -0.119219556f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/config_data.h new file mode 100644 index 00000000..749c3703 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_4_F32_INPUT_BATCHES 3 +#define CONV_MATCH_CONV_4_F32_INPUT_H 5 +#define CONV_MATCH_CONV_4_F32_INPUT_W 5 +#define CONV_MATCH_CONV_4_F32_IN_CH 3 +#define CONV_MATCH_CONV_4_F32_FILTER_H 3 +#define CONV_MATCH_CONV_4_F32_FILTER_W 2 +#define CONV_MATCH_CONV_4_F32_OUT_CH 3 +#define CONV_MATCH_CONV_4_F32_STRIDE_H 2 +#define CONV_MATCH_CONV_4_F32_STRIDE_W 2 +#define CONV_MATCH_CONV_4_F32_PADDING_H 0 +#define CONV_MATCH_CONV_4_F32_PADDING_W 0 +#define CONV_MATCH_CONV_4_F32_DILATION_H 1 +#define CONV_MATCH_CONV_4_F32_DILATION_W 1 +#define CONV_MATCH_CONV_4_F32_OUTPUT_H 2 +#define CONV_MATCH_CONV_4_F32_OUTPUT_W 2 +#define CONV_MATCH_CONV_4_F32_OUTPUT_C 3 +#define CONV_MATCH_CONV_4_F32_DST_SIZE 36 +#define CONV_MATCH_CONV_4_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_4_F32_USE_WRAPPER 1 +#define CONV_MATCH_CONV_4_F32_OUT_ACTIVATION_MIN -109.0f +#define CONV_MATCH_CONV_4_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/input_data.h new file mode 100644 index 00000000..1378f605 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/input_data.h @@ -0,0 +1,35 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_4_f32_input_data[225] = { + 0.864269555f, 0.712302327f, -0.326094419f, -0.723959625f, 0.0527689084f, 0.193368495f, -0.667928815f, + 0.530590773f, -0.739180505f, 0.486402273f, -0.674251795f, -0.51248157f, -0.123401463f, -0.01207822f, + -0.674932778f, -0.283741236f, -0.0727033094f, -0.0251916405f, -0.54580766f, -0.93331331f, -0.876137972f, + 0.394754589f, -0.591735661f, -0.108475514f, 0.756409585f, 0.290613681f, -0.741307974f, -0.43765986f, + 0.992278099f, -0.551806211f, -0.662704527f, -0.0572487749f, -0.683776438f, 0.855012417f, -0.311977804f, + -0.852075815f, 0.75723958f, 0.392988622f, 0.556995928f, -0.420294732f, -0.732201517f, -0.141530216f, + -0.368267715f, 0.272852063f, 0.118504807f, -0.653936923f, 0.651591063f, -0.231275693f, -0.619149268f, + -0.8029809f, 0.796918213f, -0.375231594f, 0.0243738648f, -0.750607431f, 0.349976718f, 0.690277398f, + 0.0913489684f, -0.0695163235f, 0.78183037f, 0.174522862f, 0.401545405f, 0.788256586f, 0.303881347f, + 0.769539177f, -0.423928738f, 0.664233208f, -0.521989286f, 0.200277686f, 0.968902647f, 0.0551027544f, + -0.26316157f, -0.814150751f, -0.761260927f, -0.0381760262f, 0.973488986f, 0.301291317f, -0.547329545f, + 0.838798106f, -0.426568091f, -0.529893339f, 0.466731459f, -0.424469829f, -0.0899960324f, -0.50711596f, + 0.308793545f, -0.583845496f, -0.900146306f, 0.324931741f, 0.179531544f, -0.636508107f, 0.949633658f, + -0.614886761f, 0.416924775f, 0.802638531f, -0.905632675f, 0.3495619f, -0.940872669f, 0.467627704f, + -0.533329427f, 0.439382553f, 0.69373554f, -0.61717397f, -0.961151898f, -0.605602801f, -0.211189747f, + 0.778335214f, -0.0176452566f, 0.79015708f, 0.904932499f, 0.751672089f, 0.832686663f, 0.936258852f, + -0.134847701f, -0.0463907793f, -0.134012893f, -0.28208077f, 0.54847759f, -0.68364501f, 0.0756253377f, + 0.29449591f, 0.741480052f, 0.410173357f, 0.860403776f, -0.701232135f, 0.100471713f, -0.229170769f, + 0.891843677f, 0.625493824f, -0.667723954f, 0.380344123f, -0.236721337f, 0.974285007f, 0.333691537f, + 0.670868993f, 0.463859528f, 0.432341337f, -0.920988917f, 0.773479104f, 0.490167379f, 0.806213677f, + 0.0217546448f, -0.126095355f, -0.282183945f, 0.793957889f, 0.0204234049f, 0.886959314f, 0.0722703636f, + 0.321219206f, -0.0781561583f, 0.389121562f, 0.044198446f, -0.149987593f, -0.266478896f, 0.00440665428f, + -0.595268369f, 0.0419120975f, -0.0272948612f, 0.799667239f, -0.600949883f, 0.821044147f, -0.44501251f, + -0.411355108f, 0.273103535f, 0.973402023f, 0.183411136f, -0.615457416f, -0.0295408051f, 0.0686913729f, + -0.223276094f, 0.563027859f, 0.655953169f, 0.455449939f, 0.214723706f, -0.662833929f, 0.496638f, + -0.751919508f, 0.772857428f, 0.817131579f, 0.0312040411f, 0.622270525f, 0.418783903f, 0.984989047f, + 0.413924098f, -0.262646466f, 0.204986274f, 0.863929808f, -0.415440947f, -0.114202105f, -0.938926995f, + 0.4571141f, 0.941200435f, -0.722297549f, 0.335018903f, 0.446684629f, 0.820238769f, -0.389461607f, + -0.140095323f, 0.400023699f, -0.458429217f, 0.434262335f, 0.313967437f, -0.866226673f, -0.769911349f, + 0.461536884f, 0.565538287f, 0.446052611f, 0.360165685f, 0.631215513f, 0.580064774f, 0.710073531f, + -0.304700941f, -0.679030001f, 0.821567893f, 0.943212807f, 0.757313132f, 0.355823398f, 0.583213508f, + 0.777127981f, 0.415718704f, 0.962087452f, -0.342607498f, 0.0645990148f, 0.468277484f, -0.0781212598f, + -0.427274913f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/output_ref_data.h new file mode 100644 index 00000000..65be6c58 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/output_ref_data.h @@ -0,0 +1,8 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_4_f32_output_ref_data[36] = { + -0.290334582f, 1.10200775f, 0.364986032f, -0.339694977f, -1.47928452f, -1.50931895f, + -0.431174546f, -0.667889476f, -1.30342293f, -1.48252249f, 0.296472967f, 0.631334484f, + -0.246771678f, -0.556117713f, 2.30441856f, 1.05779827f, -0.784546793f, -1.67325222f, + -0.295575231f, -0.372934729f, 3.93486834f, -1.40322053f, 0.687655389f, 1.91161799f, + -0.214431733f, 0.445354104f, -1.7693789f, -1.20674264f, -0.503176391f, -0.240541473f, + -0.494543195f, -0.152386323f, 1.87923288f, -2.48300409f, -0.421283424f, -0.907082617f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/weights_data.h new file mode 100644 index 00000000..49d136aa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_4_f32/weights_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_4_f32_weights_data[54] = { + -0.188020825f, -0.124044225f, 0.274847239f, 0.238533348f, -0.637880504f, 0.547646344f, -0.560829401f, + -0.415124714f, -0.665732324f, -0.665642679f, -0.418020189f, -0.693724692f, 0.629514873f, -0.643589973f, + -0.420969814f, -0.390247822f, 0.108514197f, 0.612714231f, 0.477292061f, -0.432075828f, -0.323034167f, + -0.697294235f, 0.363691151f, -0.013785609f, -0.17349124f, 0.146948084f, -0.158891052f, -0.384033382f, + -0.0868478119f, -0.219127849f, -0.310959935f, 0.556094646f, 0.0446148403f, -0.298862606f, 0.642189026f, + -0.171783105f, 0.670716882f, -0.268080413f, 0.713254392f, 0.535047293f, 0.463859379f, 0.499938786f, + 0.288595438f, 0.0639896914f, -0.196083575f, 0.158055574f, -0.347963303f, -0.236217991f, 0.0267404076f, + -0.682060182f, 0.746377945f, 0.677689612f, 0.236867324f, -0.251049638f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/biases_data.h new file mode 100644 index 00000000..8df2e845 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_5_f16_biases_data[1] = {(float16_t)-0.280273438f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/config_data.h new file mode 100644 index 00000000..38c93e9d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_5_F16_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_5_F16_INPUT_H 1 +#define CONV_MATCH_CONV_5_F16_INPUT_W 128 +#define CONV_MATCH_CONV_5_F16_IN_CH 128 +#define CONV_MATCH_CONV_5_F16_FILTER_H 3 +#define CONV_MATCH_CONV_5_F16_FILTER_W 3 +#define CONV_MATCH_CONV_5_F16_OUT_CH 1 +#define CONV_MATCH_CONV_5_F16_STRIDE_H 4 +#define CONV_MATCH_CONV_5_F16_STRIDE_W 4 +#define CONV_MATCH_CONV_5_F16_PADDING_H 1 +#define CONV_MATCH_CONV_5_F16_PADDING_W 0 +#define CONV_MATCH_CONV_5_F16_DILATION_H 1 +#define CONV_MATCH_CONV_5_F16_DILATION_W 1 +#define CONV_MATCH_CONV_5_F16_OUTPUT_H 1 +#define CONV_MATCH_CONV_5_F16_OUTPUT_W 32 +#define CONV_MATCH_CONV_5_F16_OUTPUT_C 1 +#define CONV_MATCH_CONV_5_F16_DST_SIZE 32 +#define CONV_MATCH_CONV_5_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_5_F16_USE_WRAPPER 1 +#define CONV_MATCH_CONV_5_F16_OUT_ACTIVATION_MIN (float16_t) - 88.0f +#define CONV_MATCH_CONV_5_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/input_data.h new file mode 100644 index 00000000..e1f97528 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/input_data.h @@ -0,0 +1,16385 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_5_f16_input_data[16384] = {(float16_t)-0.075378418f, + (float16_t)0.075012207f, + (float16_t)-0.0883789062f, + (float16_t)0.620605469f, + (float16_t)0.186279297f, + (float16_t)-0.748535156f, + (float16_t)0.689941406f, + (float16_t)0.780273438f, + (float16_t)0.545898438f, + (float16_t)0.199462891f, + (float16_t)-0.962402344f, + (float16_t)0.969238281f, + (float16_t)0.615234375f, + (float16_t)0.753417969f, + (float16_t)-0.773925781f, + (float16_t)0.982910156f, + (float16_t)-0.0970458984f, + (float16_t)-0.351806641f, + (float16_t)0.367675781f, + (float16_t)0.609863281f, + (float16_t)-0.410888672f, + (float16_t)0.172851562f, + (float16_t)-0.921386719f, + (float16_t)-0.594726562f, + (float16_t)0.222290039f, + (float16_t)-0.607910156f, + (float16_t)-0.241333008f, + (float16_t)0.25f, + (float16_t)-0.581542969f, + (float16_t)0.419921875f, + (float16_t)0.938964844f, + (float16_t)-0.974121094f, + (float16_t)-0.0900268555f, + (float16_t)-0.712890625f, + (float16_t)-0.541992188f, + (float16_t)0.521972656f, + (float16_t)0.543457031f, + (float16_t)0.893066406f, + (float16_t)0.416015625f, + (float16_t)-0.118408203f, + (float16_t)-0.101867676f, + (float16_t)-0.556152344f, + (float16_t)-0.323730469f, + (float16_t)0.0542297363f, + (float16_t)-0.0661010742f, + (float16_t)0.956542969f, + (float16_t)0.276611328f, + (float16_t)-0.72265625f, + (float16_t)0.454833984f, + (float16_t)0.59375f, + (float16_t)0.433105469f, + (float16_t)-0.374755859f, + (float16_t)-0.700195312f, + (float16_t)-0.651855469f, + (float16_t)-0.0429077148f, + (float16_t)0.103759766f, + (float16_t)0.828125f, + (float16_t)0.249145508f, + (float16_t)0.133056641f, + (float16_t)0.312011719f, + (float16_t)-0.0927734375f, + (float16_t)-0.815917969f, + (float16_t)0.451171875f, + (float16_t)-0.758789062f, + (float16_t)0.584960938f, + (float16_t)0.188598633f, + (float16_t)-0.119934082f, + (float16_t)0.420654297f, + (float16_t)0.791503906f, + (float16_t)0.710449219f, + (float16_t)0.817382812f, + (float16_t)-0.0715332031f, + (float16_t)-0.311767578f, + (float16_t)-0.147216797f, + (float16_t)0.955078125f, + (float16_t)0.997070312f, + (float16_t)-0.253173828f, + (float16_t)0.161132812f, + (float16_t)-0.399902344f, + (float16_t)0.448486328f, + (float16_t)-0.186035156f, + (float16_t)0.134399414f, + (float16_t)-0.648925781f, + (float16_t)-0.69140625f, + (float16_t)0.977050781f, + (float16_t)0.720703125f, + (float16_t)-0.171142578f, + (float16_t)0.0873413086f, + (float16_t)-0.58984375f, + (float16_t)0.60546875f, + (float16_t)-0.385742188f, + (float16_t)-0.549316406f, + (float16_t)-0.666992188f, + (float16_t)-0.515136719f, + (float16_t)0.296875f, + (float16_t)0.128540039f, + (float16_t)-0.907226562f, + (float16_t)0.881347656f, + (float16_t)0.806640625f, + (float16_t)-0.246337891f, + (float16_t)0.724609375f, + (float16_t)0.543457031f, + (float16_t)-0.00213241577f, + (float16_t)0.18347168f, + (float16_t)0.819335938f, + (float16_t)0.395507812f, + (float16_t)0.953613281f, + (float16_t)-0.478271484f, + (float16_t)0.427734375f, + (float16_t)0.770019531f, + (float16_t)-0.32421875f, + (float16_t)-0.247192383f, + (float16_t)-0.510253906f, + (float16_t)0.610351562f, + (float16_t)0.888671875f, + (float16_t)0.751464844f, + (float16_t)-0.437988281f, + (float16_t)0.367919922f, + (float16_t)-0.830566406f, + (float16_t)0.529785156f, + (float16_t)0.119140625f, + (float16_t)-0.571289062f, + (float16_t)0.465820312f, + (float16_t)0.750488281f, + (float16_t)0.831054688f, + (float16_t)-0.387939453f, + (float16_t)-0.982421875f, + (float16_t)-0.687011719f, + (float16_t)-0.30078125f, + (float16_t)0.01171875f, + (float16_t)0.708984375f, + (float16_t)-0.448974609f, + (float16_t)0.845214844f, + (float16_t)-0.00427627563f, + (float16_t)0.567871094f, + (float16_t)-0.795410156f, + (float16_t)0.043548584f, + (float16_t)-0.174316406f, + (float16_t)0.895507812f, + (float16_t)0.866699219f, + (float16_t)-0.140380859f, + (float16_t)-0.55078125f, + (float16_t)0.391357422f, + (float16_t)-0.559082031f, + (float16_t)-0.276611328f, + (float16_t)-0.418945312f, + (float16_t)0.413330078f, + (float16_t)-0.330078125f, + (float16_t)-0.978515625f, + (float16_t)-0.5546875f, + (float16_t)0.250732422f, + (float16_t)-0.725097656f, + (float16_t)0.701660156f, + (float16_t)-0.00695419312f, + (float16_t)0.0494384766f, + (float16_t)-0.796875f, + (float16_t)0.604492188f, + (float16_t)-0.404785156f, + (float16_t)0.435058594f, + (float16_t)0.977050781f, + (float16_t)-0.499755859f, + (float16_t)-0.969238281f, + (float16_t)-0.994628906f, + (float16_t)-0.465576172f, + (float16_t)-0.750976562f, + (float16_t)0.572753906f, + (float16_t)-0.462646484f, + (float16_t)0.200073242f, + (float16_t)0.598632812f, + (float16_t)0.413574219f, + (float16_t)0.252685547f, + (float16_t)-0.868164062f, + (float16_t)-0.657226562f, + (float16_t)0.479248047f, + (float16_t)0.96875f, + (float16_t)-0.467773438f, + (float16_t)-0.203613281f, + (float16_t)0.153076172f, + (float16_t)-0.369873047f, + (float16_t)-0.795410156f, + (float16_t)0.712890625f, + (float16_t)-0.229248047f, + (float16_t)0.369140625f, + (float16_t)-0.596679688f, + (float16_t)0.6796875f, + (float16_t)-0.216674805f, + (float16_t)-0.964355469f, + (float16_t)-0.388671875f, + (float16_t)-0.9375f, + (float16_t)-0.699707031f, + (float16_t)0.500488281f, + (float16_t)0.516601562f, + (float16_t)0.802734375f, + (float16_t)-0.0325622559f, + (float16_t)0.200439453f, + (float16_t)0.0662841797f, + (float16_t)-0.765136719f, + (float16_t)-0.0633544922f, + (float16_t)0.920410156f, + (float16_t)-0.00615310669f, + (float16_t)0.345947266f, + (float16_t)-0.450927734f, + (float16_t)-0.849609375f, + (float16_t)0.671875f, + (float16_t)-0.380371094f, + (float16_t)-0.916992188f, + (float16_t)0.983886719f, + (float16_t)-0.836914062f, + (float16_t)0.344970703f, + (float16_t)-0.506835938f, + (float16_t)-0.577148438f, + (float16_t)-0.0012922287f, + (float16_t)0.921875f, + (float16_t)0.971191406f, + (float16_t)0.0371398926f, + (float16_t)0.0756225586f, + (float16_t)0.980957031f, + (float16_t)0.3671875f, + (float16_t)0.0909423828f, + (float16_t)0.504882812f, + (float16_t)-0.280029297f, + (float16_t)-0.305419922f, + (float16_t)-0.577636719f, + (float16_t)-0.00146007538f, + (float16_t)-0.831542969f, + (float16_t)-0.250488281f, + (float16_t)-0.722167969f, + (float16_t)-0.212280273f, + (float16_t)0.0736694336f, + (float16_t)0.316162109f, + (float16_t)-0.838867188f, + (float16_t)0.303466797f, + (float16_t)0.303466797f, + (float16_t)-0.0101623535f, + (float16_t)0.856445312f, + (float16_t)-0.0969848633f, + (float16_t)-0.702148438f, + (float16_t)-0.915527344f, + (float16_t)0.270507812f, + (float16_t)-0.375976562f, + (float16_t)0.0900268555f, + (float16_t)0.786621094f, + (float16_t)-0.75390625f, + (float16_t)-0.206787109f, + (float16_t)0.0938110352f, + (float16_t)0.939453125f, + (float16_t)0.0436706543f, + (float16_t)0.803222656f, + (float16_t)0.0245666504f, + (float16_t)-0.426269531f, + (float16_t)-0.662597656f, + (float16_t)-0.748046875f, + (float16_t)-0.196899414f, + (float16_t)0.246826172f, + (float16_t)-0.681152344f, + (float16_t)-0.915527344f, + (float16_t)0.121398926f, + (float16_t)0.6328125f, + (float16_t)-0.460693359f, + (float16_t)0.0194702148f, + (float16_t)0.927246094f, + (float16_t)-0.884277344f, + (float16_t)0.493408203f, + (float16_t)0.401855469f, + (float16_t)0.0252990723f, + (float16_t)-0.981445312f, + (float16_t)-0.21105957f, + (float16_t)0.771484375f, + (float16_t)-0.525878906f, + (float16_t)-0.580566406f, + (float16_t)-0.0451049805f, + (float16_t)-0.0127868652f, + (float16_t)-0.657226562f, + (float16_t)-0.576660156f, + (float16_t)-0.918457031f, + (float16_t)-0.54296875f, + (float16_t)-0.360351562f, + (float16_t)0.51953125f, + (float16_t)-0.681640625f, + (float16_t)-0.384277344f, + (float16_t)0.200683594f, + (float16_t)-0.980957031f, + (float16_t)-0.743164062f, + (float16_t)0.478759766f, + (float16_t)0.551269531f, + (float16_t)0.557128906f, + (float16_t)-0.978515625f, + (float16_t)0.740234375f, + (float16_t)-0.0396118164f, + (float16_t)-0.742675781f, + (float16_t)-0.056427002f, + (float16_t)-0.911621094f, + (float16_t)-0.285888672f, + (float16_t)0.365478516f, + (float16_t)-0.973632812f, + (float16_t)0.207275391f, + (float16_t)-0.966308594f, + (float16_t)-0.830078125f, + (float16_t)0.016494751f, + (float16_t)0.127563477f, + (float16_t)0.491210938f, + (float16_t)-0.358642578f, + (float16_t)0.574707031f, + (float16_t)0.136352539f, + (float16_t)-0.163452148f, + (float16_t)-0.149291992f, + (float16_t)0.631835938f, + (float16_t)-0.194458008f, + (float16_t)0.990234375f, + (float16_t)0.6640625f, + (float16_t)0.610351562f, + 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(float16_t)0.166625977f, + (float16_t)0.449951172f, + (float16_t)0.247680664f, + (float16_t)-0.768554688f, + (float16_t)0.7734375f, + (float16_t)0.657226562f, + (float16_t)0.889160156f, + (float16_t)-0.0985107422f, + (float16_t)0.552734375f, + (float16_t)-0.582519531f, + (float16_t)0.0586547852f, + (float16_t)-0.18359375f, + (float16_t)-0.819335938f, + (float16_t)-0.192871094f, + (float16_t)0.166992188f, + (float16_t)0.462402344f, + (float16_t)0.676757812f, + (float16_t)-0.321777344f, + (float16_t)0.692382812f, + (float16_t)0.294921875f, + (float16_t)0.461669922f, + (float16_t)-0.536132812f, + (float16_t)0.864746094f, + (float16_t)0.962402344f, + (float16_t)0.251220703f, + (float16_t)-0.790039062f, + (float16_t)0.71484375f, + (float16_t)-0.91015625f, + (float16_t)0.0880126953f, + (float16_t)-0.437988281f, + (float16_t)0.146728516f, + (float16_t)-0.383300781f, + (float16_t)-0.4375f, + (float16_t)0.416015625f, + (float16_t)-0.0963745117f, + (float16_t)0.471679688f, + (float16_t)0.886230469f, + (float16_t)-0.208129883f, + (float16_t)-0.892089844f, + (float16_t)-0.256591797f, + (float16_t)-0.819335938f, + (float16_t)0.947265625f, + (float16_t)-0.449462891f, + (float16_t)-0.687988281f, + (float16_t)-0.8984375f, + (float16_t)0.409179688f, + (float16_t)-0.786621094f, + (float16_t)-0.0977783203f, + (float16_t)0.0397949219f, + (float16_t)0.105163574f, + (float16_t)-0.291748047f, + (float16_t)0.409667969f, + (float16_t)0.480957031f, + (float16_t)0.807617188f, + (float16_t)-0.854980469f, + (float16_t)-0.888671875f, + (float16_t)-0.939941406f, + (float16_t)0.290039062f, + (float16_t)0.384033203f, + (float16_t)0.993652344f, + (float16_t)-0.9296875f, + (float16_t)-0.486572266f, + (float16_t)-0.960449219f, + (float16_t)-0.706054688f, + (float16_t)0.727539062f, + (float16_t)0.61328125f, + (float16_t)-0.908203125f, + (float16_t)-0.095703125f, + (float16_t)-0.912597656f, + (float16_t)-0.174804688f, + (float16_t)-0.381835938f, + (float16_t)0.291259766f, + (float16_t)0.00106430054f, + (float16_t)0.484863281f, + (float16_t)-0.275146484f, + (float16_t)-0.138061523f, + (float16_t)-0.76171875f, + (float16_t)-0.676269531f, + (float16_t)-0.728515625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/output_ref_data.h new file mode 100644 index 00000000..a1e3e477 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_5_f16_output_ref_data[32] = { + (float16_t)6.59765625f, (float16_t)2.375f, (float16_t)-0.378417969f, (float16_t)7.1640625f, + (float16_t)0.460693359f, (float16_t)6.515625f, (float16_t)-4.52734375f, (float16_t)0.622070312f, + (float16_t)-8.4375f, (float16_t)-3.83203125f, (float16_t)-10.1484375f, (float16_t)-0.588378906f, + (float16_t)-3.96289062f, (float16_t)-7.73828125f, (float16_t)7.58203125f, (float16_t)-2.2578125f, + (float16_t)3.50585938f, (float16_t)-1.05078125f, (float16_t)-3.96289062f, (float16_t)2.29101562f, + (float16_t)2.66015625f, (float16_t)2.16015625f, (float16_t)3.41210938f, (float16_t)-10.46875f, + (float16_t)-5.98828125f, (float16_t)2.43164062f, (float16_t)3.796875f, (float16_t)3.08984375f, + (float16_t)-0.70703125f, (float16_t)8.0f, (float16_t)0.890625f, (float16_t)-7.05078125f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/weights_data.h new file mode 100644 index 00000000..8b9adf66 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f16/weights_data.h @@ -0,0 +1,290 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_conv_5_f16_weights_data[1152] = { + (float16_t)0.683105469f, (float16_t)-0.423583984f, (float16_t)-0.300292969f, (float16_t)0.699707031f, + (float16_t)-0.331298828f, (float16_t)0.425292969f, (float16_t)0.0638427734f, (float16_t)0.0610046387f, + (float16_t)-0.498046875f, (float16_t)0.216064453f, (float16_t)0.131591797f, (float16_t)-0.244750977f, + (float16_t)0.453369141f, (float16_t)-0.0734863281f, (float16_t)-0.735839844f, (float16_t)-0.397705078f, + (float16_t)-0.449462891f, (float16_t)-0.162719727f, (float16_t)-0.392822266f, (float16_t)-0.465576172f, + (float16_t)0.00176811218f, (float16_t)0.386474609f, (float16_t)-0.719238281f, (float16_t)-0.680664062f, + (float16_t)0.5234375f, (float16_t)-0.676757812f, (float16_t)0.518554688f, (float16_t)-0.592773438f, + (float16_t)0.631835938f, (float16_t)0.681640625f, (float16_t)0.734863281f, (float16_t)-0.502929688f, + (float16_t)0.735351562f, (float16_t)-0.558105469f, (float16_t)-0.537597656f, (float16_t)-0.192504883f, + (float16_t)-0.66796875f, (float16_t)-0.418701172f, (float16_t)-0.221679688f, (float16_t)0.173339844f, + (float16_t)0.288818359f, (float16_t)-0.318115234f, (float16_t)0.221313477f, (float16_t)-0.0426635742f, + (float16_t)0.243408203f, (float16_t)0.339111328f, (float16_t)0.236694336f, (float16_t)-0.119873047f, + (float16_t)0.673339844f, (float16_t)-0.217041016f, (float16_t)-0.474121094f, (float16_t)0.510742188f, + (float16_t)0.682128906f, (float16_t)0.547363281f, (float16_t)-0.641113281f, (float16_t)0.558105469f, + (float16_t)0.385498047f, (float16_t)-0.0222473145f, (float16_t)-0.147583008f, (float16_t)-0.0202484131f, + (float16_t)0.167236328f, (float16_t)0.197143555f, (float16_t)0.485351562f, (float16_t)-0.050567627f, + (float16_t)0.489257812f, (float16_t)0.229492188f, (float16_t)0.224243164f, (float16_t)0.427246094f, + (float16_t)-0.0800170898f, (float16_t)-0.648925781f, (float16_t)-0.307617188f, (float16_t)0.655273438f, + (float16_t)0.349853516f, (float16_t)0.44921875f, (float16_t)-0.433837891f, (float16_t)0.180908203f, + (float16_t)0.0939941406f, (float16_t)-0.5234375f, (float16_t)0.508300781f, (float16_t)0.715332031f, + (float16_t)-0.731933594f, (float16_t)0.112121582f, (float16_t)0.703613281f, (float16_t)0.545898438f, + (float16_t)0.712402344f, (float16_t)0.342041016f, (float16_t)-0.329833984f, (float16_t)0.130737305f, + (float16_t)-0.489013672f, (float16_t)0.295898438f, (float16_t)-0.174926758f, (float16_t)-0.264404297f, + (float16_t)-0.666015625f, (float16_t)0.19934082f, (float16_t)0.516601562f, (float16_t)0.0938110352f, + (float16_t)0.333740234f, (float16_t)0.282958984f, (float16_t)0.749511719f, (float16_t)-0.704101562f, + (float16_t)0.735839844f, (float16_t)-0.688476562f, (float16_t)-0.390136719f, (float16_t)0.174682617f, + (float16_t)0.105041504f, (float16_t)-0.366210938f, (float16_t)-0.698730469f, (float16_t)0.711425781f, + (float16_t)0.3515625f, (float16_t)0.612304688f, (float16_t)-0.356201172f, (float16_t)0.261230469f, + (float16_t)-0.453369141f, (float16_t)-0.00412368774f, (float16_t)-0.278808594f, (float16_t)-0.288818359f, + (float16_t)0.729003906f, (float16_t)-0.569335938f, (float16_t)-0.488037109f, (float16_t)0.428222656f, + (float16_t)0.438964844f, (float16_t)0.520996094f, (float16_t)0.284423828f, (float16_t)0.123474121f, + (float16_t)0.350585938f, (float16_t)0.0108108521f, (float16_t)-0.619628906f, (float16_t)-0.0492553711f, + (float16_t)0.307617188f, (float16_t)0.571777344f, (float16_t)-0.0618896484f, (float16_t)0.279052734f, + (float16_t)0.174072266f, (float16_t)-0.6015625f, (float16_t)-0.48046875f, (float16_t)0.0131759644f, + (float16_t)-0.363769531f, (float16_t)-0.66015625f, (float16_t)0.465576172f, (float16_t)0.690917969f, + (float16_t)0.38671875f, (float16_t)-0.736816406f, (float16_t)0.380126953f, (float16_t)-0.592285156f, + (float16_t)0.194824219f, (float16_t)-0.67578125f, (float16_t)0.719726562f, (float16_t)-0.499023438f, + (float16_t)-0.536132812f, (float16_t)0.149658203f, (float16_t)-0.671386719f, (float16_t)-0.174438477f, + (float16_t)-0.280761719f, (float16_t)-0.74609375f, (float16_t)0.625488281f, (float16_t)-0.271240234f, + (float16_t)0.579589844f, (float16_t)-0.508300781f, (float16_t)-0.0336914062f, (float16_t)-0.697265625f, + (float16_t)-0.41796875f, (float16_t)-0.210449219f, (float16_t)-0.745605469f, (float16_t)0.7265625f, + (float16_t)0.0881347656f, (float16_t)-0.380126953f, (float16_t)0.377929688f, (float16_t)0.473876953f, + (float16_t)-0.740722656f, (float16_t)0.254882812f, (float16_t)0.695800781f, (float16_t)0.144775391f, + (float16_t)-0.0843505859f, (float16_t)0.159057617f, (float16_t)0.592285156f, (float16_t)-0.284667969f, + (float16_t)-0.401123047f, (float16_t)-0.107666016f, (float16_t)-0.453125f, (float16_t)-0.626953125f, + (float16_t)0.602050781f, (float16_t)0.600585938f, (float16_t)0.627929688f, (float16_t)-0.202636719f, + (float16_t)0.134277344f, (float16_t)-0.277099609f, (float16_t)0.101379395f, (float16_t)0.330078125f, + (float16_t)-0.364013672f, (float16_t)-0.411376953f, (float16_t)-0.0786743164f, (float16_t)-0.181640625f, + (float16_t)-0.452148438f, (float16_t)0.0578308105f, (float16_t)-0.160888672f, (float16_t)0.445556641f, + (float16_t)-0.387207031f, (float16_t)-0.438964844f, (float16_t)-0.65625f, (float16_t)-0.394775391f, + (float16_t)0.522460938f, (float16_t)0.173095703f, (float16_t)0.688964844f, (float16_t)-0.441162109f, + (float16_t)-0.228149414f, (float16_t)-0.607421875f, (float16_t)-0.272949219f, (float16_t)-0.133911133f, + (float16_t)-0.483642578f, (float16_t)-0.306396484f, (float16_t)-0.666503906f, (float16_t)0.0491027832f, + (float16_t)-0.137573242f, (float16_t)-0.411132812f, (float16_t)0.327636719f, (float16_t)0.653320312f, + (float16_t)-0.402587891f, (float16_t)0.109863281f, (float16_t)0.473388672f, (float16_t)-0.651855469f, + (float16_t)-0.471923828f, (float16_t)0.363525391f, (float16_t)0.524902344f, (float16_t)-0.478515625f, + (float16_t)-0.654296875f, (float16_t)-0.156494141f, (float16_t)-0.300048828f, (float16_t)-0.391113281f, + (float16_t)-0.249633789f, (float16_t)0.579589844f, (float16_t)0.196777344f, (float16_t)-0.651855469f, + (float16_t)0.65234375f, (float16_t)-0.367919922f, (float16_t)-0.6484375f, (float16_t)0.533203125f, + (float16_t)-0.192260742f, (float16_t)-0.747070312f, (float16_t)-0.493652344f, (float16_t)0.490234375f, + (float16_t)0.247192383f, (float16_t)0.652832031f, (float16_t)-0.451416016f, (float16_t)0.725097656f, + (float16_t)0.0908813477f, (float16_t)0.136962891f, (float16_t)-0.4375f, (float16_t)0.275634766f, + (float16_t)-0.532226562f, 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(float16_t)-0.439453125f, + (float16_t)-0.17590332f, (float16_t)0.0409240723f, (float16_t)0.477539062f, (float16_t)0.241943359f, + (float16_t)0.661621094f, (float16_t)0.595214844f, (float16_t)-0.0832519531f, (float16_t)-0.368896484f, + (float16_t)0.411865234f, (float16_t)-0.359130859f, (float16_t)0.549804688f, (float16_t)0.387695312f, + (float16_t)0.381347656f, (float16_t)0.391601562f, (float16_t)0.582519531f, (float16_t)0.291992188f, + (float16_t)0.0654296875f, (float16_t)-0.246826172f, (float16_t)0.543457031f, (float16_t)-0.588867188f, + (float16_t)-0.0771484375f, (float16_t)0.156616211f, (float16_t)-0.170166016f, (float16_t)0.416503906f, + (float16_t)0.0862426758f, (float16_t)-0.254882812f, (float16_t)0.0142593384f, (float16_t)0.183227539f, + (float16_t)0.341796875f, (float16_t)-0.210449219f, (float16_t)-0.345947266f, (float16_t)-0.589355469f, + (float16_t)0.182983398f, (float16_t)0.2734375f, (float16_t)-0.677734375f, (float16_t)0.243164062f, + (float16_t)-0.212280273f, (float16_t)-0.329101562f, (float16_t)-0.482910156f, (float16_t)-0.639648438f, + (float16_t)-0.615722656f, (float16_t)0.174926758f, (float16_t)0.131103516f, (float16_t)0.411132812f, + (float16_t)-0.578125f, (float16_t)-0.547363281f, (float16_t)-0.462890625f, (float16_t)0.236694336f, + (float16_t)-0.494628906f, (float16_t)0.673828125f, (float16_t)-0.749511719f, (float16_t)0.666503906f, + (float16_t)-0.235595703f, (float16_t)-0.400390625f, (float16_t)0.521484375f, (float16_t)-0.666015625f, + (float16_t)-0.446044922f, (float16_t)-0.354003906f, (float16_t)0.438232422f, (float16_t)0.361328125f, + (float16_t)-0.645996094f, (float16_t)-0.206176758f, (float16_t)-0.0541381836f, (float16_t)0.478759766f, + (float16_t)-0.0291595459f, (float16_t)-0.705566406f, (float16_t)-0.486083984f, (float16_t)-0.375244141f, + (float16_t)0.676269531f, (float16_t)-0.595703125f, (float16_t)0.0515441895f, (float16_t)0.154663086f, + (float16_t)-0.0293121338f, (float16_t)0.322509766f, (float16_t)0.364257812f, (float16_t)0.2734375f, + (float16_t)-0.688964844f, (float16_t)0.528320312f, (float16_t)-0.645996094f, (float16_t)0.414794922f, + (float16_t)0.448242188f, (float16_t)0.444091797f, (float16_t)-0.611328125f, (float16_t)0.675292969f, + (float16_t)-0.0190887451f, (float16_t)0.484130859f, (float16_t)-0.275878906f, (float16_t)0.372070312f, + (float16_t)-0.68359375f, (float16_t)0.0786132812f, (float16_t)-0.19909668f, (float16_t)0.274902344f, + (float16_t)0.477539062f, (float16_t)0.458740234f, (float16_t)-0.533203125f, (float16_t)0.486083984f, + (float16_t)-0.162597656f, (float16_t)0.0723876953f, (float16_t)0.374511719f, (float16_t)0.584472656f, + (float16_t)-0.182739258f, (float16_t)-0.174438477f, (float16_t)0.332763672f, (float16_t)0.496826172f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/biases_data.h new file mode 100644 index 00000000..83519a5c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_5_f32_biases_data[1] = {-0.28032434f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/config_data.h new file mode 100644 index 00000000..1a94ea78 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_CONV_5_F32_INPUT_BATCHES 1 +#define CONV_MATCH_CONV_5_F32_INPUT_H 1 +#define CONV_MATCH_CONV_5_F32_INPUT_W 128 +#define CONV_MATCH_CONV_5_F32_IN_CH 128 +#define CONV_MATCH_CONV_5_F32_FILTER_H 3 +#define CONV_MATCH_CONV_5_F32_FILTER_W 3 +#define CONV_MATCH_CONV_5_F32_OUT_CH 1 +#define CONV_MATCH_CONV_5_F32_STRIDE_H 4 +#define CONV_MATCH_CONV_5_F32_STRIDE_W 4 +#define CONV_MATCH_CONV_5_F32_PADDING_H 1 +#define CONV_MATCH_CONV_5_F32_PADDING_W 0 +#define CONV_MATCH_CONV_5_F32_DILATION_H 1 +#define CONV_MATCH_CONV_5_F32_DILATION_W 1 +#define CONV_MATCH_CONV_5_F32_OUTPUT_H 1 +#define CONV_MATCH_CONV_5_F32_OUTPUT_W 32 +#define CONV_MATCH_CONV_5_F32_OUTPUT_C 1 +#define CONV_MATCH_CONV_5_F32_DST_SIZE 32 +#define CONV_MATCH_CONV_5_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_CONV_5_F32_USE_WRAPPER 1 +#define CONV_MATCH_CONV_5_F32_OUT_ACTIVATION_MIN -88.0f +#define CONV_MATCH_CONV_5_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/input_data.h new file mode 100644 index 00000000..aba4327c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/input_data.h @@ -0,0 +1,2733 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_5_f32_input_data[16384] = 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-0.78655082f, -0.0977710038f, 0.0397808403f, 0.105189532f, -0.291746795f, + 0.40968129f, 0.48089698f, 0.807515323f, -0.854865849f, -0.888632417f, -0.939866841f, + 0.290004969f, 0.384151369f, 0.993527472f, -0.929764092f, -0.486668169f, -0.960271895f, + -0.705991089f, 0.727538824f, 0.613196015f, -0.908234477f, -0.0957158133f, -0.912581503f, + -0.174862683f, -0.381755561f, 0.291326374f, 0.0010639322f, 0.484920323f, -0.275146425f, + -0.138061091f, -0.761819541f, -0.676323712f, -0.728744268f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/output_ref_data.h new file mode 100644 index 00000000..4d587a6c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_5_f32_output_ref_data[32] = { + 6.59687424f, 2.37495852f, -0.378435224f, 7.16289997f, 0.460592866f, 6.51630354f, -4.52759218f, 0.621902704f, + -8.43496323f, -3.83201647f, -10.146369f, -0.588270307f, -3.96303558f, -7.73964405f, 7.5832901f, -2.2585144f, + 3.50662327f, -1.05114758f, -3.96359205f, 2.29177952f, 2.65998316f, 2.1596508f, 3.41267443f, -10.4694328f, + -5.98876381f, 2.43104267f, 3.79732609f, 3.0892415f, -0.706797004f, 8.00033474f, 0.890414953f, -7.04979372f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/weights_data.h new file mode 100644 index 00000000..5ea7e3f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_conv_5_f32/weights_data.h @@ -0,0 +1,167 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_conv_5_f32_weights_data[1152] = { + 0.683133245f, -0.423581898f, -0.300348282f, 0.699935138f, -0.331399292f, 0.425281227f, 0.063854754f, + 0.0609982572f, -0.49812597f, 0.216096222f, 0.131600514f, -0.244808018f, 0.453332335f, -0.0734873936f, + -0.736073434f, -0.397738516f, -0.449344456f, -0.162673101f, -0.39281261f, -0.465499252f, 0.00176806818f, + 0.386407286f, -0.719389975f, -0.680686533f, 0.523412108f, -0.676534593f, 0.518681288f, -0.592755437f, + 0.632031798f, 0.681785643f, 0.735006571f, -0.502813876f, 0.735531628f, -0.558195174f, -0.537831783f, + -0.192464903f, -0.667793632f, -0.418750376f, -0.221624747f, 0.173337728f, 0.288818836f, -0.318130612f, + 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0.676405311f, -0.595484436f, 0.0515525937f, 0.15467371f, + -0.0293045137f, 0.322435647f, 0.364240915f, 0.273372084f, -0.689161122f, 0.528521597f, -0.645935237f, + 0.41482392f, 0.448147029f, 0.444052905f, -0.611468375f, 0.675420463f, -0.019092042f, 0.484214991f, + -0.2758362f, 0.37204501f, -0.683644354f, 0.0786002204f, -0.199088469f, 0.274821222f, 0.477461874f, + 0.458848298f, -0.533043444f, 0.486024976f, -0.162589535f, 0.0723659098f, 0.374626011f, 0.58458811f, + -0.182728127f, -0.174476698f, 0.332781047f, 0.496892869f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/biases_data.h new file mode 100644 index 00000000..0724e6d1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/biases_data.h @@ -0,0 +1,4 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_dilation_golden_f16_biases_data[3] = {(float16_t)-0.3046875f, + (float16_t)0.285888672f, + (float16_t)0.0186767578f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/config_data.h new file mode 100644 index 00000000..15386242 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_DILATION_GOLDEN_F16_INPUT_BATCHES 2 +#define CONV_MATCH_DILATION_GOLDEN_F16_INPUT_H 4 +#define CONV_MATCH_DILATION_GOLDEN_F16_INPUT_W 6 +#define CONV_MATCH_DILATION_GOLDEN_F16_IN_CH 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_FILTER_H 2 +#define CONV_MATCH_DILATION_GOLDEN_F16_FILTER_W 2 +#define CONV_MATCH_DILATION_GOLDEN_F16_OUT_CH 3 +#define CONV_MATCH_DILATION_GOLDEN_F16_STRIDE_H 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_STRIDE_W 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_PADDING_H 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_PADDING_W 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_DILATION_H 2 +#define CONV_MATCH_DILATION_GOLDEN_F16_DILATION_W 3 +#define CONV_MATCH_DILATION_GOLDEN_F16_OUTPUT_H 4 +#define CONV_MATCH_DILATION_GOLDEN_F16_OUTPUT_W 5 +#define CONV_MATCH_DILATION_GOLDEN_F16_OUTPUT_C 3 +#define CONV_MATCH_DILATION_GOLDEN_F16_DST_SIZE 120 +#define CONV_MATCH_DILATION_GOLDEN_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_DILATION_GOLDEN_F16_USE_WRAPPER 1 +#define CONV_MATCH_DILATION_GOLDEN_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define CONV_MATCH_DILATION_GOLDEN_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/input_data.h new file mode 100644 index 00000000..901ff093 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/input_data.h @@ -0,0 +1,14 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_dilation_golden_f16_input_data[48] = { + (float16_t)-0.0835571289f, (float16_t)0.560546875f, (float16_t)0.0511779785f, (float16_t)0.366699219f, + (float16_t)-0.666015625f, (float16_t)-0.0409240723f, (float16_t)-0.5078125f, (float16_t)-0.674804688f, + (float16_t)0.759277344f, (float16_t)0.533203125f, (float16_t)-0.661132812f, (float16_t)0.993652344f, + (float16_t)0.637207031f, (float16_t)0.666503906f, (float16_t)0.691894531f, (float16_t)0.18347168f, + (float16_t)-0.502441406f, (float16_t)0.482177734f, (float16_t)0.492675781f, (float16_t)-0.67578125f, + (float16_t)-0.133178711f, (float16_t)-0.00125980377f, (float16_t)-0.973144531f, (float16_t)0.751953125f, + (float16_t)0.19128418f, (float16_t)-0.205078125f, (float16_t)0.84765625f, (float16_t)-0.600585938f, + (float16_t)-0.62109375f, (float16_t)0.631835938f, (float16_t)-0.490966797f, (float16_t)0.112426758f, + (float16_t)0.48046875f, (float16_t)-0.338378906f, (float16_t)0.966308594f, (float16_t)-0.214477539f, + (float16_t)-0.421630859f, (float16_t)0.423095703f, (float16_t)-0.0955200195f, (float16_t)-0.251464844f, + (float16_t)0.680664062f, (float16_t)-0.257324219f, (float16_t)0.98828125f, (float16_t)-0.63671875f, + (float16_t)0.68359375f, (float16_t)-0.576171875f, (float16_t)-0.590820312f, (float16_t)0.591308594f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/output_ref_data.h new file mode 100644 index 00000000..c08a06b7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_dilation_golden_f16_output_ref_data[120] = { + (float16_t)-0.0352478027f, (float16_t)0.122070312f, (float16_t)0.451171875f, (float16_t)0.191894531f, + (float16_t)-0.111877441f, (float16_t)0.477050781f, (float16_t)-0.130737305f, (float16_t)0.0526123047f, + (float16_t)-0.15246582f, (float16_t)-0.411621094f, (float16_t)0.494140625f, (float16_t)0.353759766f, + (float16_t)-0.627441406f, (float16_t)0.582519531f, (float16_t)-0.143676758f, (float16_t)-0.0870361328f, + (float16_t)0.104064941f, (float16_t)0.37890625f, (float16_t)-0.856445312f, (float16_t)0.306152344f, + (float16_t)-0.304443359f, (float16_t)-0.309570312f, (float16_t)1.6015625f, (float16_t)-0.0864257812f, + (float16_t)-0.510253906f, (float16_t)0.630371094f, (float16_t)0.104675293f, (float16_t)-0.275390625f, + (float16_t)0.657714844f, (float16_t)-0.073425293f, (float16_t)-0.764648438f, (float16_t)-0.168701172f, + (float16_t)-0.557617188f, (float16_t)-1.08789062f, (float16_t)-0.152587891f, (float16_t)-0.43359375f, + (float16_t)-0.140258789f, (float16_t)0.0439147949f, (float16_t)0.172607422f, (float16_t)-0.206665039f, + (float16_t)-0.0245513916f, (float16_t)-0.242797852f, (float16_t)-0.0996704102f, (float16_t)0.677246094f, + (float16_t)-0.0336303711f, (float16_t)-0.680664062f, (float16_t)-0.154418945f, (float16_t)-0.4375f, + (float16_t)-0.16027832f, (float16_t)0.637695312f, (float16_t)-0.165283203f, (float16_t)0.223510742f, + (float16_t)1.09570312f, (float16_t)0.283935547f, (float16_t)-0.301757812f, (float16_t)0.487792969f, + (float16_t)-0.367675781f, (float16_t)-0.23449707f, (float16_t)0.420654297f, (float16_t)0.000553131104f, + (float16_t)-0.134155273f, (float16_t)0.182128906f, (float16_t)0.292480469f, (float16_t)-0.127563477f, + (float16_t)0.0854492188f, (float16_t)-0.0246429443f, (float16_t)-0.0298156738f, (float16_t)0.140014648f, + (float16_t)0.53515625f, (float16_t)-0.671875f, (float16_t)0.599609375f, (float16_t)-0.249755859f, + (float16_t)-0.0997924805f, (float16_t)0.0973510742f, (float16_t)0.121765137f, (float16_t)-0.799316406f, + (float16_t)-0.233032227f, (float16_t)-0.594726562f, (float16_t)0.261230469f, (float16_t)0.628417969f, + (float16_t)0.381103516f, (float16_t)-0.0601806641f, (float16_t)0.618652344f, (float16_t)0.707519531f, + (float16_t)-0.356933594f, (float16_t)0.509277344f, (float16_t)-0.599121094f, (float16_t)-0.382568359f, + (float16_t)-0.295898438f, (float16_t)0.154541016f, (float16_t)-0.323242188f, (float16_t)-0.167358398f, + (float16_t)0.0913085938f, (float16_t)-1.11132812f, (float16_t)0.814453125f, (float16_t)-0.338623047f, + (float16_t)-0.611328125f, (float16_t)-0.473388672f, (float16_t)-0.772460938f, (float16_t)-0.208007812f, + (float16_t)1.02832031f, (float16_t)0.241455078f, (float16_t)-0.0856933594f, (float16_t)-0.283691406f, + (float16_t)0.227539062f, (float16_t)-0.252685547f, (float16_t)0.346435547f, (float16_t)0.0816650391f, + (float16_t)-0.329589844f, (float16_t)0.135620117f, (float16_t)0.226074219f, (float16_t)-0.512695312f, + (float16_t)0.163818359f, (float16_t)-0.471923828f, (float16_t)-0.201416016f, (float16_t)0.379394531f, + (float16_t)0.197753906f, (float16_t)-0.400878906f, (float16_t)0.100891113f, (float16_t)0.0435180664f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/weights_data.h new file mode 100644 index 00000000..8c7fb29b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f16/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_dilation_golden_f16_weights_data[12] = {(float16_t)0.383056641f, + (float16_t)-0.543457031f, + (float16_t)-0.60546875f, + (float16_t)0.354980469f, + (float16_t)0.735351562f, + (float16_t)-0.636230469f, + (float16_t)0.556640625f, + (float16_t)-0.215576172f, + (float16_t)-0.098815918f, + (float16_t)-0.659179688f, + (float16_t)-0.304443359f, + (float16_t)0.569824219f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/biases_data.h new file mode 100644 index 00000000..7810b27a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_dilation_golden_f32_biases_data[3] = {-0.30470258f, 0.285766959f, 0.0186798833f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/config_data.h new file mode 100644 index 00000000..d5d9583c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_DILATION_GOLDEN_F32_INPUT_BATCHES 2 +#define CONV_MATCH_DILATION_GOLDEN_F32_INPUT_H 4 +#define CONV_MATCH_DILATION_GOLDEN_F32_INPUT_W 6 +#define CONV_MATCH_DILATION_GOLDEN_F32_IN_CH 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_FILTER_H 2 +#define CONV_MATCH_DILATION_GOLDEN_F32_FILTER_W 2 +#define CONV_MATCH_DILATION_GOLDEN_F32_OUT_CH 3 +#define CONV_MATCH_DILATION_GOLDEN_F32_STRIDE_H 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_STRIDE_W 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_PADDING_H 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_PADDING_W 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_DILATION_H 2 +#define CONV_MATCH_DILATION_GOLDEN_F32_DILATION_W 3 +#define CONV_MATCH_DILATION_GOLDEN_F32_OUTPUT_H 4 +#define CONV_MATCH_DILATION_GOLDEN_F32_OUTPUT_W 5 +#define CONV_MATCH_DILATION_GOLDEN_F32_OUTPUT_C 3 +#define CONV_MATCH_DILATION_GOLDEN_F32_DST_SIZE 120 +#define CONV_MATCH_DILATION_GOLDEN_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_DILATION_GOLDEN_F32_USE_WRAPPER 1 +#define CONV_MATCH_DILATION_GOLDEN_F32_OUT_ACTIVATION_MIN -128.0f +#define CONV_MATCH_DILATION_GOLDEN_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/input_data.h new file mode 100644 index 00000000..cfe51ba5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/input_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_dilation_golden_f32_input_data[48] = { + -0.0835627019f, 0.560463667f, 0.0511884131f, 0.366662502f, -0.666258156f, -0.0409321338f, -0.507796466f, + -0.67501229f, 0.759212136f, 0.53328073f, -0.661280572f, 0.993828356f, 0.637198389f, 0.666390359f, + 0.691724956f, 0.183420241f, -0.502214551f, 0.482123315f, 0.492629409f, -0.675592244f, -0.133222982f, + -0.00125965755f, -0.973261297f, 0.751835048f, 0.191265121f, -0.205127999f, 0.847616673f, -0.600703061f, + -0.621105969f, 0.631769776f, -0.491048157f, 0.112430602f, 0.480444044f, -0.338494927f, 0.966305614f, + -0.214438662f, -0.421697199f, 0.422976494f, -0.0955057889f, -0.251348197f, 0.680491209f, -0.257312268f, + 0.988480628f, -0.63659066f, 0.683592856f, -0.57594198f, -0.590773523f, 0.591548741f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/output_ref_data.h new file mode 100644 index 00000000..f08e6fef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_dilation_golden_f32_output_ref_data[120] = { + -0.0352584608f, 0.122086957f, 0.451194346f, 0.191953078f, -0.111849338f, 0.477095664f, -0.130772233f, + 0.0526142009f, -0.152519017f, -0.411582202f, 0.49409169f, 0.353690594f, -0.627523959f, 0.582597077f, + -0.143691331f, -0.0870355293f, 0.104060709f, 0.378995985f, -0.856661618f, 0.306093574f, -0.304344386f, + -0.30946669f, 1.60118079f, -0.0864092261f, -0.510473609f, 0.630544782f, 0.104656667f, -0.275279969f, + 0.657539129f, -0.0734030157f, -0.764689147f, -0.168668404f, -0.557811141f, -1.08777249f, -0.152616069f, + -0.433472931f, -0.140247002f, 0.0439180583f, 0.172657654f, -0.206644103f, -0.0245446339f, -0.242767453f, + -0.0996581987f, 0.677291036f, -0.0336386412f, -0.680722535f, -0.154441848f, -0.43741706f, -0.160320163f, + 0.63769573f, -0.165232241f, 0.223571628f, 1.0954994f, 0.283964157f, -0.301807106f, 0.487707287f, + -0.367574275f, -0.234440476f, 0.420671612f, 0.000553174352f, -0.134193152f, 0.18218711f, 0.292383373f, + -0.127578393f, 0.0854207501f, -0.0246444046f, -0.0298209228f, 0.140019417f, 0.53494072f, -0.671643555f, + 0.599418819f, -0.24976708f, -0.0997947305f, 0.097356841f, 0.121743485f, -0.799359798f, -0.233059943f, + -0.594614625f, 0.261176229f, 0.628191829f, 0.381065011f, -0.0601912253f, 0.618888855f, 0.707367241f, + -0.356943309f, 0.509445727f, -0.599160194f, -0.382658035f, -0.295950294f, 0.154574543f, -0.323263913f, + -0.167361289f, 0.0913285092f, -1.11157978f, 0.814387321f, -0.338677377f, -0.611220717f, -0.473458469f, + -0.772306442f, -0.207964778f, 1.02856076f, 0.241453007f, -0.0857221633f, -0.283770174f, 0.227492556f, + -0.252785861f, 0.346546173f, 0.0816527456f, -0.329608291f, 0.135567024f, 0.226083949f, -0.512587845f, + 0.163804263f, -0.471811086f, -0.201413319f, 0.379274637f, 0.197780207f, -0.400985599f, 0.100901656f, + 0.0435196497f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/weights_data.h new file mode 100644 index 00000000..56dea002 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_dilation_golden_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_dilation_golden_f32_weights_data[12] = {0.383066237f, + -0.54359746f, + -0.605349839f, + 0.354899645f, + 0.735494852f, + -0.636392832f, + 0.556611359f, + -0.215591922f, + -0.0988261104f, + -0.65936172f, + -0.304476053f, + 0.569688559f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/biases_data.h new file mode 100644 index 00000000..38781f53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_out_activation_f16_biases_data[2] = {(float16_t)2.39257812f, (float16_t)-17.46875f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/config_data.h new file mode 100644 index 00000000..23539032 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_OUT_ACTIVATION_F16_INPUT_BATCHES 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_INPUT_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_INPUT_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_IN_CH 4 +#define CONV_MATCH_OUT_ACTIVATION_F16_FILTER_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_FILTER_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_OUT_CH 2 +#define CONV_MATCH_OUT_ACTIVATION_F16_STRIDE_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_STRIDE_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_PADDING_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_PADDING_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_DILATION_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_DILATION_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_OUTPUT_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_OUTPUT_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F16_OUTPUT_C 2 +#define CONV_MATCH_OUT_ACTIVATION_F16_DST_SIZE 18 +#define CONV_MATCH_OUT_ACTIVATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_OUT_ACTIVATION_F16_USE_WRAPPER 1 +#define CONV_MATCH_OUT_ACTIVATION_F16_OUT_ACTIVATION_MIN (float16_t) - 61.0f +#define CONV_MATCH_OUT_ACTIVATION_F16_OUT_ACTIVATION_MAX (float16_t)107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/input_data.h new file mode 100644 index 00000000..c203e19f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_out_activation_f16_input_data[36] = { + (float16_t)-2.34375f, (float16_t)-7.3203125f, (float16_t)-6.515625f, (float16_t)5.78515625f, + (float16_t)-5.15234375f, (float16_t)-3.13867188f, (float16_t)-4.8359375f, (float16_t)-3.5859375f, + (float16_t)-0.104492188f, (float16_t)-1.35546875f, (float16_t)-1.88671875f, (float16_t)-0.715332031f, + (float16_t)5.72265625f, (float16_t)6.04296875f, (float16_t)0.422851562f, (float16_t)6.86328125f, + (float16_t)-0.351806641f, (float16_t)0.859375f, (float16_t)-7.1875f, (float16_t)0.170043945f, + (float16_t)7.375f, (float16_t)3.81054688f, (float16_t)-1.62011719f, (float16_t)6.38671875f, + (float16_t)-3.16210938f, (float16_t)4.0625f, (float16_t)0.0533447266f, (float16_t)-1.62695312f, + (float16_t)-5.0f, (float16_t)5.5078125f, (float16_t)2.28710938f, (float16_t)-2.53320312f, + (float16_t)-3.25195312f, (float16_t)-3.24804688f, (float16_t)-6.89453125f, (float16_t)-7.91015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/output_ref_data.h new file mode 100644 index 00000000..4ab0e94b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_out_activation_f16_output_ref_data[18] = {(float16_t)-14.2734375f, + (float16_t)-25.265625f, + (float16_t)33.75f, + (float16_t)-43.46875f, + (float16_t)-45.5f, + (float16_t)-40.78125f, + (float16_t)-13.9921875f, + (float16_t)-61.0f, + (float16_t)44.125f, + (float16_t)-61.0f, + (float16_t)-40.46875f, + (float16_t)-61.0f, + (float16_t)-30.65625f, + (float16_t)-9.4453125f, + (float16_t)-35.84375f, + (float16_t)21.578125f, + (float16_t)20.28125f, + (float16_t)-40.90625f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/weights_data.h new file mode 100644 index 00000000..f2aba1bd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f16/weights_data.h @@ -0,0 +1,20 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_out_activation_f16_weights_data[72] = { + (float16_t)0.67578125f, (float16_t)3.18945312f, (float16_t)-1.87109375f, (float16_t)-1.19726562f, + (float16_t)-0.987792969f, (float16_t)-3.45898438f, (float16_t)2.828125f, (float16_t)0.190795898f, + (float16_t)-2.22070312f, (float16_t)-3.04882812f, (float16_t)0.91796875f, (float16_t)-2.30664062f, + (float16_t)-0.619140625f, (float16_t)-0.881835938f, (float16_t)2.20507812f, (float16_t)1.59082031f, + (float16_t)-3.00585938f, (float16_t)1.05957031f, (float16_t)0.578125f, (float16_t)-3.08398438f, + (float16_t)0.874023438f, (float16_t)-3.51367188f, (float16_t)2.44921875f, (float16_t)0.355224609f, + (float16_t)1.15625f, (float16_t)0.507324219f, (float16_t)3.45507812f, (float16_t)3.85351562f, + (float16_t)0.582519531f, (float16_t)1.20996094f, (float16_t)0.449462891f, (float16_t)-3.3671875f, + (float16_t)-0.709472656f, (float16_t)-1.90527344f, (float16_t)-3.67773438f, (float16_t)-3.08398438f, + (float16_t)2.05273438f, (float16_t)-2.03515625f, (float16_t)-2.4765625f, (float16_t)0.0912475586f, + (float16_t)-1.83789062f, (float16_t)1.59667969f, (float16_t)1.04492188f, (float16_t)-1.81542969f, + (float16_t)-0.0270843506f, (float16_t)-2.84179688f, (float16_t)1.875f, (float16_t)1.50292969f, + (float16_t)-0.921386719f, (float16_t)-2.08984375f, (float16_t)-0.639648438f, (float16_t)-2.87304688f, + (float16_t)-3.33203125f, (float16_t)2.12695312f, (float16_t)2.33007812f, (float16_t)0.607910156f, + (float16_t)-2.4765625f, (float16_t)-1.73535156f, (float16_t)3.04882812f, (float16_t)-3.33398438f, + (float16_t)1.33886719f, (float16_t)-2.96289062f, (float16_t)2.56445312f, (float16_t)3.41992188f, + (float16_t)-3.55859375f, (float16_t)-3.9140625f, (float16_t)1.16308594f, (float16_t)3.64257812f, + (float16_t)-2.83398438f, (float16_t)-0.704589844f, (float16_t)-2.03515625f, (float16_t)-1.79980469f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/biases_data.h new file mode 100644 index 00000000..3ba8e7a9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_out_activation_f32_biases_data[2] = {2.39245725f, -17.4694023f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/config_data.h new file mode 100644 index 00000000..11bccdc0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_OUT_ACTIVATION_F32_INPUT_BATCHES 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_INPUT_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_INPUT_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_IN_CH 4 +#define CONV_MATCH_OUT_ACTIVATION_F32_FILTER_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_FILTER_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_OUT_CH 2 +#define CONV_MATCH_OUT_ACTIVATION_F32_STRIDE_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_STRIDE_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_PADDING_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_PADDING_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_DILATION_H 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_DILATION_W 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_OUTPUT_H 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_OUTPUT_W 3 +#define CONV_MATCH_OUT_ACTIVATION_F32_OUTPUT_C 2 +#define CONV_MATCH_OUT_ACTIVATION_F32_DST_SIZE 18 +#define CONV_MATCH_OUT_ACTIVATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_OUT_ACTIVATION_F32_USE_WRAPPER 1 +#define CONV_MATCH_OUT_ACTIVATION_F32_OUT_ACTIVATION_MIN -61.0f +#define CONV_MATCH_OUT_ACTIVATION_F32_OUT_ACTIVATION_MAX 107.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/input_data.h new file mode 100644 index 00000000..28b27e8e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/input_data.h @@ -0,0 +1,7 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_out_activation_f32_input_data[36] = { + -2.34456301f, -7.32184601f, -6.51738739f, 5.7859869f, -5.15253925f, -3.13855815f, -4.83480549f, -3.5858202f, + -0.104477696f, -1.35538352f, -1.88697934f, -0.715455532f, 5.72070837f, 6.04175758f, 0.422963381f, 6.86442947f, + -0.351904422f, 0.859409451f, -7.18771267f, 0.170023248f, 7.37483215f, 3.81025743f, -1.62019634f, 6.38769102f, + -3.1616652f, 4.06312418f, 0.0533585437f, -1.62724054f, -5.0011797f, 5.50949192f, 2.28732729f, -2.53402686f, + -3.25146031f, -3.24879456f, -6.89327574f, -7.90915108f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/output_ref_data.h new file mode 100644 index 00000000..fff4fb11 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_out_activation_f32_output_ref_data[18] = {-14.2760611f, + -25.2652416f, + 33.749836f, + -43.4732475f, + -45.502079f, + -40.7685318f, + -13.9932947f, + -61.0f, + 44.1100349f, + -61.0f, + -40.458252f, + -61.0f, + -30.6556034f, + -9.44801521f, + -35.8340721f, + 21.5796776f, + 20.2873688f, + -40.9218292f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/weights_data.h new file mode 100644 index 00000000..ac2dc2b3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_out_activation_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_out_activation_f32_weights_data[72] = { + 0.675576806f, 3.18996549f, -1.87097096f, -1.19678462f, -0.987966061f, -3.45952773f, 2.82745767f, + 0.19078204f, -2.2209928f, -3.04881477f, 0.91811651f, -2.30739141f, -0.619115531f, -0.881998718f, + 2.20493007f, 1.59122801f, -3.00522518f, 1.05970907f, 0.578230977f, -3.08326173f, 0.87424159f, + -3.51314855f, 2.44957447f, 0.355323672f, 1.15584779f, 0.507375121f, 3.45509863f, 3.85423517f, + 0.582753599f, 1.21029234f, 0.449484169f, -3.36746335f, -0.709526002f, -1.9056927f, -3.67855382f, + -3.08333397f, 2.05333066f, -2.03419018f, -2.47615767f, 0.0912433565f, -1.83781111f, 1.59624028f, + 1.04501379f, -1.81562757f, -0.0270776898f, -2.8426733f, 1.87546349f, 1.50323272f, -0.921551168f, + -2.08975267f, -0.639731646f, -2.87261868f, -3.33292651f, 2.12693858f, 2.33045816f, 0.607726932f, + -2.47723699f, -1.73501372f, 3.04965878f, -3.33447099f, 1.33902991f, -2.96305323f, 2.5640161f, + 3.4203856f, -3.55797601f, -3.91442084f, 1.16263521f, 3.64304829f, -2.8330698f, -0.704610944f, + -2.03583264f, -1.80015492f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/biases_data.h new file mode 100644 index 00000000..4bda48e9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_stride2pad1_f16_biases_data[1] = {(float16_t)-0.495361328f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/config_data.h new file mode 100644 index 00000000..66943e40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_STRIDE2PAD1_F16_INPUT_BATCHES 1 +#define CONV_MATCH_STRIDE2PAD1_F16_INPUT_H 7 +#define CONV_MATCH_STRIDE2PAD1_F16_INPUT_W 7 +#define CONV_MATCH_STRIDE2PAD1_F16_IN_CH 1 +#define CONV_MATCH_STRIDE2PAD1_F16_FILTER_H 3 +#define CONV_MATCH_STRIDE2PAD1_F16_FILTER_W 3 +#define CONV_MATCH_STRIDE2PAD1_F16_OUT_CH 1 +#define CONV_MATCH_STRIDE2PAD1_F16_STRIDE_H 2 +#define CONV_MATCH_STRIDE2PAD1_F16_STRIDE_W 2 +#define CONV_MATCH_STRIDE2PAD1_F16_PADDING_H 1 +#define CONV_MATCH_STRIDE2PAD1_F16_PADDING_W 1 +#define CONV_MATCH_STRIDE2PAD1_F16_DILATION_H 1 +#define CONV_MATCH_STRIDE2PAD1_F16_DILATION_W 1 +#define CONV_MATCH_STRIDE2PAD1_F16_OUTPUT_H 4 +#define CONV_MATCH_STRIDE2PAD1_F16_OUTPUT_W 4 +#define CONV_MATCH_STRIDE2PAD1_F16_OUTPUT_C 1 +#define CONV_MATCH_STRIDE2PAD1_F16_DST_SIZE 16 +#define CONV_MATCH_STRIDE2PAD1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_STRIDE2PAD1_F16_USE_WRAPPER 1 +#define CONV_MATCH_STRIDE2PAD1_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define CONV_MATCH_STRIDE2PAD1_F16_OUT_ACTIVATION_MAX (float16_t)127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/input_data.h new file mode 100644 index 00000000..d445013e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/input_data.h @@ -0,0 +1,15 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_stride2pad1_f16_input_data[49] = { + (float16_t)-0.708984375f, (float16_t)-0.942871094f, (float16_t)0.685058594f, (float16_t)0.419189453f, + (float16_t)-0.311767578f, (float16_t)0.791503906f, (float16_t)-0.693359375f, (float16_t)0.481933594f, + (float16_t)-0.120910645f, (float16_t)-0.205932617f, (float16_t)0.303710938f, (float16_t)0.478515625f, + (float16_t)0.659667969f, (float16_t)0.0454101562f, (float16_t)0.106079102f, (float16_t)-0.81640625f, + (float16_t)0.758789062f, (float16_t)0.0231323242f, (float16_t)-0.723632812f, (float16_t)-0.986328125f, + (float16_t)0.532226562f, (float16_t)-0.580566406f, (float16_t)0.775390625f, (float16_t)0.915039062f, + (float16_t)-0.350830078f, (float16_t)-0.222412109f, (float16_t)0.7578125f, (float16_t)-0.896484375f, + (float16_t)-0.454345703f, (float16_t)-0.899414062f, (float16_t)0.568359375f, (float16_t)0.611816406f, + (float16_t)-0.338867188f, (float16_t)-0.0114517212f, (float16_t)0.386962891f, (float16_t)-0.103759766f, + (float16_t)-0.97265625f, (float16_t)-0.422607422f, (float16_t)-0.159423828f, (float16_t)0.276855469f, + (float16_t)-0.250244141f, (float16_t)-0.614746094f, (float16_t)-0.756347656f, (float16_t)-0.350097656f, + (float16_t)0.309814453f, (float16_t)-0.830566406f, (float16_t)-0.0858154297f, (float16_t)-0.604980469f, + (float16_t)-0.0198364258f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/output_ref_data.h new file mode 100644 index 00000000..6e070fed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_stride2pad1_f16_output_ref_data[16] = {(float16_t)-0.588378906f, + (float16_t)-0.364990234f, + (float16_t)-0.927734375f, + (float16_t)-1.22753906f, + (float16_t)-1.51464844f, + (float16_t)0.182373047f, + (float16_t)-1.07324219f, + (float16_t)-1.13574219f, + (float16_t)0.444824219f, + (float16_t)-0.281982422f, + (float16_t)0.182617188f, + (float16_t)0.262207031f, + (float16_t)-1.29199219f, + (float16_t)-0.384277344f, + (float16_t)-0.923828125f, + (float16_t)-0.203491211f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/weights_data.h new file mode 100644 index 00000000..a8dd9237 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f16/weights_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t conv_match_stride2pad1_f16_weights_data[9] = {(float16_t)0.180786133f, + (float16_t)-0.614746094f, + (float16_t)0.486572266f, + (float16_t)0.0512390137f, + (float16_t)0.495117188f, + (float16_t)0.0369873047f, + (float16_t)-0.684082031f, + (float16_t)0.475830078f, + (float16_t)-0.528320312f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/biases_data.h new file mode 100644 index 00000000..59a4b2c1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_stride2pad1_f32_biases_data[1] = {-0.49546808f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/config_data.h new file mode 100644 index 00000000..2bc1759c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/config_data.h @@ -0,0 +1,22 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define CONV_MATCH_STRIDE2PAD1_F32_INPUT_BATCHES 1 +#define CONV_MATCH_STRIDE2PAD1_F32_INPUT_H 7 +#define CONV_MATCH_STRIDE2PAD1_F32_INPUT_W 7 +#define CONV_MATCH_STRIDE2PAD1_F32_IN_CH 1 +#define CONV_MATCH_STRIDE2PAD1_F32_FILTER_H 3 +#define CONV_MATCH_STRIDE2PAD1_F32_FILTER_W 3 +#define CONV_MATCH_STRIDE2PAD1_F32_OUT_CH 1 +#define CONV_MATCH_STRIDE2PAD1_F32_STRIDE_H 2 +#define CONV_MATCH_STRIDE2PAD1_F32_STRIDE_W 2 +#define CONV_MATCH_STRIDE2PAD1_F32_PADDING_H 1 +#define CONV_MATCH_STRIDE2PAD1_F32_PADDING_W 1 +#define CONV_MATCH_STRIDE2PAD1_F32_DILATION_H 1 +#define CONV_MATCH_STRIDE2PAD1_F32_DILATION_W 1 +#define CONV_MATCH_STRIDE2PAD1_F32_OUTPUT_H 4 +#define CONV_MATCH_STRIDE2PAD1_F32_OUTPUT_W 4 +#define CONV_MATCH_STRIDE2PAD1_F32_OUTPUT_C 1 +#define CONV_MATCH_STRIDE2PAD1_F32_DST_SIZE 16 +#define CONV_MATCH_STRIDE2PAD1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define CONV_MATCH_STRIDE2PAD1_F32_USE_WRAPPER 1 +#define CONV_MATCH_STRIDE2PAD1_F32_OUT_ACTIVATION_MIN -128.0f +#define CONV_MATCH_STRIDE2PAD1_F32_OUT_ACTIVATION_MAX 127.0f diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/input_data.h new file mode 100644 index 00000000..b0b271ef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/input_data.h @@ -0,0 +1,9 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_stride2pad1_f32_input_data[49] = { + -0.709159791f, -0.942800283f, 0.68510586f, 0.419157654f, -0.311777294f, 0.79166472f, -0.693170071f, + 0.481923252f, -0.120916486f, -0.205970064f, 0.303829879f, 0.478587538f, 0.659847915f, 0.0453958027f, + 0.106057674f, -0.816610694f, 0.75898838f, 0.0231293328f, -0.723740637f, -0.986089587f, 0.532379329f, + -0.580395818f, 0.77533716f, 0.914852798f, -0.350894183f, -0.222415954f, 0.757900655f, -0.896390498f, + -0.454371303f, -0.899198949f, 0.568175316f, 0.611923039f, -0.338917315f, -0.0114511456f, 0.387071788f, + -0.103788197f, -0.97276175f, -0.422600746f, -0.159416437f, 0.276757747f, -0.250216395f, -0.614932716f, + -0.756363213f, -0.350052536f, 0.309914649f, -0.83042258f, -0.0858245417f, -0.605047107f, -0.0198299605f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/output_ref_data.h new file mode 100644 index 00000000..3ddeec76 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_stride2pad1_f32_output_ref_data[16] = {-0.588209569f, + -0.364932209f, + -0.927749634f, + -1.22780907f, + -1.51417196f, + 0.182356f, + -1.0736407f, + -1.13600481f, + 0.444933981f, + -0.28209132f, + 0.182647541f, + 0.262234032f, + -1.29234445f, + -0.384353787f, + -0.92361331f, + -0.203475371f}; diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/test_data.h new file mode 100644 index 00000000..bc49a504 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/weights_data.h new file mode 100644 index 00000000..ed2d55ce --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/conv_match_stride2pad1_f32/weights_data.h @@ -0,0 +1,10 @@ +// Generated by conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t conv_match_stride2pad1_f32_weights_data[9] = {0.180838391f, + -0.614802361f, + 0.486519039f, + 0.0512407646f, + 0.495086998f, + 0.0370023325f, + -0.683992922f, + 0.475909203f, + -0.528368413f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/biases_data.h new file mode 100644 index 00000000..ff93bd5d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_batch2_f16_biases_data[8] = {(float16_t)0.411376953f, + (float16_t)0.431396484f, + (float16_t)-0.470703125f, + (float16_t)0.139648438f, + (float16_t)0.136352539f, + (float16_t)-0.0207061768f, + (float16_t)0.12322998f, + (float16_t)0.3046875f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/config_data.h new file mode 100644 index 00000000..d7bb986e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_2X5_OPT_BATCH2_F16_INPUT_BATCHES 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_INPUT_H 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_INPUT_W 21 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_IN_CH 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_CH_MULT 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_FILTER_H 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_FILTER_W 5 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUT_CH 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_STRIDE_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_STRIDE_W 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_PADDING_H 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_PADDING_W 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_DILATION_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_DILATION_W 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUTPUT_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUTPUT_W 17 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUTPUT_C 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_DST_SIZE 272 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_2X5_OPT_BATCH2_F16_USE_WRAPPER 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_2X5_OPT_BATCH2_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_2X5_OPT_BATCH2_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/input_data.h new file mode 100644 index 00000000..aae3dc09 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/input_data.h @@ -0,0 +1,170 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_batch2_f16_input_data[672] = { + (float16_t)-0.579101562f, (float16_t)0.727050781f, (float16_t)-0.932617188f, (float16_t)-0.6171875f, + (float16_t)-0.913574219f, (float16_t)0.251708984f, (float16_t)-0.843261719f, (float16_t)-0.791992188f, + (float16_t)0.677734375f, (float16_t)0.809082031f, (float16_t)0.422851562f, (float16_t)-0.23034668f, + (float16_t)-0.0450744629f, (float16_t)-0.472900391f, (float16_t)-0.862304688f, (float16_t)-0.643554688f, + (float16_t)0.968261719f, (float16_t)0.238769531f, (float16_t)0.399658203f, (float16_t)-0.42578125f, + (float16_t)0.0237884521f, (float16_t)-0.735351562f, (float16_t)0.932617188f, (float16_t)-0.798828125f, + (float16_t)-0.766601562f, (float16_t)-0.666503906f, (float16_t)0.0336914062f, (float16_t)0.560058594f, + (float16_t)0.909179688f, (float16_t)-0.13269043f, (float16_t)0.697753906f, (float16_t)-0.118774414f, + (float16_t)0.600585938f, (float16_t)0.058807373f, (float16_t)-0.284179688f, (float16_t)0.307128906f, + (float16_t)-0.284179688f, (float16_t)0.747558594f, (float16_t)0.735351562f, (float16_t)0.945800781f, + (float16_t)-0.81640625f, (float16_t)-0.765136719f, (float16_t)-0.984375f, (float16_t)0.244384766f, + (float16_t)-0.59375f, (float16_t)-0.770507812f, (float16_t)-0.022277832f, (float16_t)-0.369384766f, + (float16_t)-0.760742188f, (float16_t)0.0375976562f, (float16_t)-0.312744141f, (float16_t)-0.239990234f, + (float16_t)0.0661010742f, (float16_t)0.742675781f, (float16_t)-0.19152832f, (float16_t)-0.423339844f, + (float16_t)0.680664062f, (float16_t)0.34375f, (float16_t)0.260253906f, (float16_t)-0.214599609f, + (float16_t)0.489746094f, (float16_t)-0.101379395f, (float16_t)-0.438964844f, (float16_t)0.906738281f, + (float16_t)-0.0941772461f, (float16_t)0.646972656f, (float16_t)-0.269042969f, (float16_t)0.549804688f, + (float16_t)-0.0255889893f, (float16_t)0.8046875f, (float16_t)-0.908691406f, (float16_t)0.754882812f, + (float16_t)-0.916992188f, (float16_t)0.964355469f, (float16_t)-0.152709961f, (float16_t)0.563476562f, + (float16_t)0.943359375f, (float16_t)0.790527344f, (float16_t)0.467529297f, (float16_t)0.0487365723f, + (float16_t)0.937988281f, (float16_t)0.272705078f, (float16_t)0.440429688f, (float16_t)0.473144531f, + (float16_t)-0.163208008f, (float16_t)0.651367188f, (float16_t)0.446533203f, (float16_t)-0.736328125f, + (float16_t)0.375488281f, (float16_t)0.641113281f, (float16_t)-0.811035156f, (float16_t)-0.719726562f, + (float16_t)0.245239258f, (float16_t)-0.129760742f, (float16_t)0.381347656f, (float16_t)0.759277344f, + (float16_t)0.530761719f, (float16_t)-0.199951172f, (float16_t)-0.692871094f, (float16_t)0.339599609f, + (float16_t)0.666015625f, (float16_t)-0.284423828f, (float16_t)-0.108459473f, (float16_t)-0.489501953f, + (float16_t)-0.272949219f, (float16_t)-0.704101562f, (float16_t)-0.899414062f, (float16_t)0.108886719f, + (float16_t)0.7578125f, (float16_t)0.814453125f, (float16_t)0.073059082f, (float16_t)-0.409667969f, + (float16_t)-0.387939453f, (float16_t)0.267333984f, (float16_t)-0.904296875f, (float16_t)-0.395507812f, + (float16_t)-0.872070312f, (float16_t)-0.578125f, (float16_t)-0.870605469f, (float16_t)0.641601562f, + (float16_t)-0.0404663086f, (float16_t)-0.866699219f, (float16_t)0.872558594f, (float16_t)-0.377197266f, + (float16_t)-0.372802734f, (float16_t)-0.732910156f, (float16_t)-0.328857422f, (float16_t)-0.0179290771f, + (float16_t)-0.87890625f, (float16_t)0.542480469f, (float16_t)0.429199219f, (float16_t)-0.197143555f, + (float16_t)-0.298583984f, (float16_t)0.206298828f, (float16_t)0.0409851074f, (float16_t)0.5f, + (float16_t)-0.659667969f, (float16_t)-0.602539062f, (float16_t)-0.392822266f, (float16_t)0.0159912109f, + (float16_t)-0.0966186523f, (float16_t)0.364501953f, (float16_t)0.446533203f, (float16_t)0.00624465942f, + (float16_t)0.130004883f, (float16_t)0.011428833f, (float16_t)0.105773926f, (float16_t)0.141357422f, + (float16_t)-0.937011719f, (float16_t)-0.95703125f, (float16_t)-0.61328125f, (float16_t)0.0928955078f, + (float16_t)0.130981445f, (float16_t)0.679199219f, (float16_t)0.10748291f, (float16_t)-0.330078125f, + (float16_t)-0.307373047f, (float16_t)0.984863281f, (float16_t)0.391601562f, (float16_t)-0.698730469f, + (float16_t)-0.0661010742f, (float16_t)0.427246094f, (float16_t)0.194946289f, (float16_t)-0.684570312f, + (float16_t)-0.531738281f, (float16_t)-0.968261719f, (float16_t)0.444580078f, (float16_t)0.861328125f, + (float16_t)0.7578125f, (float16_t)-0.0840454102f, (float16_t)0.340576172f, (float16_t)0.792480469f, + (float16_t)0.252441406f, (float16_t)-0.307373047f, (float16_t)-0.749511719f, (float16_t)0.592773438f, + (float16_t)0.140136719f, (float16_t)0.763183594f, (float16_t)0.916015625f, (float16_t)0.128540039f, + (float16_t)-0.931640625f, (float16_t)-0.152099609f, (float16_t)0.672363281f, (float16_t)-0.802734375f, + (float16_t)0.522460938f, (float16_t)0.807128906f, (float16_t)-0.135620117f, (float16_t)-0.519042969f, + (float16_t)-0.178344727f, (float16_t)-0.259765625f, (float16_t)0.812011719f, (float16_t)-0.308837891f, + (float16_t)-0.134643555f, (float16_t)0.892089844f, (float16_t)-0.295410156f, (float16_t)-0.767578125f, + (float16_t)-0.340332031f, (float16_t)-0.512207031f, (float16_t)0.638183594f, (float16_t)-0.874023438f, + (float16_t)0.560546875f, (float16_t)-0.662597656f, (float16_t)0.673339844f, (float16_t)0.729003906f, + (float16_t)0.506835938f, (float16_t)-0.0101394653f, (float16_t)-0.573730469f, (float16_t)-0.182861328f, + (float16_t)0.906738281f, (float16_t)0.990234375f, (float16_t)-0.961914062f, (float16_t)0.565429688f, + (float16_t)0.502929688f, (float16_t)0.592773438f, (float16_t)-0.08984375f, (float16_t)0.938964844f, + (float16_t)-0.160644531f, (float16_t)0.446289062f, (float16_t)-0.846679688f, (float16_t)0.987304688f, + (float16_t)-0.181274414f, (float16_t)-0.80078125f, (float16_t)0.463867188f, (float16_t)-0.373779297f, + (float16_t)-0.182617188f, (float16_t)0.391845703f, (float16_t)0.526367188f, (float16_t)-0.962402344f, + (float16_t)-0.918945312f, (float16_t)-0.79296875f, (float16_t)0.410644531f, (float16_t)-0.55078125f, + (float16_t)-0.035949707f, (float16_t)0.572265625f, (float16_t)-0.981445312f, (float16_t)-0.965332031f, + (float16_t)0.400146484f, (float16_t)-0.434814453f, (float16_t)0.331542969f, (float16_t)-0.664550781f, + (float16_t)0.138183594f, (float16_t)-0.164428711f, (float16_t)0.96484375f, (float16_t)0.407958984f, + (float16_t)0.983886719f, (float16_t)0.256347656f, (float16_t)-0.734375f, (float16_t)-0.551757812f, + (float16_t)-0.926757812f, (float16_t)-0.55078125f, (float16_t)-0.137573242f, (float16_t)-0.691894531f, + (float16_t)0.133544922f, (float16_t)0.195556641f, (float16_t)-0.767578125f, (float16_t)0.53515625f, + 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(float16_t)0.424804688f, (float16_t)0.719238281f, (float16_t)-0.68359375f, (float16_t)-0.49609375f, + (float16_t)0.0685424805f, (float16_t)0.686523438f, (float16_t)-0.368164062f, (float16_t)0.792480469f, + (float16_t)-0.997070312f, (float16_t)0.612304688f, (float16_t)0.00637817383f, (float16_t)-0.513183594f, + (float16_t)-0.00477981567f, (float16_t)0.80078125f, (float16_t)-0.547851562f, (float16_t)-0.300292969f, + (float16_t)0.34375f, (float16_t)-0.606445312f, (float16_t)0.350097656f, (float16_t)-0.686035156f, + (float16_t)-0.025604248f, (float16_t)0.752929688f, (float16_t)-0.784667969f, (float16_t)0.213378906f, + (float16_t)0.232055664f, (float16_t)0.397460938f, (float16_t)-0.375244141f, (float16_t)0.021697998f, + (float16_t)-0.590332031f, (float16_t)0.570800781f, (float16_t)0.284423828f, (float16_t)0.238037109f, + (float16_t)0.697265625f, (float16_t)-0.985839844f, (float16_t)0.498291016f, (float16_t)-0.343017578f, + (float16_t)0.746582031f, (float16_t)0.42578125f, (float16_t)-0.991210938f, (float16_t)-0.489257812f, + (float16_t)0.724121094f, (float16_t)0.00522994995f, (float16_t)-0.655761719f, (float16_t)0.68359375f, + (float16_t)0.194702148f, (float16_t)0.427001953f, (float16_t)-0.666992188f, (float16_t)-0.0047416687f, + (float16_t)0.287353516f, (float16_t)0.708984375f, (float16_t)-0.249023438f, (float16_t)0.136962891f, + (float16_t)-0.705078125f, (float16_t)-0.795410156f, (float16_t)-0.694824219f, (float16_t)-0.372802734f, + (float16_t)-0.399169922f, (float16_t)0.101379395f, (float16_t)-0.251464844f, (float16_t)0.350830078f, + (float16_t)-0.150634766f, (float16_t)0.233886719f, (float16_t)0.353759766f, (float16_t)-0.691894531f, + (float16_t)-0.549316406f, (float16_t)-0.744140625f, (float16_t)0.325927734f, (float16_t)-0.829589844f, + (float16_t)0.249755859f, (float16_t)-0.768066406f, (float16_t)-0.865722656f, (float16_t)-0.521972656f, + (float16_t)0.269287109f, (float16_t)-0.406005859f, (float16_t)-0.592285156f, (float16_t)0.949707031f, + (float16_t)-0.693359375f, (float16_t)0.0578918457f, (float16_t)-0.452636719f, (float16_t)-0.664550781f, + (float16_t)-0.152709961f, (float16_t)0.17956543f, (float16_t)0.9296875f, (float16_t)0.493896484f, + (float16_t)-0.169189453f, (float16_t)0.677734375f, (float16_t)0.788085938f, (float16_t)0.869628906f, + (float16_t)0.0559692383f, (float16_t)-0.951660156f, (float16_t)0.0686645508f, (float16_t)0.428710938f, + (float16_t)-0.0278778076f, (float16_t)0.382324219f, (float16_t)-0.639648438f, (float16_t)0.0952758789f, + (float16_t)0.726074219f, (float16_t)0.558105469f, (float16_t)0.306152344f, (float16_t)0.520507812f, + (float16_t)-0.660644531f, (float16_t)-0.217895508f, (float16_t)-0.818359375f, (float16_t)0.155151367f, + (float16_t)-0.538085938f, (float16_t)-0.0333557129f, (float16_t)-0.389648438f, (float16_t)0.777832031f, + (float16_t)-0.865722656f, (float16_t)-0.742675781f, (float16_t)-0.337402344f, (float16_t)-0.59765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/output_ref_data.h new file mode 100644 index 00000000..87d9cefa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/output_ref_data.h @@ -0,0 +1,70 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_batch2_f16_output_ref_data[272] = { + (float16_t)1.22070312f, (float16_t)0.638183594f, (float16_t)-0.540527344f, (float16_t)1.30371094f, + (float16_t)-0.087097168f, (float16_t)0.133911133f, (float16_t)0.336914062f, (float16_t)-0.962402344f, + (float16_t)0.903808594f, (float16_t)1.12890625f, (float16_t)1.43066406f, (float16_t)0.505859375f, + (float16_t)0.760253906f, (float16_t)-1.57128906f, (float16_t)-0.309570312f, (float16_t)-1.08496094f, + (float16_t)0.184570312f, (float16_t)0.151245117f, (float16_t)-0.847167969f, (float16_t)0.418457031f, + (float16_t)0.644042969f, (float16_t)1.61523438f, (float16_t)-1.7578125f, (float16_t)0.696777344f, + (float16_t)1.56445312f, (float16_t)0.55078125f, (float16_t)-0.490478516f, (float16_t)-1.48339844f, + (float16_t)-0.135498047f, (float16_t)0.545410156f, (float16_t)-0.700195312f, (float16_t)1.73925781f, + (float16_t)0.537109375f, (float16_t)0.749023438f, (float16_t)1.28417969f, (float16_t)0.315917969f, + (float16_t)-0.466064453f, (float16_t)-0.232788086f, (float16_t)0.288574219f, (float16_t)-0.469726562f, + (float16_t)-0.0491027832f, (float16_t)1.47460938f, (float16_t)-2.1015625f, (float16_t)0.840820312f, + (float16_t)1.10546875f, (float16_t)-0.59375f, (float16_t)0.725097656f, (float16_t)-1.29980469f, + (float16_t)0.381591797f, (float16_t)1.52148438f, (float16_t)0.0822753906f, (float16_t)0.944824219f, + (float16_t)0.643066406f, (float16_t)0.423095703f, (float16_t)0.466064453f, (float16_t)0.48828125f, + (float16_t)0.608886719f, (float16_t)0.951171875f, (float16_t)-0.544433594f, (float16_t)0.161743164f, + (float16_t)0.852050781f, (float16_t)1.17480469f, (float16_t)0.283691406f, (float16_t)1.06054688f, + (float16_t)-0.23840332f, (float16_t)0.782226562f, (float16_t)-0.539550781f, (float16_t)-0.459472656f, + (float16_t)1.08398438f, (float16_t)0.578125f, (float16_t)0.225585938f, (float16_t)-0.278076172f, + (float16_t)-0.239501953f, (float16_t)-0.257568359f, (float16_t)0.47265625f, (float16_t)0.68359375f, + (float16_t)-0.63671875f, (float16_t)0.3203125f, (float16_t)-0.331054688f, (float16_t)0.695800781f, + (float16_t)0.689941406f, (float16_t)-0.775878906f, (float16_t)-0.0167694092f, (float16_t)0.172241211f, + (float16_t)-0.051361084f, (float16_t)-0.999511719f, (float16_t)1.45800781f, (float16_t)0.21875f, + (float16_t)0.227539062f, (float16_t)-0.37109375f, (float16_t)-0.397705078f, (float16_t)0.0830688477f, + (float16_t)0.575683594f, (float16_t)0.873046875f, (float16_t)0.815917969f, (float16_t)-0.755371094f, + (float16_t)0.994628906f, (float16_t)-0.886230469f, (float16_t)-0.0102005005f, (float16_t)-0.136352539f, + (float16_t)-0.0343017578f, (float16_t)1.08886719f, (float16_t)-1.31640625f, (float16_t)-0.598632812f, + (float16_t)1.2890625f, (float16_t)1.16308594f, (float16_t)-0.0825805664f, (float16_t)-0.385986328f, + (float16_t)-0.717773438f, (float16_t)-1.078125f, (float16_t)0.453125f, (float16_t)1.22949219f, + (float16_t)0.00708389282f, (float16_t)-0.0785522461f, (float16_t)-0.516601562f, (float16_t)-0.0352478027f, + (float16_t)-0.385253906f, (float16_t)-1.32617188f, (float16_t)1.93847656f, (float16_t)0.336425781f, + (float16_t)-0.393310547f, (float16_t)0.963867188f, (float16_t)-0.7890625f, (float16_t)-0.469482422f, + (float16_t)0.0732421875f, (float16_t)2.2890625f, (float16_t)-0.368896484f, (float16_t)1.04003906f, + (float16_t)0.691894531f, (float16_t)-0.522949219f, (float16_t)-0.243530273f, (float16_t)-0.0112686157f, + (float16_t)0.142578125f, (float16_t)-1.13574219f, (float16_t)-0.948242188f, (float16_t)-0.280273438f, + (float16_t)0.335205078f, (float16_t)-0.711425781f, (float16_t)-1.31835938f, (float16_t)-1.24414062f, + (float16_t)1.40136719f, (float16_t)-1.80566406f, (float16_t)1.41503906f, (float16_t)-0.0435791016f, + (float16_t)0.464111328f, (float16_t)0.918945312f, (float16_t)0.297363281f, (float16_t)0.634765625f, + (float16_t)-1.05371094f, (float16_t)-1.92675781f, (float16_t)0.486328125f, (float16_t)-0.534179688f, + (float16_t)0.375f, (float16_t)-0.931640625f, (float16_t)-0.2421875f, (float16_t)0.0468139648f, + (float16_t)0.276367188f, (float16_t)1.4765625f, (float16_t)0.337402344f, (float16_t)1.54199219f, + (float16_t)1.01367188f, (float16_t)2.23242188f, (float16_t)-0.484130859f, (float16_t)0.299072266f, + (float16_t)0.105407715f, (float16_t)0.0735473633f, (float16_t)1.72167969f, (float16_t)0.432373047f, + (float16_t)0.0573730469f, (float16_t)0.101745605f, (float16_t)-0.385009766f, (float16_t)-0.278808594f, + (float16_t)-0.213867188f, (float16_t)-0.0383605957f, (float16_t)0.278808594f, (float16_t)0.711425781f, + (float16_t)1.53222656f, (float16_t)1.34082031f, (float16_t)-0.250488281f, (float16_t)0.0800170898f, + (float16_t)0.0115966797f, (float16_t)-0.788574219f, (float16_t)0.94140625f, (float16_t)0.524902344f, + (float16_t)0.562011719f, (float16_t)0.425292969f, (float16_t)-0.5859375f, (float16_t)0.556640625f, + (float16_t)0.470458984f, (float16_t)-1.55371094f, (float16_t)0.775390625f, (float16_t)1.390625f, + (float16_t)-0.164306641f, (float16_t)0.702148438f, (float16_t)-0.966796875f, (float16_t)0.338867188f, + (float16_t)0.158081055f, (float16_t)0.69921875f, (float16_t)0.598144531f, (float16_t)0.0268249512f, + (float16_t)0.372314453f, (float16_t)-0.543945312f, (float16_t)-0.109741211f, (float16_t)0.80078125f, + (float16_t)-0.324462891f, (float16_t)-0.726074219f, (float16_t)2.63085938f, (float16_t)0.383056641f, + (float16_t)0.0681152344f, (float16_t)-0.276367188f, (float16_t)-1.18066406f, (float16_t)-0.347167969f, + (float16_t)0.217529297f, (float16_t)-0.864257812f, (float16_t)0.548828125f, (float16_t)0.532714844f, + (float16_t)0.3671875f, (float16_t)0.0163879395f, (float16_t)-0.377929688f, (float16_t)0.317626953f, + (float16_t)1.33984375f, (float16_t)0.330810547f, (float16_t)-0.390380859f, (float16_t)-0.320556641f, + (float16_t)1.52539062f, (float16_t)0.671875f, (float16_t)-1.10742188f, (float16_t)-0.932128906f, + (float16_t)-1.35449219f, (float16_t)-1.09863281f, (float16_t)1.31835938f, (float16_t)0.732421875f, + (float16_t)1.640625f, (float16_t)1.79785156f, (float16_t)-0.545898438f, (float16_t)1.11328125f, + (float16_t)-0.0706176758f, (float16_t)-0.427001953f, (float16_t)1.46582031f, (float16_t)0.791992188f, + (float16_t)-0.188354492f, (float16_t)-0.190307617f, (float16_t)-1.96289062f, (float16_t)-0.266601562f, + (float16_t)-0.136352539f, (float16_t)0.556640625f, (float16_t)-0.349853516f, (float16_t)-0.968261719f, + (float16_t)-0.19128418f, (float16_t)-0.201293945f, (float16_t)-0.406494141f, (float16_t)0.171875f, + (float16_t)-0.836914062f, (float16_t)-0.942382812f, (float16_t)-0.234863281f, (float16_t)0.955078125f, + (float16_t)0.437744141f, (float16_t)0.491943359f, (float16_t)-1.00878906f, (float16_t)-0.817871094f, + (float16_t)-0.248901367f, (float16_t)0.7109375f, (float16_t)1.72167969f, (float16_t)1.37207031f, + (float16_t)-0.293212891f, (float16_t)0.969238281f, (float16_t)-0.342041016f, (float16_t)0.825195312f, + (float16_t)0.366699219f, (float16_t)0.632324219f, (float16_t)0.867675781f, (float16_t)1.43554688f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/weights_data.h new file mode 100644 index 00000000..f852c747 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f16/weights_data.h @@ -0,0 +1,22 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_batch2_f16_weights_data[80] = { + (float16_t)0.156005859f, (float16_t)-0.696289062f, (float16_t)-0.201171875f, (float16_t)-0.111938477f, + (float16_t)-0.541992188f, (float16_t)0.0190582275f, (float16_t)-0.547851562f, (float16_t)-0.249755859f, + (float16_t)0.391601562f, (float16_t)0.610839844f, (float16_t)0.304931641f, (float16_t)0.265380859f, + (float16_t)0.672363281f, (float16_t)0.484130859f, (float16_t)-0.0780029297f, (float16_t)0.690429688f, + (float16_t)-0.0241546631f, (float16_t)0.402099609f, (float16_t)-0.698242188f, (float16_t)0.264404297f, + (float16_t)0.291748047f, (float16_t)0.210327148f, (float16_t)-0.148803711f, (float16_t)-0.0287628174f, + (float16_t)-0.403320312f, (float16_t)0.688964844f, (float16_t)0.432861328f, (float16_t)0.0907592773f, + (float16_t)0.253417969f, (float16_t)-0.668945312f, (float16_t)-0.482177734f, (float16_t)-0.749511719f, + (float16_t)-0.161987305f, (float16_t)-0.592773438f, (float16_t)-0.702148438f, (float16_t)0.0543212891f, + (float16_t)0.543457031f, (float16_t)0.460693359f, (float16_t)-0.220458984f, (float16_t)-0.167236328f, + (float16_t)-0.0830078125f, (float16_t)0.0397949219f, (float16_t)0.397949219f, (float16_t)0.665527344f, + (float16_t)-0.54296875f, (float16_t)-0.722167969f, (float16_t)-0.627929688f, (float16_t)-0.431884766f, + (float16_t)0.657226562f, (float16_t)0.595703125f, (float16_t)-0.0358886719f, (float16_t)-0.332275391f, + (float16_t)0.498535156f, (float16_t)-0.289306641f, (float16_t)-0.573242188f, (float16_t)0.549804688f, + (float16_t)0.413574219f, (float16_t)0.218994141f, (float16_t)-0.497314453f, (float16_t)-0.118408203f, + (float16_t)-0.176025391f, (float16_t)0.489013672f, (float16_t)0.260498047f, (float16_t)0.277099609f, + (float16_t)-0.643066406f, (float16_t)0.247070312f, (float16_t)0.403808594f, (float16_t)-0.447753906f, + (float16_t)0.515136719f, (float16_t)0.0830688477f, (float16_t)-0.502441406f, (float16_t)0.272949219f, + (float16_t)0.202270508f, (float16_t)0.349365234f, (float16_t)-0.552734375f, (float16_t)0.427246094f, + (float16_t)-0.0510253906f, (float16_t)-0.398681641f, (float16_t)-0.604003906f, (float16_t)-0.255371094f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/biases_data.h new file mode 100644 index 00000000..f9f14953 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_batch2_f32_biases_data[8] = + {0.411314994f, 0.431407601f, -0.470623553f, 0.139696479f, 0.136402279f, -0.0207028277f, 0.123203002f, 0.304578096f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/config_data.h new file mode 100644 index 00000000..3c79acf2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_2X5_OPT_BATCH2_F32_INPUT_BATCHES 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_INPUT_H 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_INPUT_W 21 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_IN_CH 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_CH_MULT 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_FILTER_H 2 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_FILTER_W 5 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUT_CH 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_STRIDE_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_STRIDE_W 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_PADDING_H 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_PADDING_W 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_DILATION_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_DILATION_W 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUTPUT_H 1 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUTPUT_W 17 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUTPUT_C 8 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_DST_SIZE 272 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_2X5_OPT_BATCH2_F32_USE_WRAPPER 0 +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_2X5_OPT_BATCH2_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_2X5_OPT_BATCH2_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/input_data.h new file mode 100644 index 00000000..4a3258a2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/input_data.h @@ -0,0 +1,98 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_batch2_f32_input_data[672] = { + -0.578863263f, 0.727003574f, -0.932713032f, -0.617369652f, -0.913638949f, 0.251642615f, -0.843071461f, + -0.791835785f, 0.677782297f, 0.808964074f, 0.422933072f, -0.230388045f, -0.0450696163f, -0.472925514f, + -0.862473547f, -0.64338851f, 0.968279243f, 0.238789037f, 0.399708539f, -0.425840616f, 0.023795208f, + -0.735569537f, 0.932553768f, -0.798644662f, -0.76661557f, -0.666393936f, 0.0336959958f, 0.560258865f, + 0.90935111f, -0.132636428f, 0.697693408f, -0.118775092f, 0.600812316f, 0.0588025637f, -0.284220815f, + 0.307177454f, -0.284198016f, 0.747444332f, 0.735294521f, 0.946013629f, -0.816201389f, -0.765256345f, + -0.984394431f, 0.244445756f, -0.593742669f, -0.770300567f, -0.022281114f, -0.369295537f, -0.760546744f, + 0.0375890993f, -0.31277281f, -0.239945471f, 0.0661004558f, 0.742782772f, -0.191545337f, -0.423270911f, + 0.680754006f, 0.34368524f, 0.26020357f, -0.214584783f, 0.489828378f, -0.101408035f, -0.438894212f, + 0.906818807f, -0.094147265f, 0.647054076f, -0.269062072f, 0.549667954f, -0.0255863275f, 0.804607391f, + -0.908593416f, 0.754771352f, -0.917233288f, 0.964117348f, -0.152660042f, 0.563298583f, 0.94323045f, + 0.790515959f, 0.467587948f, 0.048743505f, 0.938224852f, 0.272800177f, 0.440500081f, 0.473088413f, + -0.163233072f, 0.65130645f, 0.446445912f, -0.736327291f, 0.375534028f, 0.641128719f, -0.811081767f, + -0.719873548f, 0.245208815f, -0.129795909f, 0.381405473f, 0.759034812f, 0.530784607f, -0.199960962f, + -0.693041265f, 0.339479834f, 0.666085362f, -0.284335881f, -0.108461879f, -0.489545792f, -0.272992462f, + -0.704307318f, -0.899424493f, 0.108888075f, 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0.140176639f, 0.763203502f, 0.916084528f, 0.128511921f, -0.93176657f, -0.152125359f, + 0.672481596f, -0.802499712f, 0.522697628f, 0.807299256f, -0.135644928f, -0.518920362f, -0.178317472f, + -0.259819865f, 0.811824799f, -0.308937788f, -0.134606227f, 0.892146468f, -0.295518458f, -0.767371595f, + -0.34021008f, -0.512159407f, 0.638308525f, -0.873869359f, 0.560533464f, -0.662574649f, 0.673199534f, + 0.728903472f, 0.506824732f, -0.0101372628f, -0.573704183f, -0.182837024f, 0.906558752f, 0.990446925f, + -0.961708605f, 0.565376163f, 0.502837718f, 0.592969596f, -0.0898265317f, 0.938850224f, -0.160665184f, + 0.446378589f, -0.846504629f, 0.987498462f, -0.181218609f, -0.800620675f, 0.463840038f, -0.373863518f, + -0.18260321f, 0.391905844f, 0.526213527f, -0.962586761f, -0.918962657f, -0.792966902f, 0.410537481f, + -0.550764441f, -0.0359430201f, 0.572097421f, -0.981446624f, -0.965340972f, 0.400151461f, -0.434868515f, + 0.331508368f, -0.664637148f, 0.138232559f, -0.164466754f, 0.964848816f, 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-0.191824272f, + 0.845507324f, -0.324497789f, -0.26984486f, -0.570052266f, -0.0278550666f, -0.00670130644f, 0.178913251f, + 0.902876675f, -0.355991066f, -0.53917259f, 0.0873828828f, -0.108882718f, -0.272222251f, -0.339736462f, + 0.503971279f, 0.037532229f, -0.542632163f, -0.831446826f, 0.126430929f, -0.961242557f, 0.35163033f, + 0.109614886f, -0.183563739f, 0.281770915f, 0.428777784f, 0.342547238f, 0.302474171f, 0.563978314f, + -0.926478088f, -0.454655677f, -0.731382787f, -0.218094319f, -0.0212152991f, 0.666464567f, -0.785819709f, + 0.854464829f, 0.291582912f, -0.755162537f, 0.395651788f, -0.163425535f, 0.323998451f, 0.582435906f, + -0.15423587f, -0.916388988f, 0.737639189f, -0.334739983f, -0.714524806f, -0.524347246f, 0.02532712f, + 0.377964914f, 0.980102122f, 0.333975732f, -0.898154438f, -0.293336451f, 0.649620473f, -0.460077286f, + 0.101674899f, 0.0880954266f, -0.810345173f, -0.134453803f, 0.313271135f, -0.454901904f, 0.889322698f, + 0.805489659f, 0.879324257f, -0.0741323978f, -0.085175097f, -0.929501474f, 0.751202464f, 0.455229282f, + -0.465818971f, 0.369032741f, 0.508313596f, -0.17929031f, 0.765019178f, 0.216435209f, 0.554448903f, + -0.717391253f, -0.757977366f, 0.516693354f, 0.473485589f, -0.622799158f, -0.569735348f, -0.399851501f, + 0.281944662f, -0.561696291f, -0.564632177f, 0.117061205f, 0.719243884f, -0.685781181f, -0.738106906f, + -0.468947828f, -0.787957609f, 0.0883926824f, 0.522743583f, 0.780216038f, 0.634932101f, 0.0303295851f, + -0.727697134f, 0.857869565f, 0.526253343f, -0.918899357f, -0.437551856f, 0.775410414f, 0.385604858f, + -0.0489994586f, -0.0334704481f, -0.0986301824f, 0.191891208f, -0.787373543f, 0.421770722f, 0.382155329f, + -0.0963014364f, 0.858064055f, 0.838150024f, -0.581820428f, -0.175806597f, -0.970194519f, -0.0865546763f, + 0.74357444f, -0.505477726f, 0.36851871f, 0.21324347f, -0.75729996f, -0.0778007954f, -0.303252548f, + 0.149536476f, -0.848792076f, -0.709290445f, -0.657974422f, -0.981891274f, 0.577907503f, -0.414098829f, + 0.189881697f, -0.490014106f, 0.108584106f, 0.588603079f, 0.81750679f, 0.565790057f, 0.753465235f, + -0.897995651f, 0.796500981f, 0.45825994f, -0.709564924f, 0.0536375158f, 0.177953199f, -0.638561845f, + -0.483305424f, 0.128081247f, -0.976862371f, 0.138617843f, 0.562887132f, -0.584836125f, 0.793617606f, + 0.204783976f, -0.186474741f, -0.105617456f, 0.360067576f, 0.718544543f, 0.743668437f, 0.891161323f, + -0.160351232f, 0.18278864f, 0.430915773f, -0.829935014f, 0.0925914273f, 0.476137727f, -0.982105017f, + -0.544325173f, 0.196115151f, -0.736198604f, 0.738275528f, 0.909755409f, 0.393772066f, 0.0318772122f, + -0.289303362f, 0.362778962f, -0.221989423f, -0.808626771f, 0.30249849f, -0.64506644f, 0.138168275f, + 0.424795598f, 0.719226241f, -0.683485866f, -0.496029317f, 0.0685581639f, 0.686490834f, -0.368141204f, + 0.792617261f, -0.996834278f, 0.612466753f, 0.00637723599f, -0.513155639f, -0.00477903895f, 0.800947905f, + -0.547733963f, -0.300251037f, 0.343789995f, -0.606539071f, 0.349980444f, -0.686139345f, -0.0255995989f, + 0.753057063f, -0.784465134f, 0.213414818f, 0.232069045f, 0.397375017f, -0.375356853f, 0.0216948781f, + -0.590268552f, 0.570715904f, 0.284488589f, 0.237982884f, 0.697489262f, -0.98587811f, 0.498264045f, + -0.343071878f, 0.746504366f, 0.42584306f, -0.991286218f, -0.489175022f, 0.724250674f, 0.00522821723f, + -0.655811548f, 0.683541536f, 0.194695801f, 0.427063376f, -0.667029083f, -0.00474076113f, 0.287438124f, + 0.709055185f, -0.249021128f, 0.136926487f, -0.705291748f, -0.795194507f, -0.694688022f, -0.372858584f, + -0.399078578f, 0.10140188f, -0.251540393f, 0.350951642f, -0.15063937f, 0.233898625f, 0.353769064f, + -0.692072928f, -0.549171984f, -0.744121611f, 0.325881898f, -0.829442978f, 0.249710858f, -0.768131137f, + -0.865709364f, -0.522055864f, 0.269383937f, -0.405936807f, -0.592383802f, 0.94956708f, -0.693549454f, + 0.057895083f, -0.452641577f, -0.664329529f, -0.152756572f, 0.179603368f, 0.929730415f, 0.493886352f, + -0.169223279f, 0.677504659f, 0.787996054f, 0.869414091f, 0.0559712239f, -0.951528192f, 0.0686628893f, + 0.428652734f, -0.0278823469f, 0.382221907f, -0.639495552f, 0.095296137f, 0.726306617f, 0.557935894f, + 0.306194901f, 0.520582914f, -0.660558939f, -0.217850536f, -0.818238497f, 0.155094296f, -0.537893653f, + -0.0333435759f, -0.389579386f, 0.777832329f, -0.865780592f, -0.742732048f, -0.337373495f, -0.597443283f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/output_ref_data.h new file mode 100644 index 00000000..aab4dd64 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/output_ref_data.h @@ -0,0 +1,41 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_batch2_f32_output_ref_data[272] = { + 1.22028303f, 0.63833046f, -0.540351629f, 1.30351412f, -0.0870761573f, 0.133851483f, 0.336984307f, + -0.962425292f, 0.90375042f, 1.12882626f, 1.43041408f, 0.505725026f, 0.760202944f, -1.57114947f, + -0.30947414f, -1.08509147f, 0.184592113f, 0.151301071f, -0.847183347f, 0.418526769f, 0.643976271f, + 1.61488044f, -1.75823259f, 0.696619928f, 1.56401873f, 0.550825238f, -0.490568578f, -1.48335361f, + -0.135501519f, 0.54541713f, -0.700102448f, 1.73904181f, 0.537313998f, 0.749179721f, 1.28454924f, + 0.315926433f, -0.466052502f, -0.232842281f, 0.288680732f, -0.469779223f, -0.0490929112f, 1.47464657f, + -2.1024785f, 0.840848923f, 1.1058718f, -0.593691409f, 0.725122988f, -1.29959249f, 0.381651729f, + 1.52117562f, 0.08229509f, 0.94474709f, 0.642900169f, 0.423097014f, 0.466119558f, 0.488393784f, + 0.608808458f, 0.95097357f, -0.544642866f, 0.161787614f, 0.851994395f, 1.17517304f, 0.283740401f, + 1.06046009f, -0.238346577f, 0.782062769f, -0.53978163f, -0.459364682f, 1.08435154f, 0.578230977f, + 0.225547209f, -0.27797696f, -0.239477396f, -0.257462829f, 0.472537339f, 0.683603287f, -0.636847675f, + 0.320307583f, -0.330999374f, 0.695621192f, 0.690121651f, -0.775672972f, -0.0167683251f, 0.172282666f, + -0.0513669364f, -0.99975282f, 1.45843208f, 0.218786314f, 0.227487028f, -0.371096104f, -0.397809923f, + 0.0830807611f, 0.575577736f, 0.873056889f, 0.816079736f, -0.755135357f, 0.994469821f, -0.886392891f, + -0.0102005219f, -0.136345133f, -0.0342960469f, 1.08878994f, -1.31643498f, -0.59872067f, 1.2894398f, + 1.16272867f, -0.0825798362f, -0.385964841f, -0.717977643f, -1.0781523f, 0.45302406f, 1.2297312f, + 0.00708579412f, -0.0785653368f, -0.516794443f, -0.0352528729f, -0.385334402f, -1.3262285f, 1.93870056f, + 0.336515874f, -0.393416971f, 0.96404177f, -0.789090872f, -0.469386041f, 0.0732508078f, 2.28898311f, + -0.368919492f, 1.04005003f, 0.692061543f, -0.522903323f, -0.243471414f, -0.0112668863f, 0.142573431f, + -1.13579869f, -0.948287666f, -0.280214131f, 0.33508572f, -0.711252749f, -1.31854236f, -1.24392915f, + 1.40161037f, -1.8058629f, 1.41479039f, -0.043590337f, 0.464083135f, 0.918830752f, 0.29735443f, + 0.634570122f, -1.05337739f, -1.92696953f, 0.486361295f, -0.534260869f, 0.374887526f, -0.931838691f, + -0.242146522f, 0.0468213931f, 0.276337743f, 1.47664678f, 0.337427348f, 1.54223371f, 1.01404357f, + 2.23169518f, -0.484064281f, 0.299167931f, 0.105425596f, 0.0735268295f, 1.72147918f, 0.432467133f, + 0.0573863313f, 0.101734944f, -0.384891152f, -0.278858274f, -0.21386756f, -0.0383517109f, 0.278843731f, + 0.711331904f, 1.53199387f, 1.34053421f, -0.250374585f, 0.0800302625f, 0.011599713f, -0.788343906f, + 0.94161582f, 0.524933815f, 0.562018454f, 0.425392598f, -0.586175323f, 0.556601405f, 0.470570087f, + -1.55325925f, 0.775181711f, 1.39069724f, -0.164325714f, 0.702355742f, -0.966605544f, 0.33885929f, + 0.15804705f, 0.699022174f, 0.59820044f, 0.026821347f, 0.372282267f, -0.543704391f, -0.10976062f, + 0.800984085f, -0.324458301f, -0.726262331f, 2.63070726f, 0.383172512f, 0.0681050867f, -0.276314974f, + -1.18075061f, -0.347105414f, 0.217539147f, -0.864108264f, 0.548889995f, 0.532529056f, 0.367275596f, + 0.0163819268f, -0.377964944f, 0.317718297f, 1.33938432f, 0.330852211f, -0.390468925f, -0.320560634f, + 1.5258193f, 0.671734691f, -1.10737002f, -0.931999147f, -1.35488868f, -1.09816337f, 1.31805503f, + 0.732232928f, 1.64078987f, 1.7977699f, -0.545845807f, 1.11368525f, -0.0705999359f, -0.427120239f, + 1.46568918f, 0.79176563f, -0.188307866f, -0.190290749f, -1.96288157f, -0.266497314f, -0.13633512f, + 0.556594431f, -0.349854231f, -0.968149066f, -0.191317648f, -0.20128338f, -0.406547606f, 0.171840504f, + -0.836737871f, -0.94232893f, -0.2348748f, 0.954904377f, 0.437810183f, 0.492056698f, -1.00853956f, + -0.817837775f, -0.248888627f, 0.710819006f, 1.72182703f, 1.37250972f, -0.293120593f, 0.969191968f, + -0.342054367f, 0.825411022f, 0.366591573f, 0.632159472f, 0.867908657f, 1.43508244f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/weights_data.h new file mode 100644 index 00000000..5f790c46 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_batch2_f32/weights_data.h @@ -0,0 +1,14 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_batch2_f32_weights_data[80] = { + 0.15606612f, -0.696474612f, -0.201183692f, -0.111947782f, -0.541920781f, 0.0190570373f, -0.547615409f, + -0.249809355f, 0.39163059f, 0.610747397f, 0.304994076f, 0.265345216f, 0.672506809f, 0.48402676f, + -0.0780239478f, 0.690217912f, -0.0241520423f, 0.402158022f, -0.698457062f, 0.26449576f, 0.291844636f, + 0.210334435f, -0.14876242f, -0.0287642125f, -0.403366476f, 0.688774288f, 0.432743639f, 0.0907602683f, + 0.253411472f, -0.668735504f, -0.482281595f, -0.749722242f, -0.161979064f, -0.592873693f, -0.702030599f, + 0.0543100238f, 0.543328762f, 0.46075356f, -0.22041449f, -0.167271763f, -0.083023645f, 0.0398066454f, + 0.39786154f, 0.665287733f, -0.543164074f, -0.721988082f, -0.627800643f, -0.431794256f, 0.65708375f, + 0.595555782f, -0.035874486f, -0.332329661f, 0.498431772f, -0.289274693f, -0.5731619f, 0.549736559f, + 0.413599104f, 0.219029427f, -0.497352332f, -0.118403837f, -0.176025108f, 0.489100337f, 0.260432899f, + 0.277133495f, -0.643098414f, 0.24705714f, 0.403701395f, -0.447807997f, 0.515267372f, 0.0830788016f, + -0.502504289f, 0.272969246f, 0.202310637f, 0.349246949f, -0.55274713f, 0.427130491f, -0.0510252975f, + -0.398657471f, -0.603888154f, -0.255301565f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/biases_data.h new file mode 100644 index 00000000..cec89cf0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_nhwc_chmult16_f16_biases_data[16] = {(float16_t)0.182861328f, + (float16_t)0.346923828f, + (float16_t)-0.486816406f, + (float16_t)-0.437011719f, + (float16_t)-0.462646484f, + (float16_t)-0.195556641f, + (float16_t)-0.185302734f, + (float16_t)-0.273193359f, + (float16_t)0.207519531f, + (float16_t)-0.0116424561f, + (float16_t)-0.0386962891f, + (float16_t)0.00981903076f, + (float16_t)-0.469482422f, + (float16_t)0.100219727f, + (float16_t)0.22668457f, + (float16_t)-0.260498047f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/config_data.h new file mode 100644 index 00000000..3846fd62 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_INPUT_BATCHES 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_INPUT_H 2 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_INPUT_W 21 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_IN_CH 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_CH_MULT 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_FILTER_H 2 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_FILTER_W 5 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUT_CH 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_STRIDE_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_STRIDE_W 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_PADDING_H 0 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_PADDING_W 0 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_DILATION_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_DILATION_W 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUTPUT_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUTPUT_W 17 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUTPUT_C 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_DST_SIZE 272 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_USE_WRAPPER 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/input_data.h new file mode 100644 index 00000000..65640bea --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/input_data.h @@ -0,0 +1,13 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_nhwc_chmult16_f16_input_data[42] = { + (float16_t)0.192871094f, (float16_t)-0.130371094f, (float16_t)0.836914062f, (float16_t)-0.56640625f, + (float16_t)0.863769531f, (float16_t)0.0140838623f, (float16_t)0.829101562f, (float16_t)-0.321289062f, + (float16_t)-0.170654297f, (float16_t)-0.549804688f, (float16_t)0.362304688f, (float16_t)0.0971679688f, + (float16_t)0.875488281f, (float16_t)0.470703125f, (float16_t)-0.985839844f, (float16_t)0.0336303711f, + (float16_t)0.548339844f, (float16_t)-0.75390625f, (float16_t)0.836425781f, (float16_t)0.575683594f, + (float16_t)-0.0604858398f, (float16_t)0.888671875f, (float16_t)0.674804688f, (float16_t)-0.66015625f, + (float16_t)0.218505859f, (float16_t)0.439208984f, (float16_t)0.236328125f, (float16_t)-0.134399414f, + (float16_t)-0.110656738f, (float16_t)0.471679688f, (float16_t)0.176879883f, (float16_t)0.398925781f, + (float16_t)0.771972656f, (float16_t)-0.521972656f, (float16_t)0.978515625f, (float16_t)-0.0848999023f, + (float16_t)-0.0333251953f, (float16_t)-0.4375f, (float16_t)-0.939453125f, (float16_t)-0.962402344f, + (float16_t)0.839355469f, (float16_t)-0.908203125f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/output_ref_data.h new file mode 100644 index 00000000..e2d26c75 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/output_ref_data.h @@ -0,0 +1,70 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_nhwc_chmult16_f16_output_ref_data[272] = { + (float16_t)-0.655761719f, (float16_t)0.168334961f, (float16_t)-0.346679688f, (float16_t)-1.81054688f, + (float16_t)-1.25097656f, (float16_t)0.852539062f, (float16_t)1.99121094f, (float16_t)-0.8984375f, + (float16_t)0.491699219f, (float16_t)-0.48828125f, (float16_t)0.700195312f, (float16_t)0.223022461f, + (float16_t)-1.07519531f, (float16_t)0.275878906f, (float16_t)0.798828125f, (float16_t)1.30371094f, + (float16_t)0.23840332f, (float16_t)-0.690429688f, (float16_t)0.860839844f, (float16_t)-0.724121094f, + (float16_t)-0.7265625f, (float16_t)-0.684082031f, (float16_t)-0.780273438f, (float16_t)0.453125f, + (float16_t)-0.355957031f, (float16_t)0.388183594f, (float16_t)-0.489257812f, (float16_t)-0.291992188f, + (float16_t)0.594238281f, (float16_t)-0.0119476318f, (float16_t)-0.0948486328f, (float16_t)-1.26757812f, + (float16_t)0.334228516f, (float16_t)1.87792969f, (float16_t)-1.87890625f, (float16_t)-0.717285156f, + (float16_t)-0.501953125f, (float16_t)0.516113281f, (float16_t)0.825195312f, (float16_t)-0.563476562f, + (float16_t)1.12402344f, (float16_t)0.0916748047f, (float16_t)0.568847656f, (float16_t)0.574707031f, + (float16_t)-0.345947266f, (float16_t)2.21484375f, (float16_t)0.398925781f, (float16_t)0.977539062f, + (float16_t)-0.756347656f, (float16_t)0.33984375f, (float16_t)-0.337158203f, (float16_t)-1.31738281f, + (float16_t)-0.603027344f, (float16_t)-0.575195312f, (float16_t)-0.345947266f, (float16_t)-0.311035156f, + (float16_t)-0.18737793f, (float16_t)0.378173828f, (float16_t)-1.13183594f, (float16_t)-0.340820312f, + (float16_t)-0.40234375f, (float16_t)-0.833496094f, (float16_t)0.774414062f, (float16_t)-0.747070312f, + (float16_t)-0.629882812f, (float16_t)1.1875f, (float16_t)-0.890136719f, (float16_t)-0.674804688f, + (float16_t)-0.579589844f, (float16_t)0.251953125f, (float16_t)1.05078125f, (float16_t)-1.59179688f, + (float16_t)0.731445312f, (float16_t)-0.335449219f, (float16_t)0.828125f, (float16_t)0.940917969f, + (float16_t)-0.384521484f, (float16_t)0.662597656f, (float16_t)-0.335449219f, (float16_t)-0.122680664f, + (float16_t)0.417236328f, (float16_t)-0.602050781f, (float16_t)0.558105469f, (float16_t)-0.425048828f, + (float16_t)0.0113143921f, (float16_t)-0.719238281f, (float16_t)-0.155029297f, (float16_t)-0.37109375f, + (float16_t)-0.524414062f, (float16_t)0.364746094f, (float16_t)0.324707031f, (float16_t)0.0541992188f, + (float16_t)0.0864257812f, (float16_t)-0.768554688f, (float16_t)-0.389648438f, (float16_t)-0.601074219f, + (float16_t)0.807128906f, (float16_t)0.853027344f, (float16_t)-0.749023438f, (float16_t)0.560546875f, + (float16_t)0.206420898f, (float16_t)-0.41015625f, (float16_t)0.365722656f, (float16_t)-0.469726562f, + (float16_t)0.627441406f, (float16_t)0.30078125f, (float16_t)0.201782227f, (float16_t)0.10369873f, + (float16_t)0.0840454102f, (float16_t)0.955566406f, (float16_t)-0.902832031f, (float16_t)-0.29296875f, + (float16_t)0.475830078f, (float16_t)0.678222656f, (float16_t)-0.627929688f, (float16_t)0.166870117f, + (float16_t)-0.567382812f, (float16_t)-0.659179688f, (float16_t)-0.509765625f, (float16_t)0.151855469f, + (float16_t)-0.998046875f, (float16_t)-0.172729492f, (float16_t)-1.08886719f, (float16_t)-1.14941406f, + (float16_t)-0.869140625f, (float16_t)0.189697266f, (float16_t)-0.159545898f, (float16_t)-0.451416016f, + (float16_t)0.199707031f, (float16_t)0.0298919678f, (float16_t)-0.321044922f, (float16_t)-1.39746094f, + (float16_t)-1.19726562f, (float16_t)0.37890625f, (float16_t)0.605957031f, (float16_t)0.502929688f, + (float16_t)0.700683594f, (float16_t)0.312255859f, (float16_t)0.185913086f, (float16_t)0.53515625f, + (float16_t)0.100891113f, (float16_t)0.895996094f, (float16_t)0.360595703f, (float16_t)1.09570312f, + (float16_t)-0.937988281f, (float16_t)1.7578125f, (float16_t)-1.03808594f, (float16_t)-0.354980469f, + (float16_t)-0.846679688f, (float16_t)-0.616699219f, (float16_t)-0.117492676f, (float16_t)-0.35546875f, + (float16_t)0.0380249023f, (float16_t)-0.18737793f, (float16_t)-2.1875f, (float16_t)-1.3359375f, + (float16_t)-0.958984375f, (float16_t)0.142944336f, (float16_t)0.793945312f, (float16_t)-1.16308594f, + (float16_t)-0.555664062f, (float16_t)0.523925781f, (float16_t)-0.633789062f, (float16_t)-1.73535156f, + (float16_t)-1.26367188f, (float16_t)0.361328125f, (float16_t)0.443359375f, (float16_t)-1.12402344f, + (float16_t)-0.712402344f, (float16_t)-0.317138672f, (float16_t)1.28125f, (float16_t)1.41503906f, + (float16_t)-0.292724609f, (float16_t)0.46484375f, (float16_t)-0.347900391f, (float16_t)0.626464844f, + (float16_t)-0.397216797f, (float16_t)-0.0182342529f, (float16_t)0.625488281f, (float16_t)-0.359863281f, + (float16_t)0.328125f, (float16_t)-0.66796875f, (float16_t)1.08203125f, (float16_t)-0.876953125f, + (float16_t)1.55664062f, (float16_t)0.833007812f, (float16_t)0.274902344f, (float16_t)0.895996094f, + (float16_t)0.848144531f, (float16_t)-0.878417969f, (float16_t)-0.4453125f, (float16_t)-0.629882812f, + (float16_t)0.82421875f, (float16_t)-0.0803833008f, (float16_t)-1.00097656f, (float16_t)-0.793945312f, + (float16_t)0.899902344f, (float16_t)-0.623046875f, (float16_t)0.504394531f, (float16_t)-0.180419922f, + (float16_t)0.1640625f, (float16_t)1.45117188f, (float16_t)-0.727539062f, (float16_t)-0.737304688f, + (float16_t)-0.0979003906f, (float16_t)-0.132080078f, (float16_t)-0.0412597656f, (float16_t)0.129394531f, + (float16_t)-0.643554688f, (float16_t)0.274902344f, (float16_t)-0.768066406f, (float16_t)-0.666503906f, + (float16_t)-1.26464844f, (float16_t)0.180664062f, (float16_t)-0.959960938f, (float16_t)-0.465820312f, + (float16_t)1.24023438f, (float16_t)-0.0947875977f, (float16_t)0.241577148f, (float16_t)1.78808594f, + (float16_t)0.345458984f, (float16_t)0.295898438f, (float16_t)-0.781738281f, (float16_t)-1.09277344f, + (float16_t)-0.334472656f, (float16_t)-0.359619141f, (float16_t)-0.41015625f, (float16_t)-1.63769531f, + (float16_t)-0.637207031f, (float16_t)0.567382812f, (float16_t)0.463623047f, (float16_t)-0.0538635254f, + (float16_t)1.54199219f, (float16_t)-0.108398438f, (float16_t)-0.0389404297f, (float16_t)-0.178100586f, + (float16_t)-1.53027344f, (float16_t)-1.10449219f, (float16_t)2.60351562f, (float16_t)0.899414062f, + (float16_t)0.14074707f, (float16_t)0.0667114258f, (float16_t)-0.338623047f, (float16_t)-0.461669922f, + (float16_t)-0.749511719f, (float16_t)-0.156494141f, (float16_t)-0.96484375f, (float16_t)-0.0243377686f, + (float16_t)-0.350341797f, (float16_t)-0.644042969f, (float16_t)-1.07226562f, (float16_t)-1.77929688f, + (float16_t)-1.453125f, (float16_t)0.173828125f, (float16_t)1.46777344f, (float16_t)-1.47851562f, + (float16_t)1.01269531f, (float16_t)-4.99486923e-05f, (float16_t)-0.639648438f, (float16_t)-1.48828125f, + (float16_t)-1.74707031f, (float16_t)1.28125f, (float16_t)-0.875976562f, (float16_t)0.215576172f, + (float16_t)0.246337891f, (float16_t)-0.834960938f, (float16_t)2.578125f, (float16_t)1.66894531f, + (float16_t)-0.26171875f, (float16_t)2.43359375f, (float16_t)0.841308594f, (float16_t)1.06933594f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/weights_data.h new file mode 100644 index 00000000..a46cbf26 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f16/weights_data.h @@ -0,0 +1,42 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_2x5_opt_nhwc_chmult16_f16_weights_data[160] = { + (float16_t)0.11730957f, (float16_t)0.290527344f, (float16_t)-0.326660156f, (float16_t)0.0891723633f, + (float16_t)0.371337891f, (float16_t)0.0716552734f, (float16_t)0.393066406f, (float16_t)-0.475097656f, + (float16_t)0.404785156f, (float16_t)0.313476562f, (float16_t)0.508789062f, (float16_t)0.550292969f, + (float16_t)0.583007812f, (float16_t)0.747558594f, (float16_t)-0.740234375f, (float16_t)-0.136474609f, + (float16_t)-0.173339844f, (float16_t)-0.443847656f, (float16_t)0.508789062f, (float16_t)-0.536621094f, + (float16_t)0.479736328f, (float16_t)-0.260742188f, (float16_t)0.438964844f, (float16_t)-0.203125f, + (float16_t)-0.0804443359f, (float16_t)0.51171875f, (float16_t)-0.0498962402f, (float16_t)-0.204223633f, + (float16_t)0.2734375f, (float16_t)-0.646484375f, (float16_t)0.333251953f, (float16_t)-0.077331543f, + (float16_t)-0.647460938f, (float16_t)0.723144531f, (float16_t)-0.376708984f, (float16_t)-0.621582031f, + (float16_t)-0.503417969f, (float16_t)0.575195312f, (float16_t)0.590332031f, (float16_t)-0.557617188f, + (float16_t)0.414550781f, (float16_t)-0.42578125f, (float16_t)0.628417969f, (float16_t)0.657226562f, + (float16_t)-0.230712891f, (float16_t)0.526855469f, (float16_t)0.546386719f, (float16_t)0.570800781f, + (float16_t)-0.287353516f, (float16_t)0.375244141f, (float16_t)-0.322265625f, (float16_t)-0.408935547f, + (float16_t)-0.584472656f, (float16_t)0.119812012f, (float16_t)-0.58203125f, (float16_t)0.244995117f, + (float16_t)-0.269042969f, (float16_t)-0.142211914f, (float16_t)-0.459960938f, (float16_t)-0.124389648f, + (float16_t)0.0325012207f, (float16_t)0.659667969f, (float16_t)0.284179688f, (float16_t)-0.346679688f, + (float16_t)0.149169922f, (float16_t)-0.158447266f, (float16_t)0.00865936279f, (float16_t)-0.488525391f, + (float16_t)-0.119445801f, (float16_t)0.321777344f, (float16_t)0.602539062f, (float16_t)0.432617188f, + (float16_t)0.364501953f, (float16_t)0.165283203f, (float16_t)-0.22644043f, (float16_t)-0.517578125f, + (float16_t)-0.105712891f, (float16_t)0.456787109f, (float16_t)0.715820312f, (float16_t)0.635253906f, + (float16_t)-0.60546875f, (float16_t)-0.581054688f, (float16_t)0.647460938f, (float16_t)-0.404541016f, + (float16_t)-0.414550781f, (float16_t)0.080078125f, (float16_t)0.388916016f, (float16_t)-0.0678100586f, + (float16_t)0.305908203f, (float16_t)0.0531921387f, (float16_t)-0.0305023193f, (float16_t)0.444335938f, + (float16_t)0.459960938f, (float16_t)-0.438964844f, (float16_t)-0.411132812f, (float16_t)-0.250488281f, + (float16_t)-0.431396484f, (float16_t)0.333496094f, (float16_t)-0.549804688f, (float16_t)-0.397705078f, + (float16_t)0.164672852f, (float16_t)-0.0583496094f, (float16_t)0.647949219f, (float16_t)-0.341308594f, + (float16_t)-0.232421875f, (float16_t)0.274902344f, (float16_t)-0.5546875f, (float16_t)-0.240112305f, + (float16_t)-0.342285156f, (float16_t)-0.436767578f, (float16_t)-0.0305938721f, (float16_t)0.454101562f, + (float16_t)-0.135375977f, (float16_t)0.645507812f, (float16_t)-0.19543457f, (float16_t)0.564941406f, + (float16_t)0.397460938f, (float16_t)-0.520019531f, (float16_t)0.0258331299f, (float16_t)0.0103988647f, + (float16_t)0.481689453f, (float16_t)0.526855469f, (float16_t)-0.668457031f, (float16_t)0.128173828f, + (float16_t)0.666015625f, (float16_t)0.0575866699f, (float16_t)-0.649414062f, (float16_t)-0.451904297f, + (float16_t)0.596679688f, (float16_t)-0.496582031f, (float16_t)0.477783203f, (float16_t)-0.20715332f, + (float16_t)-0.151245117f, (float16_t)0.0419006348f, (float16_t)0.159301758f, (float16_t)0.420898438f, + (float16_t)-0.692871094f, (float16_t)0.129760742f, (float16_t)0.661621094f, (float16_t)0.222167969f, + (float16_t)0.418701172f, (float16_t)0.409423828f, (float16_t)-0.425537109f, (float16_t)0.727050781f, + (float16_t)-0.0552978516f, (float16_t)0.6328125f, (float16_t)-0.0977783203f, (float16_t)0.673828125f, + (float16_t)-0.117370605f, (float16_t)-0.170898438f, (float16_t)0.0303344727f, (float16_t)-0.279785156f, + (float16_t)-0.6015625f, (float16_t)-0.570800781f, (float16_t)-0.321289062f, (float16_t)-0.659667969f, + (float16_t)-0.474853516f, (float16_t)0.271484375f, (float16_t)-0.240478516f, (float16_t)-0.417480469f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/biases_data.h new file mode 100644 index 00000000..35b08d67 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_nhwc_chmult16_f32_biases_data[16] = {0.182861522f, + 0.346894652f, + -0.486834288f, + -0.436927557f, + -0.462665021f, + -0.195506439f, + -0.185361698f, + -0.273116261f, + 0.207508504f, + -0.0116399359f, + -0.0386982523f, + 0.00981963426f, + -0.469506055f, + 0.100227475f, + 0.226686105f, + -0.260502815f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/config_data.h new file mode 100644 index 00000000..07865234 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_INPUT_BATCHES 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_INPUT_H 2 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_INPUT_W 21 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_IN_CH 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_CH_MULT 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_FILTER_H 2 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_FILTER_W 5 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUT_CH 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_STRIDE_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_STRIDE_W 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_PADDING_H 0 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_PADDING_W 0 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_DILATION_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_DILATION_W 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUTPUT_H 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUTPUT_W 17 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUTPUT_C 16 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_DST_SIZE 272 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_USE_WRAPPER 1 +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/input_data.h new file mode 100644 index 00000000..adc9b377 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_nhwc_chmult16_f32_input_data[42] = { + 0.192811564f, -0.130386308f, 0.836830735f, -0.566606879f, 0.863662958f, 0.0140871555f, 0.828888118f, + -0.321313202f, -0.170697495f, -0.549981415f, 0.362282932f, 0.0971592665f, 0.875264049f, 0.470772117f, + -0.985958457f, 0.0336354412f, 0.548566878f, -0.75387311f, 0.836651325f, 0.575591445f, -0.0604859777f, + 0.888901055f, 0.675020635f, -0.660128117f, 0.218509644f, 0.439288825f, 0.236301422f, -0.134378016f, + -0.110660695f, 0.471626192f, 0.176913098f, 0.39894864f, 0.771873295f, -0.522054672f, 0.978722572f, + -0.0849099383f, -0.0333307162f, -0.437413454f, -0.93960768f, -0.962278903f, 0.839178562f, -0.908111095f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/output_ref_data.h new file mode 100644 index 00000000..0c83e59f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/output_ref_data.h @@ -0,0 +1,41 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_nhwc_chmult16_f32_output_ref_data[272] = { + -0.655864179f, 0.168369666f, -0.346618712f, -1.8105073f, -1.25141263f, 0.85275352f, 1.99126899f, + -0.898566008f, 0.491708487f, -0.488381386f, 0.70012629f, 0.223025709f, -1.0747627f, 0.275871515f, + 0.798837543f, 1.30348003f, 0.238399789f, -0.690236568f, 0.860723615f, -0.724049091f, -0.726685464f, + -0.683884025f, -0.78031528f, 0.453075796f, -0.356049657f, 0.388210952f, -0.489190012f, -0.29192099f, + 0.594303608f, -0.0119484365f, -0.0948404521f, -1.26798987f, 0.334278911f, 1.87753201f, -1.87845504f, + -0.717157245f, -0.501722395f, 0.516207039f, 0.825321496f, -0.563287854f, 1.12423038f, 0.0916789398f, + 0.568675518f, 0.574690342f, -0.346009046f, 2.21483564f, 0.3989833f, 0.977666259f, -0.756458521f, + 0.339963377f, -0.337192357f, -1.31774902f, -0.602928102f, -0.575042844f, -0.345906258f, -0.311078191f, + -0.187384158f, 0.378273785f, -1.13144577f, -0.340895146f, -0.402260005f, -0.833582401f, 0.774328351f, + -0.74694407f, -0.629658818f, 1.18752551f, -0.890272141f, -0.674790025f, -0.579758704f, 0.251931071f, + 1.05093825f, -1.59136677f, 0.731469095f, -0.335342914f, 0.827959418f, 0.941035748f, -0.384489924f, + 0.662420988f, -0.335517168f, -0.122698233f, 0.417190582f, -0.60189414f, 0.557917714f, -0.42515415f, + 0.0113144815f, -0.719057143f, -0.154984325f, -0.370996237f, -0.524178863f, 0.364631265f, 0.324816257f, + 0.054206118f, 0.0864200294f, -0.768368006f, -0.389769733f, -0.601079464f, 0.80712539f, 0.853112102f, + -0.749229193f, 0.560461581f, 0.206393421f, -0.410079956f, 0.365643978f, -0.469822347f, 0.627267838f, + 0.300686687f, 0.201795503f, 0.103717297f, 0.0840247571f, 0.95563817f, -0.902997851f, -0.293025196f, + 0.475952029f, 0.678280771f, -0.627818942f, 0.166880667f, -0.56729573f, -0.659216046f, -0.509616077f, + 0.151878476f, -0.99802357f, -0.172751054f, -1.08849633f, -1.14947379f, -0.86912787f, 0.189704359f, + -0.159560248f, -0.451404572f, 0.199646115f, 0.0298979878f, -0.321161211f, -1.39700556f, -1.19702482f, + 0.37896347f, 0.605967283f, 0.5028615f, 0.700604558f, 0.312141985f, 0.185945049f, 0.53521347f, + 0.100917786f, 0.895816743f, 0.360534012f, 1.09544563f, -0.937949836f, 1.75740087f, -1.0381701f, + -0.355045587f, -0.846622109f, -0.616750479f, -0.11750406f, -0.355386853f, 0.0380137414f, -0.18731761f, + -2.18757653f, -1.33565342f, -0.959203184f, 0.142885268f, 0.794167399f, -1.16262007f, -0.555749059f, + 0.523890734f, -0.633965909f, -1.73494685f, -1.26355731f, 0.361361682f, 0.443244398f, -1.12434268f, + -0.712533414f, -0.317237705f, 1.28119695f, 1.41514838f, -0.29266417f, 0.464817286f, -0.347861946f, + 0.626471043f, -0.397280455f, -0.0182318389f, 0.625359058f, -0.359879196f, 0.328153014f, -0.667846978f, + 1.08203197f, -0.876873851f, 1.55701733f, 0.833180845f, 0.275014848f, 0.896131217f, 0.848266959f, + -0.878187358f, -0.445256054f, -0.630075097f, 0.824047148f, -0.0803688765f, -1.00054717f, -0.794147432f, + 0.899701655f, -0.623280883f, 0.504504919f, -0.180410758f, 0.164123148f, 1.451038f, -0.727662861f, + -0.737534106f, -0.0978879035f, -0.132132471f, -0.0412481278f, 0.129382968f, -0.643394411f, 0.27482146f, + -0.768244445f, -0.666455805f, -1.26499426f, 0.180659726f, -0.960094869f, -0.465865821f, 1.24048781f, + -0.0947715342f, 0.241587311f, 1.78815472f, 0.345412642f, 0.29595679f, -0.781923771f, -1.0928545f, + -0.334375858f, -0.359639972f, -0.410090297f, -1.63774896f, -0.63720715f, 0.56721127f, 0.463695943f, + -0.0538785309f, 1.54205036f, -0.108403206f, -0.038941808f, -0.178150922f, -1.53012514f, -1.10409713f, + 2.60343051f, 0.899313092f, 0.140739247f, 0.0666996241f, -0.338524401f, -0.461749673f, -0.749689579f, + -0.156456962f, -0.965028405f, -0.0243452489f, -0.350425541f, -0.644226134f, -1.07256305f, -1.778988f, + -1.45266211f, 0.173786312f, 1.46778715f, -1.4780817f, 1.01314843f, -4.99784946e-05f, -0.639476836f, + -1.48858547f, -1.74731636f, 1.28155911f, -0.876033366f, 0.215553254f, 0.246363997f, -0.834825277f, + 2.57764387f, 1.66899216f, -0.261755854f, 2.43452024f, 0.841412306f, 1.06977844f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/weights_data.h new file mode 100644 index 00000000..ff42eeb3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_2x5_opt_nhwc_chmult16_f32/weights_data.h @@ -0,0 +1,25 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_2x5_opt_nhwc_chmult16_f32_weights_data[160] = { + 0.117334299f, 0.290425032f, -0.326645523f, 0.0891823098f, 0.371241391f, 0.0716729537f, 0.393114328f, + -0.475012869f, 0.404728055f, 0.313365728f, 0.508818686f, 0.550433218f, 0.583015084f, 0.747533917f, + -0.740146399f, -0.136460081f, -0.173328921f, -0.443775445f, 0.508637011f, -0.53638351f, 0.479720086f, + -0.260737926f, 0.438988417f, -0.203094378f, -0.0804625675f, 0.511614084f, -0.0498934425f, -0.204200283f, + 0.273335874f, -0.646455228f, 0.333364606f, -0.077329196f, -0.647217989f, 0.723168314f, -0.376742363f, + -0.621647716f, -0.50360465f, 0.575053155f, 0.590160489f, -0.557573795f, 0.414638162f, -0.425847203f, + 0.628370464f, 0.657378495f, -0.230773866f, 0.526778221f, 0.546309531f, 0.570831716f, -0.28744483f, + 0.375290602f, -0.322158456f, -0.408991516f, -0.584488451f, 0.119820394f, -0.582246602f, 0.244970083f, + -0.269087404f, -0.142202944f, -0.460054338f, -0.124407336f, 0.0324999951f, 0.659565866f, 0.28428492f, + -0.34664464f, 0.149174288f, -0.158497587f, 0.00866199099f, -0.488574892f, -0.119466007f, 0.321899265f, + 0.602336824f, 0.432558775f, 0.364513427f, 0.165267304f, -0.226455554f, -0.517722011f, -0.105729647f, + 0.456745565f, 0.716013968f, 0.635067105f, -0.605574608f, -0.581181347f, 0.647404373f, -0.404428482f, + -0.414628625f, 0.080075711f, 0.388964832f, -0.0678159669f, 0.30598864f, 0.0531979203f, -0.0304984748f, + 0.444303572f, 0.459977835f, -0.439016521f, -0.411185652f, -0.25053224f, -0.4313384f, 0.333512247f, + -0.549805403f, -0.397683978f, 0.164667919f, -0.0583585799f, 0.647776663f, -0.341361314f, -0.232449502f, + 0.27482152f, -0.554601014f, -0.240126058f, -0.342345953f, -0.436843365f, -0.0305915326f, 0.453998774f, + -0.135403037f, 0.645355284f, -0.195429847f, 0.565151453f, 0.397394925f, -0.51997596f, 0.025832016f, + 0.0103985211f, 0.481679738f, 0.526907563f, -0.668593228f, 0.12813507f, 0.666254938f, 0.0575793087f, + -0.649352133f, -0.451881766f, 0.596447706f, -0.496507138f, 0.477673024f, -0.207125947f, -0.151244894f, + 0.0419035703f, 0.159245804f, 0.420944542f, -0.692717135f, 0.129808843f, 0.661717832f, 0.222183436f, + 0.418654472f, 0.409499705f, -0.425654382f, 0.727283895f, -0.0553078391f, 0.632774115f, -0.0978082344f, + 0.673819959f, -0.117357276f, -0.1709418f, 0.0303407423f, -0.279859662f, -0.601751447f, -0.570778847f, + -0.321399391f, -0.659774363f, -0.474931121f, 0.271549553f, -0.240506738f, -0.417383701f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/biases_data.h new file mode 100644 index 00000000..1d279e7f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_f16_biases_data[8] = {(float16_t)-0.194580078f, + (float16_t)-0.470214844f, + (float16_t)0.136962891f, + (float16_t)0.052520752f, + (float16_t)0.222412109f, + (float16_t)0.144042969f, + (float16_t)-0.0728149414f, + (float16_t)0.153930664f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/config_data.h new file mode 100644 index 00000000..023f4df9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_BASIC_F16_INPUT_BATCHES 1 +#define DEPTHWISE_BASIC_F16_INPUT_H 9 +#define DEPTHWISE_BASIC_F16_INPUT_W 11 +#define DEPTHWISE_BASIC_F16_IN_CH 8 +#define DEPTHWISE_BASIC_F16_CH_MULT 1 +#define DEPTHWISE_BASIC_F16_FILTER_H 3 +#define DEPTHWISE_BASIC_F16_FILTER_W 3 +#define DEPTHWISE_BASIC_F16_OUT_CH 8 +#define DEPTHWISE_BASIC_F16_STRIDE_H 1 +#define DEPTHWISE_BASIC_F16_STRIDE_W 1 +#define DEPTHWISE_BASIC_F16_PADDING_H 0 +#define DEPTHWISE_BASIC_F16_PADDING_W 0 +#define DEPTHWISE_BASIC_F16_DILATION_H 1 +#define DEPTHWISE_BASIC_F16_DILATION_W 1 +#define DEPTHWISE_BASIC_F16_OUTPUT_H 7 +#define DEPTHWISE_BASIC_F16_OUTPUT_W 9 +#define DEPTHWISE_BASIC_F16_OUTPUT_C 8 +#define DEPTHWISE_BASIC_F16_DST_SIZE 504 +#define DEPTHWISE_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_BASIC_F16_USE_WRAPPER 0 +#define DEPTHWISE_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_BASIC_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/input_data.h new file mode 100644 index 00000000..570d59b0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/input_data.h @@ -0,0 +1,200 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_f16_input_data[792] = { + (float16_t)0.227416992f, (float16_t)0.486328125f, (float16_t)-0.433837891f, (float16_t)0.0367736816f, + (float16_t)0.728515625f, (float16_t)-0.923339844f, (float16_t)-0.633789062f, (float16_t)-0.372070312f, + (float16_t)-0.869628906f, (float16_t)0.513671875f, (float16_t)-0.0416564941f, (float16_t)-0.287841797f, + (float16_t)0.467773438f, (float16_t)0.194702148f, (float16_t)-0.513671875f, (float16_t)-0.099609375f, + (float16_t)-0.70703125f, (float16_t)0.199462891f, (float16_t)-0.0665283203f, (float16_t)0.559082031f, + (float16_t)0.0409545898f, (float16_t)-0.342773438f, (float16_t)-0.0247802734f, (float16_t)0.932128906f, + (float16_t)-0.534179688f, (float16_t)0.118896484f, (float16_t)0.441162109f, (float16_t)0.573730469f, + (float16_t)0.382080078f, (float16_t)-0.574707031f, (float16_t)0.700683594f, (float16_t)0.618164062f, + (float16_t)0.998046875f, (float16_t)0.971191406f, (float16_t)-0.0928955078f, (float16_t)0.706054688f, + (float16_t)-0.778808594f, (float16_t)-0.923828125f, (float16_t)-0.23828125f, (float16_t)0.249145508f, + (float16_t)0.936523438f, (float16_t)-0.137939453f, (float16_t)-0.491699219f, (float16_t)0.0402526855f, + (float16_t)0.421142578f, (float16_t)0.366699219f, (float16_t)0.0389709473f, (float16_t)-0.561035156f, + (float16_t)0.618652344f, (float16_t)0.716796875f, (float16_t)-0.180908203f, (float16_t)0.217651367f, + (float16_t)0.66015625f, (float16_t)0.807617188f, (float16_t)0.917480469f, (float16_t)0.673339844f, + (float16_t)-0.421386719f, (float16_t)-0.70703125f, (float16_t)-0.448974609f, (float16_t)0.352050781f, + (float16_t)0.491210938f, (float16_t)0.135620117f, (float16_t)-0.475585938f, (float16_t)0.900390625f, + (float16_t)-0.436767578f, (float16_t)-0.114013672f, (float16_t)-0.719238281f, (float16_t)0.426757812f, + (float16_t)-0.862304688f, (float16_t)-0.876464844f, (float16_t)0.90625f, (float16_t)0.35546875f, + (float16_t)-0.548339844f, (float16_t)0.805175781f, (float16_t)0.308349609f, (float16_t)-0.270507812f, + (float16_t)0.883300781f, (float16_t)0.775878906f, (float16_t)0.108154297f, (float16_t)0.285644531f, + (float16_t)-0.922851562f, (float16_t)0.854003906f, (float16_t)0.4375f, (float16_t)-0.616210938f, + (float16_t)0.0389099121f, (float16_t)-0.608398438f, (float16_t)0.821777344f, (float16_t)0.656738281f, + (float16_t)0.362304688f, (float16_t)0.942382812f, (float16_t)-0.239135742f, (float16_t)-0.909667969f, + (float16_t)-0.647460938f, 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(float16_t)0.203735352f, + (float16_t)-0.620117188f, (float16_t)-0.180541992f, (float16_t)-0.455810547f, (float16_t)-0.656738281f, + (float16_t)-0.0952148438f, (float16_t)0.414794922f, (float16_t)0.276855469f, (float16_t)-0.27734375f, + (float16_t)0.0941162109f, (float16_t)-0.0970458984f, (float16_t)-0.880859375f, (float16_t)-0.361328125f, + (float16_t)0.553710938f, (float16_t)-0.333007812f, (float16_t)0.0580444336f, (float16_t)-0.986816406f, + (float16_t)0.0986328125f, (float16_t)-0.267333984f, (float16_t)-0.0327758789f, (float16_t)0.693359375f, + (float16_t)-0.197387695f, (float16_t)-0.69140625f, (float16_t)0.429199219f, (float16_t)-0.734863281f, + (float16_t)0.388427734f, (float16_t)0.774414062f, (float16_t)0.984863281f, (float16_t)0.670898438f, + (float16_t)-0.08984375f, (float16_t)-0.813476562f, (float16_t)0.861328125f, (float16_t)-0.479736328f, + (float16_t)0.414550781f, (float16_t)0.797851562f, (float16_t)0.887207031f, (float16_t)-0.911132812f, + (float16_t)0.0307312012f, (float16_t)0.850097656f, (float16_t)0.274169922f, (float16_t)0.481689453f, + (float16_t)-0.838378906f, (float16_t)0.12512207f, (float16_t)0.264892578f, (float16_t)0.709472656f, + (float16_t)-0.659667969f, (float16_t)-0.354980469f, (float16_t)-0.733398438f, (float16_t)-0.0453491211f, + (float16_t)0.134155273f, (float16_t)-0.892089844f, (float16_t)0.504882812f, (float16_t)0.171875f, + (float16_t)-0.0272369385f, (float16_t)-0.224609375f, (float16_t)-0.343261719f, (float16_t)-0.993164062f, + (float16_t)-0.516113281f, (float16_t)0.114257812f, (float16_t)0.403808594f, (float16_t)0.769042969f, + (float16_t)-0.956054688f, (float16_t)-0.659179688f, (float16_t)-0.194091797f, (float16_t)0.619140625f, + (float16_t)0.451660156f, (float16_t)0.727050781f, (float16_t)-0.265380859f, (float16_t)0.334228516f, + (float16_t)0.419921875f, (float16_t)-0.161376953f, (float16_t)0.528320312f, (float16_t)-0.534179688f, + (float16_t)-0.681640625f, (float16_t)-0.040802002f, (float16_t)0.585449219f, (float16_t)0.909667969f, + (float16_t)0.897460938f, (float16_t)0.821777344f, (float16_t)-0.919921875f, (float16_t)0.377685547f, + (float16_t)-0.193481445f, (float16_t)-0.0801391602f, (float16_t)0.260253906f, (float16_t)-0.371337891f, + (float16_t)0.192871094f, (float16_t)-0.840332031f, (float16_t)0.789550781f, (float16_t)-0.268798828f, + (float16_t)0.665039062f, (float16_t)-0.745117188f, (float16_t)-0.862792969f, (float16_t)0.51171875f, + (float16_t)0.102478027f, (float16_t)-0.299316406f, (float16_t)0.0635986328f, (float16_t)0.795898438f, + (float16_t)0.0700073242f, (float16_t)0.345703125f, (float16_t)-0.547851562f, (float16_t)0.963378906f, + (float16_t)0.1953125f, (float16_t)0.981933594f, (float16_t)-0.793945312f, (float16_t)-0.940917969f, + (float16_t)0.900878906f, (float16_t)0.700683594f, (float16_t)-0.607910156f, (float16_t)-0.876953125f, + (float16_t)-0.0585021973f, (float16_t)0.220581055f, (float16_t)-0.0879516602f, (float16_t)-0.545898438f, + (float16_t)0.115722656f, (float16_t)0.538574219f, (float16_t)0.205688477f, (float16_t)0.0253601074f, + (float16_t)-0.369628906f, (float16_t)0.729980469f, (float16_t)-0.637207031f, (float16_t)0.46875f, + (float16_t)-0.854492188f, (float16_t)0.844726562f, (float16_t)-0.313232422f, (float16_t)-0.9296875f, + (float16_t)0.336181641f, (float16_t)0.0588989258f, (float16_t)-0.440185547f, (float16_t)-0.465576172f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/output_ref_data.h new file mode 100644 index 00000000..49e9f0d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/output_ref_data.h @@ -0,0 +1,128 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_f16_output_ref_data[504] = { + (float16_t)-0.143798828f, (float16_t)-1.13476562f, (float16_t)-0.866699219f, (float16_t)1.28515625f, + (float16_t)0.562988281f, (float16_t)-0.698242188f, (float16_t)0.347167969f, (float16_t)0.783203125f, + (float16_t)-0.854980469f, (float16_t)-1.94335938f, (float16_t)-0.330810547f, (float16_t)-0.145263672f, + (float16_t)-0.0545349121f, (float16_t)-0.895996094f, (float16_t)-0.225219727f, (float16_t)-0.0504455566f, + (float16_t)-1.36035156f, (float16_t)-0.874023438f, (float16_t)0.192138672f, (float16_t)0.0417175293f, + (float16_t)-0.713867188f, (float16_t)-0.827636719f, (float16_t)0.0869750977f, (float16_t)1.75292969f, + (float16_t)-1.30078125f, (float16_t)-0.969238281f, (float16_t)1.11035156f, (float16_t)-1.45800781f, + (float16_t)1.08886719f, (float16_t)0.198364258f, (float16_t)-0.255615234f, (float16_t)-0.142944336f, + (float16_t)0.79296875f, (float16_t)-0.215332031f, (float16_t)-0.608886719f, (float16_t)0.603515625f, + (float16_t)0.566894531f, (float16_t)0.0720214844f, (float16_t)-0.179931641f, (float16_t)0.014541626f, + (float16_t)0.10333252f, (float16_t)0.101989746f, (float16_t)1.34179688f, (float16_t)-0.0938720703f, + (float16_t)0.450927734f, (float16_t)-0.0205841064f, (float16_t)0.314453125f, (float16_t)1.23046875f, + (float16_t)-0.682617188f, (float16_t)0.703613281f, (float16_t)-0.203491211f, (float16_t)-0.0377197266f, + (float16_t)-1.14550781f, (float16_t)1.04589844f, (float16_t)-1.38183594f, (float16_t)-0.360107422f, + (float16_t)-0.478759766f, (float16_t)-0.111328125f, (float16_t)-0.472412109f, (float16_t)-0.580078125f, + (float16_t)0.431396484f, (float16_t)0.0476074219f, (float16_t)0.488525391f, (float16_t)0.863769531f, + (float16_t)0.40234375f, (float16_t)-1.83984375f, (float16_t)0.250976562f, (float16_t)0.172607422f, + (float16_t)-0.714355469f, (float16_t)-0.601074219f, (float16_t)-1.33886719f, (float16_t)0.642089844f, + (float16_t)-0.666992188f, (float16_t)-0.213989258f, (float16_t)0.364257812f, (float16_t)-0.245605469f, + (float16_t)0.815917969f, (float16_t)0.275146484f, (float16_t)-0.462158203f, (float16_t)0.271240234f, + (float16_t)0.204467773f, (float16_t)-0.62890625f, (float16_t)0.81640625f, (float16_t)-1.02832031f, + (float16_t)0.273925781f, (float16_t)-0.140625f, (float16_t)0.537597656f, (float16_t)-0.0734863281f, + (float16_t)-0.838867188f, (float16_t)-0.611328125f, (float16_t)0.856933594f, (float16_t)0.4140625f, + (float16_t)-0.662597656f, (float16_t)0.264160156f, (float16_t)0.131835938f, (float16_t)0.958984375f, + (float16_t)-0.999511719f, (float16_t)0.890625f, (float16_t)-0.452636719f, (float16_t)0.257080078f, + (float16_t)-0.429443359f, (float16_t)0.0942993164f, (float16_t)0.629394531f, (float16_t)0.407226562f, + (float16_t)-0.889648438f, (float16_t)-1.01269531f, (float16_t)1.10742188f, (float16_t)-0.123535156f, + (float16_t)0.394775391f, (float16_t)1.51269531f, (float16_t)-0.529785156f, (float16_t)0.00202178955f, + (float16_t)0.0217285156f, (float16_t)-0.815429688f, (float16_t)0.764648438f, (float16_t)-0.0147857666f, + (float16_t)0.967773438f, (float16_t)-0.110778809f, (float16_t)0.00122737885f, (float16_t)-0.137084961f, + (float16_t)0.173095703f, (float16_t)-0.530761719f, (float16_t)-0.0594482422f, (float16_t)-0.5625f, + (float16_t)0.178344727f, (float16_t)-0.420654297f, (float16_t)-0.329833984f, (float16_t)-0.246826172f, + (float16_t)-1.37109375f, (float16_t)0.0963745117f, (float16_t)1.74023438f, (float16_t)0.459472656f, + (float16_t)-0.292480469f, (float16_t)-0.128051758f, (float16_t)-1.109375f, (float16_t)0.0850830078f, + (float16_t)0.916503906f, (float16_t)-0.488525391f, (float16_t)-2.34179688f, (float16_t)0.182128906f, + (float16_t)0.0181884766f, (float16_t)0.185791016f, (float16_t)-0.67578125f, (float16_t)0.0540771484f, + (float16_t)0.104736328f, (float16_t)-0.634765625f, (float16_t)0.473388672f, (float16_t)0.14465332f, + (float16_t)-0.784179688f, (float16_t)0.687988281f, (float16_t)-0.5390625f, (float16_t)0.952148438f, + (float16_t)-1.57421875f, (float16_t)-0.826660156f, (float16_t)-0.209960938f, (float16_t)0.670410156f, + (float16_t)0.121459961f, (float16_t)0.493164062f, (float16_t)0.604980469f, (float16_t)-0.0602111816f, + (float16_t)-0.129272461f, (float16_t)-0.259033203f, (float16_t)-0.594726562f, (float16_t)-1.34570312f, + (float16_t)0.566894531f, (float16_t)0.0597229004f, (float16_t)-1.01855469f, (float16_t)0.626464844f, + (float16_t)-0.706542969f, (float16_t)0.149291992f, (float16_t)-0.0670776367f, (float16_t)0.263916016f, + (float16_t)-0.847167969f, (float16_t)0.424560547f, (float16_t)0.3671875f, (float16_t)0.81640625f, + (float16_t)-0.634277344f, (float16_t)-0.483886719f, (float16_t)0.459716797f, (float16_t)0.110961914f, + (float16_t)0.375488281f, (float16_t)-0.0390625f, (float16_t)-0.230712891f, (float16_t)-0.181762695f, + (float16_t)-0.664550781f, (float16_t)-1.29296875f, (float16_t)-0.333740234f, (float16_t)-1.25f, + (float16_t)0.787109375f, (float16_t)0.654296875f, (float16_t)-0.310546875f, (float16_t)-0.732421875f, + (float16_t)-1.33105469f, (float16_t)-1.54980469f, (float16_t)1.01855469f, (float16_t)0.857910156f, + (float16_t)-0.497314453f, (float16_t)-0.205078125f, (float16_t)0.0273132324f, (float16_t)1.5546875f, + (float16_t)-0.269287109f, (float16_t)-0.275634766f, (float16_t)-1.28320312f, (float16_t)-0.0299682617f, + (float16_t)1.42578125f, (float16_t)0.406738281f, (float16_t)-0.370117188f, (float16_t)-1.50488281f, + (float16_t)-0.4375f, (float16_t)-1.24902344f, (float16_t)-0.233276367f, (float16_t)-1.19628906f, + (float16_t)0.199829102f, (float16_t)-0.437255859f, (float16_t)0.573730469f, (float16_t)-0.643066406f, + (float16_t)0.182983398f, (float16_t)-0.0817871094f, (float16_t)-0.438964844f, (float16_t)-0.353271484f, + (float16_t)1.20214844f, (float16_t)-0.447998047f, (float16_t)-0.174438477f, (float16_t)1.63769531f, + (float16_t)0.0740966797f, (float16_t)-0.326416016f, (float16_t)-0.219604492f, (float16_t)-0.694824219f, + (float16_t)0.805175781f, (float16_t)0.268310547f, (float16_t)1.64257812f, (float16_t)0.260253906f, + (float16_t)-0.8828125f, (float16_t)-0.499023438f, (float16_t)-0.692382812f, (float16_t)0.873046875f, + (float16_t)-0.0207519531f, (float16_t)-0.432617188f, (float16_t)-0.757324219f, (float16_t)0.142822266f, + (float16_t)0.438232422f, (float16_t)-0.0240020752f, (float16_t)-0.399169922f, (float16_t)-0.983398438f, + (float16_t)0.0546569824f, (float16_t)0.00862121582f, (float16_t)-0.174560547f, (float16_t)0.251220703f, + (float16_t)-0.700683594f, (float16_t)-1.79882812f, (float16_t)-0.228149414f, (float16_t)-0.191772461f, + (float16_t)0.583496094f, (float16_t)-0.446289062f, (float16_t)0.210571289f, (float16_t)0.173950195f, + (float16_t)0.603515625f, (float16_t)-1.39257812f, (float16_t)0.721191406f, (float16_t)0.0284576416f, + (float16_t)0.294189453f, (float16_t)0.195556641f, (float16_t)0.0581359863f, (float16_t)1.11621094f, + (float16_t)-1.26171875f, (float16_t)-0.255371094f, (float16_t)0.404785156f, (float16_t)-0.606445312f, + (float16_t)-0.542480469f, (float16_t)-0.387939453f, (float16_t)-0.791015625f, (float16_t)0.0675048828f, + (float16_t)1.09667969f, (float16_t)-0.152709961f, (float16_t)1.37109375f, (float16_t)-0.474609375f, + (float16_t)-0.628417969f, (float16_t)0.401855469f, (float16_t)-1.28125f, (float16_t)0.0523071289f, + (float16_t)-0.384277344f, (float16_t)0.484619141f, (float16_t)0.720214844f, (float16_t)1.19433594f, + (float16_t)0.6875f, (float16_t)0.316162109f, (float16_t)0.519042969f, (float16_t)-0.00880432129f, + (float16_t)-0.866699219f, (float16_t)0.583496094f, (float16_t)0.880859375f, (float16_t)0.729003906f, + (float16_t)1.84375f, (float16_t)-0.240356445f, (float16_t)-0.896972656f, (float16_t)-0.819824219f, + (float16_t)1.34960938f, (float16_t)0.12121582f, (float16_t)-1.39160156f, (float16_t)0.290527344f, + (float16_t)-0.221069336f, (float16_t)-0.342041016f, (float16_t)0.696289062f, (float16_t)0.430908203f, + (float16_t)-1.03417969f, (float16_t)0.188964844f, (float16_t)0.0825195312f, (float16_t)-0.8046875f, + (float16_t)-0.0589599609f, (float16_t)0.501464844f, (float16_t)-0.174804688f, (float16_t)-0.707519531f, + (float16_t)0.189453125f, (float16_t)0.2734375f, (float16_t)1.02832031f, (float16_t)0.363769531f, + (float16_t)1.35253906f, (float16_t)0.972167969f, (float16_t)0.388183594f, (float16_t)0.327148438f, + (float16_t)-0.611328125f, (float16_t)0.0991210938f, (float16_t)0.198608398f, (float16_t)-0.710449219f, + (float16_t)0.53515625f, (float16_t)0.783203125f, (float16_t)1.8046875f, (float16_t)0.368408203f, + (float16_t)0.523925781f, (float16_t)0.190063477f, (float16_t)-0.445800781f, (float16_t)-0.274414062f, + (float16_t)-0.781738281f, (float16_t)0.193725586f, (float16_t)-1.16308594f, (float16_t)1.09179688f, + (float16_t)-0.107788086f, (float16_t)0.240600586f, (float16_t)-0.872070312f, (float16_t)0.593261719f, + (float16_t)0.0491333008f, (float16_t)0.654785156f, (float16_t)0.116943359f, (float16_t)-1.68066406f, + (float16_t)-0.994140625f, (float16_t)-0.904296875f, (float16_t)0.533691406f, (float16_t)-0.908203125f, + (float16_t)0.427978516f, (float16_t)-0.123474121f, (float16_t)-1.14160156f, (float16_t)1.13574219f, + (float16_t)-0.364501953f, (float16_t)-0.927734375f, (float16_t)0.0679321289f, (float16_t)-0.709472656f, + (float16_t)-0.356933594f, (float16_t)0.0209503174f, (float16_t)0.0287628174f, (float16_t)-2.12890625f, + (float16_t)-0.543457031f, (float16_t)-1.14453125f, (float16_t)-1.35839844f, (float16_t)0.276123047f, + (float16_t)0.911621094f, (float16_t)0.260253906f, (float16_t)-0.58984375f, (float16_t)-0.0634765625f, + (float16_t)-0.0431213379f, (float16_t)-1.65429688f, (float16_t)0.130737305f, (float16_t)-0.170043945f, + (float16_t)0.132202148f, (float16_t)0.113037109f, (float16_t)-0.429443359f, (float16_t)0.908203125f, + (float16_t)0.195556641f, (float16_t)-1.50976562f, (float16_t)2.06835938f, (float16_t)-0.662109375f, + (float16_t)1.50488281f, (float16_t)0.837402344f, (float16_t)0.547851562f, (float16_t)-0.341064453f, + (float16_t)-0.486816406f, (float16_t)-2.82617188f, (float16_t)0.0314941406f, (float16_t)0.633300781f, + (float16_t)0.00126266479f, (float16_t)1.02441406f, (float16_t)0.0928955078f, (float16_t)1.01464844f, + (float16_t)0.150634766f, (float16_t)-0.877441406f, (float16_t)0.108398438f, (float16_t)-0.23815918f, + (float16_t)-0.571777344f, (float16_t)0.754394531f, (float16_t)1.58398438f, (float16_t)-0.739746094f, + (float16_t)-0.694824219f, (float16_t)-0.510742188f, (float16_t)-1.60546875f, (float16_t)1.50976562f, + (float16_t)0.442382812f, (float16_t)0.0696411133f, (float16_t)0.28515625f, (float16_t)-0.217041016f, + (float16_t)0.984863281f, (float16_t)-1.40039062f, (float16_t)-0.242919922f, (float16_t)-1.50292969f, + (float16_t)-0.695800781f, (float16_t)-0.86328125f, (float16_t)-0.152832031f, (float16_t)-0.568847656f, + (float16_t)-0.138427734f, (float16_t)0.370849609f, (float16_t)0.511230469f, (float16_t)0.494140625f, + (float16_t)0.152954102f, (float16_t)-0.358398438f, (float16_t)1.04589844f, (float16_t)0.530273438f, + (float16_t)0.0687866211f, (float16_t)-0.450683594f, (float16_t)1.35742188f, (float16_t)-0.727050781f, + (float16_t)1.33789062f, (float16_t)0.430664062f, (float16_t)0.150390625f, (float16_t)-0.939941406f, + (float16_t)-1.12207031f, (float16_t)-0.934570312f, (float16_t)0.649414062f, (float16_t)-0.185424805f, + (float16_t)1.15332031f, (float16_t)-0.366455078f, (float16_t)-0.608886719f, (float16_t)0.627929688f, + (float16_t)-0.480712891f, (float16_t)-0.6015625f, (float16_t)1.36230469f, (float16_t)-0.447753906f, + (float16_t)0.567382812f, (float16_t)-0.529785156f, (float16_t)0.229248047f, (float16_t)-1.58203125f, + (float16_t)0.467529297f, (float16_t)0.199462891f, (float16_t)-0.981445312f, (float16_t)0.692382812f, + (float16_t)-0.546386719f, (float16_t)-0.0533752441f, (float16_t)0.260742188f, (float16_t)-0.0612792969f, + (float16_t)-0.623535156f, (float16_t)0.151977539f, (float16_t)-0.395019531f, (float16_t)0.38671875f, + (float16_t)0.26953125f, (float16_t)0.366455078f, (float16_t)-0.083984375f, (float16_t)-0.732421875f, + (float16_t)0.677246094f, (float16_t)-0.866210938f, (float16_t)-0.120056152f, (float16_t)0.517089844f, + (float16_t)0.148193359f, (float16_t)-0.654296875f, (float16_t)-0.197265625f, (float16_t)-0.0156173706f, + (float16_t)0.0661010742f, (float16_t)-0.767578125f, (float16_t)1.03027344f, (float16_t)-0.726074219f, + (float16_t)-0.0279388428f, (float16_t)0.526855469f, (float16_t)0.852539062f, (float16_t)-1.41796875f, + (float16_t)-0.0380859375f, (float16_t)0.00134181976f, (float16_t)0.184448242f, (float16_t)-0.196044922f, + (float16_t)-0.436767578f, (float16_t)1.02050781f, (float16_t)-1.35742188f, (float16_t)0.581054688f, + (float16_t)0.430175781f, (float16_t)0.0753173828f, (float16_t)-1.18359375f, (float16_t)0.643066406f, + (float16_t)0.690917969f, (float16_t)0.575195312f, (float16_t)0.780761719f, (float16_t)-0.383056641f, + (float16_t)-0.390625f, (float16_t)1.23046875f, (float16_t)-0.751953125f, (float16_t)0.915039062f, + (float16_t)0.264648438f, (float16_t)0.169189453f, (float16_t)-0.8984375f, (float16_t)-0.535644531f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/weights_data.h new file mode 100644 index 00000000..f198094a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f16/weights_data.h @@ -0,0 +1,20 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_f16_weights_data[72] = { + (float16_t)0.600097656f, (float16_t)-0.0904541016f, (float16_t)0.533203125f, (float16_t)-0.278320312f, + (float16_t)0.302001953f, (float16_t)0.381591797f, (float16_t)-0.716796875f, (float16_t)0.208007812f, + (float16_t)-0.0520019531f, (float16_t)-0.254638672f, (float16_t)0.326660156f, (float16_t)-0.588378906f, + (float16_t)-0.490966797f, (float16_t)0.214477539f, (float16_t)0.451904297f, (float16_t)0.621582031f, + (float16_t)0.139038086f, (float16_t)-0.0452880859f, (float16_t)-0.659179688f, (float16_t)0.342041016f, + (float16_t)0.231445312f, (float16_t)0.196166992f, (float16_t)-0.345214844f, (float16_t)0.354492188f, + (float16_t)0.192749023f, (float16_t)-0.639160156f, (float16_t)0.72265625f, (float16_t)-0.338623047f, + (float16_t)-0.462158203f, (float16_t)-0.11328125f, (float16_t)0.109802246f, (float16_t)0.474121094f, + (float16_t)0.472900391f, (float16_t)0.548339844f, (float16_t)-0.544921875f, (float16_t)0.718261719f, + (float16_t)-0.47265625f, (float16_t)-0.00606918335f, (float16_t)0.645996094f, (float16_t)-0.412841797f, + (float16_t)-0.326660156f, (float16_t)-0.4765625f, (float16_t)-0.07421875f, (float16_t)-0.511230469f, + (float16_t)0.65234375f, (float16_t)-0.450195312f, (float16_t)-0.120117188f, (float16_t)0.451171875f, + (float16_t)-0.425048828f, (float16_t)0.512207031f, (float16_t)-0.157958984f, (float16_t)0.0795898438f, + (float16_t)0.14440918f, (float16_t)-0.255859375f, (float16_t)0.377929688f, (float16_t)0.546386719f, + (float16_t)0.517089844f, (float16_t)0.693359375f, (float16_t)0.254882812f, (float16_t)-0.22277832f, + (float16_t)-0.584472656f, (float16_t)0.627441406f, (float16_t)0.457519531f, (float16_t)-0.535644531f, + (float16_t)0.25f, (float16_t)0.634765625f, (float16_t)0.451904297f, (float16_t)0.0791015625f, + (float16_t)-0.111083984f, (float16_t)0.305175781f, (float16_t)0.0693969727f, (float16_t)0.660644531f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/biases_data.h new file mode 100644 index 00000000..47f1a4b5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_f32_biases_data[8] = {-0.194589406f, + -0.470234275f, + 0.136926427f, + 0.0525091663f, + 0.22237812f, + 0.144056782f, + -0.0727997869f, + 0.153881207f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/config_data.h new file mode 100644 index 00000000..b88f90bb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_BASIC_F32_INPUT_BATCHES 1 +#define DEPTHWISE_BASIC_F32_INPUT_H 9 +#define DEPTHWISE_BASIC_F32_INPUT_W 11 +#define DEPTHWISE_BASIC_F32_IN_CH 8 +#define DEPTHWISE_BASIC_F32_CH_MULT 1 +#define DEPTHWISE_BASIC_F32_FILTER_H 3 +#define DEPTHWISE_BASIC_F32_FILTER_W 3 +#define DEPTHWISE_BASIC_F32_OUT_CH 8 +#define DEPTHWISE_BASIC_F32_STRIDE_H 1 +#define DEPTHWISE_BASIC_F32_STRIDE_W 1 +#define DEPTHWISE_BASIC_F32_PADDING_H 0 +#define DEPTHWISE_BASIC_F32_PADDING_W 0 +#define DEPTHWISE_BASIC_F32_DILATION_H 1 +#define DEPTHWISE_BASIC_F32_DILATION_W 1 +#define DEPTHWISE_BASIC_F32_OUTPUT_H 7 +#define DEPTHWISE_BASIC_F32_OUTPUT_W 9 +#define DEPTHWISE_BASIC_F32_OUTPUT_C 8 +#define DEPTHWISE_BASIC_F32_DST_SIZE 504 +#define DEPTHWISE_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_BASIC_F32_USE_WRAPPER 0 +#define DEPTHWISE_BASIC_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_BASIC_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_BASIC_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/input_data.h new file mode 100644 index 00000000..026cff12 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/input_data.h @@ -0,0 +1,116 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_f32_input_data[792] = { + 0.227461159f, 0.48641184f, -0.433916092f, 0.0367808603f, 0.728409946f, -0.923234344f, -0.63399446f, + -0.372102946f, -0.869392574f, 0.513439596f, -0.0416554809f, -0.287963688f, 0.467863679f, 0.194679305f, + -0.513872802f, -0.0996303484f, -0.706916213f, 0.199453786f, -0.0665481612f, 0.559212208f, 0.0409679972f, + -0.342830122f, -0.0247745179f, 0.931917965f, -0.534002662f, 0.118892208f, 0.441206127f, 0.573934257f, + 0.382139713f, -0.574650705f, 0.700828612f, 0.618381798f, 0.997826517f, 0.971284151f, -0.092898123f, + 0.706062198f, -0.778919399f, -0.92370671f, -0.23831369f, 0.249151856f, 0.936721504f, -0.137967467f, + -0.491629273f, 0.0402529985f, 0.421183079f, 0.366761982f, 0.0389698856f, -0.561271906f, 0.618740857f, + 0.716881335f, -0.180942878f, 0.217631757f, 0.659954071f, 0.807587743f, 0.917621195f, 0.673554718f, + -0.421328932f, -0.70708555f, -0.448988169f, 0.35203588f, 0.491164744f, 0.135561422f, -0.475541323f, + 0.900191426f, -0.436862022f, -0.114018351f, -0.719254673f, 0.426830351f, -0.862061143f, -0.8766554f, + 0.90609479f, 0.355396599f, -0.548520088f, 0.805369437f, 0.308452398f, -0.270446569f, 0.883171916f, + 0.77611506f, 0.108169891f, 0.285590559f, -0.922692895f, 0.854084432f, 0.437533289f, -0.616330624f, + 0.0389013812f, -0.6082142f, 0.821737409f, 0.656878948f, 0.362274498f, 0.942361236f, -0.239092469f, + -0.909679055f, -0.64748174f, 0.104818903f, 0.695379972f, 0.00987109914f, 0.0128592793f, 0.549489081f, + 0.530018747f, 0.949921846f, -0.00612965273f, -0.770340979f, -0.649073958f, -0.458941907f, -0.822815359f, + -0.473959953f, 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1.34953272f, 0.121220224f, -1.39122558f, 0.290436178f, -0.221043542f, + -0.342033803f, 0.696347356f, 0.430854648f, -1.03452253f, 0.188948035f, 0.0824932605f, -0.804814935f, + -0.0589724444f, 0.501503646f, -0.174806967f, -0.707703233f, 0.189486191f, 0.273324579f, 1.02868259f, + 0.363780469f, 1.35246301f, 0.972130656f, 0.388240218f, 0.327164501f, -0.611499131f, 0.099147141f, + 0.198548302f, -0.710431755f, 0.535257697f, 0.78323406f, 1.80464244f, 0.368314654f, 0.523753285f, + 0.19006896f, -0.445718586f, -0.274492443f, -0.781941712f, 0.193728939f, -1.16269255f, 1.09181082f, + -0.107798122f, 0.240544468f, -0.872148693f, 0.593465686f, 0.0491210185f, 0.654557884f, 0.116935894f, + -1.68075311f, -0.994221449f, -0.904221594f, 0.53371042f, -0.908047557f, 0.427915752f, -0.123460762f, + -1.14153302f, 1.13587153f, -0.364400119f, -0.92784071f, 0.0679379925f, -0.709396899f, -0.356939673f, + 0.0209541805f, 0.0287553705f, -2.128479f, -0.543217182f, -1.14463484f, -1.3586148f, 0.276177436f, + 0.911599219f, 0.260313153f, -0.590074718f, -0.0634772927f, -0.0431152284f, -1.65452456f, 0.130754054f, + -0.170072928f, 0.132158384f, 0.113026515f, -0.429547012f, 0.908418596f, 0.195608884f, -1.50929654f, + 2.06886101f, -0.661923885f, 1.50440228f, 0.837328374f, 0.547977865f, -0.341119349f, -0.486916125f, + -2.82630634f, 0.0315091535f, 0.633410931f, 0.00126277725f, 1.02430081f, 0.0928870812f, 1.01496637f, + 0.15066196f, -0.877332509f, 0.10841582f, -0.238212094f, -0.57177937f, 0.75421983f, 1.58396459f, + -0.739605606f, -0.694735408f, -0.510823429f, -1.60552287f, 1.50947392f, 0.442365855f, 0.0696437582f, + 0.285099655f, -0.217010006f, 0.984911144f, -1.39995396f, -0.242888525f, -1.50337136f, -0.695816994f, + -0.8631531f, -0.152870119f, -0.568966448f, -0.138476521f, 0.370930731f, 0.511078537f, 0.494178653f, + 0.152894035f, -0.358415633f, 1.04628015f, 0.53029716f, 0.0688062012f, -0.450653434f, 1.35729051f, + -0.727040887f, 1.33784616f, 0.430573344f, 0.150414139f, -0.939875305f, -1.12248182f, -0.934419215f, + 0.649592698f, -0.185466424f, 1.15299046f, -0.36643526f, -0.609092355f, 0.628139257f, -0.480798811f, + -0.601377249f, 1.36272085f, -0.447690696f, 0.567373514f, -0.52990675f, 0.229229808f, -1.58182073f, + 0.467578381f, 0.199475139f, -0.981360614f, 0.692332089f, -0.54649967f, -0.0533776246f, 0.260780871f, + -0.0612677671f, -0.623554111f, 0.151952729f, -0.395031273f, 0.386798412f, 0.269506633f, 0.366463035f, + -0.0840148926f, -0.732307017f, 0.677481592f, -0.866444707f, -0.120066494f, 0.517260015f, 0.148144081f, + -0.654331744f, -0.197221801f, -0.0156205976f, 0.0660951436f, -0.767409444f, 1.03042352f, -0.726048052f, + -0.0279352423f, 0.52694118f, 0.852573395f, -1.41825533f, -0.0380906351f, 0.00134212442f, 0.184419766f, + -0.196011275f, -0.436710268f, 1.0205487f, -1.3577745f, 0.58092773f, 0.430276275f, 0.0752953142f, + -1.18404591f, 0.642934322f, 0.690962791f, 0.57530117f, 0.78091222f, -0.383039504f, -0.390589803f, + 1.23012257f, -0.751887739f, 0.915024817f, 0.264532089f, 0.169153109f, -0.898452342f, -0.535434663f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/weights_data.h new file mode 100644 index 00000000..766ab93b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_f32/weights_data.h @@ -0,0 +1,13 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_f32_weights_data[72] = { + 0.600200534f, -0.0904421285f, 0.533148408f, -0.278412998f, 0.302062213f, 0.381634057f, -0.716575027f, + 0.208000273f, -0.0519933961f, -0.254645675f, 0.32654798f, -0.588366807f, -0.491059303f, 0.214441895f, + 0.451919198f, 0.621720672f, 0.139044181f, -0.0452794582f, -0.659279704f, 0.341954947f, 0.231432363f, + 0.196136788f, -0.345171452f, 0.35452798f, 0.192733243f, -0.639377534f, 0.722452283f, -0.338681668f, + -0.462167025f, -0.113309674f, 0.109808706f, 0.474071532f, 0.472809345f, 0.548186243f, -0.544988036f, + 0.718018174f, -0.472697288f, -0.00606817519f, 0.646072209f, -0.412896961f, -0.326701313f, -0.476567358f, + -0.0742184445f, -0.511037588f, 0.652374983f, -0.450087517f, -0.120112844f, 0.451097935f, -0.425026596f, + 0.512308657f, -0.157916605f, 0.0795721486f, 0.144381002f, -0.255845308f, 0.378033906f, 0.546284974f, + 0.517190874f, 0.693530619f, 0.254821241f, -0.222800031f, -0.584508419f, 0.627417564f, 0.457558513f, + -0.535736799f, 0.25009039f, 0.634729087f, 0.451922834f, 0.0791218355f, -0.111087777f, 0.305226028f, + 0.0694135427f, 0.660591424f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/biases_data.h new file mode 100644 index 00000000..c40a3598 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/biases_data.h @@ -0,0 +1,7 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_smallc_nhwc_f16_biases_data[6] = {(float16_t)-0.0908203125f, + (float16_t)-0.169189453f, + (float16_t)0.184326172f, + (float16_t)0.268798828f, + (float16_t)0.106872559f, + (float16_t)0.459960938f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/config_data.h new file mode 100644 index 00000000..02f3af0f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_INPUT_BATCHES 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_INPUT_H 9 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_INPUT_W 11 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_IN_CH 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_CH_MULT 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_FILTER_H 3 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_FILTER_W 3 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUT_CH 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_STRIDE_H 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_STRIDE_W 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_PADDING_H 0 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_PADDING_W 0 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_DILATION_H 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_DILATION_W 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUTPUT_H 7 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUTPUT_W 9 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUTPUT_C 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_DST_SIZE 378 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_USE_WRAPPER 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_BASIC_SMALLC_NHWC_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/input_data.h new file mode 100644 index 00000000..d2e6b225 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/input_data.h @@ -0,0 +1,151 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_smallc_nhwc_f16_input_data[594] = { + (float16_t)0.0461425781f, (float16_t)-0.774902344f, (float16_t)-0.349609375f, (float16_t)-0.840332031f, + (float16_t)-0.534179688f, (float16_t)-0.0485839844f, (float16_t)0.0231323242f, (float16_t)0.150146484f, + (float16_t)0.94921875f, (float16_t)0.949707031f, (float16_t)-0.728027344f, (float16_t)0.966308594f, + (float16_t)-0.81640625f, (float16_t)0.731933594f, (float16_t)0.424804688f, (float16_t)-0.544921875f, + (float16_t)0.229736328f, (float16_t)0.86328125f, (float16_t)0.831542969f, (float16_t)-0.448242188f, + (float16_t)0.665527344f, (float16_t)-0.372802734f, (float16_t)0.267822266f, (float16_t)0.819824219f, + (float16_t)-0.378173828f, (float16_t)0.834960938f, (float16_t)-0.297851562f, (float16_t)0.834472656f, + (float16_t)-0.386230469f, (float16_t)0.0897216797f, (float16_t)0.384277344f, (float16_t)-0.515625f, + (float16_t)-0.870605469f, (float16_t)-0.848632812f, (float16_t)0.682128906f, (float16_t)0.783691406f, + (float16_t)0.538085938f, (float16_t)0.205322266f, (float16_t)0.564453125f, (float16_t)-0.786132812f, + (float16_t)0.478271484f, (float16_t)0.774414062f, (float16_t)0.226318359f, (float16_t)0.751464844f, + (float16_t)-0.285888672f, (float16_t)-0.546386719f, (float16_t)0.63671875f, (float16_t)-0.200195312f, + (float16_t)-0.573730469f, (float16_t)0.237670898f, (float16_t)-0.486572266f, (float16_t)0.9375f, + (float16_t)0.767089844f, (float16_t)-0.722167969f, (float16_t)0.842285156f, (float16_t)-0.435546875f, + (float16_t)0.600097656f, (float16_t)-0.288818359f, (float16_t)-0.0581054688f, (float16_t)-0.932128906f, + (float16_t)-0.00458145142f, (float16_t)-0.788574219f, (float16_t)0.347412109f, (float16_t)0.692382812f, + (float16_t)0.322753906f, (float16_t)0.189208984f, (float16_t)-0.784179688f, (float16_t)0.581542969f, + (float16_t)-0.381347656f, (float16_t)0.98046875f, (float16_t)-0.606445312f, (float16_t)0.729003906f, + (float16_t)-0.333984375f, (float16_t)-0.777832031f, (float16_t)-0.729492188f, (float16_t)0.45703125f, + (float16_t)0.8984375f, (float16_t)0.108520508f, (float16_t)-0.833984375f, (float16_t)0.0908813477f, + (float16_t)0.893554688f, (float16_t)-0.400390625f, (float16_t)0.505859375f, (float16_t)-0.331787109f, + (float16_t)0.907226562f, (float16_t)-0.548339844f, (float16_t)0.798828125f, (float16_t)-0.755371094f, + (float16_t)-0.0724487305f, (float16_t)0.216186523f, (float16_t)-0.876953125f, (float16_t)-0.925292969f, + (float16_t)0.791015625f, (float16_t)0.763183594f, (float16_t)0.104064941f, (float16_t)0.915039062f, + (float16_t)-0.451660156f, (float16_t)0.813964844f, (float16_t)-0.0907592773f, (float16_t)0.467773438f, + (float16_t)-0.711425781f, (float16_t)0.185058594f, (float16_t)0.231689453f, (float16_t)0.0439758301f, + (float16_t)-0.927734375f, (float16_t)-0.101135254f, (float16_t)0.368164062f, (float16_t)-0.391601562f, + (float16_t)0.658691406f, (float16_t)-0.629882812f, (float16_t)-0.647460938f, (float16_t)-0.94140625f, + (float16_t)-0.633789062f, (float16_t)0.966796875f, (float16_t)-0.541992188f, (float16_t)0.00743103027f, + (float16_t)-0.673339844f, (float16_t)-0.567382812f, (float16_t)-0.9453125f, (float16_t)0.948242188f, + (float16_t)-0.455078125f, (float16_t)0.946777344f, (float16_t)0.159301758f, (float16_t)0.986328125f, + (float16_t)-0.0819091797f, (float16_t)0.0583496094f, (float16_t)-0.417236328f, (float16_t)0.847167969f, + (float16_t)-0.856933594f, (float16_t)-0.870117188f, (float16_t)-0.466308594f, (float16_t)-0.661132812f, + (float16_t)-0.24206543f, (float16_t)0.275878906f, (float16_t)0.136962891f, (float16_t)-0.160766602f, + (float16_t)-0.673339844f, (float16_t)0.130737305f, (float16_t)0.681640625f, (float16_t)-0.0292053223f, + (float16_t)0.430419922f, (float16_t)0.457275391f, (float16_t)-0.61328125f, (float16_t)0.453125f, + (float16_t)-0.138793945f, (float16_t)-0.653320312f, (float16_t)0.417236328f, (float16_t)0.240234375f, + (float16_t)-0.0791625977f, (float16_t)-0.692382812f, (float16_t)0.436523438f, (float16_t)-0.350585938f, + 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(float16_t)0.217285156f, (float16_t)-0.0531005859f, (float16_t)-0.316650391f, (float16_t)-0.991699219f, + (float16_t)0.474609375f, (float16_t)-0.657226562f, (float16_t)0.318847656f, (float16_t)0.547363281f, + (float16_t)-0.247314453f, (float16_t)-0.462646484f, (float16_t)-0.903320312f, (float16_t)-0.488525391f, + (float16_t)-0.262695312f, (float16_t)-0.783691406f, (float16_t)-0.499267578f, (float16_t)-0.304199219f, + (float16_t)0.34375f, (float16_t)0.771972656f, (float16_t)-0.766601562f, (float16_t)0.875f, + (float16_t)0.880859375f, (float16_t)-0.41015625f, (float16_t)-0.540039062f, (float16_t)-0.109069824f, + (float16_t)-0.405761719f, (float16_t)0.0904541016f, (float16_t)-0.736328125f, (float16_t)-0.990234375f, + (float16_t)-0.56640625f, (float16_t)-0.351318359f, (float16_t)0.774414062f, (float16_t)-0.229003906f, + (float16_t)-0.143188477f, (float16_t)-0.815917969f, (float16_t)0.502929688f, (float16_t)-0.212158203f, + (float16_t)-0.8984375f, (float16_t)0.0934448242f, 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(float16_t)-0.538085938f, + (float16_t)-0.388427734f, (float16_t)-0.458007812f, (float16_t)-0.576171875f, (float16_t)0.615722656f, + (float16_t)-0.00332260132f, (float16_t)-0.765136719f, (float16_t)-0.685058594f, (float16_t)0.670410156f, + (float16_t)0.775390625f, (float16_t)-0.164672852f, (float16_t)0.981933594f, (float16_t)0.459716797f, + (float16_t)0.768554688f, (float16_t)0.352783203f, (float16_t)0.727539062f, (float16_t)0.173217773f, + (float16_t)-0.958984375f, (float16_t)-0.608398438f, (float16_t)-0.712402344f, (float16_t)0.796386719f, + (float16_t)0.0186309814f, (float16_t)-0.837402344f, (float16_t)-0.353759766f, (float16_t)-0.112365723f, + (float16_t)0.159667969f, (float16_t)0.665527344f, (float16_t)0.25390625f, (float16_t)-0.237548828f, + (float16_t)-0.442382812f, (float16_t)0.439453125f, (float16_t)-0.181030273f, (float16_t)0.818847656f, + (float16_t)0.759765625f, (float16_t)-0.685546875f, (float16_t)-0.849121094f, (float16_t)0.706054688f, + (float16_t)-0.55859375f, (float16_t)0.388671875f, (float16_t)0.116638184f, (float16_t)-0.84375f, + (float16_t)0.900390625f, (float16_t)0.0325927734f, (float16_t)0.0217132568f, (float16_t)0.430664062f, + (float16_t)0.96484375f, (float16_t)0.791015625f, (float16_t)0.616210938f, (float16_t)0.193115234f, + (float16_t)-0.258300781f, (float16_t)-0.0921630859f, (float16_t)-0.79296875f, (float16_t)-0.790527344f, + (float16_t)0.113647461f, (float16_t)0.379150391f, (float16_t)-0.503417969f, (float16_t)0.456054688f, + (float16_t)0.908203125f, (float16_t)0.275878906f, (float16_t)0.816894531f, (float16_t)0.101501465f, + (float16_t)-0.0233459473f, (float16_t)-0.563964844f, (float16_t)0.950683594f, (float16_t)0.201171875f, + (float16_t)0.414794922f, (float16_t)0.127319336f, (float16_t)0.301757812f, (float16_t)0.0592956543f, + (float16_t)0.320800781f, (float16_t)-0.933105469f, (float16_t)-0.312744141f, (float16_t)-0.602539062f, + (float16_t)-0.851074219f, (float16_t)-0.158935547f, (float16_t)-0.293212891f, (float16_t)0.763671875f, + (float16_t)-0.833984375f, (float16_t)0.745605469f, (float16_t)0.025680542f, (float16_t)-0.564941406f, + (float16_t)-0.788085938f, (float16_t)-0.104248047f, (float16_t)0.247558594f, (float16_t)-0.0291900635f, + (float16_t)0.854980469f, (float16_t)-0.948730469f, (float16_t)0.581542969f, (float16_t)-0.978515625f, + (float16_t)0.34375f, (float16_t)0.968261719f, (float16_t)0.912109375f, (float16_t)0.0709838867f, + (float16_t)-0.840332031f, (float16_t)-0.19921875f, (float16_t)0.283935547f, (float16_t)-0.45703125f, + (float16_t)0.345947266f, (float16_t)0.299072266f, (float16_t)-0.343505859f, (float16_t)0.854492188f, + (float16_t)0.643554688f, (float16_t)-0.00321006775f, (float16_t)-0.7734375f, (float16_t)-0.547851562f, + (float16_t)-0.313720703f, (float16_t)-0.506835938f, (float16_t)0.181274414f, (float16_t)-0.5390625f, + (float16_t)0.60546875f, (float16_t)0.0684814453f, (float16_t)-0.264404297f, (float16_t)0.512207031f, + (float16_t)-0.458740234f, (float16_t)0.627929688f, (float16_t)-0.110900879f, (float16_t)0.862304688f, + (float16_t)0.174926758f, (float16_t)0.802246094f, (float16_t)-0.3515625f, (float16_t)-0.497070312f, + (float16_t)-0.125366211f, (float16_t)0.760253906f, (float16_t)-0.587402344f, (float16_t)0.8125f, + (float16_t)0.296875f, (float16_t)0.889160156f, (float16_t)0.4921875f, (float16_t)0.060546875f, + (float16_t)-0.316650391f, (float16_t)0.937988281f, (float16_t)-0.994140625f, (float16_t)0.26171875f, + (float16_t)-0.68359375f, (float16_t)-0.807128906f, (float16_t)0.514648438f, (float16_t)0.895996094f, + (float16_t)0.519042969f, (float16_t)0.556640625f, (float16_t)0.0247650146f, (float16_t)0.614746094f, + (float16_t)0.960449219f, (float16_t)-0.879394531f, (float16_t)0.176025391f, (float16_t)-0.852050781f, + (float16_t)0.531738281f, (float16_t)-0.86328125f, (float16_t)-0.55078125f, (float16_t)-0.482177734f, + (float16_t)-0.609375f, (float16_t)0.828613281f, (float16_t)-0.557617188f, (float16_t)0.259521484f, + (float16_t)0.541992188f, (float16_t)0.0704345703f, (float16_t)0.724609375f, (float16_t)0.479248047f, + (float16_t)-0.8203125f, (float16_t)0.114868164f, (float16_t)0.820800781f, (float16_t)-0.649414062f, + (float16_t)-0.0514831543f, (float16_t)0.635253906f, (float16_t)0.0402832031f, (float16_t)-0.508300781f, + (float16_t)0.0258178711f, (float16_t)0.8515625f, (float16_t)-0.687988281f, (float16_t)-0.646484375f, + (float16_t)-0.383544922f, (float16_t)0.0863647461f, (float16_t)-0.424072266f, (float16_t)-0.584472656f, + (float16_t)0.7421875f, (float16_t)-0.239257812f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..bb3800fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/output_ref_data.h @@ -0,0 +1,97 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_smallc_nhwc_f16_output_ref_data[378] = { + (float16_t)-1.10058594f, (float16_t)-0.872070312f, (float16_t)-0.690429688f, (float16_t)0.288818359f, + (float16_t)-0.104919434f, (float16_t)-0.199707031f, (float16_t)0.768554688f, (float16_t)0.142211914f, + (float16_t)1.50390625f, (float16_t)0.967773438f, (float16_t)-0.317382812f, (float16_t)-0.687011719f, + (float16_t)-0.5859375f, (float16_t)-1.89355469f, (float16_t)0.0511474609f, (float16_t)0.0925292969f, + (float16_t)-0.536132812f, (float16_t)-0.357666016f, (float16_t)0.0300140381f, (float16_t)0.107299805f, + (float16_t)0.872558594f, (float16_t)0.023727417f, (float16_t)0.537109375f, (float16_t)-1.00976562f, + (float16_t)0.41015625f, (float16_t)-0.819824219f, (float16_t)-0.491210938f, (float16_t)1.81933594f, + (float16_t)-0.214355469f, (float16_t)-0.0963745117f, (float16_t)-0.318359375f, (float16_t)0.266357422f, + (float16_t)-0.509277344f, (float16_t)0.231567383f, (float16_t)0.364746094f, (float16_t)-0.117980957f, + (float16_t)-0.79296875f, (float16_t)-0.268066406f, (float16_t)0.355957031f, (float16_t)-0.408447266f, + (float16_t)0.784667969f, (float16_t)-0.264404297f, (float16_t)0.72265625f, (float16_t)-0.377929688f, + (float16_t)-0.411865234f, (float16_t)0.103393555f, (float16_t)0.221679688f, (float16_t)0.836425781f, + (float16_t)-0.394775391f, (float16_t)0.526367188f, (float16_t)-0.413574219f, (float16_t)-0.319335938f, + (float16_t)0.244750977f, (float16_t)0.880371094f, (float16_t)-0.850097656f, (float16_t)-1.60644531f, + (float16_t)0.503417969f, (float16_t)0.276367188f, (float16_t)-0.500976562f, (float16_t)-0.137451172f, + (float16_t)1.60351562f, (float16_t)0.921386719f, (float16_t)-1.35253906f, (float16_t)1.73535156f, + (float16_t)-0.392089844f, (float16_t)1.078125f, (float16_t)-0.735839844f, (float16_t)-0.330810547f, + (float16_t)0.641601562f, (float16_t)0.152954102f, (float16_t)0.0693359375f, (float16_t)0.291503906f, + (float16_t)0.184082031f, (float16_t)-1.39550781f, (float16_t)-0.850097656f, (float16_t)0.110656738f, + (float16_t)-0.562011719f, (float16_t)0.247924805f, (float16_t)-0.443359375f, (float16_t)-0.105102539f, + (float16_t)0.379150391f, (float16_t)1.08105469f, (float16_t)0.930175781f, (float16_t)0.157104492f, + (float16_t)-0.214477539f, (float16_t)-0.457763672f, (float16_t)1.34863281f, (float16_t)1.79882812f, + (float16_t)-0.153320312f, (float16_t)0.475830078f, (float16_t)-0.394042969f, (float16_t)-0.826171875f, + (float16_t)1.203125f, (float16_t)0.956542969f, (float16_t)-0.0629882812f, (float16_t)0.329345703f, + (float16_t)-0.55078125f, (float16_t)-0.556152344f, (float16_t)-0.406005859f, (float16_t)0.188354492f, + (float16_t)0.620605469f, (float16_t)-0.0161895752f, (float16_t)0.369384766f, (float16_t)0.171386719f, + (float16_t)-1.49707031f, (float16_t)-0.491455078f, (float16_t)0.174438477f, (float16_t)1.27734375f, + (float16_t)-0.491699219f, (float16_t)0.592773438f, (float16_t)0.166870117f, (float16_t)0.166381836f, + (float16_t)-0.217041016f, (float16_t)1.26953125f, (float16_t)0.6171875f, (float16_t)-0.800292969f, + (float16_t)0.184204102f, (float16_t)-0.653320312f, (float16_t)-0.180786133f, (float16_t)0.613769531f, + (float16_t)-0.363769531f, (float16_t)0.105834961f, (float16_t)-1.0390625f, (float16_t)0.461181641f, + (float16_t)0.417236328f, (float16_t)0.19128418f, (float16_t)-0.430175781f, (float16_t)0.889160156f, + (float16_t)0.891601562f, (float16_t)0.169189453f, (float16_t)-0.729003906f, (float16_t)0.811523438f, + (float16_t)-1.11914062f, (float16_t)0.898925781f, (float16_t)-0.604492188f, (float16_t)-0.0606994629f, + (float16_t)0.551757812f, (float16_t)1.14160156f, (float16_t)1.62207031f, (float16_t)-0.565429688f, + (float16_t)0.967285156f, (float16_t)0.209350586f, (float16_t)-0.113830566f, (float16_t)1.83691406f, + (float16_t)-0.595703125f, (float16_t)0.485595703f, (float16_t)-1.203125f, (float16_t)-0.772460938f, + (float16_t)-0.387451172f, (float16_t)1.44921875f, (float16_t)0.00625610352f, (float16_t)-0.376953125f, + (float16_t)1.35839844f, (float16_t)-0.759277344f, (float16_t)0.292724609f, (float16_t)1.69726562f, + (float16_t)0.496582031f, (float16_t)-0.427246094f, (float16_t)0.149414062f, (float16_t)1.37109375f, + (float16_t)1.51855469f, (float16_t)0.743164062f, (float16_t)-0.730957031f, (float16_t)1.15625f, + (float16_t)-0.708984375f, (float16_t)-0.812988281f, (float16_t)0.275878906f, (float16_t)0.0462036133f, + (float16_t)-0.65234375f, (float16_t)-1.09472656f, (float16_t)0.75390625f, (float16_t)-0.384765625f, + (float16_t)-0.81640625f, (float16_t)-0.253662109f, (float16_t)0.913085938f, (float16_t)0.650878906f, + (float16_t)0.294189453f, (float16_t)0.401611328f, (float16_t)-0.22668457f, (float16_t)0.864257812f, + (float16_t)-0.712890625f, (float16_t)-1.09472656f, (float16_t)-0.295166016f, (float16_t)0.0721435547f, + (float16_t)-0.027557373f, (float16_t)0.0391845703f, (float16_t)0.951171875f, (float16_t)-0.685546875f, + (float16_t)1.02050781f, (float16_t)0.246582031f, (float16_t)-0.578125f, (float16_t)0.755371094f, + (float16_t)-1.16796875f, (float16_t)-1.08984375f, (float16_t)0.302246094f, (float16_t)0.766113281f, + (float16_t)-0.360351562f, (float16_t)1.32324219f, (float16_t)0.0290985107f, (float16_t)-0.0367736816f, + (float16_t)-0.771972656f, (float16_t)-0.0144042969f, (float16_t)-0.0363464355f, (float16_t)0.997558594f, + (float16_t)-0.251220703f, (float16_t)-1.09179688f, (float16_t)0.123352051f, (float16_t)1.04492188f, + (float16_t)0.353515625f, (float16_t)1.62988281f, (float16_t)-1.06445312f, (float16_t)-0.957519531f, + (float16_t)1.00683594f, (float16_t)-0.613769531f, (float16_t)0.381103516f, (float16_t)-0.745117188f, + (float16_t)-0.755371094f, (float16_t)-1.54394531f, (float16_t)1.92675781f, (float16_t)0.272705078f, + (float16_t)-0.042388916f, (float16_t)-0.295166016f, (float16_t)0.356445312f, (float16_t)1.08398438f, + (float16_t)-0.275390625f, (float16_t)0.0871582031f, (float16_t)-0.622558594f, (float16_t)1.32910156f, + (float16_t)-0.371582031f, (float16_t)-0.580566406f, (float16_t)-0.452880859f, (float16_t)0.361816406f, + (float16_t)0.300292969f, (float16_t)-0.0942382812f, (float16_t)0.471191406f, (float16_t)0.874511719f, + (float16_t)1.27734375f, (float16_t)1.19921875f, (float16_t)-1.5f, (float16_t)0.438964844f, + (float16_t)-0.813964844f, (float16_t)0.795410156f, (float16_t)1.14550781f, (float16_t)0.293212891f, + (float16_t)0.24206543f, (float16_t)0.993164062f, (float16_t)-0.743652344f, (float16_t)1.69726562f, + (float16_t)0.584960938f, (float16_t)1.55957031f, (float16_t)-0.261474609f, (float16_t)-0.671875f, + (float16_t)1.02832031f, (float16_t)-0.197753906f, (float16_t)-0.622070312f, (float16_t)0.5625f, + (float16_t)0.214599609f, (float16_t)0.984375f, (float16_t)-0.803222656f, (float16_t)0.723144531f, + (float16_t)0.71484375f, (float16_t)-0.249511719f, (float16_t)1.10058594f, (float16_t)-0.385498047f, + (float16_t)0.903808594f, (float16_t)0.0955200195f, (float16_t)-0.0980224609f, (float16_t)0.0381164551f, + (float16_t)-0.432861328f, (float16_t)0.866699219f, (float16_t)0.48828125f, (float16_t)-0.567871094f, + (float16_t)0.148803711f, (float16_t)-0.868164062f, (float16_t)0.363037109f, (float16_t)0.791015625f, + (float16_t)-0.768554688f, (float16_t)0.628417969f, (float16_t)0.741210938f, (float16_t)-0.448242188f, + (float16_t)-0.957519531f, (float16_t)1.171875f, (float16_t)-0.209594727f, (float16_t)-0.716796875f, + (float16_t)-0.754394531f, (float16_t)1.27734375f, (float16_t)0.212280273f, (float16_t)1.109375f, + (float16_t)-0.813964844f, (float16_t)-0.387939453f, (float16_t)0.704589844f, (float16_t)-1.22949219f, + (float16_t)-0.33984375f, (float16_t)1.42480469f, (float16_t)0.817871094f, (float16_t)-0.353027344f, + (float16_t)-0.0687866211f, (float16_t)0.462890625f, (float16_t)0.574707031f, (float16_t)0.167724609f, + (float16_t)-0.325927734f, (float16_t)-0.196044922f, (float16_t)0.445068359f, (float16_t)0.0772705078f, + (float16_t)-0.131347656f, (float16_t)1.05957031f, (float16_t)-1.01074219f, (float16_t)0.324462891f, + (float16_t)-0.753417969f, (float16_t)0.202026367f, (float16_t)-0.0296936035f, (float16_t)-0.195556641f, + (float16_t)0.36328125f, (float16_t)1.35644531f, (float16_t)1.77636719f, (float16_t)-0.776855469f, + (float16_t)0.911621094f, (float16_t)-0.365478516f, (float16_t)0.0676269531f, (float16_t)-0.603515625f, + (float16_t)-0.319091797f, (float16_t)0.255615234f, (float16_t)-0.499267578f, (float16_t)-0.119934082f, + (float16_t)0.0498352051f, (float16_t)-0.104431152f, (float16_t)-0.274414062f, (float16_t)1.06445312f, + (float16_t)-0.997070312f, (float16_t)-0.204711914f, (float16_t)-0.310302734f, (float16_t)-0.327392578f, + (float16_t)0.840332031f, (float16_t)0.729003906f, (float16_t)0.234863281f, (float16_t)0.971679688f, + (float16_t)-0.227294922f, (float16_t)-0.564941406f, (float16_t)1.32226562f, (float16_t)-0.16003418f, + (float16_t)-0.0162200928f, (float16_t)0.964355469f, (float16_t)0.7265625f, (float16_t)-0.670898438f, + (float16_t)0.181030273f, (float16_t)1.08496094f, (float16_t)0.548828125f, (float16_t)0.705566406f, + (float16_t)-0.0696411133f, (float16_t)-0.685546875f, (float16_t)-2.22460938f, (float16_t)-0.387207031f, + (float16_t)-0.0942382812f, (float16_t)0.139282227f, (float16_t)-1.05273438f, (float16_t)-1.33105469f, + (float16_t)0.714355469f, (float16_t)0.413574219f, (float16_t)0.424316406f, (float16_t)0.879394531f, + (float16_t)1.02832031f, (float16_t)-1.11621094f, (float16_t)1.06054688f, (float16_t)0.955566406f, + (float16_t)-0.524414062f, (float16_t)-0.20703125f, (float16_t)0.332519531f, (float16_t)-1.34472656f, + (float16_t)-1.38378906f, (float16_t)-0.0811767578f, (float16_t)0.269775391f, (float16_t)0.315429688f, + (float16_t)-0.5625f, (float16_t)-0.302246094f, (float16_t)-0.311767578f, (float16_t)0.622070312f, + (float16_t)0.0706787109f, (float16_t)-0.141845703f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/weights_data.h new file mode 100644 index 00000000..e6b28058 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f16/weights_data.h @@ -0,0 +1,16 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_basic_smallc_nhwc_f16_weights_data[54] = { + (float16_t)-0.108276367f, (float16_t)-0.597167969f, (float16_t)0.580078125f, (float16_t)0.419921875f, + (float16_t)0.241088867f, (float16_t)-0.325439453f, (float16_t)-0.434082031f, (float16_t)0.385009766f, + (float16_t)-0.708984375f, (float16_t)-0.294677734f, (float16_t)-0.724609375f, (float16_t)-0.21875f, + (float16_t)0.404541016f, (float16_t)-0.716796875f, (float16_t)-0.174682617f, (float16_t)-0.606933594f, + (float16_t)0.076965332f, (float16_t)-0.623046875f, (float16_t)0.0640869141f, (float16_t)0.181396484f, + (float16_t)-0.470214844f, (float16_t)0.591796875f, (float16_t)0.0643920898f, (float16_t)0.11114502f, + (float16_t)0.302978516f, (float16_t)0.608886719f, (float16_t)0.554199219f, (float16_t)-0.254882812f, + (float16_t)-0.474609375f, (float16_t)-0.698730469f, (float16_t)-0.0495605469f, (float16_t)0.312011719f, + (float16_t)0.337890625f, (float16_t)0.502441406f, (float16_t)-0.638671875f, (float16_t)-0.260009766f, + (float16_t)0.574707031f, (float16_t)-0.144042969f, (float16_t)0.0850830078f, (float16_t)0.0856323242f, + (float16_t)0.134521484f, (float16_t)-0.300292969f, (float16_t)-0.602050781f, (float16_t)-0.34765625f, + (float16_t)-0.574707031f, (float16_t)0.286132812f, (float16_t)-0.333984375f, (float16_t)0.239379883f, + (float16_t)0.0339050293f, (float16_t)0.504394531f, (float16_t)0.561523438f, (float16_t)-0.241333008f, + (float16_t)-0.584472656f, (float16_t)0.125366211f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/biases_data.h new file mode 100644 index 00000000..b58172c9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_smallc_nhwc_f32_biases_data[6] = + {-0.0907983407f, -0.169143111f, 0.184338167f, 0.268792182f, 0.106893525f, 0.459879994f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/config_data.h new file mode 100644 index 00000000..adcbb268 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_INPUT_BATCHES 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_INPUT_H 9 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_INPUT_W 11 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_IN_CH 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_CH_MULT 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_FILTER_H 3 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_FILTER_W 3 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUT_CH 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_STRIDE_H 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_STRIDE_W 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_PADDING_H 0 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_PADDING_W 0 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_DILATION_H 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_DILATION_W 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUTPUT_H 7 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUTPUT_W 9 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUTPUT_C 6 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_DST_SIZE 378 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_USE_WRAPPER 1 +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_BASIC_SMALLC_NHWC_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/input_data.h new file mode 100644 index 00000000..c6701303 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/input_data.h @@ -0,0 +1,87 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_smallc_nhwc_f32_input_data[594] = { + 0.0461506993f, -0.775034487f, -0.34951672f, -0.840281785f, -0.534254253f, -0.0485910252f, 0.0231395736f, + 0.150120497f, 0.949248612f, 0.949508965f, -0.72816509f, 0.96649152f, -0.816307664f, 0.731841564f, + 0.424732864f, -0.545021892f, 0.229707003f, 0.863180876f, 0.831528485f, -0.448235571f, 0.665720344f, + -0.372921318f, 0.267928272f, 0.819706321f, -0.378174841f, 0.834845185f, -0.297861516f, 0.834429204f, + -0.386256486f, 0.089705199f, 0.384233028f, -0.515556157f, -0.870803893f, -0.848754108f, 0.682212353f, + 0.783567309f, 0.538158059f, 0.205351263f, 0.56423986f, -0.78612721f, 0.478357106f, 0.774361134f, + 0.226268649f, 0.75169605f, -0.285795331f, -0.546179771f, 0.636537373f, -0.200183064f, -0.573805034f, + 0.23763448f, -0.486691058f, 0.937471211f, 0.766984642f, -0.722242594f, 0.842116833f, -0.435511559f, + 0.599931002f, -0.288757831f, -0.0581042171f, -0.932370722f, -0.00458242558f, -0.788780808f, 0.347325057f, + 0.692482293f, 0.322744042f, 0.189150453f, -0.784282088f, 0.581486404f, -0.381299764f, 0.980657935f, + -0.60620141f, 0.728825748f, -0.333877295f, -0.77763027f, -0.729290605f, 0.457021058f, 0.898545623f, + 0.108502686f, -0.834059477f, 0.0909020826f, 0.893384159f, -0.400440097f, 0.506074786f, -0.331727535f, + 0.907245219f, -0.548221886f, 0.799000025f, -0.75541389f, -0.0724322721f, 0.21618773f, -0.876811206f, + -0.925110102f, 0.790902317f, 0.763374567f, 0.104042925f, 0.915043056f, -0.45170173f, 0.813825548f, + -0.0907640308f, 0.467712462f, -0.711401761f, 0.185076371f, 0.231707647f, 0.043963585f, -0.927606106f, + -0.101153642f, 0.368051142f, -0.391574234f, 0.658836782f, -0.629952669f, -0.647319794f, -0.94162482f, + -0.633835554f, 0.966897547f, -0.541832924f, 0.00743071502f, -0.673266768f, -0.567570806f, -0.94550848f, + 0.948262811f, -0.455168009f, 0.946933091f, 0.159272909f, 0.986418903f, -0.0819233283f, 0.0583622642f, + -0.417262107f, 0.847207665f, -0.856745601f, -0.870105684f, -0.466329545f, -0.661137879f, -0.242074504f, + 0.275889188f, 0.136933833f, -0.160822585f, -0.673267663f, 0.130791113f, 0.681418955f, -0.0292105209f, + 0.430419356f, 0.457350969f, -0.61318022f, 0.453198791f, -0.138852313f, -0.653414249f, 0.417237222f, + 0.240272403f, -0.0791540518f, -0.692580581f, 0.436556697f, -0.350535154f, 0.50049752f, 0.777767718f, + -0.152762264f, -0.149278522f, 0.243319839f, -0.249626279f, 0.00451186113f, 0.55026716f, 0.882009327f, + -0.381742746f, -0.825726986f, -0.814327538f, -0.237728f, 0.725842774f, 0.0890971795f, 0.120248288f, + -0.687417448f, -0.926531255f, -0.75391674f, 0.265781969f, -0.0535063632f, -0.723428547f, 0.244145781f, + 0.336841464f, 0.782653689f, 0.877220869f, -0.302406132f, -0.891634047f, -0.377770245f, 0.518775225f, + 0.393047243f, 0.445783377f, 0.0983802155f, -0.0317637771f, -0.598086774f, -0.238686293f, -0.884603679f, + 0.499450982f, -0.230629131f, -0.933981478f, 0.308948994f, -0.815382361f, -0.86683768f, -0.815314591f, + 0.821623147f, 0.613773763f, -0.51137507f, 0.45877564f, 0.895182312f, -0.246854618f, 0.0214037262f, + 0.838172793f, 0.880209625f, 0.158243626f, 0.84390682f, -0.764028728f, -0.940009356f, -0.557908952f, + -0.770049512f, -0.858647108f, 0.485483527f, 0.754374087f, 0.795023441f, -0.283917189f, -0.606767774f, + 0.862670839f, -0.591131985f, 0.218220934f, 0.0804467425f, 0.340621263f, -0.614667177f, 0.0529857017f, + 0.320891142f, 0.225292787f, -0.335037977f, 0.380637884f, 0.329288274f, -0.003725074f, -0.721434712f, + 0.414622903f, 0.27116847f, -0.270457566f, 0.431563914f, 0.131485254f, -0.968694508f, -0.645439208f, + -0.583783925f, -0.725240648f, 0.15798834f, 0.755468607f, -0.0129764928f, -0.327239573f, 0.391568363f, + -0.522482276f, 0.750388563f, -0.595434725f, 0.238839909f, -0.412171572f, 0.878078997f, -0.791814983f, + -0.281829f, -0.968351781f, -0.137329787f, -0.977818966f, -0.251747668f, -0.40413174f, -0.219014809f, + 0.933584273f, -0.816695452f, 0.709871829f, -0.819530666f, 0.768319905f, 0.358438432f, 0.714419663f, + -0.666453719f, -0.984229922f, 0.703391254f, 0.830747366f, 0.901661396f, -0.453124136f, -0.974340141f, + -0.260816246f, 0.677147627f, 0.377822518f, -0.308812588f, 0.997778058f, -0.771019399f, 0.0949156806f, + 0.698250175f, 0.971073866f, -0.357357562f, -0.28075698f, 0.179460317f, 0.171609655f, -0.763040543f, + -0.878554463f, -0.9357391f, -0.990139902f, -0.878707409f, -0.386837453f, 0.726979434f, 0.84535265f, + 0.916345477f, -0.418299437f, -0.629754841f, 0.0316003114f, -0.321313888f, -0.111522973f, -0.9417153f, + 0.459496111f, -0.891671717f, -0.560201049f, 0.217333063f, -0.0530869365f, -0.316732466f, -0.991662085f, + 0.474718511f, -0.657185853f, 0.318855733f, 0.547416389f, -0.247307375f, -0.462646067f, -0.903408766f, + -0.488645822f, -0.262635678f, -0.783649564f, -0.499364406f, -0.304199696f, 0.343648851f, 0.771914005f, + -0.766380906f, 0.875162005f, 0.880926132f, -0.410222977f, -0.539913654f, -0.109049626f, -0.40575248f, + 0.0904778019f, -0.736429274f, -0.990286112f, -0.566227078f, -0.351276457f, 0.774620891f, -0.229064181f, + -0.143186718f, -0.815924704f, 0.502927601f, -0.212136135f, -0.898452759f, 0.0934632868f, 0.701911509f, + 0.658166707f, 0.169161722f, 0.770366669f, 0.446173131f, -0.852414012f, -0.95550555f, -0.414016724f, + 0.506459594f, 0.36365366f, 0.0966983438f, -0.83223027f, 0.262280345f, 0.521374285f, -0.75463748f, + -0.119186021f, 0.704472363f, 0.515432656f, -0.857438445f, 0.937313378f, 0.4857077f, 0.759706974f, + 0.0109049622f, -0.867220879f, 0.368219763f, 0.600506783f, 0.978464901f, 0.311559737f, 0.879831135f, + 0.67708391f, 0.205152422f, 0.0336357094f, 0.723314464f, 0.211275741f, 0.626336336f, -0.111185834f, + -0.364659458f, 0.508442461f, -0.724148214f, 0.150044829f, -0.469038367f, 0.866069913f, 0.871059656f, + -0.393287003f, 0.395920753f, -0.0601241291f, 0.115646064f, -0.371749997f, 0.555284321f, 0.459844112f, + 0.534863412f, 0.0886852592f, 0.960114539f, -0.00828926824f, -0.0297105778f, -0.426364839f, 0.200210378f, + -0.220919043f, 0.317605287f, 0.131218776f, -0.0910425037f, -0.660699606f, -0.762315929f, 0.452837706f, + 0.704705477f, 0.40975675f, -0.644276142f, 0.242083043f, 0.439379632f, -0.225474805f, -0.204259023f, + 0.106189542f, -0.913652897f, 0.52049917f, -0.215640828f, -0.932341397f, 0.272824168f, -0.538319945f, + -0.388518602f, -0.457888812f, -0.576337337f, 0.615734577f, -0.00332241016f, -0.765071511f, -0.685281992f, + 0.670358121f, 0.775356174f, -0.164671421f, 0.981745839f, 0.459646225f, 0.768698752f, 0.352773041f, + 0.727704048f, 0.173196524f, -0.958934665f, -0.608179569f, -0.712465882f, 0.796336055f, 0.0186300855f, + -0.837368548f, -0.353722543f, -0.112363935f, 0.159613699f, 0.66575408f, 0.253790438f, -0.237586975f, + -0.442279667f, 0.439365923f, -0.18100673f, 0.819041848f, 0.759590209f, -0.685518444f, -0.848922431f, + 0.706256092f, -0.558751345f, 0.388626337f, 0.116654269f, -0.843783438f, 0.900232613f, 0.0325961821f, + 0.0217110291f, 0.430556089f, 0.964812279f, 0.790863156f, 0.615982652f, 0.193102136f, -0.258274287f, + -0.0921551213f, -0.792865872f, -0.790449262f, 0.113635689f, 0.379143745f, -0.503289223f, 0.45616284f, + 0.908367395f, 0.275820583f, 0.816913784f, 0.101516828f, -0.0233429149f, -0.564100981f, 0.950588942f, + 0.20114626f, 0.414727122f, 0.127302304f, 0.301670253f, 0.059302479f, 0.320895433f, -0.933256507f, + -0.312828481f, -0.602770686f, -0.850928843f, -0.158993214f, -0.293215781f, 0.763442755f, -0.834123611f, + 0.745729029f, 0.0256781187f, -0.564999402f, -0.788015246f, -0.104233779f, 0.247524142f, -0.0291831959f, + 0.854846239f, -0.948824108f, 0.581330061f, -0.978466034f, 0.343809456f, 0.968357682f, 0.911944687f, + 0.0709637254f, -0.840368509f, -0.199161395f, 0.28389591f, -0.456953257f, 0.346062124f, 0.299016923f, + -0.34346664f, 0.854661703f, 0.643587887f, -0.00321057229f, -0.773554265f, -0.548024356f, -0.31369704f, + -0.5069049f, 0.181261927f, -0.539186418f, 0.605420649f, 0.0684717447f, -0.264448464f, 0.512151241f, + -0.458849281f, 0.628112853f, -0.110912606f, 0.862521112f, 0.174966469f, 0.802370131f, -0.351630062f, + -0.49711886f, -0.125354543f, 0.760392725f, -0.587279797f, 0.812723458f, 0.296967685f, 0.888991952f, + 0.492154241f, 0.0605526641f, -0.316635042f, 0.937923908f, -0.993903518f, 0.261778921f, -0.683466733f, + -0.806980908f, 0.514467239f, 0.896002531f, 0.518979132f, 0.556438446f, 0.0247643013f, 0.614546061f, + 0.960555136f, -0.879587471f, 0.175975055f, -0.852062404f, 0.531722605f, -0.863461375f, -0.55059123f, + -0.482084394f, -0.609529257f, 0.828512073f, -0.557594299f, 0.25952816f, 0.541909993f, 0.0704215467f, + 0.724696696f, 0.479173899f, -0.820200562f, 0.114863701f, 0.820827544f, -0.649466038f, -0.0514933877f, + 0.635310531f, 0.0402941518f, -0.50838393f, 0.0258178432f, 0.851475894f, -0.68789494f, -0.64647156f, + -0.383603215f, 0.0863882601f, -0.424005449f, -0.584533811f, 0.742364824f, -0.239304423f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..7fca6d05 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_smallc_nhwc_f32_output_ref_data[378] = { + -1.10010612f, -0.871972084f, -0.690476358f, 0.288925022f, -0.104903892f, -0.199727908f, 0.768360317f, + 0.142227635f, 1.50351501f, 0.967853904f, -0.317392141f, -0.687004805f, -0.585839391f, -1.89326322f, + 0.0511576347f, 0.0925056934f, -0.536185861f, -0.357755005f, 0.0300114583f, 0.107305206f, 0.87255156f, + 0.0237213708f, 0.537165105f, -1.00978649f, 0.410042703f, -0.82004267f, -0.491200835f, 1.81903839f, + -0.214415997f, -0.0963722691f, -0.318328351f, 0.26626274f, -0.509115636f, 0.231604367f, 0.36468786f, + -0.117989406f, -0.792876661f, -0.268070608f, 0.356071264f, -0.408398867f, 0.7847507f, -0.264481276f, + 0.722482264f, -0.377997309f, -0.411913604f, 0.103379853f, 0.221630901f, 0.836328089f, -0.394681096f, + 0.526137233f, -0.413641155f, -0.319294006f, 0.244746312f, 0.880392253f, -0.850270987f, -1.60689545f, + 0.503269851f, 0.276316226f, -0.501129985f, -0.137404129f, 1.60317159f, 0.921519756f, -1.35291362f, + 1.73518717f, -0.391996175f, 1.07795429f, -0.735841811f, -0.330838203f, 0.641786516f, 0.152984351f, + 0.0693130046f, 0.291581452f, 0.184093744f, -1.3956573f, -0.849893093f, 0.110627942f, -0.561865389f, + 0.247963816f, -0.443337232f, -0.105115168f, 0.379075348f, 1.08096814f, 0.93008095f, 0.157130763f, + -0.214495763f, -0.457840741f, 1.3487668f, 1.79899073f, -0.153282225f, 0.475768775f, -0.394119471f, + -0.826274395f, 1.20297837f, 0.956537247f, -0.0629657283f, 0.329385847f, -0.55064708f, -0.556394815f, + -0.405955911f, 0.188332155f, 0.620693505f, -0.0161832888f, 0.369403988f, 0.17141147f, -1.4968586f, + -0.491353422f, 0.174457058f, 1.27744806f, -0.491788685f, 0.592717946f, 0.16690366f, 0.16633071f, + -0.217041224f, 1.26936889f, 0.617331624f, -0.800410926f, 0.184225902f, -0.653419793f, -0.180748314f, + 0.613772452f, -0.363680005f, 0.105825663f, -1.03880811f, 0.46123448f, 0.417253464f, 0.191293746f, + -0.430133671f, 0.889236689f, 0.891803384f, 0.169218361f, -0.728880346f, 0.811339915f, -1.1189208f, + 0.898798227f, -0.604266405f, -0.0607044324f, 0.551588714f, 1.14116907f, 1.62255514f, -0.565496325f, + 0.967483819f, 0.209375843f, -0.113829307f, 1.83656156f, -0.595717967f, 0.485601902f, -1.2035886f, + -0.772442758f, -0.387360573f, 1.44929671f, 0.00625429535f, -0.376908898f, 1.35863996f, -0.75949049f, + 0.292654514f, 1.69699872f, 0.496658325f, -0.427351624f, 0.149438828f, 1.37081134f, 1.5181762f, + 0.743137121f, -0.730795979f, 1.15657926f, -0.708947957f, -0.812798619f, 0.275765508f, 0.0461959578f, + -0.652492344f, -1.09487021f, 0.754148901f, -0.384687066f, -0.816472352f, -0.253570288f, 0.913133144f, + 0.651024222f, 0.294183165f, 0.401510805f, -0.226695865f, 0.864173114f, -0.712884963f, -1.09479344f, + -0.295143157f, 0.0721624419f, -0.0275622122f, 0.0391981788f, 0.951071084f, -0.685689569f, 1.02013242f, + 0.246556476f, -0.577902079f, 0.755232096f, -1.16826701f, -1.09019291f, 0.302365363f, 0.766259491f, + -0.360385597f, 1.32301366f, 0.0290916599f, -0.036761377f, -0.771853209f, -0.0144028943f, -0.0363315642f, + 0.997349679f, -0.251246721f, -1.09209311f, 0.123367719f, 1.04508698f, 0.353617728f, 1.63017571f, + -1.06413591f, -0.957411826f, 1.0069294f, -0.613557875f, 0.381219298f, -0.745027661f, -0.755484879f, + -1.54353082f, 1.92713559f, 0.272780955f, -0.0423825718f, -0.295272291f, 0.35650146f, 1.08422089f, + -0.275483131f, 0.0871846899f, -0.622370183f, 1.32916665f, -0.371656746f, -0.580370843f, -0.452815473f, + 0.361759871f, 0.300382584f, -0.0942248777f, 0.47121492f, 0.87437135f, 1.27730083f, 1.19895339f, + -1.49968791f, 0.438949019f, -0.814184904f, 0.795427203f, 1.1453824f, 0.293276042f, 0.242103145f, + 0.993199468f, -0.743451595f, 1.69712102f, 0.58510679f, 1.55994618f, -0.261554718f, -0.67167902f, + 1.02848494f, -0.197762653f, -0.622025311f, 0.562278271f, 0.214595199f, 0.984201491f, -0.803260088f, + 0.722949207f, 0.714877069f, -0.249466866f, 1.1004225f, -0.385569811f, 0.90389961f, 0.0955225229f, + -0.0980034173f, 0.0381221212f, -0.432794243f, 0.866581321f, 0.48834753f, -0.567807496f, 0.148836896f, + -0.868213594f, 0.363072306f, 0.791115463f, -0.768558085f, 0.628623486f, 0.741078019f, -0.448185116f, + -0.957453966f, 1.17211854f, -0.209605798f, -0.717038691f, -0.754505157f, 1.2772907f, 0.212326184f, + 1.10960031f, -0.814207911f, -0.38782078f, 0.704350531f, -1.22994626f, -0.339724332f, 1.42458904f, + 0.817862332f, -0.352953315f, -0.0688010305f, 0.462863892f, 0.574714124f, 0.167713419f, -0.325966477f, + -0.196056694f, 0.445154786f, 0.0772478357f, -0.131358519f, 1.05916023f, -1.01068723f, 0.32456997f, + -0.753326595f, 0.202031776f, -0.0296886228f, -0.195582598f, 0.363346457f, 1.35684228f, 1.7765131f, + -0.776678264f, 0.911473036f, -0.365517288f, 0.0676370859f, -0.603625298f, -0.319135159f, 0.255618364f, + -0.499371201f, -0.119929634f, 0.0498280972f, -0.104449339f, -0.274528921f, 1.0648644f, -0.996873498f, + -0.204666272f, -0.310234845f, -0.327468455f, 0.840101421f, 0.729152083f, 0.234906018f, 0.971677125f, + -0.2272342f, -0.565027297f, 1.32184267f, -0.159990817f, -0.0162259582f, 0.964278102f, 0.726777375f, + -0.670874834f, 0.181081384f, 1.0850054f, 0.548804164f, 0.705562115f, -0.0696426108f, -0.685662091f, + -2.22386098f, -0.387092352f, -0.0942458957f, 0.139327437f, -1.05241346f, -1.33098674f, 0.714577198f, + 0.413553923f, 0.424210489f, 0.879238248f, 1.02872157f, -1.11658454f, 1.06043339f, 0.955563307f, + -0.524501324f, -0.206976339f, 0.332440883f, -1.34484637f, -1.38370943f, -0.0811475962f, 0.269758701f, + 0.315400153f, -0.562459588f, -0.302180409f, -0.311727524f, 0.621868908f, 0.070667237f, -0.141861171f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/weights_data.h new file mode 100644 index 00000000..71501bc1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_basic_smallc_nhwc_f32/weights_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_basic_smallc_nhwc_f32_weights_data[54] = { + -0.108272232f, -0.597294271f, 0.580228806f, 0.420006514f, 0.241141245f, -0.325408429f, -0.434191346f, + 0.385082245f, -0.709216475f, -0.294621825f, -0.724681199f, -0.218810186f, 0.404493809f, -0.716842771f, + -0.174629554f, -0.606981814f, 0.0769502521f, -0.622962832f, 0.0640635714f, 0.181376323f, -0.47018221f, + 0.591931045f, 0.0643882155f, 0.111149885f, 0.302995801f, 0.609086096f, 0.554321587f, -0.254782408f, + -0.474623054f, -0.698962271f, -0.0495626852f, 0.312034965f, 0.337827176f, 0.502341032f, -0.638766348f, + -0.259903997f, 0.57462877f, -0.144099131f, 0.0850858912f, 0.0856513605f, 0.134512976f, -0.300195575f, + -0.601966083f, -0.34774366f, -0.574568093f, 0.286100835f, -0.333900571f, 0.239438236f, 0.0339160673f, + 0.504330933f, 0.561322331f, -0.2412799f, -0.584404707f, 0.125399336f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/biases_data.h new file mode 100644 index 00000000..d0edce1d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_ic1_to_conv_nhwc_f16_biases_data[16] = {(float16_t)-0.481689453f, + (float16_t)-0.258544922f, + (float16_t)-0.29296875f, + (float16_t)-0.197998047f, + (float16_t)0.479003906f, + (float16_t)-0.4921875f, + (float16_t)-0.253662109f, + (float16_t)-0.312988281f, + (float16_t)0.461669922f, + (float16_t)-0.102905273f, + (float16_t)0.457275391f, + (float16_t)0.339111328f, + (float16_t)0.497558594f, + (float16_t)0.185546875f, + (float16_t)-0.148071289f, + (float16_t)0.480712891f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/config_data.h new file mode 100644 index 00000000..3faa0810 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_BATCHES 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_H 9 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_W 11 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_IN_CH 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_CH_MULT 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_FILTER_H 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_FILTER_W 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_CH 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_STRIDE_H 2 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_STRIDE_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_PADDING_H 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_PADDING_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_DILATION_H 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_DILATION_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_H 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_W 10 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_C 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_DST_SIZE 640 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_USE_WRAPPER 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_IC1_TO_CONV_NHWC_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/input_data.h new file mode 100644 index 00000000..b37b7841 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/input_data.h @@ -0,0 +1,27 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_ic1_to_conv_nhwc_f16_input_data[99] = { + (float16_t)-0.577636719f, (float16_t)0.000984191895f, (float16_t)0.408935547f, (float16_t)0.875f, + (float16_t)0.438476562f, (float16_t)0.0464172363f, (float16_t)-0.0484008789f, (float16_t)-0.431152344f, + (float16_t)-0.389404297f, (float16_t)-0.0173339844f, (float16_t)0.047668457f, (float16_t)0.909179688f, + (float16_t)-0.833496094f, (float16_t)0.615722656f, (float16_t)0.366699219f, (float16_t)0.440673828f, + (float16_t)-0.415283203f, (float16_t)-0.325439453f, (float16_t)-0.422363281f, (float16_t)0.511230469f, + (float16_t)0.57421875f, (float16_t)-0.580566406f, (float16_t)-0.799804688f, (float16_t)-0.295410156f, + (float16_t)-0.516113281f, (float16_t)0.196289062f, (float16_t)-0.075012207f, (float16_t)-0.376220703f, + (float16_t)-0.324462891f, (float16_t)-0.809570312f, (float16_t)0.337646484f, (float16_t)0.833496094f, + (float16_t)0.229248047f, (float16_t)0.0610961914f, (float16_t)0.704101562f, (float16_t)-0.915039062f, + (float16_t)0.273193359f, (float16_t)-0.868164062f, (float16_t)-0.321044922f, (float16_t)0.967773438f, + (float16_t)0.668945312f, (float16_t)0.31640625f, (float16_t)0.293212891f, (float16_t)0.202026367f, + (float16_t)-0.362792969f, (float16_t)0.915039062f, (float16_t)-0.125f, (float16_t)0.354980469f, + (float16_t)0.504882812f, (float16_t)-0.763671875f, (float16_t)-0.299804688f, (float16_t)-0.520996094f, + (float16_t)-0.0223083496f, (float16_t)0.776367188f, (float16_t)0.91796875f, (float16_t)0.355712891f, + (float16_t)0.871582031f, (float16_t)-0.530273438f, (float16_t)-0.622558594f, (float16_t)-0.220581055f, + (float16_t)-0.143188477f, (float16_t)0.232543945f, (float16_t)-0.426757812f, (float16_t)-0.0204772949f, + (float16_t)0.415039062f, (float16_t)0.173706055f, (float16_t)0.00126552582f, (float16_t)-0.391601562f, + (float16_t)-0.889648438f, (float16_t)0.279785156f, (float16_t)0.562011719f, (float16_t)0.0971069336f, + (float16_t)0.881835938f, (float16_t)0.678710938f, (float16_t)-0.778320312f, (float16_t)0.986816406f, + (float16_t)0.197753906f, (float16_t)-0.192382812f, (float16_t)0.685546875f, (float16_t)0.0820922852f, + (float16_t)-0.219604492f, (float16_t)-0.851074219f, (float16_t)0.0352172852f, (float16_t)-0.973144531f, + (float16_t)0.857910156f, (float16_t)-0.682617188f, (float16_t)0.598144531f, (float16_t)0.153076172f, + (float16_t)0.887695312f, (float16_t)0.133300781f, (float16_t)0.392333984f, (float16_t)0.135864258f, + (float16_t)0.552246094f, (float16_t)0.213500977f, (float16_t)-0.633300781f, (float16_t)0.448486328f, + (float16_t)0.632324219f, (float16_t)0.966308594f, (float16_t)0.0295562744f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..fc6eee6c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/output_ref_data.h @@ -0,0 +1,162 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_ic1_to_conv_nhwc_f16_output_ref_data[640] = { + (float16_t)-0.0700683594f, (float16_t)-1.84863281f, (float16_t)-0.227294922f, (float16_t)-0.645996094f, + (float16_t)2.15625f, (float16_t)-1.21289062f, (float16_t)-0.0192565918f, (float16_t)-1.38769531f, + (float16_t)1.07617188f, (float16_t)-0.336425781f, (float16_t)0.553222656f, (float16_t)1.92773438f, + (float16_t)0.802246094f, (float16_t)0.118835449f, (float16_t)-0.447998047f, (float16_t)0.886230469f, + (float16_t)-0.478027344f, (float16_t)0.443847656f, (float16_t)-1.08691406f, (float16_t)-0.6953125f, + (float16_t)0.518066406f, (float16_t)0.531738281f, (float16_t)0.310058594f, (float16_t)0.93359375f, + (float16_t)1.07324219f, (float16_t)-1.47753906f, (float16_t)1.63085938f, (float16_t)-0.244018555f, + (float16_t)-0.0452575684f, (float16_t)1.95605469f, (float16_t)-0.505859375f, (float16_t)0.313964844f, + (float16_t)0.251220703f, (float16_t)-0.862792969f, (float16_t)-0.541992188f, (float16_t)0.868164062f, + (float16_t)1.4140625f, (float16_t)-1.16113281f, (float16_t)-2.0078125f, (float16_t)-0.63671875f, + (float16_t)0.912109375f, (float16_t)-1.46777344f, (float16_t)-0.155029297f, (float16_t)0.528808594f, + (float16_t)0.0881958008f, (float16_t)0.0191192627f, (float16_t)-0.903808594f, (float16_t)1.1953125f, + (float16_t)-0.409179688f, (float16_t)0.233642578f, (float16_t)-0.87890625f, (float16_t)-0.264892578f, + (float16_t)-0.0983886719f, (float16_t)0.0433654785f, (float16_t)-0.825195312f, (float16_t)1.36621094f, + (float16_t)0.354003906f, (float16_t)-1.12304688f, (float16_t)0.297607422f, (float16_t)-0.123840332f, + (float16_t)0.412841797f, (float16_t)0.127441406f, (float16_t)-0.146240234f, (float16_t)-0.498535156f, + (float16_t)-0.755859375f, (float16_t)-1.10058594f, (float16_t)-0.279785156f, (float16_t)-1.25195312f, + (float16_t)0.129150391f, (float16_t)-0.395751953f, (float16_t)-0.109130859f, (float16_t)-0.142333984f, + (float16_t)0.56640625f, (float16_t)0.338134766f, (float16_t)0.453125f, (float16_t)1.12304688f, + (float16_t)1.05566406f, (float16_t)-0.658691406f, (float16_t)0.205200195f, (float16_t)0.429443359f, + (float16_t)-1.23144531f, (float16_t)-0.454101562f, (float16_t)-1.13867188f, (float16_t)-0.655273438f, + (float16_t)0.791992188f, (float16_t)-0.790039062f, (float16_t)0.317138672f, (float16_t)0.0673217773f, + (float16_t)1.01171875f, (float16_t)0.89453125f, (float16_t)0.689941406f, (float16_t)1.15234375f, + (float16_t)1.14648438f, (float16_t)-0.665527344f, (float16_t)-0.305908203f, (float16_t)0.812988281f, + (float16_t)-1.33886719f, (float16_t)-0.618164062f, (float16_t)-0.427001953f, (float16_t)-0.68359375f, + (float16_t)0.993652344f, (float16_t)-1.10839844f, (float16_t)-0.147460938f, (float16_t)-1.10449219f, + (float16_t)0.91796875f, (float16_t)1.203125f, (float16_t)-0.215209961f, (float16_t)0.309326172f, + (float16_t)1.31640625f, (float16_t)0.287597656f, (float16_t)0.0316772461f, (float16_t)0.643066406f, + (float16_t)-0.0218353271f, (float16_t)-0.0999145508f, (float16_t)-0.118103027f, (float16_t)0.0252532959f, + (float16_t)1.14355469f, (float16_t)-1.1171875f, (float16_t)-1.10058594f, (float16_t)-1.74902344f, + (float16_t)1.18164062f, (float16_t)-0.371582031f, (float16_t)0.776367188f, (float16_t)-0.124084473f, + (float16_t)0.744628906f, (float16_t)0.643554688f, (float16_t)-0.404296875f, (float16_t)0.884277344f, + (float16_t)0.577148438f, (float16_t)0.835449219f, (float16_t)0.548339844f, (float16_t)1.5f, + (float16_t)0.922851562f, (float16_t)-1.21191406f, (float16_t)-1.62402344f, (float16_t)0.220336914f, + (float16_t)0.301269531f, (float16_t)-1.27929688f, (float16_t)-0.259765625f, (float16_t)-0.111755371f, + (float16_t)-0.182861328f, (float16_t)0.748535156f, (float16_t)0.322021484f, (float16_t)-0.608886719f, + (float16_t)-0.478515625f, (float16_t)0.0459594727f, (float16_t)0.26171875f, (float16_t)-0.389648438f, + (float16_t)-0.287353516f, (float16_t)0.23840332f, (float16_t)0.637207031f, (float16_t)0.319580078f, + (float16_t)-0.788085938f, (float16_t)-0.450195312f, (float16_t)0.393066406f, (float16_t)-0.347412109f, + (float16_t)0.0302429199f, (float16_t)0.727050781f, (float16_t)0.0953979492f, (float16_t)-0.574707031f, + (float16_t)0.658691406f, (float16_t)0.676269531f, (float16_t)-0.463134766f, (float16_t)1.3984375f, + (float16_t)1.92773438f, (float16_t)-2.44335938f, (float16_t)-1.77050781f, (float16_t)-0.876953125f, + (float16_t)0.230224609f, (float16_t)0.329589844f, (float16_t)1.30859375f, (float16_t)0.637695312f, + (float16_t)0.273193359f, (float16_t)-0.334716797f, (float16_t)0.593261719f, (float16_t)-0.0518188477f, + (float16_t)-0.337402344f, (float16_t)-0.0391845703f, (float16_t)0.0143051147f, (float16_t)-0.480712891f, + (float16_t)0.150146484f, (float16_t)0.381591797f, (float16_t)1.95996094f, (float16_t)0.468994141f, + (float16_t)-0.975097656f, (float16_t)-0.930664062f, (float16_t)-0.653808594f, (float16_t)-0.397460938f, + (float16_t)0.970214844f, (float16_t)0.663085938f, (float16_t)0.755371094f, (float16_t)-1.0625f, + (float16_t)-1.16015625f, (float16_t)0.406005859f, (float16_t)-0.584472656f, (float16_t)0.212524414f, + (float16_t)-0.724609375f, (float16_t)-0.284179688f, (float16_t)0.332275391f, (float16_t)-0.117614746f, + (float16_t)-0.820800781f, (float16_t)0.0936279297f, (float16_t)2.66601562f, (float16_t)1.16601562f, + (float16_t)-0.3203125f, (float16_t)0.7578125f, (float16_t)0.141113281f, (float16_t)0.423828125f, + (float16_t)-1.46972656f, (float16_t)0.0391845703f, (float16_t)-0.530273438f, (float16_t)0.852539062f, + (float16_t)1.61816406f, (float16_t)-1.26074219f, (float16_t)-0.327880859f, (float16_t)0.223632812f, + (float16_t)-0.162597656f, (float16_t)0.525390625f, (float16_t)-1.08398438f, (float16_t)-0.131469727f, + (float16_t)0.486816406f, (float16_t)-0.792480469f, (float16_t)-0.257080078f, (float16_t)0.11907959f, + (float16_t)-1.60546875f, (float16_t)-0.3515625f, (float16_t)-0.80078125f, (float16_t)-1.36425781f, + (float16_t)0.655761719f, (float16_t)-0.00405883789f, (float16_t)0.876953125f, (float16_t)-0.617675781f, + (float16_t)0.8359375f, (float16_t)2.2109375f, (float16_t)0.162475586f, (float16_t)-0.870117188f, + (float16_t)0.613769531f, (float16_t)0.690429688f, (float16_t)-0.0293273926f, (float16_t)0.786132812f, + (float16_t)-0.4296875f, (float16_t)-1.04296875f, (float16_t)-1.671875f, (float16_t)0.658691406f, + (float16_t)1.73828125f, (float16_t)-1.45996094f, (float16_t)-0.522949219f, (float16_t)-1.93847656f, + (float16_t)1.59179688f, (float16_t)0.826171875f, (float16_t)-0.245727539f, (float16_t)0.747558594f, + (float16_t)1.81738281f, (float16_t)-1.02539062f, (float16_t)-1.68066406f, (float16_t)2.37890625f, + (float16_t)1.23535156f, (float16_t)-0.412597656f, (float16_t)-0.0352172852f, (float16_t)0.165527344f, + (float16_t)1.45703125f, (float16_t)-0.846191406f, (float16_t)-0.96875f, (float16_t)-0.677246094f, + (float16_t)1.21679688f, (float16_t)-1.00390625f, (float16_t)0.272705078f, (float16_t)1.53515625f, + (float16_t)0.699707031f, (float16_t)0.592285156f, (float16_t)0.273925781f, (float16_t)-0.303466797f, + (float16_t)0.663574219f, (float16_t)-0.629882812f, (float16_t)-0.218505859f, (float16_t)-0.180541992f, + (float16_t)0.269775391f, (float16_t)-0.0398864746f, (float16_t)0.295166016f, (float16_t)-0.224121094f, + (float16_t)-0.073425293f, (float16_t)-2.62304688f, (float16_t)1.75292969f, (float16_t)1.29882812f, + (float16_t)0.124023438f, (float16_t)1.95019531f, (float16_t)0.0927124023f, (float16_t)-0.432373047f, + (float16_t)-0.981445312f, (float16_t)0.994628906f, (float16_t)-0.071472168f, (float16_t)0.67578125f, + (float16_t)-0.170166016f, (float16_t)-0.0563354492f, (float16_t)-1.41015625f, (float16_t)0.811035156f, + (float16_t)-0.7890625f, (float16_t)-2.58789062f, (float16_t)0.762207031f, (float16_t)-1.70800781f, + (float16_t)-0.694824219f, (float16_t)2.390625f, (float16_t)-0.975585938f, (float16_t)-1.19433594f, + (float16_t)0.140380859f, (float16_t)0.859863281f, (float16_t)-0.033782959f, (float16_t)-0.192504883f, + (float16_t)0.124572754f, (float16_t)-0.0597229004f, (float16_t)-1.25292969f, (float16_t)0.481689453f, + (float16_t)-0.198974609f, (float16_t)-0.564453125f, (float16_t)0.228027344f, (float16_t)-2.07421875f, + (float16_t)-0.225585938f, (float16_t)0.0736694336f, (float16_t)0.200439453f, (float16_t)-0.571777344f, + (float16_t)-1.37304688f, (float16_t)-0.853515625f, (float16_t)-1.3828125f, (float16_t)1.34960938f, + (float16_t)0.886230469f, (float16_t)-0.928222656f, (float16_t)-0.702148438f, (float16_t)0.983886719f, + (float16_t)0.553710938f, (float16_t)-0.608398438f, (float16_t)-0.442382812f, (float16_t)1.52636719f, + (float16_t)0.303466797f, (float16_t)0.608398438f, (float16_t)-0.930175781f, (float16_t)0.573242188f, + (float16_t)-0.570800781f, (float16_t)-0.608886719f, (float16_t)-0.324707031f, (float16_t)-1.53222656f, + (float16_t)0.896972656f, (float16_t)-0.853515625f, (float16_t)-0.512207031f, (float16_t)0.0387268066f, + (float16_t)-0.274414062f, (float16_t)-0.4609375f, (float16_t)1.36914062f, (float16_t)0.701660156f, + (float16_t)1.37890625f, (float16_t)-0.469970703f, (float16_t)0.245117188f, (float16_t)-0.243286133f, + (float16_t)-0.681152344f, (float16_t)-1.14160156f, (float16_t)0.770996094f, (float16_t)-2.37890625f, + (float16_t)0.0333251953f, (float16_t)-0.0439453125f, (float16_t)0.561035156f, (float16_t)-0.879394531f, + (float16_t)2.03710938f, (float16_t)-0.953125f, (float16_t)1.08691406f, (float16_t)0.1640625f, + (float16_t)1.66503906f, (float16_t)-0.0873413086f, (float16_t)-0.785644531f, (float16_t)1.89648438f, + (float16_t)-0.690429688f, (float16_t)1.22558594f, (float16_t)0.996582031f, (float16_t)0.862304688f, + (float16_t)0.404296875f, (float16_t)-1.56835938f, (float16_t)-2.48046875f, (float16_t)-0.412109375f, + (float16_t)1.33105469f, (float16_t)0.803222656f, (float16_t)-0.403808594f, (float16_t)-1.30371094f, + (float16_t)-0.425048828f, (float16_t)0.689941406f, (float16_t)-0.707519531f, (float16_t)0.719726562f, + (float16_t)0.450439453f, (float16_t)0.0345458984f, (float16_t)0.397460938f, (float16_t)-0.944335938f, + (float16_t)-0.950195312f, (float16_t)0.150634766f, (float16_t)0.3671875f, (float16_t)-1.28417969f, + (float16_t)0.0674438477f, (float16_t)0.517089844f, (float16_t)0.0894165039f, (float16_t)-0.455810547f, + (float16_t)1.24511719f, (float16_t)-1.00878906f, (float16_t)0.747070312f, (float16_t)0.772949219f, + (float16_t)-0.194091797f, (float16_t)-0.0674438477f, (float16_t)0.373046875f, (float16_t)0.904296875f, + (float16_t)-0.933105469f, (float16_t)-0.657714844f, (float16_t)-0.346923828f, (float16_t)-1.16210938f, + 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(float16_t)-0.424316406f, (float16_t)0.578613281f, + (float16_t)-0.765625f, (float16_t)0.556640625f, (float16_t)-0.833007812f, (float16_t)-1.29492188f, + (float16_t)0.228637695f, (float16_t)1.24707031f, (float16_t)0.254638672f, (float16_t)-1.30371094f, + (float16_t)0.0178375244f, (float16_t)-0.756835938f, (float16_t)0.868652344f, (float16_t)-0.773925781f, + (float16_t)1.2265625f, (float16_t)-0.811035156f, (float16_t)-0.675292969f, (float16_t)-1.47753906f, + (float16_t)0.310546875f, (float16_t)0.521484375f, (float16_t)0.875488281f, (float16_t)0.422607422f, + (float16_t)0.528320312f, (float16_t)-0.225463867f, (float16_t)0.355957031f, (float16_t)0.324462891f, + (float16_t)0.362304688f, (float16_t)-0.341796875f, (float16_t)0.99609375f, (float16_t)-0.89453125f, + (float16_t)0.21887207f, (float16_t)-0.366210938f, (float16_t)1.10253906f, (float16_t)-0.208251953f, + (float16_t)0.267333984f, (float16_t)-0.896972656f, (float16_t)1.15917969f, (float16_t)0.227905273f, + (float16_t)0.913574219f, (float16_t)0.500976562f, (float16_t)0.274414062f, (float16_t)0.902832031f, + (float16_t)-0.442871094f, (float16_t)0.0926513672f, (float16_t)0.696777344f, (float16_t)-0.42578125f, + (float16_t)-0.676269531f, (float16_t)0.297363281f, (float16_t)0.434326172f, (float16_t)-0.489746094f, + (float16_t)0.102416992f, (float16_t)-0.997558594f, (float16_t)1.10351562f, (float16_t)-0.209228516f, + (float16_t)0.0456848145f, (float16_t)0.217041016f, (float16_t)-1.50195312f, (float16_t)1.48339844f, + (float16_t)0.078125f, (float16_t)0.153198242f, (float16_t)0.617675781f, (float16_t)-0.779785156f, + (float16_t)-0.748046875f, (float16_t)0.817382812f, (float16_t)-0.0382080078f, (float16_t)0.691894531f, + (float16_t)1.46679688f, (float16_t)0.118774414f, (float16_t)1.40332031f, (float16_t)1.5234375f, + (float16_t)-0.6875f, (float16_t)-0.642578125f, (float16_t)0.279541016f, (float16_t)1.66601562f, + (float16_t)-1.10644531f, (float16_t)-2.17578125f, (float16_t)1.04101562f, (float16_t)-1.140625f, + (float16_t)0.0173339844f, (float16_t)-0.255371094f, (float16_t)-0.29296875f, (float16_t)-1.57519531f, + (float16_t)1.01074219f, (float16_t)0.73046875f, (float16_t)-0.315673828f, (float16_t)0.896972656f, + (float16_t)-0.0384216309f, (float16_t)0.579589844f, (float16_t)-0.19921875f, (float16_t)2.29101562f, + (float16_t)-1.08203125f, (float16_t)0.780273438f, (float16_t)-1.17578125f, (float16_t)0.42578125f, + (float16_t)-0.383789062f, (float16_t)-1.07324219f, (float16_t)-1.66113281f, (float16_t)-0.328369141f, + (float16_t)0.172485352f, (float16_t)-0.313232422f, (float16_t)1.27441406f, (float16_t)-0.913574219f, + (float16_t)1.0f, (float16_t)-0.692382812f, (float16_t)-1.25292969f, (float16_t)1.35546875f, + (float16_t)1.33886719f, (float16_t)-0.587890625f, (float16_t)2.30664062f, (float16_t)-1.56445312f, + (float16_t)0.098449707f, (float16_t)0.153076172f, (float16_t)-1.29296875f, (float16_t)-1.41992188f, + (float16_t)1.16992188f, (float16_t)-0.703125f, (float16_t)-0.530273438f, (float16_t)-0.417236328f, + (float16_t)0.151367188f, (float16_t)-0.224365234f, (float16_t)1.43359375f, (float16_t)0.032623291f, + (float16_t)0.0270843506f, (float16_t)0.217407227f, (float16_t)-0.0336608887f, (float16_t)0.569824219f, + (float16_t)-0.830078125f, (float16_t)0.332275391f, (float16_t)-0.138427734f, (float16_t)0.0960083008f, + (float16_t)0.264648438f, (float16_t)-0.487548828f, (float16_t)1.27050781f, (float16_t)-0.0704345703f, + (float16_t)-1.37597656f, (float16_t)1.68164062f, (float16_t)0.0845336914f, (float16_t)0.615234375f, + (float16_t)-0.902832031f, (float16_t)0.245605469f, (float16_t)-0.662597656f, (float16_t)0.6875f, + (float16_t)-0.333740234f, (float16_t)0.231445312f, (float16_t)-0.524414062f, (float16_t)0.692382812f, + (float16_t)-1.37402344f, (float16_t)-0.5625f, (float16_t)-0.286621094f, (float16_t)-0.594238281f, + (float16_t)-0.187133789f, (float16_t)0.00540924072f, (float16_t)-0.261230469f, (float16_t)-0.334472656f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/weights_data.h new file mode 100644 index 00000000..836b0988 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f16/weights_data.h @@ -0,0 +1,66 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_ic1_to_conv_nhwc_f16_weights_data[256] = { + (float16_t)-0.0912475586f, (float16_t)-0.0733642578f, (float16_t)-0.661621094f, (float16_t)-0.0943603516f, + (float16_t)0.572265625f, (float16_t)-0.176025391f, (float16_t)0.504882812f, (float16_t)0.147460938f, + (float16_t)-0.30859375f, (float16_t)-0.0586547852f, (float16_t)-0.319091797f, (float16_t)-0.624023438f, + (float16_t)0.646972656f, (float16_t)-0.476074219f, (float16_t)0.165161133f, (float16_t)-0.0290222168f, + (float16_t)-0.535644531f, (float16_t)0.235473633f, (float16_t)-0.343017578f, (float16_t)0.312744141f, + (float16_t)0.700683594f, (float16_t)-0.542480469f, (float16_t)-0.664550781f, (float16_t)0.173461914f, + (float16_t)-0.467529297f, (float16_t)0.544433594f, (float16_t)0.109130859f, (float16_t)-0.328125f, + (float16_t)-0.488525391f, (float16_t)0.433105469f, (float16_t)0.517578125f, (float16_t)-0.743164062f, + (float16_t)-0.74609375f, (float16_t)-0.0412597656f, (float16_t)-0.637207031f, (float16_t)0.355224609f, + (float16_t)-0.286621094f, (float16_t)0.331054688f, (float16_t)0.439697266f, (float16_t)0.591308594f, + (float16_t)-0.63671875f, (float16_t)-0.311523438f, (float16_t)-0.245239258f, (float16_t)0.312988281f, + (float16_t)-0.108642578f, (float16_t)0.244384766f, (float16_t)0.156738281f, (float16_t)-0.618164062f, + (float16_t)0.507324219f, (float16_t)0.207397461f, (float16_t)-0.684082031f, (float16_t)0.375244141f, + (float16_t)0.0645751953f, (float16_t)-0.119140625f, (float16_t)0.263427734f, (float16_t)0.135375977f, + (float16_t)-0.619140625f, (float16_t)-0.0944824219f, (float16_t)0.374267578f, (float16_t)0.364990234f, + (float16_t)-0.107666016f, (float16_t)-0.265136719f, (float16_t)0.651367188f, (float16_t)-0.360595703f, + (float16_t)0.165405273f, (float16_t)-0.522460938f, (float16_t)0.437011719f, (float16_t)-0.501464844f, + (float16_t)-0.157714844f, (float16_t)-0.137329102f, (float16_t)-0.438720703f, (float16_t)-0.417724609f, + (float16_t)0.635742188f, (float16_t)0.657226562f, (float16_t)-0.225219727f, (float16_t)0.301757812f, + (float16_t)0.0478820801f, (float16_t)-0.293701172f, (float16_t)0.559570312f, (float16_t)0.258544922f, + (float16_t)-0.252929688f, (float16_t)0.321533203f, (float16_t)-0.357910156f, (float16_t)-0.457519531f, + (float16_t)-0.591308594f, (float16_t)0.631835938f, (float16_t)-0.180786133f, (float16_t)0.530273438f, + (float16_t)-0.237915039f, (float16_t)-0.419677734f, (float16_t)0.0362548828f, (float16_t)-0.6796875f, + (float16_t)0.181640625f, (float16_t)-0.576660156f, (float16_t)-0.373046875f, (float16_t)-0.15222168f, + (float16_t)-0.18359375f, (float16_t)-0.174926758f, (float16_t)-0.286376953f, (float16_t)0.668457031f, + (float16_t)-0.010269165f, (float16_t)-0.359375f, (float16_t)-0.37890625f, (float16_t)0.26953125f, + (float16_t)-0.242919922f, (float16_t)-0.409179688f, (float16_t)-0.184326172f, (float16_t)-0.125366211f, + (float16_t)-0.349365234f, (float16_t)0.322753906f, (float16_t)-0.386230469f, (float16_t)0.421386719f, + (float16_t)0.655273438f, (float16_t)-0.345458984f, (float16_t)0.610351562f, (float16_t)-0.680664062f, + (float16_t)0.0363769531f, (float16_t)0.698730469f, (float16_t)0.188232422f, (float16_t)0.0867919922f, + (float16_t)0.556152344f, (float16_t)-0.641113281f, (float16_t)0.665527344f, (float16_t)0.601074219f, + (float16_t)-0.572265625f, (float16_t)0.216186523f, (float16_t)0.289550781f, (float16_t)0.410644531f, + (float16_t)-0.086730957f, (float16_t)0.300292969f, (float16_t)-0.297607422f, (float16_t)-0.618652344f, + (float16_t)-0.462890625f, (float16_t)0.62109375f, (float16_t)0.699707031f, (float16_t)0.592773438f, + (float16_t)-0.337890625f, (float16_t)-0.509277344f, (float16_t)0.728515625f, (float16_t)-0.406738281f, + (float16_t)0.0618286133f, (float16_t)0.51953125f, (float16_t)-0.0853881836f, (float16_t)-0.696289062f, + (float16_t)0.448242188f, (float16_t)-0.403808594f, (float16_t)0.17980957f, (float16_t)0.243530273f, + (float16_t)0.237670898f, (float16_t)-0.281982422f, (float16_t)-0.41796875f, (float16_t)-0.0367736816f, + (float16_t)-0.435302734f, (float16_t)-0.681640625f, (float16_t)-0.377197266f, (float16_t)0.308105469f, + (float16_t)0.154663086f, (float16_t)-0.00953674316f, (float16_t)0.0912475586f, (float16_t)-0.690429688f, + (float16_t)0.417724609f, (float16_t)0.263427734f, (float16_t)-0.549316406f, (float16_t)0.6171875f, + (float16_t)-0.0805664062f, (float16_t)0.128051758f, (float16_t)-0.485107422f, (float16_t)0.278076172f, + (float16_t)0.0585632324f, (float16_t)-0.515625f, (float16_t)0.0513916016f, (float16_t)-0.0538024902f, + (float16_t)-0.107543945f, (float16_t)0.199707031f, (float16_t)-0.130126953f, (float16_t)-0.146972656f, + (float16_t)0.119750977f, (float16_t)-0.108398438f, (float16_t)-0.237060547f, (float16_t)-0.0807495117f, + (float16_t)0.227416992f, (float16_t)0.280761719f, (float16_t)-0.0113983154f, (float16_t)-0.453857422f, + (float16_t)0.169921875f, (float16_t)9.06586647e-05f, (float16_t)-0.249633789f, (float16_t)-0.736328125f, + (float16_t)-0.264892578f, (float16_t)0.577148438f, (float16_t)-0.347900391f, (float16_t)-0.00104999542f, + (float16_t)-0.0953369141f, (float16_t)-0.578125f, (float16_t)-0.166137695f, (float16_t)-0.310058594f, + (float16_t)-0.496337891f, (float16_t)0.367675781f, (float16_t)0.625976562f, (float16_t)-0.544433594f, + (float16_t)-0.487792969f, (float16_t)0.243286133f, (float16_t)0.602539062f, (float16_t)0.490234375f, + (float16_t)0.151489258f, (float16_t)-0.583984375f, (float16_t)-0.302001953f, (float16_t)0.723144531f, + (float16_t)-0.00565338135f, (float16_t)0.494628906f, (float16_t)0.501953125f, (float16_t)0.148803711f, + (float16_t)-0.735351562f, (float16_t)0.587402344f, (float16_t)0.280517578f, (float16_t)0.689453125f, + (float16_t)-0.445068359f, (float16_t)0.389404297f, (float16_t)-0.620117188f, (float16_t)-0.701171875f, + (float16_t)-0.66015625f, (float16_t)0.587890625f, (float16_t)0.655273438f, (float16_t)-0.648925781f, + (float16_t)0.603515625f, (float16_t)0.564941406f, (float16_t)0.435546875f, (float16_t)0.386962891f, + (float16_t)-0.0152893066f, (float16_t)0.0445556641f, (float16_t)-0.479003906f, (float16_t)-0.196166992f, + (float16_t)-0.00798034668f, (float16_t)-0.583007812f, (float16_t)0.197998047f, (float16_t)-0.681640625f, + (float16_t)-0.491699219f, (float16_t)-0.153564453f, (float16_t)-0.240356445f, (float16_t)-0.0598754883f, + (float16_t)-0.07421875f, (float16_t)0.08984375f, (float16_t)0.650390625f, (float16_t)-0.273681641f, + (float16_t)-0.590820312f, (float16_t)-0.0609436035f, (float16_t)-0.230712891f, (float16_t)-0.452880859f, + (float16_t)-0.448486328f, (float16_t)-0.224609375f, (float16_t)0.101745605f, (float16_t)-0.612304688f, + (float16_t)0.186157227f, (float16_t)0.473144531f, (float16_t)0.306152344f, (float16_t)-0.229370117f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/biases_data.h new file mode 100644 index 00000000..2d265d5c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_ic1_to_conv_nhwc_f32_biases_data[16] = {-0.481616676f, + -0.258648247f, + -0.292894572f, + -0.198041558f, + 0.479033113f, + -0.492067754f, + -0.253637612f, + -0.313004792f, + 0.461565048f, + -0.102920398f, + 0.457162857f, + 0.33921051f, + 0.497558504f, + 0.185501352f, + -0.148013398f, + 0.480720788f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/config_data.h new file mode 100644 index 00000000..e17c562d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_BATCHES 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_H 9 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_W 11 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_IN_CH 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_CH_MULT 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_FILTER_H 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_FILTER_W 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_CH 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_STRIDE_H 2 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_STRIDE_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_PADDING_H 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_PADDING_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_DILATION_H 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_DILATION_W 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_H 4 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_W 10 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_C 16 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_DST_SIZE 640 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_USE_WRAPPER 1 +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_IC1_TO_CONV_NHWC_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/input_data.h new file mode 100644 index 00000000..8c5fb73b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/input_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_ic1_to_conv_nhwc_f32_input_data[99] = { + -0.577801228f, 0.000984549988f, 0.408933282f, 0.8752262f, 0.438545555f, 0.0464316458f, -0.0484103188f, + -0.431046188f, -0.389419466f, -0.0173361283f, 0.0476731844f, 0.909294546f, -0.833289385f, 0.615796745f, + 0.366582781f, 0.440698594f, -0.415292025f, -0.32538262f, -0.422286838f, 0.511144042f, 0.574195385f, + -0.580718935f, -0.799660146f, -0.295520246f, -0.516073942f, 0.196234331f, -0.0750000253f, -0.376253635f, + -0.324381769f, -0.80962199f, 0.33770296f, 0.833618462f, 0.229240343f, 0.0610953979f, 0.704182804f, + -0.915279746f, 0.273250461f, -0.86805737f, -0.321091741f, 0.967612684f, 0.668790817f, 0.316408306f, + 0.293202132f, 0.202083528f, -0.362725645f, 0.914833903f, -0.124983221f, 0.354871064f, 0.504687428f, + -0.76375097f, -0.299687803f, -0.521078825f, -0.0223054253f, 0.776437581f, 0.917877793f, 0.355614603f, + 0.871474385f, -0.530131876f, -0.622328818f, -0.22056295f, -0.14315562f, 0.23257798f, -0.426656663f, + -0.0204726681f, 0.415052712f, 0.173760116f, 0.00126594782f, -0.391489387f, -0.889751256f, 0.279796511f, + 0.562051117f, 0.0971218646f, 0.881752789f, 0.678634286f, -0.778122783f, 0.986737967f, 0.197711468f, + -0.192438111f, 0.685777426f, 0.0821166933f, -0.219629332f, -0.851004243f, 0.0352022536f, -0.973383248f, + 0.85799253f, -0.682578385f, 0.598285854f, 0.153081313f, 0.887699842f, 0.13329725f, 0.392285675f, + 0.135824695f, 0.552115977f, 0.213522613f, -0.633063495f, 0.448443979f, 0.632413387f, 0.966548264f, + 0.0295490008f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..59d2440d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/output_ref_data.h @@ -0,0 +1,94 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_ic1_to_conv_nhwc_f32_output_ref_data[640] = { + -0.0700490177f, -1.84821069f, -0.227287695f, -0.645942211f, 2.15646791f, -1.2132231f, -0.0192639362f, + -1.38784742f, 1.07583284f, -0.336312324f, 0.553461134f, 1.92735589f, 0.802458584f, 0.11882776f, + -0.448023617f, 0.886143148f, -0.478090405f, 0.44385767f, -1.08654034f, -0.695356905f, 0.51808852f, + 0.5315575f, 0.309996843f, 0.933514178f, 1.07299185f, -1.47726023f, 1.63038123f, -0.244029224f, + -0.0452566706f, 1.95638311f, -0.505622745f, 0.31389764f, 0.251245916f, -0.862618327f, -0.542177379f, + 0.867964089f, 1.41448963f, -1.16091537f, -2.006989f, -0.636695862f, 0.912039936f, -1.46816397f, + -0.155085832f, 0.528854966f, 0.0882069319f, 0.0191213489f, -0.903601766f, 1.19496143f, -0.40929389f, + 0.233682021f, -0.878678918f, -0.26494652f, -0.0983748063f, 0.0433711782f, -0.825072706f, 1.36627102f, + 0.353913844f, -1.12278414f, 0.297658026f, -0.123843506f, 0.412883312f, 0.127441302f, -0.14624925f, + -0.498612732f, -0.755961955f, -1.10036528f, -0.27989766f, -1.25174105f, 0.12917304f, -0.395805091f, + -0.109161325f, -0.142363578f, 0.566222012f, 0.3380211f, 0.453139782f, 1.1229564f, 1.05611205f, + -0.658837736f, 0.205182895f, 0.429361075f, -1.23168659f, -0.454002529f, -1.1382587f, -0.655178785f, + 0.792094946f, -0.789876401f, 0.31720081f, 0.0673454404f, 1.01219285f, 0.894608796f, 0.690177917f, + 1.15238833f, 1.14613628f, -0.665385008f, -0.305928379f, 0.81283021f, -1.33880627f, -0.618332326f, + -0.426928908f, -0.683665097f, 0.993887186f, -1.10850132f, -0.147489265f, -1.10468781f, 0.91786325f, + 1.20352674f, -0.215227067f, 0.309243172f, 1.31651044f, 0.287662923f, 0.0316653885f, 0.64315778f, + -0.0218351725f, -0.0999381542f, -0.118103474f, 0.0252529755f, 1.14321709f, -1.11670232f, -1.10027277f, + -1.74890983f, 1.18170536f, -0.371487111f, 0.776267886f, -0.124101065f, 0.744695902f, 0.643334031f, + -0.404396415f, 0.884196281f, 0.577306688f, 0.835294306f, 0.54813689f, 1.50024903f, 0.923039615f, + -1.21155477f, -1.6240375f, 0.220293149f, 0.301237345f, -1.27881956f, -0.259828955f, -0.111764893f, + -0.182845786f, 0.748433113f, 0.322141469f, -0.608830273f, -0.47859031f, 0.0459461287f, 0.261704743f, + -0.389725059f, -0.287455261f, 0.238430783f, 0.637133598f, 0.319570661f, -0.787914515f, -0.450317025f, + 0.393156052f, -0.347500741f, 0.0302485395f, 0.727102578f, 0.095413357f, -0.574505091f, 0.658783674f, + 0.676462471f, -0.4630768f, 1.3986721f, 1.92816699f, -2.44313622f, -1.77078962f, -0.876839101f, + 0.230179012f, 0.329563677f, 1.3084408f, 0.637529194f, 0.273108542f, -0.334753752f, 0.593125522f, + -0.0518076085f, -0.337441593f, -0.0391860493f, 0.014304501f, -0.480600059f, 0.150101349f, 0.381608933f, + 1.96013832f, 0.469113439f, -0.974983215f, -0.930540979f, -0.653711498f, -0.397353142f, 0.970193863f, + 0.663052976f, 0.755484283f, -1.06234169f, -1.15985835f, 0.405930728f, -0.584536672f, 0.212522835f, + -0.724630952f, -0.284156471f, 0.332220733f, -0.117634691f, -0.820745051f, 0.0936385319f, 2.66648555f, + 1.16592908f, -0.320294499f, 0.757632911f, 0.141160056f, 0.423806697f, -1.46966839f, 0.0391953513f, + -0.530439913f, 0.85272032f, 1.61776769f, -1.2603972f, -0.327900738f, 0.223690197f, -0.162596121f, + 0.525632739f, -1.08356667f, -0.131513759f, 0.486709714f, -0.792334199f, -0.257121205f, 0.11907284f, + -1.60498083f, -0.351520151f, -0.800664306f, -1.36423838f, 0.655535579f, -0.00406035688f, 0.876718938f, + -0.617652655f, 0.835790396f, 2.21184278f, 0.16250816f, -0.869993746f, 0.613840103f, 0.690256238f, + -0.0293289069f, 0.785924077f, -0.429574609f, -1.04248512f, -1.6714232f, 0.658702731f, 1.73794639f, + -1.45977175f, -0.52315706f, -1.93888617f, 1.59201074f, 0.825968862f, -0.245780677f, 0.747790992f, + 1.81735063f, -1.02562523f, -1.68105567f, 2.37950397f, 1.23519957f, -0.412493318f, -0.0352141596f, + 0.165512949f, 1.45659387f, -0.846047699f, -0.968717277f, -0.677016318f, 1.21694946f, -1.0037837f, + 0.272715628f, 1.53499734f, 0.699577332f, 0.592292964f, 0.274003297f, -0.303534538f, 0.663356245f, + -0.629660726f, -0.218454078f, -0.180579126f, 0.269860595f, -0.0398894064f, 0.295136154f, -0.22412537f, + -0.0734514073f, -2.6226089f, 1.75338149f, 1.29913568f, 0.124010034f, 1.94992733f, 0.0927006677f, + -0.432489663f, -0.981368482f, 0.994494259f, -0.0714638308f, 0.675833404f, -0.170204341f, -0.0563389733f, + -1.41034198f, 0.810934782f, -0.789207458f, -2.58788252f, 0.762276649f, -1.70810175f, -0.694960237f, + 2.39003444f, -0.975780964f, -1.19430494f, 0.140404776f, 0.859825075f, -0.033793021f, -0.192534208f, + 0.12459901f, -0.0597328693f, -1.25326896f, 0.481627285f, -0.198929176f, -0.564616621f, 0.228054509f, + -2.07364917f, -0.225622311f, 0.0736534894f, 0.200432673f, -0.571845114f, -1.37258685f, -0.853663564f, + -1.38245225f, 1.34960938f, 0.886018038f, -0.928329229f, -0.702362299f, 0.984016836f, 0.553563833f, + -0.608169079f, -0.442374438f, 1.52635992f, 0.30352211f, 0.608599603f, -0.930342197f, 0.573025823f, + -0.570736527f, -0.608978748f, -0.324615538f, -1.53202283f, 0.897171676f, -0.853620291f, -0.511971593f, + 0.0387205929f, -0.274496794f, -0.461029649f, 1.36959565f, 0.701882422f, 1.37924206f, -0.46988374f, + 0.245135278f, -0.243242279f, -0.681213081f, -1.14118707f, 0.771152973f, -2.37796187f, 0.0333316326f, + -0.0439579226f, 0.56101048f, -0.879440367f, 2.03655338f, -0.953138947f, 1.08688474f, 0.164069429f, + 1.66462541f, -0.0873398259f, -0.785552919f, 1.8962487f, -0.690468907f, 1.22546864f, 0.996455848f, + 0.862472415f, 0.404405534f, -1.56823599f, -2.48027039f, -0.412020206f, 1.33099139f, 0.803430259f, + -0.403876901f, -1.30335641f, -0.425128967f, 0.690154672f, -0.707727551f, 0.719904184f, 0.450506151f, + 0.0345417634f, 0.397345155f, -0.944499612f, -0.95041734f, 0.150640503f, 0.367198884f, -1.28404164f, + 0.0674581379f, 0.516860902f, 0.0894012079f, -0.455827594f, 1.2451005f, -1.00835526f, 0.747222304f, + 0.773092628f, -0.194111258f, -0.0674602911f, 0.373115957f, 0.904081047f, -0.933285773f, -0.657509089f, + -0.347011209f, -1.16235375f, -0.415910959f, 0.11593169f, 0.813242197f, 1.46706879f, -0.0301746987f, + 0.20298335f, 0.675648451f, 0.164878786f, -0.889707208f, 0.878525138f, -0.606342852f, 1.13829017f, + 1.00815451f, -0.273220062f, 0.0735104159f, 0.834835708f, -0.99123925f, 0.336443305f, 0.219985798f, + 0.183095247f, -1.15320373f, 0.87675494f, 0.733147621f, -1.49925506f, -1.2213099f, -0.732854366f, + -0.764796078f, -1.04981863f, 0.192156598f, 0.313972384f, 1.62078106f, -0.264032304f, -1.15410244f, + 0.505153656f, 1.10527074f, -0.192732692f, 0.278140455f, 1.39395165f, 1.32553518f, 0.118360527f, + 0.292769521f, -0.622686505f, -1.14166605f, 0.685777962f, 1.43552744f, -0.621207058f, -0.638156474f, + -0.868312359f, -0.0829402208f, -2.05992627f, 1.64795601f, 0.397354245f, -0.0556526557f, -0.312963128f, + -0.984705687f, 1.68604791f, -0.738512516f, 0.801193118f, -0.586083472f, 0.890452683f, 0.0940506458f, + -0.281714112f, -1.25516963f, 1.65818787f, -0.548437893f, -1.0236088f, -1.00170743f, -1.62204742f, + -0.465606987f, 1.09089231f, 0.282096714f, -1.12088704f, -1.84663224f, 0.854488194f, -0.711048961f, + 0.813310683f, -0.554145455f, -0.145157769f, -0.424239695f, 0.578670144f, -0.765621066f, 0.556805849f, + -0.832963109f, -1.29475081f, 0.228614852f, 1.2468096f, 0.254585564f, -1.30389678f, 0.0178375989f, + -0.756977379f, 0.868604183f, -0.77373594f, 1.2260747f, -0.8112306f, -0.675504208f, -1.47716916f, + 0.310433507f, 0.521378398f, 0.875448704f, 0.422585309f, 0.528458178f, -0.22545898f, 0.355941385f, + 0.324389011f, 0.362185389f, -0.341918021f, 0.996198654f, -0.894525826f, 0.218869627f, -0.366139263f, + 1.10209632f, -0.208265722f, 0.267357141f, -0.89677453f, 1.15940654f, 0.227962151f, 0.913425326f, + 0.50086081f, 0.274388522f, 0.902824819f, -0.442973614f, 0.0926382765f, 0.696626604f, -0.425873548f, + -0.676471353f, 0.29729408f, 0.434304714f, -0.48967883f, 0.102422856f, -0.997565567f, 1.10375845f, + -0.209264129f, 0.0456745811f, 0.216997966f, -1.50240505f, 1.48359048f, 0.07813932f, 0.153179243f, + 0.617800891f, -0.779851675f, -0.748180509f, 0.817190111f, -0.0381977037f, 0.691774368f, 1.46646369f, + 0.118782185f, 1.40324867f, 1.52340746f, -0.687547982f, -0.642618358f, 0.279491693f, 1.66579556f, + -1.10680556f, -2.17522168f, 1.04056489f, -1.14060402f, 0.0173285808f, -0.255405724f, -0.293017983f, + -1.57544029f, 1.0106802f, 0.730295002f, -0.315764099f, 0.897104263f, -0.0384168737f, 0.579565406f, + -0.199237004f, 2.29024076f, -1.08240187f, 0.78011322f, -1.17607486f, 0.425730526f, -0.38372457f, + -1.07317984f, -1.6606741f, -0.328284174f, 0.172447115f, -0.313143939f, 1.27422249f, -0.913568139f, + 1.00027311f, -0.692252457f, -1.25286102f, 1.35583556f, 1.33880234f, -0.58781594f, 2.30746365f, + -1.56450939f, 0.098440513f, 0.153078064f, -1.29336059f, -1.41944242f, 1.17006469f, -0.70307982f, + -0.530316532f, -0.417143077f, 0.151316866f, -0.224361613f, 1.43407822f, 0.0326175615f, 0.0270895679f, + 0.21740438f, -0.0336623266f, 0.570039511f, -0.829930067f, 0.332199335f, -0.138474762f, 0.096033752f, + 0.264714301f, -0.487462759f, 1.27033055f, -0.070455499f, -1.37554801f, 1.6813724f, 0.0845528618f, + 0.615037322f, -0.902919948f, 0.245611474f, -0.662745714f, 0.687474489f, -0.333729744f, 0.231473461f, + -0.524563134f, 0.692479014f, -1.37406683f, -0.56263274f, -0.286730617f, -0.59438473f, -0.187177077f, + 0.00540861906f, -0.261250496f, -0.334579647f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/weights_data.h new file mode 100644 index 00000000..d7678562 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_ic1_to_conv_nhwc_f32/weights_data.h @@ -0,0 +1,39 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_ic1_to_conv_nhwc_f32_weights_data[256] = { + -0.0912582055f, -0.0733436272f, -0.661615193f, -0.0943607464f, 0.572121024f, -0.175988704f, 0.504772961f, + 0.147515953f, -0.308557451f, -0.0586693697f, -0.319145113f, -0.623854458f, 0.64697963f, -0.476059169f, + 0.165210143f, -0.0290233828f, -0.535828292f, 0.235530853f, -0.343005866f, 0.312837213f, 0.700608432f, + -0.542627811f, -0.664605737f, 0.173419297f, -0.467473298f, 0.544445634f, 0.109135844f, -0.32805267f, + -0.488554627f, 0.433065772f, 0.51775068f, -0.74294585f, -0.746018648f, -0.0412466004f, -0.63715452f, + 0.355258703f, -0.286690354f, 0.331170678f, 0.439818919f, 0.591163874f, -0.636737227f, -0.311481714f, + -0.245286435f, 0.313049048f, -0.108668253f, 0.244397327f, 0.1567332f, -0.618376315f, 0.507461429f, + 0.207398012f, -0.684002221f, 0.375189036f, 0.0645987019f, -0.119145662f, 0.263487458f, 0.135362729f, + -0.619125545f, -0.0944691747f, 0.374335617f, 0.364892781f, -0.107671231f, -0.265097529f, 0.651578128f, + -0.360535413f, 0.16546613f, -0.522301793f, 0.436955243f, -0.501307726f, -0.157748163f, -0.137350529f, + -0.438671798f, -0.417787343f, 0.635763288f, 0.657390952f, -0.225164473f, 0.301815569f, 0.0478837825f, + -0.293726295f, 0.559718192f, 0.25849551f, -0.252812535f, 0.321650803f, -0.357881188f, -0.45760718f, + -0.591225147f, 0.631724358f, -0.180748656f, 0.530175447f, -0.237869874f, -0.419792801f, 0.0362518728f, + -0.679775894f, 0.181646273f, -0.576579571f, -0.373116612f, -0.152231291f, -0.183535025f, -0.174979195f, + -0.286331296f, 0.668228745f, -0.010267335f, -0.359415203f, -0.3788203f, 0.269591421f, -0.242895231f, + -0.409276903f, -0.184381798f, -0.125333309f, -0.349290639f, 0.322839856f, -0.386121601f, 0.421440363f, + 0.65549475f, -0.345377237f, 0.610441685f, -0.680596173f, 0.0363858417f, 0.698927402f, 0.18827714f, + 0.0867858976f, 0.556123495f, -0.641084135f, 0.665403664f, 0.600846291f, -0.572143435f, 0.216193035f, + 0.289511353f, 0.410737604f, -0.0867454857f, 0.300185055f, -0.297526181f, -0.618411303f, -0.462993652f, + 0.621034801f, 0.69994539f, 0.592766047f, -0.338008195f, -0.509464562f, 0.728441775f, -0.406668663f, + 0.0618190654f, 0.519625306f, -0.0853846744f, -0.696092963f, 0.448137015f, -0.403870046f, 0.179761961f, + 0.243523493f, 0.237656176f, -0.282079399f, -0.418060541f, -0.0367835127f, -0.435414225f, -0.681875765f, + -0.377269179f, 0.308123887f, 0.154633388f, -0.00953871477f, 0.0912742913f, -0.69032383f, 0.417745441f, + 0.263441354f, -0.549271703f, 0.617143929f, -0.0805537403f, 0.128016561f, -0.485069841f, 0.278157055f, + 0.0585487746f, -0.515865684f, 0.0513781384f, -0.0538167208f, -0.107562922f, 0.199679047f, -0.130088776f, + -0.146981657f, 0.119770683f, -0.108424962f, -0.237108216f, -0.0807543993f, 0.227379844f, 0.280757099f, + -0.0113964323f, -0.453873813f, 0.169870123f, 9.06450732e-05f, -0.249670327f, -0.736420333f, -0.264798373f, + 0.577295363f, -0.347880065f, -0.00105039927f, -0.0953342691f, -0.578088582f, -0.166191354f, -0.310164958f, + -0.496269107f, 0.367784262f, 0.625895679f, -0.544206619f, -0.48787275f, 0.243346676f, 0.602533221f, + 0.490260214f, 0.151457012f, -0.584162116f, -0.302037597f, 0.723012507f, -0.0056518456f, 0.494612485f, + 0.501906216f, 0.148765966f, -0.735548258f, 0.587259948f, 0.280600667f, 0.689324617f, -0.445138305f, + 0.389443308f, -0.620204926f, -0.701267004f, -0.659932256f, 0.587960064f, 0.655108571f, -0.64885968f, + 0.603511155f, 0.56477809f, 0.435425043f, 0.386893958f, -0.0152863245f, 0.0445698127f, -0.479080111f, + -0.196172029f, -0.00798054133f, -0.582886457f, 0.197997928f, -0.681689322f, -0.4917548f, -0.153541848f, + -0.240319297f, -0.059874922f, -0.0742324442f, 0.0898144767f, 0.650376558f, -0.273612499f, -0.591009974f, + -0.0609476119f, -0.230739921f, -0.452875584f, -0.448448598f, -0.224555299f, 0.101753712f, -0.612362266f, + 0.186123431f, 0.473163903f, 0.306034744f, -0.229421631f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/biases_data.h new file mode 100644 index 00000000..f46d8fb3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_batch2_f16_biases_data[8] = {(float16_t)0.318603516f, + (float16_t)-0.354003906f, + (float16_t)0.480957031f, + (float16_t)-0.307373047f, + (float16_t)-0.396972656f, + (float16_t)-0.448242188f, + (float16_t)-0.0251312256f, + (float16_t)0.245849609f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/config_data.h new file mode 100644 index 00000000..1e55da2c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_INPUT_BATCHES 2 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_INPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_INPUT_W 21 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_IN_CH 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_CH_MULT 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_FILTER_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_FILTER_W 3 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUT_CH 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_STRIDE_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_STRIDE_W 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_PADDING_H 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_PADDING_W 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_DILATION_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_DILATION_W 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUTPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUTPUT_W 19 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUTPUT_C 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_DST_SIZE 304 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_USE_WRAPPER 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_K3_1D_OPT_BATCH2_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/input_data.h new file mode 100644 index 00000000..b922d1f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/input_data.h @@ -0,0 +1,86 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_batch2_f16_input_data[336] = { + (float16_t)-0.16809082f, (float16_t)-0.237548828f, (float16_t)0.515136719f, (float16_t)-0.0524597168f, + (float16_t)-0.222167969f, (float16_t)0.818359375f, (float16_t)-0.507324219f, (float16_t)-0.526855469f, + (float16_t)0.328125f, (float16_t)0.89453125f, (float16_t)0.127075195f, (float16_t)0.436279297f, + (float16_t)-0.784667969f, (float16_t)0.627441406f, (float16_t)-0.706542969f, (float16_t)-0.285644531f, + (float16_t)0.251464844f, (float16_t)-0.667480469f, (float16_t)0.124938965f, (float16_t)0.159179688f, + (float16_t)0.303710938f, (float16_t)-0.842285156f, (float16_t)0.0363464355f, (float16_t)-0.0867919922f, + (float16_t)-0.0952758789f, (float16_t)0.371582031f, (float16_t)0.751464844f, (float16_t)-0.0847167969f, + (float16_t)-0.268310547f, (float16_t)0.288085938f, (float16_t)0.364501953f, (float16_t)0.241210938f, + (float16_t)-0.889648438f, (float16_t)0.943847656f, (float16_t)-0.608398438f, (float16_t)-0.284423828f, + (float16_t)-0.783203125f, (float16_t)-0.947753906f, (float16_t)-0.741210938f, (float16_t)0.416748047f, + (float16_t)-0.764160156f, (float16_t)-0.645996094f, (float16_t)-0.828613281f, (float16_t)-0.00126838684f, + (float16_t)-0.117492676f, (float16_t)-0.276611328f, (float16_t)-0.260986328f, (float16_t)-0.831054688f, + (float16_t)-0.59375f, (float16_t)0.344726562f, (float16_t)-0.234008789f, (float16_t)0.619628906f, + (float16_t)-0.884765625f, (float16_t)-0.00680541992f, (float16_t)0.833496094f, (float16_t)-0.622070312f, + (float16_t)0.343994141f, (float16_t)0.10144043f, (float16_t)0.3046875f, (float16_t)-0.491699219f, + (float16_t)-0.100830078f, (float16_t)-0.755859375f, (float16_t)-0.615234375f, (float16_t)-0.765625f, + (float16_t)-0.298828125f, (float16_t)0.243041992f, (float16_t)-0.209960938f, (float16_t)-0.141113281f, + (float16_t)-0.771484375f, (float16_t)-0.362304688f, (float16_t)-0.650390625f, (float16_t)0.28515625f, + (float16_t)-0.253173828f, (float16_t)0.566894531f, (float16_t)-0.393066406f, (float16_t)0.204833984f, + (float16_t)0.553710938f, (float16_t)0.593261719f, (float16_t)-0.823730469f, (float16_t)-0.92578125f, + (float16_t)0.0907592773f, (float16_t)0.197753906f, (float16_t)0.803222656f, (float16_t)0.909179688f, + (float16_t)0.872558594f, (float16_t)-0.666015625f, (float16_t)-0.148803711f, (float16_t)-0.430175781f, + (float16_t)0.395019531f, (float16_t)0.0980224609f, (float16_t)-0.592285156f, (float16_t)-0.496826172f, + (float16_t)-0.893554688f, (float16_t)-0.576171875f, (float16_t)0.628417969f, (float16_t)-0.997070312f, + (float16_t)-0.300537109f, (float16_t)0.421630859f, (float16_t)0.911621094f, (float16_t)0.704589844f, + (float16_t)0.793945312f, (float16_t)0.488037109f, (float16_t)0.513671875f, (float16_t)-0.843261719f, + (float16_t)0.708984375f, (float16_t)0.0670776367f, (float16_t)0.483886719f, (float16_t)-0.689941406f, + (float16_t)0.934570312f, (float16_t)0.756835938f, (float16_t)0.935546875f, (float16_t)-0.475341797f, + (float16_t)0.769042969f, (float16_t)-0.010559082f, (float16_t)-0.375f, (float16_t)-0.846191406f, + (float16_t)0.0507202148f, (float16_t)-0.838378906f, (float16_t)0.504394531f, (float16_t)0.125244141f, + (float16_t)0.859863281f, (float16_t)0.446289062f, (float16_t)0.841796875f, (float16_t)-0.88671875f, + (float16_t)-0.140625f, (float16_t)-0.514648438f, (float16_t)0.454833984f, (float16_t)0.37890625f, + (float16_t)0.158691406f, (float16_t)-0.551269531f, (float16_t)-0.726074219f, (float16_t)0.00600814819f, + (float16_t)0.428710938f, (float16_t)0.735351562f, (float16_t)-0.0850830078f, (float16_t)-0.922851562f, + (float16_t)0.377197266f, (float16_t)0.699707031f, (float16_t)-0.331298828f, (float16_t)-0.108154297f, + (float16_t)-0.549316406f, (float16_t)-0.626464844f, (float16_t)0.531738281f, (float16_t)0.471923828f, + (float16_t)-0.348144531f, (float16_t)-0.615722656f, (float16_t)0.530761719f, (float16_t)0.785644531f, + (float16_t)-0.480224609f, (float16_t)-0.217407227f, (float16_t)0.740722656f, (float16_t)0.303222656f, + (float16_t)-0.0765380859f, (float16_t)-0.805175781f, (float16_t)0.674804688f, (float16_t)0.366210938f, + (float16_t)-0.442138672f, (float16_t)-0.748535156f, (float16_t)0.750488281f, (float16_t)0.66796875f, + (float16_t)0.129272461f, (float16_t)-0.325195312f, (float16_t)0.390625f, (float16_t)-0.502929688f, + (float16_t)0.830566406f, (float16_t)0.381347656f, (float16_t)-0.017074585f, (float16_t)-0.754394531f, + (float16_t)-0.654785156f, (float16_t)0.739257812f, (float16_t)-0.125976562f, (float16_t)0.172363281f, + (float16_t)0.886230469f, (float16_t)-0.0763549805f, (float16_t)0.156005859f, (float16_t)-0.0591125488f, + (float16_t)0.787109375f, (float16_t)-0.758300781f, (float16_t)-0.140136719f, (float16_t)0.524902344f, + (float16_t)0.0434875488f, (float16_t)0.646972656f, (float16_t)0.528808594f, (float16_t)0.956542969f, + (float16_t)0.818359375f, (float16_t)0.720703125f, (float16_t)-0.865722656f, (float16_t)-0.872070312f, + (float16_t)0.239868164f, (float16_t)0.924316406f, (float16_t)-0.12121582f, (float16_t)0.979492188f, + (float16_t)-0.674316406f, (float16_t)-0.108032227f, (float16_t)0.496337891f, (float16_t)-0.20715332f, + (float16_t)0.591308594f, (float16_t)0.0600280762f, (float16_t)-0.728027344f, (float16_t)0.902832031f, + (float16_t)-0.044128418f, (float16_t)-0.416748047f, (float16_t)0.242675781f, (float16_t)-0.649414062f, + (float16_t)-0.597167969f, (float16_t)0.161621094f, (float16_t)0.099609375f, (float16_t)0.0101165771f, + (float16_t)-0.987304688f, (float16_t)-0.134155273f, (float16_t)0.307617188f, (float16_t)-0.595703125f, + (float16_t)0.0872192383f, (float16_t)-0.638671875f, (float16_t)0.815917969f, (float16_t)-0.771972656f, + (float16_t)-0.967773438f, (float16_t)0.8125f, (float16_t)-0.750976562f, (float16_t)-0.239868164f, + (float16_t)0.907714844f, (float16_t)-0.390380859f, (float16_t)-0.577148438f, (float16_t)-0.222412109f, + (float16_t)-0.631347656f, (float16_t)-0.0914306641f, (float16_t)-0.0676269531f, (float16_t)0.378662109f, + (float16_t)0.407226562f, (float16_t)-0.081237793f, (float16_t)0.397705078f, (float16_t)-0.406982422f, + (float16_t)-0.426025391f, (float16_t)-0.709472656f, (float16_t)-0.190673828f, (float16_t)-0.964355469f, + (float16_t)0.939941406f, (float16_t)0.618652344f, (float16_t)0.764160156f, (float16_t)0.430175781f, + (float16_t)0.605957031f, (float16_t)0.206420898f, (float16_t)0.357666016f, (float16_t)0.486083984f, + (float16_t)0.958984375f, (float16_t)0.39453125f, (float16_t)-0.781738281f, (float16_t)-0.193237305f, + (float16_t)0.117553711f, (float16_t)0.990234375f, (float16_t)-0.294189453f, (float16_t)-0.996582031f, + (float16_t)-0.624023438f, (float16_t)0.3046875f, (float16_t)-0.504394531f, (float16_t)-0.534179688f, + (float16_t)0.859375f, (float16_t)0.419433594f, (float16_t)0.662109375f, (float16_t)0.163574219f, + (float16_t)-0.334716797f, (float16_t)-0.815429688f, (float16_t)-0.103210449f, (float16_t)0.926269531f, + (float16_t)0.899414062f, (float16_t)0.513671875f, (float16_t)-0.461181641f, (float16_t)-0.0908203125f, + (float16_t)0.661132812f, (float16_t)-0.978027344f, (float16_t)0.947753906f, (float16_t)-0.316162109f, + (float16_t)0.266601562f, (float16_t)0.986816406f, (float16_t)-0.0985717773f, (float16_t)-0.447021484f, + (float16_t)-0.294677734f, (float16_t)0.587890625f, (float16_t)-0.64453125f, (float16_t)-0.405517578f, + (float16_t)0.539550781f, (float16_t)0.655273438f, (float16_t)-0.700195312f, (float16_t)0.325195312f, + (float16_t)-0.14855957f, (float16_t)0.892089844f, (float16_t)0.449462891f, (float16_t)0.661621094f, + (float16_t)-0.624511719f, (float16_t)-0.795898438f, (float16_t)0.553710938f, (float16_t)0.864257812f, + (float16_t)-0.916015625f, (float16_t)0.0240020752f, (float16_t)-0.0458679199f, (float16_t)0.461669922f, + (float16_t)0.650390625f, (float16_t)-0.912109375f, (float16_t)0.216552734f, (float16_t)0.216430664f, + (float16_t)-0.921875f, (float16_t)0.00431060791f, (float16_t)0.155517578f, (float16_t)-0.876953125f, + (float16_t)0.533203125f, (float16_t)-0.4609375f, (float16_t)-0.481689453f, (float16_t)0.584960938f, + (float16_t)0.156982422f, (float16_t)0.394042969f, (float16_t)-0.709960938f, (float16_t)0.601074219f, + (float16_t)0.981445312f, (float16_t)0.319335938f, (float16_t)-0.3203125f, (float16_t)0.883300781f, + (float16_t)-0.281738281f, (float16_t)-0.345458984f, (float16_t)-0.310302734f, (float16_t)0.908203125f, + (float16_t)0.37109375f, (float16_t)-0.18347168f, (float16_t)0.102416992f, (float16_t)-0.136962891f, + (float16_t)0.721679688f, (float16_t)-0.590820312f, (float16_t)-0.928222656f, (float16_t)0.507324219f, + (float16_t)0.461914062f, (float16_t)0.273925781f, (float16_t)0.00660705566f, (float16_t)0.181640625f, + (float16_t)-0.969238281f, (float16_t)0.557128906f, (float16_t)-0.245483398f, (float16_t)-0.0904541016f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/output_ref_data.h new file mode 100644 index 00000000..cc47df31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/output_ref_data.h @@ -0,0 +1,78 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_batch2_f16_output_ref_data[304] = { + (float16_t)0.0450439453f, (float16_t)-0.588867188f, (float16_t)0.762695312f, (float16_t)-0.347167969f, + (float16_t)-0.138916016f, (float16_t)-1.9453125f, (float16_t)0.0525512695f, (float16_t)0.0841064453f, + (float16_t)0.0961303711f, (float16_t)-0.17578125f, (float16_t)0.583984375f, (float16_t)-0.314453125f, + (float16_t)-0.802734375f, (float16_t)-0.185058594f, (float16_t)0.0549621582f, (float16_t)0.190063477f, + (float16_t)0.788574219f, (float16_t)0.205078125f, (float16_t)0.0602416992f, (float16_t)-0.338134766f, + (float16_t)-0.697265625f, (float16_t)-0.687988281f, (float16_t)0.0635375977f, (float16_t)0.176147461f, + (float16_t)1.32324219f, (float16_t)-0.5f, (float16_t)1.22265625f, (float16_t)-0.270507812f, + (float16_t)-0.381835938f, (float16_t)-0.275878906f, (float16_t)-0.0650024414f, (float16_t)-0.0155944824f, + (float16_t)1.4609375f, (float16_t)-0.181884766f, (float16_t)0.525390625f, (float16_t)-0.20703125f, + (float16_t)-1.06054688f, (float16_t)0.354248047f, (float16_t)0.000248670578f, (float16_t)0.732421875f, + (float16_t)0.771484375f, (float16_t)-0.293457031f, (float16_t)0.105957031f, (float16_t)-0.5f, + (float16_t)-0.314697266f, (float16_t)-0.76953125f, (float16_t)0.10534668f, (float16_t)-0.067565918f, + (float16_t)0.518066406f, (float16_t)-0.153076172f, (float16_t)0.127319336f, (float16_t)-0.211303711f, + (float16_t)-1.02734375f, (float16_t)-0.24609375f, (float16_t)-0.098449707f, (float16_t)0.350830078f, + (float16_t)0.507324219f, (float16_t)0.0401306152f, (float16_t)0.75390625f, (float16_t)-0.283691406f, + (float16_t)0.0266113281f, (float16_t)0.681640625f, (float16_t)0.178100586f, (float16_t)-0.509765625f, + (float16_t)0.533691406f, (float16_t)-0.0952758789f, (float16_t)0.664550781f, (float16_t)-0.170166016f, + (float16_t)-0.222045898f, (float16_t)-1.00195312f, (float16_t)0.0993652344f, (float16_t)0.751464844f, + (float16_t)0.136474609f, (float16_t)-0.194580078f, (float16_t)-0.308349609f, (float16_t)-0.529296875f, + (float16_t)-0.909667969f, (float16_t)-0.852050781f, (float16_t)0.041229248f, (float16_t)-0.263427734f, + (float16_t)0.221679688f, (float16_t)-0.0646972656f, (float16_t)1.45898438f, (float16_t)-0.0206756592f, + (float16_t)0.431152344f, (float16_t)0.674316406f, (float16_t)-0.0556335449f, (float16_t)0.311279297f, + (float16_t)-0.0648803711f, (float16_t)-0.217529297f, (float16_t)-0.371337891f, (float16_t)-0.590820312f, + (float16_t)-0.264892578f, (float16_t)0.171386719f, (float16_t)-0.24597168f, (float16_t)0.0170135498f, + (float16_t)-0.427001953f, (float16_t)-0.30078125f, (float16_t)0.759277344f, (float16_t)-0.313476562f, + (float16_t)-0.342529297f, (float16_t)-1.79394531f, (float16_t)-0.168212891f, (float16_t)0.055480957f, + (float16_t)-0.909179688f, (float16_t)-0.0866088867f, (float16_t)1.11621094f, (float16_t)-0.406494141f, + (float16_t)-0.243652344f, (float16_t)-0.8203125f, (float16_t)-0.240966797f, (float16_t)0.0168609619f, + (float16_t)-0.583496094f, (float16_t)-0.587890625f, (float16_t)-0.334960938f, (float16_t)-0.235961914f, + (float16_t)-0.122558594f, (float16_t)0.923828125f, (float16_t)-0.0989379883f, (float16_t)-0.0796508789f, + (float16_t)-0.328369141f, (float16_t)-0.00746536255f, (float16_t)1.41015625f, (float16_t)-0.398925781f, + (float16_t)-0.814941406f, (float16_t)-0.955078125f, (float16_t)-0.17175293f, (float16_t)1.00292969f, + (float16_t)0.233154297f, (float16_t)-0.632324219f, (float16_t)0.256347656f, (float16_t)-0.135009766f, + (float16_t)-0.446044922f, (float16_t)-0.728515625f, (float16_t)-0.0958862305f, (float16_t)-0.407470703f, + (float16_t)0.422119141f, (float16_t)-0.877929688f, (float16_t)0.0412597656f, (float16_t)-0.364257812f, + (float16_t)-0.688964844f, (float16_t)-0.402832031f, (float16_t)-0.203735352f, (float16_t)0.500488281f, + (float16_t)0.42578125f, (float16_t)-0.776855469f, (float16_t)0.4921875f, (float16_t)-0.401855469f, + (float16_t)0.0183410645f, (float16_t)0.398193359f, (float16_t)-0.145874023f, (float16_t)-0.0872802734f, + (float16_t)-0.36328125f, (float16_t)-0.00595855713f, (float16_t)0.373291016f, (float16_t)-0.546875f, + (float16_t)-0.046295166f, (float16_t)-0.147827148f, (float16_t)-0.0322265625f, (float16_t)-0.0302581787f, + (float16_t)-0.0866088867f, (float16_t)-0.3515625f, (float16_t)0.943359375f, (float16_t)-0.173217773f, + (float16_t)-0.10736084f, (float16_t)-1.38964844f, (float16_t)-0.0300292969f, (float16_t)0.483398438f, + (float16_t)0.424804688f, (float16_t)-0.543457031f, (float16_t)-0.343505859f, (float16_t)-0.472167969f, + (float16_t)-0.774414062f, (float16_t)-1.00292969f, (float16_t)-0.0235900879f, (float16_t)0.331298828f, + (float16_t)1.17578125f, (float16_t)-0.529785156f, (float16_t)0.706542969f, (float16_t)-0.341552734f, + (float16_t)-0.0842285156f, (float16_t)-1.01757812f, (float16_t)0.00384902954f, (float16_t)0.548339844f, + (float16_t)1.47851562f, (float16_t)0.050994873f, (float16_t)0.374023438f, (float16_t)-0.313964844f, + (float16_t)-0.069519043f, (float16_t)-0.448486328f, (float16_t)0.036895752f, (float16_t)0.576660156f, + (float16_t)1.64160156f, (float16_t)-0.255615234f, (float16_t)1.10449219f, (float16_t)-0.2421875f, + (float16_t)-0.323486328f, (float16_t)0.160400391f, (float16_t)-0.224121094f, (float16_t)-0.149291992f, + (float16_t)1.31640625f, (float16_t)-0.702148438f, (float16_t)0.00208091736f, (float16_t)-0.575195312f, + (float16_t)0.273925781f, (float16_t)0.285400391f, (float16_t)-0.0132522583f, (float16_t)0.422119141f, + (float16_t)0.469482422f, (float16_t)-0.387939453f, (float16_t)0.588867188f, (float16_t)-0.0334777832f, + (float16_t)0.0547485352f, (float16_t)-0.487792969f, (float16_t)0.00895690918f, (float16_t)-0.223999023f, + (float16_t)0.057800293f, (float16_t)0.193969727f, (float16_t)0.11541748f, (float16_t)-0.654296875f, + (float16_t)-0.679199219f, (float16_t)-0.893554688f, (float16_t)-0.105957031f, (float16_t)0.448730469f, + (float16_t)-0.482177734f, (float16_t)0.117919922f, (float16_t)0.96484375f, (float16_t)-0.0838623047f, + (float16_t)-0.215698242f, (float16_t)-1.44921875f, (float16_t)0.120300293f, (float16_t)0.611328125f, + (float16_t)-0.754882812f, (float16_t)0.140991211f, (float16_t)-0.146972656f, (float16_t)-0.428466797f, + (float16_t)-0.131591797f, (float16_t)-0.841308594f, (float16_t)-0.0539855957f, (float16_t)-0.561035156f, + (float16_t)-0.704101562f, (float16_t)0.376708984f, (float16_t)1.16796875f, (float16_t)-0.369140625f, + (float16_t)-0.76953125f, (float16_t)1.08007812f, (float16_t)0.0823364258f, (float16_t)0.802246094f, + (float16_t)-0.500976562f, (float16_t)0.2890625f, (float16_t)0.148803711f, (float16_t)-0.181518555f, + (float16_t)-0.252685547f, (float16_t)0.478027344f, (float16_t)-0.254394531f, (float16_t)0.423583984f, + (float16_t)0.253417969f, (float16_t)-0.581054688f, (float16_t)0.883300781f, (float16_t)-0.221557617f, + (float16_t)-0.946289062f, (float16_t)-1.35546875f, (float16_t)0.0989379883f, (float16_t)-0.188354492f, + (float16_t)0.104736328f, (float16_t)-0.982421875f, (float16_t)-0.270263672f, (float16_t)-0.372070312f, + (float16_t)-0.766601562f, (float16_t)-1.06640625f, (float16_t)-0.124084473f, (float16_t)0.176635742f, + (float16_t)-0.130126953f, (float16_t)-0.897949219f, (float16_t)0.663574219f, (float16_t)-0.157592773f, + (float16_t)-0.371337891f, (float16_t)-0.19934082f, (float16_t)0.0720214844f, (float16_t)1.05371094f, + (float16_t)-0.81640625f, (float16_t)-0.359130859f, (float16_t)0.859863281f, (float16_t)-0.205810547f, + (float16_t)-0.817871094f, (float16_t)-0.916992188f, (float16_t)-0.0217437744f, (float16_t)-0.309326172f, + (float16_t)-0.685546875f, (float16_t)-0.446289062f, (float16_t)0.359130859f, (float16_t)-0.424560547f, + (float16_t)0.0919799805f, (float16_t)-0.915039062f, (float16_t)0.211303711f, (float16_t)0.238891602f, + (float16_t)-0.550292969f, (float16_t)-0.196044922f, (float16_t)0.212280273f, (float16_t)-0.181396484f, + (float16_t)-1.13671875f, (float16_t)0.512207031f, (float16_t)0.050201416f, (float16_t)0.463134766f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/weights_data.h new file mode 100644 index 00000000..add1ffb3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f16/weights_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_batch2_f16_weights_data[24] = { + (float16_t)-0.390380859f, (float16_t)0.107971191f, (float16_t)0.658691406f, (float16_t)0.078125f, + (float16_t)0.256835938f, (float16_t)-0.680175781f, (float16_t)-0.176391602f, (float16_t)0.533203125f, + (float16_t)-0.59375f, (float16_t)0.204956055f, (float16_t)-0.571289062f, (float16_t)-0.153808594f, + (float16_t)-0.189575195f, (float16_t)-0.586914062f, (float16_t)0.0103988647f, (float16_t)-0.486328125f, + (float16_t)-0.575195312f, (float16_t)0.587890625f, (float16_t)0.12109375f, (float16_t)0.197631836f, + (float16_t)0.547851562f, (float16_t)0.679199219f, (float16_t)-0.12322998f, (float16_t)0.225463867f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/biases_data.h new file mode 100644 index 00000000..769fe04a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_batch2_f32_biases_data[8] = + {0.318682313f, -0.35397172f, 0.48092863f, -0.307419658f, -0.396993905f, -0.448122829f, -0.025135383f, 0.24585925f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/config_data.h new file mode 100644 index 00000000..fe7ffcfb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_INPUT_BATCHES 2 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_INPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_INPUT_W 21 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_IN_CH 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_CH_MULT 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_FILTER_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_FILTER_W 3 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUT_CH 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_STRIDE_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_STRIDE_W 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_PADDING_H 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_PADDING_W 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_DILATION_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_DILATION_W 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUTPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUTPUT_W 19 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUTPUT_C 8 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_DST_SIZE 304 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_USE_WRAPPER 0 +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_K3_1D_OPT_BATCH2_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/input_data.h new file mode 100644 index 00000000..d1707afd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/input_data.h @@ -0,0 +1,50 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_batch2_f32_input_data[336] = { + -0.168129608f, -0.237536818f, 0.515160024f, -0.0524687245f, -0.222151682f, 0.818511546f, -0.507236779f, + -0.527021587f, 0.3280527f, 0.894520283f, 0.127052292f, 0.436180085f, -0.784431696f, 0.627493918f, + -0.706332266f, -0.285601079f, 0.251413256f, -0.667496324f, 0.124909043f, 0.15919964f, 0.303727001f, + -0.842187822f, 0.0363536701f, -0.0867863223f, -0.0952540785f, 0.371598244f, 0.751264572f, -0.0846918225f, + -0.268358678f, 0.287987947f, 0.364522427f, 0.241184384f, -0.889822245f, 0.944030523f, -0.608305156f, + -0.284532547f, -0.783366084f, -0.947519243f, -0.74108392f, 0.416752666f, -0.764149725f, -0.646123528f, + -0.828584313f, -0.00126844773f, -0.117507339f, -0.276614696f, -0.261011809f, -0.831264794f, -0.59374702f, + 0.344729811f, -0.233980924f, 0.619421184f, -0.884894729f, -0.00680601643f, 0.833283126f, -0.62190485f, + 0.344021171f, 0.101418845f, 0.304733545f, -0.491698086f, -0.100811236f, -0.755807161f, -0.615079284f, + -0.765460312f, -0.298912764f, 0.2430913f, -0.20995298f, -0.141073152f, -0.771300733f, -0.362363338f, + -0.650165856f, 0.285164207f, -0.253180176f, 0.567081392f, -0.392977118f, 0.204839855f, 0.55370903f, + 0.59323293f, -0.82385385f, -0.925846517f, 0.0907814205f, 0.197697073f, 0.803064287f, 0.909243643f, + 0.87237215f, -0.666010261f, -0.148779318f, -0.430134028f, 0.394939452f, 0.0980427191f, -0.592265427f, + -0.496842176f, -0.893637896f, -0.575963259f, 0.628587484f, -0.997205913f, -0.300475508f, 0.421668172f, + 0.911791801f, 0.704784214f, 0.793733716f, 0.488000453f, 0.513739705f, -0.84325707f, 0.708928049f, + 0.0670982078f, 0.48393321f, -0.689831138f, 0.934614658f, 0.756842911f, 0.935507417f, -0.475229532f, + 0.768967032f, -0.0105622392f, -0.375090957f, -0.846406579f, 0.0507191606f, -0.838477552f, 0.504521549f, + 0.125240147f, 0.860088527f, 0.446217924f, 0.84180516f, -0.886833012f, -0.140620574f, -0.514520764f, + 0.454731554f, 0.378815413f, 0.158648446f, -0.551455855f, -0.726173937f, 0.00600666972f, 0.428605855f, + 0.735562444f, -0.0850723684f, -0.922957301f, 0.377178848f, 0.69980222f, -0.331283897f, -0.108132735f, + -0.549114466f, -0.626380265f, 0.531563222f, 0.471882761f, -0.348259449f, -0.615917385f, 0.530796707f, + 0.785666704f, -0.480208218f, -0.217426285f, 0.740754962f, 0.303260863f, -0.0765518993f, -0.805303872f, + 0.674961507f, 0.366156936f, -0.44202292f, -0.748588204f, 0.750365853f, 0.667824805f, 0.129286423f, + -0.325118721f, 0.39071843f, -0.50304848f, 0.830343962f, 0.381440759f, -0.017070869f, -0.754331172f, + -0.6549595f, 0.739172876f, -0.126004264f, 0.172362253f, 0.886314631f, -0.0763742551f, 0.155972719f, + -0.0591276065f, 0.787008286f, -0.758119702f, -0.140185907f, 0.52490598f, 0.0434777476f, 0.646816134f, + 0.528912187f, 0.95674181f, 0.818304181f, 0.720617175f, -0.865892589f, -0.872192204f, 0.239893898f, + 0.924495757f, -0.121237144f, 0.97934705f, -0.674218476f, -0.10800498f, 0.496334195f, -0.207157865f, + 0.591409147f, 0.060029529f, -0.727938592f, 0.902759552f, -0.0441245586f, -0.416712433f, 0.24270843f, + -0.649240077f, -0.597041309f, 0.16168046f, 0.0996308103f, 0.0101131899f, -0.987449884f, -0.134209856f, + 0.307659179f, -0.59569931f, 0.0872220621f, -0.638792574f, 0.815675795f, -0.771748841f, -0.967889607f, + 0.812273681f, -0.750865281f, -0.239871219f, 0.907881498f, -0.390415639f, -0.577346325f, -0.222418368f, + -0.631107628f, -0.0914102867f, -0.0676362216f, 0.378645569f, 0.407328963f, -0.0812553465f, 0.397793055f, + -0.407044172f, -0.426137894f, -0.709611833f, -0.190615326f, -0.964161873f, 0.940107048f, 0.618631363f, + 0.763968647f, 0.430137426f, 0.605969787f, 0.206421614f, 0.357679367f, 0.486132145f, 0.959100306f, + 0.394565225f, -0.781950235f, -0.193286926f, 0.117571309f, 0.990455925f, -0.294303924f, -0.996471822f, + -0.624178886f, 0.304658413f, -0.504332244f, -0.534317374f, 0.859611273f, 0.419520974f, 0.661996067f, + 0.163595408f, -0.334606439f, -0.815233529f, -0.10320276f, 0.926202416f, 0.899490952f, 0.513786912f, + -0.461232841f, -0.0908226445f, 0.661169291f, -0.978145778f, 0.94763279f, -0.316160917f, 0.266665846f, + 0.987016797f, -0.0985984132f, -0.447021365f, -0.294669867f, 0.587953568f, -0.644662619f, -0.405638576f, + 0.539315343f, 0.655257821f, -0.700072229f, 0.325114101f, -0.148547679f, 0.891880751f, 0.449416757f, + 0.661754847f, -0.624395669f, -0.795847416f, 0.553872108f, 0.86409688f, -0.915817797f, 0.0240042154f, + -0.045865871f, 0.461648345f, 0.650283217f, -0.912063956f, 0.216501102f, 0.21646066f, -0.922094405f, + 0.00431178277f, 0.155530959f, -0.876942217f, 0.533237159f, -0.460976601f, -0.481704265f, 0.585094988f, + 0.157029316f, 0.39392662f, -0.709924638f, 0.60092324f, 0.981483757f, 0.31935209f, -0.320305616f, + 0.883517742f, -0.281627387f, -0.345533669f, -0.310225695f, 0.908402741f, 0.371001154f, -0.183531523f, + 0.10239546f, -0.137018204f, 0.721575379f, -0.59104234f, -0.928080142f, 0.507318676f, 0.461949766f, + 0.273958355f, 0.00660763355f, 0.181625098f, -0.96909368f, 0.556920767f, -0.245434746f, -0.0904282555f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/output_ref_data.h new file mode 100644 index 00000000..3915ad19 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/output_ref_data.h @@ -0,0 +1,46 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_batch2_f32_output_ref_data[304] = { + 0.0450320281f, -0.588637292f, 0.762669146f, -0.347119182f, -0.13897641f, -1.94532001f, 0.0525420979f, + 0.0841131881f, 0.0961331651f, -0.17576386f, 0.584211469f, -0.314561069f, -0.802941203f, -0.185118124f, + 0.0549628139f, 0.190109923f, 0.788782418f, 0.205023721f, 0.0602392107f, -0.338202298f, -0.697255075f, + -0.687832236f, 0.0635460839f, 0.17620571f, 1.32349718f, -0.500151157f, 1.22280765f, -0.270536304f, + -0.381777525f, -0.275861651f, -0.0650028884f, -0.015595397f, 1.46118069f, -0.18183051f, 0.525485218f, + -0.207014129f, -1.06054401f, 0.354200035f, 0.000248699682f, 0.732272923f, 0.771651924f, -0.293469369f, + 0.105951048f, -0.499958009f, -0.314661384f, -0.769358277f, 0.105365723f, -0.0675746799f, 0.518248796f, + -0.153069168f, 0.12731643f, -0.211304098f, -1.02755082f, -0.246071815f, -0.0984589085f, 0.350754023f, + 0.50736779f, 0.040139351f, 0.753943324f, -0.283653408f, 0.0266168378f, 0.681702316f, 0.178137034f, + -0.509826243f, 0.533473432f, -0.0952836573f, 0.66449368f, -0.170208529f, -0.222100481f, -1.00217772f, + 0.0993490815f, 0.751321793f, 0.136534557f, -0.194586545f, -0.308416039f, -0.529435992f, -0.909692049f, + -0.852080345f, 0.0412399843f, -0.263539612f, 0.221619487f, -0.0646690354f, 1.45855057f, -0.0206683259f, + 0.431120932f, 0.674487591f, -0.0556426756f, 0.311383784f, -0.0648974851f, -0.217517614f, -0.371408492f, + -0.590968251f, -0.264906794f, 0.171426997f, -0.245966241f, 0.0170075819f, -0.427002281f, -0.300895274f, + 0.759355068f, -0.313592166f, -0.342584968f, -1.79386306f, -0.16821599f, 0.0554734394f, -0.909210324f, + -0.0866007581f, 1.11585402f, -0.406469464f, -0.243695408f, -0.820429981f, -0.240983486f, 0.0168646239f, + -0.583341658f, -0.587804198f, -0.334994078f, -0.23599878f, -0.122545727f, 0.923925638f, -0.0989476144f, + -0.0796538666f, -0.328260541f, -0.00746724382f, 1.41000628f, -0.399006546f, -0.815116048f, -0.955274343f, + -0.171748206f, 1.00320494f, 0.233140126f, -0.632126212f, 0.256361157f, -0.13502042f, -0.445923269f, + -0.728615522f, -0.0958620906f, -0.407577246f, 0.422130555f, -0.878010511f, 0.0412640683f, -0.364297658f, + -0.689071715f, -0.402896374f, -0.203675404f, 0.500587225f, 0.425760895f, -0.776644349f, 0.492097974f, + -0.401774913f, 0.0183364861f, 0.398237824f, -0.145934671f, -0.0872633159f, -0.3632963f, -0.00595682953f, + 0.373180628f, -0.547071397f, -0.0462908447f, -0.1477689f, -0.0322196148f, -0.0302629024f, -0.0866028666f, + -0.351620793f, 0.943492591f, -0.173244983f, -0.107348628f, -1.38995147f, -0.0300358776f, 0.483311236f, + 0.424697429f, -0.543242395f, -0.343412638f, -0.472046316f, -0.774538457f, -1.00246751f, -0.0235877428f, + 0.331394643f, 1.17603552f, -0.529934585f, 0.706325293f, -0.341526747f, -0.0842263103f, -1.0177753f, + 0.003848664f, 0.548472941f, 1.47864938f, 0.0509839617f, 0.374002576f, -0.313962907f, -0.0694966763f, + -0.44842425f, 0.0368988514f, 0.576489925f, 1.64172018f, -0.25570944f, 1.10434103f, -0.24223122f, + -0.323559105f, 0.160385802f, -0.224073976f, -0.149343029f, 1.31630623f, -0.702112496f, 0.00208103145f, + -0.574969351f, 0.273833722f, 0.285398543f, -0.0132521056f, 0.4221766f, 0.469568014f, -0.387950361f, + 0.588630319f, -0.0334878936f, 0.0547523275f, -0.487884223f, 0.00895919185f, -0.224034622f, 0.0577977337f, + 0.193910152f, 0.115401506f, -0.654472947f, -0.679332614f, -0.893595159f, -0.105931148f, 0.448846638f, + -0.482094288f, 0.117922731f, 0.96478349f, -0.0838776752f, -0.215749592f, -1.44912899f, 0.120309487f, + 0.611445308f, -0.75475049f, 0.140973538f, -0.146970645f, -0.428382605f, -0.131651372f, -0.841382921f, + -0.0539733283f, -0.560901165f, -0.704246163f, 0.376812518f, 1.16841364f, -0.369035333f, -0.769620895f, + 1.07993722f, 0.0823544264f, 0.802205563f, -0.501005769f, 0.288975596f, 0.148772717f, -0.181514144f, + -0.252773732f, 0.478104323f, -0.254417896f, 0.423705965f, 0.253529668f, -0.580893993f, 0.88309896f, + -0.221530527f, -0.946138978f, -1.35523725f, 0.0989385694f, -0.1883035f, 0.104714312f, -0.982453942f, + -0.270278782f, -0.372098207f, -0.766637921f, -1.06603563f, -0.12407352f, 0.176590219f, -0.130135641f, + -0.897813737f, 0.663574934f, -0.157535523f, -0.371320873f, -0.199392736f, 0.0720414296f, 1.054093f, + -0.816206515f, -0.359223217f, 0.859921575f, -0.205829352f, -0.817746103f, -0.916963339f, -0.0217392165f, + -0.309384465f, -0.685468733f, -0.446174532f, 0.359169424f, -0.424644232f, 0.0919506624f, -0.914816141f, + 0.211248443f, 0.238892883f, -0.550458968f, -0.196069926f, 0.212318093f, -0.181419551f, -1.13689363f, + 0.512131274f, 0.0501918048f, 0.463231236f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/weights_data.h new file mode 100644 index 00000000..1239f1c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_batch2_f32/weights_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_batch2_f32_weights_data[24] = { + -0.390495718f, 0.107981429f, 0.658447564f, 0.0781304762f, 0.256724447f, -0.680308521f, + -0.176448569f, 0.53336972f, -0.593551397f, 0.204963148f, -0.571377218f, -0.153764799f, + -0.189536929f, -0.586883903f, 0.0103984363f, -0.486395776f, -0.575101078f, 0.587808013f, + 0.121123418f, 0.197670236f, 0.547763288f, 0.679291189f, -0.123205423f, 0.225425601f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/biases_data.h new file mode 100644 index 00000000..d45cc1e9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_nhwc_f16_biases_data[8] = {(float16_t)-0.136474609f, + (float16_t)0.154541016f, + (float16_t)-0.068359375f, + (float16_t)0.0939331055f, + (float16_t)-0.0619506836f, + (float16_t)-0.444824219f, + (float16_t)0.348388672f, + (float16_t)-0.178222656f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/config_data.h new file mode 100644 index 00000000..376577b5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_INPUT_BATCHES 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_INPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_INPUT_W 21 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_IN_CH 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_CH_MULT 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_FILTER_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_FILTER_W 3 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUT_CH 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_STRIDE_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_STRIDE_W 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_PADDING_H 0 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_PADDING_W 0 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_DILATION_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_DILATION_W 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUTPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUTPUT_W 19 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUTPUT_C 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_DST_SIZE 152 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_USE_WRAPPER 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_K3_1D_OPT_NHWC_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/input_data.h new file mode 100644 index 00000000..57659c48 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/input_data.h @@ -0,0 +1,44 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_nhwc_f16_input_data[168] = { + (float16_t)0.292724609f, (float16_t)-0.83984375f, (float16_t)0.567871094f, (float16_t)0.42578125f, + (float16_t)0.595703125f, (float16_t)-0.907714844f, (float16_t)0.510742188f, (float16_t)-0.881835938f, + (float16_t)0.775390625f, (float16_t)0.0877685547f, (float16_t)0.481933594f, (float16_t)-0.615722656f, + (float16_t)0.435058594f, (float16_t)0.405761719f, (float16_t)0.751464844f, (float16_t)0.108276367f, + (float16_t)-0.6640625f, (float16_t)0.769042969f, (float16_t)0.835449219f, (float16_t)-0.664550781f, + (float16_t)-0.717285156f, (float16_t)0.905761719f, (float16_t)0.00504302979f, (float16_t)-0.0761108398f, + (float16_t)-0.234985352f, (float16_t)-0.0741577148f, (float16_t)0.856445312f, (float16_t)0.304199219f, + (float16_t)0.227416992f, (float16_t)-0.4609375f, (float16_t)0.0579528809f, (float16_t)-0.780273438f, + (float16_t)-0.0609436035f, (float16_t)0.0823974609f, (float16_t)-0.408935547f, (float16_t)-0.705078125f, + (float16_t)-0.754394531f, (float16_t)0.627929688f, (float16_t)0.508300781f, (float16_t)0.333251953f, + (float16_t)0.309326172f, (float16_t)-0.5f, (float16_t)-0.307373047f, (float16_t)0.159790039f, + (float16_t)-0.551757812f, (float16_t)0.872070312f, (float16_t)0.394287109f, (float16_t)0.802246094f, + (float16_t)0.50390625f, (float16_t)-0.0468139648f, (float16_t)0.282470703f, (float16_t)0.411132812f, + (float16_t)0.71484375f, (float16_t)0.657714844f, (float16_t)0.282958984f, (float16_t)-0.814941406f, + (float16_t)0.708007812f, (float16_t)-0.397460938f, (float16_t)0.776855469f, (float16_t)0.397949219f, + (float16_t)-0.721679688f, (float16_t)0.714355469f, (float16_t)-0.154296875f, (float16_t)-0.681152344f, + (float16_t)0.91015625f, (float16_t)-0.105773926f, (float16_t)-0.314697266f, (float16_t)0.344726562f, + (float16_t)-0.466308594f, (float16_t)-0.631835938f, (float16_t)-0.783691406f, (float16_t)0.938964844f, + (float16_t)-0.385986328f, (float16_t)0.634765625f, (float16_t)0.930175781f, (float16_t)-0.26953125f, + (float16_t)-0.178222656f, (float16_t)0.795410156f, (float16_t)0.295410156f, (float16_t)0.857421875f, + (float16_t)-0.689453125f, (float16_t)-0.894042969f, (float16_t)0.939453125f, (float16_t)0.534667969f, + (float16_t)0.0617980957f, (float16_t)0.336181641f, (float16_t)0.0587768555f, (float16_t)-0.61328125f, + (float16_t)0.164916992f, (float16_t)-0.688476562f, (float16_t)-0.754394531f, (float16_t)-0.470947266f, + (float16_t)0.966796875f, (float16_t)-0.0408325195f, (float16_t)0.633300781f, (float16_t)0.0193634033f, + (float16_t)0.718261719f, (float16_t)0.583984375f, (float16_t)-0.724121094f, (float16_t)0.00807189941f, + (float16_t)0.158447266f, (float16_t)0.6484375f, (float16_t)-0.0522460938f, (float16_t)0.709472656f, + (float16_t)0.317138672f, (float16_t)0.492675781f, (float16_t)0.599609375f, (float16_t)-0.198608398f, + (float16_t)-0.447509766f, (float16_t)-0.649414062f, (float16_t)-0.571289062f, (float16_t)0.107971191f, + (float16_t)0.295654297f, (float16_t)0.432128906f, (float16_t)0.2109375f, (float16_t)0.12310791f, + (float16_t)0.400146484f, (float16_t)-0.929199219f, (float16_t)0.9140625f, (float16_t)-0.299804688f, + (float16_t)-0.0579528809f, (float16_t)-0.985351562f, (float16_t)0.076965332f, (float16_t)-0.341308594f, + (float16_t)0.841308594f, (float16_t)-0.181030273f, (float16_t)-0.567871094f, (float16_t)0.732421875f, + (float16_t)-0.244262695f, (float16_t)-0.54296875f, (float16_t)-0.731445312f, (float16_t)0.821289062f, + (float16_t)0.075012207f, (float16_t)0.986816406f, (float16_t)-0.208618164f, (float16_t)0.131958008f, + (float16_t)-0.872558594f, (float16_t)0.325927734f, (float16_t)0.365722656f, (float16_t)0.337402344f, + (float16_t)-0.660644531f, (float16_t)-0.507324219f, (float16_t)0.250976562f, (float16_t)0.395263672f, + (float16_t)0.536132812f, (float16_t)0.479248047f, (float16_t)-0.471923828f, (float16_t)-0.049621582f, + (float16_t)-0.879882812f, (float16_t)-0.409423828f, (float16_t)-0.756347656f, (float16_t)0.862304688f, + (float16_t)-0.0475158691f, (float16_t)-0.953613281f, (float16_t)0.658203125f, (float16_t)-0.12902832f, + (float16_t)-0.532714844f, (float16_t)0.631835938f, (float16_t)0.91015625f, (float16_t)-0.237915039f, + (float16_t)0.658691406f, (float16_t)0.833496094f, (float16_t)-0.401855469f, (float16_t)0.904296875f, + (float16_t)0.122070312f, (float16_t)0.917480469f, (float16_t)0.235595703f, (float16_t)-0.228881836f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..b47ea21a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_nhwc_f16_output_ref_data[152] = { + (float16_t)-0.217895508f, (float16_t)0.050994873f, (float16_t)-0.385498047f, (float16_t)0.559082031f, + (float16_t)-0.369140625f, (float16_t)-0.135742188f, (float16_t)0.446044922f, (float16_t)-0.451904297f, + (float16_t)0.100524902f, (float16_t)0.0745849609f, (float16_t)-0.475585938f, (float16_t)-0.470703125f, + (float16_t)-0.291015625f, (float16_t)-0.854492188f, (float16_t)0.488769531f, (float16_t)-0.5546875f, + (float16_t)0.116149902f, (float16_t)0.407470703f, (float16_t)-1.046875f, (float16_t)-0.0412597656f, + (float16_t)-0.168212891f, (float16_t)-0.0515441895f, (float16_t)0.507324219f, (float16_t)-0.391113281f, + (float16_t)-0.205688477f, (float16_t)0.0220336914f, (float16_t)-0.500488281f, (float16_t)0.166625977f, + (float16_t)-0.606445312f, (float16_t)-0.198120117f, (float16_t)0.48828125f, (float16_t)0.106506348f, + (float16_t)-0.481933594f, (float16_t)0.228637695f, (float16_t)0.386230469f, (float16_t)-0.495117188f, + (float16_t)0.240478516f, (float16_t)-0.342041016f, (float16_t)0.525390625f, (float16_t)-0.10546875f, + (float16_t)-0.726074219f, (float16_t)-0.0679321289f, (float16_t)0.212890625f, (float16_t)0.0839233398f, + (float16_t)-0.029083252f, (float16_t)-0.266357422f, (float16_t)0.369873047f, (float16_t)-0.61328125f, + (float16_t)-0.939941406f, (float16_t)0.166381836f, (float16_t)-0.651855469f, (float16_t)0.262939453f, + (float16_t)-0.663574219f, (float16_t)-0.886230469f, (float16_t)0.149902344f, (float16_t)-0.292724609f, + (float16_t)-0.478759766f, (float16_t)0.177612305f, (float16_t)-0.151855469f, (float16_t)0.478515625f, + (float16_t)-0.137939453f, (float16_t)0.0611877441f, (float16_t)0.401367188f, (float16_t)0.436767578f, + (float16_t)0.163085938f, (float16_t)-0.132202148f, (float16_t)-0.00952148438f, (float16_t)0.0913085938f, + (float16_t)0.00855255127f, (float16_t)-0.478271484f, (float16_t)0.244018555f, (float16_t)0.212890625f, + (float16_t)0.151855469f, (float16_t)0.309082031f, (float16_t)-1.23339844f, (float16_t)0.144165039f, + (float16_t)0.4296875f, (float16_t)-0.533691406f, (float16_t)0.593261719f, (float16_t)-0.166015625f, + (float16_t)-0.398193359f, (float16_t)0.0894165039f, (float16_t)-0.520507812f, (float16_t)0.390869141f, + (float16_t)0.313720703f, (float16_t)-0.141601562f, (float16_t)0.352783203f, (float16_t)-0.024810791f, + (float16_t)-0.612304688f, (float16_t)-0.0187683105f, (float16_t)0.857421875f, (float16_t)-0.132080078f, + (float16_t)-0.420654297f, (float16_t)-0.884277344f, (float16_t)0.274414062f, (float16_t)0.197875977f, + (float16_t)-0.539550781f, (float16_t)0.356201172f, (float16_t)0.177246094f, (float16_t)0.0357971191f, + (float16_t)-0.0648193359f, (float16_t)-0.7109375f, (float16_t)0.612792969f, (float16_t)-0.0454101562f, + (float16_t)-0.294677734f, (float16_t)0.0503234863f, (float16_t)-0.499267578f, (float16_t)0.104980469f, + (float16_t)0.543457031f, (float16_t)-0.3203125f, (float16_t)0.0956420898f, (float16_t)0.0767822266f, + (float16_t)-0.0582580566f, (float16_t)0.289306641f, (float16_t)-0.4296875f, (float16_t)-0.163208008f, + (float16_t)0.161376953f, (float16_t)0.0334777832f, (float16_t)0.423095703f, (float16_t)0.116699219f, + (float16_t)0.370605469f, (float16_t)-0.00696182251f, (float16_t)0.292236328f, (float16_t)-0.177490234f, + (float16_t)-0.515625f, (float16_t)-0.897949219f, (float16_t)0.330078125f, (float16_t)0.309082031f, + (float16_t)0.0340576172f, (float16_t)0.0521240234f, (float16_t)0.0922241211f, (float16_t)0.666015625f, + (float16_t)-0.688964844f, (float16_t)-0.507324219f, (float16_t)0.0852050781f, (float16_t)0.458496094f, + (float16_t)-0.173828125f, (float16_t)0.00191497803f, (float16_t)0.088684082f, (float16_t)0.353027344f, + (float16_t)-0.445800781f, (float16_t)-0.0713500977f, (float16_t)0.657714844f, (float16_t)0.220703125f, + (float16_t)-0.515625f, (float16_t)0.577148438f, (float16_t)-0.168701172f, (float16_t)-0.285888672f, + (float16_t)0.00886535645f, (float16_t)-0.178222656f, (float16_t)0.313476562f, (float16_t)-0.116577148f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/weights_data.h new file mode 100644 index 00000000..2848adbc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f16/weights_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_k3_1d_opt_nhwc_f16_weights_data[24] = { + (float16_t)-0.190429688f, (float16_t)0.295654297f, (float16_t)-0.606445312f, (float16_t)0.638183594f, + (float16_t)-0.217773438f, (float16_t)0.00703811646f, (float16_t)0.162841797f, (float16_t)0.323486328f, + (float16_t)-0.419433594f, (float16_t)-0.118286133f, (float16_t)-0.419433594f, (float16_t)0.0878295898f, + (float16_t)0.329101562f, (float16_t)-0.2265625f, (float16_t)0.0169830322f, (float16_t)0.447998047f, + (float16_t)-0.451171875f, (float16_t)0.201660156f, (float16_t)0.274658203f, (float16_t)-0.372314453f, + (float16_t)0.447265625f, (float16_t)0.449707031f, (float16_t)0.308837891f, (float16_t)0.483886719f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/biases_data.h new file mode 100644 index 00000000..8b9b7a83 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/biases_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_nhwc_f32_biases_data[8] = {-0.136435211f, + 0.154553384f, + -0.068371132f, + 0.0939170346f, + -0.0619552918f, + -0.444835246f, + 0.348479062f, + -0.178226918f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/config_data.h new file mode 100644 index 00000000..a3502a1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_INPUT_BATCHES 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_INPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_INPUT_W 21 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_IN_CH 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_CH_MULT 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_FILTER_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_FILTER_W 3 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUT_CH 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_STRIDE_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_STRIDE_W 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_PADDING_H 0 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_PADDING_W 0 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_DILATION_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_DILATION_W 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUTPUT_H 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUTPUT_W 19 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUTPUT_C 8 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_DST_SIZE 152 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_USE_WRAPPER 1 +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_K3_1D_OPT_NHWC_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/input_data.h new file mode 100644 index 00000000..a8a0f354 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/input_data.h @@ -0,0 +1,26 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_nhwc_f32_input_data[168] = { + 0.292631f, -0.839865148f, 0.567898214f, 0.425681621f, 0.595517635f, -0.907935977f, 0.510708451f, + -0.88164109f, 0.775633335f, 0.0877880305f, 0.481834501f, -0.615650237f, 0.434943259f, 0.405868232f, + 0.751611471f, 0.108261533f, -0.664282143f, 0.769242108f, 0.835287571f, -0.664728701f, -0.717292666f, + 0.905762553f, 0.00504148053f, -0.0761172622f, -0.234975651f, -0.0741531104f, 0.856415749f, 0.304139793f, + 0.227392107f, -0.460958302f, 0.057964202f, -0.780465424f, -0.0609496236f, 0.0823921636f, -0.408979088f, + -0.704889178f, -0.754289329f, 0.627995133f, 0.508459508f, 0.333199024f, 0.309282631f, -0.499928296f, + -0.307414263f, 0.159782946f, -0.551725447f, 0.871902347f, 0.394225657f, 0.802056432f, 0.50380981f, + -0.0468038097f, 0.282580256f, 0.411210507f, 0.715046048f, 0.65792048f, 0.282963216f, -0.814916015f, + 0.707774043f, -0.397508144f, 0.776713431f, 0.397864401f, -0.721581221f, 0.714285553f, -0.154257715f, + -0.68120873f, 0.91015017f, -0.105803154f, -0.314764619f, 0.344783753f, -0.466263533f, -0.632023275f, + -0.783688247f, 0.938782215f, -0.386073947f, 0.63486886f, 0.930021822f, -0.269484192f, -0.178268611f, + 0.79542917f, 0.29538089f, 0.85754174f, -0.689284623f, -0.894004464f, 0.939289749f, 0.534568787f, + 0.0617941692f, 0.336245477f, 0.0587823763f, -0.613188505f, 0.164921641f, -0.688685656f, -0.754614174f, + -0.471010745f, 0.966974735f, -0.0408360139f, 0.633437872f, 0.0193624664f, 0.718062043f, 0.583891332f, + -0.723962367f, 0.00807134993f, 0.158467621f, 0.648250639f, -0.0522323027f, 0.709249854f, 0.31725204f, + 0.492689699f, 0.599368572f, -0.198662862f, -0.447526097f, -0.649618149f, -0.571112752f, 0.107946083f, + 0.295705885f, 0.432241589f, 0.210951656f, 0.123102978f, 0.400178045f, -0.929157019f, 0.913945198f, + -0.299749911f, -0.0579434186f, -0.985594869f, 0.0769689232f, -0.341284662f, 0.841225684f, -0.181075439f, + -0.567763686f, 0.732417524f, -0.244235501f, -0.542942703f, -0.731491387f, 0.821118176f, 0.0750202686f, + 0.986662447f, -0.208614767f, 0.131960228f, -0.872317612f, 0.325859517f, 0.365665287f, 0.337485135f, + -0.660537422f, -0.507239044f, 0.250981301f, 0.395250589f, 0.536348462f, 0.479170024f, -0.471917808f, + -0.0496360436f, -0.879699588f, -0.409369886f, -0.756212294f, 0.862077117f, -0.0475034341f, -0.953506052f, + 0.658178687f, -0.129061893f, -0.532752752f, 0.632041156f, 0.910165012f, -0.237921402f, 0.658549964f, + 0.833405256f, -0.401764214f, 0.904438317f, 0.12204805f, 0.917544961f, 0.23559092f, -0.228903845f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..61f58725 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_nhwc_f32_output_ref_data[152] = { + -0.217872337f, 0.050981123f, -0.385385841f, 0.559021413f, -0.369249433f, -0.135761321f, 0.445964992f, + -0.45182699f, 0.100508161f, 0.0745879412f, -0.475647449f, -0.470677823f, -0.291118771f, -0.854560852f, + 0.488855571f, -0.554886818f, 0.116144314f, 0.40741387f, -1.04652929f, -0.0412519872f, -0.168163985f, + -0.0515313894f, 0.507342458f, -0.391210675f, -0.20563817f, 0.0220408831f, -0.500653923f, 0.16664879f, + -0.606486976f, -0.198178247f, 0.488327742f, 0.106525019f, -0.481846154f, 0.228596255f, 0.386240602f, + -0.495072991f, 0.240475997f, -0.342034787f, 0.525374293f, -0.105451241f, -0.725971818f, -0.0679272041f, + 0.212931052f, 0.0839120969f, -0.0290808212f, -0.266478866f, 0.369830221f, -0.613280356f, -0.939876676f, + 0.166379139f, -0.652023911f, 0.262989938f, -0.663722336f, -0.886348844f, 0.149860874f, -0.292812586f, + -0.478870004f, 0.177578881f, -0.151850894f, 0.478489935f, -0.137980029f, 0.0611971579f, 0.401287049f, + 0.4367176f, 0.163128644f, -0.132141858f, -0.00951961149f, 0.0913131237f, 0.00855559763f, -0.478268027f, + 0.244045004f, 0.212939486f, 0.151844993f, 0.309095562f, -1.23370659f, 0.144209817f, 0.429616332f, + -0.533795953f, 0.59323734f, -0.166033164f, -0.398273081f, 0.0894070268f, -0.520379364f, 0.390758693f, + 0.31374383f, -0.141622096f, 0.35267809f, -0.0248151291f, -0.612184942f, -0.0187648218f, 0.857613623f, + -0.132066756f, -0.420533448f, -0.884217501f, 0.274324954f, 0.197927192f, -0.539673924f, 0.356139988f, + 0.177221522f, 0.0357887037f, -0.0648354813f, -0.711028934f, 0.612579405f, -0.0454172194f, -0.294763863f, + 0.0503250808f, -0.499195218f, 0.104976565f, 0.543418288f, -0.320321649f, 0.0956343561f, 0.0767967626f, + -0.0582672693f, 0.289406002f, -0.429565787f, -0.163173079f, 0.161331475f, 0.0334736891f, 0.423214018f, + 0.116670355f, 0.370571941f, -0.00696012145f, 0.292250156f, -0.177439466f, -0.515863895f, -0.897839665f, + 0.330010355f, 0.309082657f, 0.0340563655f, 0.0521194935f, 0.0921976045f, 0.666170835f, -0.689098179f, + -0.507099926f, 0.0851878747f, 0.458579063f, -0.173866719f, 0.00191497477f, 0.088658534f, 0.353025168f, + -0.445880532f, -0.0713434368f, 0.65763998f, 0.220664307f, -0.515738428f, 0.577113926f, -0.168643177f, + -0.285810798f, 0.00886669103f, -0.178201199f, 0.313577712f, -0.116602138f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/weights_data.h new file mode 100644 index 00000000..2e86f9c4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_k3_1d_opt_nhwc_f32/weights_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_k3_1d_opt_nhwc_f32_weights_data[24] = { + -0.19045116f, 0.295709223f, -0.606447279f, 0.638311744f, -0.217813939f, 0.00703659281f, + 0.162831932f, 0.323558807f, -0.419494003f, -0.118256174f, -0.419460714f, 0.0878431723f, + 0.329167724f, -0.226587668f, 0.0169888362f, 0.447887033f, -0.451115996f, 0.20171164f, + 0.274751216f, -0.372283399f, 0.447169483f, 0.449817032f, 0.308889359f, 0.483809948f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/biases_data.h new file mode 100644 index 00000000..8e813d08 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_2x2_f16_biases_data[4] = {(float16_t)0.239746094f, + (float16_t)-0.100097656f, + (float16_t)0.461669922f, + (float16_t)-0.11138916f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/config_data.h new file mode 100644 index 00000000..d627c45a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_2X2_F16_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_2X2_F16_INPUT_H 6 +#define DEPTHWISE_KERNEL_2X2_F16_INPUT_W 5 +#define DEPTHWISE_KERNEL_2X2_F16_IN_CH 4 +#define DEPTHWISE_KERNEL_2X2_F16_CH_MULT 1 +#define DEPTHWISE_KERNEL_2X2_F16_FILTER_H 2 +#define DEPTHWISE_KERNEL_2X2_F16_FILTER_W 2 +#define DEPTHWISE_KERNEL_2X2_F16_OUT_CH 4 +#define DEPTHWISE_KERNEL_2X2_F16_STRIDE_H 1 +#define DEPTHWISE_KERNEL_2X2_F16_STRIDE_W 1 +#define DEPTHWISE_KERNEL_2X2_F16_PADDING_H 0 +#define DEPTHWISE_KERNEL_2X2_F16_PADDING_W 0 +#define DEPTHWISE_KERNEL_2X2_F16_DILATION_H 1 +#define DEPTHWISE_KERNEL_2X2_F16_DILATION_W 1 +#define DEPTHWISE_KERNEL_2X2_F16_OUTPUT_H 5 +#define DEPTHWISE_KERNEL_2X2_F16_OUTPUT_W 4 +#define DEPTHWISE_KERNEL_2X2_F16_OUTPUT_C 4 +#define DEPTHWISE_KERNEL_2X2_F16_DST_SIZE 80 +#define DEPTHWISE_KERNEL_2X2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_2X2_F16_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_2X2_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_KERNEL_2X2_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_KERNEL_2X2_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/input_data.h new file mode 100644 index 00000000..c55d69be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/input_data.h @@ -0,0 +1,32 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_2x2_f16_input_data[120] = { + (float16_t)0.625488281f, (float16_t)-0.480957031f, (float16_t)-0.0805053711f, (float16_t)0.715332031f, + (float16_t)-0.9140625f, (float16_t)0.212768555f, (float16_t)-0.234863281f, (float16_t)0.909179688f, + (float16_t)-0.871582031f, (float16_t)-0.811523438f, (float16_t)0.765136719f, (float16_t)0.286376953f, + (float16_t)0.585449219f, (float16_t)-0.564941406f, (float16_t)-0.202270508f, (float16_t)0.431396484f, + (float16_t)-0.307861328f, (float16_t)-0.943359375f, (float16_t)-0.875488281f, (float16_t)-0.0992431641f, + (float16_t)0.7734375f, (float16_t)-0.152954102f, (float16_t)0.400878906f, (float16_t)-0.178466797f, + (float16_t)-0.15637207f, (float16_t)-0.685058594f, (float16_t)0.399658203f, (float16_t)0.21105957f, + (float16_t)-0.344726562f, (float16_t)-0.788085938f, (float16_t)0.303955078f, (float16_t)-0.997070312f, + (float16_t)0.190795898f, (float16_t)0.108154297f, (float16_t)-0.203979492f, (float16_t)0.672851562f, + (float16_t)0.840332031f, (float16_t)-0.895507812f, (float16_t)0.773925781f, (float16_t)0.132446289f, + (float16_t)-0.67578125f, (float16_t)0.668945312f, (float16_t)-0.491943359f, (float16_t)0.115478516f, + (float16_t)0.734863281f, (float16_t)0.589355469f, (float16_t)-0.6171875f, (float16_t)0.491943359f, + (float16_t)-0.44140625f, (float16_t)0.223876953f, (float16_t)0.895996094f, (float16_t)0.69921875f, + (float16_t)-0.869140625f, (float16_t)0.275390625f, (float16_t)-0.138183594f, (float16_t)0.923339844f, + (float16_t)0.388916016f, (float16_t)-0.684082031f, (float16_t)-0.495117188f, (float16_t)0.620605469f, + (float16_t)0.678222656f, (float16_t)-0.860839844f, (float16_t)-0.0182189941f, (float16_t)0.64453125f, + (float16_t)0.132080078f, (float16_t)0.33984375f, (float16_t)-0.00343132019f, (float16_t)-0.819824219f, + (float16_t)0.79296875f, (float16_t)-0.808105469f, (float16_t)0.627929688f, (float16_t)-0.931640625f, + (float16_t)-0.7890625f, (float16_t)-0.412841797f, (float16_t)0.186401367f, (float16_t)0.545410156f, + (float16_t)-0.846191406f, (float16_t)-0.00130558014f, (float16_t)0.704101562f, (float16_t)0.186645508f, + (float16_t)0.285400391f, (float16_t)-0.456298828f, (float16_t)-0.22644043f, (float16_t)0.253173828f, + (float16_t)0.944824219f, (float16_t)0.382568359f, (float16_t)0.729003906f, (float16_t)0.576660156f, + (float16_t)0.900878906f, (float16_t)-0.0679931641f, (float16_t)-0.400146484f, (float16_t)0.775878906f, + (float16_t)0.694335938f, (float16_t)0.476318359f, (float16_t)0.312255859f, (float16_t)0.58203125f, + (float16_t)-0.0797119141f, (float16_t)0.520019531f, (float16_t)-0.359619141f, (float16_t)-0.857421875f, + (float16_t)-0.406738281f, (float16_t)-0.393310547f, (float16_t)0.691894531f, (float16_t)0.60546875f, + (float16_t)0.264404297f, (float16_t)-0.495849609f, (float16_t)-0.854980469f, (float16_t)-0.344238281f, + (float16_t)0.948730469f, (float16_t)-0.549804688f, (float16_t)0.0575561523f, (float16_t)-0.442138672f, + (float16_t)0.362548828f, (float16_t)0.838378906f, (float16_t)-0.880859375f, (float16_t)-0.242675781f, + (float16_t)-0.326416016f, (float16_t)-0.759765625f, (float16_t)-0.377441406f, (float16_t)0.408691406f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/output_ref_data.h new file mode 100644 index 00000000..b1f41fa7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/output_ref_data.h @@ -0,0 +1,22 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_2x2_f16_output_ref_data[80] = { + (float16_t)0.299804688f, (float16_t)-0.170532227f, (float16_t)0.708984375f, (float16_t)-0.668945312f, + (float16_t)-0.447509766f, (float16_t)-0.0473937988f, (float16_t)0.422363281f, (float16_t)0.813964844f, + (float16_t)-0.140869141f, (float16_t)0.69921875f, (float16_t)0.336181641f, (float16_t)-1.3671875f, + (float16_t)0.383056641f, (float16_t)0.168457031f, (float16_t)1.03613281f, (float16_t)0.494384766f, + (float16_t)0.728027344f, (float16_t)0.305908203f, (float16_t)0.0351867676f, (float16_t)-0.540527344f, + (float16_t)-0.0218963623f, (float16_t)0.479736328f, (float16_t)0.677734375f, (float16_t)0.513671875f, + (float16_t)0.279052734f, (float16_t)0.289794922f, (float16_t)0.440429688f, (float16_t)-1.02441406f, + (float16_t)0.666503906f, (float16_t)-0.126831055f, (float16_t)0.0753173828f, (float16_t)0.30859375f, + (float16_t)-0.185913086f, (float16_t)-0.334960938f, (float16_t)0.691894531f, (float16_t)0.559570312f, + (float16_t)0.467285156f, (float16_t)-0.831054688f, (float16_t)0.561523438f, (float16_t)-0.352050781f, + (float16_t)-0.278808594f, (float16_t)-0.293701172f, (float16_t)0.469238281f, (float16_t)-1.40039062f, + (float16_t)0.0581665039f, (float16_t)0.108337402f, (float16_t)0.905761719f, (float16_t)0.103210449f, + (float16_t)0.503417969f, (float16_t)0.418212891f, (float16_t)0.758300781f, (float16_t)0.481445312f, + (float16_t)0.172729492f, (float16_t)-0.0651245117f, (float16_t)0.147460938f, (float16_t)0.0825805664f, + (float16_t)0.284423828f, (float16_t)0.627441406f, (float16_t)0.444824219f, (float16_t)-0.655273438f, + (float16_t)-0.575683594f, (float16_t)0.189575195f, (float16_t)0.107788086f, (float16_t)0.902832031f, + (float16_t)0.672363281f, (float16_t)-0.126098633f, (float16_t)-0.0368347168f, (float16_t)0.238891602f, + (float16_t)0.84375f, (float16_t)-0.356445312f, (float16_t)0.472900391f, (float16_t)-0.347167969f, + (float16_t)0.71484375f, (float16_t)0.177001953f, (float16_t)0.0724487305f, (float16_t)-0.334960938f, + (float16_t)0.634277344f, (float16_t)-0.973144531f, (float16_t)0.339599609f, (float16_t)0.248901367f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/weights_data.h new file mode 100644 index 00000000..be39cdc8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f16/weights_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_2x2_f16_weights_data[16] = {(float16_t)0.633300781f, + (float16_t)-0.476074219f, + (float16_t)-0.135253906f, + (float16_t)0.387695312f, + (float16_t)0.218383789f, + (float16_t)-0.372070312f, + (float16_t)-0.268310547f, + (float16_t)-0.642089844f, + (float16_t)-0.205322266f, + (float16_t)-0.20690918f, + (float16_t)0.0244903564f, + (float16_t)0.678222656f, + (float16_t)-0.143920898f, + (float16_t)0.367675781f, + (float16_t)0.409667969f, + (float16_t)-0.615722656f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/biases_data.h new file mode 100644 index 00000000..212e6301 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_2x2_f32_biases_data[4] = {0.23972787f, -0.100074098f, 0.461551815f, -0.111373886f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/config_data.h new file mode 100644 index 00000000..0cbaed03 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_2X2_F32_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_2X2_F32_INPUT_H 6 +#define DEPTHWISE_KERNEL_2X2_F32_INPUT_W 5 +#define DEPTHWISE_KERNEL_2X2_F32_IN_CH 4 +#define DEPTHWISE_KERNEL_2X2_F32_CH_MULT 1 +#define DEPTHWISE_KERNEL_2X2_F32_FILTER_H 2 +#define DEPTHWISE_KERNEL_2X2_F32_FILTER_W 2 +#define DEPTHWISE_KERNEL_2X2_F32_OUT_CH 4 +#define DEPTHWISE_KERNEL_2X2_F32_STRIDE_H 1 +#define DEPTHWISE_KERNEL_2X2_F32_STRIDE_W 1 +#define DEPTHWISE_KERNEL_2X2_F32_PADDING_H 0 +#define DEPTHWISE_KERNEL_2X2_F32_PADDING_W 0 +#define DEPTHWISE_KERNEL_2X2_F32_DILATION_H 1 +#define DEPTHWISE_KERNEL_2X2_F32_DILATION_W 1 +#define DEPTHWISE_KERNEL_2X2_F32_OUTPUT_H 5 +#define DEPTHWISE_KERNEL_2X2_F32_OUTPUT_W 4 +#define DEPTHWISE_KERNEL_2X2_F32_OUTPUT_C 4 +#define DEPTHWISE_KERNEL_2X2_F32_DST_SIZE 80 +#define DEPTHWISE_KERNEL_2X2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_2X2_F32_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_2X2_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_KERNEL_2X2_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_KERNEL_2X2_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/input_data.h new file mode 100644 index 00000000..468dd9c1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/input_data.h @@ -0,0 +1,20 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_2x2_f32_input_data[120] = { + 0.625604331f, -0.480847389f, -0.0804906189f, 0.715177596f, -0.913837671f, 0.212774247f, -0.23485069f, + 0.909229875f, -0.871446729f, -0.811367333f, 0.765162408f, 0.286490947f, 0.585423887f, -0.564758897f, + -0.202279747f, 0.431421727f, -0.307938486f, -0.943592548f, -0.87534672f, -0.0992412642f, 0.773452699f, + -0.152907133f, 0.400836647f, -0.1785236f, -0.156316772f, -0.68506372f, 0.399654955f, 0.21108599f, + -0.344770998f, -0.788090348f, 0.304055125f, -0.996989548f, 0.190850899f, 0.108160153f, -0.203937247f, + 0.673055351f, 0.840356708f, -0.895337343f, 0.773735046f, 0.132454395f, -0.675762057f, 0.669127524f, + -0.491985798f, 0.115477763f, 0.734874487f, 0.589123428f, -0.617287397f, 0.491848975f, -0.441481501f, + 0.223827198f, 0.896204293f, 0.698992193f, -0.869286597f, 0.275505513f, -0.138233989f, 0.923482955f, + 0.388836056f, -0.684090376f, -0.495021194f, 0.62069416f, 0.678233862f, -0.860801339f, -0.0182139222f, + 0.644648671f, 0.132028326f, 0.33976388f, -0.00343101961f, -0.81994313f, 0.792947352f, -0.808253288f, + 0.627961874f, -0.931625187f, -0.789225519f, -0.412899494f, 0.186378941f, 0.545519054f, -0.846030772f, + -0.00130546058f, 0.70389396f, 0.186592445f, 0.285303324f, -0.456410557f, -0.226427272f, 0.253117234f, + 0.944857121f, 0.382626742f, 0.729152858f, 0.576794088f, 0.901108921f, -0.0680089295f, -0.400161445f, + 0.775658309f, 0.694108367f, 0.476350546f, 0.312280357f, 0.58226335f, -0.0797177255f, 0.519876897f, + -0.359685361f, -0.857636392f, -0.406750083f, -0.393318295f, 0.692080319f, 0.605293691f, 0.264400929f, + -0.495887429f, -0.855144024f, -0.344324231f, 0.948884666f, -0.549608409f, 0.0575656258f, -0.442084789f, + 0.36264649f, 0.838544965f, -0.880944848f, -0.242666885f, -0.326376885f, -0.759685814f, -0.377443731f, + 0.408682555f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/output_ref_data.h new file mode 100644 index 00000000..32a4bae2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/output_ref_data.h @@ -0,0 +1,14 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_2x2_f32_output_ref_data[80] = { + 0.29988423f, -0.170584962f, 0.708982468f, -0.668817818f, -0.447426498f, -0.0473818891f, 0.422309726f, + 0.813978374f, -0.140808657f, 0.699122488f, 0.33627215f, -1.36760545f, 0.382945299f, 0.168411076f, + 1.03575289f, 0.494463146f, 0.728260815f, 0.30581364f, 0.0351965837f, -0.540530026f, -0.0219016671f, + 0.479726881f, 0.677900672f, 0.513810873f, 0.278934389f, 0.289781064f, 0.440485626f, -1.0244056f, + 0.666640401f, -0.126821116f, 0.0753383264f, 0.308692217f, -0.18590048f, -0.334851384f, 0.691882312f, + 0.559444845f, 0.467303425f, -0.831215799f, 0.561668217f, -0.352026999f, -0.27886501f, -0.293731004f, + 0.469178319f, -1.4007659f, 0.0581639558f, 0.108320139f, 0.905979872f, 0.103224844f, 0.503353357f, + 0.418300718f, 0.758067071f, 0.481560409f, 0.172737166f, -0.0651542544f, 0.147440642f, 0.0825528577f, + 0.284407645f, 0.627508163f, 0.444735676f, -0.655177891f, -0.575786233f, 0.189522669f, 0.107767724f, + 0.903032541f, 0.672186375f, -0.126150981f, -0.0368374065f, 0.238837719f, 0.843837321f, -0.356352448f, + 0.472974271f, -0.347112834f, 0.714752734f, 0.176979631f, 0.0724190697f, -0.334893137f, 0.63427484f, + -0.973047495f, 0.339664221f, 0.248856604f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/weights_data.h new file mode 100644 index 00000000..26ae7d84 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_2x2_f32/weights_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_2x2_f32_weights_data[16] = {0.633113146f, + -0.476002634f, + -0.135227725f, + 0.387677908f, + 0.218401656f, + -0.372167856f, + -0.268354416f, + -0.641995132f, + -0.205366001f, + -0.206876129f, + 0.0244883653f, + 0.678099275f, + -0.143954962f, + 0.367616296f, + 0.40962106f, + -0.615505397f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/biases_data.h new file mode 100644 index 00000000..da62afd0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_f16_biases_data[5] = {(float16_t)0.157592773f, + (float16_t)-0.4765625f, + (float16_t)-0.381835938f, + (float16_t)0.140991211f, + (float16_t)0.445068359f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/config_data.h new file mode 100644 index 00000000..675c8170 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_3X3_F16_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_3X3_F16_INPUT_H 5 +#define DEPTHWISE_KERNEL_3X3_F16_INPUT_W 4 +#define DEPTHWISE_KERNEL_3X3_F16_IN_CH 5 +#define DEPTHWISE_KERNEL_3X3_F16_CH_MULT 1 +#define DEPTHWISE_KERNEL_3X3_F16_FILTER_H 3 +#define DEPTHWISE_KERNEL_3X3_F16_FILTER_W 3 +#define DEPTHWISE_KERNEL_3X3_F16_OUT_CH 5 +#define DEPTHWISE_KERNEL_3X3_F16_STRIDE_H 2 +#define DEPTHWISE_KERNEL_3X3_F16_STRIDE_W 2 +#define DEPTHWISE_KERNEL_3X3_F16_PADDING_H 1 +#define DEPTHWISE_KERNEL_3X3_F16_PADDING_W 0 +#define DEPTHWISE_KERNEL_3X3_F16_DILATION_H 1 +#define DEPTHWISE_KERNEL_3X3_F16_DILATION_W 1 +#define DEPTHWISE_KERNEL_3X3_F16_OUTPUT_H 3 +#define DEPTHWISE_KERNEL_3X3_F16_OUTPUT_W 1 +#define DEPTHWISE_KERNEL_3X3_F16_OUTPUT_C 5 +#define DEPTHWISE_KERNEL_3X3_F16_DST_SIZE 15 +#define DEPTHWISE_KERNEL_3X3_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_3X3_F16_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_3X3_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_KERNEL_3X3_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_KERNEL_3X3_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/input_data.h new file mode 100644 index 00000000..eec1781a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/input_data.h @@ -0,0 +1,27 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_f16_input_data[100] = { + (float16_t)-0.427246094f, (float16_t)-0.958007812f, (float16_t)-0.111328125f, (float16_t)0.65625f, + (float16_t)-0.258544922f, (float16_t)0.858398438f, (float16_t)0.445068359f, (float16_t)0.719726562f, + (float16_t)-0.458740234f, (float16_t)-0.595703125f, (float16_t)0.759277344f, (float16_t)-0.704101562f, + (float16_t)0.932617188f, (float16_t)0.164306641f, (float16_t)0.505371094f, (float16_t)-0.578613281f, + (float16_t)0.908691406f, (float16_t)0.633300781f, (float16_t)0.765136719f, (float16_t)-0.0566101074f, + (float16_t)-0.832519531f, (float16_t)-0.846679688f, (float16_t)-0.891601562f, (float16_t)-0.391845703f, + (float16_t)-0.00601196289f, (float16_t)0.393066406f, (float16_t)0.219238281f, (float16_t)0.680664062f, + (float16_t)0.0271453857f, (float16_t)-0.516601562f, (float16_t)0.331542969f, (float16_t)0.683105469f, + (float16_t)-0.429931641f, (float16_t)0.220703125f, (float16_t)0.460693359f, (float16_t)-0.0263671875f, + (float16_t)-0.385742188f, (float16_t)-0.176513672f, (float16_t)0.383300781f, (float16_t)0.699707031f, + (float16_t)0.989257812f, (float16_t)0.4140625f, (float16_t)-0.766113281f, (float16_t)-0.500488281f, + (float16_t)0.17565918f, (float16_t)-0.739746094f, (float16_t)-0.672851562f, (float16_t)0.255615234f, + (float16_t)0.278808594f, (float16_t)-0.272705078f, (float16_t)-0.731933594f, (float16_t)-0.431640625f, + (float16_t)0.200439453f, (float16_t)-0.658203125f, (float16_t)0.104309082f, (float16_t)0.661132812f, + (float16_t)-0.340332031f, (float16_t)0.257568359f, (float16_t)-0.422851562f, (float16_t)-0.448730469f, + (float16_t)-0.639160156f, (float16_t)-0.240966797f, (float16_t)-0.126586914f, (float16_t)-0.0600891113f, + (float16_t)-0.640136719f, (float16_t)0.14465332f, (float16_t)0.236083984f, (float16_t)0.464111328f, + (float16_t)-0.264404297f, (float16_t)-0.122802734f, (float16_t)-0.847167969f, (float16_t)0.478027344f, + (float16_t)-0.551757812f, (float16_t)0.239624023f, (float16_t)0.573242188f, (float16_t)0.678222656f, + (float16_t)0.337646484f, (float16_t)-0.281005859f, (float16_t)0.8359375f, (float16_t)-0.689941406f, + (float16_t)0.267333984f, (float16_t)0.759277344f, (float16_t)0.52734375f, (float16_t)-0.297119141f, + (float16_t)-0.0587158203f, (float16_t)0.862792969f, (float16_t)-0.290039062f, (float16_t)0.967773438f, + (float16_t)0.930175781f, (float16_t)-0.433105469f, (float16_t)-0.113464355f, (float16_t)-0.685546875f, + (float16_t)0.941894531f, (float16_t)0.772460938f, (float16_t)0.311035156f, (float16_t)0.948730469f, + (float16_t)-0.705566406f, (float16_t)-0.974121094f, (float16_t)0.0887451172f, (float16_t)-0.0581970215f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/output_ref_data.h new file mode 100644 index 00000000..fbf6f67a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/output_ref_data.h @@ -0,0 +1,16 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_f16_output_ref_data[15] = {(float16_t)-0.00125312805f, + (float16_t)0.363037109f, + (float16_t)0.684082031f, + (float16_t)0.0251464844f, + (float16_t)0.164306641f, + (float16_t)0.862304688f, + (float16_t)0.475341797f, + (float16_t)-1.79492188f, + (float16_t)-0.127929688f, + (float16_t)0.849609375f, + (float16_t)-0.620605469f, + (float16_t)0.153442383f, + (float16_t)0.0204620361f, + (float16_t)-0.0582885742f, + (float16_t)0.524414062f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/weights_data.h new file mode 100644 index 00000000..52dd9f6a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f16/weights_data.h @@ -0,0 +1,14 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_f16_weights_data[45] = { + (float16_t)0.632324219f, (float16_t)-0.150390625f, (float16_t)0.692382812f, (float16_t)0.430908203f, + (float16_t)-0.0573425293f, (float16_t)-0.694824219f, (float16_t)0.724121094f, (float16_t)-0.448730469f, + (float16_t)0.515136719f, (float16_t)-0.707519531f, (float16_t)0.506347656f, (float16_t)0.5f, + (float16_t)0.738769531f, (float16_t)-0.515136719f, (float16_t)0.513183594f, (float16_t)0.241455078f, + (float16_t)-0.286865234f, (float16_t)0.697265625f, (float16_t)0.100952148f, (float16_t)0.3359375f, + (float16_t)0.022064209f, (float16_t)-0.332519531f, (float16_t)0.327880859f, (float16_t)0.15234375f, + (float16_t)0.594726562f, (float16_t)-0.62890625f, (float16_t)-0.445068359f, (float16_t)0.447021484f, + (float16_t)-0.0328674316f, (float16_t)-0.196533203f, (float16_t)-0.304199219f, (float16_t)-0.68359375f, + (float16_t)-0.522460938f, (float16_t)0.289794922f, (float16_t)0.577148438f, (float16_t)0.732910156f, + (float16_t)-0.146484375f, (float16_t)-0.218017578f, (float16_t)0.0127944946f, (float16_t)-0.0609741211f, + (float16_t)-0.416748047f, (float16_t)-0.215209961f, (float16_t)-0.403320312f, (float16_t)0.0285797119f, + (float16_t)0.502929688f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/biases_data.h new file mode 100644 index 00000000..e033024b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_f32_biases_data[5] = {0.157603428f, + -0.476453811f, + -0.38180697f, + 0.141017616f, + 0.445060611f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/config_data.h new file mode 100644 index 00000000..0e58d77c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_3X3_F32_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_3X3_F32_INPUT_H 5 +#define DEPTHWISE_KERNEL_3X3_F32_INPUT_W 4 +#define DEPTHWISE_KERNEL_3X3_F32_IN_CH 5 +#define DEPTHWISE_KERNEL_3X3_F32_CH_MULT 1 +#define DEPTHWISE_KERNEL_3X3_F32_FILTER_H 3 +#define DEPTHWISE_KERNEL_3X3_F32_FILTER_W 3 +#define DEPTHWISE_KERNEL_3X3_F32_OUT_CH 5 +#define DEPTHWISE_KERNEL_3X3_F32_STRIDE_H 2 +#define DEPTHWISE_KERNEL_3X3_F32_STRIDE_W 2 +#define DEPTHWISE_KERNEL_3X3_F32_PADDING_H 1 +#define DEPTHWISE_KERNEL_3X3_F32_PADDING_W 0 +#define DEPTHWISE_KERNEL_3X3_F32_DILATION_H 1 +#define DEPTHWISE_KERNEL_3X3_F32_DILATION_W 1 +#define DEPTHWISE_KERNEL_3X3_F32_OUTPUT_H 3 +#define DEPTHWISE_KERNEL_3X3_F32_OUTPUT_W 1 +#define DEPTHWISE_KERNEL_3X3_F32_OUTPUT_C 5 +#define DEPTHWISE_KERNEL_3X3_F32_DST_SIZE 15 +#define DEPTHWISE_KERNEL_3X3_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_3X3_F32_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_3X3_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_KERNEL_3X3_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_KERNEL_3X3_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/input_data.h new file mode 100644 index 00000000..e49ba10f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/input_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_f32_input_data[100] = { + -0.427356601f, -0.957908094f, -0.111306764f, 0.656263471f, -0.258462518f, 0.8582443f, 0.445004106f, + 0.719540834f, -0.458815992f, -0.595798731f, 0.759295464f, -0.703949153f, 0.932859182f, 0.164360061f, + 0.505152941f, -0.578500867f, 0.908699393f, 0.633463979f, 0.765098572f, -0.056620691f, -0.832288384f, + -0.846487761f, -0.891679227f, -0.391788453f, -0.00601079175f, 0.393070668f, 0.219191149f, 0.680811524f, + 0.0271397643f, -0.516547978f, 0.331538826f, 0.68298173f, -0.429917455f, 0.220714346f, 0.460596085f, + -0.026371859f, -0.385669529f, -0.176491991f, 0.383268386f, 0.699580312f, 0.989327729f, 0.414087534f, + -0.76602304f, -0.500510395f, 0.175672024f, -0.739769638f, -0.672909439f, 0.255654216f, 0.27890262f, + -0.272691369f, -0.731701851f, -0.431577265f, 0.20042704f, -0.658211172f, 0.104281247f, 0.661359608f, + -0.340295494f, 0.257479757f, -0.422878504f, -0.448652953f, -0.639080465f, -0.240976751f, -0.126634672f, + -0.0600877516f, -0.640264213f, 0.144656941f, 0.236119375f, 0.464121789f, -0.264350355f, -0.122785076f, + -0.847043395f, 0.477942258f, -0.55192095f, 0.239621103f, 0.573387384f, 0.678176582f, 0.3375431f, + -0.280979782f, 0.835860014f, -0.690111339f, 0.267317086f, 0.759520948f, 0.527227163f, -0.297042578f, + -0.0587255657f, 0.86257273f, -0.289972872f, 0.967817426f, 0.930321515f, -0.433057636f, -0.113448776f, + -0.685425937f, 0.941962063f, 0.772687376f, 0.311071664f, 0.948912621f, -0.705579996f, -0.973996222f, + 0.0887400061f, -0.0582057312f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/output_ref_data.h new file mode 100644 index 00000000..da07d4d3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/output_ref_data.h @@ -0,0 +1,16 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_f32_output_ref_data[15] = {-0.00125297182f, + 0.363015801f, + 0.684281468f, + 0.0251492243f, + 0.164335564f, + 0.862511694f, + 0.475340724f, + -1.79528952f, + -0.127959192f, + 0.849850476f, + -0.620791018f, + 0.153396323f, + 0.0204567481f, + -0.0582945198f, + 0.524565458f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/weights_data.h new file mode 100644 index 00000000..c18a53b3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_f32_weights_data[45] = { + 0.632108152f, -0.150356606f, 0.692375183f, 0.430963069f, -0.0573538542f, -0.694621205f, 0.724364221f, + -0.448710322f, 0.515344977f, -0.707502961f, 0.50632602f, 0.499886721f, 0.738785267f, -0.515367031f, + 0.513213098f, 0.241430208f, -0.286792308f, 0.697157681f, 0.10097152f, 0.335893124f, 0.0220592599f, + -0.332532227f, 0.327947736f, 0.152308315f, 0.594562352f, -0.629088402f, -0.445071757f, 0.446934909f, + -0.0328657553f, -0.196585581f, -0.304115534f, -0.683441162f, -0.522358239f, 0.289708197f, 0.577232718f, + 0.73292166f, -0.146445811f, -0.218006849f, 0.0127969095f, -0.0609827675f, -0.416692376f, -0.21524106f, + -0.403410524f, 0.0285800174f, 0.502837777f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/biases_data.h new file mode 100644 index 00000000..807a770f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_null_bias_f16_biases_data[5] = {(float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/config_data.h new file mode 100644 index 00000000..5e1c8289 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_INPUT_H 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_INPUT_W 4 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_IN_CH 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_CH_MULT 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_FILTER_H 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_FILTER_W 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUT_CH 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_STRIDE_H 2 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_STRIDE_W 2 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_PADDING_H 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_PADDING_W 0 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_DILATION_H 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_DILATION_W 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUTPUT_H 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUTPUT_W 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUTPUT_C 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_DST_SIZE 15 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16_USE_NULL_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/input_data.h new file mode 100644 index 00000000..0e3ed54b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/input_data.h @@ -0,0 +1,27 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_null_bias_f16_input_data[100] = { + (float16_t)-0.789550781f, (float16_t)-0.689453125f, (float16_t)0.727050781f, (float16_t)0.256835938f, + (float16_t)-0.994628906f, (float16_t)0.334960938f, (float16_t)-0.060546875f, (float16_t)0.275146484f, + (float16_t)0.823730469f, (float16_t)-0.714355469f, (float16_t)-0.0614318848f, (float16_t)0.997558594f, + (float16_t)0.954101562f, (float16_t)0.189331055f, (float16_t)0.145874023f, (float16_t)-0.945800781f, + (float16_t)0.703125f, (float16_t)-0.429443359f, (float16_t)-0.319335938f, (float16_t)-0.0495300293f, + (float16_t)-0.661132812f, (float16_t)-0.831054688f, (float16_t)0.954589844f, (float16_t)0.560546875f, + (float16_t)-0.352050781f, (float16_t)0.626953125f, (float16_t)-0.317871094f, (float16_t)-0.156494141f, + (float16_t)0.998535156f, (float16_t)-0.613769531f, (float16_t)-0.930664062f, (float16_t)0.530273438f, + (float16_t)-0.232177734f, (float16_t)0.239013672f, (float16_t)-0.665527344f, (float16_t)0.454833984f, + (float16_t)-0.937011719f, (float16_t)0.418701172f, (float16_t)0.9765625f, (float16_t)-0.363037109f, + (float16_t)0.178222656f, (float16_t)-0.467285156f, (float16_t)-0.495849609f, (float16_t)-0.715820312f, + (float16_t)-0.520996094f, (float16_t)0.0864257812f, (float16_t)0.557617188f, (float16_t)-0.444091797f, + (float16_t)0.490722656f, (float16_t)-0.540039062f, (float16_t)-0.693847656f, (float16_t)0.979003906f, + (float16_t)-0.0806274414f, (float16_t)0.982421875f, (float16_t)0.814941406f, (float16_t)-0.102539062f, + (float16_t)-0.110229492f, (float16_t)0.437988281f, (float16_t)0.104919434f, (float16_t)-0.199584961f, + (float16_t)0.573730469f, (float16_t)0.673828125f, (float16_t)-0.758789062f, (float16_t)-0.458251953f, + (float16_t)-0.582519531f, (float16_t)0.122253418f, (float16_t)-0.198486328f, (float16_t)0.799804688f, + (float16_t)0.947753906f, (float16_t)0.250244141f, (float16_t)0.691894531f, (float16_t)-0.602539062f, + (float16_t)0.987792969f, (float16_t)-0.0722045898f, (float16_t)0.696289062f, (float16_t)-0.669921875f, + (float16_t)0.131713867f, (float16_t)0.51171875f, (float16_t)0.459716797f, (float16_t)0.0916748047f, + (float16_t)0.163574219f, (float16_t)0.455810547f, (float16_t)0.707519531f, (float16_t)-0.467773438f, + (float16_t)0.662597656f, (float16_t)-0.6796875f, (float16_t)0.117126465f, (float16_t)-0.959472656f, + (float16_t)-0.0175628662f, (float16_t)0.458496094f, (float16_t)0.748535156f, (float16_t)0.446044922f, + (float16_t)0.389160156f, (float16_t)-0.732910156f, (float16_t)-0.271728516f, (float16_t)-0.260742188f, + (float16_t)-0.804199219f, (float16_t)-0.88671875f, (float16_t)0.575195312f, (float16_t)-0.282226562f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/output_ref_data.h new file mode 100644 index 00000000..bf1f7d6a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/output_ref_data.h @@ -0,0 +1,16 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_null_bias_f16_output_ref_data[15] = {(float16_t)0.0380249023f, + (float16_t)-0.442871094f, + (float16_t)0.368652344f, + (float16_t)0.172851562f, + (float16_t)-1.26757812f, + (float16_t)0.165161133f, + (float16_t)0.776367188f, + (float16_t)-0.907226562f, + (float16_t)1.08105469f, + (float16_t)-0.72265625f, + (float16_t)0.530273438f, + (float16_t)0.322753906f, + (float16_t)-0.278076172f, + (float16_t)0.0431213379f, + (float16_t)0.138549805f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/weights_data.h new file mode 100644 index 00000000..9e708c38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f16/weights_data.h @@ -0,0 +1,14 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_kernel_3x3_null_bias_f16_weights_data[45] = { + (float16_t)0.331054688f, (float16_t)0.159667969f, (float16_t)0.167480469f, (float16_t)-0.577148438f, + (float16_t)0.133911133f, (float16_t)0.251464844f, (float16_t)0.453857422f, (float16_t)-0.529785156f, + (float16_t)-0.00604248047f, (float16_t)-0.274169922f, (float16_t)-0.195922852f, (float16_t)-0.0503845215f, + (float16_t)0.279052734f, (float16_t)-0.666992188f, (float16_t)-0.577636719f, (float16_t)-0.616699219f, + (float16_t)0.13684082f, (float16_t)0.200439453f, (float16_t)-0.596679688f, (float16_t)0.322265625f, + (float16_t)-0.709472656f, (float16_t)0.442382812f, (float16_t)0.0196685791f, (float16_t)-0.286132812f, + (float16_t)0.610839844f, (float16_t)0.0857543945f, (float16_t)0.360351562f, (float16_t)-0.323242188f, + (float16_t)0.747558594f, (float16_t)-0.713867188f, (float16_t)-0.123901367f, (float16_t)0.418945312f, + (float16_t)0.403564453f, (float16_t)-0.24230957f, (float16_t)0.627929688f, (float16_t)0.223266602f, + (float16_t)0.20690918f, (float16_t)-0.736328125f, (float16_t)0.484863281f, (float16_t)-0.056427002f, + (float16_t)0.460205078f, (float16_t)-0.503417969f, (float16_t)-0.108947754f, (float16_t)0.300292969f, + (float16_t)0.331298828f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/biases_data.h new file mode 100644 index 00000000..ab41ad6c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_null_bias_f32_biases_data[5] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/config_data.h new file mode 100644 index 00000000..92dca029 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_INPUT_BATCHES 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_INPUT_H 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_INPUT_W 4 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_IN_CH 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_CH_MULT 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_FILTER_H 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_FILTER_W 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUT_CH 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_STRIDE_H 2 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_STRIDE_W 2 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_PADDING_H 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_PADDING_W 0 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_DILATION_H 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_DILATION_W 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUTPUT_H 3 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUTPUT_W 1 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUTPUT_C 5 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_DST_SIZE 15 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_USE_WRAPPER 0 +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUT_ACTIVATION_MIN -1000.0f +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_OUT_ACTIVATION_MAX 1000.0f +#define DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32_USE_NULL_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/input_data.h new file mode 100644 index 00000000..3ce4710a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/input_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_null_bias_f32_input_data[100] = { + -0.789746881f, -0.68960017f, 0.727114975f, 0.256756634f, -0.9946419f, 0.334961534f, -0.060546346f, + 0.275044233f, 0.823655903f, -0.714277208f, -0.0614239685f, 0.997402906f, 0.953877568f, 0.189344481f, + 0.145907238f, -0.945780218f, 0.703238308f, -0.429360569f, -0.31926021f, -0.0495199524f, -0.660933852f, + -0.831294775f, 0.95451355f, 0.560545206f, -0.351931185f, 0.627141118f, -0.31782642f, -0.156490296f, + 0.998402536f, -0.61385721f, -0.930555642f, 0.530351996f, -0.232128128f, 0.239041537f, -0.665762961f, + 0.454955399f, -0.936900318f, 0.418823153f, 0.976500332f, -0.36294964f, 0.178185225f, -0.46740362f, + -0.495879501f, -0.716003358f, -0.52108413f, 0.0864288211f, 0.557744265f, -0.444110811f, 0.490730256f, + -0.539911926f, -0.6938802f, 0.978813171f, -0.0806528702f, 0.982300818f, 0.814729929f, -0.102567807f, + -0.110244401f, 0.437934041f, 0.104902461f, -0.199595541f, 0.573556304f, 0.673936665f, -0.758593261f, + -0.458354145f, -0.582665384f, 0.122253858f, -0.19844076f, 0.79996109f, 0.947912574f, 0.250281364f, + 0.692109287f, -0.602615595f, 0.98762542f, -0.0721752644f, 0.69647193f, -0.669873953f, 0.131693557f, + 0.511770606f, 0.459700346f, 0.091674909f, 0.163522094f, 0.455696166f, 0.707464814f, -0.467781603f, + 0.662615716f, -0.679446697f, 0.117129974f, -0.959341586f, -0.0175613053f, 0.458508313f, 0.748750269f, + 0.446008265f, 0.389110625f, -0.732773721f, -0.271664947f, -0.260777384f, -0.804033697f, -0.886547744f, + 0.575298071f, -0.282122165f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/output_ref_data.h new file mode 100644 index 00000000..686e1741 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/output_ref_data.h @@ -0,0 +1,16 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_null_bias_f32_output_ref_data[15] = {0.0380151831f, + -0.442923576f, + 0.368538588f, + 0.172841564f, + -1.26777923f, + 0.165180206f, + 0.776353776f, + -0.907046676f, + 1.08136308f, + -0.72251606f, + 0.530382931f, + 0.322742969f, + -0.278043002f, + 0.0431247763f, + 0.138510838f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/weights_data.h new file mode 100644 index 00000000..b821196d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_kernel_3x3_null_bias_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_kernel_3x3_null_bias_f32_weights_data[45] = { + 0.330965757f, 0.159647107f, 0.167421967f, -0.576920986f, 0.13387318f, 0.251441509f, 0.453952432f, + -0.529795468f, -0.00604302622f, -0.274142981f, -0.195878848f, -0.0503826477f, 0.279139638f, -0.667109311f, + -0.577638805f, -0.616915762f, 0.136892438f, 0.200408831f, -0.596632779f, 0.322216272f, -0.709507465f, + 0.442448527f, 0.0196741782f, -0.286087424f, 0.610788345f, 0.0857324153f, 0.360232949f, -0.323340923f, + 0.747635663f, -0.713669837f, -0.123875342f, 0.418831259f, 0.403662533f, -0.242304489f, 0.627774358f, + 0.223269418f, 0.206948042f, -0.736423612f, 0.484922081f, -0.056432832f, 0.460115671f, -0.503600419f, + -0.108949415f, 0.300295383f, 0.331343293f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/biases_data.h new file mode 100644 index 00000000..0cf34be9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_basic_f16_biases_data[1] = {(float16_t)-0.412353516f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/config_data.h new file mode 100644 index 00000000..79443d88 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_BASIC_F16_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_BASIC_F16_INPUT_H 8 +#define DEPTHWISE_MATCH_BASIC_F16_INPUT_W 5 +#define DEPTHWISE_MATCH_BASIC_F16_IN_CH 1 +#define DEPTHWISE_MATCH_BASIC_F16_CH_MULT 1 +#define DEPTHWISE_MATCH_BASIC_F16_FILTER_H 4 +#define DEPTHWISE_MATCH_BASIC_F16_FILTER_W 2 +#define DEPTHWISE_MATCH_BASIC_F16_OUT_CH 1 +#define DEPTHWISE_MATCH_BASIC_F16_STRIDE_H 1 +#define DEPTHWISE_MATCH_BASIC_F16_STRIDE_W 1 +#define DEPTHWISE_MATCH_BASIC_F16_PADDING_H 0 +#define DEPTHWISE_MATCH_BASIC_F16_PADDING_W 0 +#define DEPTHWISE_MATCH_BASIC_F16_DILATION_H 1 +#define DEPTHWISE_MATCH_BASIC_F16_DILATION_W 1 +#define DEPTHWISE_MATCH_BASIC_F16_OUTPUT_H 5 +#define DEPTHWISE_MATCH_BASIC_F16_OUTPUT_W 4 +#define DEPTHWISE_MATCH_BASIC_F16_OUTPUT_C 1 +#define DEPTHWISE_MATCH_BASIC_F16_DST_SIZE 20 +#define DEPTHWISE_MATCH_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_BASIC_F16_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define DEPTHWISE_MATCH_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define DEPTHWISE_MATCH_BASIC_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/input_data.h new file mode 100644 index 00000000..e20e9a5a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/input_data.h @@ -0,0 +1,12 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_basic_f16_input_data[40] = { + (float16_t)0.432617188f, (float16_t)0.796386719f, (float16_t)0.514648438f, (float16_t)0.48046875f, + (float16_t)-0.0737915039f, (float16_t)0.173706055f, (float16_t)-0.350341797f, (float16_t)-0.548828125f, + (float16_t)0.202392578f, (float16_t)-0.775390625f, (float16_t)0.756835938f, (float16_t)0.0152664185f, + (float16_t)0.858398438f, (float16_t)0.272460938f, (float16_t)-0.341308594f, (float16_t)0.788085938f, + (float16_t)-0.138671875f, (float16_t)0.328613281f, (float16_t)-0.637207031f, (float16_t)-0.7890625f, + (float16_t)0.119506836f, (float16_t)0.219970703f, (float16_t)-0.290039062f, (float16_t)-0.601074219f, + (float16_t)-0.750976562f, (float16_t)-0.765625f, (float16_t)-0.790527344f, (float16_t)0.383789062f, + (float16_t)-0.686035156f, (float16_t)-0.0629882812f, (float16_t)0.69921875f, (float16_t)-0.424316406f, + (float16_t)-0.396728516f, (float16_t)-0.849609375f, (float16_t)-0.447265625f, (float16_t)0.884765625f, + (float16_t)-0.981933594f, (float16_t)0.208984375f, (float16_t)-0.566894531f, (float16_t)0.705078125f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/output_ref_data.h new file mode 100644 index 00000000..50b919d5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_basic_f16_output_ref_data[20] = { + (float16_t)-0.87109375f, (float16_t)-0.883789062f, (float16_t)-0.79296875f, (float16_t)-0.0607910156f, + (float16_t)0.288085938f, (float16_t)-0.743164062f, (float16_t)-0.373291016f, (float16_t)0.131713867f, + (float16_t)-0.663574219f, (float16_t)-0.0963745117f, (float16_t)-0.751464844f, (float16_t)0.472900391f, + (float16_t)-0.64453125f, (float16_t)-1.38769531f, (float16_t)0.516113281f, (float16_t)-0.0415039062f, + (float16_t)-0.571777344f, (float16_t)0.478759766f, (float16_t)-0.0931396484f, (float16_t)0.597167969f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/weights_data.h new file mode 100644 index 00000000..9953e59e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f16/weights_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_basic_f16_weights_data[8] = {(float16_t)0.126098633f, + (float16_t)-0.6328125f, + (float16_t)-0.17578125f, + (float16_t)-0.0864257812f, + (float16_t)0.53515625f, + (float16_t)-0.702148438f, + (float16_t)-0.414550781f, + (float16_t)0.553710938f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/biases_data.h new file mode 100644 index 00000000..dee9f11f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_basic_f32_biases_data[1] = {-0.412409931f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/config_data.h new file mode 100644 index 00000000..d9b11b67 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_BASIC_F32_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_BASIC_F32_INPUT_H 8 +#define DEPTHWISE_MATCH_BASIC_F32_INPUT_W 5 +#define DEPTHWISE_MATCH_BASIC_F32_IN_CH 1 +#define DEPTHWISE_MATCH_BASIC_F32_CH_MULT 1 +#define DEPTHWISE_MATCH_BASIC_F32_FILTER_H 4 +#define DEPTHWISE_MATCH_BASIC_F32_FILTER_W 2 +#define DEPTHWISE_MATCH_BASIC_F32_OUT_CH 1 +#define DEPTHWISE_MATCH_BASIC_F32_STRIDE_H 1 +#define DEPTHWISE_MATCH_BASIC_F32_STRIDE_W 1 +#define DEPTHWISE_MATCH_BASIC_F32_PADDING_H 0 +#define DEPTHWISE_MATCH_BASIC_F32_PADDING_W 0 +#define DEPTHWISE_MATCH_BASIC_F32_DILATION_H 1 +#define DEPTHWISE_MATCH_BASIC_F32_DILATION_W 1 +#define DEPTHWISE_MATCH_BASIC_F32_OUTPUT_H 5 +#define DEPTHWISE_MATCH_BASIC_F32_OUTPUT_W 4 +#define DEPTHWISE_MATCH_BASIC_F32_OUTPUT_C 1 +#define DEPTHWISE_MATCH_BASIC_F32_DST_SIZE 20 +#define DEPTHWISE_MATCH_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_BASIC_F32_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_BASIC_F32_OUT_ACTIVATION_MIN -128.0f +#define DEPTHWISE_MATCH_BASIC_F32_OUT_ACTIVATION_MAX 127.0f +#define DEPTHWISE_MATCH_BASIC_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/input_data.h new file mode 100644 index 00000000..da215a3b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_basic_f32_input_data[40] = { + 0.432514697f, 0.796235442f, 0.514622688f, 0.480490327f, -0.0738143176f, 0.173660055f, -0.350337952f, + -0.548597574f, 0.20234406f, -0.775429487f, 0.756653011f, 0.0152687049f, 0.858630419f, 0.272507548f, + -0.341314256f, 0.787966967f, -0.138709292f, 0.328636229f, -0.637032688f, -0.788827062f, 0.119496837f, + 0.220009133f, -0.290133208f, -0.600914121f, -0.750792563f, -0.765610397f, -0.790354311f, 0.383742183f, + -0.685997665f, -0.062992081f, 0.699255586f, -0.424256921f, -0.396682739f, -0.849551857f, -0.447202921f, + 0.884639323f, -0.981924951f, 0.208983332f, -0.566703379f, 0.705281436f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/output_ref_data.h new file mode 100644 index 00000000..8aaed613 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_basic_f32_output_ref_data[20] = { + -0.871009111f, -0.883775473f, -0.793173313f, -0.0607912838f, 0.288111955f, -0.743109345f, -0.373355985f, + 0.131687954f, -0.663728237f, -0.0963539258f, -0.751330435f, 0.472973853f, -0.644739389f, -1.38744032f, + 0.516103566f, -0.0415150784f, -0.57178998f, 0.478716433f, -0.0931512415f, 0.597332597f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/weights_data.h new file mode 100644 index 00000000..138e25e4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_basic_f32/weights_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_basic_f32_weights_data[8] = {0.1261556f, + -0.632649183f, + -0.175735846f, + -0.0864114761f, + 0.535107136f, + -0.702003777f, + -0.414459974f, + 0.553470552f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/biases_data.h new file mode 100644 index 00000000..703a38ff --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_dilation_f16_biases_data[1] = {(float16_t)0.0693969727f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/config_data.h new file mode 100644 index 00000000..30b0fb52 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_DILATION_F16_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_DILATION_F16_INPUT_H 7 +#define DEPTHWISE_MATCH_DILATION_F16_INPUT_W 7 +#define DEPTHWISE_MATCH_DILATION_F16_IN_CH 1 +#define DEPTHWISE_MATCH_DILATION_F16_CH_MULT 1 +#define DEPTHWISE_MATCH_DILATION_F16_FILTER_H 3 +#define DEPTHWISE_MATCH_DILATION_F16_FILTER_W 3 +#define DEPTHWISE_MATCH_DILATION_F16_OUT_CH 1 +#define DEPTHWISE_MATCH_DILATION_F16_STRIDE_H 1 +#define DEPTHWISE_MATCH_DILATION_F16_STRIDE_W 1 +#define DEPTHWISE_MATCH_DILATION_F16_PADDING_H 2 +#define DEPTHWISE_MATCH_DILATION_F16_PADDING_W 2 +#define DEPTHWISE_MATCH_DILATION_F16_DILATION_H 2 +#define DEPTHWISE_MATCH_DILATION_F16_DILATION_W 2 +#define DEPTHWISE_MATCH_DILATION_F16_OUTPUT_H 7 +#define DEPTHWISE_MATCH_DILATION_F16_OUTPUT_W 7 +#define DEPTHWISE_MATCH_DILATION_F16_OUTPUT_C 1 +#define DEPTHWISE_MATCH_DILATION_F16_DST_SIZE 49 +#define DEPTHWISE_MATCH_DILATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_DILATION_F16_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_DILATION_F16_OUT_ACTIVATION_MIN (float16_t) - 70.0f +#define DEPTHWISE_MATCH_DILATION_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define DEPTHWISE_MATCH_DILATION_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/input_data.h new file mode 100644 index 00000000..cfd2b170 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/input_data.h @@ -0,0 +1,15 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_dilation_f16_input_data[49] = { + (float16_t)-0.828125f, (float16_t)0.850097656f, (float16_t)-0.0319213867f, (float16_t)-0.820800781f, + (float16_t)-0.251708984f, (float16_t)0.0287017822f, (float16_t)-0.908691406f, (float16_t)0.991699219f, + (float16_t)0.5703125f, (float16_t)-0.945800781f, (float16_t)0.740234375f, (float16_t)-0.328857422f, + (float16_t)0.480224609f, (float16_t)0.98046875f, (float16_t)0.050994873f, (float16_t)0.890625f, + (float16_t)0.526367188f, (float16_t)0.965820312f, (float16_t)-0.899414062f, (float16_t)-0.268066406f, + (float16_t)0.335693359f, (float16_t)-0.529296875f, (float16_t)0.797851562f, (float16_t)0.991210938f, + (float16_t)-0.0313720703f, (float16_t)0.787109375f, (float16_t)0.881835938f, (float16_t)0.939941406f, + (float16_t)0.459228516f, (float16_t)0.751464844f, (float16_t)0.520996094f, (float16_t)-0.380371094f, + (float16_t)0.709960938f, (float16_t)-0.168701172f, (float16_t)0.247314453f, (float16_t)0.667480469f, + (float16_t)-0.12298584f, (float16_t)0.797363281f, (float16_t)-0.303466797f, (float16_t)-0.538085938f, + (float16_t)0.320556641f, (float16_t)-0.952148438f, (float16_t)0.501953125f, (float16_t)-0.385986328f, + (float16_t)0.850097656f, (float16_t)-0.0701904297f, (float16_t)0.922363281f, (float16_t)-0.00805664062f, + (float16_t)-0.330810547f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/output_ref_data.h new file mode 100644 index 00000000..71e11832 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/output_ref_data.h @@ -0,0 +1,15 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_dilation_f16_output_ref_data[49] = { + (float16_t)-0.0762939453f, (float16_t)-0.40234375f, (float16_t)1.26953125f, (float16_t)0.353271484f, + (float16_t)-0.547851562f, (float16_t)0.599121094f, (float16_t)0.389160156f, (float16_t)-1.05957031f, + (float16_t)0.481201172f, (float16_t)-0.562988281f, (float16_t)-0.702636719f, (float16_t)0.856933594f, + (float16_t)-0.105529785f, (float16_t)0.619628906f, (float16_t)-0.216186523f, (float16_t)1.52050781f, + (float16_t)-0.449462891f, (float16_t)-0.716308594f, (float16_t)0.693359375f, (float16_t)-0.861816406f, + (float16_t)0.361328125f, (float16_t)1.19921875f, (float16_t)-0.233276367f, (float16_t)1.31054688f, + (float16_t)-0.433837891f, (float16_t)-1.14746094f, (float16_t)0.451171875f, (float16_t)-0.78515625f, + (float16_t)-0.497314453f, (float16_t)-0.570800781f, (float16_t)0.572753906f, (float16_t)0.368652344f, + (float16_t)-0.0267333984f, (float16_t)0.525390625f, (float16_t)-0.583007812f, (float16_t)-0.738769531f, + (float16_t)0.345214844f, (float16_t)-0.9140625f, (float16_t)-0.0933837891f, (float16_t)-0.565429688f, + (float16_t)0.526367188f, (float16_t)1.15722656f, (float16_t)-0.0164794922f, (float16_t)0.652832031f, + (float16_t)-0.331054688f, (float16_t)0.528808594f, (float16_t)-0.416503906f, (float16_t)-0.0679931641f, + (float16_t)-0.138061523f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/weights_data.h new file mode 100644 index 00000000..b17f295a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f16/weights_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_dilation_f16_weights_data[9] = {(float16_t)0.314697266f, + (float16_t)0.372558594f, + (float16_t)-0.673339844f, + (float16_t)-0.625f, + (float16_t)-0.161010742f, + (float16_t)0.205444336f, + (float16_t)0.138183594f, + (float16_t)0.418701172f, + (float16_t)-0.558105469f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/biases_data.h new file mode 100644 index 00000000..6b972b8e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_dilation_f32_biases_data[1] = {0.069405131f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/config_data.h new file mode 100644 index 00000000..1206cf47 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_DILATION_F32_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_DILATION_F32_INPUT_H 7 +#define DEPTHWISE_MATCH_DILATION_F32_INPUT_W 7 +#define DEPTHWISE_MATCH_DILATION_F32_IN_CH 1 +#define DEPTHWISE_MATCH_DILATION_F32_CH_MULT 1 +#define DEPTHWISE_MATCH_DILATION_F32_FILTER_H 3 +#define DEPTHWISE_MATCH_DILATION_F32_FILTER_W 3 +#define DEPTHWISE_MATCH_DILATION_F32_OUT_CH 1 +#define DEPTHWISE_MATCH_DILATION_F32_STRIDE_H 1 +#define DEPTHWISE_MATCH_DILATION_F32_STRIDE_W 1 +#define DEPTHWISE_MATCH_DILATION_F32_PADDING_H 2 +#define DEPTHWISE_MATCH_DILATION_F32_PADDING_W 2 +#define DEPTHWISE_MATCH_DILATION_F32_DILATION_H 2 +#define DEPTHWISE_MATCH_DILATION_F32_DILATION_W 2 +#define DEPTHWISE_MATCH_DILATION_F32_OUTPUT_H 7 +#define DEPTHWISE_MATCH_DILATION_F32_OUTPUT_W 7 +#define DEPTHWISE_MATCH_DILATION_F32_OUTPUT_C 1 +#define DEPTHWISE_MATCH_DILATION_F32_DST_SIZE 49 +#define DEPTHWISE_MATCH_DILATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_DILATION_F32_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_DILATION_F32_OUT_ACTIVATION_MIN -70.0f +#define DEPTHWISE_MATCH_DILATION_F32_OUT_ACTIVATION_MAX 127.0f +#define DEPTHWISE_MATCH_DILATION_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/input_data.h new file mode 100644 index 00000000..30538736 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/input_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_dilation_f32_input_data[49] = { + -0.828087568f, 0.850230992f, -0.0319085307f, -0.820751309f, -0.251685053f, 0.0287064817f, -0.908589721f, + 0.9919101f, 0.570520997f, -0.94571209f, 0.740135908f, -0.328968823f, 0.480218679f, 0.980441332f, + 0.0509939566f, 0.890427113f, 0.526411653f, 0.965975046f, -0.899173737f, -0.268081546f, 0.33571887f, + -0.529268324f, 0.79772377f, 0.991452873f, -0.0313800462f, 0.786916971f, 0.881841242f, 0.93976742f, + 0.459142268f, 0.751399279f, 0.521087706f, -0.380272657f, 0.709911168f, -0.168711528f, 0.247369409f, + 0.667658925f, -0.122973911f, 0.797531724f, -0.30352515f, -0.538328171f, 0.320600867f, -0.952080727f, + 0.501843154f, -0.38589555f, 0.850248814f, -0.0701903477f, 0.922218204f, -0.00805893075f, -0.330788642f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/output_ref_data.h new file mode 100644 index 00000000..29c03e2d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/output_ref_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_dilation_f32_output_ref_data[49] = { + -0.0762793422f, -0.402309477f, 1.26962805f, 0.353256732f, -0.547917843f, 0.598970175f, 0.389239788f, + -1.0594883f, 0.481174827f, -0.56299293f, -0.702631354f, 0.85688889f, -0.105512857f, 0.619457543f, + -0.216225818f, 1.52072966f, -0.449514687f, -0.716349304f, 0.69329983f, -0.861950994f, 0.36129424f, + 1.19903362f, -0.233315796f, 1.31025839f, -0.433936507f, -1.14794385f, 0.451210976f, -0.785001516f, + -0.497232974f, -0.570720375f, 0.572713971f, 0.368757099f, -0.0267356299f, 0.52528441f, -0.583034515f, + -0.738914251f, 0.345163614f, -0.913834214f, -0.0933557153f, -0.56545347f, 0.526138186f, 1.15686607f, + -0.0164763201f, 0.653063357f, -0.330939144f, 0.528617322f, -0.416421205f, -0.0679712296f, -0.138088211f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/weights_data.h new file mode 100644 index 00000000..2fcde207 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_dilation_f32/weights_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_dilation_f32_weights_data[9] = {0.314733684f, + 0.372551739f, + -0.673268497f, + -0.624940336f, + -0.160970256f, + 0.205443457f, + 0.138224602f, + 0.418738872f, + -0.558079779f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/biases_data.h new file mode 100644 index 00000000..9cd4544e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/biases_data.h @@ -0,0 +1,4 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_out_activation_f16_biases_data[3] = {(float16_t)-0.394042969f, + (float16_t)0.0171813965f, + (float16_t)0.126464844f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/config_data.h new file mode 100644 index 00000000..3d8556e5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_INPUT_H 5 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_INPUT_W 6 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_IN_CH 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_CH_MULT 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_FILTER_H 4 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_FILTER_W 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUT_CH 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_STRIDE_H 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_STRIDE_W 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_PADDING_H 0 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_PADDING_W 0 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_DILATION_H 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_DILATION_W 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUTPUT_H 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUTPUT_W 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUTPUT_C 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_DST_SIZE 6 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUT_ACTIVATION_MIN (float16_t) - 45.0f +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_OUT_ACTIVATION_MAX (float16_t)103.0f +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/input_data.h new file mode 100644 index 00000000..9d4fa732 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/input_data.h @@ -0,0 +1,25 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_out_activation_f16_input_data[90] = { + (float16_t)0.812011719f, (float16_t)0.183349609f, (float16_t)-0.480224609f, (float16_t)0.779296875f, + (float16_t)0.8828125f, (float16_t)-0.0215606689f, (float16_t)-0.394775391f, (float16_t)-0.571289062f, + (float16_t)0.266601562f, (float16_t)0.856445312f, (float16_t)-0.0308990479f, (float16_t)-0.169311523f, + (float16_t)-0.873046875f, (float16_t)0.246337891f, (float16_t)-0.68359375f, (float16_t)-0.150268555f, + (float16_t)-0.870605469f, (float16_t)-0.286376953f, (float16_t)-0.77734375f, (float16_t)0.309082031f, + (float16_t)-0.443847656f, (float16_t)-0.575683594f, (float16_t)0.449951172f, (float16_t)0.743164062f, + (float16_t)0.417236328f, (float16_t)-0.385498047f, (float16_t)0.565917969f, (float16_t)-0.882324219f, + (float16_t)0.711914062f, (float16_t)0.815429688f, (float16_t)-0.46875f, (float16_t)0.760253906f, + (float16_t)-0.938964844f, (float16_t)0.986816406f, (float16_t)-0.288574219f, (float16_t)-0.158813477f, + (float16_t)-0.00915527344f, (float16_t)-0.938964844f, (float16_t)-0.543457031f, (float16_t)0.134277344f, + (float16_t)0.525390625f, (float16_t)-0.429199219f, (float16_t)0.100952148f, (float16_t)-0.040435791f, + (float16_t)0.77734375f, (float16_t)-0.0136032104f, (float16_t)-0.720703125f, (float16_t)-0.653808594f, + (float16_t)0.698242188f, (float16_t)0.883300781f, (float16_t)0.813476562f, (float16_t)-0.693359375f, + (float16_t)0.316162109f, (float16_t)-0.6953125f, (float16_t)-0.553710938f, (float16_t)-0.164672852f, + (float16_t)0.294921875f, (float16_t)-0.812011719f, (float16_t)0.794921875f, (float16_t)0.284179688f, + (float16_t)-0.995605469f, (float16_t)0.433349609f, (float16_t)0.0115280151f, (float16_t)-0.736328125f, + (float16_t)-0.454589844f, (float16_t)0.256591797f, (float16_t)-0.406738281f, (float16_t)-0.916015625f, + (float16_t)0.826171875f, (float16_t)0.00414276123f, (float16_t)-0.180664062f, (float16_t)-0.553710938f, + (float16_t)0.801269531f, (float16_t)-0.482910156f, (float16_t)0.874023438f, (float16_t)-0.938964844f, + (float16_t)-0.99609375f, (float16_t)0.926757812f, (float16_t)0.168823242f, (float16_t)-0.814453125f, + (float16_t)0.231933594f, (float16_t)0.908203125f, (float16_t)0.741699219f, (float16_t)0.649414062f, + (float16_t)-0.442138672f, (float16_t)-0.784667969f, (float16_t)0.296386719f, (float16_t)-0.892089844f, + (float16_t)-0.520019531f, (float16_t)-0.739746094f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/output_ref_data.h new file mode 100644 index 00000000..4b10b2ac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_out_activation_f16_output_ref_data[6] = {(float16_t)-1.37792969f, + (float16_t)-0.474853516f, + (float16_t)0.841308594f, + (float16_t)-1.3671875f, + (float16_t)-0.433837891f, + (float16_t)-0.456787109f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/weights_data.h new file mode 100644 index 00000000..0a2d4d7c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_out_activation_f16_weights_data[36] = { + (float16_t)0.129638672f, (float16_t)0.360107422f, (float16_t)-0.638671875f, (float16_t)-0.302978516f, + (float16_t)0.584960938f, (float16_t)0.309814453f, (float16_t)0.345947266f, (float16_t)-0.229370117f, + (float16_t)0.502441406f, (float16_t)-0.0346984863f, (float16_t)-0.387695312f, (float16_t)0.618164062f, + (float16_t)-0.315673828f, (float16_t)-0.15637207f, (float16_t)-0.0296173096f, (float16_t)-0.534179688f, + (float16_t)-0.0115966797f, (float16_t)0.0524291992f, (float16_t)0.731933594f, (float16_t)0.712890625f, + (float16_t)-0.538574219f, (float16_t)0.219360352f, (float16_t)-0.0311431885f, (float16_t)0.257568359f, + (float16_t)0.0958862305f, (float16_t)-0.338134766f, (float16_t)0.267578125f, (float16_t)0.628417969f, + (float16_t)-0.467041016f, (float16_t)0.353759766f, (float16_t)0.565429688f, (float16_t)-0.481445312f, + (float16_t)0.186279297f, (float16_t)-0.0726928711f, (float16_t)-0.0966796875f, (float16_t)0.0292663574f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/biases_data.h new file mode 100644 index 00000000..536dae8d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_out_activation_f32_biases_data[3] = {-0.393930942f, 0.0171751585f, 0.12652187f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/config_data.h new file mode 100644 index 00000000..d458fb62 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_INPUT_H 5 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_INPUT_W 6 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_IN_CH 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_CH_MULT 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_FILTER_H 4 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_FILTER_W 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUT_CH 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_STRIDE_H 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_STRIDE_W 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_PADDING_H 0 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_PADDING_W 0 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_DILATION_H 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_DILATION_W 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUTPUT_H 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUTPUT_W 2 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUTPUT_C 3 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_DST_SIZE 6 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUT_ACTIVATION_MIN -45.0f +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_OUT_ACTIVATION_MAX 103.0f +#define DEPTHWISE_MATCH_OUT_ACTIVATION_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/input_data.h new file mode 100644 index 00000000..45ee8650 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/input_data.h @@ -0,0 +1,15 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_out_activation_f32_input_data[90] = { + 0.812041938f, 0.183310568f, -0.480301946f, 0.779252648f, 0.88299334f, -0.0215533916f, -0.394744784f, + -0.571116328f, 0.266626209f, 0.856517971f, -0.0309023038f, -0.169317603f, -0.872832f, 0.246327192f, + -0.68360281f, -0.150221825f, -0.870432556f, -0.286377311f, -0.777382076f, 0.309045821f, -0.44376123f, + -0.57549578f, 0.449891895f, 0.743158281f, 0.41728732f, -0.385542989f, 0.565809429f, -0.882493138f, + 0.711823046f, 0.81560123f, -0.468860537f, 0.76020813f, -0.939036667f, 0.986981332f, -0.288634777f, + -0.158810958f, -0.00915836263f, -0.939174592f, -0.543462336f, 0.134329408f, 0.525385082f, -0.429282278f, + 0.100928411f, -0.0404509418f, 0.777240455f, -0.0136043001f, -0.720914423f, -0.65359807f, 0.698440611f, + 0.88328737f, 0.813476622f, -0.693402529f, 0.316063136f, -0.695260465f, -0.553558171f, -0.164702132f, + 0.294939071f, -0.811847448f, 0.794934988f, 0.284072578f, -0.995649278f, 0.433386981f, 0.0115255555f, + -0.736465991f, -0.454583853f, 0.256617308f, -0.406763256f, -0.915876985f, 0.826279402f, 0.00414400641f, + -0.180648088f, -0.55381453f, 0.801302016f, -0.482812792f, 0.874200583f, -0.939043522f, -0.996209741f, + 0.926810324f, 0.168789968f, -0.814409971f, 0.231908187f, 0.908078253f, 0.741515577f, 0.649510801f, + -0.442023247f, -0.78464973f, 0.296432287f, -0.892001867f, -0.519800663f, -0.739597499f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/output_ref_data.h new file mode 100644 index 00000000..92d71310 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/output_ref_data.h @@ -0,0 +1,3 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_out_activation_f32_output_ref_data[6] = + {-1.37783527f, -0.474913478f, 0.841549814f, -1.36714721f, -0.433901936f, -0.456859261f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/weights_data.h new file mode 100644 index 00000000..1d2da9fe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_out_activation_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_out_activation_f32_weights_data[36] = { + 0.129597068f, 0.360217392f, -0.638557017f, -0.302993149f, 0.584758639f, 0.309760898f, + 0.346028686f, -0.229330033f, 0.50256753f, -0.0347020775f, -0.387734979f, 0.618014216f, + -0.315622658f, -0.156334519f, -0.0296149347f, -0.533955157f, -0.0115975095f, 0.0524187572f, + 0.731944263f, 0.712757945f, -0.538647115f, 0.219379961f, -0.0311433244f, 0.257649422f, + 0.0958638564f, -0.338231325f, 0.267460585f, 0.628627241f, -0.467092216f, 0.353749692f, + 0.565442145f, -0.481409401f, 0.186303392f, -0.0726646632f, -0.0966792628f, 0.029260112f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/biases_data.h new file mode 100644 index 00000000..c07100bc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_stride2pad1_f16_biases_data[1] = {(float16_t)0.463867188f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/config_data.h new file mode 100644 index 00000000..215e8346 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_INPUT_H 7 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_INPUT_W 7 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_IN_CH 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_CH_MULT 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_FILTER_H 3 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_FILTER_W 3 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUT_CH 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_STRIDE_H 2 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_STRIDE_W 2 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_PADDING_H 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_PADDING_W 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_DILATION_H 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_DILATION_W 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUTPUT_H 4 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUTPUT_W 4 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUTPUT_C 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_DST_SIZE 16 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define DEPTHWISE_MATCH_STRIDE2PAD1_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/input_data.h new file mode 100644 index 00000000..8cb72014 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/input_data.h @@ -0,0 +1,15 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_stride2pad1_f16_input_data[49] = { + (float16_t)-0.379882812f, (float16_t)0.971191406f, (float16_t)-0.471923828f, (float16_t)-0.00421142578f, + (float16_t)0.357177734f, (float16_t)-0.322265625f, (float16_t)-0.658691406f, (float16_t)-0.871582031f, + (float16_t)-0.804199219f, (float16_t)0.348388672f, (float16_t)-0.785644531f, (float16_t)0.922851562f, + (float16_t)-0.258789062f, (float16_t)0.509765625f, (float16_t)0.398925781f, (float16_t)-0.471923828f, + (float16_t)-0.818847656f, (float16_t)-0.131835938f, (float16_t)-0.786132812f, (float16_t)0.0885009766f, + (float16_t)0.903320312f, (float16_t)-0.455566406f, (float16_t)-0.450195312f, (float16_t)-0.969726562f, + (float16_t)-0.908203125f, (float16_t)-0.119995117f, (float16_t)-0.0972900391f, (float16_t)0.522949219f, + (float16_t)0.931152344f, (float16_t)-0.634765625f, (float16_t)-0.660644531f, (float16_t)0.38671875f, + (float16_t)-0.554199219f, (float16_t)-0.297851562f, (float16_t)-0.978515625f, (float16_t)0.740722656f, + (float16_t)-0.605957031f, (float16_t)0.265136719f, (float16_t)-0.647460938f, (float16_t)0.682128906f, + (float16_t)-0.806152344f, (float16_t)-0.917480469f, (float16_t)-0.647460938f, (float16_t)-0.681152344f, + (float16_t)-0.637207031f, (float16_t)-0.923828125f, (float16_t)-0.18371582f, (float16_t)-0.0879516602f, + (float16_t)0.642578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/output_ref_data.h new file mode 100644 index 00000000..13170c2a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_stride2pad1_f16_output_ref_data[16] = {(float16_t)-0.388916016f, + (float16_t)0.194580078f, + (float16_t)0.8359375f, + (float16_t)1.04785156f, + (float16_t)0.891113281f, + (float16_t)0.630371094f, + (float16_t)0.509277344f, + (float16_t)0.331542969f, + (float16_t)1.10839844f, + (float16_t)1.59863281f, + (float16_t)0.907714844f, + (float16_t)0.108764648f, + (float16_t)1.09765625f, + (float16_t)1.61914062f, + (float16_t)1.29785156f, + (float16_t)0.588378906f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/weights_data.h new file mode 100644 index 00000000..f7ec19ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f16/weights_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_stride2pad1_f16_weights_data[9] = {(float16_t)-0.0761108398f, + (float16_t)-0.250976562f, + (float16_t)-0.653320312f, + (float16_t)-0.371337891f, + (float16_t)-0.310791016f, + (float16_t)-0.326416016f, + (float16_t)0.141601562f, + (float16_t)0.580078125f, + (float16_t)0.184570312f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/biases_data.h new file mode 100644 index 00000000..7da281df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_stride2pad1_f32_biases_data[1] = {0.463939458f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/config_data.h new file mode 100644 index 00000000..7519c129 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_INPUT_H 7 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_INPUT_W 7 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_IN_CH 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_CH_MULT 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_FILTER_H 3 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_FILTER_W 3 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUT_CH 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_STRIDE_H 2 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_STRIDE_W 2 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_PADDING_H 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_PADDING_W 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_DILATION_H 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_DILATION_W 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUTPUT_H 4 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUTPUT_W 4 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUTPUT_C 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_DST_SIZE 16 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUT_ACTIVATION_MIN -128.0f +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_OUT_ACTIVATION_MAX 127.0f +#define DEPTHWISE_MATCH_STRIDE2PAD1_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/input_data.h new file mode 100644 index 00000000..5aa28754 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/input_data.h @@ -0,0 +1,9 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_stride2pad1_f32_input_data[49] = { + -0.379838735f, 0.971112132f, -0.471989214f, -0.00421106862f, 0.357232302f, -0.322177202f, -0.658907175f, + -0.871695518f, -0.804245532f, 0.348477006f, -0.785861373f, 0.922931373f, -0.258741051f, 0.50993675f, + 0.398807228f, -0.472036511f, -0.818627298f, -0.131825224f, -0.78617692f, 0.0884840488f, 0.903312922f, + -0.455529511f, -0.450246841f, -0.969737649f, -0.908206701f, -0.120024167f, -0.097268872f, 0.522713482f, + 0.931110561f, -0.634784341f, -0.660433829f, 0.386663884f, -0.554014325f, -0.297946274f, -0.978648901f, + 0.740839124f, -0.606140792f, 0.265085459f, -0.647329092f, 0.682100892f, -0.806185305f, -0.917459607f, + -0.647696137f, -0.680974603f, -0.63704592f, -0.923824847f, -0.183671996f, -0.0879695714f, 0.642405331f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/output_ref_data.h new file mode 100644 index 00000000..04209fe1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_stride2pad1_f32_output_ref_data[16] = {-0.388918936f, + 0.194553345f, + 0.835785985f, + 1.04738033f, + 0.891135752f, + 0.630228639f, + 0.509426475f, + 0.331423759f, + 1.10805166f, + 1.59816003f, + 0.90769738f, + 0.108734891f, + 1.09766459f, + 1.61893654f, + 1.29764664f, + 0.58859688f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/weights_data.h new file mode 100644 index 00000000..2ef00fb3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_stride2pad1_f32/weights_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_stride2pad1_f32_weights_data[9] = {-0.0761408731f, + -0.250961989f, + -0.653435647f, + -0.371298999f, + -0.310764402f, + -0.326433122f, + 0.14162825f, + 0.579869986f, + 0.184552491f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/biases_data.h new file mode 100644 index 00000000..6499ee55 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/biases_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_sub_block_f16_biases_data[9] = {(float16_t)-0.375f, + (float16_t)0.357177734f, + (float16_t)-0.463378906f, + (float16_t)-0.0497741699f, + (float16_t)0.0266876221f, + (float16_t)-0.145996094f, + (float16_t)0.466796875f, + (float16_t)-0.343017578f, + (float16_t)-0.412597656f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/config_data.h new file mode 100644 index 00000000..34cb12c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_INPUT_H 5 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_INPUT_W 7 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_IN_CH 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_CH_MULT 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_FILTER_H 2 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_FILTER_W 2 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUT_CH 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_STRIDE_H 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_STRIDE_W 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_PADDING_H 0 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_PADDING_W 0 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_DILATION_H 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_DILATION_W 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUTPUT_H 4 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUTPUT_W 6 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUTPUT_C 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_DST_SIZE 216 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define DEPTHWISE_MATCH_SUB_BLOCK_F16_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/input_data.h new file mode 100644 index 00000000..a70475f7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/input_data.h @@ -0,0 +1,81 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_sub_block_f16_input_data[315] = { + (float16_t)-0.836914062f, (float16_t)-0.158935547f, (float16_t)-0.744628906f, (float16_t)-0.130126953f, + (float16_t)0.582519531f, (float16_t)-0.66796875f, (float16_t)0.184204102f, (float16_t)0.870605469f, + (float16_t)0.00199890137f, (float16_t)-0.721679688f, (float16_t)-0.0311126709f, (float16_t)0.377929688f, + (float16_t)0.692871094f, (float16_t)0.549316406f, (float16_t)0.0394897461f, (float16_t)0.00993347168f, + (float16_t)0.9140625f, (float16_t)-0.109191895f, (float16_t)-0.332519531f, (float16_t)0.198608398f, + (float16_t)-0.395751953f, (float16_t)-0.152709961f, (float16_t)0.823242188f, (float16_t)0.342529297f, + (float16_t)0.281738281f, (float16_t)0.725585938f, (float16_t)-0.6171875f, (float16_t)-0.023147583f, + (float16_t)-0.617675781f, (float16_t)-0.21105957f, (float16_t)-0.88671875f, (float16_t)-0.532714844f, + (float16_t)0.423828125f, (float16_t)0.112426758f, (float16_t)0.544433594f, (float16_t)-0.360595703f, + (float16_t)0.736816406f, (float16_t)-0.634765625f, (float16_t)0.912597656f, (float16_t)-0.543945312f, + (float16_t)0.935546875f, (float16_t)0.544433594f, (float16_t)-0.531738281f, (float16_t)-0.194091797f, + (float16_t)0.874511719f, (float16_t)-0.799316406f, (float16_t)0.666015625f, (float16_t)0.00268363953f, + (float16_t)0.744628906f, (float16_t)-0.661621094f, (float16_t)-0.924316406f, (float16_t)-0.304199219f, + (float16_t)-0.722167969f, (float16_t)-0.864257812f, (float16_t)-0.872558594f, (float16_t)0.0469665527f, + (float16_t)0.435791016f, (float16_t)0.580078125f, (float16_t)0.875f, (float16_t)-0.791015625f, + (float16_t)0.266601562f, (float16_t)0.835449219f, (float16_t)0.430664062f, (float16_t)0.238891602f, + (float16_t)-0.344482422f, (float16_t)-0.366943359f, (float16_t)0.799804688f, (float16_t)0.518554688f, + (float16_t)-0.850097656f, (float16_t)0.818359375f, (float16_t)-0.301025391f, (float16_t)0.630371094f, + (float16_t)0.319580078f, (float16_t)-0.0263061523f, (float16_t)-0.556640625f, (float16_t)-0.541015625f, + (float16_t)0.9765625f, (float16_t)-0.139404297f, (float16_t)-0.571777344f, (float16_t)-0.811523438f, + (float16_t)0.916503906f, (float16_t)0.489746094f, (float16_t)-0.298828125f, (float16_t)0.129516602f, + (float16_t)0.993164062f, (float16_t)-0.516601562f, (float16_t)-0.90234375f, (float16_t)-0.945800781f, + (float16_t)-0.668945312f, (float16_t)-0.184082031f, (float16_t)0.15612793f, (float16_t)-0.252929688f, + (float16_t)-0.489501953f, (float16_t)-0.6328125f, (float16_t)-0.718261719f, (float16_t)-0.765625f, + (float16_t)0.26953125f, (float16_t)-0.118347168f, (float16_t)-0.715332031f, (float16_t)-0.957519531f, + (float16_t)0.486816406f, (float16_t)0.745117188f, (float16_t)0.220336914f, (float16_t)-0.617675781f, + (float16_t)0.0382080078f, (float16_t)0.00022149086f, (float16_t)0.620117188f, (float16_t)-0.711914062f, + (float16_t)0.167480469f, (float16_t)-0.704589844f, (float16_t)0.0132598877f, (float16_t)0.0105438232f, + (float16_t)-0.991210938f, (float16_t)0.857910156f, (float16_t)0.160888672f, (float16_t)0.0733032227f, + (float16_t)0.614257812f, (float16_t)-0.517578125f, (float16_t)-0.300537109f, (float16_t)0.633300781f, + (float16_t)-0.828125f, (float16_t)-0.386230469f, (float16_t)-0.819824219f, (float16_t)-0.665039062f, + (float16_t)-0.0284881592f, (float16_t)-0.811523438f, (float16_t)-0.126220703f, (float16_t)0.473388672f, + (float16_t)-0.265136719f, (float16_t)-0.107421875f, (float16_t)-0.685546875f, (float16_t)-0.906738281f, + (float16_t)-0.641113281f, (float16_t)0.284912109f, (float16_t)0.220947266f, (float16_t)0.97265625f, + (float16_t)0.913574219f, (float16_t)0.500488281f, (float16_t)-0.973144531f, (float16_t)0.841308594f, + (float16_t)0.203125f, (float16_t)0.272216797f, (float16_t)-0.406738281f, (float16_t)-0.134643555f, + (float16_t)0.247192383f, (float16_t)-0.891601562f, (float16_t)0.221801758f, (float16_t)-0.676757812f, + (float16_t)0.076171875f, (float16_t)0.243530273f, (float16_t)0.890136719f, (float16_t)-0.125244141f, + (float16_t)-0.650390625f, (float16_t)-0.94921875f, (float16_t)0.388671875f, (float16_t)-0.881835938f, + (float16_t)0.211791992f, (float16_t)-0.0559082031f, (float16_t)0.791015625f, (float16_t)0.854003906f, + (float16_t)0.821777344f, (float16_t)0.47265625f, (float16_t)-0.596679688f, (float16_t)0.708007812f, + (float16_t)0.400878906f, (float16_t)-0.620605469f, (float16_t)0.674804688f, (float16_t)-0.984375f, + (float16_t)0.318847656f, (float16_t)0.201782227f, (float16_t)0.956542969f, (float16_t)-0.964355469f, + (float16_t)-0.272705078f, (float16_t)0.227539062f, (float16_t)0.139770508f, (float16_t)0.924804688f, + (float16_t)0.5625f, (float16_t)0.225830078f, (float16_t)-0.0932617188f, (float16_t)-0.0299530029f, + (float16_t)0.94140625f, (float16_t)0.361572266f, (float16_t)0.725585938f, (float16_t)-0.0684204102f, + (float16_t)0.962402344f, (float16_t)-0.700683594f, (float16_t)-0.664550781f, (float16_t)0.296142578f, + (float16_t)-0.353027344f, (float16_t)0.842773438f, (float16_t)-0.080078125f, (float16_t)0.992675781f, + (float16_t)0.710449219f, (float16_t)0.615234375f, (float16_t)0.112243652f, (float16_t)0.480957031f, + (float16_t)0.0389709473f, (float16_t)-0.314453125f, (float16_t)0.103210449f, (float16_t)0.790039062f, + (float16_t)-0.870117188f, (float16_t)0.679199219f, (float16_t)-0.549316406f, (float16_t)-0.137817383f, + (float16_t)0.758789062f, (float16_t)-0.631835938f, (float16_t)0.619140625f, (float16_t)0.712402344f, + (float16_t)-0.286132812f, (float16_t)0.616210938f, (float16_t)0.809570312f, (float16_t)-0.518554688f, + (float16_t)0.0740966797f, (float16_t)-0.8984375f, (float16_t)-0.14074707f, (float16_t)0.626953125f, + (float16_t)0.0308074951f, (float16_t)0.532714844f, (float16_t)0.618652344f, (float16_t)-0.24609375f, + (float16_t)-0.873046875f, (float16_t)0.614746094f, (float16_t)0.381591797f, (float16_t)-0.809570312f, + (float16_t)-0.137573242f, (float16_t)-0.05859375f, (float16_t)0.0787353516f, (float16_t)0.507324219f, + (float16_t)-0.343261719f, (float16_t)0.133422852f, (float16_t)-0.0318603516f, (float16_t)-0.497070312f, + (float16_t)0.345947266f, (float16_t)0.832519531f, (float16_t)0.643554688f, (float16_t)0.66796875f, + (float16_t)-0.421630859f, (float16_t)0.0748291016f, (float16_t)-0.461669922f, (float16_t)0.53515625f, + (float16_t)-0.0397033691f, (float16_t)-0.130249023f, (float16_t)0.770996094f, (float16_t)-0.363037109f, + (float16_t)0.541992188f, (float16_t)0.762207031f, (float16_t)0.670410156f, (float16_t)-0.8359375f, + (float16_t)0.0218048096f, (float16_t)0.765136719f, (float16_t)-0.325195312f, (float16_t)-0.188842773f, + (float16_t)0.740722656f, (float16_t)0.230834961f, (float16_t)-0.830078125f, (float16_t)-0.742675781f, + (float16_t)0.108032227f, (float16_t)-0.84375f, (float16_t)-0.68359375f, (float16_t)0.243286133f, + (float16_t)-0.760253906f, (float16_t)0.314208984f, (float16_t)0.931640625f, (float16_t)0.806152344f, + (float16_t)-0.921875f, (float16_t)-0.421875f, (float16_t)0.963378906f, (float16_t)-0.392333984f, + (float16_t)0.583984375f, (float16_t)0.473144531f, (float16_t)0.47265625f, (float16_t)-0.985351562f, + (float16_t)0.879882812f, (float16_t)-0.931152344f, (float16_t)0.822753906f, (float16_t)0.165771484f, + (float16_t)0.568359375f, (float16_t)0.906738281f, (float16_t)-0.850585938f, (float16_t)0.0246429443f, + (float16_t)0.391601562f, (float16_t)0.254882812f, (float16_t)-0.53125f, (float16_t)0.6640625f, + (float16_t)0.296875f, (float16_t)-0.700683594f, (float16_t)-0.869140625f, (float16_t)0.709960938f, + (float16_t)0.186279297f, (float16_t)-0.483886719f, (float16_t)0.364746094f, (float16_t)0.673339844f, + (float16_t)0.132080078f, (float16_t)-0.666015625f, (float16_t)0.418212891f, (float16_t)0.962890625f, + (float16_t)-0.340087891f, (float16_t)0.547851562f, (float16_t)-0.58203125f, (float16_t)0.0968017578f, + (float16_t)-0.398925781f, (float16_t)0.406982422f, (float16_t)0.0567626953f, (float16_t)-0.520996094f, + (float16_t)-0.825683594f, (float16_t)-0.0914916992f, (float16_t)-0.945800781f, (float16_t)0.298828125f, + (float16_t)0.592773438f, (float16_t)0.17565918f, (float16_t)-0.157592773f, (float16_t)0.155639648f, + (float16_t)-0.225708008f, (float16_t)-0.947753906f, (float16_t)-0.546386719f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/output_ref_data.h new file mode 100644 index 00000000..5da958da --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_sub_block_f16_output_ref_data[216] = { + (float16_t)-0.0171356201f, (float16_t)0.342285156f, (float16_t)-0.442871094f, (float16_t)0.169677734f, + (float16_t)0.173095703f, (float16_t)-0.968261719f, (float16_t)0.583496094f, (float16_t)0.532226562f, + (float16_t)-0.226806641f, (float16_t)0.0296173096f, (float16_t)0.394287109f, (float16_t)-0.157592773f, + (float16_t)0.236328125f, (float16_t)0.196289062f, (float16_t)-0.265625f, (float16_t)0.126342773f, + (float16_t)0.427246094f, (float16_t)-0.6484375f, (float16_t)-0.184570312f, (float16_t)0.647949219f, + (float16_t)-0.555175781f, (float16_t)0.240478516f, (float16_t)-0.733398438f, (float16_t)-0.518554688f, + (float16_t)0.390380859f, (float16_t)0.0416564941f, (float16_t)-0.854980469f, (float16_t)-0.47265625f, + (float16_t)0.540527344f, (float16_t)-0.481689453f, (float16_t)-0.459228516f, (float16_t)0.0159759521f, + (float16_t)-0.274658203f, (float16_t)0.370605469f, (float16_t)-0.759765625f, (float16_t)-0.625976562f, + (float16_t)-0.811035156f, (float16_t)0.614257812f, (float16_t)-0.414306641f, (float16_t)0.0454711914f, + (float16_t)-0.881347656f, (float16_t)0.0969848633f, (float16_t)0.260009766f, (float16_t)-0.798828125f, + (float16_t)0.13684082f, (float16_t)-0.254882812f, (float16_t)0.140258789f, (float16_t)-0.59765625f, + (float16_t)-0.433105469f, (float16_t)-0.00899505615f, (float16_t)-0.35546875f, (float16_t)0.335449219f, + (float16_t)0.0855712891f, (float16_t)-1.02734375f, (float16_t)-0.209594727f, (float16_t)0.162353516f, + (float16_t)-0.651367188f, (float16_t)-0.594238281f, (float16_t)-0.247680664f, (float16_t)-1.07519531f, + (float16_t)0.357421875f, (float16_t)-0.548828125f, (float16_t)-0.184936523f, (float16_t)-0.391601562f, + (float16_t)1.11230469f, (float16_t)-0.915527344f, (float16_t)-1.16503906f, (float16_t)-0.142578125f, + (float16_t)-0.221801758f, (float16_t)0.302490234f, (float16_t)-0.529785156f, (float16_t)-0.2421875f, + (float16_t)-0.805175781f, (float16_t)0.173461914f, (float16_t)-0.313720703f, (float16_t)-0.274902344f, + (float16_t)0.198364258f, (float16_t)-0.477783203f, (float16_t)0.838378906f, (float16_t)-0.794433594f, + (float16_t)-0.310302734f, (float16_t)-0.739746094f, (float16_t)-0.0363464355f, (float16_t)-0.358642578f, + (float16_t)-0.337646484f, (float16_t)0.354980469f, (float16_t)-0.194946289f, (float16_t)0.880371094f, + (float16_t)-0.244262695f, (float16_t)-0.505859375f, (float16_t)-0.120544434f, (float16_t)0.862792969f, + (float16_t)-0.39453125f, (float16_t)-0.366210938f, (float16_t)0.590820312f, (float16_t)-0.465332031f, + (float16_t)0.688964844f, (float16_t)-0.300292969f, (float16_t)-0.7734375f, (float16_t)-0.314697266f, + (float16_t)0.451171875f, (float16_t)-0.688964844f, (float16_t)0.00450134277f, (float16_t)0.947265625f, + (float16_t)0.361816406f, (float16_t)0.11907959f, (float16_t)-0.501464844f, (float16_t)-0.319091797f, + (float16_t)-0.137939453f, (float16_t)-0.563964844f, (float16_t)-0.710449219f, (float16_t)0.812011719f, + (float16_t)0.622070312f, (float16_t)-0.510253906f, (float16_t)0.771972656f, (float16_t)-0.222900391f, + (float16_t)-0.119689941f, (float16_t)-0.630371094f, (float16_t)0.873535156f, (float16_t)-0.0728149414f, + (float16_t)0.766113281f, (float16_t)-0.649414062f, (float16_t)-0.073059082f, (float16_t)0.827636719f, + (float16_t)-0.243408203f, (float16_t)-0.391357422f, (float16_t)-0.634765625f, (float16_t)0.0863647461f, + (float16_t)-0.821777344f, (float16_t)0.281494141f, (float16_t)-0.348388672f, (float16_t)0.232177734f, + (float16_t)0.880371094f, (float16_t)0.494628906f, (float16_t)-0.75f, (float16_t)-0.0326538086f, + (float16_t)0.0108947754f, (float16_t)-1.13867188f, (float16_t)-0.393554688f, (float16_t)-0.194702148f, + (float16_t)0.418457031f, (float16_t)0.772460938f, (float16_t)-0.350830078f, (float16_t)0.0777587891f, + (float16_t)-0.0930175781f, (float16_t)0.0408325195f, (float16_t)-0.395263672f, (float16_t)-0.3125f, + (float16_t)-0.181030273f, (float16_t)-0.479980469f, (float16_t)0.480224609f, (float16_t)-0.367431641f, + (float16_t)0.078918457f, (float16_t)0.145141602f, (float16_t)0.166625977f, (float16_t)-0.358154297f, + (float16_t)0.405273438f, (float16_t)-0.585449219f, (float16_t)0.316650391f, (float16_t)0.490478516f, + (float16_t)-0.0129013062f, (float16_t)-0.587890625f, (float16_t)-0.684570312f, (float16_t)-0.359619141f, + (float16_t)-0.0113677979f, (float16_t)-1.00488281f, (float16_t)-0.344970703f, (float16_t)-0.467285156f, + (float16_t)0.56640625f, (float16_t)-0.981933594f, (float16_t)-0.274658203f, (float16_t)-0.200927734f, + (float16_t)0.952636719f, (float16_t)-1.28613281f, (float16_t)-1.13476562f, (float16_t)0.162475586f, + (float16_t)0.368408203f, (float16_t)0.378417969f, (float16_t)-0.935546875f, (float16_t)-0.440917969f, + (float16_t)-0.702148438f, (float16_t)-0.231201172f, (float16_t)-0.603027344f, (float16_t)-0.8984375f, + (float16_t)0.582519531f, (float16_t)0.129150391f, (float16_t)0.33203125f, (float16_t)-0.386474609f, + (float16_t)0.12512207f, (float16_t)-0.355224609f, (float16_t)0.493164062f, (float16_t)-0.390625f, + (float16_t)-0.052154541f, (float16_t)0.735839844f, (float16_t)0.231445312f, (float16_t)0.298828125f, + (float16_t)-0.55078125f, (float16_t)-0.601074219f, (float16_t)-0.117431641f, (float16_t)0.159545898f, + (float16_t)-0.462402344f, (float16_t)0.138671875f, (float16_t)0.0170135498f, (float16_t)-0.440917969f, + (float16_t)0.285888672f, (float16_t)-0.10144043f, (float16_t)-0.0370788574f, (float16_t)-0.959472656f, + (float16_t)-0.333496094f, (float16_t)-0.76171875f, (float16_t)-0.209228516f, (float16_t)0.276855469f, + (float16_t)0.172973633f, (float16_t)0.530273438f, (float16_t)0.0955200195f, (float16_t)-0.2734375f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/weights_data.h new file mode 100644 index 00000000..3333730f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t depthwise_match_sub_block_f16_weights_data[36] = { + (float16_t)-0.474121094f, (float16_t)-0.243652344f, (float16_t)0.376220703f, (float16_t)0.116699219f, + (float16_t)-0.464599609f, (float16_t)0.490722656f, (float16_t)0.260986328f, (float16_t)-0.0271453857f, + (float16_t)0.384521484f, (float16_t)0.157592773f, (float16_t)-0.481689453f, (float16_t)0.0564575195f, + (float16_t)0.00324058533f, (float16_t)0.126831055f, (float16_t)0.171630859f, (float16_t)0.383056641f, + (float16_t)0.482421875f, (float16_t)0.0545959473f, (float16_t)-0.000357866287f, (float16_t)0.232788086f, + (float16_t)-0.390869141f, (float16_t)0.682128906f, (float16_t)0.403076172f, (float16_t)0.568359375f, + (float16_t)0.289794922f, (float16_t)-0.120056152f, (float16_t)-0.116638184f, (float16_t)0.234619141f, + (float16_t)-0.439453125f, (float16_t)-0.244140625f, (float16_t)0.578613281f, (float16_t)0.141601562f, + (float16_t)0.131469727f, (float16_t)0.301513672f, (float16_t)-0.520019531f, (float16_t)0.288574219f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/biases_data.h new file mode 100644 index 00000000..b70a4740 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/biases_data.h @@ -0,0 +1,10 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_sub_block_f32_biases_data[9] = {-0.375010908f, + 0.357140869f, + -0.463421792f, + -0.04976511f, + 0.0266925879f, + -0.145963833f, + 0.466690183f, + -0.343103439f, + -0.412703067f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/config_data.h new file mode 100644 index 00000000..de500d3d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_INPUT_BATCHES 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_INPUT_H 5 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_INPUT_W 7 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_IN_CH 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_CH_MULT 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_FILTER_H 2 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_FILTER_W 2 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUT_CH 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_STRIDE_H 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_STRIDE_W 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_PADDING_H 0 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_PADDING_W 0 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_DILATION_H 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_DILATION_W 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUTPUT_H 4 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUTPUT_W 6 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUTPUT_C 9 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_DST_SIZE 216 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_USE_WRAPPER 1 +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUT_ACTIVATION_MIN -128.0f +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_OUT_ACTIVATION_MAX 127.0f +#define DEPTHWISE_MATCH_SUB_BLOCK_F32_USE_NULL_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/input_data.h new file mode 100644 index 00000000..ecf4adef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/input_data.h @@ -0,0 +1,47 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_sub_block_f32_input_data[315] = { + -0.836701274f, -0.158884481f, -0.744542122f, -0.130173579f, 0.582365334f, -0.668177366f, 0.18423973f, + 0.870691657f, 0.00199850928f, -0.721766889f, -0.0311121717f, 0.378047138f, 0.69269526f, 0.549334764f, + 0.0394966193f, 0.00993445236f, 0.913962841f, -0.109191656f, -0.332456827f, 0.198625505f, -0.395684302f, + -0.152720377f, 0.82305032f, 0.342409015f, 0.281677574f, 0.725577116f, -0.617255449f, -0.0231521204f, + -0.617478073f, -0.211050704f, -0.886725426f, -0.532798648f, 0.423763812f, 0.112446651f, 0.544481397f, + -0.360645771f, 0.737056494f, -0.634892464f, 0.912456512f, -0.544144988f, 0.935504615f, 0.544256806f, + -0.531680286f, -0.194110364f, 0.87456423f, -0.79930228f, 0.665948272f, 0.0026842095f, 0.74486804f, + -0.66174978f, -0.924358487f, -0.304287583f, -0.722371161f, -0.864259481f, -0.872585118f, 0.0469701812f, + 0.435880959f, 0.579941094f, 0.875123322f, -0.790954232f, 0.26650089f, 0.835601747f, 0.430754215f, + 0.238894135f, -0.344512105f, -0.367015123f, 0.799836457f, 0.518773496f, -0.850006521f, 0.818567991f, + -0.301029742f, 0.630406559f, 0.319471508f, -0.0263118967f, -0.556580007f, -0.540947974f, 0.976348996f, + -0.139432967f, -0.571884394f, -0.811337888f, 0.916597545f, 0.489636868f, -0.29892084f, 0.12952742f, + 0.993113697f, -0.516671419f, -0.90239352f, -0.945672452f, -0.66891712f, -0.184097707f, 0.156092197f, + -0.252957582f, -0.489613771f, -0.632961631f, -0.718246281f, -0.765720844f, 0.269430876f, -0.118333168f, + -0.715230405f, -0.957678378f, 0.486885279f, 0.744984567f, 0.220286831f, -0.617799222f, 0.0382047258f, + 0.000221473718f, 0.620285988f, -0.712007523f, 0.167466044f, -0.704722881f, 0.0132606961f, 0.0105445208f, + -0.991132259f, 0.858088732f, 0.160897046f, 0.073297374f, 0.614357054f, -0.517405033f, -0.300617903f, + 0.633134782f, -0.82815057f, -0.386239558f, -0.819959939f, -0.665275991f, -0.0284906626f, -0.811441958f, + -0.126168504f, 0.473434597f, -0.265102416f, -0.107392423f, -0.685557485f, -0.906861007f, -0.641024828f, + 0.284961611f, 0.221008092f, 0.972787857f, 0.913812339f, 0.500594974f, -0.973016024f, 0.841412187f, + 0.203124851f, 0.272252738f, -0.406643569f, -0.134701714f, 0.247171476f, -0.891371012f, 0.221834093f, + -0.67687428f, 0.0761705488f, 0.243563145f, 0.889965415f, -0.125198215f, -0.650242984f, -0.949278951f, + 0.388698429f, -0.881810546f, 0.211768866f, -0.0558972657f, 0.791035831f, 0.853821099f, 0.821574628f, + 0.472753376f, -0.596663356f, 0.707898021f, 0.400809824f, -0.620579958f, 0.674593508f, -0.98415935f, + 0.31876111f, 0.201801687f, 0.956421256f, -0.964253902f, -0.27261883f, 0.227522358f, 0.139777526f, + 0.924728513f, 0.562570751f, 0.225788817f, -0.0932644308f, -0.0299596041f, 0.941547692f, 0.361693561f, + 0.725623369f, -0.0684259534f, 0.96261543f, -0.700549364f, -0.664350569f, 0.296202242f, -0.353077859f, + 0.842573345f, -0.08006908f, 0.992781341f, 0.710444391f, 0.615199029f, 0.112238489f, 0.480954766f, + 0.0389754213f, -0.314403474f, 0.103198238f, 0.789959431f, -0.870147049f, 0.679133892f, -0.549350679f, + -0.137783438f, 0.758801043f, -0.632006705f, 0.619168282f, 0.712272048f, -0.286204457f, 0.616034508f, + 0.809331298f, -0.518622756f, 0.0740685984f, -0.898202062f, -0.140693143f, 0.627008736f, 0.0308128484f, + 0.532762587f, 0.618709803f, -0.246096075f, -0.8728531f, 0.614901006f, 0.381496191f, -0.809639156f, + -0.137566522f, -0.0585791953f, 0.0787620619f, 0.507310867f, -0.343241006f, 0.133370012f, -0.0318544134f, + -0.497177869f, 0.345865637f, 0.832740307f, 0.643431783f, 0.667750478f, -0.421556294f, 0.0748400241f, + -0.461616933f, 0.534987867f, -0.0396947004f, -0.130216107f, 0.771126449f, -0.362953275f, 0.542123556f, + 0.76224786f, 0.670183539f, -0.836052597f, 0.0218070932f, 0.765286505f, -0.325270534f, -0.18888554f, + 0.740849495f, 0.230882898f, -0.829960227f, -0.742543995f, 0.108041853f, -0.843830824f, -0.683525264f, + 0.243282378f, -0.76017642f, 0.314251304f, 0.931800842f, 0.806191266f, -0.921760142f, -0.42188406f, + 0.963257134f, -0.392428726f, 0.58402288f, 0.473135591f, 0.472763419f, -0.985388696f, 0.88000983f, + -0.931042194f, 0.822789192f, 0.165803969f, 0.568531871f, 0.9067325f, -0.850426137f, 0.0246358272f, + 0.391481698f, 0.254876643f, -0.531106234f, 0.664294243f, 0.296996325f, -0.700707436f, -0.869193733f, + 0.710121691f, 0.18623881f, -0.483767033f, 0.364646345f, 0.673280656f, 0.132080734f, -0.666174352f, + 0.418091536f, 0.962738812f, -0.340117335f, 0.547681153f, -0.582223237f, 0.096819289f, -0.39889437f, + 0.407013059f, 0.0567638911f, -0.52105087f, -0.82573539f, -0.0915205032f, -0.945980906f, 0.29892984f, + 0.592720509f, 0.175648049f, -0.157614753f, 0.155660346f, -0.225720987f, -0.947569609f, -0.546197176f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/output_ref_data.h new file mode 100644 index 00000000..36faaee6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/output_ref_data.h @@ -0,0 +1,33 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_sub_block_f32_output_ref_data[216] = { + -0.0171283837f, 0.342194587f, -0.442888021f, 0.169673368f, 0.173118457f, -0.968362451f, 0.583432853f, + 0.532199204f, -0.226855829f, 0.0296134092f, 0.394296676f, -0.157561019f, 0.236382708f, 0.196316049f, + -0.265719026f, 0.126360625f, 0.42736125f, -0.64844203f, -0.184570387f, 0.647722542f, -0.555326879f, + 0.240474284f, -0.733244717f, -0.51872921f, 0.390372336f, 0.0416683741f, -0.854750812f, -0.472661436f, + 0.540531635f, -0.481811076f, -0.459147334f, 0.0159722734f, -0.274771005f, 0.370581031f, -0.759915411f, + -0.625751138f, -0.81080091f, 0.614162028f, -0.414376527f, 0.0454719476f, -0.881243229f, 0.0969921574f, + 0.2600061f, -0.798840761f, 0.13683778f, -0.254961312f, 0.140280217f, -0.597560763f, -0.433008999f, + -0.00899871252f, -0.355389625f, 0.335460842f, 0.0855477601f, -1.02744341f, -0.209651843f, 0.162390962f, + -0.651538372f, -0.594480872f, -0.247715488f, -1.07553983f, 0.357538939f, -0.548965037f, -0.184880033f, + -0.391696036f, 1.11202347f, -0.915387988f, -1.16486895f, -0.142545745f, -0.22175698f, 0.302497625f, + -0.529760778f, -0.24222064f, -0.805408001f, 0.173460066f, -0.313753605f, -0.274954289f, 0.198413312f, + -0.477667809f, 0.83842212f, -0.794290304f, -0.310248375f, -0.739579082f, -0.0363346227f, -0.358748138f, + -0.337617785f, 0.35497275f, -0.194981426f, 0.880612075f, -0.244322479f, -0.50571537f, -0.120563999f, + 0.862555921f, -0.394568741f, -0.366319984f, 0.59079659f, -0.465268701f, 0.68880403f, -0.300314695f, + -0.773195446f, -0.314675093f, 0.451228499f, -0.68877995f, 0.00450007012f, 0.947193623f, 0.361913234f, + 0.119101919f, -0.501693785f, -0.319146961f, -0.137997478f, -0.564172983f, -0.710554659f, 0.812007666f, + 0.622202516f, -0.510373712f, 0.771805465f, -0.222937837f, -0.119670086f, -0.630237699f, 0.873500407f, + -0.0727846697f, 0.765950978f, -0.649429679f, -0.0730719268f, 0.82771796f, -0.243388161f, -0.391241699f, + -0.634807944f, 0.0863560215f, -0.821599662f, 0.281419545f, -0.348388791f, 0.232202441f, 0.880520523f, + 0.494543284f, -0.749809623f, -0.0326663665f, 0.0108914291f, -1.13832104f, -0.393506825f, -0.194710821f, + 0.418502122f, 0.772238493f, -0.350753605f, 0.0777320266f, -0.093033433f, 0.0408279598f, -0.395144463f, + -0.312477648f, -0.18098937f, -0.480044901f, 0.480261952f, -0.367366076f, 0.078936249f, 0.145164579f, + 0.16657941f, -0.358147711f, 0.405177146f, -0.585235119f, 0.316719562f, 0.490357518f, -0.0128988987f, + -0.587963521f, -0.684798121f, -0.359582782f, -0.0113686258f, -1.00441277f, -0.345006168f, -0.467394561f, + 0.566364646f, -0.981951237f, -0.274583697f, -0.200900137f, 0.952500641f, -1.2860595f, -1.13522494f, + 0.162531555f, 0.368320912f, 0.378337353f, -0.935522914f, -0.441007495f, -0.702277839f, -0.231194451f, + -0.602906704f, -0.898349583f, 0.582598567f, 0.129152045f, 0.332003236f, -0.386578053f, 0.125089079f, + -0.355161637f, 0.493135452f, -0.390622288f, -0.0521492399f, 0.735630572f, 0.231431827f, 0.298771262f, + -0.550671279f, -0.601149559f, -0.117413759f, 0.159566328f, -0.462506503f, 0.138711557f, 0.0170071926f, + -0.440889895f, 0.285892308f, -0.101421818f, -0.0370940268f, -0.959448576f, -0.333616495f, -0.761567891f, + -0.209226519f, 0.276850402f, 0.172978193f, 0.530343354f, 0.0955323502f, -0.273432434f}; diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/test_data.h new file mode 100644 index 00000000..1e33d8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/weights_data.h new file mode 100644 index 00000000..c0e69bc8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/depthwise_match_sub_block_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by depthwise_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t depthwise_match_sub_block_f32_weights_data[36] = { + -0.474163175f, -0.243674815f, 0.376290292f, 0.116687976f, -0.464623392f, 0.490622252f, + 0.260909885f, -0.0271504447f, 0.384584218f, 0.157564119f, -0.481651098f, 0.0564608797f, + 0.00324152829f, 0.126877233f, 0.171583354f, 0.382941991f, 0.482312262f, 0.0545904078f, + -0.000357892539f, 0.232825175f, -0.390903801f, 0.681910276f, 0.403094739f, 0.568253934f, + 0.289827049f, -0.120068029f, -0.116638839f, 0.234636426f, -0.439476937f, -0.244143248f, + 0.578676581f, 0.141541079f, 0.131485268f, 0.301415354f, -0.520107329f, 0.288642883f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/config_data.h new file mode 100644 index 00000000..579a0fa1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/config_data.h @@ -0,0 +1,39 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DS_CNN_S_BODY_F16_INPUT_BATCHES 1 +#define DS_CNN_S_BODY_F16_INPUT_H 25 +#define DS_CNN_S_BODY_F16_INPUT_W 5 +#define DS_CNN_S_BODY_F16_INPUT_C 64 +#define DS_CNN_S_BODY_F16_CHANNELS 64 +#define DS_CNN_S_BODY_F16_NUM_BLOCKS 4 +#define DS_CNN_S_BODY_F16_NUM_CLASSES 12 +#define DS_CNN_S_BODY_F16_FEATURE_MAP_SIZE 8000 +#define DS_CNN_S_BODY_F16_POOLED_SIZE 64 +#define DS_CNN_S_BODY_F16_DST_SIZE 12 +#define DS_CNN_S_BODY_F16_SOFTMAX_ROWS 1 +#define DS_CNN_S_BODY_F16_SOFTMAX_COLS 12 +#define DS_CNN_S_BODY_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define DS_CNN_S_BODY_F16_DEPTHWISE_CH_MULT 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_H 3 +#define DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_W 3 +#define DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_H 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_W 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_H 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_W 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_H 1 +#define DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_W 1 +#define DS_CNN_S_BODY_F16_POINTWISE_FILTER_H 1 +#define DS_CNN_S_BODY_F16_POINTWISE_FILTER_W 1 +#define DS_CNN_S_BODY_F16_POINTWISE_STRIDE_H 1 +#define DS_CNN_S_BODY_F16_POINTWISE_STRIDE_W 1 +#define DS_CNN_S_BODY_F16_POINTWISE_PADDING_H 0 +#define DS_CNN_S_BODY_F16_POINTWISE_PADDING_W 0 +#define DS_CNN_S_BODY_F16_POINTWISE_DILATION_H 1 +#define DS_CNN_S_BODY_F16_POINTWISE_DILATION_W 1 +#define DS_CNN_S_BODY_F16_AVGPOOL_FILTER_H 25 +#define DS_CNN_S_BODY_F16_AVGPOOL_FILTER_W 5 +#define DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_H 1 +#define DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_W 1 +#define DS_CNN_S_BODY_F16_RELU_MIN (float16_t)0.0f +#define DS_CNN_S_BODY_F16_RELU_MAX (float16_t)65504.0f +#define DS_CNN_S_BODY_F16_IDENTITY_MIN (float16_t) - 65504.0f +#define DS_CNN_S_BODY_F16_IDENTITY_MAX (float16_t)65504.0f diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_biases_data.h new file mode 100644 index 00000000..60cdaeb6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv0_biases_data[64] = { + (float16_t)0.0421142578f, (float16_t)0.0346679688f, (float16_t)-0.0225219727f, (float16_t)0.0123519897f, + (float16_t)-0.0083694458f, (float16_t)0.021270752f, (float16_t)0.00528335571f, (float16_t)0.0358581543f, + (float16_t)0.0156860352f, (float16_t)0.0154800415f, (float16_t)0.0228424072f, (float16_t)-0.0187530518f, + (float16_t)-0.0168151855f, (float16_t)-0.0133895874f, (float16_t)-0.0356750488f, (float16_t)-0.0144958496f, + (float16_t)-0.016998291f, (float16_t)0.0254974365f, (float16_t)0.047668457f, (float16_t)-0.0268249512f, + (float16_t)-0.025894165f, (float16_t)-0.00775909424f, (float16_t)0.00547409058f, (float16_t)-0.0453186035f, + (float16_t)-0.0207061768f, (float16_t)0.018081665f, (float16_t)0.0221862793f, (float16_t)0.0193481445f, + (float16_t)-0.0258178711f, (float16_t)-0.00661849976f, (float16_t)0.0209960938f, (float16_t)-8.73804092e-05f, + (float16_t)-0.0186920166f, (float16_t)-0.0140228271f, (float16_t)-0.00281333923f, (float16_t)-0.00187492371f, + (float16_t)0.0153198242f, (float16_t)0.0256500244f, (float16_t)-0.0455627441f, (float16_t)0.01902771f, + (float16_t)0.0306243896f, (float16_t)-0.0102081299f, (float16_t)-0.0389099121f, (float16_t)0.0358886719f, + (float16_t)-0.0375671387f, (float16_t)0.0440979004f, (float16_t)-0.0372619629f, (float16_t)-0.00388145447f, + (float16_t)-0.0169219971f, (float16_t)-0.0401306152f, (float16_t)-0.0320129395f, (float16_t)-0.00127601624f, + (float16_t)-0.0400085449f, (float16_t)-0.0470275879f, (float16_t)-0.0262298584f, (float16_t)-0.0421447754f, + (float16_t)-0.0294036865f, (float16_t)0.0445861816f, (float16_t)-0.0121612549f, (float16_t)-0.004737854f, + (float16_t)-0.00889587402f, (float16_t)-0.0371398926f, (float16_t)0.0141448975f, (float16_t)-0.0390319824f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_weights_data.h new file mode 100644 index 00000000..dc74865e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv0_weights_data.h @@ -0,0 +1,146 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv0_weights_data[576] = { + (float16_t)-0.0331420898f, (float16_t)0.151367188f, (float16_t)-0.123474121f, (float16_t)-0.00984954834f, + (float16_t)0.0756835938f, (float16_t)0.222900391f, (float16_t)-0.0586547852f, (float16_t)0.0641479492f, + (float16_t)0.0895996094f, (float16_t)-0.0154724121f, (float16_t)0.202148438f, (float16_t)0.0745849609f, + (float16_t)-0.14831543f, (float16_t)-0.220092773f, (float16_t)-0.00749969482f, (float16_t)-0.2265625f, + (float16_t)-0.159912109f, (float16_t)-0.145019531f, (float16_t)0.0698852539f, (float16_t)0.0916137695f, + (float16_t)0.136108398f, (float16_t)0.221557617f, (float16_t)-0.109069824f, (float16_t)-0.188232422f, + (float16_t)0.182861328f, (float16_t)-0.239135742f, (float16_t)0.182006836f, (float16_t)0.170532227f, + (float16_t)-0.158691406f, (float16_t)-0.0595703125f, (float16_t)-0.237182617f, (float16_t)-0.0419311523f, + (float16_t)-0.0521850586f, (float16_t)-0.249633789f, (float16_t)0.20300293f, (float16_t)0.197753906f, + (float16_t)-0.202148438f, (float16_t)0.185791016f, (float16_t)0.129760742f, (float16_t)0.01222229f, + (float16_t)0.006690979f, (float16_t)0.139404297f, (float16_t)-0.0662231445f, (float16_t)-0.109375f, + (float16_t)0.0673217773f, (float16_t)0.039855957f, (float16_t)0.154785156f, (float16_t)-0.193237305f, + (float16_t)0.23828125f, (float16_t)-0.114807129f, (float16_t)-0.157470703f, (float16_t)-0.0928955078f, + (float16_t)0.204711914f, (float16_t)-0.0724487305f, (float16_t)0.0726318359f, (float16_t)0.0725708008f, + (float16_t)0.0708007812f, (float16_t)-0.133178711f, (float16_t)-0.100036621f, (float16_t)0.045501709f, + (float16_t)0.0369262695f, (float16_t)0.0611877441f, (float16_t)0.187744141f, (float16_t)0.172973633f, + (float16_t)-0.111999512f, (float16_t)-0.213745117f, (float16_t)0.185913086f, (float16_t)-0.186523438f, + (float16_t)0.122436523f, (float16_t)-0.0406799316f, (float16_t)0.205566406f, (float16_t)0.134765625f, + (float16_t)-0.0369873047f, (float16_t)-0.102233887f, (float16_t)0.0226898193f, (float16_t)0.222045898f, + (float16_t)-0.0698852539f, (float16_t)0.230957031f, (float16_t)-0.171630859f, (float16_t)-0.235595703f, + (float16_t)0.0961303711f, (float16_t)0.210571289f, (float16_t)0.206787109f, (float16_t)0.0699462891f, + (float16_t)0.0792236328f, (float16_t)-0.139770508f, (float16_t)-0.116088867f, (float16_t)0.106323242f, + (float16_t)0.215576172f, (float16_t)0.11932373f, (float16_t)0.162719727f, (float16_t)-0.131958008f, + (float16_t)0.180175781f, 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(float16_t)0.15612793f, (float16_t)-0.192382812f, (float16_t)-0.24230957f, (float16_t)0.0619506836f, + (float16_t)0.160522461f, (float16_t)0.156738281f, (float16_t)-0.182739258f, (float16_t)-0.0292816162f, + (float16_t)0.048828125f, (float16_t)-0.208007812f, (float16_t)-0.139282227f, (float16_t)0.107116699f, + (float16_t)0.130615234f, (float16_t)-0.129272461f, (float16_t)0.144042969f, (float16_t)0.107788086f, + (float16_t)0.122680664f, (float16_t)0.103271484f, (float16_t)-0.100769043f, (float16_t)0.14465332f, + (float16_t)-0.178466797f, (float16_t)0.0657348633f, (float16_t)0.124694824f, (float16_t)0.241455078f, + (float16_t)-0.172973633f, (float16_t)0.135131836f, (float16_t)0.166992188f, (float16_t)0.19909668f, + (float16_t)-0.0503234863f, (float16_t)-0.0735473633f, (float16_t)-0.243041992f, (float16_t)-0.0044593811f, + (float16_t)0.0717773438f, (float16_t)0.0887451172f, (float16_t)-0.167724609f, (float16_t)0.160522461f, + (float16_t)0.241210938f, (float16_t)-0.0913085938f, (float16_t)-0.16418457f, (float16_t)-0.0910644531f, + (float16_t)-0.0101699829f, (float16_t)-0.169433594f, (float16_t)0.156738281f, (float16_t)-0.225708008f, + (float16_t)-0.122497559f, (float16_t)0.221801758f, (float16_t)-0.172607422f, (float16_t)0.23815918f, + (float16_t)0.0649414062f, (float16_t)-0.0550537109f, (float16_t)0.0975952148f, (float16_t)-0.0979614258f, + (float16_t)-0.154174805f, (float16_t)0.0503234863f, (float16_t)0.0845336914f, (float16_t)0.23425293f, + (float16_t)-0.241333008f, (float16_t)0.0299224854f, (float16_t)-0.102661133f, (float16_t)-0.24597168f, + (float16_t)-0.142211914f, (float16_t)0.16418457f, (float16_t)-0.099609375f, (float16_t)0.164794922f, + (float16_t)0.0508728027f, (float16_t)-0.130004883f, (float16_t)0.187866211f, (float16_t)0.131469727f, + (float16_t)-0.242431641f, (float16_t)-0.0447692871f, (float16_t)0.146972656f, (float16_t)-0.0658569336f, + (float16_t)-0.132568359f, (float16_t)0.174072266f, (float16_t)0.216186523f, (float16_t)-0.0633544922f, + (float16_t)0.0731201172f, (float16_t)-0.238891602f, (float16_t)0.157958984f, (float16_t)0.126464844f, + (float16_t)-0.139892578f, (float16_t)-0.0357971191f, (float16_t)-0.0245361328f, (float16_t)-0.056060791f, + (float16_t)0.127075195f, (float16_t)-0.0461120605f, (float16_t)0.205566406f, (float16_t)-0.227050781f, + (float16_t)-0.0816040039f, (float16_t)-0.0309143066f, (float16_t)0.12109375f, (float16_t)0.057220459f, + (float16_t)0.204223633f, (float16_t)-0.112854004f, (float16_t)0.0576171875f, (float16_t)-0.215942383f, + (float16_t)-0.0347900391f, (float16_t)-0.225463867f, (float16_t)-0.125366211f, (float16_t)0.0980834961f, + (float16_t)-0.0731811523f, (float16_t)0.192504883f, (float16_t)0.173217773f, (float16_t)-0.125f, + (float16_t)0.137939453f, (float16_t)0.158447266f, (float16_t)-0.148071289f, (float16_t)0.218261719f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_biases_data.h new file mode 100644 index 00000000..64bdc16f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv1_biases_data[64] = { + (float16_t)-0.0403442383f, (float16_t)-0.00492477417f, (float16_t)0.0486755371f, (float16_t)-0.0479431152f, + (float16_t)0.0307922363f, (float16_t)0.0142593384f, (float16_t)-0.0321044922f, (float16_t)-0.00685119629f, + (float16_t)0.0364379883f, (float16_t)0.0364990234f, (float16_t)-0.0316467285f, (float16_t)-0.0260009766f, + (float16_t)0.0194091797f, (float16_t)-0.0442810059f, (float16_t)-0.0406494141f, (float16_t)-0.0174255371f, + (float16_t)0.00061416626f, (float16_t)-0.0478210449f, (float16_t)0.0282592773f, (float16_t)0.0395507812f, + (float16_t)-0.0162963867f, (float16_t)-0.0303955078f, (float16_t)-0.0387573242f, (float16_t)-0.0016412735f, + (float16_t)-0.040222168f, (float16_t)0.0325622559f, (float16_t)0.0267791748f, (float16_t)0.0334777832f, + (float16_t)-0.004737854f, (float16_t)-0.0256195068f, (float16_t)-0.0405578613f, (float16_t)-0.0439147949f, + (float16_t)0.0392456055f, (float16_t)-0.0426330566f, (float16_t)-0.0418395996f, (float16_t)-0.0321350098f, + (float16_t)-0.00518798828f, (float16_t)-0.0274200439f, (float16_t)0.0245513916f, (float16_t)0.0214233398f, + (float16_t)0.024597168f, (float16_t)0.0305023193f, (float16_t)0.0250854492f, (float16_t)0.00100421906f, + (float16_t)-0.00284576416f, (float16_t)-0.0137786865f, (float16_t)-0.00552749634f, (float16_t)-0.0333557129f, + (float16_t)-0.0494384766f, (float16_t)0.0323486328f, (float16_t)-0.0402832031f, (float16_t)0.0394897461f, + (float16_t)-0.0186920166f, (float16_t)0.0270996094f, (float16_t)0.0438842773f, (float16_t)0.0288848877f, + (float16_t)0.00520706177f, (float16_t)-0.00571060181f, (float16_t)0.0401000977f, (float16_t)0.039855957f, + (float16_t)0.0366210938f, (float16_t)0.0109176636f, (float16_t)0.0476074219f, (float16_t)0.0379638672f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_weights_data.h new file mode 100644 index 00000000..e1bd8119 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv1_weights_data.h @@ -0,0 +1,146 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv1_weights_data[576] = { + (float16_t)-0.15234375f, (float16_t)-0.0489196777f, (float16_t)0.0818481445f, (float16_t)0.226074219f, + (float16_t)-0.174194336f, (float16_t)-0.169433594f, (float16_t)-0.165283203f, (float16_t)0.0369262695f, + (float16_t)-0.217895508f, (float16_t)0.221801758f, (float16_t)-0.0873413086f, (float16_t)-0.0426940918f, + (float16_t)-0.147460938f, (float16_t)-0.163696289f, (float16_t)-0.0958251953f, (float16_t)0.0514831543f, + (float16_t)-0.196899414f, (float16_t)0.126098633f, (float16_t)-0.120239258f, (float16_t)0.164672852f, + (float16_t)-0.161254883f, (float16_t)0.103942871f, (float16_t)0.0157470703f, (float16_t)0.0473022461f, + (float16_t)-0.104309082f, (float16_t)-0.0443725586f, (float16_t)0.112792969f, (float16_t)0.0960693359f, + (float16_t)-0.117736816f, (float16_t)0.133666992f, (float16_t)0.0700073242f, (float16_t)-0.0393981934f, + (float16_t)0.14465332f, (float16_t)-0.246826172f, (float16_t)0.0390014648f, (float16_t)0.123779297f, + (float16_t)-0.106506348f, (float16_t)0.178100586f, (float16_t)0.0126953125f, (float16_t)0.214355469f, + (float16_t)-0.0686645508f, (float16_t)-0.030380249f, (float16_t)0.00503540039f, (float16_t)-0.170410156f, + (float16_t)-0.0394897461f, (float16_t)0.231445312f, (float16_t)0.122192383f, (float16_t)-0.1328125f, + (float16_t)-0.229980469f, (float16_t)-0.21105957f, (float16_t)-0.112243652f, (float16_t)0.000114142895f, + (float16_t)0.00204277039f, (float16_t)0.141601562f, (float16_t)-0.0631103516f, (float16_t)0.0579528809f, + (float16_t)-0.0147781372f, (float16_t)0.143920898f, (float16_t)0.194213867f, (float16_t)0.00187492371f, + (float16_t)0.0425109863f, (float16_t)-0.00197029114f, (float16_t)-0.112365723f, (float16_t)-0.125976562f, + (float16_t)-0.203491211f, (float16_t)-0.0713500977f, (float16_t)-0.189208984f, (float16_t)0.00629425049f, + (float16_t)-0.100341797f, (float16_t)0.0991821289f, (float16_t)0.238769531f, (float16_t)0.0405578613f, + (float16_t)0.142333984f, (float16_t)0.244262695f, (float16_t)-0.000453948975f, (float16_t)0.0838012695f, + (float16_t)-0.217773438f, (float16_t)-0.0891113281f, (float16_t)-0.00319290161f, (float16_t)-0.0419616699f, + (float16_t)-0.243896484f, (float16_t)-0.221069336f, (float16_t)0.102172852f, (float16_t)0.0868530273f, + (float16_t)0.0755004883f, (float16_t)0.103881836f, (float16_t)-0.0429992676f, (float16_t)-0.161987305f, + (float16_t)-0.0617980957f, (float16_t)-0.219116211f, (float16_t)0.0403442383f, (float16_t)-0.0825805664f, + (float16_t)0.161987305f, (float16_t)-0.146972656f, (float16_t)0.201049805f, (float16_t)-0.114196777f, + (float16_t)0.105407715f, (float16_t)-0.239013672f, (float16_t)0.0141296387f, (float16_t)0.244506836f, + (float16_t)0.110534668f, (float16_t)-0.0511779785f, (float16_t)-0.162109375f, (float16_t)0.145019531f, + (float16_t)0.108398438f, (float16_t)-0.14465332f, (float16_t)0.0819702148f, (float16_t)0.0661010742f, + (float16_t)0.106872559f, (float16_t)-0.0193328857f, (float16_t)0.196533203f, (float16_t)-0.0925292969f, + (float16_t)0.101989746f, (float16_t)-0.128173828f, (float16_t)0.0767822266f, (float16_t)0.185180664f, + (float16_t)-0.0364074707f, (float16_t)0.158203125f, (float16_t)-0.19128418f, (float16_t)-0.162353516f, + (float16_t)0.21081543f, (float16_t)-0.167236328f, (float16_t)-0.162597656f, (float16_t)-0.161132812f, + (float16_t)0.225341797f, (float16_t)-0.227783203f, (float16_t)0.0777587891f, (float16_t)0.0974121094f, + (float16_t)-0.0485229492f, (float16_t)-0.17175293f, (float16_t)0.0641479492f, (float16_t)0.127441406f, + (float16_t)-0.0220031738f, (float16_t)-0.0100784302f, (float16_t)0.0246124268f, (float16_t)-0.225463867f, + (float16_t)0.0670166016f, (float16_t)0.203857422f, (float16_t)-0.159423828f, (float16_t)0.0177307129f, + (float16_t)-0.194335938f, (float16_t)-0.138793945f, (float16_t)-0.113647461f, (float16_t)-0.141113281f, + (float16_t)0.0407104492f, (float16_t)0.0386047363f, (float16_t)-0.0307617188f, (float16_t)0.203857422f, + (float16_t)-0.183227539f, (float16_t)-0.216308594f, (float16_t)-0.194213867f, (float16_t)0.0632324219f, + (float16_t)-0.183837891f, (float16_t)-0.178710938f, (float16_t)-0.194946289f, (float16_t)0.0437011719f, + (float16_t)0.200683594f, (float16_t)0.124633789f, (float16_t)-0.162109375f, (float16_t)-0.21862793f, + (float16_t)0.00885009766f, (float16_t)0.0553283691f, (float16_t)0.157836914f, (float16_t)-0.0658569336f, + (float16_t)-0.100219727f, (float16_t)-0.050567627f, (float16_t)0.161376953f, (float16_t)0.0667114258f, + (float16_t)0.217041016f, (float16_t)-0.16784668f, (float16_t)0.24597168f, (float16_t)0.248168945f, + (float16_t)0.143920898f, (float16_t)-0.0476989746f, (float16_t)0.0419616699f, (float16_t)0.231323242f, + (float16_t)-0.116455078f, (float16_t)0.178833008f, (float16_t)-0.190429688f, (float16_t)-0.17175293f, + (float16_t)0.122802734f, (float16_t)0.22253418f, (float16_t)0.228637695f, (float16_t)0.155517578f, + (float16_t)-0.233764648f, (float16_t)-0.00521469116f, (float16_t)-0.0825805664f, (float16_t)-0.0147323608f, + (float16_t)0.209594727f, (float16_t)0.0930786133f, (float16_t)0.239990234f, (float16_t)0.0430603027f, + (float16_t)0.00169277191f, (float16_t)-0.194213867f, (float16_t)-0.18359375f, (float16_t)-0.171630859f, + (float16_t)0.184936523f, (float16_t)0.181152344f, (float16_t)-0.234863281f, (float16_t)0.10357666f, + (float16_t)-0.233886719f, (float16_t)0.194335938f, (float16_t)-0.128051758f, (float16_t)0.162719727f, + (float16_t)0.0465698242f, (float16_t)0.0409545898f, (float16_t)0.104736328f, (float16_t)0.191772461f, + (float16_t)-0.0409851074f, (float16_t)0.239257812f, (float16_t)-0.0617370605f, (float16_t)0.0232391357f, + (float16_t)-0.0760498047f, (float16_t)0.139282227f, (float16_t)-0.248779297f, (float16_t)0.236572266f, + (float16_t)0.155273438f, (float16_t)0.151367188f, (float16_t)0.0975341797f, (float16_t)0.101013184f, + (float16_t)-0.211669922f, (float16_t)0.0512390137f, (float16_t)-0.0303039551f, (float16_t)0.00213623047f, + (float16_t)-0.0425720215f, (float16_t)0.0634155273f, (float16_t)0.0950927734f, (float16_t)0.0709838867f, + (float16_t)-0.153320312f, (float16_t)-0.225219727f, (float16_t)-0.176025391f, (float16_t)-0.165405273f, + (float16_t)0.0764160156f, (float16_t)0.183349609f, (float16_t)-0.248535156f, (float16_t)0.0248413086f, + (float16_t)0.23828125f, (float16_t)-0.176513672f, (float16_t)0.249023438f, (float16_t)-0.0422973633f, + (float16_t)0.172607422f, (float16_t)-0.0554504395f, (float16_t)-0.189575195f, (float16_t)0.24230957f, + (float16_t)0.0704956055f, (float16_t)-0.145751953f, (float16_t)0.224731445f, (float16_t)0.120727539f, + (float16_t)0.0282592773f, (float16_t)0.0966796875f, (float16_t)0.0454101562f, (float16_t)0.151855469f, + (float16_t)-0.0776367188f, (float16_t)-0.211547852f, (float16_t)0.128540039f, (float16_t)0.178344727f, + (float16_t)-0.0795288086f, (float16_t)-0.131225586f, (float16_t)-0.0764770508f, (float16_t)0.122802734f, + (float16_t)-0.177734375f, (float16_t)-0.0446777344f, (float16_t)0.00198936462f, (float16_t)-0.158691406f, + (float16_t)-0.153686523f, (float16_t)-0.178344727f, (float16_t)-0.0563049316f, (float16_t)0.10333252f, + (float16_t)-0.129882812f, (float16_t)0.180297852f, (float16_t)0.0942382812f, (float16_t)0.229736328f, + (float16_t)0.115783691f, (float16_t)0.146728516f, (float16_t)0.0100402832f, (float16_t)0.0661010742f, + (float16_t)-0.0941162109f, (float16_t)-0.00186824799f, (float16_t)-0.16027832f, (float16_t)-0.220214844f, + (float16_t)-0.149291992f, (float16_t)0.107666016f, (float16_t)0.229736328f, (float16_t)0.0969238281f, + (float16_t)-0.189819336f, (float16_t)-0.0676879883f, (float16_t)0.248535156f, (float16_t)-0.182128906f, + (float16_t)-0.148925781f, (float16_t)-0.138305664f, (float16_t)2.70605087e-05f, (float16_t)-0.189086914f, + (float16_t)0.200927734f, (float16_t)0.0785522461f, (float16_t)-0.114990234f, (float16_t)-0.19934082f, + (float16_t)0.112976074f, (float16_t)0.0998535156f, (float16_t)0.0309143066f, (float16_t)-0.231323242f, + (float16_t)-0.016418457f, (float16_t)0.0430603027f, (float16_t)0.108093262f, (float16_t)-0.151611328f, + (float16_t)0.105163574f, (float16_t)0.0403442383f, (float16_t)0.187744141f, (float16_t)-0.128173828f, + (float16_t)0.0279388428f, (float16_t)-0.0795898438f, (float16_t)0.24987793f, (float16_t)-0.192016602f, + (float16_t)0.0588684082f, (float16_t)-0.0944824219f, (float16_t)-0.0934448242f, (float16_t)0.124023438f, + (float16_t)-0.214477539f, (float16_t)-0.0269012451f, (float16_t)-0.158569336f, (float16_t)0.215454102f, + (float16_t)0.0913696289f, (float16_t)-0.248046875f, (float16_t)0.0814819336f, (float16_t)-0.0398864746f, + (float16_t)0.135864258f, (float16_t)0.0511169434f, (float16_t)0.202026367f, (float16_t)0.217773438f, + (float16_t)0.130493164f, (float16_t)0.105651855f, (float16_t)-0.187866211f, (float16_t)0.198120117f, + (float16_t)0.0999755859f, (float16_t)0.144897461f, (float16_t)-0.0379638672f, (float16_t)-0.0827636719f, + (float16_t)0.125610352f, (float16_t)0.0585021973f, (float16_t)0.171875f, (float16_t)0.0471496582f, + (float16_t)-0.209350586f, (float16_t)0.190917969f, (float16_t)0.116577148f, (float16_t)0.00464248657f, + (float16_t)0.0216827393f, (float16_t)-0.0213317871f, (float16_t)0.196044922f, (float16_t)-0.177246094f, + (float16_t)0.0667114258f, (float16_t)0.232421875f, (float16_t)-0.228515625f, (float16_t)-0.208129883f, + (float16_t)-0.02394104f, (float16_t)0.152587891f, (float16_t)-0.0620422363f, (float16_t)-0.0715332031f, + (float16_t)0.193359375f, (float16_t)-0.0199584961f, (float16_t)-0.19909668f, (float16_t)0.111206055f, + (float16_t)0.164306641f, (float16_t)0.21484375f, (float16_t)-0.11138916f, (float16_t)0.011100769f, + (float16_t)-0.224731445f, (float16_t)0.166381836f, (float16_t)-0.122497559f, (float16_t)-0.106872559f, + (float16_t)0.0715942383f, (float16_t)0.195678711f, (float16_t)-0.0805664062f, (float16_t)-0.173706055f, + (float16_t)0.241699219f, (float16_t)0.195556641f, (float16_t)0.0339355469f, (float16_t)-0.108642578f, + (float16_t)0.0188293457f, (float16_t)0.232177734f, (float16_t)0.236572266f, (float16_t)0.0784912109f, + (float16_t)0.0107498169f, (float16_t)-0.0891723633f, (float16_t)0.239135742f, (float16_t)-0.0563964844f, + (float16_t)0.0664672852f, (float16_t)0.0257568359f, (float16_t)-0.207275391f, (float16_t)0.239624023f, + (float16_t)0.00317955017f, (float16_t)0.150024414f, (float16_t)0.239013672f, (float16_t)0.196533203f, + (float16_t)-0.19909668f, (float16_t)0.0609436035f, (float16_t)-0.198852539f, (float16_t)0.234863281f, + (float16_t)-0.131713867f, (float16_t)-0.202514648f, (float16_t)0.0240325928f, (float16_t)0.201538086f, + (float16_t)0.0972900391f, (float16_t)-0.165527344f, (float16_t)0.136352539f, (float16_t)-0.246826172f, + (float16_t)-0.0594787598f, (float16_t)0.181396484f, (float16_t)-0.159545898f, (float16_t)-0.0717163086f, + (float16_t)0.0639648438f, (float16_t)0.0767822266f, (float16_t)0.200439453f, (float16_t)-0.186645508f, + (float16_t)0.170166016f, (float16_t)-0.0930175781f, (float16_t)0.0961914062f, (float16_t)0.162231445f, + (float16_t)-0.0166778564f, (float16_t)0.0252990723f, (float16_t)-0.200073242f, (float16_t)-0.0981445312f, + (float16_t)0.145263672f, (float16_t)-0.128051758f, (float16_t)0.0290222168f, (float16_t)0.0888061523f, + (float16_t)0.0624694824f, (float16_t)-0.160522461f, (float16_t)0.209472656f, (float16_t)-0.147338867f, + (float16_t)-0.0174255371f, (float16_t)0.214599609f, (float16_t)-0.0374450684f, (float16_t)0.0953979492f, + (float16_t)-0.143554688f, (float16_t)-0.188354492f, (float16_t)0.158325195f, (float16_t)-0.107055664f, + (float16_t)0.183837891f, (float16_t)0.140258789f, (float16_t)0.247192383f, (float16_t)-0.0835571289f, + (float16_t)-0.197387695f, (float16_t)-0.240234375f, (float16_t)-0.215820312f, (float16_t)0.0157928467f, + (float16_t)-0.155273438f, (float16_t)0.0432128906f, (float16_t)0.133666992f, (float16_t)0.236083984f, + (float16_t)0.248657227f, (float16_t)-0.247924805f, (float16_t)-0.226928711f, (float16_t)-0.240478516f, + (float16_t)-0.243164062f, (float16_t)-0.201660156f, (float16_t)-0.196533203f, (float16_t)0.231445312f, + (float16_t)-0.109924316f, (float16_t)-0.141601562f, (float16_t)0.127929688f, (float16_t)-0.113525391f, + (float16_t)-0.19140625f, (float16_t)0.103271484f, (float16_t)-0.217651367f, (float16_t)0.167724609f, + (float16_t)-0.0662231445f, (float16_t)-0.172485352f, (float16_t)-0.18347168f, (float16_t)0.140136719f, + (float16_t)0.131469727f, (float16_t)-0.0731811523f, (float16_t)0.175292969f, (float16_t)0.0604553223f, + (float16_t)-0.00343894958f, (float16_t)0.135864258f, (float16_t)0.142333984f, (float16_t)0.227783203f, + (float16_t)0.230224609f, (float16_t)-0.0743408203f, (float16_t)-0.197021484f, (float16_t)0.202148438f, + (float16_t)-0.235473633f, (float16_t)-0.00981903076f, (float16_t)-0.0462646484f, (float16_t)0.0356750488f, + (float16_t)-0.124389648f, (float16_t)-0.0439453125f, (float16_t)-0.0941772461f, (float16_t)-0.233032227f, + (float16_t)-0.0825195312f, (float16_t)0.152832031f, (float16_t)0.226928711f, (float16_t)-0.0566101074f, + (float16_t)0.117919922f, (float16_t)-0.143066406f, (float16_t)-0.137695312f, (float16_t)0.0134735107f, + (float16_t)0.0960693359f, (float16_t)-0.161376953f, (float16_t)0.0108947754f, (float16_t)-0.187866211f, + (float16_t)-0.106872559f, (float16_t)-0.146240234f, (float16_t)-0.081237793f, (float16_t)-0.189941406f, + (float16_t)0.231201172f, (float16_t)-0.0111618042f, (float16_t)-0.142822266f, (float16_t)0.135498047f, + (float16_t)-0.0895385742f, (float16_t)0.0541381836f, (float16_t)0.0479125977f, (float16_t)-0.19921875f, + (float16_t)-0.208862305f, (float16_t)-0.00736618042f, (float16_t)0.118225098f, (float16_t)-0.0775756836f, + (float16_t)-0.0775756836f, (float16_t)-0.0510559082f, (float16_t)0.124389648f, (float16_t)0.162109375f, + (float16_t)0.0110244751f, (float16_t)-0.0560302734f, (float16_t)-0.196533203f, (float16_t)0.0348815918f, + (float16_t)-0.232299805f, (float16_t)-0.109863281f, (float16_t)-0.149536133f, (float16_t)0.0739135742f, + (float16_t)-0.00211715698f, (float16_t)0.205322266f, (float16_t)-0.12878418f, (float16_t)-0.1484375f, + (float16_t)0.0956420898f, (float16_t)0.169555664f, (float16_t)-0.227539062f, (float16_t)0.194091797f, + (float16_t)0.102966309f, (float16_t)0.0736694336f, (float16_t)0.0932617188f, (float16_t)0.202392578f, + (float16_t)0.236450195f, (float16_t)-0.241088867f, (float16_t)-0.10534668f, (float16_t)0.172729492f, + (float16_t)0.24230957f, (float16_t)-0.0107574463f, (float16_t)0.196899414f, (float16_t)0.14440918f, + (float16_t)0.0549316406f, (float16_t)0.143554688f, (float16_t)0.0990600586f, (float16_t)0.0247650146f, + (float16_t)0.221801758f, (float16_t)0.0946044922f, (float16_t)0.220458984f, (float16_t)0.112426758f, + (float16_t)-0.0511474609f, (float16_t)-0.0274353027f, (float16_t)0.11932373f, (float16_t)-0.24597168f, + (float16_t)-0.108642578f, (float16_t)0.143554688f, (float16_t)0.127441406f, (float16_t)-0.0094909668f, + (float16_t)-0.0595703125f, (float16_t)0.179321289f, (float16_t)-0.138061523f, (float16_t)0.21081543f, + (float16_t)0.147949219f, (float16_t)0.0751342773f, (float16_t)0.176757812f, (float16_t)-0.0879516602f, + (float16_t)0.0458374023f, (float16_t)0.218261719f, (float16_t)-0.162231445f, (float16_t)-0.0150680542f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_biases_data.h new file mode 100644 index 00000000..6d3c19a0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv2_biases_data[64] = { + (float16_t)0.00397491455f, (float16_t)-0.00168132782f, (float16_t)-0.008644104f, (float16_t)0.0483093262f, + (float16_t)0.0135726929f, (float16_t)-0.0173950195f, (float16_t)0.021194458f, (float16_t)-0.0317687988f, + (float16_t)0.00262641907f, (float16_t)0.015411377f, (float16_t)0.0223083496f, (float16_t)0.00270271301f, + (float16_t)-0.0258331299f, (float16_t)-0.0152359009f, (float16_t)0.00456237793f, (float16_t)-0.00443649292f, + (float16_t)-0.00221633911f, (float16_t)0.0322265625f, (float16_t)0.0242156982f, (float16_t)0.0247955322f, + (float16_t)0.0461425781f, (float16_t)0.00839996338f, (float16_t)-0.0186157227f, (float16_t)0.0491943359f, + (float16_t)0.0227813721f, (float16_t)-0.0408935547f, (float16_t)-0.00636672974f, (float16_t)-0.0350341797f, + (float16_t)-0.0455932617f, (float16_t)-0.0363769531f, (float16_t)-0.036315918f, (float16_t)-0.0317687988f, + (float16_t)0.0300750732f, (float16_t)-0.0192718506f, (float16_t)-0.0193939209f, (float16_t)-0.0431518555f, + (float16_t)-0.00131320953f, (float16_t)0.0294494629f, (float16_t)0.0222320557f, (float16_t)-0.033782959f, + (float16_t)-0.0466918945f, (float16_t)-0.0466308594f, (float16_t)0.0284729004f, (float16_t)-0.0145492554f, + (float16_t)0.0485839844f, (float16_t)0.0149459839f, (float16_t)0.0110244751f, (float16_t)0.0292358398f, + (float16_t)0.0462036133f, (float16_t)-0.0264892578f, (float16_t)0.0400085449f, (float16_t)0.014503479f, + (float16_t)-0.0031452179f, (float16_t)0.0222167969f, (float16_t)-0.0199584961f, (float16_t)0.0117340088f, + (float16_t)0.0262908936f, (float16_t)-0.000274896622f, (float16_t)-0.0108032227f, (float16_t)0.0192565918f, + (float16_t)0.0422363281f, (float16_t)0.0305328369f, (float16_t)-0.0149612427f, (float16_t)0.0464477539f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_weights_data.h new file mode 100644 index 00000000..667eb691 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv2_weights_data.h @@ -0,0 +1,146 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv2_weights_data[576] = { + (float16_t)-0.194946289f, (float16_t)0.110107422f, (float16_t)-0.166015625f, (float16_t)-0.134155273f, + (float16_t)-0.040435791f, (float16_t)0.0837402344f, (float16_t)-0.226928711f, (float16_t)0.0811157227f, + (float16_t)0.192382812f, (float16_t)-0.0717163086f, (float16_t)0.0249938965f, (float16_t)-0.193237305f, + (float16_t)0.241577148f, (float16_t)-0.168212891f, (float16_t)-0.0838012695f, (float16_t)-0.135498047f, + (float16_t)-0.100097656f, (float16_t)0.197509766f, (float16_t)0.0981445312f, (float16_t)-0.0921020508f, + (float16_t)-0.215942383f, (float16_t)0.155883789f, (float16_t)-0.0919799805f, (float16_t)-0.146240234f, + (float16_t)-0.000464200974f, (float16_t)0.219238281f, (float16_t)-0.243530273f, (float16_t)0.244018555f, + (float16_t)0.0637207031f, (float16_t)-0.102844238f, (float16_t)0.227783203f, (float16_t)-0.193725586f, + (float16_t)-0.0258636475f, (float16_t)0.114807129f, (float16_t)-0.0557556152f, (float16_t)-0.232421875f, + (float16_t)-0.0689086914f, (float16_t)-0.0293884277f, (float16_t)0.242431641f, (float16_t)0.130249023f, + (float16_t)-0.0125198364f, (float16_t)-0.14465332f, (float16_t)0.213256836f, (float16_t)-0.0447692871f, + (float16_t)-0.0974121094f, (float16_t)0.159423828f, (float16_t)-0.125610352f, (float16_t)0.0306396484f, + (float16_t)0.00345611572f, (float16_t)-0.125366211f, (float16_t)-0.0232086182f, (float16_t)-0.0288238525f, + (float16_t)-0.160522461f, (float16_t)-0.235961914f, (float16_t)0.187133789f, (float16_t)0.09375f, + (float16_t)0.161132812f, (float16_t)0.0374145508f, (float16_t)-0.0917358398f, (float16_t)0.08203125f, + (float16_t)-0.153930664f, (float16_t)-0.0814208984f, (float16_t)0.229858398f, (float16_t)0.133178711f, + (float16_t)-0.157226562f, (float16_t)0.187133789f, (float16_t)-0.244995117f, (float16_t)0.0875854492f, + (float16_t)0.153076172f, (float16_t)0.244628906f, (float16_t)0.0607910156f, (float16_t)0.184570312f, + (float16_t)0.206420898f, (float16_t)0.247558594f, (float16_t)0.227783203f, (float16_t)0.0265655518f, + (float16_t)0.212646484f, (float16_t)0.0358581543f, (float16_t)0.200561523f, (float16_t)0.0856933594f, + (float16_t)-0.188110352f, (float16_t)-0.0988769531f, (float16_t)-0.0244140625f, (float16_t)-0.223754883f, + (float16_t)0.184204102f, (float16_t)0.177490234f, (float16_t)0.00622940063f, (float16_t)0.223144531f, + (float16_t)-0.174804688f, (float16_t)0.0833129883f, (float16_t)-0.120361328f, (float16_t)0.192504883f, + (float16_t)0.11895752f, (float16_t)0.0178222656f, (float16_t)0.0376281738f, (float16_t)0.15625f, + (float16_t)-0.0309448242f, (float16_t)0.0755004883f, (float16_t)-0.0707397461f, (float16_t)0.0961914062f, + (float16_t)0.0850219727f, (float16_t)-0.228149414f, (float16_t)-0.117858887f, (float16_t)0.139160156f, + (float16_t)-0.238525391f, (float16_t)-0.220092773f, (float16_t)0.106262207f, (float16_t)-0.202880859f, + (float16_t)-0.165893555f, (float16_t)0.190307617f, (float16_t)0.138671875f, (float16_t)-0.125244141f, + (float16_t)-0.144287109f, (float16_t)-0.176879883f, (float16_t)0.042755127f, (float16_t)0.0603637695f, + (float16_t)-0.0854492188f, (float16_t)0.22277832f, (float16_t)-0.235595703f, (float16_t)-0.0306854248f, + (float16_t)-0.150878906f, (float16_t)0.126708984f, (float16_t)-0.0564880371f, (float16_t)0.110229492f, + (float16_t)0.0737304688f, (float16_t)0.0745239258f, (float16_t)0.228881836f, (float16_t)0.0855102539f, + (float16_t)0.105224609f, (float16_t)-0.0941162109f, (float16_t)0.018371582f, (float16_t)0.115478516f, + (float16_t)-0.107177734f, (float16_t)-0.151245117f, (float16_t)-0.165893555f, (float16_t)0.0308227539f, + (float16_t)-0.00969696045f, (float16_t)0.109741211f, (float16_t)0.0570983887f, (float16_t)0.107971191f, + (float16_t)0.194580078f, (float16_t)0.037322998f, (float16_t)0.146972656f, (float16_t)0.166992188f, + (float16_t)-0.186523438f, (float16_t)-0.244384766f, (float16_t)0.174682617f, (float16_t)0.204711914f, + (float16_t)-0.125244141f, (float16_t)0.155029297f, (float16_t)0.00745010376f, (float16_t)0.0326538086f, + (float16_t)0.0480957031f, (float16_t)-0.0974121094f, (float16_t)-0.22253418f, (float16_t)0.151733398f, + (float16_t)0.217529297f, (float16_t)-0.0656738281f, (float16_t)0.151489258f, (float16_t)-0.182250977f, + (float16_t)-0.0186767578f, (float16_t)-0.0266113281f, (float16_t)0.248168945f, (float16_t)0.177368164f, + (float16_t)-0.0175476074f, (float16_t)-0.0759887695f, (float16_t)-0.215332031f, (float16_t)0.245117188f, + 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(float16_t)0.0609130859f, (float16_t)-0.244750977f, (float16_t)-0.241210938f, + (float16_t)0.246948242f, (float16_t)-0.0264587402f, (float16_t)-0.0665893555f, (float16_t)-0.101379395f, + (float16_t)0.0634765625f, (float16_t)-0.212280273f, (float16_t)0.0212554932f, (float16_t)0.209228516f, + (float16_t)0.0678100586f, (float16_t)-0.168823242f, (float16_t)-0.0169219971f, (float16_t)-0.119689941f, + (float16_t)0.133056641f, (float16_t)0.0448913574f, (float16_t)-0.203979492f, (float16_t)0.0585632324f, + (float16_t)0.155761719f, (float16_t)0.115234375f, (float16_t)0.0252227783f, (float16_t)0.12097168f, + (float16_t)0.231689453f, (float16_t)0.0247192383f, (float16_t)0.215820312f, (float16_t)0.100585938f, + (float16_t)-0.0281982422f, (float16_t)0.114746094f, (float16_t)0.0294494629f, (float16_t)0.0872802734f, + (float16_t)-0.0245666504f, (float16_t)-0.140869141f, (float16_t)0.197021484f, (float16_t)0.140258789f, + (float16_t)0.244628906f, (float16_t)0.124145508f, (float16_t)0.0458679199f, (float16_t)0.0845947266f, + (float16_t)0.129272461f, (float16_t)0.225830078f, (float16_t)0.176757812f, (float16_t)-0.058380127f, + (float16_t)0.0437011719f, (float16_t)0.0206604004f, (float16_t)0.0858154297f, (float16_t)0.0148010254f, + (float16_t)0.124816895f, (float16_t)0.109313965f, (float16_t)-0.0364074707f, (float16_t)-0.138793945f, + (float16_t)-0.174316406f, (float16_t)0.0221099854f, (float16_t)0.230834961f, (float16_t)0.184814453f, + (float16_t)-6.93202019e-05f, (float16_t)-0.229003906f, (float16_t)-0.129638672f, (float16_t)-0.0361328125f, + (float16_t)-0.142578125f, (float16_t)0.0299530029f, (float16_t)-0.16394043f, (float16_t)-0.134399414f, + (float16_t)0.175415039f, (float16_t)-0.149414062f, (float16_t)0.183959961f, (float16_t)0.206420898f, + (float16_t)-0.0382080078f, (float16_t)-0.162475586f, (float16_t)0.0520324707f, (float16_t)-0.0679321289f, + (float16_t)0.173828125f, (float16_t)0.227050781f, (float16_t)-0.12298584f, (float16_t)-0.128173828f, + (float16_t)-0.0878295898f, (float16_t)-0.224121094f, (float16_t)0.220947266f, (float16_t)0.200195312f, + (float16_t)0.216186523f, (float16_t)-0.0969238281f, (float16_t)0.0390014648f, (float16_t)-0.247314453f, + (float16_t)-0.0362854004f, (float16_t)-0.167724609f, (float16_t)-0.0767822266f, (float16_t)-0.23449707f, + (float16_t)0.241455078f, (float16_t)0.238769531f, (float16_t)0.0634765625f, (float16_t)0.139892578f, + (float16_t)-0.146850586f, (float16_t)-0.0970458984f, (float16_t)0.0838623047f, (float16_t)-0.244873047f, + (float16_t)-0.130493164f, (float16_t)0.167236328f, (float16_t)0.110717773f, (float16_t)-0.0899658203f, + (float16_t)-0.0290527344f, (float16_t)0.0265655518f, (float16_t)0.143676758f, (float16_t)-0.129760742f, + (float16_t)0.154663086f, (float16_t)0.24621582f, (float16_t)0.228149414f, (float16_t)0.143432617f, + (float16_t)-0.0207366943f, (float16_t)0.0492553711f, (float16_t)-0.210205078f, (float16_t)-0.00547409058f, + (float16_t)-0.240844727f, (float16_t)-0.0916748047f, (float16_t)0.0643920898f, (float16_t)-0.223876953f, + (float16_t)0.194458008f, (float16_t)-0.0469055176f, (float16_t)0.0251617432f, (float16_t)-0.173828125f, + (float16_t)0.0613708496f, (float16_t)-0.104980469f, (float16_t)0.193481445f, (float16_t)-0.19934082f, + (float16_t)0.234008789f, (float16_t)-0.138061523f, (float16_t)-0.203125f, (float16_t)0.0740356445f, + (float16_t)-0.177856445f, (float16_t)-0.0160064697f, (float16_t)-0.14440918f, (float16_t)-0.160522461f, + (float16_t)0.176513672f, (float16_t)-0.0180206299f, (float16_t)0.095703125f, (float16_t)-0.215087891f, + (float16_t)0.225341797f, (float16_t)0.14050293f, (float16_t)0.162963867f, (float16_t)-0.0695800781f, + (float16_t)-0.191650391f, (float16_t)0.076171875f, (float16_t)0.122497559f, (float16_t)0.177978516f, + (float16_t)-0.0313110352f, (float16_t)0.197021484f, (float16_t)-0.0474243164f, (float16_t)0.133544922f, + (float16_t)-0.00401306152f, (float16_t)0.190307617f, (float16_t)0.245605469f, (float16_t)0.10760498f, + (float16_t)0.0547790527f, (float16_t)-0.0553283691f, (float16_t)0.0942382812f, (float16_t)-0.0443115234f, + (float16_t)-0.204467773f, (float16_t)0.118530273f, (float16_t)-0.0708007812f, (float16_t)0.235229492f, + (float16_t)0.131469727f, (float16_t)-0.181640625f, (float16_t)0.0788574219f, (float16_t)-0.123413086f, + (float16_t)0.0747070312f, (float16_t)-0.196166992f, (float16_t)0.00297927856f, (float16_t)-0.162597656f, + (float16_t)-0.182861328f, (float16_t)0.14465332f, (float16_t)0.243286133f, (float16_t)0.0538024902f, + (float16_t)0.169433594f, (float16_t)-0.179199219f, (float16_t)0.248291016f, (float16_t)0.0667114258f, + (float16_t)0.157714844f, (float16_t)0.115966797f, (float16_t)-0.0886230469f, (float16_t)-0.130126953f, + (float16_t)-0.111450195f, (float16_t)-0.112487793f, (float16_t)0.130859375f, (float16_t)-0.061126709f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_biases_data.h new file mode 100644 index 00000000..adf273c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv3_biases_data[64] = { + (float16_t)0.0133514404f, (float16_t)-0.0243530273f, (float16_t)0.00271606445f, (float16_t)0.00584030151f, + (float16_t)0.0429382324f, (float16_t)-0.00430297852f, (float16_t)-0.0199584961f, (float16_t)0.0335388184f, + (float16_t)0.0236053467f, (float16_t)-0.00170993805f, (float16_t)-0.0299072266f, (float16_t)-0.0353393555f, + (float16_t)0.0391845703f, (float16_t)-0.0148849487f, (float16_t)0.0134811401f, (float16_t)-0.0453491211f, + (float16_t)-0.0202026367f, (float16_t)0.0457458496f, (float16_t)-0.0335388184f, (float16_t)0.049621582f, + (float16_t)0.0447692871f, (float16_t)-0.0461730957f, (float16_t)-0.03125f, (float16_t)-0.00560379028f, + (float16_t)0.0426330566f, (float16_t)-0.0244293213f, (float16_t)-0.0215148926f, (float16_t)-0.00766372681f, + (float16_t)-0.022644043f, (float16_t)0.0237426758f, (float16_t)-0.0483093262f, (float16_t)0.0340576172f, + (float16_t)0.00310325623f, (float16_t)-0.0355834961f, (float16_t)0.0318603516f, (float16_t)-0.0381164551f, + (float16_t)0.0406188965f, (float16_t)-0.0411682129f, (float16_t)0.0254058838f, (float16_t)-0.0393371582f, + (float16_t)-0.0327453613f, (float16_t)0.035369873f, (float16_t)-0.00932312012f, (float16_t)0.0204772949f, + (float16_t)0.0497741699f, (float16_t)0.0159606934f, (float16_t)-0.0331726074f, (float16_t)-0.0441589355f, + (float16_t)0.0192565918f, (float16_t)0.0167694092f, (float16_t)0.0418701172f, (float16_t)0.00492095947f, + (float16_t)0.0284423828f, (float16_t)-0.0279083252f, (float16_t)-0.0293884277f, (float16_t)-0.00240516663f, + (float16_t)0.0369567871f, (float16_t)0.0459594727f, (float16_t)-0.0048789978f, (float16_t)0.0115356445f, + (float16_t)0.0190429688f, (float16_t)-0.0297698975f, (float16_t)0.0258331299f, (float16_t)0.0263519287f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_weights_data.h new file mode 100644 index 00000000..ccd4e380 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/dwconv3_weights_data.h @@ -0,0 +1,146 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_dwconv3_weights_data[576] = { + (float16_t)0.128295898f, (float16_t)0.00933074951f, (float16_t)0.0558166504f, (float16_t)-0.183349609f, + (float16_t)0.223632812f, (float16_t)0.150634766f, (float16_t)0.101623535f, (float16_t)0.075012207f, + (float16_t)0.195556641f, (float16_t)-0.14855957f, (float16_t)0.0756225586f, (float16_t)-0.239135742f, + (float16_t)-0.16015625f, (float16_t)-0.197753906f, (float16_t)0.249511719f, (float16_t)0.170898438f, + (float16_t)-0.215209961f, (float16_t)-0.0911865234f, (float16_t)-0.0795898438f, (float16_t)-0.0678100586f, + (float16_t)0.158813477f, (float16_t)0.0966796875f, (float16_t)0.18371582f, (float16_t)0.0781860352f, + (float16_t)0.142944336f, (float16_t)-0.0507202148f, (float16_t)0.0533447266f, (float16_t)0.21105957f, + (float16_t)0.143066406f, (float16_t)0.0916748047f, (float16_t)-0.229980469f, (float16_t)-0.0305633545f, + (float16_t)0.0272369385f, (float16_t)-0.121704102f, (float16_t)-0.0520019531f, (float16_t)-0.133422852f, + (float16_t)0.166503906f, (float16_t)-0.200683594f, (float16_t)-0.211669922f, (float16_t)-0.145507812f, + (float16_t)-0.161987305f, (float16_t)0.178222656f, (float16_t)0.222290039f, (float16_t)0.203491211f, + (float16_t)-0.117980957f, (float16_t)-0.172729492f, (float16_t)-0.00901794434f, (float16_t)-0.0245666504f, + (float16_t)-0.0422363281f, (float16_t)0.151367188f, (float16_t)-0.0194854736f, (float16_t)-0.110778809f, + (float16_t)0.124816895f, (float16_t)-0.0635375977f, (float16_t)0.14465332f, (float16_t)-0.177734375f, + (float16_t)0.0852661133f, (float16_t)0.149902344f, (float16_t)-0.224609375f, (float16_t)0.127197266f, + (float16_t)0.230957031f, (float16_t)-0.108764648f, (float16_t)-0.0871582031f, (float16_t)-0.198974609f, + (float16_t)0.227905273f, (float16_t)0.136230469f, (float16_t)-0.222900391f, (float16_t)0.0708618164f, + (float16_t)-0.244873047f, (float16_t)0.177978516f, (float16_t)0.0773925781f, (float16_t)0.0333557129f, + (float16_t)-0.206176758f, (float16_t)0.101257324f, (float16_t)-0.203125f, (float16_t)-0.194091797f, + (float16_t)0.214111328f, (float16_t)-0.106750488f, (float16_t)-0.133544922f, (float16_t)-0.139404297f, + (float16_t)0.186157227f, (float16_t)0.232543945f, (float16_t)-0.142456055f, (float16_t)0.169799805f, + (float16_t)-0.166015625f, (float16_t)0.16418457f, (float16_t)0.174682617f, (float16_t)0.0919189453f, + (float16_t)0.0238342285f, (float16_t)-0.149047852f, (float16_t)0.182739258f, (float16_t)0.121398926f, + (float16_t)-0.15222168f, (float16_t)-0.181030273f, (float16_t)-0.190185547f, (float16_t)-0.211791992f, + (float16_t)0.0638427734f, (float16_t)-0.114624023f, (float16_t)-0.079284668f, (float16_t)-0.204345703f, + (float16_t)0.0935668945f, (float16_t)0.134765625f, (float16_t)-0.233764648f, (float16_t)-0.05078125f, + (float16_t)0.151489258f, (float16_t)0.0585632324f, (float16_t)0.0662231445f, (float16_t)0.193969727f, + (float16_t)-0.185546875f, (float16_t)-0.117126465f, (float16_t)0.0139694214f, (float16_t)0.0883789062f, + (float16_t)0.0428466797f, (float16_t)-0.188110352f, (float16_t)0.0881958008f, (float16_t)-0.19128418f, + (float16_t)0.0662841797f, (float16_t)0.092956543f, (float16_t)0.0025138855f, (float16_t)-0.246459961f, + (float16_t)0.180053711f, (float16_t)0.208129883f, (float16_t)-0.0333862305f, (float16_t)-0.145874023f, + (float16_t)0.198120117f, (float16_t)0.142089844f, (float16_t)0.0589294434f, (float16_t)-0.228515625f, + (float16_t)-0.0338439941f, (float16_t)-0.0497741699f, (float16_t)0.0116958618f, (float16_t)0.00737762451f, + (float16_t)0.161743164f, (float16_t)0.212768555f, (float16_t)-0.103820801f, (float16_t)-0.192016602f, + (float16_t)-0.101196289f, (float16_t)-0.248779297f, (float16_t)-0.182495117f, (float16_t)-0.0517883301f, + (float16_t)0.197753906f, (float16_t)-0.196655273f, (float16_t)-0.0822143555f, (float16_t)-0.181152344f, + (float16_t)0.144165039f, (float16_t)0.191040039f, (float16_t)-0.0184936523f, (float16_t)0.00368881226f, + (float16_t)0.224731445f, (float16_t)0.138427734f, (float16_t)-0.112548828f, (float16_t)0.135253906f, + (float16_t)-0.156005859f, (float16_t)-0.000400543213f, (float16_t)-0.166381836f, (float16_t)-0.173950195f, + (float16_t)-0.129516602f, (float16_t)-0.202758789f, (float16_t)-0.152587891f, (float16_t)0.0946044922f, + (float16_t)0.072265625f, (float16_t)0.191772461f, (float16_t)-0.0700683594f, (float16_t)-0.174194336f, + (float16_t)0.116943359f, (float16_t)0.127197266f, (float16_t)0.236328125f, (float16_t)-0.171386719f, + (float16_t)-0.115234375f, (float16_t)0.0294189453f, (float16_t)-0.0386962891f, (float16_t)-0.0692138672f, + (float16_t)0.0211334229f, (float16_t)0.0939941406f, (float16_t)-0.155273438f, (float16_t)-0.034576416f, + (float16_t)-0.209228516f, (float16_t)-0.0322265625f, (float16_t)0.137939453f, (float16_t)-0.203857422f, + (float16_t)0.101013184f, (float16_t)-0.200927734f, (float16_t)-0.0262756348f, (float16_t)0.2265625f, + (float16_t)0.0366516113f, (float16_t)-0.151367188f, (float16_t)0.185302734f, (float16_t)-0.16003418f, + (float16_t)-0.161254883f, (float16_t)-0.193603516f, (float16_t)-0.235839844f, (float16_t)0.0369873047f, + (float16_t)-0.248046875f, (float16_t)0.137207031f, (float16_t)-0.100097656f, (float16_t)0.0585021973f, + (float16_t)0.0171203613f, (float16_t)0.113525391f, (float16_t)-0.05859375f, (float16_t)0.209106445f, + (float16_t)-0.0306549072f, (float16_t)0.21484375f, (float16_t)-0.0983886719f, (float16_t)-5.78165054e-05f, + (float16_t)0.156005859f, (float16_t)-0.0634765625f, (float16_t)0.248779297f, (float16_t)0.0431213379f, + (float16_t)-0.137451172f, (float16_t)0.130737305f, (float16_t)-0.188720703f, (float16_t)-0.0994873047f, + (float16_t)0.0763549805f, (float16_t)-0.179931641f, (float16_t)-0.247314453f, (float16_t)-0.230712891f, + (float16_t)0.184936523f, (float16_t)0.24597168f, (float16_t)-0.0974731445f, (float16_t)-0.174682617f, + (float16_t)-0.147827148f, (float16_t)-0.0516357422f, (float16_t)-0.2421875f, (float16_t)-0.180908203f, + (float16_t)0.105895996f, (float16_t)-0.202392578f, (float16_t)0.0157928467f, (float16_t)0.0485839844f, + (float16_t)0.214111328f, (float16_t)0.210327148f, (float16_t)-0.194091797f, (float16_t)0.105285645f, + (float16_t)0.0846557617f, (float16_t)-0.222412109f, (float16_t)0.10559082f, (float16_t)0.0246429443f, + (float16_t)0.185791016f, (float16_t)0.0332641602f, (float16_t)-0.0989379883f, (float16_t)0.175537109f, + (float16_t)0.0426940918f, (float16_t)-0.127807617f, (float16_t)-0.079284668f, (float16_t)-0.146972656f, + (float16_t)-0.114562988f, (float16_t)-0.0604858398f, (float16_t)-0.188110352f, (float16_t)-0.0295257568f, + (float16_t)-0.228271484f, (float16_t)0.0637207031f, (float16_t)0.0484008789f, (float16_t)0.137939453f, + (float16_t)-0.0673828125f, (float16_t)0.122802734f, (float16_t)0.00981140137f, (float16_t)0.0510559082f, + (float16_t)-0.0870361328f, (float16_t)-0.0716552734f, (float16_t)0.021987915f, (float16_t)-0.158813477f, + (float16_t)0.209228516f, (float16_t)0.167724609f, (float16_t)0.00413131714f, (float16_t)0.191040039f, + (float16_t)0.133056641f, (float16_t)0.0350646973f, (float16_t)0.0589904785f, (float16_t)-0.204101562f, + (float16_t)-0.134399414f, (float16_t)0.100219727f, (float16_t)0.130981445f, (float16_t)0.0646972656f, + (float16_t)-0.0356445312f, (float16_t)0.0630493164f, (float16_t)-0.23034668f, (float16_t)0.210449219f, + (float16_t)0.241577148f, (float16_t)0.151245117f, (float16_t)0.0139312744f, (float16_t)-0.0631713867f, + (float16_t)-0.133178711f, (float16_t)0.101257324f, (float16_t)0.0317382812f, (float16_t)0.0932006836f, + (float16_t)0.0621948242f, (float16_t)0.0625f, (float16_t)-0.11541748f, (float16_t)-0.219970703f, + (float16_t)0.195922852f, (float16_t)0.16809082f, (float16_t)-0.153442383f, (float16_t)0.188232422f, + (float16_t)0.0237884521f, (float16_t)0.0961303711f, (float16_t)0.233764648f, (float16_t)-0.0260467529f, + (float16_t)-0.237304688f, (float16_t)-0.0372619629f, (float16_t)0.135253906f, (float16_t)-0.24230957f, + (float16_t)0.219970703f, (float16_t)0.170288086f, (float16_t)0.0389404297f, (float16_t)-0.142456055f, + (float16_t)-0.13671875f, (float16_t)0.053527832f, (float16_t)0.0928955078f, (float16_t)0.00923919678f, + (float16_t)-0.000328779221f, (float16_t)-0.0775756836f, (float16_t)0.153564453f, (float16_t)-0.20715332f, + (float16_t)0.0422363281f, (float16_t)0.0106964111f, (float16_t)0.120788574f, (float16_t)-0.0861206055f, + (float16_t)-0.153564453f, (float16_t)0.214477539f, (float16_t)0.135620117f, (float16_t)-0.099609375f, + (float16_t)-0.0574035645f, (float16_t)-0.0551147461f, (float16_t)0.149780273f, (float16_t)0.190185547f, + (float16_t)0.0377502441f, (float16_t)-0.13293457f, (float16_t)0.106994629f, (float16_t)0.101867676f, + (float16_t)-0.244873047f, (float16_t)-0.0880737305f, (float16_t)-0.0673828125f, (float16_t)0.158081055f, + (float16_t)0.201293945f, (float16_t)0.127197266f, (float16_t)0.0522766113f, (float16_t)0.111206055f, + (float16_t)-0.232177734f, (float16_t)-0.248168945f, (float16_t)0.121887207f, (float16_t)-0.00532150269f, + (float16_t)-0.134399414f, (float16_t)0.164794922f, (float16_t)-0.0704345703f, (float16_t)0.0659179688f, + (float16_t)0.209594727f, (float16_t)-0.144165039f, (float16_t)0.108093262f, (float16_t)-0.145996094f, + (float16_t)-0.0731201172f, (float16_t)0.028717041f, (float16_t)-0.148925781f, (float16_t)-0.0677490234f, + (float16_t)0.0649414062f, (float16_t)0.0138092041f, (float16_t)-0.15637207f, (float16_t)0.0382080078f, + (float16_t)-0.212158203f, (float16_t)-0.00849151611f, (float16_t)-0.176635742f, (float16_t)0.085144043f, + (float16_t)-0.227661133f, (float16_t)-0.0317382812f, (float16_t)-0.00576782227f, (float16_t)0.103942871f, + (float16_t)0.0718383789f, (float16_t)0.0645751953f, (float16_t)-0.0511474609f, (float16_t)0.0280761719f, + (float16_t)-0.174682617f, (float16_t)0.0846557617f, (float16_t)-0.133911133f, (float16_t)0.226806641f, + (float16_t)-0.108032227f, (float16_t)-0.154052734f, (float16_t)0.0953979492f, (float16_t)0.0642700195f, + (float16_t)0.149536133f, (float16_t)-0.146118164f, (float16_t)0.0702514648f, (float16_t)0.0901489258f, + (float16_t)0.0650634766f, (float16_t)0.0335693359f, (float16_t)0.084777832f, (float16_t)-0.0999145508f, + (float16_t)0.151367188f, (float16_t)-0.170776367f, (float16_t)0.205566406f, (float16_t)0.194824219f, + (float16_t)-0.156005859f, (float16_t)0.0775756836f, (float16_t)0.00340080261f, (float16_t)0.186279297f, + (float16_t)-0.0422363281f, (float16_t)0.211669922f, (float16_t)-0.140014648f, (float16_t)0.0160522461f, + (float16_t)0.00509643555f, (float16_t)-0.23059082f, (float16_t)0.189575195f, (float16_t)-0.000174283981f, + (float16_t)0.0235443115f, (float16_t)-0.220825195f, (float16_t)0.0149917603f, (float16_t)-0.0642089844f, + (float16_t)0.00233650208f, (float16_t)0.10723877f, (float16_t)-0.0868530273f, (float16_t)-0.18347168f, + (float16_t)0.169555664f, (float16_t)0.029586792f, (float16_t)-0.21105957f, (float16_t)-0.0821533203f, + (float16_t)-0.215820312f, (float16_t)0.0216827393f, (float16_t)-0.124328613f, (float16_t)-0.0110855103f, + (float16_t)0.108398438f, (float16_t)-0.143432617f, (float16_t)-0.126953125f, (float16_t)0.202026367f, + (float16_t)0.24987793f, (float16_t)0.0780639648f, (float16_t)-0.041229248f, (float16_t)-0.0689697266f, + (float16_t)0.00548553467f, (float16_t)-0.0975952148f, (float16_t)-0.198120117f, (float16_t)-0.0643920898f, + (float16_t)-0.0687255859f, (float16_t)-0.139038086f, (float16_t)0.0243682861f, (float16_t)-0.234130859f, + (float16_t)0.156494141f, (float16_t)-0.160400391f, (float16_t)0.147094727f, (float16_t)0.155517578f, + (float16_t)0.148925781f, (float16_t)0.0989379883f, (float16_t)-0.173095703f, (float16_t)-0.223266602f, + (float16_t)0.109069824f, (float16_t)-0.214599609f, (float16_t)-0.050201416f, (float16_t)0.107299805f, + (float16_t)-0.126464844f, (float16_t)0.182617188f, (float16_t)-0.190795898f, (float16_t)-0.0553894043f, + (float16_t)0.143798828f, (float16_t)-0.190551758f, (float16_t)0.00197029114f, (float16_t)0.0854492188f, + (float16_t)-0.0508422852f, (float16_t)-0.110595703f, (float16_t)-0.0135803223f, (float16_t)-0.245361328f, + (float16_t)0.197143555f, (float16_t)0.166381836f, (float16_t)0.21887207f, (float16_t)0.0281677246f, + (float16_t)0.025604248f, (float16_t)0.000285387039f, (float16_t)0.00394439697f, (float16_t)-0.19519043f, + (float16_t)0.0853881836f, (float16_t)0.196655273f, (float16_t)0.175048828f, (float16_t)-0.102478027f, + (float16_t)-0.181396484f, (float16_t)-0.204345703f, (float16_t)-0.173706055f, (float16_t)-0.0217437744f, + (float16_t)0.00517654419f, (float16_t)0.211669922f, (float16_t)0.12890625f, (float16_t)0.194824219f, + (float16_t)0.197875977f, (float16_t)-0.239501953f, (float16_t)0.23815918f, (float16_t)-0.0374145508f, + (float16_t)-0.128295898f, (float16_t)-0.166625977f, (float16_t)-0.211914062f, (float16_t)-0.0178375244f, + (float16_t)-0.18762207f, (float16_t)0.0634155273f, (float16_t)0.149658203f, (float16_t)-0.132202148f, + (float16_t)0.184936523f, (float16_t)-0.080078125f, (float16_t)-0.0665893555f, (float16_t)0.146118164f, + (float16_t)0.057800293f, (float16_t)0.127563477f, (float16_t)-0.157470703f, (float16_t)-0.109191895f, + (float16_t)0.2109375f, (float16_t)-0.187866211f, (float16_t)0.234130859f, (float16_t)0.141601562f, + (float16_t)0.172607422f, (float16_t)-0.0079574585f, (float16_t)-0.159667969f, (float16_t)0.210693359f, + (float16_t)0.0462646484f, (float16_t)0.0179595947f, (float16_t)-0.0439453125f, (float16_t)0.176757812f, + (float16_t)-0.232910156f, (float16_t)-0.0565490723f, (float16_t)-0.0401000977f, (float16_t)-0.23059082f, + (float16_t)-0.219360352f, (float16_t)-0.0130462646f, (float16_t)0.241699219f, (float16_t)0.12109375f, + (float16_t)-0.196777344f, (float16_t)-0.0602111816f, (float16_t)-0.107116699f, (float16_t)0.231933594f, + (float16_t)-0.181640625f, (float16_t)0.185668945f, (float16_t)0.00137424469f, (float16_t)0.233032227f, + (float16_t)0.166015625f, (float16_t)0.205810547f, (float16_t)-0.184814453f, (float16_t)-0.0115737915f, + (float16_t)-0.031829834f, (float16_t)-0.0425109863f, (float16_t)-0.008934021f, (float16_t)0.0610961914f, + (float16_t)-0.181762695f, (float16_t)0.0423278809f, (float16_t)-0.194824219f, (float16_t)0.229248047f, + (float16_t)-0.0671386719f, (float16_t)0.151123047f, (float16_t)0.0941162109f, (float16_t)-0.187988281f, + (float16_t)0.0025138855f, (float16_t)-0.164550781f, (float16_t)0.156982422f, (float16_t)0.172973633f, + (float16_t)0.015914917f, (float16_t)-0.121154785f, (float16_t)-0.065612793f, (float16_t)0.178344727f, + (float16_t)-0.238647461f, (float16_t)-0.00176429749f, (float16_t)-0.187255859f, (float16_t)0.210327148f, + (float16_t)-0.1796875f, (float16_t)0.0466918945f, (float16_t)0.0661010742f, (float16_t)0.0319519043f, + (float16_t)-0.232177734f, (float16_t)-0.247558594f, (float16_t)-0.181640625f, (float16_t)0.0395507812f, + (float16_t)0.155151367f, (float16_t)-0.156005859f, (float16_t)-0.0207977295f, (float16_t)-0.116943359f, + (float16_t)0.106506348f, (float16_t)0.137207031f, (float16_t)-0.229980469f, (float16_t)0.0158081055f, + (float16_t)0.154418945f, (float16_t)-0.205932617f, (float16_t)-0.179077148f, (float16_t)0.1328125f, + (float16_t)0.0628662109f, (float16_t)-0.206787109f, (float16_t)-0.111633301f, (float16_t)-0.151245117f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_biases_data.h new file mode 100644 index 00000000..9dea0ea4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_biases_data.h @@ -0,0 +1,13 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_fc_biases_data[12] = {(float16_t)-0.0290374756f, + (float16_t)-0.00316047668f, + (float16_t)0.0229492188f, + (float16_t)0.00551605225f, + (float16_t)0.0250091553f, + (float16_t)-0.0442810059f, + (float16_t)0.0367126465f, + (float16_t)0.0375671387f, + (float16_t)0.0320739746f, + (float16_t)0.0360107422f, + (float16_t)0.0263671875f, + (float16_t)0.00165367126f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_weights_data.h new file mode 100644 index 00000000..6194ddb5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/fc_weights_data.h @@ -0,0 +1,194 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_fc_weights_data[768] = { + (float16_t)-0.0241546631f, (float16_t)-0.139526367f, (float16_t)0.0682983398f, (float16_t)0.117004395f, + (float16_t)0.0824584961f, (float16_t)-0.127929688f, (float16_t)0.0872192383f, (float16_t)0.194702148f, + (float16_t)-0.120178223f, (float16_t)-0.146728516f, (float16_t)-0.141845703f, (float16_t)0.11315918f, + (float16_t)0.0381164551f, (float16_t)0.0645751953f, (float16_t)-0.0819091797f, (float16_t)-0.121948242f, + (float16_t)-0.109558105f, (float16_t)-0.113830566f, (float16_t)-0.0776367188f, (float16_t)0.136108398f, + (float16_t)-0.00663375854f, (float16_t)0.0880737305f, (float16_t)0.0873413086f, (float16_t)0.0947265625f, + (float16_t)-0.166259766f, (float16_t)-0.113647461f, (float16_t)-0.165771484f, (float16_t)-0.0558776855f, + (float16_t)0.0567321777f, (float16_t)-0.10168457f, (float16_t)0.154418945f, (float16_t)0.0830688477f, + (float16_t)-0.136230469f, (float16_t)-0.159790039f, (float16_t)0.19519043f, (float16_t)-0.0884399414f, + (float16_t)-0.0744018555f, (float16_t)-0.130859375f, (float16_t)-0.0115737915f, (float16_t)0.19128418f, + (float16_t)0.0602722168f, (float16_t)-0.133911133f, (float16_t)0.0906982422f, (float16_t)-0.061126709f, + (float16_t)0.049621582f, (float16_t)-0.166503906f, (float16_t)-0.102783203f, (float16_t)0.155639648f, + (float16_t)0.184204102f, (float16_t)0.111816406f, (float16_t)-0.00616073608f, (float16_t)-0.000730037689f, + (float16_t)0.180908203f, (float16_t)-0.145019531f, (float16_t)0.111999512f, (float16_t)0.134643555f, + (float16_t)-0.193481445f, (float16_t)0.106323242f, (float16_t)-0.0816040039f, (float16_t)-0.0772094727f, + (float16_t)0.0744628906f, (float16_t)0.129394531f, (float16_t)-0.178833008f, (float16_t)0.0173034668f, + (float16_t)0.0196990967f, (float16_t)0.0841674805f, (float16_t)0.0592651367f, (float16_t)-0.0532226562f, + (float16_t)-0.0413208008f, (float16_t)0.085144043f, (float16_t)0.10559082f, (float16_t)-0.184692383f, + (float16_t)0.194580078f, (float16_t)0.180297852f, (float16_t)-0.143554688f, (float16_t)-0.0822753906f, + (float16_t)-0.101318359f, (float16_t)-0.0437011719f, (float16_t)-0.192749023f, (float16_t)-0.196044922f, + (float16_t)-0.0628662109f, (float16_t)-0.055480957f, (float16_t)-0.101623535f, (float16_t)-0.0186309814f, + (float16_t)0.0653076172f, (float16_t)-0.163818359f, (float16_t)-0.106567383f, (float16_t)0.1015625f, + (float16_t)-0.0653686523f, (float16_t)0.0617370605f, (float16_t)0.121520996f, (float16_t)0.114501953f, + (float16_t)-0.174316406f, (float16_t)-0.0110092163f, (float16_t)0.119689941f, (float16_t)0.00344848633f, + (float16_t)0.030090332f, (float16_t)-0.147094727f, (float16_t)-0.0318908691f, (float16_t)-0.11706543f, + (float16_t)-0.0510864258f, (float16_t)0.111328125f, (float16_t)0.0273895264f, (float16_t)0.114868164f, + (float16_t)-0.0265960693f, (float16_t)-0.131469727f, (float16_t)-0.0972290039f, (float16_t)-0.094543457f, + (float16_t)-0.162719727f, (float16_t)0.186767578f, (float16_t)0.114562988f, (float16_t)-0.0302124023f, + (float16_t)-0.0862426758f, (float16_t)0.0313720703f, (float16_t)-0.165649414f, (float16_t)-0.0235443115f, + (float16_t)0.171020508f, (float16_t)0.0933837891f, (float16_t)0.099609375f, (float16_t)-0.0960083008f, + (float16_t)-0.00387001038f, (float16_t)-0.0557861328f, (float16_t)-0.165771484f, (float16_t)0.171264648f, + (float16_t)0.0462646484f, (float16_t)-0.192871094f, (float16_t)0.105163574f, (float16_t)-0.0273284912f, + (float16_t)0.0531616211f, (float16_t)0.00317955017f, (float16_t)0.171508789f, (float16_t)-0.0922241211f, + (float16_t)-0.170898438f, (float16_t)0.197631836f, (float16_t)0.0353393555f, (float16_t)-0.0841064453f, + (float16_t)-0.12878418f, (float16_t)-0.170654297f, (float16_t)0.134277344f, (float16_t)-0.092956543f, + (float16_t)-0.0279693604f, (float16_t)0.0623168945f, (float16_t)0.103759766f, (float16_t)-0.183959961f, + (float16_t)0.125244141f, (float16_t)-0.193237305f, (float16_t)-0.194946289f, (float16_t)-0.0449829102f, + (float16_t)0.045135498f, (float16_t)0.13293457f, (float16_t)-0.142333984f, (float16_t)-0.174194336f, + (float16_t)0.104614258f, (float16_t)0.00763320923f, (float16_t)-0.0195007324f, (float16_t)0.065612793f, + (float16_t)0.0356750488f, (float16_t)-0.0443725586f, (float16_t)0.124816895f, (float16_t)0.186889648f, + (float16_t)0.181396484f, (float16_t)0.179321289f, (float16_t)-0.00738525391f, (float16_t)0.0366821289f, + (float16_t)-0.0288696289f, (float16_t)0.12890625f, (float16_t)-0.0966796875f, (float16_t)-0.0702514648f, + (float16_t)0.196777344f, (float16_t)0.1015625f, (float16_t)-0.0217590332f, (float16_t)-0.0806274414f, + (float16_t)-0.0829467773f, (float16_t)0.0695800781f, (float16_t)0.0769042969f, (float16_t)-0.0703125f, + (float16_t)0.127807617f, (float16_t)-0.0934448242f, (float16_t)0.113220215f, (float16_t)-0.0185546875f, + (float16_t)-0.080871582f, (float16_t)-0.10168457f, (float16_t)0.135009766f, (float16_t)0.0587158203f, + (float16_t)-0.106323242f, (float16_t)-0.179199219f, (float16_t)-0.14440918f, (float16_t)-0.000296831131f, + (float16_t)0.0375671387f, (float16_t)0.188232422f, (float16_t)0.0518798828f, (float16_t)-0.0619812012f, + (float16_t)-0.169677734f, (float16_t)-0.0720825195f, (float16_t)0.0420532227f, (float16_t)-0.0141220093f, + (float16_t)-0.121948242f, (float16_t)0.030380249f, (float16_t)-0.0734863281f, (float16_t)-0.0947265625f, + (float16_t)0.0211181641f, (float16_t)0.0255432129f, (float16_t)0.0237731934f, (float16_t)-0.113891602f, + (float16_t)0.0755004883f, (float16_t)0.0780639648f, (float16_t)0.135742188f, (float16_t)-0.10723877f, + (float16_t)0.194702148f, (float16_t)0.00264167786f, (float16_t)-0.0519714355f, (float16_t)-0.0198822021f, + (float16_t)0.021270752f, (float16_t)-0.139770508f, (float16_t)0.111633301f, (float16_t)-0.145263672f, + (float16_t)-0.0897827148f, (float16_t)-0.0275726318f, (float16_t)-0.141113281f, (float16_t)-0.194580078f, + (float16_t)-0.19934082f, (float16_t)-0.157958984f, (float16_t)-0.192382812f, (float16_t)0.17199707f, + (float16_t)0.163208008f, (float16_t)0.0690307617f, (float16_t)0.139526367f, (float16_t)-0.0835571289f, + (float16_t)0.0548706055f, (float16_t)0.129760742f, (float16_t)-0.0131378174f, (float16_t)0.0855712891f, + (float16_t)0.0170135498f, (float16_t)0.0338439941f, (float16_t)-0.189208984f, (float16_t)0.169921875f, + (float16_t)0.15625f, (float16_t)-0.151733398f, (float16_t)0.170776367f, (float16_t)0.137573242f, + (float16_t)-0.134521484f, (float16_t)-0.197875977f, (float16_t)0.0249023438f, (float16_t)0.120422363f, + (float16_t)0.184204102f, (float16_t)0.151733398f, (float16_t)-0.18371582f, (float16_t)-0.176513672f, + (float16_t)0.178588867f, (float16_t)0.16809082f, (float16_t)-0.00897216797f, (float16_t)-0.183349609f, + (float16_t)0.193237305f, (float16_t)0.0567321777f, (float16_t)0.024017334f, 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(float16_t)-0.161987305f, (float16_t)0.00436782837f, (float16_t)-0.189575195f, (float16_t)-0.160644531f, + (float16_t)-0.165893555f, (float16_t)0.135498047f, (float16_t)0.00309371948f, (float16_t)-0.193969727f, + (float16_t)-0.0979003906f, (float16_t)-0.103637695f, (float16_t)0.00231361389f, (float16_t)-0.0115890503f, + (float16_t)0.176391602f, (float16_t)0.190063477f, (float16_t)0.00694656372f, (float16_t)0.117614746f, + (float16_t)-0.0572814941f, (float16_t)-0.0564575195f, (float16_t)0.130371094f, (float16_t)0.0809936523f, + (float16_t)-0.00182247162f, (float16_t)-0.056427002f, (float16_t)-0.165527344f, (float16_t)-0.0153808594f, + (float16_t)-0.12322998f, (float16_t)-0.162719727f, (float16_t)-0.0795288086f, (float16_t)0.0910644531f, + (float16_t)0.11730957f, (float16_t)-0.0273284912f, (float16_t)0.0134735107f, (float16_t)0.152832031f, + (float16_t)0.101135254f, (float16_t)-0.00870513916f, (float16_t)-0.0903930664f, (float16_t)0.0359191895f, + (float16_t)-0.00860595703f, (float16_t)-0.119934082f, (float16_t)0.0245819092f, (float16_t)0.117614746f, + (float16_t)-0.00594711304f, (float16_t)-0.0943603516f, (float16_t)0.109191895f, (float16_t)-0.0844726562f, + (float16_t)-0.150512695f, (float16_t)0.00199127197f, (float16_t)0.139160156f, (float16_t)0.102844238f, + (float16_t)-0.0441589355f, (float16_t)0.0404968262f, (float16_t)0.113098145f, (float16_t)-0.0438537598f, + (float16_t)-0.15222168f, (float16_t)-0.0986938477f, (float16_t)-0.154418945f, (float16_t)-0.044921875f, + (float16_t)-0.175048828f, (float16_t)0.151123047f, (float16_t)-0.182739258f, (float16_t)0.0936279297f, + (float16_t)-0.149902344f, (float16_t)-0.0608520508f, (float16_t)0.152099609f, (float16_t)-0.160522461f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/input_data.h new file mode 100644 index 00000000..062c418d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/input_data.h @@ -0,0 +1,8001 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_input_data[8000] = {(float16_t)-0.541992188f, + (float16_t)-0.345214844f, + (float16_t)0.206787109f, + (float16_t)0.925292969f, + (float16_t)-0.942871094f, + (float16_t)0.71484375f, + (float16_t)0.0718383789f, + (float16_t)-0.64453125f, + (float16_t)-0.660644531f, + (float16_t)0.231445312f, + (float16_t)0.438232422f, + (float16_t)-0.531738281f, + (float16_t)-0.493652344f, + (float16_t)0.300292969f, + (float16_t)-0.044128418f, + (float16_t)-0.792480469f, + (float16_t)0.642578125f, + (float16_t)0.65625f, + (float16_t)-0.350097656f, + (float16_t)0.604980469f, + (float16_t)0.0475463867f, + (float16_t)-0.499267578f, + (float16_t)0.74609375f, + (float16_t)0.258789062f, + (float16_t)0.973632812f, + (float16_t)0.795898438f, + (float16_t)0.7578125f, + (float16_t)0.375488281f, + (float16_t)0.606445312f, + (float16_t)0.587402344f, + (float16_t)-0.225585938f, + 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(float16_t)0.961425781f, + (float16_t)-0.672363281f, + (float16_t)0.301269531f, + (float16_t)-0.909179688f, + (float16_t)0.904296875f, + (float16_t)-0.130737305f, + (float16_t)0.616699219f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/output_ref_data.h new file mode 100644 index 00000000..d43219d9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_output_ref_data[12] = {(float16_t)0.0775146484f, + (float16_t)0.081237793f, + (float16_t)0.0840454102f, + (float16_t)0.0834960938f, + (float16_t)0.0865478516f, + (float16_t)0.0793457031f, + (float16_t)0.0885620117f, + (float16_t)0.0869750977f, + (float16_t)0.0872802734f, + (float16_t)0.0823974609f, + (float16_t)0.0816040039f, + (float16_t)0.0809936523f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_biases_data.h new file mode 100644 index 00000000..01371dc8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv0_biases_data[64] = { + (float16_t)0.0314941406f, (float16_t)0.0272064209f, (float16_t)0.0285949707f, (float16_t)0.00788879395f, + (float16_t)-0.0286254883f, (float16_t)0.0269927979f, (float16_t)0.0354919434f, (float16_t)-0.0121231079f, + (float16_t)-0.000853538513f, (float16_t)-0.0225982666f, (float16_t)-0.0423278809f, (float16_t)-0.00415802002f, + (float16_t)0.0209197998f, (float16_t)-0.00294494629f, (float16_t)-0.0483093262f, (float16_t)-0.026260376f, + (float16_t)0.0343933105f, (float16_t)0.0064125061f, (float16_t)-0.0351257324f, (float16_t)-0.0348815918f, + (float16_t)0.00340461731f, (float16_t)0.00402069092f, (float16_t)0.0137863159f, (float16_t)-0.0195922852f, + (float16_t)0.0352783203f, (float16_t)-0.0342712402f, (float16_t)0.0420227051f, (float16_t)-0.020401001f, + (float16_t)0.0260314941f, (float16_t)-0.0454406738f, (float16_t)0.0411987305f, (float16_t)-0.040435791f, + (float16_t)0.0356445312f, (float16_t)0.0411987305f, (float16_t)0.00714111328f, (float16_t)0.0411682129f, + (float16_t)0.0393981934f, (float16_t)-0.022354126f, (float16_t)-0.029296875f, (float16_t)0.03515625f, + (float16_t)0.0236816406f, (float16_t)-0.0445556641f, (float16_t)0.0259399414f, (float16_t)0.0473022461f, + (float16_t)0.0264129639f, (float16_t)0.00407409668f, (float16_t)-0.0294036865f, (float16_t)-0.0143661499f, + (float16_t)0.0346984863f, (float16_t)0.0161743164f, (float16_t)0.042388916f, (float16_t)-0.00215148926f, + (float16_t)0.00261497498f, (float16_t)-0.0159759521f, (float16_t)-0.0420532227f, (float16_t)-0.0205078125f, + (float16_t)0.0419006348f, (float16_t)-0.0307312012f, (float16_t)-0.0343933105f, (float16_t)0.0106887817f, + (float16_t)0.0304260254f, (float16_t)0.00632095337f, (float16_t)0.0210723877f, (float16_t)0.040802002f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_weights_data.h new file mode 100644 index 00000000..e2d2c7c0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv0_weights_data.h @@ -0,0 +1,1026 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv0_weights_data[4096] = { + (float16_t)-0.0307769775f, (float16_t)0.0419006348f, (float16_t)-0.0147018433f, (float16_t)0.0605773926f, + (float16_t)0.0949707031f, (float16_t)-0.0708618164f, (float16_t)0.0899047852f, (float16_t)0.0461120605f, + (float16_t)-0.0978393555f, (float16_t)0.0200042725f, (float16_t)0.00561904907f, (float16_t)0.0817260742f, + (float16_t)-0.107666016f, (float16_t)0.0107574463f, (float16_t)0.0957641602f, (float16_t)-0.0554504395f, + (float16_t)0.0181732178f, (float16_t)0.104858398f, (float16_t)0.0784912109f, (float16_t)-0.114135742f, + (float16_t)0.018661499f, (float16_t)-0.119506836f, (float16_t)0.0667724609f, (float16_t)-0.0127410889f, + (float16_t)0.108154297f, (float16_t)0.000259399414f, (float16_t)0.104431152f, (float16_t)0.0201721191f, + (float16_t)-0.0677490234f, (float16_t)-0.0575866699f, (float16_t)-0.0313110352f, (float16_t)-0.109680176f, + (float16_t)-0.0538024902f, (float16_t)-0.0200042725f, (float16_t)-0.00473022461f, (float16_t)0.0177001953f, + (float16_t)0.0538635254f, (float16_t)-0.0169525146f, (float16_t)-0.0989379883f, (float16_t)-0.0600891113f, + (float16_t)-0.0556640625f, (float16_t)0.105285645f, (float16_t)0.0639648438f, (float16_t)0.105834961f, + (float16_t)0.00434875488f, (float16_t)0.0953979492f, (float16_t)-0.11340332f, (float16_t)0.027923584f, + (float16_t)-0.0321655273f, (float16_t)-0.000427484512f, (float16_t)-0.0417175293f, (float16_t)0.11529541f, + (float16_t)0.0414123535f, (float16_t)0.048248291f, (float16_t)0.0901489258f, (float16_t)0.0947875977f, + (float16_t)-0.00651931763f, (float16_t)0.0371398926f, (float16_t)-0.112243652f, (float16_t)-0.0699462891f, + (float16_t)0.0289154053f, (float16_t)-0.109375f, (float16_t)-0.0289459229f, (float16_t)0.0187530518f, + (float16_t)-0.00656890869f, (float16_t)-0.0323791504f, (float16_t)-0.0780639648f, (float16_t)0.0671386719f, + (float16_t)0.069519043f, (float16_t)-0.0274810791f, (float16_t)-0.0416870117f, (float16_t)-0.0541687012f, + (float16_t)-0.0955810547f, (float16_t)0.068359375f, (float16_t)0.00183582306f, (float16_t)-0.0310668945f, + (float16_t)-0.0765380859f, (float16_t)-0.0942993164f, (float16_t)0.0496520996f, (float16_t)0.114440918f, + (float16_t)0.0536499023f, (float16_t)-0.0635375977f, (float16_t)-0.109069824f, (float16_t)-0.0357666016f, + (float16_t)0.0596618652f, (float16_t)0.0726928711f, (float16_t)0.0973510742f, (float16_t)0.0885009766f, + (float16_t)-0.0928344727f, (float16_t)-0.0447998047f, (float16_t)0.109741211f, (float16_t)0.0532226562f, + (float16_t)-0.111877441f, (float16_t)0.0439147949f, (float16_t)-0.0920410156f, (float16_t)-0.0280609131f, + (float16_t)-0.0271453857f, (float16_t)0.0948486328f, (float16_t)0.106445312f, (float16_t)-0.0686035156f, + (float16_t)0.0588989258f, (float16_t)-0.00419998169f, (float16_t)0.0874633789f, (float16_t)0.0455627441f, + (float16_t)-0.0113220215f, (float16_t)0.0259094238f, (float16_t)0.0190582275f, (float16_t)0.0666503906f, + (float16_t)0.000953197479f, (float16_t)0.0847167969f, (float16_t)-0.0438537598f, (float16_t)0.0463562012f, + (float16_t)-0.0449523926f, (float16_t)0.10748291f, (float16_t)-0.0812988281f, (float16_t)-0.0463562012f, + (float16_t)-0.00426101685f, (float16_t)0.00912475586f, (float16_t)0.0172271729f, (float16_t)-0.0978393555f, + (float16_t)-0.0500793457f, (float16_t)0.0134277344f, (float16_t)-0.0991210938f, (float16_t)-0.117614746f, + (float16_t)-0.0309143066f, (float16_t)0.0891113281f, (float16_t)-0.00843811035f, (float16_t)0.0776367188f, + (float16_t)0.113464355f, (float16_t)-0.0129928589f, (float16_t)0.0106506348f, (float16_t)-0.0773925781f, + (float16_t)-0.110290527f, (float16_t)0.0892333984f, (float16_t)0.0677490234f, (float16_t)0.069519043f, + (float16_t)0.114746094f, (float16_t)-0.0261688232f, (float16_t)0.0361328125f, (float16_t)-0.00547790527f, + (float16_t)0.113464355f, (float16_t)0.0339050293f, (float16_t)-0.0498046875f, (float16_t)0.0283050537f, + (float16_t)-0.0171203613f, (float16_t)0.0673217773f, (float16_t)-0.119384766f, (float16_t)0.110961914f, + (float16_t)-0.112976074f, (float16_t)-0.0913085938f, (float16_t)0.0686035156f, (float16_t)0.0235900879f, + (float16_t)-0.0729370117f, (float16_t)-0.0407714844f, (float16_t)-0.105834961f, (float16_t)-0.0418701172f, + (float16_t)-0.0257263184f, (float16_t)0.0435791016f, (float16_t)-0.0297393799f, (float16_t)-0.032409668f, + (float16_t)0.0127258301f, (float16_t)-0.040435791f, (float16_t)-0.0265655518f, (float16_t)0.0144271851f, + (float16_t)0.114624023f, (float16_t)-0.0858764648f, (float16_t)0.0161132812f, (float16_t)-0.0473937988f, + (float16_t)-0.0963134766f, (float16_t)0.0191192627f, (float16_t)-0.0921630859f, (float16_t)-0.00334358215f, + (float16_t)-0.0417785645f, (float16_t)-0.0941772461f, (float16_t)0.0203552246f, (float16_t)0.0105438232f, + (float16_t)0.073425293f, (float16_t)0.0816650391f, (float16_t)-0.0384521484f, (float16_t)0.0933837891f, + (float16_t)-0.023651123f, (float16_t)0.057434082f, (float16_t)-0.104003906f, (float16_t)-0.0740356445f, + (float16_t)-0.0886230469f, (float16_t)0.115661621f, (float16_t)-0.0389404297f, (float16_t)0.0575256348f, + (float16_t)0.0911865234f, (float16_t)-0.112548828f, (float16_t)-0.107849121f, (float16_t)-0.11151123f, + (float16_t)-0.116394043f, (float16_t)-0.0385131836f, (float16_t)-0.0522155762f, (float16_t)0.0734863281f, + (float16_t)-0.0495605469f, (float16_t)-0.0178222656f, (float16_t)-0.0479431152f, (float16_t)0.00639343262f, + (float16_t)0.0592956543f, (float16_t)-0.0948486328f, (float16_t)-0.00361251831f, (float16_t)0.101623535f, + (float16_t)-0.101928711f, (float16_t)0.0638427734f, (float16_t)0.0274200439f, (float16_t)-0.0439758301f, + (float16_t)-0.040802002f, (float16_t)0.0241241455f, (float16_t)-0.101745605f, (float16_t)-0.0818481445f, + (float16_t)-0.0313720703f, (float16_t)-0.0708007812f, (float16_t)-0.105041504f, (float16_t)0.103881836f, + (float16_t)0.0207214355f, (float16_t)-0.0205230713f, (float16_t)-0.118774414f, (float16_t)-0.0157623291f, + (float16_t)-0.0153808594f, (float16_t)-0.0380554199f, (float16_t)0.0450744629f, (float16_t)0.0281219482f, + (float16_t)0.0269622803f, (float16_t)0.0210113525f, (float16_t)-0.0935668945f, (float16_t)-0.069152832f, + (float16_t)0.000899791718f, (float16_t)-0.102294922f, (float16_t)-0.0474853516f, (float16_t)-0.115844727f, + (float16_t)0.0308685303f, (float16_t)-0.0184631348f, (float16_t)-0.0677490234f, (float16_t)0.118713379f, + (float16_t)-0.0697631836f, (float16_t)0.0321350098f, (float16_t)0.077331543f, (float16_t)0.108093262f, + (float16_t)0.046081543f, (float16_t)-0.0329589844f, (float16_t)0.107788086f, (float16_t)-0.0160064697f, + (float16_t)-0.102966309f, (float16_t)0.0166168213f, (float16_t)0.0453186035f, (float16_t)-0.0811767578f, + (float16_t)0.102355957f, (float16_t)-0.092590332f, (float16_t)0.0179138184f, (float16_t)-0.0221557617f, + (float16_t)-0.0550231934f, (float16_t)-0.0850219727f, (float16_t)-0.0302581787f, (float16_t)-0.0219421387f, + (float16_t)0.0177612305f, (float16_t)0.0640258789f, (float16_t)-0.0963134766f, (float16_t)0.0943603516f, + (float16_t)-0.0537109375f, (float16_t)0.0541992188f, (float16_t)0.0791625977f, (float16_t)0.0816040039f, + (float16_t)0.0576477051f, (float16_t)-0.0135345459f, (float16_t)-0.105407715f, (float16_t)-0.0684814453f, + (float16_t)-0.00960540771f, (float16_t)0.107910156f, (float16_t)-0.0874633789f, 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(float16_t)0.0286560059f, (float16_t)-0.0369262695f, (float16_t)-0.10760498f, + (float16_t)-0.0687255859f, (float16_t)0.00833129883f, (float16_t)0.115905762f, (float16_t)0.0420837402f, + (float16_t)0.024597168f, (float16_t)-0.0761108398f, (float16_t)0.0944213867f, (float16_t)0.104248047f, + (float16_t)-0.0523376465f, (float16_t)0.110961914f, (float16_t)-0.0117416382f, (float16_t)0.0319213867f, + (float16_t)-0.037322998f, (float16_t)-0.0701904297f, (float16_t)0.0530395508f, (float16_t)-0.0189056396f, + (float16_t)-0.0737304688f, (float16_t)0.112915039f, (float16_t)0.103759766f, (float16_t)0.0464782715f, + (float16_t)0.0203399658f, (float16_t)0.0351257324f, (float16_t)0.0860595703f, (float16_t)0.0811767578f, + (float16_t)-0.0250549316f, (float16_t)0.114624023f, (float16_t)0.0747680664f, (float16_t)-0.00445556641f, + (float16_t)0.00757980347f, (float16_t)0.0556030273f, (float16_t)0.0580444336f, (float16_t)-0.105285645f, + (float16_t)0.0707397461f, (float16_t)-0.0555419922f, (float16_t)0.0380859375f, (float16_t)0.0414123535f, + (float16_t)0.0948486328f, (float16_t)-0.112121582f, (float16_t)-0.0415344238f, (float16_t)0.0534057617f, + (float16_t)0.0400695801f, (float16_t)0.0753173828f, (float16_t)-0.110900879f, (float16_t)0.110351562f, + (float16_t)0.0746459961f, (float16_t)-0.0492553711f, (float16_t)0.00710296631f, (float16_t)-0.0625f, + (float16_t)0.0439453125f, (float16_t)-0.0822143555f, (float16_t)0.0173187256f, (float16_t)-0.111755371f, + (float16_t)0.0115356445f, (float16_t)0.00908660889f, (float16_t)-0.075012207f, (float16_t)-0.0132522583f, + (float16_t)0.0847167969f, (float16_t)-0.0131835938f, (float16_t)0.101867676f, (float16_t)0.0288848877f, + (float16_t)-0.00676727295f, (float16_t)-0.110656738f, (float16_t)-0.102966309f, (float16_t)0.0639038086f, + (float16_t)-0.0963745117f, (float16_t)0.0696411133f, (float16_t)-0.0960693359f, (float16_t)0.0779418945f, + (float16_t)-0.0394592285f, (float16_t)0.0991210938f, (float16_t)-0.0561828613f, 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(float16_t)-0.0918579102f, (float16_t)-0.0579223633f, (float16_t)0.0244903564f, (float16_t)-0.0185089111f, + (float16_t)0.039642334f, (float16_t)0.0931396484f, (float16_t)0.0294494629f, (float16_t)-0.0281677246f, + (float16_t)-0.00576782227f, (float16_t)0.00426483154f, (float16_t)0.0552062988f, (float16_t)-0.0965576172f, + (float16_t)0.00102996826f, (float16_t)-0.115539551f, (float16_t)0.0946044922f, (float16_t)0.118713379f, + (float16_t)0.056427002f, (float16_t)-0.11730957f, (float16_t)0.0684204102f, (float16_t)0.0285491943f, + (float16_t)-0.0808105469f, (float16_t)-0.0068359375f, (float16_t)0.0889282227f, (float16_t)-0.0106735229f, + (float16_t)-0.0316772461f, (float16_t)-0.0542297363f, (float16_t)0.0807495117f, (float16_t)-0.0698852539f, + (float16_t)-0.00336456299f, (float16_t)-0.0307159424f, (float16_t)-0.0792236328f, (float16_t)-0.000944137573f, + (float16_t)0.0993652344f, (float16_t)0.108947754f, (float16_t)0.100158691f, (float16_t)0.00566101074f, + (float16_t)-0.11730957f, (float16_t)-0.104858398f, (float16_t)0.0770874023f, (float16_t)0.0491027832f, + (float16_t)0.0893554688f, (float16_t)-0.0688476562f, (float16_t)0.0215454102f, (float16_t)0.0977783203f, + (float16_t)-0.0841064453f, (float16_t)-0.0581054688f, (float16_t)0.0390319824f, (float16_t)0.0718383789f, + (float16_t)0.111816406f, (float16_t)0.116027832f, (float16_t)-0.106506348f, (float16_t)0.11328125f, + (float16_t)-0.0881347656f, (float16_t)-0.104125977f, (float16_t)-0.0616455078f, (float16_t)0.0249176025f, + (float16_t)-0.0439758301f, (float16_t)0.0267486572f, (float16_t)0.0578918457f, (float16_t)-0.0724487305f, + (float16_t)0.029510498f, (float16_t)0.0790405273f, (float16_t)0.000895500183f, (float16_t)0.0599365234f, + (float16_t)-0.061920166f, (float16_t)-0.0159606934f, (float16_t)0.0531005859f, (float16_t)-0.0844726562f, + (float16_t)-0.023223877f, (float16_t)0.0415039062f, (float16_t)0.0646972656f, (float16_t)0.0935668945f, + (float16_t)0.0477905273f, (float16_t)0.0466918945f, (float16_t)0.0040435791f, (float16_t)0.0339355469f, + (float16_t)0.033416748f, (float16_t)-0.0621337891f, (float16_t)0.101318359f, (float16_t)0.0798339844f, + (float16_t)-0.0302124023f, (float16_t)-0.0194854736f, (float16_t)0.101196289f, (float16_t)0.0686035156f, + (float16_t)0.0263977051f, (float16_t)-0.0348815918f, (float16_t)0.0439453125f, (float16_t)0.0281982422f, + (float16_t)-0.0101089478f, (float16_t)0.113098145f, (float16_t)-0.0913696289f, (float16_t)0.0247650146f, + (float16_t)0.000550270081f, (float16_t)-0.0873413086f, (float16_t)-0.0486755371f, (float16_t)-0.0382995605f, + (float16_t)-0.0855102539f, (float16_t)-0.0753173828f, (float16_t)-0.00484466553f, (float16_t)0.0891723633f, + (float16_t)-0.00154590607f, (float16_t)0.116638184f, (float16_t)0.0356140137f, (float16_t)-0.00947570801f, + (float16_t)-0.0129928589f, (float16_t)-0.0645751953f, (float16_t)-0.111328125f, (float16_t)0.0819702148f, + (float16_t)0.0717773438f, (float16_t)-0.000141143799f, (float16_t)0.0744628906f, (float16_t)-0.0775146484f, + (float16_t)-0.0993041992f, (float16_t)-0.0681762695f, (float16_t)0.0828857422f, (float16_t)0.0757446289f, + (float16_t)0.00413513184f, (float16_t)0.0634155273f, (float16_t)-0.0726318359f, (float16_t)-0.116210938f, + (float16_t)0.00376701355f, (float16_t)0.0222930908f, (float16_t)-0.037689209f, (float16_t)-0.0943603516f, + (float16_t)0.112426758f, (float16_t)-0.028793335f, (float16_t)-0.0300598145f, (float16_t)-0.0991821289f, + (float16_t)0.114990234f, (float16_t)-0.0469970703f, (float16_t)-0.0998535156f, (float16_t)0.026473999f, + (float16_t)-0.0915527344f, (float16_t)0.000893592834f, (float16_t)-0.000560760498f, (float16_t)-0.0504150391f, + (float16_t)0.0856323242f, (float16_t)-0.103820801f, (float16_t)-0.111206055f, (float16_t)-0.0493469238f, + (float16_t)-0.0602722168f, (float16_t)-0.00303077698f, (float16_t)0.0954589844f, (float16_t)0.109680176f, + (float16_t)0.00526809692f, (float16_t)-0.000248432159f, (float16_t)0.00843048096f, (float16_t)0.0844726562f, + (float16_t)0.0236663818f, (float16_t)0.0972290039f, (float16_t)0.0921020508f, (float16_t)-0.0202331543f, + (float16_t)-0.0539550781f, (float16_t)-0.0140914917f, (float16_t)0.0240783691f, (float16_t)0.112731934f, + (float16_t)0.0176849365f, (float16_t)-0.0371704102f, (float16_t)-0.080871582f, (float16_t)-0.0135498047f, + (float16_t)0.033416748f, (float16_t)-0.0888671875f, (float16_t)0.0947875977f, (float16_t)-0.0184173584f, + (float16_t)-0.0797119141f, (float16_t)-0.00873565674f, (float16_t)0.0013999939f, (float16_t)0.104797363f, + (float16_t)0.11517334f, (float16_t)-0.0604248047f, (float16_t)-0.0330810547f, (float16_t)0.0197143555f, + (float16_t)0.0640258789f, (float16_t)0.0339050293f, (float16_t)-0.0760498047f, (float16_t)-0.0844726562f, + (float16_t)0.0987548828f, (float16_t)-0.117797852f, (float16_t)-0.0627441406f, (float16_t)0.102172852f, + (float16_t)0.0935668945f, (float16_t)-0.0985717773f, (float16_t)-0.0695800781f, (float16_t)-0.00647354126f, + (float16_t)0.0360412598f, (float16_t)0.0955200195f, (float16_t)-0.0822753906f, (float16_t)-0.00270271301f, + (float16_t)-0.0491333008f, (float16_t)0.00255012512f, (float16_t)0.0795288086f, (float16_t)0.00040268898f, + (float16_t)0.0397033691f, (float16_t)0.00439834595f, (float16_t)0.00849914551f, (float16_t)0.0145339966f, + (float16_t)0.038482666f, (float16_t)0.0299987793f, (float16_t)-0.0817871094f, (float16_t)-0.02293396f, + (float16_t)-0.0765991211f, (float16_t)0.113098145f, (float16_t)-0.00311088562f, (float16_t)0.0505065918f, + (float16_t)0.112243652f, (float16_t)0.0534362793f, (float16_t)0.0331115723f, (float16_t)0.0202484131f, + (float16_t)-0.0264434814f, (float16_t)-0.0634155273f, (float16_t)-0.0977783203f, (float16_t)-0.0276489258f, + (float16_t)-0.0915527344f, (float16_t)-0.0386657715f, (float16_t)-0.0212249756f, (float16_t)-0.00661849976f, + (float16_t)0.0810546875f, (float16_t)-0.0366821289f, (float16_t)-0.0174865723f, (float16_t)-0.110107422f, + (float16_t)-0.00644683838f, (float16_t)0.0175933838f, (float16_t)0.0386657715f, (float16_t)0.0973510742f, + (float16_t)-0.0266876221f, (float16_t)-0.067565918f, (float16_t)0.034942627f, (float16_t)0.0901489258f, + (float16_t)0.0293426514f, (float16_t)-0.0275268555f, (float16_t)0.0581970215f, (float16_t)-0.0363769531f, + (float16_t)-0.0259246826f, (float16_t)0.0902709961f, (float16_t)-0.0563659668f, (float16_t)0.0163726807f, + (float16_t)0.0465087891f, (float16_t)-0.106018066f, (float16_t)-0.0010137558f, (float16_t)-0.102355957f, + (float16_t)-0.00513458252f, (float16_t)0.0258789062f, (float16_t)-0.0429077148f, (float16_t)0.106872559f, + (float16_t)0.100097656f, (float16_t)-0.0932006836f, (float16_t)-0.00172138214f, (float16_t)0.0591125488f, + (float16_t)0.108947754f, (float16_t)0.0988769531f, (float16_t)-0.0277252197f, (float16_t)0.0276031494f, + (float16_t)0.00239562988f, (float16_t)-0.0203094482f, (float16_t)-0.0835571289f, (float16_t)0.105102539f, + (float16_t)0.025894165f, (float16_t)-0.0917358398f, (float16_t)0.05078125f, (float16_t)0.00210952759f, + (float16_t)0.109619141f, (float16_t)0.106567383f, (float16_t)0.0608215332f, (float16_t)0.0540771484f, + (float16_t)0.0562133789f, (float16_t)-0.0819091797f, (float16_t)0.0241241455f, (float16_t)-0.0928955078f, + (float16_t)-0.0810546875f, (float16_t)0.115905762f, (float16_t)-0.0121383667f, (float16_t)0.0791625977f, + (float16_t)0.0184326172f, (float16_t)0.069152832f, (float16_t)-0.0336303711f, (float16_t)-0.0198974609f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_biases_data.h new file mode 100644 index 00000000..7434bb39 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv1_biases_data[64] = { + (float16_t)0.0108947754f, (float16_t)0.0380249023f, (float16_t)0.0389404297f, (float16_t)-0.000238180161f, + (float16_t)-0.0472106934f, (float16_t)-0.0354003906f, (float16_t)-0.0316467285f, (float16_t)-0.0165557861f, + (float16_t)0.0411376953f, (float16_t)-0.033203125f, (float16_t)-0.0274810791f, (float16_t)0.0473022461f, + (float16_t)0.0272369385f, (float16_t)-0.0433959961f, (float16_t)-0.0499572754f, (float16_t)-0.0218658447f, + (float16_t)0.0131149292f, (float16_t)0.049987793f, (float16_t)0.0292816162f, (float16_t)-0.0483093262f, + (float16_t)-0.0375061035f, (float16_t)0.020614624f, (float16_t)-0.0436096191f, (float16_t)0.0262145996f, + (float16_t)0.01121521f, (float16_t)0.0411682129f, (float16_t)-0.0355529785f, (float16_t)-0.00168609619f, + (float16_t)0.0415039062f, (float16_t)-0.0193328857f, (float16_t)-0.0188751221f, (float16_t)0.00734329224f, + (float16_t)0.0331115723f, (float16_t)0.0155334473f, (float16_t)0.0305328369f, (float16_t)-0.0496826172f, + (float16_t)-0.0225524902f, (float16_t)0.0434265137f, (float16_t)-0.0431518555f, (float16_t)0.0129241943f, + (float16_t)-0.0445556641f, (float16_t)0.0425415039f, (float16_t)0.0241546631f, (float16_t)0.0224151611f, + (float16_t)-0.0123062134f, (float16_t)0.0412597656f, (float16_t)0.00578689575f, (float16_t)-0.0340576172f, + (float16_t)2.58088112e-05f, (float16_t)-0.0366210938f, (float16_t)0.0481262207f, (float16_t)-0.0057220459f, + (float16_t)-0.030670166f, (float16_t)0.0447998047f, (float16_t)-0.0352172852f, (float16_t)-0.0334777832f, + (float16_t)0.00871276855f, (float16_t)-0.0317077637f, (float16_t)0.025970459f, (float16_t)0.0420227051f, + (float16_t)0.0354309082f, (float16_t)-0.0333862305f, (float16_t)0.0422363281f, (float16_t)0.0161590576f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_weights_data.h new file mode 100644 index 00000000..d8b283df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv1_weights_data.h @@ -0,0 +1,1026 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv1_weights_data[4096] = { + (float16_t)0.0908203125f, (float16_t)-0.0354003906f, (float16_t)-0.0145874023f, (float16_t)-0.0803222656f, + (float16_t)-0.0894775391f, (float16_t)-0.0673217773f, (float16_t)0.00138282776f, (float16_t)0.0865478516f, + (float16_t)-0.0795288086f, (float16_t)-0.0892333984f, (float16_t)0.0686645508f, (float16_t)-0.0564575195f, + (float16_t)0.0418395996f, (float16_t)-0.09765625f, (float16_t)-0.0358581543f, (float16_t)0.0429992676f, + (float16_t)-0.0750732422f, (float16_t)-0.0246582031f, (float16_t)0.0712890625f, (float16_t)-0.0946655273f, + (float16_t)-0.0040512085f, (float16_t)0.087097168f, (float16_t)-0.0988769531f, (float16_t)-0.00712585449f, + (float16_t)-0.0617370605f, (float16_t)-0.0590820312f, (float16_t)-0.107788086f, (float16_t)-0.0273284912f, + (float16_t)0.0171813965f, (float16_t)-0.094909668f, (float16_t)0.0594787598f, (float16_t)-0.0889282227f, + (float16_t)-0.107971191f, (float16_t)-0.0552062988f, (float16_t)-0.0574951172f, (float16_t)0.0850830078f, + (float16_t)0.0120162964f, (float16_t)0.0115127563f, (float16_t)0.0675048828f, (float16_t)0.114501953f, + (float16_t)0.0395202637f, (float16_t)0.0610656738f, (float16_t)0.00845336914f, (float16_t)0.0262298584f, + (float16_t)0.0972900391f, (float16_t)0.0916748047f, (float16_t)-0.0545654297f, (float16_t)0.037109375f, + (float16_t)-0.0975341797f, (float16_t)-0.015045166f, (float16_t)-0.0188140869f, (float16_t)-0.0369873047f, + (float16_t)-0.087097168f, (float16_t)0.0188140869f, (float16_t)-0.10559082f, (float16_t)-0.00690841675f, + (float16_t)0.0913696289f, (float16_t)0.0629882812f, (float16_t)-0.113098145f, (float16_t)0.0451049805f, + (float16_t)0.114074707f, (float16_t)-0.0933227539f, (float16_t)0.0839233398f, (float16_t)0.0202178955f, + (float16_t)0.0321044922f, (float16_t)0.0631103516f, (float16_t)-0.0421447754f, (float16_t)0.0209960938f, + (float16_t)0.0757446289f, (float16_t)0.105041504f, (float16_t)-0.0787353516f, (float16_t)0.0768432617f, + (float16_t)0.0099105835f, (float16_t)-0.0985717773f, (float16_t)0.044921875f, (float16_t)0.0355834961f, + (float16_t)-0.0107040405f, (float16_t)0.0113830566f, (float16_t)-0.0407409668f, (float16_t)-0.094909668f, + (float16_t)0.111572266f, (float16_t)0.0772705078f, (float16_t)-0.00596237183f, (float16_t)0.0833129883f, + (float16_t)-0.114135742f, (float16_t)-0.0236968994f, (float16_t)0.0468139648f, (float16_t)0.119567871f, + (float16_t)0.0079498291f, (float16_t)-0.0804443359f, (float16_t)0.0569152832f, (float16_t)-0.0228118896f, + (float16_t)0.000790119171f, (float16_t)-0.0650024414f, (float16_t)-0.0221557617f, (float16_t)0.0672607422f, + (float16_t)0.0715942383f, (float16_t)-0.0109863281f, (float16_t)-0.067199707f, (float16_t)0.0495605469f, + (float16_t)0.0109710693f, (float16_t)0.0491333008f, (float16_t)-0.0150604248f, (float16_t)-0.0695800781f, + (float16_t)0.049987793f, (float16_t)0.108825684f, (float16_t)-0.00136566162f, (float16_t)0.0296020508f, + (float16_t)0.109375f, (float16_t)-0.0241851807f, (float16_t)0.0132598877f, (float16_t)-0.112243652f, + (float16_t)0.0942382812f, (float16_t)-0.00580215454f, (float16_t)0.0850830078f, (float16_t)-0.0477905273f, + (float16_t)0.0581970215f, (float16_t)0.102905273f, (float16_t)-0.0283050537f, (float16_t)0.040222168f, + (float16_t)-0.0872192383f, (float16_t)-0.0993652344f, (float16_t)-0.0458679199f, (float16_t)0.0616149902f, + (float16_t)-0.0630493164f, (float16_t)0.00810241699f, (float16_t)-0.0381469727f, (float16_t)-0.00279045105f, + (float16_t)-0.0533752441f, (float16_t)0.117553711f, (float16_t)0.0118865967f, (float16_t)-0.105285645f, + (float16_t)-0.0679321289f, (float16_t)0.046295166f, (float16_t)0.0192565918f, (float16_t)-0.043548584f, + (float16_t)0.0575256348f, (float16_t)0.0293731689f, (float16_t)0.0303344727f, (float16_t)-0.109985352f, + (float16_t)-0.115844727f, (float16_t)0.0936889648f, (float16_t)0.0740356445f, (float16_t)-0.069152832f, + (float16_t)-0.0576477051f, (float16_t)0.0673217773f, (float16_t)-8.91685486e-05f, (float16_t)-0.0856933594f, + (float16_t)-0.110656738f, (float16_t)0.106262207f, (float16_t)-0.0796508789f, (float16_t)0.0622253418f, + (float16_t)-0.114562988f, (float16_t)-0.00136184692f, (float16_t)-0.0807495117f, (float16_t)-0.0439147949f, + (float16_t)-0.00496292114f, (float16_t)-0.11315918f, (float16_t)-0.0359191895f, (float16_t)0.0822753906f, + (float16_t)-0.0458984375f, (float16_t)-0.00975036621f, (float16_t)-0.084777832f, (float16_t)0.0617370605f, + (float16_t)0.106567383f, (float16_t)0.114685059f, (float16_t)-0.0258026123f, (float16_t)0.0586547852f, + (float16_t)0.0104446411f, (float16_t)-0.0798950195f, (float16_t)-0.0866699219f, (float16_t)0.0928344727f, + (float16_t)-0.00783538818f, (float16_t)0.0462341309f, (float16_t)-0.0460510254f, (float16_t)-0.0377807617f, + (float16_t)-0.0803833008f, (float16_t)-0.0173492432f, (float16_t)-0.0538024902f, (float16_t)0.0877685547f, + (float16_t)0.0977783203f, (float16_t)-0.0177001953f, (float16_t)-0.0432434082f, (float16_t)0.0684814453f, + (float16_t)-0.115783691f, (float16_t)-0.0687866211f, (float16_t)0.105285645f, (float16_t)0.0229644775f, + (float16_t)-0.0614318848f, (float16_t)-0.0667724609f, (float16_t)0.0242004395f, (float16_t)-0.0855102539f, + (float16_t)-0.0114593506f, (float16_t)0.08984375f, (float16_t)-0.00479888916f, (float16_t)-0.1171875f, + (float16_t)-0.102539062f, (float16_t)0.0717163086f, (float16_t)-0.00307655334f, (float16_t)-0.0172119141f, + (float16_t)-0.03125f, (float16_t)0.0283660889f, (float16_t)-0.107849121f, (float16_t)0.0499572754f, + (float16_t)0.011390686f, (float16_t)0.0955200195f, (float16_t)0.0993041992f, (float16_t)0.0927124023f, + (float16_t)-0.083190918f, (float16_t)-0.0207977295f, (float16_t)0.0407409668f, (float16_t)-0.0196685791f, + (float16_t)0.0145263672f, (float16_t)0.0998535156f, (float16_t)0.00967407227f, (float16_t)-0.00433349609f, + (float16_t)-0.0540161133f, (float16_t)0.0120010376f, (float16_t)0.0666503906f, (float16_t)0.0494995117f, + (float16_t)-0.0158996582f, (float16_t)0.108032227f, (float16_t)-0.0650634766f, (float16_t)-0.042755127f, + (float16_t)0.0208129883f, (float16_t)0.0225982666f, (float16_t)0.0168762207f, (float16_t)0.108886719f, + (float16_t)0.116271973f, (float16_t)0.0625f, (float16_t)0.0875244141f, (float16_t)0.0888061523f, + (float16_t)-0.0729980469f, (float16_t)0.09375f, (float16_t)0.00492477417f, (float16_t)0.0903930664f, + (float16_t)-0.0433959961f, (float16_t)-0.011680603f, (float16_t)0.111694336f, (float16_t)-0.107727051f, + (float16_t)-0.0512084961f, (float16_t)-0.0340270996f, (float16_t)-0.0614624023f, (float16_t)0.0834960938f, + (float16_t)0.0254211426f, (float16_t)0.09765625f, (float16_t)-0.0508117676f, (float16_t)-0.00791931152f, + (float16_t)-0.0340270996f, (float16_t)-0.102722168f, (float16_t)-0.108276367f, (float16_t)-0.0731811523f, + (float16_t)0.0989990234f, (float16_t)0.0852050781f, (float16_t)-0.106872559f, (float16_t)0.00696563721f, + (float16_t)0.0941772461f, (float16_t)0.0904541016f, (float16_t)0.0990600586f, (float16_t)-0.0928344727f, + (float16_t)-0.116638184f, (float16_t)-0.00566482544f, (float16_t)0.0590820312f, (float16_t)-0.055267334f, + (float16_t)-0.0880737305f, (float16_t)-0.108642578f, (float16_t)0.0341186523f, (float16_t)0.118652344f, + (float16_t)0.0527648926f, (float16_t)-0.00257110596f, (float16_t)0.0076751709f, (float16_t)0.072265625f, + (float16_t)-0.0279541016f, (float16_t)0.00715255737f, (float16_t)-0.0225830078f, (float16_t)-0.0117645264f, + (float16_t)-0.0298156738f, (float16_t)0.0943603516f, (float16_t)0.0101165771f, (float16_t)0.025680542f, + (float16_t)-0.0128250122f, (float16_t)-0.103088379f, (float16_t)-0.0270996094f, (float16_t)-0.0507202148f, + (float16_t)-0.0625f, (float16_t)-0.0275421143f, (float16_t)-0.00653457642f, (float16_t)0.0734863281f, + (float16_t)-0.00143527985f, (float16_t)0.0110397339f, (float16_t)6.58035278e-05f, (float16_t)-0.0447692871f, + (float16_t)0.0577392578f, (float16_t)0.00457763672f, (float16_t)-0.0400085449f, (float16_t)0.0631103516f, + (float16_t)0.0459594727f, (float16_t)0.0993041992f, (float16_t)-0.0511779785f, (float16_t)-0.0254364014f, + (float16_t)0.065612793f, (float16_t)0.0829467773f, (float16_t)0.0861206055f, (float16_t)0.0422058105f, + (float16_t)-0.0306396484f, (float16_t)-0.0135574341f, (float16_t)-0.0305480957f, (float16_t)-0.0121841431f, + (float16_t)-0.109313965f, (float16_t)0.0434570312f, (float16_t)0.00592803955f, (float16_t)-0.115234375f, + (float16_t)-0.0189208984f, (float16_t)0.0651245117f, (float16_t)-0.0298919678f, (float16_t)0.0639648438f, + (float16_t)-0.100097656f, (float16_t)-0.0803222656f, (float16_t)0.0473632812f, (float16_t)-0.100219727f, + (float16_t)-0.0995483398f, (float16_t)0.0758056641f, (float16_t)0.116210938f, (float16_t)-0.0888671875f, + (float16_t)0.00234031677f, (float16_t)0.00509262085f, (float16_t)-0.0347900391f, (float16_t)-0.0963134766f, + (float16_t)0.0821533203f, (float16_t)0.0341186523f, (float16_t)-0.0920410156f, (float16_t)0.0111465454f, + (float16_t)0.094543457f, (float16_t)0.0225524902f, (float16_t)0.0247955322f, (float16_t)-0.082824707f, + (float16_t)-0.103515625f, (float16_t)0.0803833008f, (float16_t)0.0443115234f, (float16_t)0.115905762f, + (float16_t)0.0932006836f, (float16_t)-0.0249023438f, (float16_t)-0.119262695f, (float16_t)0.0328063965f, + (float16_t)-0.0757446289f, (float16_t)0.114135742f, (float16_t)0.11505127f, (float16_t)0.0994873047f, + (float16_t)-0.0546264648f, (float16_t)0.0863647461f, (float16_t)-0.0334472656f, (float16_t)0.00397872925f, + (float16_t)-0.0874023438f, (float16_t)-0.0872192383f, (float16_t)-0.114379883f, (float16_t)0.01512146f, + (float16_t)0.0458068848f, (float16_t)0.115600586f, (float16_t)0.0701904297f, (float16_t)-0.0990600586f, + (float16_t)0.0821533203f, (float16_t)0.0446777344f, (float16_t)-0.00588989258f, (float16_t)-0.104248047f, + (float16_t)-0.04296875f, (float16_t)0.0202484131f, (float16_t)0.0727539062f, (float16_t)0.0806884766f, + (float16_t)0.0953369141f, (float16_t)0.00353622437f, (float16_t)0.0420227051f, (float16_t)0.0725097656f, + (float16_t)0.0587463379f, (float16_t)0.00437927246f, (float16_t)-0.0301513672f, (float16_t)-0.0846557617f, + (float16_t)-0.0165252686f, (float16_t)-0.0286712646f, (float16_t)-0.079284668f, (float16_t)0.0356445312f, + (float16_t)0.106384277f, (float16_t)0.0312805176f, (float16_t)0.0781860352f, (float16_t)0.0566711426f, + (float16_t)-0.047668457f, (float16_t)-0.0068397522f, (float16_t)0.105651855f, (float16_t)-0.0938110352f, + (float16_t)-0.106079102f, (float16_t)-0.0855712891f, (float16_t)-0.0313110352f, (float16_t)0.0310668945f, + (float16_t)-0.0579833984f, (float16_t)-0.0121078491f, (float16_t)-0.0947875977f, (float16_t)-0.0456848145f, + (float16_t)-0.0554199219f, (float16_t)-0.0207824707f, (float16_t)0.0673217773f, (float16_t)0.0491333008f, + 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(float16_t)-0.107788086f, (float16_t)0.0784912109f, (float16_t)-0.0977172852f, (float16_t)-0.117797852f, + (float16_t)-0.0638427734f, (float16_t)0.108825684f, (float16_t)-0.00917816162f, (float16_t)-0.116821289f, + (float16_t)0.119445801f, (float16_t)0.0814819336f, (float16_t)0.0801391602f, (float16_t)-0.0876464844f, + (float16_t)0.045501709f, (float16_t)0.0244445801f, (float16_t)-0.00904083252f, (float16_t)0.0227966309f, + (float16_t)-0.0836181641f, (float16_t)-0.0714111328f, (float16_t)-0.0637817383f, (float16_t)0.0469665527f, + (float16_t)-0.116760254f, (float16_t)-0.0994262695f, (float16_t)-0.0756225586f, (float16_t)-0.116210938f, + (float16_t)0.0244293213f, (float16_t)0.00484848022f, (float16_t)-0.0858764648f, (float16_t)-0.0493164062f, + (float16_t)0.0931396484f, (float16_t)0.000136494637f, (float16_t)0.0745849609f, (float16_t)-0.0792236328f, + (float16_t)-0.106628418f, (float16_t)-0.0868530273f, (float16_t)-0.0786132812f, (float16_t)-0.0885620117f, + (float16_t)0.11114502f, (float16_t)-0.0745849609f, (float16_t)0.113525391f, (float16_t)-0.0725097656f, + (float16_t)0.00343513489f, (float16_t)-0.0118026733f, (float16_t)-0.00953674316f, (float16_t)0.0923461914f, + (float16_t)-0.0513916016f, (float16_t)0.0184783936f, (float16_t)-0.0908203125f, (float16_t)-0.0534667969f, + (float16_t)-0.0533752441f, (float16_t)0.00316429138f, (float16_t)0.096862793f, (float16_t)0.079284668f, + (float16_t)0.0916137695f, (float16_t)-0.116638184f, (float16_t)-0.114990234f, (float16_t)0.0642700195f, + (float16_t)0.0415649414f, (float16_t)0.0241241455f, (float16_t)0.10748291f, (float16_t)-0.0338439941f, + (float16_t)0.100952148f, (float16_t)0.0550231934f, (float16_t)0.0111160278f, (float16_t)-0.0222320557f, + (float16_t)0.100830078f, (float16_t)0.0094833374f, (float16_t)-0.041595459f, (float16_t)-0.116699219f, + (float16_t)0.0404052734f, (float16_t)-0.0541687012f, (float16_t)-0.0431213379f, (float16_t)0.076171875f, + (float16_t)-0.0211181641f, (float16_t)-0.04296875f, (float16_t)-0.0692749023f, (float16_t)0.0136795044f, + (float16_t)0.053527832f, (float16_t)0.0850830078f, (float16_t)0.0940551758f, (float16_t)0.117370605f, + (float16_t)-0.003490448f, (float16_t)0.0909423828f, (float16_t)0.100280762f, (float16_t)0.0178985596f, + (float16_t)-0.109008789f, (float16_t)-0.0258636475f, (float16_t)0.0954589844f, (float16_t)-0.104675293f, + (float16_t)0.00838470459f, (float16_t)-0.0210113525f, (float16_t)0.0436401367f, (float16_t)0.0919799805f, + (float16_t)0.0493469238f, (float16_t)0.0274353027f, (float16_t)-0.0211181641f, (float16_t)0.00777816772f, + (float16_t)0.101257324f, (float16_t)-0.0979614258f, (float16_t)-0.104858398f, (float16_t)0.0705566406f, + (float16_t)0.0994262695f, (float16_t)-0.0930175781f, (float16_t)-0.10949707f, (float16_t)0.0778198242f, + (float16_t)-0.0366516113f, (float16_t)0.108642578f, (float16_t)0.00483322144f, (float16_t)0.101928711f, + (float16_t)0.0126800537f, (float16_t)0.116577148f, (float16_t)0.0330810547f, (float16_t)0.0428161621f, + (float16_t)-0.0168609619f, (float16_t)0.0855712891f, (float16_t)-0.0726318359f, (float16_t)0.0153961182f, + (float16_t)0.00811767578f, (float16_t)-0.0253753662f, (float16_t)0.0411376953f, (float16_t)-0.0880126953f, + (float16_t)-0.0855712891f, (float16_t)-0.0856323242f, (float16_t)-0.0176849365f, (float16_t)-0.116149902f, + (float16_t)0.0916748047f, (float16_t)-0.0377807617f, (float16_t)0.0202026367f, (float16_t)-0.00727081299f, + (float16_t)0.0627441406f, (float16_t)0.0796508789f, (float16_t)0.0448913574f, (float16_t)-0.0731811523f, + (float16_t)-0.09375f, (float16_t)0.0417785645f, (float16_t)-0.025604248f, (float16_t)0.0393066406f, + (float16_t)-0.114990234f, (float16_t)0.0606994629f, (float16_t)0.0483398438f, (float16_t)-0.0454711914f, + (float16_t)0.0168762207f, (float16_t)0.0357971191f, (float16_t)-0.0597839355f, (float16_t)0.0734863281f, + (float16_t)-0.0374145508f, (float16_t)0.0480957031f, (float16_t)0.0863037109f, (float16_t)-0.0109481812f, + (float16_t)-0.0445861816f, (float16_t)0.100219727f, (float16_t)-0.00484466553f, (float16_t)0.076171875f, + (float16_t)0.0311431885f, (float16_t)-0.0062713623f, (float16_t)0.117675781f, (float16_t)-0.0389709473f, + (float16_t)-0.111633301f, (float16_t)0.0793457031f, (float16_t)0.0493774414f, (float16_t)0.0193939209f, + (float16_t)0.0093460083f, (float16_t)0.117126465f, (float16_t)0.100097656f, (float16_t)-0.0858154297f, + (float16_t)-0.0806274414f, (float16_t)0.0188903809f, (float16_t)-0.103393555f, (float16_t)0.0627441406f, + (float16_t)0.0521850586f, (float16_t)0.0906982422f, (float16_t)-0.0545043945f, (float16_t)-0.0473022461f, + (float16_t)0.0513305664f, (float16_t)0.0539550781f, (float16_t)-0.106201172f, (float16_t)0.00817871094f, + (float16_t)0.0132675171f, (float16_t)-0.0159759521f, (float16_t)0.0605163574f, (float16_t)0.0821533203f, + (float16_t)0.046875f, (float16_t)0.0613708496f, (float16_t)-0.078125f, (float16_t)-0.00622558594f, + (float16_t)0.0477294922f, (float16_t)-0.116516113f, (float16_t)-0.10949707f, (float16_t)-0.00644683838f, + (float16_t)0.0463562012f, (float16_t)0.0173187256f, (float16_t)-0.0287017822f, (float16_t)-0.108581543f, + (float16_t)0.0769042969f, (float16_t)0.0497741699f, (float16_t)0.00611114502f, (float16_t)-0.0745849609f, + (float16_t)0.0583190918f, (float16_t)-0.102478027f, (float16_t)0.0115585327f, (float16_t)-0.0446472168f, + (float16_t)-0.0609130859f, (float16_t)-0.0358581543f, (float16_t)0.0476989746f, (float16_t)0.0348510742f, + (float16_t)-0.111328125f, (float16_t)0.0758056641f, (float16_t)0.00313949585f, (float16_t)-0.0732421875f, + (float16_t)-0.117980957f, (float16_t)0.0585021973f, (float16_t)0.0170135498f, (float16_t)0.0543823242f, + (float16_t)-0.090637207f, (float16_t)-0.107177734f, (float16_t)0.109680176f, (float16_t)0.0587768555f, + (float16_t)-0.0246887207f, (float16_t)0.0401000977f, (float16_t)-0.0653686523f, (float16_t)0.0388183594f, + (float16_t)-0.0119247437f, (float16_t)0.098449707f, (float16_t)0.0935668945f, (float16_t)0.0126190186f, + (float16_t)0.0342407227f, (float16_t)0.0645751953f, (float16_t)-0.112426758f, (float16_t)-0.0326843262f, + (float16_t)0.0164031982f, (float16_t)0.0823974609f, (float16_t)0.0335388184f, (float16_t)-0.0933837891f, + (float16_t)-0.105895996f, (float16_t)0.0390014648f, (float16_t)-0.0804443359f, (float16_t)-0.0991210938f, + (float16_t)-0.0357666016f, (float16_t)-0.0319519043f, (float16_t)-0.0680541992f, (float16_t)0.0426940918f, + (float16_t)-0.0464782715f, (float16_t)-0.0166625977f, (float16_t)-0.0190887451f, (float16_t)-0.0689697266f, + (float16_t)0.0419921875f, (float16_t)-0.0966186523f, (float16_t)-0.0205688477f, (float16_t)0.0124053955f, + (float16_t)0.031829834f, (float16_t)0.0100631714f, (float16_t)0.0678100586f, (float16_t)0.0320129395f, + (float16_t)0.0566711426f, (float16_t)0.0892944336f, (float16_t)-0.00498962402f, (float16_t)0.0626831055f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_biases_data.h new file mode 100644 index 00000000..018f91e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv2_biases_data[64] = { + (float16_t)-0.0430297852f, (float16_t)-0.00845336914f, (float16_t)-0.0346374512f, (float16_t)-0.0414733887f, + (float16_t)0.0115661621f, (float16_t)-0.0394897461f, (float16_t)-0.0136795044f, (float16_t)-0.036529541f, + (float16_t)-0.0123825073f, (float16_t)-0.0364685059f, (float16_t)-0.0430908203f, (float16_t)-0.0388793945f, + (float16_t)-0.0356140137f, (float16_t)0.00366592407f, (float16_t)-0.0105438232f, (float16_t)-0.0330200195f, + (float16_t)0.0225372314f, (float16_t)-0.00901794434f, (float16_t)-0.0272064209f, (float16_t)0.0463867188f, + (float16_t)-0.0266265869f, (float16_t)0.0194549561f, (float16_t)-0.0471496582f, (float16_t)-0.021987915f, + (float16_t)-0.0272827148f, (float16_t)-0.0436706543f, (float16_t)0.00968933105f, (float16_t)-0.0205688477f, + (float16_t)-0.0492553711f, (float16_t)0.0497741699f, (float16_t)0.00910186768f, (float16_t)0.0283660889f, + (float16_t)-0.0194549561f, (float16_t)0.0433959961f, (float16_t)0.0044631958f, (float16_t)-0.0287017822f, + (float16_t)0.041809082f, (float16_t)0.00562667847f, (float16_t)0.0375671387f, (float16_t)-0.0165252686f, + (float16_t)-0.0438842773f, (float16_t)-0.014793396f, (float16_t)-0.0445861816f, (float16_t)-0.00212097168f, + (float16_t)0.0326538086f, (float16_t)-0.0307159424f, (float16_t)0.044708252f, (float16_t)0.00253105164f, + (float16_t)-0.038269043f, (float16_t)0.038848877f, (float16_t)-0.0391845703f, (float16_t)0.00288391113f, + (float16_t)0.010925293f, (float16_t)-0.000226855278f, (float16_t)0.0493164062f, (float16_t)0.0387878418f, + (float16_t)0.0454711914f, (float16_t)0.0357971191f, (float16_t)0.0480957031f, (float16_t)-0.0429992676f, + (float16_t)0.0497131348f, (float16_t)0.032623291f, (float16_t)-0.00807189941f, (float16_t)-0.0350036621f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_weights_data.h new file mode 100644 index 00000000..67e9f5a7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv2_weights_data.h @@ -0,0 +1,1026 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv2_weights_data[4096] = { + (float16_t)-0.0227966309f, (float16_t)-0.023727417f, (float16_t)-0.00386428833f, (float16_t)-0.0671386719f, + (float16_t)0.0472412109f, (float16_t)0.0452575684f, (float16_t)0.100219727f, (float16_t)0.118591309f, + (float16_t)0.0954589844f, (float16_t)-0.0830688477f, (float16_t)-0.113586426f, (float16_t)-0.0681762695f, + (float16_t)-0.051361084f, (float16_t)0.0110855103f, (float16_t)0.0900878906f, (float16_t)0.0437316895f, + (float16_t)0.0918579102f, (float16_t)0.0651245117f, (float16_t)0.0331115723f, (float16_t)0.0205535889f, + (float16_t)0.0841674805f, (float16_t)-0.0444335938f, (float16_t)-0.0698242188f, (float16_t)0.033416748f, + (float16_t)-0.0672607422f, (float16_t)-0.0617370605f, (float16_t)0.0128250122f, (float16_t)-4.17232513e-07f, + (float16_t)-0.0419921875f, (float16_t)0.0569458008f, (float16_t)6.07967377e-06f, (float16_t)0.0399169922f, + (float16_t)-0.0215301514f, (float16_t)-0.0751953125f, (float16_t)0.0521850586f, (float16_t)0.0914916992f, + (float16_t)0.0224151611f, (float16_t)-0.0947265625f, (float16_t)-0.107849121f, (float16_t)0.0351867676f, + (float16_t)0.0997314453f, (float16_t)0.0692138672f, (float16_t)-0.0587768555f, (float16_t)-0.0172424316f, + (float16_t)0.0834350586f, (float16_t)0.117797852f, (float16_t)-0.0469360352f, (float16_t)0.0911865234f, + 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(float16_t)-0.0770263672f, (float16_t)0.0312194824f, (float16_t)-0.0711669922f, (float16_t)-0.0594787598f, + (float16_t)-0.0568847656f, (float16_t)-0.104614258f, (float16_t)0.0305175781f, (float16_t)-0.0650024414f, + (float16_t)-0.11895752f, (float16_t)0.0646972656f, (float16_t)-0.106384277f, (float16_t)-0.0284423828f, + (float16_t)0.0678100586f, (float16_t)0.087890625f, (float16_t)-0.0938110352f, (float16_t)0.117919922f, + (float16_t)-0.0947875977f, (float16_t)-0.0604858398f, (float16_t)0.0918579102f, (float16_t)0.0508422852f, + (float16_t)0.0664672852f, (float16_t)-0.0244903564f, (float16_t)0.087890625f, (float16_t)0.100280762f, + (float16_t)-0.0213928223f, (float16_t)-0.0285949707f, (float16_t)0.103088379f, (float16_t)-0.106384277f, + (float16_t)-0.0162658691f, (float16_t)-0.00137424469f, (float16_t)-0.0916748047f, (float16_t)-0.0995483398f, + (float16_t)-0.00646972656f, (float16_t)0.0538330078f, (float16_t)-0.116516113f, (float16_t)0.0734863281f, + (float16_t)0.0909423828f, (float16_t)-0.117980957f, (float16_t)0.100708008f, (float16_t)0.0675048828f, + (float16_t)-0.10144043f, (float16_t)-0.0502929688f, (float16_t)-0.0708007812f, (float16_t)0.0992431641f, + (float16_t)0.0484924316f, (float16_t)0.0288238525f, (float16_t)-0.0175476074f, (float16_t)-0.108093262f, + (float16_t)0.00834655762f, (float16_t)-0.0506896973f, (float16_t)0.0519104004f, (float16_t)-0.0562438965f, + (float16_t)-0.0982666016f, (float16_t)-0.106750488f, (float16_t)-0.0105514526f, (float16_t)-0.0390930176f, + (float16_t)-0.0541992188f, (float16_t)-0.106262207f, (float16_t)-0.0275421143f, (float16_t)0.0183105469f, + (float16_t)-0.0809936523f, (float16_t)0.0567016602f, (float16_t)-0.0263366699f, (float16_t)0.0440063477f, + (float16_t)-0.0369567871f, (float16_t)-0.0234680176f, (float16_t)-0.0628051758f, (float16_t)0.02003479f, + (float16_t)0.075378418f, (float16_t)-0.0520935059f, (float16_t)0.015914917f, (float16_t)-0.0844116211f, + (float16_t)0.0122375488f, (float16_t)0.0358276367f, (float16_t)-0.0158233643f, (float16_t)0.109069824f, + (float16_t)-0.0135421753f, (float16_t)-0.0925292969f, (float16_t)0.0464172363f, (float16_t)-0.0206756592f, + (float16_t)-0.0292663574f, (float16_t)0.0578918457f, (float16_t)-0.0706176758f, (float16_t)0.0176086426f, + (float16_t)0.00769805908f, (float16_t)-0.0555725098f, (float16_t)-0.0528564453f, (float16_t)-0.0564880371f, + (float16_t)-0.0487976074f, (float16_t)0.076965332f, (float16_t)-0.0132980347f, (float16_t)-0.042755127f, + (float16_t)-0.076965332f, (float16_t)0.0916137695f, (float16_t)0.0129547119f, (float16_t)0.0700683594f, + (float16_t)0.0346374512f, (float16_t)-0.0679321289f, (float16_t)-0.0452880859f, (float16_t)-0.0383605957f, + (float16_t)0.0530395508f, (float16_t)-0.111572266f, (float16_t)0.10559082f, (float16_t)0.0640258789f, + (float16_t)0.0326843262f, (float16_t)0.0359802246f, (float16_t)-0.112487793f, (float16_t)-0.108276367f, + (float16_t)0.0424194336f, (float16_t)0.0247955322f, (float16_t)0.0712890625f, (float16_t)-0.112609863f, + (float16_t)0.0402526855f, (float16_t)0.0444030762f, (float16_t)-0.0866699219f, (float16_t)0.0184631348f, + (float16_t)0.0764770508f, (float16_t)0.0491943359f, (float16_t)0.0966186523f, (float16_t)-0.065246582f, + (float16_t)0.0678100586f, (float16_t)0.0648803711f, (float16_t)-0.089050293f, (float16_t)0.0508422852f, + (float16_t)0.0134658813f, (float16_t)-0.00433731079f, (float16_t)0.0891113281f, (float16_t)0.0804443359f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_biases_data.h new file mode 100644 index 00000000..44df92a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_biases_data.h @@ -0,0 +1,18 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv3_biases_data[64] = { + (float16_t)0.0272979736f, (float16_t)-0.0305938721f, (float16_t)-0.0338745117f, (float16_t)0.0445861816f, + (float16_t)-0.0433959961f, (float16_t)-0.0499267578f, (float16_t)0.00705718994f, (float16_t)-0.0483093262f, + (float16_t)-0.0275421143f, (float16_t)0.0361938477f, (float16_t)0.0411682129f, (float16_t)-0.0482177734f, + (float16_t)0.0284118652f, (float16_t)-0.00737380981f, (float16_t)0.0356445312f, (float16_t)0.0477905273f, + (float16_t)-0.0361328125f, (float16_t)0.0172424316f, (float16_t)-0.0438537598f, (float16_t)0.0333251953f, + (float16_t)-0.0390930176f, (float16_t)0.00852966309f, (float16_t)-0.0129318237f, (float16_t)0.0449523926f, + (float16_t)0.0151596069f, (float16_t)-0.0336303711f, (float16_t)0.0428161621f, (float16_t)0.0125350952f, + (float16_t)-0.0393371582f, (float16_t)-0.00080871582f, (float16_t)0.0344848633f, (float16_t)-0.0409545898f, + (float16_t)0.0169219971f, (float16_t)0.0480957031f, (float16_t)0.0346374512f, (float16_t)0.0157318115f, + (float16_t)0.0390930176f, (float16_t)0.012336731f, (float16_t)0.0223693848f, (float16_t)-0.00405502319f, + (float16_t)-0.00408554077f, (float16_t)0.0242919922f, (float16_t)-0.0262145996f, (float16_t)-0.0443725586f, + (float16_t)0.0374450684f, (float16_t)0.0230407715f, (float16_t)0.00875854492f, (float16_t)0.0482177734f, + (float16_t)0.0117416382f, (float16_t)0.0416870117f, (float16_t)-0.00574111938f, (float16_t)-0.0410461426f, + (float16_t)0.0232696533f, (float16_t)-0.035369873f, (float16_t)-0.0107879639f, (float16_t)-0.0382995605f, + (float16_t)0.0397033691f, (float16_t)-0.0261230469f, (float16_t)0.00910949707f, (float16_t)-0.0491027832f, + (float16_t)0.0485839844f, (float16_t)0.0226593018f, (float16_t)-0.00535964966f, (float16_t)0.0376586914f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_weights_data.h new file mode 100644 index 00000000..08efebfa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/pwconv3_weights_data.h @@ -0,0 +1,1026 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t ds_cnn_s_body_f16_pwconv3_weights_data[4096] = { + (float16_t)0.0492858887f, (float16_t)0.00119972229f, (float16_t)0.100280762f, (float16_t)-0.0650024414f, + (float16_t)-0.0670166016f, (float16_t)0.02784729f, (float16_t)-0.0690917969f, (float16_t)0.0353088379f, + (float16_t)-0.0424804688f, (float16_t)0.0510253906f, (float16_t)-0.0991821289f, (float16_t)-0.0551147461f, + (float16_t)-0.0667114258f, (float16_t)0.0399169922f, (float16_t)0.0169830322f, (float16_t)0.00899505615f, + (float16_t)0.0210418701f, (float16_t)0.0299072266f, (float16_t)-0.0505371094f, (float16_t)-0.0488891602f, + (float16_t)0.061706543f, (float16_t)0.0809936523f, (float16_t)-0.0646362305f, (float16_t)-0.056640625f, + (float16_t)-0.0124282837f, (float16_t)-0.000993728638f, (float16_t)-0.0806274414f, (float16_t)-0.0818481445f, + (float16_t)0.0886230469f, (float16_t)-0.0687255859f, (float16_t)0.0230102539f, (float16_t)-0.102294922f, + (float16_t)0.0868530273f, (float16_t)0.0905761719f, (float16_t)-0.0777587891f, (float16_t)0.0428771973f, + (float16_t)0.117492676f, (float16_t)0.0397033691f, (float16_t)0.0841064453f, (float16_t)0.0413818359f, + (float16_t)0.0594177246f, (float16_t)-0.00199890137f, (float16_t)-0.0272979736f, (float16_t)-0.0311889648f, + (float16_t)0.118835449f, (float16_t)-0.0263061523f, (float16_t)0.0246887207f, (float16_t)0.105773926f, + (float16_t)-0.0880126953f, (float16_t)0.024307251f, (float16_t)-0.0632324219f, (float16_t)0.0813598633f, + (float16_t)0.0223846436f, (float16_t)-0.110412598f, (float16_t)0.0313415527f, (float16_t)-0.0788574219f, + (float16_t)-0.0360107422f, (float16_t)0.0202026367f, (float16_t)-0.0979614258f, (float16_t)-0.111572266f, + (float16_t)-0.0563354492f, (float16_t)0.035736084f, (float16_t)-0.0213165283f, (float16_t)-0.0896606445f, + (float16_t)-0.0596008301f, (float16_t)0.0756225586f, (float16_t)-0.0100708008f, (float16_t)0.0885009766f, + (float16_t)-0.0744018555f, (float16_t)0.0620117188f, (float16_t)-0.0511474609f, (float16_t)-0.0735473633f, + (float16_t)0.0801391602f, (float16_t)-0.0503845215f, (float16_t)-0.0243682861f, (float16_t)0.118347168f, + (float16_t)-0.0919189453f, (float16_t)-0.0635375977f, (float16_t)-0.0495910645f, (float16_t)0.0727539062f, + (float16_t)-0.0092010498f, (float16_t)-0.104370117f, (float16_t)-0.107788086f, (float16_t)0.0219421387f, + (float16_t)0.0252685547f, (float16_t)-0.0152053833f, (float16_t)-0.0374450684f, (float16_t)0.047454834f, + (float16_t)-0.0682983398f, (float16_t)-0.029510498f, (float16_t)0.0221252441f, (float16_t)-0.0682983398f, + (float16_t)0.0173492432f, (float16_t)0.0592041016f, (float16_t)-0.0189971924f, (float16_t)0.0311737061f, + (float16_t)-0.0488891602f, (float16_t)0.0885620117f, (float16_t)-0.0737304688f, (float16_t)0.0933837891f, + (float16_t)0.00780487061f, (float16_t)-0.0720214844f, (float16_t)-0.0092010498f, (float16_t)0.0699462891f, + (float16_t)-0.0267486572f, (float16_t)-0.0518798828f, (float16_t)-0.0651855469f, (float16_t)-0.0603637695f, + (float16_t)-0.0421447754f, (float16_t)0.0335388184f, (float16_t)0.00817871094f, (float16_t)0.0726318359f, + (float16_t)-0.0236816406f, (float16_t)0.0667724609f, (float16_t)-0.114807129f, (float16_t)0.11517334f, + (float16_t)0.0319519043f, (float16_t)0.0498046875f, (float16_t)0.10546875f, (float16_t)-0.0711669922f, + (float16_t)-0.0921630859f, (float16_t)0.0718994141f, (float16_t)-0.110473633f, (float16_t)-0.0864868164f, + (float16_t)-0.0242462158f, (float16_t)0.0919189453f, (float16_t)-0.0485229492f, (float16_t)-0.0191192627f, + (float16_t)0.0215759277f, (float16_t)0.0826416016f, (float16_t)-0.106445312f, (float16_t)-0.116943359f, + (float16_t)-0.0334472656f, (float16_t)0.0375671387f, (float16_t)-0.0540466309f, (float16_t)0.111206055f, + (float16_t)0.102661133f, (float16_t)0.11517334f, (float16_t)-0.0258483887f, (float16_t)0.0930175781f, + (float16_t)-0.091003418f, (float16_t)-0.0267791748f, (float16_t)-0.0991821289f, (float16_t)0.0121688843f, + (float16_t)-0.0426635742f, (float16_t)0.00332450867f, (float16_t)-0.113098145f, (float16_t)-0.0686645508f, + (float16_t)0.0429382324f, (float16_t)-0.0233306885f, (float16_t)0.090637207f, (float16_t)-0.0238800049f, + (float16_t)-0.0772705078f, (float16_t)0.0703125f, (float16_t)-0.0281677246f, (float16_t)0.0204620361f, + (float16_t)0.00648117065f, (float16_t)0.0316772461f, (float16_t)0.0143585205f, (float16_t)-0.0272521973f, + (float16_t)-0.00231170654f, (float16_t)-0.117553711f, (float16_t)-0.0357055664f, (float16_t)-0.0352478027f, + (float16_t)-0.0133132935f, (float16_t)-0.0947265625f, (float16_t)0.0333557129f, (float16_t)-0.105102539f, + (float16_t)0.098449707f, (float16_t)-0.0344848633f, (float16_t)-0.0612487793f, (float16_t)0.0168609619f, + (float16_t)0.107299805f, (float16_t)0.104064941f, (float16_t)-0.0228271484f, (float16_t)-0.0884399414f, + (float16_t)-0.063293457f, (float16_t)0.083984375f, (float16_t)0.0353088379f, 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(float16_t)0.0963745117f, (float16_t)0.0076713562f, (float16_t)-0.083190918f, (float16_t)0.119873047f, + (float16_t)-0.11315918f, (float16_t)-0.0641479492f, (float16_t)0.102355957f, (float16_t)-0.0648193359f, + (float16_t)-0.0820922852f, (float16_t)0.00762176514f, (float16_t)0.0620727539f, (float16_t)-0.0444641113f, + (float16_t)-0.00730895996f, (float16_t)-0.0177001953f, (float16_t)-0.102233887f, (float16_t)-0.0509033203f, + (float16_t)0.00987243652f, (float16_t)-0.0686645508f, (float16_t)0.0176239014f, (float16_t)-0.110412598f, + (float16_t)0.058807373f, (float16_t)0.0291748047f, (float16_t)0.00118350983f, (float16_t)0.00145626068f, + (float16_t)0.0159301758f, (float16_t)-0.0332336426f, (float16_t)-0.0537109375f, (float16_t)-0.0895996094f, + (float16_t)0.0222320557f, (float16_t)-0.0481567383f, (float16_t)0.0697021484f, (float16_t)0.0245361328f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/test_data.h new file mode 100644 index 00000000..619cfee9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f16/test_data.h @@ -0,0 +1,22 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "dwconv0_biases_data.h" +#include "dwconv0_weights_data.h" +#include "dwconv1_biases_data.h" +#include "dwconv1_weights_data.h" +#include "dwconv2_biases_data.h" +#include "dwconv2_weights_data.h" +#include "dwconv3_biases_data.h" +#include "dwconv3_weights_data.h" +#include "fc_biases_data.h" +#include "fc_weights_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "pwconv0_biases_data.h" +#include "pwconv0_weights_data.h" +#include "pwconv1_biases_data.h" +#include "pwconv1_weights_data.h" +#include "pwconv2_biases_data.h" +#include "pwconv2_weights_data.h" +#include "pwconv3_biases_data.h" +#include "pwconv3_weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/config_data.h new file mode 100644 index 00000000..f7c68b09 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/config_data.h @@ -0,0 +1,39 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +#define DS_CNN_S_BODY_F32_INPUT_BATCHES 1 +#define DS_CNN_S_BODY_F32_INPUT_H 25 +#define DS_CNN_S_BODY_F32_INPUT_W 5 +#define DS_CNN_S_BODY_F32_INPUT_C 64 +#define DS_CNN_S_BODY_F32_CHANNELS 64 +#define DS_CNN_S_BODY_F32_NUM_BLOCKS 4 +#define DS_CNN_S_BODY_F32_NUM_CLASSES 12 +#define DS_CNN_S_BODY_F32_FEATURE_MAP_SIZE 8000 +#define DS_CNN_S_BODY_F32_POOLED_SIZE 64 +#define DS_CNN_S_BODY_F32_DST_SIZE 12 +#define DS_CNN_S_BODY_F32_SOFTMAX_ROWS 1 +#define DS_CNN_S_BODY_F32_SOFTMAX_COLS 12 +#define DS_CNN_S_BODY_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define DS_CNN_S_BODY_F32_DEPTHWISE_CH_MULT 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_H 3 +#define DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_W 3 +#define DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_H 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_W 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_H 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_W 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_H 1 +#define DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_W 1 +#define DS_CNN_S_BODY_F32_POINTWISE_FILTER_H 1 +#define DS_CNN_S_BODY_F32_POINTWISE_FILTER_W 1 +#define DS_CNN_S_BODY_F32_POINTWISE_STRIDE_H 1 +#define DS_CNN_S_BODY_F32_POINTWISE_STRIDE_W 1 +#define DS_CNN_S_BODY_F32_POINTWISE_PADDING_H 0 +#define DS_CNN_S_BODY_F32_POINTWISE_PADDING_W 0 +#define DS_CNN_S_BODY_F32_POINTWISE_DILATION_H 1 +#define DS_CNN_S_BODY_F32_POINTWISE_DILATION_W 1 +#define DS_CNN_S_BODY_F32_AVGPOOL_FILTER_H 25 +#define DS_CNN_S_BODY_F32_AVGPOOL_FILTER_W 5 +#define DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_H 1 +#define DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_W 1 +#define DS_CNN_S_BODY_F32_RELU_MIN 0.0f +#define DS_CNN_S_BODY_F32_RELU_MAX 3.40282347e+38f +#define DS_CNN_S_BODY_F32_IDENTITY_MIN -3.40282347e+38f +#define DS_CNN_S_BODY_F32_IDENTITY_MAX 3.40282347e+38f diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_biases_data.h new file mode 100644 index 00000000..ce04f6bc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_biases_data.h @@ -0,0 +1,13 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv0_biases_data[64] = { + 0.0421280116f, 0.0346825346f, -0.0225146748f, 0.0123509541f, -0.00837002974f, 0.0212684348f, + 0.00528240949f, 0.03587294f, 0.0156866089f, 0.0154814953f, 0.0228421204f, -0.018753795f, + -0.0168222301f, -0.0133913243f, -0.0356760025f, -0.0144922463f, -0.017002834f, 0.0254964828f, + 0.0476734154f, -0.0268174671f, -0.0258927587f, -0.00775855687f, 0.00547267823f, -0.0453145318f, + -0.0207091551f, 0.0180791095f, 0.0221850444f, 0.0193505716f, -0.0258227456f, -0.00661873445f, + 0.0209932495f, -8.74013422e-05f, -0.0186978076f, -0.0140220337f, -0.00281400583f, -0.00187518739f, + 0.0153199844f, 0.0256453454f, -0.0455697291f, 0.0190223604f, 0.0306222569f, -0.0102063045f, + -0.0389048867f, 0.0358844995f, -0.037553709f, 0.0441004373f, -0.0372633226f, -0.00388116203f, + -0.0169288665f, -0.0401217826f, -0.0320250951f, -0.00127591053f, -0.0400041007f, -0.0470256209f, + -0.0262327697f, -0.0421363488f, -0.0294107161f, 0.0445869304f, -0.012161999f, -0.00473673642f, + -0.0088923648f, -0.0371293277f, 0.0141461967f, -0.0390256122f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_weights_data.h new file mode 100644 index 00000000..1b990d54 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv0_weights_data.h @@ -0,0 +1,98 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv0_weights_data[576] = { + -0.0331523009f, 0.1514121f, -0.123502798f, -0.00984851737f, 0.0756718963f, 0.222855136f, + -0.0586460158f, 0.0641651601f, 0.089626953f, -0.0154694756f, 0.202144727f, 0.0745572746f, + -0.148285046f, -0.2200616f, -0.0075008953f, -0.226605177f, -0.159969851f, -0.1449797f, + 0.0698615685f, 0.0915941373f, 0.136134282f, 0.221586689f, -0.109090656f, -0.188171998f, + 0.18290861f, -0.239196762f, 0.182002306f, 0.170497626f, -0.158693254f, -0.0595799536f, + -0.237134412f, -0.0419333205f, -0.0521955192f, -0.249636739f, 0.203058153f, 0.197750136f, + -0.202163681f, 0.185769767f, 0.129709467f, 0.012224487f, 0.00668972405f, 0.139400691f, + -0.0661988109f, -0.109348096f, 0.0673248321f, 0.0398503877f, 0.154804409f, -0.193235368f, + 0.238242194f, -0.114824556f, -0.15742594f, -0.0929126367f, 0.20473969f, -0.0724594668f, + 0.0726143494f, 0.072544761f, 0.0708171874f, -0.133186713f, -0.100041009f, 0.0454886556f, + 0.0369164497f, 0.0611978322f, 0.187794462f, 0.172915816f, -0.112019874f, -0.213726252f, + 0.185938373f, -0.186565533f, 0.12243814f, -0.0406709053f, 0.205523193f, 0.134794012f, + -0.036986351f, -0.102206901f, 0.0226856656f, 0.222005665f, -0.0699069872f, 0.230913967f, + -0.171598181f, -0.235606715f, 0.0961523727f, 0.210531011f, 0.206745416f, 0.0699581876f, + 0.0791945979f, -0.139736742f, -0.116073959f, 0.106299996f, 0.215630785f, 0.119315289f, + 0.162775353f, -0.131916821f, 0.180173174f, -0.00379125378f, 0.151431084f, -0.242360726f, + 0.172570691f, -0.232375607f, -0.0433189385f, -0.123218499f, 0.00116367801f, -0.0119981691f, + -0.237906054f, 0.13103731f, 0.102818944f, 0.115475237f, -0.169470802f, -0.126257971f, + 0.154415131f, -0.0799380541f, 0.245793253f, -0.0962371603f, 0.130960464f, 0.0614325441f, + 0.00278517231f, -0.0342581794f, -0.1348418f, -0.110743314f, -0.194788218f, 0.0573471971f, + 0.241646349f, -0.177279904f, 0.137385353f, -0.189511508f, -0.0240212679f, -0.0833267793f, + -0.169304222f, 0.16970408f, -0.125090376f, 0.0999049842f, 0.0988951772f, 0.0960577205f, + -0.164764091f, -0.0715991482f, 0.0213036537f, -0.102852136f, 0.160612524f, -0.188977391f, + -0.0997707844f, 0.134909227f, -0.0129447961f, -0.102520823f, -0.0984525904f, 0.118965223f, + -0.143148556f, -0.200590506f, -0.246897608f, 0.177879766f, -0.140581116f, 0.239648581f, + 0.0668921545f, -0.233766183f, -0.0596440285f, -0.0145752253f, 0.234351382f, -0.0666546375f, + 0.189244121f, -0.193207473f, -0.186604246f, 0.0917253718f, 0.09043663f, 0.0345070176f, + -0.104397394f, 0.0559749193f, 0.0918210074f, -0.232013032f, 0.194912508f, -0.170289069f, + -0.118605837f, -0.0132637117f, 0.115161441f, -0.156233117f, -0.122466989f, -0.148953065f, + 0.240036756f, 0.2039309f, -0.0105046323f, -0.150684655f, -0.151965618f, 0.12005233f, + -0.094192937f, -0.0011158922f, -0.0555564985f, -0.166472867f, -0.0422444716f, -0.220771864f, + -0.00906970259f, 0.15163219f, -0.0829812065f, 0.115437172f, 0.084522903f, 0.208388567f, + -0.103721954f, -0.0423124172f, -0.228732526f, -0.141175956f, -0.00412856881f, 0.246662423f, + -0.151860058f, 0.0824815407f, -0.116887927f, 0.0910483003f, -0.184380889f, -0.0484478958f, + -0.0278419144f, -0.0823857337f, -0.243607894f, -0.0383054018f, 0.140452698f, -0.136199757f, + 0.21257478f, -0.217714533f, -0.117294014f, 0.134542421f, 0.0962423235f, 0.232435465f, + -0.160029083f, -0.133362994f, 0.182700649f, -0.163804948f, -0.195592061f, -0.100163184f, + 0.110995442f, 0.150406078f, -0.162078559f, -0.0737849846f, 0.118128538f, -0.20681791f, + 0.186672524f, -0.00278071477f, 0.0453966297f, 0.216313854f, 0.156873599f, -0.0429814719f, + -0.0295081642f, 0.0795878917f, 0.0629301593f, -0.166259021f, 0.035964705f, 0.169558942f, + 0.0482955724f, 0.0206385814f, -0.0212575067f, 0.0203019902f, -0.177333623f, -0.220403492f, + -0.142292157f, -0.245813534f, 0.116195552f, -0.0895119533f, 0.163967878f, 0.200790614f, + -0.171666965f, 0.0569628812f, -0.0737624839f, 0.206961185f, 0.161743656f, -0.167139202f, + 0.177670822f, 0.00481896335f, -0.181053922f, -0.156307057f, -0.24391602f, -0.0362259969f, + 0.185796604f, -0.140128866f, -0.0835598409f, -0.138556942f, 0.00316764531f, 0.142072693f, + 0.112568542f, 0.0266025215f, -0.132893249f, -0.153081387f, -0.248910278f, -0.213556886f, + -0.0237528682f, 0.0964731649f, -0.0671756417f, 0.244151279f, -0.00657798583f, -0.0831584707f, + -0.218507126f, 0.112698615f, -0.00822386332f, 0.242895618f, -0.16346918f, 0.220103115f, + -0.232752934f, 0.150414571f, 0.0556297414f, 0.11738763f, -0.105233461f, 0.184593141f, + 0.0840354636f, 0.234183982f, -0.079667002f, 0.142722696f, 0.0333263017f, 0.0633940697f, + 0.178269833f, 0.0161215793f, -0.0739536062f, -0.175106063f, -0.0887630209f, 0.155233353f, + 0.103259102f, 0.113717072f, 0.0869355723f, 0.111571431f, 0.0680829212f, -0.111385919f, + -0.0892195776f, 0.146258578f, 0.193529114f, -0.0463341027f, 0.0146828787f, 0.00527403178f, + 0.159702525f, -0.160988286f, 0.129169732f, -0.00129910966f, 0.172397107f, 0.0472389571f, + -0.246731758f, -0.158940941f, -0.00533873262f, -0.0955863521f, -0.21916382f, 0.0371852331f, + -0.036484763f, 0.12793608f, 0.201511517f, 0.165747404f, -0.0569098443f, -0.221537247f, + 0.134692296f, 0.0458750091f, 0.231057465f, -0.0547551289f, 0.0737761632f, 0.204973325f, + 0.164583296f, 0.196959555f, -0.0667520612f, 0.0066844332f, 0.133996829f, -0.0514950119f, + -0.114512742f, 0.0812250003f, -0.0894055814f, 0.146318063f, 0.0826893598f, 0.218582839f, + -0.0605653711f, 0.128411964f, -0.0872579664f, -0.204144031f, 0.167146981f, 0.0120458752f, + 0.107678317f, -0.196910217f, -0.0102685168f, -0.0294108111f, -0.158590496f, 0.216366872f, + -0.00917323399f, -0.0424689278f, 0.213428631f, -0.110617101f, -0.0743878707f, -0.0673951358f, + -0.0333368629f, -0.00280999811f, -0.0305848867f, 0.112688862f, -0.226706147f, -0.151573583f, + -0.237820253f, 0.0015386591f, 0.138704628f, 0.0341284201f, 0.227545887f, 0.0934216306f, + 0.0968951359f, -0.048474364f, 0.0760732442f, -0.0174676944f, 0.19413051f, 0.218137532f, + 0.0539973155f, 0.24289453f, 0.21518153f, -0.0116689429f, 0.0520492457f, -0.167498872f, + 0.0694618374f, -0.0240962319f, -0.20908305f, -0.119557351f, 0.0325521044f, -0.105667956f, + 0.150064185f, 0.118584506f, 0.0257718153f, -0.236536354f, 0.233940542f, -0.058653634f, + -0.196242586f, -0.198706865f, -0.245094612f, 0.0168640893f, 0.0108287493f, 0.0626276284f, + 0.215922162f, -0.0775094852f, -0.0636390522f, -0.237023667f, 0.0640716255f, 0.20092985f, + 0.00460074749f, 0.00227969349f, 0.0927301943f, -0.0176936779f, -0.0238029286f, 0.0122374697f, + -0.0647719428f, -0.0031870245f, 0.0773111656f, 0.140146434f, -0.034628462f, -0.049105186f, + -0.0835859403f, -0.085570395f, 0.0407676212f, -0.0340494476f, -0.0352494866f, -0.158820704f, + -0.0544754229f, 0.127444714f, 0.00577727426f, -0.141358927f, -0.103063114f, 0.150505319f, + -0.230377123f, -0.110128172f, -0.145633429f, 0.169683173f, -0.156367809f, 0.0680247545f, + -0.186615899f, 0.221604019f, -0.115186714f, -0.184591457f, -0.206378788f, 0.105023421f, + 0.00301379315f, 0.0807217732f, 0.0249986947f, 0.131484434f, -0.182724655f, 0.0753553212f, + -0.011810828f, -0.0279636998f, 0.124853864f, -0.0566720776f, -0.170347467f, -0.192559749f, + 0.156174883f, -0.192356244f, -0.242313728f, 0.0619577356f, 0.160509408f, 0.15669769f, + -0.182727173f, -0.0292819254f, 0.0488405302f, -0.207950786f, -0.139319077f, 0.107130066f, + 0.13065508f, -0.12924099f, 0.144062877f, 0.107765384f, 0.122710086f, 0.103245847f, + -0.100743927f, 0.144628644f, -0.178463921f, 0.0657250062f, 0.124669895f, 0.241481066f, + -0.173025295f, 0.135187954f, 0.166978866f, 0.199074388f, -0.0503351577f, -0.0735432878f, + -0.243054837f, -0.00445953663f, 0.071774438f, 0.0887347609f, -0.167698726f, 0.160478458f, + 0.241191536f, -0.0913013741f, -0.164239421f, -0.0910620093f, -0.0101677077f, -0.169414163f, + 0.156755194f, -0.225666717f, -0.122503452f, 0.221838221f, -0.172607958f, 0.238194853f, + 0.0649400577f, -0.0550577566f, 0.0976088792f, -0.0979822725f, -0.154185101f, 0.0503381714f, + 0.0845140219f, 0.234240159f, -0.241362825f, 0.0299288668f, -0.102638245f, -0.246029526f, + -0.142171532f, 0.16421333f, -0.0995989218f, 0.164764389f, 0.0508613884f, -0.130034342f, + 0.187850669f, 0.131520987f, -0.24248223f, -0.044770807f, 0.146985695f, -0.0658545941f, + -0.132530332f, 0.174100488f, 0.216246545f, -0.063329488f, 0.0731302649f, -0.238891244f, + 0.158009559f, 0.126430854f, -0.139850885f, -0.0357943699f, -0.0245353077f, -0.0560530946f, + 0.127076164f, -0.0461119749f, 0.205552757f, -0.226991341f, -0.0816141367f, -0.0309068747f, + 0.121112369f, 0.057214722f, 0.204242527f, -0.112832569f, 0.0576258712f, -0.215975672f, + -0.0347874872f, -0.225497708f, -0.125329122f, 0.09807612f, -0.0731896162f, 0.192532808f, + 0.173191801f, -0.124977216f, 0.137989521f, 0.158429503f, -0.148077145f, 0.218253091f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_biases_data.h new file mode 100644 index 00000000..bf592eda --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv1_biases_data[64] = { + -0.0403422192f, -0.0049253637f, 0.0486633182f, -0.0479502529f, 0.0307862666f, 0.014258571f, -0.0321090296f, + -0.00685146218f, 0.036438901f, 0.0364948809f, -0.031647291f, -0.0259957537f, 0.0194073878f, -0.0442877747f, + -0.0406474099f, -0.0174206384f, 0.0006143215f, -0.0478272401f, 0.028260475f, 0.0395379215f, -0.0162912067f, + -0.0303900819f, -0.0387655944f, -0.00164123124f, -0.0402157977f, 0.0325474255f, 0.0267807636f, 0.0334855504f, + -0.00473810267f, -0.0256177485f, -0.0405447073f, -0.0439170338f, 0.0392591357f, -0.0426195748f, -0.0418412425f, + -0.0321296677f, -0.00518627046f, -0.0274173431f, 0.0245511569f, 0.0214212742f, 0.0246006325f, 0.0305096898f, + 0.0250873528f, 0.00100394175f, -0.00284610176f, -0.0137778465f, -0.00552659668f, -0.0333621018f, -0.0494412184f, + 0.0323428549f, -0.0402754247f, 0.0394983515f, -0.0186855048f, 0.0270948969f, 0.043885909f, 0.0288913064f, + 0.00520614907f, -0.0057123648f, 0.0401113741f, 0.0398555622f, 0.0366195701f, 0.0109159863f, 0.0476051606f, + 0.0379757509f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_weights_data.h new file mode 100644 index 00000000..fd686ff7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv1_weights_data.h @@ -0,0 +1,85 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv1_weights_data[576] = { + -0.152381808f, -0.0489313342f, 0.0818722695f, 0.226061195f, -0.174238801f, -0.169413254f, -0.165290073f, + 0.0369145647f, -0.217946157f, 0.221820012f, -0.087324813f, -0.0427014753f, -0.147460207f, -0.16365777f, + -0.0958414227f, 0.051485613f, -0.196877778f, 0.126058042f, -0.120213054f, 0.164709464f, -0.161211878f, + 0.103971079f, 0.0157475192f, 0.0473099798f, -0.104336567f, -0.0443679057f, 0.112797812f, 0.0960444212f, + -0.117748871f, 0.133703113f, 0.069991149f, -0.0393836498f, 0.144701689f, -0.246833831f, 0.0389962941f, + 0.123792633f, -0.106526859f, 0.178059563f, 0.0126926387f, 0.214389428f, -0.068646282f, -0.0303794928f, + 0.0050344523f, -0.170465648f, -0.0394985788f, 0.231469795f, 0.122185446f, -0.132827803f, -0.230015635f, + -0.211018965f, -0.112253413f, 0.000114149763f, 0.0020424244f, 0.141587824f, -0.0631073266f, 0.0579454303f, + -0.0147818541f, 0.143873438f, 0.194241315f, 0.00187526504f, 0.0425251052f, -0.00196939916f, -0.112376615f, + -0.125934839f, -0.203478217f, -0.0713611394f, -0.18915619f, 0.00629344257f, -0.100365788f, 0.0991806313f, + 0.238830194f, 0.0405428223f, 0.142355606f, 0.244220629f, -0.000453882734f, 0.0837811083f, -0.217750341f, + -0.0891199186f, -0.0031927675f, -0.0419581085f, -0.243843973f, -0.221072525f, 0.102144696f, 0.0868333951f, + 0.0754903331f, 0.103882492f, -0.0430080816f, -0.162011966f, -0.0618079603f, -0.219100967f, 0.0403506905f, + -0.0825853124f, 0.16202341f, -0.147009894f, 0.201033548f, -0.114184134f, 0.105428353f, -0.239018306f, + 0.0141313374f, 0.244470268f, 0.11051546f, -0.0511741526f, -0.162137762f, 0.144985244f, 0.108405486f, + -0.144600451f, 0.0819693431f, 0.0660770759f, 0.106881283f, -0.0193296671f, 0.196534351f, -0.0925131738f, + 0.101980142f, -0.1282074f, 0.0767977163f, 0.185220718f, -0.0364007652f, 0.158175737f, -0.191334128f, + -0.162307575f, 0.210860774f, -0.167204395f, -0.162655979f, -0.161105365f, 0.225326598f, -0.227778837f, + 0.0777739361f, 0.0974284932f, -0.0485141166f, -0.171759307f, 0.0641277283f, 0.127476439f, -0.0219961796f, + -0.0100766635f, 0.0246087313f, -0.225472733f, 0.0670070127f, 0.203855291f, -0.159380138f, 0.0177327525f, + -0.194395959f, -0.138831019f, -0.113650672f, -0.141115084f, 0.0406991653f, 0.0386104695f, -0.0307560191f, + 0.203900278f, -0.183284f, -0.216356739f, -0.194193006f, 0.0632108226f, -0.183855474f, -0.178758353f, + -0.194954976f, 0.0437011383f, 0.200638235f, 0.124619737f, -0.162105128f, -0.218600988f, 0.00884779915f, + 0.0553298257f, 0.157832623f, -0.0658561438f, -0.100202225f, -0.0505614765f, 0.161397278f, 0.0667188838f, + 0.217097998f, -0.167804807f, 0.246025056f, 0.248140812f, 0.143897533f, -0.0476843379f, 0.0419718921f, + 0.231277272f, -0.116467953f, 0.178881839f, -0.190468341f, -0.171767071f, 0.122800954f, 0.222491711f, + 0.228685766f, 0.155481219f, -0.233784601f, -0.00521378079f, -0.0825862065f, -0.0147293527f, 0.209538966f, + 0.0931000561f, 0.240041077f, 0.0430590063f, 0.00169252371f, -0.194201767f, -0.183613166f, -0.17164427f, + 0.184922218f, 0.181129694f, -0.234816089f, 0.103557006f, -0.233844101f, 0.194328696f, -0.128014341f, + 0.162746131f, 0.0465604924f, 0.0409497544f, 0.104738824f, 0.19174397f, -0.0409788974f, 0.239247128f, + -0.0617503226f, 0.0232359469f, -0.0760724321f, 0.139252514f, -0.248767227f, 0.236528918f, 0.155279383f, + 0.151417777f, 0.0975325257f, 0.10099557f, -0.211662665f, 0.0512321815f, -0.0302993171f, 0.00213657389f, + -0.0425854847f, 0.0634187758f, 0.0950976908f, 0.0709600672f, -0.153339669f, -0.225258023f, -0.176077187f, + -0.165356532f, 0.0764361098f, 0.183295771f, -0.248541176f, 0.0248434152f, 0.238310099f, -0.176454619f, + 0.248965427f, -0.0422894172f, 0.172570422f, -0.0554560311f, -0.189564601f, 0.242290378f, 0.0704660043f, + -0.145800605f, 0.224696219f, 0.120756797f, 0.0282622259f, 0.0966525972f, 0.0454127267f, 0.151887178f, + -0.0776365697f, -0.211527526f, 0.128504381f, 0.178305507f, -0.079502821f, -0.131255612f, -0.0764535069f, + 0.1228f, -0.177781254f, -0.0446736887f, 0.00198972272f, -0.15863885f, -0.153667808f, 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0.167781815f, -0.066244714f, -0.172532365f, -0.183522344f, 0.140089855f, 0.131510869f, + -0.0731974766f, 0.175243095f, 0.0604515746f, -0.00343962829f, 0.13589485f, 0.142373919f, 0.227746323f, + 0.23021546f, -0.0743390173f, -0.196984917f, 0.202088594f, -0.23543492f, -0.00982249528f, -0.0462601222f, + 0.0356797241f, -0.124373116f, -0.0439353809f, -0.0941476971f, -0.233088925f, -0.0825365931f, 0.152772114f, + 0.226942822f, -0.056613382f, 0.117889434f, -0.143101424f, -0.137650698f, 0.0134716751f, 0.0960480124f, + -0.161333174f, 0.0108959014f, -0.187853843f, -0.106899165f, -0.146285981f, -0.0812146664f, -0.189970717f, + 0.231230944f, -0.0111611411f, -0.142873093f, 0.135461316f, -0.0895338878f, 0.0541292429f, 0.0479233116f, + -0.199209094f, -0.208827585f, -0.00736538321f, 0.118211761f, -0.0775724947f, -0.0775607303f, -0.051046811f, + 0.124413148f, 0.162125215f, 0.0110209305f, -0.0560210049f, -0.196540311f, 0.0348874331f, -0.232251927f, + -0.109884374f, -0.14948386f, 0.0739397705f, -0.0021164401f, 0.205345407f, -0.128736377f, -0.148423165f, + 0.0956119522f, 0.169602126f, -0.227578714f, 0.194130048f, 0.1029412f, 0.0736462697f, 0.0932543427f, + 0.202392042f, 0.236402467f, -0.241034806f, -0.105331026f, 0.172737181f, 0.242337406f, -0.0107579874f, + 0.19690837f, 0.144389257f, 0.0549231991f, 0.14350526f, 0.0990673155f, 0.024761172f, 0.221811518f, + 0.0946242735f, 0.220410764f, 0.112428814f, -0.0511388928f, -0.0274293013f, 0.119311288f, -0.245987043f, + -0.108650267f, 0.143496543f, 0.127487987f, -0.0094910413f, -0.0595748983f, 0.179289609f, -0.138021037f, + 0.210796997f, 0.147949859f, 0.075138323f, 0.17673476f, -0.087975882f, 0.0458272919f, 0.218215093f, + -0.162235633f, -0.0150662009f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_biases_data.h new file mode 100644 index 00000000..a212aa5f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv2_biases_data[64] = { + 0.00397531549f, -0.00168164971f, -0.00864610448f, 0.0483056754f, 0.0135763967f, -0.0173974149f, 0.0211912896f, + -0.0317728445f, 0.00262633874f, 0.0154102761f, 0.0223040637f, 0.0027032916f, -0.0258330666f, -0.0152372168f, + 0.00456206733f, -0.00443630153f, -0.00221544318f, 0.0322305709f, 0.0242129862f, 0.0247907173f, 0.0461446084f, + 0.0084034279f, -0.0186216943f, 0.0492069982f, 0.0227832235f, -0.0408986807f, -0.00636833301f, -0.03504733f, + -0.0455790795f, -0.0363696329f, -0.0363083184f, -0.0317747593f, 0.0300787017f, -0.019277459f, -0.0193896368f, + -0.0431539118f, -0.00131299614f, 0.0294524785f, 0.0222361665f, -0.0337962359f, -0.0466861539f, -0.0466204695f, + 0.0284685064f, -0.0145465797f, 0.0485703498f, 0.0149480905f, 0.0110248132f, 0.0292314105f, 0.0462050736f, + -0.0264948364f, 0.040017236f, 0.0145003628f, -0.00314491848f, 0.0222218875f, -0.0199620333f, 0.0117305042f, + 0.026284704f, -0.000274877762f, -0.0107994983f, 0.019259192f, 0.0422355421f, 0.0305306353f, -0.0149649642f, + 0.0464530401f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_weights_data.h new file mode 100644 index 00000000..abb4a04a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv2_weights_data.h @@ -0,0 +1,98 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv2_weights_data[576] = { + -0.194962084f, 0.110098526f, -0.166045308f, -0.134146705f, -0.0404374413f, 0.0837464333f, + -0.226967931f, 0.0810972303f, 0.192422256f, -0.0716861188f, 0.0249972288f, -0.19322817f, + 0.24153772f, -0.168179378f, -0.083818838f, -0.135459632f, -0.100078642f, 0.197460055f, + 0.0981743857f, -0.0920910463f, -0.215954661f, 0.155836865f, -0.0919869542f, -0.146185562f, + -0.00046418549f, 0.219293863f, -0.243470192f, 0.243976787f, 0.063693516f, -0.102839731f, + 0.227808565f, -0.193769336f, -0.0258595273f, 0.114819713f, -0.0557566658f, -0.23245354f, + -0.0689340904f, -0.0293821264f, 0.242450312f, 0.130256295f, -0.0125214597f, -0.144600868f, + 0.213248432f, -0.0447755642f, -0.0974388272f, 0.159381464f, -0.125614464f, 0.0306334384f, + 0.00345684611f, -0.125373974f, -0.0232047401f, -0.0288256202f, -0.160581544f, -0.23591046f, + 0.187082455f, 0.0937565491f, 0.161128432f, 0.0374292731f, -0.0917225406f, 0.0820356309f, + -0.153976813f, -0.0814081803f, 0.229803294f, 0.133124113f, -0.157284915f, 0.187160984f, + -0.244954452f, 0.0876004919f, 0.153028861f, 0.244576007f, 0.060775917f, 0.184514165f, + 0.206476837f, 0.247577086f, 0.227822885f, 0.026563704f, 0.212621912f, 0.0358624347f, + 0.200586021f, 0.0856883228f, -0.188109085f, -0.0988873467f, -0.0244097561f, -0.22371541f, + 0.1841712f, 0.177541375f, 0.00623051031f, 0.223095119f, -0.174759984f, 0.083310999f, + -0.120360062f, 0.192556992f, 0.118935011f, 0.0178155918f, 0.0376325697f, 0.156264782f, + -0.0309376698f, 0.075525336f, -0.0707151964f, 0.096166417f, 0.085030593f, -0.228111684f, + -0.117865846f, 0.139175475f, -0.238573149f, -0.22012794f, 0.106282689f, -0.20282574f, + -0.165896893f, 0.190255627f, 0.138662666f, -0.125301763f, -0.144236997f, -0.176840678f, + 0.0427572131f, 0.0603575706f, -0.0854776204f, 0.222792655f, -0.235648274f, -0.0306869112f, + -0.150932655f, 0.126683056f, -0.056499131f, 0.110217489f, 0.0737146363f, 0.074507691f, + 0.228866801f, 0.0855011195f, 0.10523504f, -0.0941083655f, 0.0183788631f, 0.115503587f, + -0.10716331f, -0.151293665f, -0.165904373f, 0.0308258366f, -0.00970003009f, 0.10972327f, + 0.057090953f, 0.107948288f, 0.194588825f, 0.0373189747f, 0.146923095f, 0.166978061f, + -0.186495543f, -0.24436371f, 0.174689427f, 0.204667151f, -0.125209823f, 0.154994681f, + 0.00744836871f, 0.0326523259f, 0.0480835699f, -0.097413376f, -0.222563073f, 0.151730835f, + 0.217498466f, -0.0656866878f, 0.151496038f, -0.182228699f, -0.018670857f, -0.0266093798f, + 0.248174012f, 0.177390859f, -0.017550569f, -0.0759860873f, -0.215355664f, 0.24509187f, + 0.162089869f, -0.0615923144f, 0.22281298f, 0.107293427f, 0.0990277305f, 0.0632246509f, + -0.0540574864f, -0.165336043f, 0.068074055f, 0.141154036f, 0.167159766f, -0.0586354844f, + -0.15926674f, 0.0595439039f, -0.0820861906f, 0.0168603119f, -0.120171376f, 0.220678419f, + 0.245907545f, -0.231189564f, -0.172757566f, 0.0708781481f, -0.0576741025f, -0.0213194806f, + 0.00101152214f, -0.240747005f, 0.025808841f, 0.0749096274f, 0.097854279f, 0.0970129296f, + 0.187720314f, -0.00892521162f, -0.147643834f, -0.0554970466f, 0.109910399f, -0.0519063771f, + -0.230245352f, 0.0689501464f, 0.0198865328f, 0.12553665f, -0.0440246463f, 0.2344722f, + 0.18948175f, -0.141256586f, -0.0961556137f, -0.0878932178f, 0.0890938863f, 0.0682759061f, + 0.0503444336f, 0.104441941f, 0.0160615742f, 0.225296915f, 0.229559675f, 0.194941923f, + 0.00595383998f, 0.0151102021f, 0.0100983391f, -0.048121199f, -0.142784193f, 0.0283158757f, + 0.203590944f, -0.247002229f, -0.200926617f, 0.0346098505f, 0.0168459266f, 0.0870971307f, + -0.173141941f, 0.0344577171f, 0.233817443f, 0.0575626008f, -0.0949081555f, -0.203758687f, + -0.181129977f, -0.151739344f, 0.0940021053f, -0.110482775f, -0.0681488514f, -0.0499266982f, + -0.0877245963f, 0.122314997f, -0.102988139f, 0.146215588f, 0.076686196f, 0.192922831f, + 0.0777213871f, 0.0649720728f, -0.243030176f, 0.0151289282f, 0.160602823f, -0.137045652f, + -0.215433449f, 0.0760072023f, 0.242944002f, -0.121328771f, 0.0312955752f, 0.0991382375f, + 0.20867075f, -0.103445679f, 0.120112814f, 0.030679483f, 0.170976236f, -0.243025437f, + 0.15652445f, -0.125599265f, 0.10053087f, 0.11163079f, -0.0719337314f, -0.240244135f, + 0.188653216f, 0.174003214f, 0.0641860738f, 0.121288985f, 0.0775991008f, -0.108328044f, + -0.0780456141f, -0.16583626f, -0.145902202f, 0.229919478f, 0.0262374151f, 0.137970686f, + 0.221152633f, -0.131182596f, 0.242065787f, 0.0901609734f, 0.191120163f, 0.240011483f, + -0.132564321f, -0.0931205153f, 0.138558105f, -0.229962468f, 0.0872385055f, -0.0082992008f, + -0.197110862f, 0.142416716f, 0.0745129064f, 0.151851609f, 0.0447299741f, 0.0305558965f, + -0.176383212f, 0.188350946f, -0.0515560843f, -0.198164627f, -0.158319116f, 0.209456131f, + 0.0269749016f, -0.00527604437f, -0.184841901f, -0.0326047502f, -0.197498351f, -0.0783279762f, + -0.144266456f, -0.112399369f, -0.201765805f, -0.130331039f, 0.155385792f, -0.189264685f, + 0.235204846f, 0.170716301f, 0.249827296f, -0.0208991598f, -0.171822175f, -0.08407747f, + 0.113507591f, -0.139029622f, 0.00384695781f, 0.217636287f, -0.119151339f, -0.212489545f, + 0.102691248f, -0.028295368f, 0.158595294f, 0.197093353f, 0.0477747656f, -0.157869622f, + 0.151523516f, 0.228808418f, -0.080706954f, -0.094531998f, 0.197087958f, -0.0629420802f, + -0.126978263f, -0.22787188f, -0.195104986f, -0.00294601149f, -0.0383908525f, 0.131172657f, + 0.193450913f, -0.00702785328f, 0.117966264f, 0.0442852788f, -0.10880623f, 0.0132705728f, + 0.0987898484f, -0.109213769f, 0.227387995f, -0.247008175f, 0.0744883418f, 0.0635955185f, + 0.102475584f, 0.0968924984f, -0.211714208f, -0.0260522179f, -0.0731709227f, 0.19629392f, + -0.0356444679f, 0.0148916719f, -0.160774559f, -0.179246977f, -0.0439384282f, -0.176072583f, + -0.135649517f, -0.199083179f, -0.00600360986f, 0.121830732f, -0.0169907957f, 0.208687246f, + -0.0567675419f, -0.167537794f, -0.0350372382f, -0.0517791659f, 0.0847488716f, -0.104262464f, + -0.0319373608f, -0.144238234f, 0.0580122359f, 0.0609127507f, -0.24476622f, -0.241260424f, + 0.246934623f, -0.0264530014f, -0.06656795f, -0.101370052f, 0.0635043606f, -0.212291807f, + 0.0212579928f, 0.209203944f, 0.0678214803f, -0.168791801f, -0.0169224311f, -0.119694643f, + 0.133080468f, 0.0448826067f, -0.204003751f, 0.0585706271f, 0.155812979f, 0.115249053f, + 0.0252229441f, 0.120969705f, 0.23166199f, 0.0247252844f, 0.215828463f, 0.100597337f, + -0.0282036811f, 0.114773288f, 0.0294475798f, 0.0872755274f, -0.0245729201f, -0.140827194f, + 0.197078913f, 0.140235931f, 0.244650319f, 0.124125473f, 0.045852676f, 0.0845704004f, + 0.129321605f, 0.225864857f, 0.176806852f, -0.0583789162f, 0.0436888002f, 0.0206597485f, + 0.0858156085f, 0.0147997709f, 0.124819741f, 0.109305784f, -0.0364041924f, -0.138780355f, + -0.174332276f, 0.0221149642f, 0.230858281f, 0.184782177f, -6.9324371e-05f, -0.228969261f, + -0.129589334f, -0.036140684f, -0.142583683f, 0.0299541559f, -0.163911119f, -0.134376645f, + 0.175469071f, -0.149361983f, 0.183924824f, 0.206391066f, -0.0382037424f, -0.162489191f, + 0.0520347506f, -0.067959711f, 0.173881769f, 0.227027804f, -0.12296582f, -0.128197759f, + -0.0878420472f, -0.224140614f, 0.220918253f, 0.20025377f, 0.216131732f, -0.0969288871f, + 0.0390135832f, -0.247319564f, -0.0362940729f, -0.167689636f, -0.0768008009f, -0.234526917f, + 0.241430327f, 0.238714665f, 0.0634573027f, 0.139861465f, -0.146832809f, -0.0970208421f, + 0.0838508904f, -0.244925454f, -0.130460843f, 0.167192534f, 0.110717058f, -0.0899708867f, + -0.0290601309f, 0.0265609343f, 0.143626869f, -0.129785895f, 0.154641435f, 0.246191323f, + 0.228133619f, 0.143421397f, -0.0207300857f, 0.0492408015f, -0.210262954f, -0.00547447428f, + -0.240825132f, -0.0916878954f, 0.0643828437f, -0.223902985f, 0.194412172f, -0.0469116569f, + 0.0251655597f, -0.173859984f, 0.0613699742f, -0.105001897f, 0.193445712f, -0.199366361f, + 0.233974054f, -0.138118967f, -0.203089878f, 0.0740090087f, -0.177857503f, -0.0160038881f, + -0.144405112f, -0.160473853f, 0.17645292f, -0.0180149991f, 0.0956963971f, -0.215125531f, + 0.225282729f, 0.140548214f, 0.16293329f, -0.0695518777f, -0.191684529f, 0.0761629716f, + 0.12247657f, 0.177970022f, -0.0313149728f, 0.197055802f, -0.0474149957f, 0.133543178f, + -0.00401257537f, 0.190313101f, 0.245630652f, 0.107616827f, 0.0547698736f, -0.0553132109f, + 0.0942211151f, -0.0443101116f, -0.204432696f, 0.118548207f, -0.0708247796f, 0.235187843f, + 0.131437108f, -0.18164587f, 0.0788662061f, -0.123431146f, 0.074727118f, -0.196172446f, + 0.00297887414f, -0.162632018f, -0.182894066f, 0.144663796f, 0.243317291f, 0.0538040027f, + 0.169408187f, -0.179161966f, 0.248314351f, 0.0666955784f, 0.157720193f, 0.115965441f, + -0.0886170045f, -0.130078137f, -0.111477688f, -0.112516701f, 0.130834088f, -0.0611249134f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_biases_data.h new file mode 100644 index 00000000..6889db6e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv3_biases_data[64] = { + 0.0133514246f, -0.0243466925f, 0.00271566841f, 0.00583982188f, 0.0429494865f, -0.00430197129f, -0.0199601855f, + 0.0335537791f, 0.023608746f, -0.00170954235f, -0.0299079698f, -0.0353333764f, 0.0391831063f, -0.0148872174f, + 0.0134804947f, -0.0453439429f, -0.0202074852f, 0.0457332432f, -0.0335510299f, 0.0496340245f, 0.0447684936f, + -0.0461610481f, -0.0312513299f, -0.00560295023f, 0.0426363908f, -0.0244220756f, -0.0215146188f, -0.00766331656f, + -0.0226389468f, 0.0237416122f, -0.0483128019f, 0.0340567417f, 0.0031035461f, -0.0355887823f, 0.0318539515f, + -0.0381162837f, 0.0406263657f, -0.0411715358f, 0.0254072696f, -0.0393293947f, -0.032749135f, 0.0353782475f, + -0.00932193734f, 0.0204844903f, 0.0497883484f, 0.0159632266f, -0.0331816748f, -0.0441509411f, 0.0192561224f, + 0.0167736709f, 0.0418711156f, 0.00492136786f, 0.0284382906f, -0.0279011671f, -0.0293948706f, -0.00240541832f, + 0.0369717926f, 0.0459468178f, -0.00487865834f, 0.0115355775f, 0.0190407485f, -0.0297629796f, 0.0258329529f, + 0.0263551343f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_weights_data.h new file mode 100644 index 00000000..9e925a1d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/dwconv3_weights_data.h @@ -0,0 +1,98 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_dwconv3_weights_data[576] = { + 0.128238469f, 0.00933288503f, 0.0558225773f, -0.18332009f, 0.223593295f, 0.150682122f, + 0.10164126f, 0.0750369579f, 0.195577189f, -0.148549452f, 0.0756452233f, -0.239150867f, + -0.16015166f, -0.197812751f, 0.249480844f, 0.170870051f, -0.215165883f, -0.0912138447f, + -0.0795635954f, -0.0678064227f, 0.158833057f, 0.0967090651f, 0.183774456f, 0.0781895146f, + 0.142997608f, -0.0507251695f, 0.0533466041f, 0.211090222f, 0.143019855f, 0.0916576833f, + -0.229938805f, -0.0305589344f, 0.0272395462f, -0.121708564f, -0.0519889332f, -0.133459777f, + 0.16649881f, -0.200638652f, -0.211665437f, -0.145516589f, -0.162008539f, 0.178198233f, + 0.222231939f, 0.203511998f, -0.117950767f, -0.172737807f, -0.00901525002f, -0.0245597214f, + -0.0422400087f, 0.151368156f, -0.019484235f, -0.110780649f, 0.124841079f, -0.0635218024f, + 0.144667864f, -0.177789509f, 0.0852719173f, 0.14989163f, -0.22460039f, 0.127204612f, + 0.230898157f, -0.108738318f, -0.0871673822f, -0.198990852f, 0.227930427f, 0.136257127f, + -0.222861335f, 0.0708450377f, -0.244932696f, 0.177997038f, 0.0773785636f, 0.0333410241f, + -0.206153765f, 0.101246633f, -0.203103304f, -0.194135725f, 0.214087546f, -0.106756672f, + -0.133554548f, -0.139359087f, 0.186163947f, 0.232512787f, -0.142490014f, 0.16984567f, + -0.166017234f, 0.164156199f, 0.174623743f, 0.0919118375f, 0.0238279924f, -0.14900659f, + 0.182717353f, 0.121406086f, -0.152244151f, -0.18104659f, -0.190147385f, -0.211832628f, + 0.0638633072f, -0.114611633f, -0.0793118626f, -0.204348445f, 0.0935887769f, 0.1348124f, + -0.233772397f, -0.0507699698f, 0.151542187f, 0.0585648715f, 0.0662196353f, 0.193909749f, + -0.185550466f, -0.117098406f, 0.0139667727f, 0.0883980319f, 0.0428572185f, -0.188113451f, + 0.088169679f, -0.19130221f, 0.0662566796f, 0.0929559916f, 0.00251384918f, -0.246429652f, + 0.180003777f, 0.208118036f, -0.0333792381f, -0.145820126f, 0.198088944f, 0.142107531f, + 0.0589221716f, -0.228538439f, -0.0338385552f, -0.0497642234f, 0.011698748f, 0.00737856515f, + 0.161696792f, 0.21276176f, -0.103794321f, -0.191994727f, -0.101214133f, -0.248776138f, + -0.182443276f, -0.051779516f, 0.197714269f, -0.19660756f, -0.0822334066f, -0.181138515f, + 0.144196466f, 0.191082403f, -0.0184973348f, 0.00368960924f, 0.224779576f, 0.138464913f, + -0.112560377f, 0.135219529f, -0.155998766f, -0.000400556397f, -0.166346908f, -0.173918441f, + -0.129520431f, -0.20274426f, -0.152602062f, 0.0946124792f, 0.0722868741f, 0.191779837f, + -0.0700867027f, -0.174142793f, 0.116967589f, 0.127177298f, 0.236375183f, -0.171439767f, + -0.11520569f, 0.0294204131f, -0.0386919379f, -0.0691973865f, 0.0211303011f, 0.0939984024f, + -0.155331463f, -0.0345635861f, -0.209280774f, -0.0322236493f, 0.137977645f, -0.203914911f, + 0.101015754f, -0.200875476f, -0.0262686908f, 0.226505741f, 0.0366499126f, -0.151310757f, + 0.185304537f, -0.160045639f, -0.161277786f, -0.193594128f, -0.235863969f, 0.0369984396f, + -0.248072475f, 0.137215883f, -0.100094445f, 0.0585034974f, 0.0171257984f, 0.113555141f, + -0.05859432f, 0.209115341f, -0.0306581613f, 0.214864433f, -0.0983751193f, -5.78371182e-05f, + 0.155963778f, -0.0635069087f, 0.248838246f, 0.0431072488f, -0.137484893f, 0.130721346f, + -0.188676775f, -0.099501051f, 0.0763268843f, -0.179892957f, -0.247291028f, -0.230659828f, + 0.184966803f, 0.245926097f, -0.0974486694f, -0.174725205f, -0.147849947f, -0.0516364425f, + -0.242226616f, -0.180887282f, 0.105888918f, -0.202442259f, 0.0157967173f, 0.048582565f, + 0.214131892f, 0.210320026f, -0.194089562f, 0.105297558f, 0.0846496671f, -0.222395703f, + 0.105602682f, 0.0246473271f, 0.185804114f, 0.0332782269f, -0.0989247039f, 0.17548801f, + 0.0427081436f, -0.127751902f, -0.0792686492f, -0.146989569f, -0.114555247f, -0.0604799204f, + -0.188114852f, -0.0295222197f, -0.228229627f, 0.0637368411f, 0.0483900867f, 0.13790445f, + -0.0673791245f, 0.122780323f, 0.00981002301f, 0.0510625876f, -0.0870476291f, -0.0716522783f, + 0.0219889935f, -0.158794448f, 0.209219843f, 0.167713344f, 0.00413032295f, 0.191081464f, + 0.133006364f, 0.0350778662f, 0.058990214f, -0.204074472f, -0.134382591f, 0.100191839f, + 0.131004453f, 0.0646688491f, -0.0356595926f, 0.0630314127f, -0.230297565f, 0.210463643f, + 0.241551071f, 0.15128471f, 0.0139346449f, -0.0631579384f, -0.133153036f, 0.101240896f, + 0.0317304358f, 0.093207635f, 0.0621946491f, 0.0625102296f, -0.115420736f, -0.219972804f, + 0.195882052f, 0.168051049f, -0.153445631f, 0.188172638f, 0.0237928722f, 0.0961534455f, + 0.233759567f, -0.0260534566f, -0.237274006f, -0.0372497104f, 0.135299206f, -0.242263675f, + 0.219915748f, 0.170243084f, 0.0389365032f, -0.142485678f, -0.136689588f, 0.0535241812f, + 0.0928898007f, 0.00923628733f, -0.00032888251f, -0.0775638819f, 0.153603613f, -0.207110286f, + 0.0422332659f, 0.0106930193f, 0.120761245f, -0.0861200318f, -0.153518632f, 0.214459017f, + 0.13564232f, -0.0996323153f, -0.0574156456f, -0.0550999753f, 0.149753705f, 0.190184116f, + 0.0377537683f, -0.132961452f, 0.107002117f, 0.101882108f, -0.244881332f, -0.0880615935f, + -0.0673604831f, 0.158043757f, 0.20133622f, 0.1272223f, 0.0522834808f, 0.111186847f, + -0.232196063f, -0.248193204f, 0.121889435f, -0.00532125868f, -0.134394854f, 0.164761394f, + -0.0704330206f, 0.0659077913f, 0.209649488f, -0.144212157f, 0.108067185f, -0.145962387f, + -0.0731442422f, 0.0287193879f, -0.148928225f, -0.0677222237f, 0.0649372861f, 0.0138092758f, + -0.156425923f, 0.0382211469f, -0.212191388f, -0.00849167723f, -0.176617533f, 0.0851489305f, + -0.227682874f, -0.0317491852f, -0.00576724391f, 0.103967734f, 0.0718595237f, 0.0645762235f, + -0.0511434488f, 0.0280834176f, -0.17467092f, 0.0846755132f, -0.133868039f, 0.226846218f, + -0.108021989f, -0.154057771f, 0.0953859985f, 0.0642770752f, 0.149499863f, -0.14616026f, + 0.0702817366f, 0.0901341438f, 0.0650376454f, 0.0335566439f, 0.0847928375f, -0.0998870507f, + 0.151384041f, -0.170728043f, 0.20557338f, 0.194804356f, -0.155968443f, 0.0775897428f, + 0.00339992391f, 0.186294705f, -0.0422473326f, 0.21173057f, -0.140035167f, 0.0160535872f, + 0.00509712193f, -0.230567768f, 0.189520642f, -0.000174273329f, 0.0235411897f, -0.220770329f, + 0.0149909975f, -0.0641895905f, 0.00233622454f, 0.107265428f, -0.086875774f, -0.183487192f, + 0.169585139f, 0.0295800455f, -0.211048424f, -0.0821636245f, -0.215817049f, 0.021686092f, + -0.124348029f, -0.0110818883f, 0.108418196f, -0.143386811f, -0.127013639f, 0.202055112f, + 0.249845341f, 0.0780455917f, -0.0412311554f, -0.0689474121f, 0.00548394956f, -0.0975774229f, + -0.19817622f, -0.0643949956f, -0.0687440038f, -0.139077961f, 0.0243679378f, -0.234146357f, + 0.156538233f, -0.160345957f, 0.147105753f, 0.155575052f, 0.1489162f, 0.0989150405f, + -0.173141226f, -0.223231897f, 0.109054834f, -0.214642361f, -0.0501947999f, 0.107288122f, + -0.126494154f, 0.182574168f, -0.190786749f, -0.0554038994f, 0.14385052f, -0.190548465f, + 0.0019704767f, 0.0854792595f, -0.0508570299f, -0.110589281f, -0.0135772675f, -0.245326743f, + 0.19714573f, 0.166439876f, 0.218843326f, 0.0281700678f, 0.0256031025f, 0.000285422575f, + 0.00394561049f, -0.195152894f, 0.0853983462f, 0.196673751f, 0.175080732f, -0.10249503f, + -0.181348577f, -0.2042889f, -0.173688456f, -0.0217503235f, 0.00517543172f, 0.211692959f, + 0.128880009f, 0.19486329f, 0.197857127f, -0.239486411f, 0.23816736f, -0.0374263115f, + -0.128279269f, -0.166632786f, -0.211898744f, -0.0178374052f, -0.187582061f, 0.063439481f, + 0.149675891f, -0.132168964f, 0.18496497f, -0.0800563246f, -0.0665890127f, 0.146123692f, + 0.0577863604f, 0.127568692f, -0.15746361f, -0.109191775f, 0.210902229f, -0.187874362f, + 0.23416248f, 0.141640246f, 0.172640607f, -0.00795886479f, -0.159714997f, 0.210678861f, + 0.046274256f, 0.0179610997f, -0.0439326353f, 0.176724806f, -0.232900873f, -0.0565589964f, + -0.0400855877f, -0.230548799f, -0.219340861f, -0.0130485967f, 0.241755024f, 0.121063456f, + -0.196791351f, -0.0602031015f, -0.107105672f, 0.231955037f, -0.181584015f, 0.185680106f, + 0.00137403945f, 0.233006686f, 0.166029587f, 0.205838785f, -0.184832126f, -0.0115766078f, + -0.03184136f, -0.0425005183f, -0.00893112179f, 0.0611068793f, -0.181787521f, 0.0423393324f, + -0.19486475f, 0.229216933f, -0.0671143979f, 0.151072815f, 0.094090499f, -0.187947184f, + 0.0025145309f, -0.164501742f, 0.156971887f, 0.172998115f, 0.0159182325f, -0.12112508f, + -0.0655921474f, 0.178321913f, -0.238682479f, -0.00176446571f, -0.187212586f, 0.210341185f, + -0.179686248f, 0.0466954857f, 0.0660978407f, 0.0319569558f, -0.232155576f, -0.247592866f, + -0.181660339f, 0.0395429842f, 0.155117348f, -0.156011522f, -0.0207947027f, -0.116966158f, + 0.106533907f, 0.13725704f, -0.230025008f, 0.0158080608f, 0.154442281f, -0.205944136f, + -0.179053441f, 0.13278757f, 0.0628754869f, -0.206813917f, -0.111649014f, -0.151259139f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_biases_data.h new file mode 100644 index 00000000..da2e659c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_biases_data.h @@ -0,0 +1,13 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_fc_biases_data[12] = {-0.0290437583f, + -0.00316095375f, + 0.022950083f, + 0.00551534025f, + 0.0250060037f, + -0.0442860201f, + 0.0367247313f, + 0.0375697464f, + 0.0320638902f, + 0.0360117666f, + 0.0263689999f, + 0.00165376649f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_weights_data.h new file mode 100644 index 00000000..934f38c2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/fc_weights_data.h @@ -0,0 +1,130 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_fc_weights_data[768] = { + -0.0241556931f, -0.1394912f, 0.0683103204f, 0.116987787f, 0.0824718699f, -0.127961427f, + 0.0871893615f, 0.194641635f, -0.120208502f, -0.146786571f, -0.14184463f, 0.113149501f, + 0.0381219164f, 0.0645868704f, -0.0819247141f, -0.12193156f, -0.109532729f, -0.11381416f, + -0.0776196569f, 0.136100352f, -0.00663476251f, 0.0880578905f, 0.087343201f, 0.0947566628f, + -0.166274682f, -0.113617606f, -0.165715218f, -0.0558896363f, 0.0567252524f, -0.101690494f, + 0.154474914f, 0.0830418915f, -0.136262536f, -0.159805864f, 0.195167959f, -0.0884605497f, + -0.0743965805f, -0.130870029f, -0.0115703829f, 0.19132264f, 0.0602714121f, 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-0.154417098f, -0.0449176915f, -0.175066814f, 0.151180059f, + -0.182728052f, 0.0936387777f, -0.149874657f, -0.0608488396f, 0.152061224f, -0.160489172f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/input_data.h new file mode 100644 index 00000000..42e93679 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/input_data.h @@ -0,0 +1,1336 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_input_data[8000] = { + -0.54212302f, -0.345118225f, 0.206792012f, 0.92534101f, -0.942779481f, 0.714954019f, + 0.0718123987f, -0.64453274f, -0.660411417f, 0.2314803f, 0.438321501f, -0.531636536f, + -0.493766814f, 0.300249398f, -0.0441280268f, -0.792246759f, 0.642595112f, 0.656173825f, + -0.350048631f, 0.6048491f, 0.0475399122f, -0.499352276f, 0.746240079f, 0.258723587f, + 0.973807096f, 0.795941174f, 0.757621825f, 0.37559706f, 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0.904436827f, + -0.130680084f, 0.616674185f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/output_ref_data.h new file mode 100644 index 00000000..6f73f436 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_output_ref_data[12] = {0.0775370002f, + 0.0812419951f, + 0.0840318874f, + 0.0835080817f, + 0.0865597203f, + 0.079363212f, + 0.0885824189f, + 0.0869727209f, + 0.0872648433f, + 0.0823692083f, + 0.0815939382f, + 0.0809749439f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_biases_data.h new file mode 100644 index 00000000..067b6adb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv0_biases_data[64] = { + 0.0314875282f, 0.0272063576f, 0.0285951328f, 0.00788791943f, -0.0286328048f, 0.0269956049f, 0.0354814939f, + -0.0121227624f, -0.000853603822f, -0.02259399f, -0.0423340946f, -0.00415864401f, 0.0209239684f, -0.00294428389f, + -0.0483013131f, -0.0262545384f, 0.0343918391f, 0.00641389331f, -0.0351278074f, -0.0348909199f, 0.00340457121f, + 0.00402239477f, 0.0137897041f, -0.0195854995f, 0.0352905132f, -0.034285292f, 0.0420166962f, -0.0203992594f, + 0.0260299705f, -0.0454419851f, 0.041186735f, -0.0404302888f, 0.0356451161f, 0.041195225f, 0.00714180246f, + 0.0411704816f, 0.0394049101f, -0.0223489888f, -0.0292913336f, 0.0351621769f, 0.0236852039f, -0.0445439927f, + 0.0259404536f, 0.0473167785f, 0.0264060479f, 0.00407406827f, -0.0293987896f, -0.0143637806f, 0.0347032696f, + 0.0161740854f, 0.0423943698f, -0.00215222407f, 0.00261533889f, -0.0159724038f, -0.0420554429f, -0.0205047075f, + 0.0419111848f, -0.0307371113f, -0.0343802385f, 0.0106870038f, 0.0304290056f, 0.00632284302f, 0.0210757349f, + 0.0408095419f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_weights_data.h new file mode 100644 index 00000000..1c755bca --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv0_weights_data.h @@ -0,0 +1,685 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv0_weights_data[4096] = { + -0.0307769142f, 0.0418983549f, -0.0147056524f, 0.0605719723f, 0.0949632078f, -0.0708899871f, + 0.0898904055f, 0.0461036488f, -0.0978183746f, 0.020005377f, 0.00561837899f, 0.0816973001f, + -0.107663088f, 0.0107583152f, 0.0957897231f, -0.0554618984f, 0.0181807764f, 0.104841553f, + 0.0784835592f, -0.11412999f, 0.0186609067f, 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+ 0.0085015744f, 0.0145348078f, 0.0384676792f, 0.0299927071f, -0.0817648172f, -0.0229334589f, + -0.0765770823f, 0.113073021f, -0.00311076082f, 0.0505022481f, 0.112248212f, 0.0534294248f, + 0.0331100598f, 0.0202460568f, -0.0264424365f, -0.0633970648f, -0.0977716148f, -0.0276451837f, + -0.0915571526f, -0.0386655144f, -0.0212322567f, -0.00661702966f, 0.0810472444f, -0.0366677158f, + -0.0174862351f, -0.110128157f, -0.00644707214f, 0.0175963845f, 0.0386655442f, 0.0973412991f, + -0.0266941264f, -0.0675621778f, 0.0349422619f, 0.090143241f, 0.0293430742f, -0.0275199208f, + 0.0581965856f, -0.0363687687f, -0.0259297937f, 0.0902517065f, -0.0563724563f, 0.0163784381f, + 0.0464943871f, -0.106008388f, -0.00101382879f, -0.102330536f, -0.00513361953f, 0.025878679f, + -0.0429021493f, 0.106860831f, 0.100084297f, -0.0932111666f, -0.00172106526f, 0.0591162778f, + 0.108970098f, 0.098898761f, -0.0277267862f, 0.0276026409f, 0.00239580101f, -0.0203095749f, + -0.0835620239f, 0.105094619f, 0.0258993618f, -0.0917557254f, 0.0507871024f, 0.00210973434f, + 0.109649405f, 0.106554911f, 0.0608151294f, 0.0540782399f, 0.0562076867f, -0.0819226578f, + 0.0241262764f, -0.0928813294f, -0.0810399354f, 0.11589101f, -0.0121370349f, 0.0791483968f, + 0.0184281543f, 0.069134362f, -0.0336310118f, -0.0199031029f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_biases_data.h new file mode 100644 index 00000000..0f07c1cf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv1_biases_data[64] = { + 0.0108924704f, 0.03802488f, 0.0389525071f, -0.000238192675f, -0.0472146384f, -0.0353890546f, -0.0316382758f, + -0.0165487658f, 0.0411459059f, -0.0332114734f, -0.0274842773f, 0.0473045819f, 0.0272396524f, -0.0433808044f, + -0.0499667563f, -0.0218679272f, 0.013117305f, 0.0499819294f, 0.0292762648f, -0.0483056158f, -0.0375058018f, + 0.0206177011f, -0.0436190143f, 0.0262136031f, 0.0112138921f, 0.0411590934f, -0.0355522446f, -0.00168568233f, + 0.0414928719f, -0.0193278398f, -0.0188803077f, 0.00734265847f, 0.0331225432f, 0.0155341933f, 0.03053133f, + -0.0496800579f, -0.0225490965f, 0.0434114598f, -0.0431387424f, 0.0129241608f, -0.0445610024f, 0.0425353907f, + 0.0241547506f, 0.0224093888f, -0.0123048835f, 0.0412665196f, 0.00578615023f, -0.0340683125f, 2.58199198e-05f, + -0.0366087332f, 0.0481384024f, -0.00572383497f, -0.0306695122f, 0.0447919369f, -0.0352078415f, -0.0334836841f, + 0.00871017575f, -0.0317134261f, 0.025968425f, 0.0420259126f, 0.0354287364f, -0.033389803f, 0.0422319956f, + 0.0161640141f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_weights_data.h new file mode 100644 index 00000000..2174482d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv1_weights_data.h @@ -0,0 +1,685 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv1_weights_data[4096] = { + 0.0908407345f, -0.0354128405f, -0.0145870605f, -0.0803154483f, -0.089480184f, -0.0673051178f, + 0.00138307107f, 0.0865723416f, -0.0795178041f, -0.0892082378f, 0.0686734766f, -0.0564463213f, + 0.0418340564f, -0.0976371095f, -0.0358592272f, 0.0429850183f, -0.0750633255f, -0.0246515553f, + 0.071294196f, -0.0946361348f, -0.004050049f, 0.087080054f, -0.098857224f, -0.00712525239f, + -0.0617232397f, -0.0590962619f, -0.1077732f, -0.0273293871f, 0.0171772763f, -0.0949047804f, + 0.0594646148f, -0.0889502466f, -0.107977241f, -0.0551971607f, -0.057480596f, 0.0850979537f, + 0.0120171597f, 0.0115104355f, 0.0675188303f, 0.11449834f, 0.0395224541f, 0.061068669f, + 0.00845212862f, 0.026228115f, 0.0972736627f, 0.0916479081f, -0.0545801707f, 0.0371046998f, + 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0.0543719195f, -0.0906563252f, -0.107187562f, 0.1096882f, 0.058762297f, + -0.0246827379f, 0.0401122645f, -0.065353103f, 0.0388216786f, -0.0119210407f, 0.0984404087f, + 0.0935904682f, 0.0126195475f, 0.0342466719f, 0.0645646751f, -0.112450846f, -0.0326721743f, + 0.0164004061f, 0.0823795646f, 0.0335366353f, -0.093368046f, -0.10591913f, 0.0390131623f, + -0.0804302692f, -0.0991000161f, -0.0357598476f, -0.0319506563f, -0.0680399314f, 0.0427079573f, + -0.0464694574f, -0.016663013f, -0.0190844666f, -0.0689787418f, 0.0419947729f, -0.0966222659f, + -0.0205732938f, 0.0124068223f, 0.0318202898f, 0.01006593f, 0.0677970797f, 0.0319980532f, + 0.0566683002f, 0.0893118903f, -0.00498895394f, 0.0626715273f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_biases_data.h new file mode 100644 index 00000000..2138b613 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv2_biases_data[64] = { + -0.0430229791f, -0.00845040102f, -0.0346276015f, -0.0414757356f, 0.0115681766f, -0.0394880362f, -0.0136820842f, + -0.0365239754f, -0.0123803969f, -0.0364604779f, -0.0431027785f, -0.0388819724f, -0.0356166512f, 0.0036665001f, + -0.0105403103f, -0.0330186486f, 0.022544276f, -0.00901633222f, -0.0272135008f, 0.0463961922f, -0.0266230274f, + 0.0194584131f, -0.0471620895f, -0.0219892077f, -0.0272775795f, -0.0436660647f, 0.00968848355f, -0.0205635112f, + -0.0492681563f, 0.0497731976f, 0.00910512637f, 0.028366955f, -0.0194506813f, 0.0433911756f, 0.00446484098f, + -0.0286971703f, 0.0418177284f, 0.00562752457f, 0.0375783816f, -0.0165188592f, -0.0438901968f, -0.0147913955f, + -0.0445731059f, -0.00212176726f, 0.03266472f, -0.0307204574f, 0.0446961708f, 0.00253031822f, -0.0382691287f, + 0.0388375595f, -0.0391993113f, 0.00288369763f, 0.0109256562f, -0.000226850636f, 0.0493150279f, 0.0387781449f, + 0.0454741903f, 0.0357961692f, 0.0481036566f, -0.0430088639f, 0.0497001559f, 0.0326313488f, -0.0080747474f, + -0.0349953398f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_weights_data.h new file mode 100644 index 00000000..fb356098 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv2_weights_data.h @@ -0,0 +1,685 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv2_weights_data[4096] = { + -0.0227964278f, -0.0237301737f, -0.00386335957f, -0.0671156421f, 0.047237806f, 0.0452650152f, + 0.100199439f, 0.118586786f, 0.0954515412f, -0.0830814466f, -0.113558985f, -0.0682055727f, + -0.0513496809f, 0.0110852625f, 0.0901141837f, 0.0437230505f, 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-0.112622283f, + 0.04026559f, 0.0444181338f, -0.0866668448f, 0.0184633639f, 0.0764718056f, 0.0491885245f, + 0.0966079533f, -0.0652691051f, 0.0678183958f, 0.0648844093f, -0.0890424252f, 0.0508383922f, + 0.0134696392f, -0.00433620485f, 0.0891025141f, 0.0804242119f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_biases_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_biases_data.h new file mode 100644 index 00000000..171daece --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_biases_data.h @@ -0,0 +1,12 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv3_biases_data[64] = { + 0.027301589f, -0.030588083f, -0.0338828042f, 0.0445916578f, -0.0433980599f, -0.0499300323f, 0.0070562358f, + -0.0482956804f, -0.0275382772f, 0.0361950658f, 0.0411588959f, -0.0482161194f, 0.0284184162f, -0.00737481238f, + 0.0356405638f, 0.0477969684f, -0.0361279733f, 0.017248489f, -0.0438606143f, 0.0333388746f, -0.0390965678f, + 0.00852669124f, -0.0129317828f, 0.0449515283f, 0.0151562011f, -0.0336233974f, 0.0428035706f, 0.0125375623f, + -0.0393420123f, -0.000808478857f, 0.034489762f, -0.0409512259f, 0.0169265345f, 0.0480882786f, 0.0346526764f, + 0.0157321375f, 0.0390809439f, 0.0123341708f, 0.0223702006f, -0.00405360339f, -0.00408416195f, 0.0242932048f, + -0.0262117535f, -0.0443615913f, 0.0374502353f, 0.0230382755f, 0.00875673257f, 0.0482314043f, 0.0117443493f, + 0.0416886471f, -0.0057396898f, -0.0410434231f, 0.0232680384f, -0.0353847705f, -0.0107884789f, -0.0383011736f, + 0.0397002995f, -0.0261226166f, 0.0091072144f, -0.0490902849f, 0.04858445f, 0.0226620845f, -0.00535885198f, + 0.0376569852f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_weights_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_weights_data.h new file mode 100644 index 00000000..5dce5f80 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/pwconv3_weights_data.h @@ -0,0 +1,685 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t ds_cnn_s_body_f32_pwconv3_weights_data[4096] = { + 0.0492954813f, 0.00120000436f, 0.100307077f, -0.0650077388f, -0.0670322552f, 0.0278484356f, + -0.0690849796f, 0.0353215113f, -0.0424957275f, 0.0510348566f, -0.0991815105f, -0.0551059283f, + -0.0667195171f, 0.0399216861f, 0.0169782452f, 0.00899198838f, 0.0210432336f, 0.0299049243f, + -0.0505340584f, -0.0488840304f, 0.0616914444f, 0.0810104758f, -0.0646653995f, -0.0566294976f, + -0.0124266222f, -0.000993527472f, -0.080627799f, -0.0818260536f, 0.0886214003f, -0.068703942f, + 0.0230090488f, -0.102280267f, 0.086839743f, 0.0905605108f, -0.0777613074f, 0.0428892486f, + 0.117515713f, 0.0397169888f, 0.0841015279f, 0.0413783789f, 0.0594088547f, -0.00199880404f, + -0.0272944197f, -0.03118697f, 0.118834056f, -0.0263039786f, 0.0246934611f, 0.105778061f, + 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-0.0458047278f, + -0.0488300584f, -0.0439001098f, 0.0464970954f, -0.0499346107f, -0.109708853f, -0.0174941029f, + 0.0246828347f, -0.0253565125f, 0.0170273967f, -0.0612149388f, -0.0504586622f, -0.0379899256f, + 0.0874522924f, -0.000234797029f, 0.0964033827f, 0.00767090078f, -0.0831824616f, 0.119864352f, + -0.113172732f, -0.0641516149f, 0.102371469f, -0.0648361593f, -0.0821113586f, 0.0076220734f, + 0.0620589405f, -0.044454366f, -0.00730966032f, -0.0176990721f, -0.102206998f, -0.0509099327f, + 0.00987561792f, -0.0686740503f, 0.0176218227f, -0.110409595f, 0.0588162057f, 0.0291683301f, + 0.00118328421f, 0.00145605172f, 0.0159305632f, -0.0332448632f, -0.0536969341f, -0.0895701051f, + 0.022224851f, -0.0481650755f, 0.0697115287f, 0.0245350674f}; diff --git a/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/test_data.h new file mode 100644 index 00000000..619cfee9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/ds_cnn_s_body_f32/test_data.h @@ -0,0 +1,22 @@ +// Generated by ds_cnn_s_body_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "dwconv0_biases_data.h" +#include "dwconv0_weights_data.h" +#include "dwconv1_biases_data.h" +#include "dwconv1_weights_data.h" +#include "dwconv2_biases_data.h" +#include "dwconv2_weights_data.h" +#include "dwconv3_biases_data.h" +#include "dwconv3_weights_data.h" +#include "fc_biases_data.h" +#include "fc_weights_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "pwconv0_biases_data.h" +#include "pwconv0_weights_data.h" +#include "pwconv1_biases_data.h" +#include "pwconv1_weights_data.h" +#include "pwconv2_biases_data.h" +#include "pwconv2_weights_data.h" +#include "pwconv3_biases_data.h" +#include "pwconv3_weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/biases_data.h new file mode 100644 index 00000000..7ddc89b8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_batch2_f16_biases_data[2] = {(float16_t)0.303955078f, (float16_t)0.528808594f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/config_data.h new file mode 100644 index 00000000..217fd21e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_BATCH2_F16_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_IN_CH 1 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_INPUT_W 8 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_INPUT_H 8 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_INPUT_SIZE 64 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_OUT_ACTIVATION_MIN (float16_t) - 10.0f +#define FULLY_CONNECTED_2OUT_BATCH2_F16_OUT_ACTIVATION_MAX (float16_t)10.0f +#define FULLY_CONNECTED_2OUT_BATCH2_F16_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_BATCH2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_BATCH2_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/input_data.h new file mode 100644 index 00000000..481bd987 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/input_data.h @@ -0,0 +1,34 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_batch2_f16_input_data[128] = { + (float16_t)1.33300781f, (float16_t)1.80957031f, (float16_t)-0.643066406f, (float16_t)1.05859375f, + (float16_t)-1.7421875f, (float16_t)0.989746094f, (float16_t)0.0924072266f, (float16_t)-0.469482422f, + (float16_t)1.38378906f, (float16_t)-1.56933594f, (float16_t)-1.67089844f, (float16_t)-1.22460938f, + (float16_t)-0.12487793f, (float16_t)-0.874023438f, (float16_t)-0.409423828f, (float16_t)-0.0464782715f, + (float16_t)0.465576172f, (float16_t)1.24316406f, (float16_t)-0.936523438f, (float16_t)0.191772461f, + (float16_t)-1.93652344f, (float16_t)-0.417724609f, (float16_t)-1.57617188f, (float16_t)-1.5234375f, + (float16_t)-1.19921875f, (float16_t)1.72363281f, (float16_t)-1.015625f, (float16_t)0.823242188f, + (float16_t)-0.316894531f, (float16_t)-1.00195312f, (float16_t)1.17773438f, (float16_t)-1.41113281f, + (float16_t)0.887207031f, (float16_t)1.82519531f, (float16_t)0.126831055f, (float16_t)0.561523438f, + (float16_t)-1.22460938f, (float16_t)1.90429688f, (float16_t)-1.80273438f, (float16_t)1.09960938f, + (float16_t)-0.861816406f, (float16_t)-1.53320312f, (float16_t)-0.470703125f, (float16_t)-1.65625f, + (float16_t)-0.228637695f, (float16_t)0.376708984f, (float16_t)0.225952148f, (float16_t)-1.02246094f, + (float16_t)1.70898438f, (float16_t)-0.310546875f, (float16_t)-1.83984375f, (float16_t)-0.105041504f, + (float16_t)1.45703125f, (float16_t)-1.61523438f, (float16_t)-1.33300781f, (float16_t)-1.3125f, + (float16_t)-1.53613281f, (float16_t)1.9609375f, (float16_t)0.660644531f, (float16_t)-1.63476562f, + (float16_t)1.59082031f, (float16_t)0.983398438f, (float16_t)-1.92578125f, (float16_t)-0.984375f, + (float16_t)-1.08691406f, (float16_t)1.13085938f, (float16_t)0.0401916504f, (float16_t)-0.266845703f, + (float16_t)0.0117797852f, (float16_t)0.0899047852f, (float16_t)-0.0506286621f, (float16_t)0.87890625f, + (float16_t)1.55078125f, (float16_t)0.0471496582f, (float16_t)1.98632812f, (float16_t)1.8984375f, + (float16_t)1.16992188f, (float16_t)-0.959472656f, (float16_t)-1.36035156f, (float16_t)0.953613281f, + (float16_t)0.459716797f, (float16_t)-0.0393981934f, (float16_t)-1.36425781f, (float16_t)-0.449462891f, + (float16_t)0.681640625f, (float16_t)0.712890625f, (float16_t)-0.940429688f, (float16_t)0.827636719f, + (float16_t)-1.68261719f, (float16_t)1.84863281f, (float16_t)-0.650390625f, (float16_t)-1.80957031f, + (float16_t)0.74609375f, (float16_t)0.132202148f, (float16_t)0.936523438f, (float16_t)0.259277344f, + (float16_t)1.78027344f, (float16_t)1.16796875f, (float16_t)1.30761719f, (float16_t)0.612304688f, + (float16_t)-0.267333984f, (float16_t)-0.784179688f, (float16_t)1.671875f, (float16_t)0.0366210938f, + (float16_t)-0.522949219f, (float16_t)0.356201172f, (float16_t)-1.60546875f, (float16_t)-0.425048828f, + (float16_t)-0.684082031f, (float16_t)1.3671875f, (float16_t)0.182250977f, (float16_t)0.852539062f, + (float16_t)-0.916503906f, (float16_t)-1.01464844f, (float16_t)-1.66210938f, (float16_t)-0.371337891f, + (float16_t)0.353027344f, (float16_t)1.69238281f, (float16_t)-0.0748901367f, (float16_t)-0.556152344f, + (float16_t)1.13769531f, (float16_t)0.192993164f, (float16_t)-1.78613281f, (float16_t)1.22265625f, + (float16_t)0.884277344f, (float16_t)-1.6640625f, (float16_t)0.80078125f, (float16_t)-1.69335938f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/output_ref_data.h new file mode 100644 index 00000000..3398389b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_batch2_f16_output_ref_data[4] = {(float16_t)1.828125f, + (float16_t)-1.3828125f, + (float16_t)7.28125f, + (float16_t)10.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/weights_data.h new file mode 100644 index 00000000..37c16e7f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f16/weights_data.h @@ -0,0 +1,34 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_batch2_f16_weights_data[128] = { + (float16_t)0.321533203f, (float16_t)1.39941406f, (float16_t)-0.602539062f, (float16_t)-0.315673828f, + (float16_t)-1.29492188f, (float16_t)-0.0983886719f, (float16_t)0.364257812f, (float16_t)0.764160156f, + (float16_t)-1.35546875f, (float16_t)0.514160156f, (float16_t)0.00950622559f, (float16_t)0.702148438f, + (float16_t)0.293945312f, (float16_t)-0.514160156f, (float16_t)-0.580566406f, (float16_t)0.813476562f, + (float16_t)-0.950195312f, (float16_t)0.195922852f, (float16_t)-1.45703125f, (float16_t)1.39257812f, + (float16_t)-0.78515625f, (float16_t)1.48828125f, (float16_t)0.258544922f, (float16_t)-0.00137710571f, + (float16_t)-1.0078125f, (float16_t)1.20996094f, (float16_t)1.19433594f, (float16_t)0.687988281f, + (float16_t)-0.978027344f, (float16_t)0.555175781f, (float16_t)0.692871094f, (float16_t)-0.219482422f, + (float16_t)-1.06347656f, (float16_t)-1.31054688f, (float16_t)-1.37207031f, (float16_t)0.237915039f, + (float16_t)-1.26269531f, (float16_t)-1.37890625f, (float16_t)1.00292969f, (float16_t)0.882324219f, + (float16_t)1.21582031f, (float16_t)0.100585938f, (float16_t)0.338623047f, (float16_t)0.278076172f, + (float16_t)-1.46386719f, (float16_t)0.77734375f, (float16_t)-0.661132812f, (float16_t)1.4453125f, + (float16_t)-0.295166016f, (float16_t)-0.546875f, (float16_t)0.114074707f, (float16_t)0.475830078f, + (float16_t)0.262695312f, (float16_t)-0.480224609f, (float16_t)0.544921875f, (float16_t)-1.32324219f, + (float16_t)0.782226562f, (float16_t)0.359863281f, (float16_t)-0.104187012f, (float16_t)-0.886230469f, + (float16_t)-0.75390625f, (float16_t)0.534179688f, (float16_t)0.136962891f, (float16_t)-0.773925781f, + (float16_t)-1.34375f, (float16_t)-0.627441406f, (float16_t)0.734375f, (float16_t)1.31054688f, + (float16_t)-1.06933594f, (float16_t)-0.413574219f, (float16_t)-1.40722656f, (float16_t)1.17675781f, + (float16_t)0.889160156f, (float16_t)-1.109375f, (float16_t)-1.07714844f, (float16_t)1.17578125f, + (float16_t)1.00976562f, (float16_t)1.125f, (float16_t)0.0048828125f, (float16_t)0.820800781f, + (float16_t)-0.948242188f, (float16_t)-0.0561828613f, (float16_t)-1.16699219f, (float16_t)-0.56640625f, + (float16_t)0.204711914f, (float16_t)-1.16992188f, (float16_t)1.04199219f, (float16_t)-0.867675781f, + (float16_t)0.0922851562f, (float16_t)0.538574219f, (float16_t)-1.03515625f, (float16_t)0.229370117f, + (float16_t)-0.0892944336f, (float16_t)0.683105469f, (float16_t)-0.153930664f, (float16_t)0.298095703f, + (float16_t)-0.842285156f, (float16_t)-1.00097656f, (float16_t)-0.170898438f, (float16_t)-0.0589904785f, + (float16_t)-0.497070312f, (float16_t)-0.471923828f, (float16_t)0.518554688f, (float16_t)0.399658203f, + (float16_t)-0.791015625f, (float16_t)0.817871094f, (float16_t)1.24414062f, (float16_t)-0.0689086914f, + (float16_t)-0.818359375f, (float16_t)0.327392578f, (float16_t)-0.241333008f, (float16_t)0.937011719f, + (float16_t)-1.10546875f, (float16_t)-1.02929688f, (float16_t)-0.343505859f, (float16_t)1.0390625f, + (float16_t)0.578613281f, (float16_t)-0.651367188f, (float16_t)-1.35253906f, (float16_t)-1.00488281f, + (float16_t)1.4296875f, (float16_t)0.378662109f, (float16_t)-1.359375f, (float16_t)-1.27929688f, + (float16_t)1.26660156f, (float16_t)-1.45703125f, (float16_t)1.42480469f, (float16_t)0.289550781f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/biases_data.h new file mode 100644 index 00000000..3dbc62a1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_batch2_f32_biases_data[2] = {-0.120329626f, 0.518665552f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/config_data.h new file mode 100644 index 00000000..1b5cfbb8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_BATCH2_F32_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_IN_CH 1 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_INPUT_W 8 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_INPUT_H 8 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_INPUT_SIZE 64 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_OUT_ACTIVATION_MIN -10.0f +#define FULLY_CONNECTED_2OUT_BATCH2_F32_OUT_ACTIVATION_MAX 10.0f +#define FULLY_CONNECTED_2OUT_BATCH2_F32_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_BATCH2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_BATCH2_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/input_data.h new file mode 100644 index 00000000..de63bfb2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/input_data.h @@ -0,0 +1,21 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_batch2_f32_input_data[128] = { + -0.215575024f, 1.79584289f, 1.71320438f, 0.630657434f, -0.510590255f, 0.855434716f, -0.141665012f, + -1.6544764f, 1.91209483f, 0.237798318f, -0.795520544f, -0.418973178f, -1.58932889f, -0.419499129f, + -1.8110429f, 1.41556001f, -1.54615343f, 0.636822045f, -0.784827292f, -1.52429414f, -0.583683491f, + 1.79699051f, 0.549193263f, -0.888990819f, -1.59371221f, -1.96233284f, -1.9791702f, -0.708013892f, + -0.0341752023f, -0.450265825f, 0.528377175f, 1.53982258f, -1.50762343f, 1.35376966f, -0.780115247f, + -0.37586844f, -0.344580024f, -0.0857018009f, 1.34154773f, -0.777727425f, 0.508975685f, 0.65506947f, + 0.289979458f, -1.00538766f, 1.76171398f, 1.56290805f, -1.31567276f, -0.443438143f, -0.527190149f, + 0.521141648f, 0.672858536f, -0.433843046f, -0.906853914f, -0.0588332489f, -1.12486136f, 0.751166701f, + -0.474026144f, -0.756207824f, 0.552224636f, -1.76922977f, 0.779949129f, -0.126823738f, -1.66116714f, + 1.35042906f, -0.247148916f, 0.822143853f, -0.292916954f, -0.356431991f, 0.680055618f, 1.56292224f, + 1.90125942f, -1.36825216f, -1.67361462f, -1.5934211f, 0.501728892f, 0.92273587f, 1.32489109f, + 0.762496114f, 0.570388734f, 0.664821088f, -0.269168973f, 0.620140076f, 0.782797992f, -1.83588886f, + 0.135944977f, -0.0273134038f, -1.47289741f, -0.0995977744f, 1.27113271f, 0.208210438f, -0.625672817f, + -0.857081473f, 0.936623275f, 0.410117686f, -1.5792923f, 0.529672503f, 1.59247112f, 1.71110141f, + 1.29216802f, 0.488510787f, -1.33029401f, -1.80059171f, 1.71222973f, 0.746406674f, -1.59550858f, + 1.58559096f, 1.58543217f, -1.74714756f, -1.70716751f, -0.997443676f, -0.0801201314f, 1.174824f, + 1.64524388f, 0.109829254f, -0.045676101f, 0.127977252f, -1.9912231f, 1.20486414f, -1.87923276f, + -0.348734528f, 0.101313613f, -1.93024576f, -1.84039116f, -0.845619798f, -0.889916182f, 0.456342131f, + 0.459479451f, 1.95179963f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/output_ref_data.h new file mode 100644 index 00000000..da07b0f5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_batch2_f32_output_ref_data[4] = {0.838216066f, + 3.60571194f, + -4.30911446f, + 0.432630181f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/weights_data.h new file mode 100644 index 00000000..eb3945a0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_batch2_f32/weights_data.h @@ -0,0 +1,21 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_batch2_f32_weights_data[128] = { + 0.516488135f, -0.682179689f, 0.901430488f, -0.306382805f, -1.30391204f, 0.811374426f, -0.637004972f, + -1.14501023f, 0.106105447f, -0.0915178806f, -1.06027269f, 0.0072067338f, -1.28417003f, 0.231403932f, + -0.103095911f, -0.703431964f, 1.07890522f, 1.41266143f, -0.451252103f, -0.183898553f, 1.49865937f, + -0.999017119f, -1.39126301f, 0.349677682f, -0.291246176f, -1.39725077f, 0.681518674f, 0.641381919f, + 1.19450235f, -0.196761698f, 0.767211676f, 0.508988857f, -1.29312396f, 0.0415900312f, 0.52829802f, + 0.466559649f, 0.645361245f, -0.627977192f, -0.751555085f, 0.896730363f, -0.310848594f, 0.233991176f, + 1.05256295f, 1.34393489f, -0.314604133f, -1.4252193f, -0.123927072f, -1.39948058f, 0.85248071f, + 1.01414204f, -0.972751677f, -0.36598894f, -0.53283143f, -1.16565561f, -0.984646082f, 0.706086278f, + -0.413005263f, 0.0437129438f, 1.16185105f, 0.338804454f, -0.142973974f, 0.550713062f, -0.0385985002f, + -1.23958528f, -0.0161596164f, 0.604425609f, 0.946752965f, 0.84434396f, -0.429954648f, -0.77633214f, + 1.28068829f, -0.955991209f, -0.351872057f, -1.03841496f, 0.50170368f, 0.840720952f, -1.02506518f, + 1.2459563f, 0.1109666f, -0.406214297f, -0.938735485f, 0.382159621f, -0.21110107f, 0.0692494065f, + -0.843169868f, -0.752653003f, 0.277810812f, 1.4068625f, 0.40659073f, -0.449287087f, 0.374388218f, + 1.04113853f, 1.30890775f, 1.00609267f, 0.872967064f, 1.07795656f, -1.28613412f, -0.207568362f, + 0.264157623f, -0.665298104f, 1.38657176f, 1.25992656f, -0.0544868857f, 0.881894648f, -1.40357494f, + -0.704514742f, 0.974439204f, 0.00670412229f, 1.01925766f, 0.0981215462f, 0.680183828f, 1.12623072f, + 1.29465485f, -1.01750505f, 1.42172158f, -0.979807138f, 0.648712814f, 1.35290515f, 0.136727765f, + 0.536024928f, 1.3226074f, -0.0309580695f, 0.284093916f, -0.281762719f, 0.361164868f, -0.616732657f, + -0.555695832f, -0.772637844f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/biases_data.h new file mode 100644 index 00000000..c0ed48ec --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail17_f16_biases_data[2] = {(float16_t)0.448730469f, (float16_t)-0.380859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/config_data.h new file mode 100644 index 00000000..8b5425d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_TAIL17_F16_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_IN_CH 17 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_INPUT_W 1 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_INPUT_H 1 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_INPUT_SIZE 17 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_OUT_ACTIVATION_MIN (float16_t) - 10.0f +#define FULLY_CONNECTED_2OUT_TAIL17_F16_OUT_ACTIVATION_MAX (float16_t)10.0f +#define FULLY_CONNECTED_2OUT_TAIL17_F16_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_TAIL17_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_TAIL17_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/input_data.h new file mode 100644 index 00000000..3b888f03 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail17_f16_input_data[34] = { + (float16_t)-1.51855469f, (float16_t)-0.977539062f, (float16_t)-0.0757446289f, (float16_t)-1.58398438f, + (float16_t)-0.0718383789f, (float16_t)-1.09082031f, (float16_t)-1.234375f, (float16_t)1.44628906f, + (float16_t)1.77050781f, (float16_t)1.82324219f, (float16_t)-1.76367188f, (float16_t)1.27539062f, + (float16_t)0.517578125f, (float16_t)1.78808594f, (float16_t)1.38769531f, (float16_t)-0.485107422f, + (float16_t)1.47363281f, (float16_t)0.486083984f, (float16_t)1.78222656f, (float16_t)1.13378906f, + (float16_t)-0.169921875f, (float16_t)1.77148438f, (float16_t)-0.971191406f, (float16_t)0.414550781f, + (float16_t)-0.547851562f, (float16_t)0.212890625f, (float16_t)0.359619141f, (float16_t)0.515136719f, + (float16_t)1.07128906f, (float16_t)1.66210938f, (float16_t)0.849609375f, (float16_t)-0.518066406f, + (float16_t)0.417724609f, (float16_t)0.798828125f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/output_ref_data.h new file mode 100644 index 00000000..d0fdca4b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail17_f16_output_ref_data[4] = {(float16_t)2.56835938f, + (float16_t)-2.39648438f, + (float16_t)-5.9375f, + (float16_t)-0.566894531f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/weights_data.h new file mode 100644 index 00000000..49f4025c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f16/weights_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail17_f16_weights_data[34] = { + (float16_t)0.743164062f, (float16_t)-1.48046875f, (float16_t)0.186157227f, (float16_t)-1.24316406f, + (float16_t)-0.0759277344f, (float16_t)-0.780273438f, (float16_t)-0.743164062f, (float16_t)0.313720703f, + (float16_t)1.11230469f, (float16_t)-0.459472656f, (float16_t)0.168823242f, (float16_t)-0.207397461f, + (float16_t)-1.44140625f, (float16_t)-0.499267578f, (float16_t)0.12310791f, (float16_t)-1.35449219f, + (float16_t)-1.453125f, (float16_t)1.109375f, (float16_t)-1.0625f, (float16_t)-1.39746094f, + (float16_t)-0.143920898f, (float16_t)0.272460938f, (float16_t)-1.28027344f, (float16_t)-0.879882812f, + (float16_t)-1.39746094f, (float16_t)0.883300781f, (float16_t)0.037689209f, (float16_t)0.157104492f, + (float16_t)0.116333008f, (float16_t)0.906738281f, (float16_t)-0.777832031f, (float16_t)-1.00097656f, + (float16_t)-0.651855469f, (float16_t)-1.12304688f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/biases_data.h new file mode 100644 index 00000000..bb939792 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail17_f32_biases_data[2] = {0.262449205f, -0.387271762f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/config_data.h new file mode 100644 index 00000000..860e088f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_TAIL17_F32_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_IN_CH 17 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_INPUT_W 1 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_INPUT_H 1 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_INPUT_SIZE 17 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_OUT_ACTIVATION_MIN -10.0f +#define FULLY_CONNECTED_2OUT_TAIL17_F32_OUT_ACTIVATION_MAX 10.0f +#define FULLY_CONNECTED_2OUT_TAIL17_F32_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_TAIL17_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_TAIL17_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/input_data.h new file mode 100644 index 00000000..c1645377 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/input_data.h @@ -0,0 +1,7 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail17_f32_input_data[34] = { + 1.32455671f, -0.895787537f, 1.03667164f, -1.86833119f, -0.86836946f, 1.47601604f, -1.33391178f, + -0.101422831f, -1.63426661f, 1.47316492f, -1.10570669f, -1.46678913f, 1.36111379f, 1.26035106f, + 1.26414061f, 0.579231679f, -1.32370627f, 0.149604395f, -1.84693134f, 1.80726826f, -1.53700078f, + 1.04103112f, -1.90798771f, -0.610601783f, 1.52125943f, 1.07525909f, 1.02146852f, 1.83939326f, + 0.154961124f, 1.0025636f, 1.16782761f, 1.70538521f, 1.54755175f, -0.383960247f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/output_ref_data.h new file mode 100644 index 00000000..90645f85 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail17_f32_output_ref_data[4] = {3.29120994f, + 1.72403085f, + 4.135993f, + -1.38057745f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/weights_data.h new file mode 100644 index 00000000..f35b0984 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail17_f32/weights_data.h @@ -0,0 +1,7 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail17_f32_weights_data[34] = { + 1.39107823f, -1.1524477f, -0.561025918f, -0.277095556f, 0.562091887f, 1.12107301f, 0.0762709305f, + -1.22176623f, 1.04287326f, 0.510640085f, 0.669526577f, -0.311446667f, -0.67461282f, -0.148083195f, + 0.95446378f, 1.40055037f, 0.489881605f, 1.20714557f, 0.704170704f, 1.22716558f, -0.241612762f, + 1.31307518f, 0.215865955f, -0.148264199f, -0.678672254f, 0.135642141f, 0.619134963f, -1.46900296f, + 0.491822898f, -0.369105458f, 0.335035384f, -0.926282108f, 0.75168556f, 0.606652856f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/biases_data.h new file mode 100644 index 00000000..37646fca --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail21_f16_biases_data[2] = {(float16_t)0.115234375f, (float16_t)-0.5859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/config_data.h new file mode 100644 index 00000000..0c4cf47c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_TAIL21_F16_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_IN_CH 21 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_INPUT_W 1 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_INPUT_H 1 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_INPUT_SIZE 21 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_OUT_ACTIVATION_MIN (float16_t) - 10.0f +#define FULLY_CONNECTED_2OUT_TAIL21_F16_OUT_ACTIVATION_MAX (float16_t)10.0f +#define FULLY_CONNECTED_2OUT_TAIL21_F16_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_TAIL21_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_TAIL21_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/input_data.h new file mode 100644 index 00000000..8b19f6df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/input_data.h @@ -0,0 +1,13 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail21_f16_input_data[42] = { + (float16_t)-1.25878906f, (float16_t)-0.935058594f, (float16_t)-1.97460938f, (float16_t)-0.487060547f, + (float16_t)0.968261719f, (float16_t)0.6015625f, (float16_t)-0.583007812f, (float16_t)-0.688964844f, + (float16_t)1.15039062f, (float16_t)0.769042969f, (float16_t)0.838867188f, (float16_t)1.42871094f, + (float16_t)1.65722656f, (float16_t)1.734375f, (float16_t)0.0180664062f, (float16_t)-1.27148438f, + (float16_t)-0.304931641f, (float16_t)1.41503906f, (float16_t)0.0697021484f, (float16_t)1.49707031f, + (float16_t)-1.90234375f, (float16_t)1.14355469f, (float16_t)1.62695312f, (float16_t)-1.98242188f, + (float16_t)-0.202758789f, (float16_t)1.13964844f, (float16_t)-1.80664062f, (float16_t)-1.16308594f, + (float16_t)-1.29296875f, (float16_t)-0.927734375f, (float16_t)-0.222045898f, (float16_t)-0.00867462158f, + (float16_t)-1.55175781f, (float16_t)0.38671875f, (float16_t)1.22753906f, (float16_t)1.71777344f, + (float16_t)0.639160156f, (float16_t)-1.10742188f, (float16_t)1.11132812f, (float16_t)-1.58984375f, + (float16_t)-1.31640625f, (float16_t)0.243774414f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/output_ref_data.h new file mode 100644 index 00000000..7f3775e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail21_f16_output_ref_data[4] = {(float16_t)-5.86328125f, + (float16_t)-2.375f, + (float16_t)-7.125f, + (float16_t)4.81640625f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/weights_data.h new file mode 100644 index 00000000..1836a4c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f16/weights_data.h @@ -0,0 +1,13 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_2out_tail21_f16_weights_data[42] = { + (float16_t)-1.29492188f, (float16_t)-1.20800781f, (float16_t)-0.067565918f, (float16_t)0.766113281f, + (float16_t)-0.858398438f, (float16_t)0.41015625f, (float16_t)0.139404297f, (float16_t)0.738769531f, + (float16_t)-0.893554688f, (float16_t)-0.0297393799f, (float16_t)1.35449219f, (float16_t)-0.166503906f, + (float16_t)-1.45410156f, (float16_t)-1.39550781f, (float16_t)-1.32421875f, (float16_t)0.853027344f, + (float16_t)-0.454589844f, (float16_t)0.243164062f, (float16_t)0.299072266f, (float16_t)-1.30175781f, + (float16_t)-0.111877441f, (float16_t)1.06445312f, (float16_t)-0.624511719f, (float16_t)-0.803222656f, + (float16_t)0.25f, (float16_t)-0.408447266f, (float16_t)-0.93359375f, (float16_t)-0.829589844f, + (float16_t)1.47070312f, (float16_t)0.523925781f, (float16_t)0.245361328f, (float16_t)0.179443359f, + (float16_t)-1.02636719f, (float16_t)0.633300781f, (float16_t)1.49023438f, (float16_t)-0.944824219f, + (float16_t)-0.783203125f, (float16_t)-0.178588867f, (float16_t)-0.887695312f, (float16_t)-1.09179688f, + (float16_t)-0.831054688f, (float16_t)1.35449219f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/biases_data.h new file mode 100644 index 00000000..f13bf355 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail21_f32_biases_data[2] = {0.25679338f, 0.606104136f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/config_data.h new file mode 100644 index 00000000..8f0e0112 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_2OUT_TAIL21_F32_OUT_CH 2 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_IN_CH 21 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_INPUT_W 1 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_INPUT_H 1 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_DST_SIZE 4 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_INPUT_SIZE 21 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_OUT_ACTIVATION_MIN -10.0f +#define FULLY_CONNECTED_2OUT_TAIL21_F32_OUT_ACTIVATION_MAX 10.0f +#define FULLY_CONNECTED_2OUT_TAIL21_F32_INPUT_BATCHES 2 +#define FULLY_CONNECTED_2OUT_TAIL21_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_2OUT_TAIL21_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/input_data.h new file mode 100644 index 00000000..7dca55da --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/input_data.h @@ -0,0 +1,8 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail21_f32_input_data[42] = { + 1.3880229f, 0.283811063f, -1.39805019f, 0.193800062f, -0.0546646491f, -1.88280833f, -1.66230369f, + 1.89948475f, 1.85183609f, -0.0236763824f, 1.3232044f, -0.20375362f, 0.608960092f, -0.812781572f, + -1.58325398f, 1.34862971f, -0.182739437f, 0.115184262f, -0.353920162f, 0.59546417f, 0.660296321f, + -1.21959376f, -1.70491254f, 0.680950642f, -0.984788656f, -0.620765567f, -0.940562427f, -0.752914846f, + -1.34191084f, -1.03029633f, -0.457984746f, 1.18187904f, -1.06592381f, 0.511000276f, 0.445497841f, + 0.0942531377f, 1.03173161f, -0.56230402f, -1.0821923f, 1.14345789f, 0.151186362f, -1.52405286f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/output_ref_data.h new file mode 100644 index 00000000..a54fb35f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/output_ref_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail21_f32_output_ref_data[4] = {5.00465202f, 1.03846145f, 10.0f, -2.68611073f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/weights_data.h new file mode 100644 index 00000000..61f61642 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_2out_tail21_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_2out_tail21_f32_weights_data[42] = { + -0.350494951f, -1.41482162f, -0.356010258f, -0.867707908f, -1.21946383f, -1.32922506f, -1.40370953f, + 0.987245739f, 0.0746712238f, 0.483532816f, 0.451302707f, -0.256148696f, 0.800049424f, -0.0163596496f, + 1.45192814f, 1.4065026f, 0.561231315f, -1.32672918f, 1.4921838f, -1.4718672f, -1.03965116f, + 0.788863003f, -1.43446195f, 0.217111498f, 1.00235641f, -1.01245177f, 1.3655405f, -0.960148215f, + 1.20780861f, 0.4755238f, 1.38832438f, -1.36112523f, -0.33662653f, 0.17339839f, -1.21649253f, + 0.682741821f, -0.396286488f, -0.0465278514f, 0.687896073f, 0.066489473f, 0.668196619f, -0.873956621f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/biases_data.h new file mode 100644 index 00000000..61749328 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/biases_data.h @@ -0,0 +1,18 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_large_f16_biases_data[64] = { + (float16_t)0.0359802246f, (float16_t)-0.288330078f, (float16_t)-0.0761108398f, (float16_t)0.0844726562f, + (float16_t)0.353027344f, (float16_t)0.299072266f, (float16_t)-0.342529297f, (float16_t)0.0765991211f, + (float16_t)-0.135131836f, (float16_t)0.0894165039f, (float16_t)-0.250732422f, (float16_t)-0.328857422f, + (float16_t)-0.0844116211f, (float16_t)0.0754394531f, (float16_t)0.295166016f, (float16_t)0.317871094f, + (float16_t)-0.0661010742f, (float16_t)0.0252227783f, (float16_t)-0.341064453f, (float16_t)-0.306396484f, + (float16_t)-0.121154785f, (float16_t)0.0190887451f, (float16_t)0.395263672f, (float16_t)-0.199462891f, + (float16_t)-0.372558594f, (float16_t)-0.363525391f, (float16_t)-0.354492188f, (float16_t)0.094909668f, + (float16_t)0.299804688f, (float16_t)0.227783203f, (float16_t)-0.0345458984f, (float16_t)-0.30859375f, + (float16_t)-0.185058594f, (float16_t)0.285644531f, (float16_t)0.284423828f, (float16_t)0.297119141f, + (float16_t)0.00994873047f, (float16_t)-0.0927734375f, (float16_t)0.333251953f, (float16_t)-0.398925781f, + (float16_t)-0.0784912109f, (float16_t)0.126708984f, (float16_t)-0.292236328f, (float16_t)-0.260009766f, + (float16_t)0.134277344f, (float16_t)-0.0977172852f, (float16_t)0.383544922f, (float16_t)-0.269287109f, + (float16_t)-0.181640625f, (float16_t)0.155029297f, (float16_t)0.281738281f, (float16_t)-0.197753906f, + (float16_t)-0.185302734f, (float16_t)-0.141479492f, (float16_t)0.276367188f, (float16_t)-0.278320312f, + (float16_t)0.318603516f, (float16_t)0.0202941895f, (float16_t)-0.172973633f, (float16_t)-0.0606079102f, + (float16_t)0.185058594f, (float16_t)-0.147216797f, (float16_t)0.176391602f, (float16_t)0.00656890869f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/config_data.h new file mode 100644 index 00000000..166cdaab --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_LARGE_F16_OUT_CH 64 +#define FULLY_CONNECTED_LARGE_F16_IN_CH 1 +#define FULLY_CONNECTED_LARGE_F16_INPUT_W 32 +#define FULLY_CONNECTED_LARGE_F16_INPUT_H 16 +#define FULLY_CONNECTED_LARGE_F16_DST_SIZE 128 +#define FULLY_CONNECTED_LARGE_F16_INPUT_SIZE 512 +#define FULLY_CONNECTED_LARGE_F16_OUT_ACTIVATION_MIN (float16_t) - 100.0f +#define FULLY_CONNECTED_LARGE_F16_OUT_ACTIVATION_MAX (float16_t)100.0f +#define FULLY_CONNECTED_LARGE_F16_INPUT_BATCHES 2 +#define FULLY_CONNECTED_LARGE_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_LARGE_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/input_data.h new file mode 100644 index 00000000..cf558db5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/input_data.h @@ -0,0 +1,258 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_large_f16_input_data[1024] = { + (float16_t)-1.16503906f, (float16_t)-0.505371094f, (float16_t)0.409667969f, (float16_t)-0.390869141f, + (float16_t)-0.999511719f, (float16_t)-0.196777344f, (float16_t)0.174072266f, (float16_t)0.671875f, + (float16_t)-1.14453125f, 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(float16_t)-0.747070312f, (float16_t)1.12890625f, (float16_t)-0.41796875f, + (float16_t)-0.574707031f, (float16_t)-0.00949859619f, (float16_t)-0.159179688f, (float16_t)-1.18652344f, + (float16_t)0.673828125f, (float16_t)0.101379395f, (float16_t)0.361816406f, (float16_t)0.833007812f, + (float16_t)-1.03417969f, (float16_t)0.292480469f, (float16_t)-0.139892578f, (float16_t)0.793457031f, + (float16_t)-0.500488281f, (float16_t)0.151367188f, (float16_t)-0.284179688f, (float16_t)-0.343261719f, + (float16_t)0.431396484f, (float16_t)-0.891601562f, (float16_t)-0.904296875f, (float16_t)-1.16796875f, + (float16_t)0.0368652344f, (float16_t)-0.916992188f, (float16_t)0.576171875f, (float16_t)0.5f, + (float16_t)-0.387939453f, (float16_t)0.703125f, (float16_t)-0.10369873f, (float16_t)0.423339844f, + (float16_t)0.580078125f, (float16_t)-0.02734375f, (float16_t)0.769042969f, (float16_t)-0.73828125f, + (float16_t)0.634277344f, (float16_t)1.23730469f, (float16_t)-0.317871094f, (float16_t)-0.986816406f, + (float16_t)1.19726562f, (float16_t)0.433105469f, (float16_t)1.13964844f, (float16_t)-0.6484375f, + (float16_t)-0.324707031f, (float16_t)-0.881347656f, (float16_t)-0.633789062f, (float16_t)0.237670898f, + (float16_t)-0.228027344f, (float16_t)0.782714844f, (float16_t)-1.09667969f, (float16_t)0.850585938f, + (float16_t)-0.368164062f, (float16_t)-0.00509643555f, (float16_t)0.171142578f, (float16_t)0.137451172f, + (float16_t)0.187866211f, (float16_t)-0.856445312f, (float16_t)-0.328125f, (float16_t)0.716308594f, + (float16_t)0.679199219f, (float16_t)-1.0f, (float16_t)-0.756835938f, (float16_t)-0.766601562f, + (float16_t)0.855957031f, (float16_t)-0.51953125f, (float16_t)0.63671875f, (float16_t)-0.901367188f, + (float16_t)-1.24902344f, (float16_t)-0.84765625f, (float16_t)-0.133300781f, (float16_t)0.478515625f, + (float16_t)0.335449219f, (float16_t)-0.826660156f, (float16_t)0.301025391f, (float16_t)0.518066406f, + (float16_t)0.297363281f, (float16_t)0.979492188f, (float16_t)0.0183105469f, (float16_t)0.935546875f, + (float16_t)0.263916016f, (float16_t)-0.346923828f, (float16_t)-0.890136719f, (float16_t)1.22070312f, + (float16_t)0.0915527344f, (float16_t)0.477294922f, (float16_t)0.00959014893f, (float16_t)-0.423583984f, + (float16_t)0.547363281f, (float16_t)0.208740234f, (float16_t)-0.416259766f, (float16_t)0.220214844f, + (float16_t)-0.850097656f, (float16_t)-0.525878906f, (float16_t)-0.8125f, (float16_t)1.12109375f, + (float16_t)-0.912597656f, (float16_t)0.792480469f, (float16_t)0.214599609f, (float16_t)-1.05664062f, + (float16_t)0.426513672f, (float16_t)-0.670410156f, (float16_t)-0.498291016f, (float16_t)1.21289062f, + (float16_t)-0.510253906f, (float16_t)1.03125f, (float16_t)-1.13867188f, (float16_t)-1.01953125f, + (float16_t)0.0580749512f, (float16_t)0.215820312f, (float16_t)-0.210449219f, (float16_t)-0.299804688f, + (float16_t)-1.23144531f, (float16_t)0.332763672f, (float16_t)-1.10546875f, (float16_t)-0.705078125f, + (float16_t)-0.576171875f, (float16_t)-0.270507812f, (float16_t)0.869628906f, (float16_t)-0.501464844f, + (float16_t)1.23925781f, (float16_t)-0.700683594f, (float16_t)0.13659668f, (float16_t)0.153076172f, + (float16_t)1.0859375f, (float16_t)0.730957031f, (float16_t)-0.252197266f, (float16_t)-0.793457031f, + (float16_t)-1.08007812f, (float16_t)-0.3828125f, (float16_t)-0.843261719f, (float16_t)-0.0297241211f, + (float16_t)-1.15625f, (float16_t)0.0497741699f, (float16_t)0.602050781f, (float16_t)-1.15722656f, + (float16_t)-0.382324219f, (float16_t)-0.679199219f, (float16_t)-0.8359375f, (float16_t)0.962890625f, + (float16_t)0.375976562f, (float16_t)-0.964355469f, (float16_t)-0.437988281f, (float16_t)0.0407104492f, + (float16_t)0.544921875f, (float16_t)-1.06640625f, (float16_t)-0.903808594f, (float16_t)-0.225097656f, + (float16_t)0.823730469f, (float16_t)0.296875f, (float16_t)0.735351562f, (float16_t)1.12597656f, + (float16_t)-0.359863281f, (float16_t)0.0764160156f, (float16_t)0.147583008f, (float16_t)0.616699219f, + (float16_t)0.924316406f, (float16_t)1.10742188f, (float16_t)0.592285156f, (float16_t)0.32421875f, + (float16_t)-0.287597656f, (float16_t)0.167602539f, (float16_t)0.982421875f, (float16_t)-0.753417969f, + (float16_t)0.103088379f, (float16_t)-1.04882812f, (float16_t)1.01464844f, (float16_t)1.02734375f, + (float16_t)0.428710938f, (float16_t)0.404541016f, (float16_t)-1.18066406f, (float16_t)-0.535644531f, + (float16_t)0.168579102f, (float16_t)-0.924316406f, (float16_t)0.755859375f, (float16_t)0.291503906f, + (float16_t)0.705566406f, (float16_t)-0.83984375f, (float16_t)1.10351562f, (float16_t)-0.878417969f, + (float16_t)-1.02539062f, (float16_t)-0.476318359f, (float16_t)0.461914062f, (float16_t)-0.0396728516f, + (float16_t)0.833496094f, (float16_t)0.944335938f, (float16_t)-0.654296875f, (float16_t)-1.15429688f, + (float16_t)-1.13769531f, (float16_t)0.935058594f, (float16_t)0.8125f, (float16_t)-0.237792969f, + (float16_t)-0.996582031f, (float16_t)0.236694336f, (float16_t)1.16894531f, (float16_t)-0.487792969f, + (float16_t)-0.0510253906f, (float16_t)0.9921875f, (float16_t)-0.814941406f, (float16_t)0.274414062f, + (float16_t)1.13574219f, (float16_t)0.110046387f, (float16_t)0.481933594f, (float16_t)0.496582031f, + (float16_t)-1.05371094f, (float16_t)-1.22851562f, (float16_t)0.923339844f, (float16_t)-0.835449219f, + (float16_t)0.6796875f, (float16_t)-0.930175781f, (float16_t)-1.21582031f, (float16_t)-1.06542969f, + (float16_t)0.737304688f, (float16_t)0.535644531f, (float16_t)0.260253906f, (float16_t)-0.703613281f, + (float16_t)-1.23632812f, (float16_t)-1.23144531f, (float16_t)0.190307617f, (float16_t)0.781738281f, + (float16_t)-0.923828125f, (float16_t)-0.724609375f, (float16_t)0.871582031f, (float16_t)0.140869141f, + (float16_t)1.02539062f, (float16_t)0.63671875f, (float16_t)-0.713378906f, (float16_t)0.827148438f, + (float16_t)-0.332275391f, (float16_t)-1.10742188f, (float16_t)1.23730469f, (float16_t)-0.814453125f, + (float16_t)1.23632812f, (float16_t)0.252685547f, (float16_t)-0.642089844f, (float16_t)0.273193359f, + (float16_t)0.104614258f, (float16_t)-0.408447266f, (float16_t)-0.119567871f, (float16_t)-0.226074219f, + (float16_t)1.16503906f, (float16_t)0.0101852417f, (float16_t)0.499755859f, (float16_t)-0.981933594f, + (float16_t)0.409912109f, (float16_t)0.412597656f, (float16_t)-0.0778198242f, (float16_t)0.65625f, + (float16_t)-0.321289062f, (float16_t)0.929199219f, (float16_t)-0.382080078f, (float16_t)0.381835938f, + (float16_t)-0.145019531f, (float16_t)1.22460938f, (float16_t)-0.642578125f, (float16_t)-1.21386719f, + (float16_t)-0.203491211f, (float16_t)-0.15246582f, (float16_t)-0.328369141f, (float16_t)0.503417969f, + (float16_t)-0.486572266f, (float16_t)1.00683594f, (float16_t)0.339355469f, (float16_t)0.0935668945f, + (float16_t)-0.776855469f, (float16_t)-0.496337891f, (float16_t)0.609375f, (float16_t)-0.625488281f, + (float16_t)-0.559082031f, (float16_t)0.41796875f, (float16_t)-0.955078125f, (float16_t)0.605957031f, + (float16_t)-0.282226562f, (float16_t)0.380615234f, (float16_t)0.900390625f, (float16_t)-0.460205078f, + (float16_t)0.753417969f, (float16_t)0.904785156f, (float16_t)-0.703613281f, (float16_t)-0.134521484f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/output_ref_data.h new file mode 100644 index 00000000..9f5993a3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/output_ref_data.h @@ -0,0 +1,34 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_large_f16_output_ref_data[128] = { + (float16_t)-3.00195312f, (float16_t)-5.7578125f, (float16_t)-2.90039062f, (float16_t)-9.890625f, + (float16_t)-5.91796875f, (float16_t)14.109375f, (float16_t)21.03125f, (float16_t)-8.4765625f, + (float16_t)1.93261719f, (float16_t)-12.84375f, (float16_t)1.34765625f, (float16_t)7.26171875f, + (float16_t)0.350097656f, (float16_t)5.86328125f, (float16_t)11.4453125f, (float16_t)1.55957031f, + (float16_t)-11.296875f, (float16_t)-9.1484375f, (float16_t)1.34765625f, (float16_t)3.18945312f, + (float16_t)2.36914062f, (float16_t)11.5625f, (float16_t)-6.05859375f, (float16_t)-2.22265625f, + (float16_t)3.99023438f, (float16_t)-12.5078125f, (float16_t)-4.0546875f, (float16_t)4.4375f, + (float16_t)4.42578125f, (float16_t)5.58203125f, (float16_t)-1.05078125f, (float16_t)7.3671875f, + (float16_t)13.7265625f, (float16_t)5.14453125f, (float16_t)-4.7265625f, (float16_t)-8.6875f, + (float16_t)-2.7890625f, (float16_t)3.3359375f, (float16_t)-3.41601562f, (float16_t)0.146484375f, + (float16_t)-8.4921875f, (float16_t)4.59765625f, (float16_t)-0.858398438f, (float16_t)7.86328125f, + (float16_t)0.476074219f, (float16_t)-4.265625f, (float16_t)14.5390625f, (float16_t)-1.84863281f, + (float16_t)7.828125f, (float16_t)-9.8359375f, (float16_t)9.2578125f, (float16_t)3.46289062f, + (float16_t)1.04785156f, (float16_t)-8.84375f, (float16_t)-4.12890625f, (float16_t)-6.02734375f, + (float16_t)9.3828125f, (float16_t)-0.157348633f, (float16_t)4.15234375f, (float16_t)0.966796875f, + (float16_t)1.13671875f, (float16_t)-1.77148438f, (float16_t)9.7734375f, (float16_t)-12.046875f, + (float16_t)6.2578125f, (float16_t)0.549804688f, (float16_t)-1.38964844f, (float16_t)12.390625f, + (float16_t)-4.94140625f, (float16_t)6.2734375f, (float16_t)5.8203125f, (float16_t)1.54785156f, + (float16_t)2.109375f, (float16_t)7.6796875f, (float16_t)10.5625f, (float16_t)-8.109375f, + (float16_t)-0.121582031f, (float16_t)1.65234375f, (float16_t)8.234375f, (float16_t)9.6640625f, + (float16_t)-3.61523438f, (float16_t)2.34570312f, (float16_t)-3.1171875f, (float16_t)-5.62890625f, + (float16_t)-1.87988281f, (float16_t)4.42578125f, (float16_t)13.296875f, (float16_t)1.71484375f, + (float16_t)-4.42578125f, (float16_t)-10.15625f, (float16_t)0.322998047f, (float16_t)4.59765625f, + (float16_t)-5.5078125f, (float16_t)7.6484375f, (float16_t)1.61132812f, (float16_t)12.609375f, + (float16_t)-5.5078125f, (float16_t)1.0f, (float16_t)14.984375f, (float16_t)1.34082031f, + (float16_t)-15.7734375f, (float16_t)5.62890625f, (float16_t)3.13085938f, (float16_t)-2.20703125f, + (float16_t)9.8125f, (float16_t)-5.93359375f, (float16_t)5.54296875f, (float16_t)13.53125f, + (float16_t)-5.02734375f, (float16_t)-0.352294922f, (float16_t)-6.51171875f, (float16_t)-19.59375f, + (float16_t)-4.64453125f, (float16_t)-4.24609375f, (float16_t)7.5859375f, (float16_t)-9.390625f, + (float16_t)2.28710938f, (float16_t)-8.71875f, (float16_t)14.421875f, (float16_t)-1.65917969f, + (float16_t)-1.86132812f, (float16_t)9.125f, (float16_t)11.9921875f, (float16_t)2.1875f, + (float16_t)2.32617188f, (float16_t)7.21875f, (float16_t)-1.06445312f, (float16_t)-5.85546875f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/weights_data.h new file mode 100644 index 00000000..02eddda1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f16/weights_data.h @@ -0,0 +1,32769 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_large_f16_weights_data[32768] = {(float16_t)-0.111633301f, + (float16_t)-0.170043945f, + (float16_t)-0.297363281f, + (float16_t)-0.194091797f, + (float16_t)0.404296875f, + (float16_t)0.520019531f, + (float16_t)-0.0542297363f, + (float16_t)0.418212891f, + (float16_t)0.292480469f, + (float16_t)-0.621582031f, + (float16_t)-0.250244141f, + (float16_t)-0.5859375f, + (float16_t)0.00710678101f, + (float16_t)0.0659179688f, + (float16_t)-0.431884766f, + (float16_t)-0.544433594f, + (float16_t)0.444335938f, + (float16_t)0.260253906f, + (float16_t)-0.157104492f, + (float16_t)0.0546569824f, + (float16_t)-0.218017578f, + (float16_t)-0.0610351562f, + (float16_t)0.677246094f, + (float16_t)-0.423828125f, + (float16_t)0.520507812f, + (float16_t)-0.625488281f, + (float16_t)0.410888672f, + (float16_t)0.328857422f, + (float16_t)-0.359375f, + (float16_t)-0.546875f, + (float16_t)0.579101562f, + (float16_t)-0.235961914f, + (float16_t)0.361816406f, + (float16_t)-0.0855102539f, + (float16_t)0.694335938f, + (float16_t)-0.206298828f, + (float16_t)0.549804688f, + (float16_t)0.337646484f, + (float16_t)0.47265625f, + (float16_t)-0.239379883f, + (float16_t)0.357666016f, + (float16_t)-0.307128906f, + (float16_t)0.50390625f, + (float16_t)0.315673828f, + (float16_t)0.138061523f, + (float16_t)-0.259033203f, + (float16_t)0.169189453f, + (float16_t)-0.462402344f, + (float16_t)0.528808594f, + (float16_t)0.215576172f, + (float16_t)0.532226562f, + (float16_t)-0.139282227f, + (float16_t)0.166870117f, + (float16_t)0.165161133f, + (float16_t)-0.565917969f, + (float16_t)0.616210938f, + (float16_t)-0.421386719f, + (float16_t)0.58203125f, + (float16_t)-0.517089844f, + (float16_t)0.731933594f, + (float16_t)0.404785156f, + (float16_t)-0.71484375f, + (float16_t)0.401367188f, + (float16_t)-0.347412109f, + (float16_t)-0.220703125f, + (float16_t)0.572265625f, + (float16_t)0.159301758f, + (float16_t)0.616210938f, + (float16_t)0.309570312f, + (float16_t)0.489257812f, + (float16_t)-0.217163086f, + (float16_t)0.436523438f, + (float16_t)0.0119400024f, + (float16_t)0.311279297f, + (float16_t)-0.139038086f, + (float16_t)-0.441162109f, + (float16_t)-0.199829102f, + (float16_t)-0.421142578f, + (float16_t)-0.736328125f, + (float16_t)0.422607422f, + (float16_t)0.237426758f, + (float16_t)0.310546875f, + (float16_t)-0.173828125f, + (float16_t)-0.180541992f, + (float16_t)-0.607910156f, + (float16_t)0.670898438f, + (float16_t)0.244262695f, + (float16_t)0.374755859f, + (float16_t)-0.245117188f, + (float16_t)-0.410400391f, + (float16_t)0.352539062f, + (float16_t)-0.350585938f, + (float16_t)-0.263183594f, + (float16_t)-0.696289062f, + (float16_t)0.0745239258f, + (float16_t)0.646484375f, + (float16_t)0.456298828f, + (float16_t)-0.306396484f, + (float16_t)-0.114196777f, + (float16_t)0.593261719f, + (float16_t)0.687011719f, + (float16_t)-0.696777344f, + (float16_t)0.327392578f, + (float16_t)0.292236328f, + (float16_t)-0.568847656f, + (float16_t)-0.502929688f, + (float16_t)0.715820312f, + (float16_t)0.211669922f, + (float16_t)0.276367188f, + (float16_t)-0.0441894531f, + (float16_t)-0.391845703f, + (float16_t)0.0770263672f, + (float16_t)-0.349609375f, + (float16_t)-0.5234375f, + (float16_t)0.0868530273f, + (float16_t)0.5078125f, + (float16_t)0.201660156f, + (float16_t)-0.0121459961f, + (float16_t)0.232421875f, + (float16_t)-0.474121094f, + (float16_t)-0.716796875f, + (float16_t)0.616699219f, + (float16_t)0.169067383f, + (float16_t)-0.576171875f, + (float16_t)0.280761719f, + (float16_t)-0.603515625f, + (float16_t)-0.293945312f, + (float16_t)0.235839844f, + (float16_t)0.376953125f, + (float16_t)0.646972656f, + (float16_t)-0.531738281f, + (float16_t)0.736328125f, + (float16_t)0.460693359f, + (float16_t)0.674316406f, + (float16_t)0.441650391f, + (float16_t)-0.231689453f, + (float16_t)0.280029297f, + (float16_t)0.173217773f, + (float16_t)-0.467285156f, + (float16_t)-0.117919922f, + (float16_t)0.695800781f, + (float16_t)-0.504394531f, + (float16_t)0.686035156f, + (float16_t)0.0671386719f, + (float16_t)-0.441894531f, + (float16_t)-0.458007812f, + (float16_t)0.419189453f, + (float16_t)0.314941406f, + (float16_t)0.508300781f, + (float16_t)0.45703125f, + (float16_t)-0.554199219f, + (float16_t)0.437011719f, + (float16_t)0.353271484f, + (float16_t)-0.333007812f, + (float16_t)0.347412109f, + (float16_t)-0.174316406f, + (float16_t)-0.139892578f, + (float16_t)0.17956543f, + (float16_t)-0.345214844f, + (float16_t)0.146118164f, + (float16_t)-0.479980469f, + (float16_t)-0.351806641f, + (float16_t)-0.665527344f, + (float16_t)0.484130859f, + (float16_t)-0.0207672119f, + (float16_t)-0.0751342773f, + (float16_t)-0.294433594f, + (float16_t)0.564941406f, + (float16_t)0.278564453f, + (float16_t)0.430419922f, + (float16_t)0.072265625f, + (float16_t)0.723144531f, + (float16_t)0.182006836f, + (float16_t)0.536621094f, + (float16_t)-0.28125f, + (float16_t)0.0571289062f, + (float16_t)0.0131912231f, + (float16_t)-0.349853516f, + (float16_t)0.0751953125f, + (float16_t)-0.723632812f, + (float16_t)-0.377197266f, + (float16_t)0.491455078f, + (float16_t)-0.394287109f, + (float16_t)0.337646484f, + (float16_t)-0.592285156f, + (float16_t)-0.135742188f, + (float16_t)0.324707031f, + (float16_t)-0.30859375f, + (float16_t)-0.116210938f, + (float16_t)-0.16003418f, + (float16_t)0.682128906f, + (float16_t)0.702148438f, + (float16_t)-0.145874023f, + (float16_t)0.435791016f, + (float16_t)0.545898438f, + (float16_t)-0.620117188f, + (float16_t)-0.457763672f, + (float16_t)-0.0218658447f, + (float16_t)0.592773438f, + (float16_t)0.549316406f, + (float16_t)-0.610839844f, + (float16_t)-0.458251953f, + (float16_t)0.496826172f, + (float16_t)-0.121582031f, + (float16_t)-0.0368041992f, + (float16_t)0.422363281f, + (float16_t)0.526855469f, + (float16_t)0.308105469f, + (float16_t)-0.493896484f, + (float16_t)-0.314453125f, + (float16_t)-0.741699219f, + (float16_t)-0.466064453f, + (float16_t)-0.275390625f, + (float16_t)0.700683594f, + (float16_t)-0.268310547f, + (float16_t)0.478515625f, + (float16_t)0.00898742676f, + (float16_t)0.302490234f, + (float16_t)0.692871094f, + (float16_t)0.367675781f, + (float16_t)-0.14440918f, + (float16_t)0.106750488f, + (float16_t)0.59375f, + (float16_t)-0.0343322754f, + (float16_t)0.708496094f, + (float16_t)-0.711914062f, + (float16_t)-0.334228516f, + (float16_t)0.7109375f, + (float16_t)-0.261962891f, + (float16_t)0.535644531f, + (float16_t)0.0792236328f, + (float16_t)0.120605469f, + (float16_t)-0.0728149414f, + (float16_t)0.473388672f, + (float16_t)-0.406494141f, + (float16_t)0.00139045715f, + (float16_t)-0.247436523f, + (float16_t)-0.729003906f, + (float16_t)-0.0270233154f, + (float16_t)0.549804688f, + (float16_t)0.689453125f, + (float16_t)-0.421386719f, + (float16_t)-0.640136719f, + (float16_t)0.306152344f, + (float16_t)-0.0254974365f, + (float16_t)0.509765625f, + (float16_t)0.59765625f, + (float16_t)0.0249481201f, + (float16_t)-0.669433594f, + (float16_t)-0.57421875f, + (float16_t)-0.553222656f, + (float16_t)0.124328613f, + (float16_t)0.188476562f, + (float16_t)-0.349609375f, + (float16_t)-0.113098145f, + (float16_t)-0.435058594f, + (float16_t)-0.440185547f, + (float16_t)-0.265869141f, + (float16_t)0.0567932129f, + (float16_t)-0.457275391f, + (float16_t)0.39453125f, + (float16_t)-0.210083008f, + (float16_t)-0.332275391f, + (float16_t)-0.408203125f, + (float16_t)-0.362060547f, + (float16_t)0.364990234f, + (float16_t)-0.562011719f, + (float16_t)0.497802734f, + (float16_t)0.237792969f, + (float16_t)0.682128906f, + (float16_t)-0.6796875f, + (float16_t)-0.374755859f, + (float16_t)-0.391113281f, + (float16_t)-0.735351562f, + (float16_t)0.6171875f, + (float16_t)0.676757812f, + (float16_t)-0.259033203f, + (float16_t)-0.380126953f, + (float16_t)0.458007812f, + (float16_t)0.444580078f, + (float16_t)-0.133544922f, + (float16_t)-0.220092773f, + (float16_t)-0.6875f, + (float16_t)-0.054473877f, + (float16_t)0.718261719f, + (float16_t)0.310546875f, + (float16_t)-0.311767578f, + (float16_t)-0.657714844f, + (float16_t)-0.568847656f, + (float16_t)-0.461669922f, + (float16_t)0.485351562f, + (float16_t)0.560058594f, + (float16_t)-0.353515625f, + (float16_t)0.303710938f, + 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(float16_t)0.643066406f, + (float16_t)-0.448974609f, + (float16_t)-0.611816406f, + (float16_t)0.018951416f, + (float16_t)0.392089844f, + (float16_t)0.316162109f, + (float16_t)0.191894531f, + (float16_t)0.149169922f, + (float16_t)0.494384766f, + (float16_t)0.489501953f, + (float16_t)-0.557128906f, + (float16_t)0.458496094f, + (float16_t)0.363037109f, + (float16_t)-0.189697266f, + (float16_t)-0.646972656f, + (float16_t)-0.0355224609f, + (float16_t)-0.595703125f, + (float16_t)0.645996094f, + (float16_t)-0.651855469f, + (float16_t)-0.142211914f, + (float16_t)0.549316406f, + (float16_t)-0.369384766f, + (float16_t)0.251708984f, + (float16_t)-0.141479492f, + (float16_t)-0.148681641f, + (float16_t)0.711425781f, + (float16_t)0.685058594f, + (float16_t)-0.436035156f, + (float16_t)-0.209838867f, + (float16_t)0.00473403931f, + (float16_t)0.485595703f, + (float16_t)-0.0870361328f, + (float16_t)0.109802246f, + (float16_t)0.247192383f, + (float16_t)0.300537109f, + (float16_t)-0.210083008f, + (float16_t)0.270263672f, + (float16_t)-0.378662109f, + (float16_t)0.542480469f, + (float16_t)0.491943359f, + (float16_t)-0.668457031f, + (float16_t)0.0356750488f, + (float16_t)-0.556152344f, + (float16_t)-0.636230469f, + (float16_t)-0.441650391f, + (float16_t)-0.514160156f, + (float16_t)0.0520324707f, + (float16_t)-0.263183594f, + (float16_t)-0.0686645508f, + (float16_t)-0.740234375f, + (float16_t)0.573730469f, + (float16_t)0.096862793f, + (float16_t)0.393798828f, + (float16_t)-0.174926758f, + (float16_t)-0.0582275391f, + (float16_t)0.1484375f, + (float16_t)-0.263183594f, + (float16_t)0.472900391f, + (float16_t)0.732421875f, + (float16_t)-0.565917969f, + (float16_t)-0.635253906f, + (float16_t)-0.470703125f, + (float16_t)0.657714844f, + (float16_t)0.155761719f, + (float16_t)-0.58203125f, + (float16_t)-0.685546875f, + (float16_t)-0.584472656f, + (float16_t)0.0559692383f, + (float16_t)-0.0936279297f, + (float16_t)-0.158203125f, + (float16_t)0.6015625f, + (float16_t)0.572753906f, + (float16_t)-0.201904297f, + (float16_t)-0.521972656f, + (float16_t)0.129638672f, + (float16_t)0.600097656f, + (float16_t)-0.733886719f, + (float16_t)-0.335449219f, + (float16_t)-0.405761719f, + (float16_t)0.0186004639f, + (float16_t)-0.0136108398f, + (float16_t)-0.553222656f, + (float16_t)0.660644531f, + (float16_t)0.715332031f, + (float16_t)-0.306152344f, + (float16_t)0.177978516f, + (float16_t)-0.345458984f, + (float16_t)0.122558594f, + (float16_t)-0.421875f, + (float16_t)-0.40234375f, + (float16_t)-0.279296875f, + (float16_t)-0.534179688f, + (float16_t)-0.321044922f, + (float16_t)0.0714111328f, + (float16_t)-0.271972656f, + (float16_t)-0.00756454468f, + (float16_t)-0.679199219f, + (float16_t)-0.315429688f, + (float16_t)-0.274658203f, + (float16_t)-0.19921875f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/biases_data.h new file mode 100644 index 00000000..0e40ca9a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/biases_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_large_f32_biases_data[64] = { + -0.325522542f, 0.272401452f, -0.160263985f, 0.0939930603f, 0.398132145f, 0.152784646f, 0.207884252f, + -0.0696865171f, -0.0946049541f, 0.166148946f, -0.111410365f, -0.109236777f, 0.348677427f, -0.16363506f, + -0.006594365f, 0.154868007f, -0.136526749f, 0.349910945f, 0.144986153f, -0.222805873f, 0.256947994f, + 0.291131973f, -0.346687138f, -0.368030816f, -0.128237128f, 0.0845169201f, -0.171919331f, -0.0771812052f, + 0.246094137f, -0.0820646062f, 0.390092582f, 0.21154809f, 0.091534324f, 0.216656521f, -0.352303982f, + -0.21313107f, -0.247619629f, 0.171210453f, -0.1119859f, 0.122437693f, 0.00500846794f, 0.326596141f, + 0.311264664f, -0.38476035f, -0.187967047f, 0.160117745f, 0.0341780595f, -0.230218768f, 0.11668852f, + -0.270534337f, -1.45258182e-05f, 0.178893447f, 0.138357133f, -0.341742545f, -0.346452147f, 0.287246197f, + 0.286955446f, 0.0571875349f, 0.34407416f, -0.237185195f, -0.397513986f, 0.272589296f, 0.18273066f, + 0.076252386f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/config_data.h new file mode 100644 index 00000000..e61d2ab4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_LARGE_F32_OUT_CH 64 +#define FULLY_CONNECTED_LARGE_F32_IN_CH 1 +#define FULLY_CONNECTED_LARGE_F32_INPUT_W 32 +#define FULLY_CONNECTED_LARGE_F32_INPUT_H 16 +#define FULLY_CONNECTED_LARGE_F32_DST_SIZE 128 +#define FULLY_CONNECTED_LARGE_F32_INPUT_SIZE 512 +#define FULLY_CONNECTED_LARGE_F32_OUT_ACTIVATION_MIN -100.0f +#define FULLY_CONNECTED_LARGE_F32_OUT_ACTIVATION_MAX 100.0f +#define FULLY_CONNECTED_LARGE_F32_INPUT_BATCHES 2 +#define FULLY_CONNECTED_LARGE_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_LARGE_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/input_data.h new file mode 100644 index 00000000..68711f10 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/input_data.h @@ -0,0 +1,149 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_large_f32_input_data[1024] = { + -1.01039922f, 1.15048063f, -0.264392346f, 0.537314951f, 0.805545509f, 0.841798246f, 0.814971566f, + -0.181879371f, 1.04536378f, 0.818982899f, -1.15185368f, 1.217556f, 0.70761919f, -0.621381998f, + 0.377182096f, -0.013491475f, -1.24691153f, 1.10493672f, -0.398437232f, -0.559559584f, -0.650198519f, + -0.406149507f, 0.0239068028f, 0.805091679f, 0.324669898f, -0.747063816f, 1.20841038f, -0.711261213f, + 0.261475623f, -0.804553747f, 0.837663293f, -0.112519406f, 0.113845602f, 1.24320233f, 1.24136996f, + 0.388473302f, 0.284660071f, 0.330638736f, 0.676642597f, -1.18575835f, 0.756000578f, -1.04075503f, + 0.656195164f, -0.990888774f, -1.10952473f, 0.84517467f, -1.10872889f, -1.01023161f, 0.357245892f, + 0.253169179f, 0.391929746f, 0.286952615f, 0.896738768f, -0.690620959f, -1.18520284f, 0.848747075f, + -0.851099849f, -0.0319245681f, 0.386177927f, -0.710528016f, 0.908195257f, -1.21099031f, 0.335511535f, + 1.05366385f, -0.527705431f, 0.138304293f, -0.93386966f, 1.13916421f, -0.18503283f, -0.793832362f, + -0.72534132f, -0.457599819f, 0.714501917f, -0.635391772f, 0.625593483f, 0.990787685f, 0.729634881f, + -1.21718764f, 0.404931098f, -1.08061087f, -0.7003057f, -0.550670862f, -0.0505613014f, 0.794376671f, + -0.895667374f, -0.279422104f, -0.90953964f, 0.493198991f, -0.513155401f, -0.0219843313f, 0.269155979f, + -0.578433275f, 0.81410104f, -0.146935165f, 1.11967385f, -0.12401513f, -1.00527775f, -0.96378237f, + -0.643497467f, -0.0512195304f, -1.00822186f, -1.13476789f, 0.610428691f, 0.793130398f, 0.918233335f, + 0.316909581f, -0.233249173f, 0.56453526f, -0.691548467f, -0.892399669f, -0.462251574f, 0.33705619f, + 0.805817187f, -0.672961354f, -0.0611276366f, 1.16007614f, -1.19165397f, -0.140632153f, 0.633658469f, + -1.22079051f, 1.10186458f, -0.0374834687f, 0.837126493f, 1.13921416f, 0.313026011f, -0.536427021f, + -0.0204402339f, -0.452354342f, -0.500201404f, -1.05721545f, -0.836042464f, -0.870305538f, 0.954271734f, + 0.0795934349f, -0.231572509f, -0.155004993f, -1.23840213f, -0.145617768f, 0.670349836f, -0.0563427247f, + -0.490258992f, -1.11383665f, 0.630469441f, 0.503137946f, -0.158140913f, -0.883952379f, -0.85486573f, + 0.435627997f, 0.664356589f, -0.702392578f, 0.42970261f, 0.189107433f, -0.92007798f, 0.420061409f, + -0.493676841f, -1.18188453f, -0.912990987f, -0.491036147f, 0.0669021308f, 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-0.87317723f, -1.05524468f, -0.312748432f, 1.11325586f, 0.135905474f, -0.753491819f, -0.363316685f, + -1.07772481f, 1.1244148f, 0.520026803f, 1.1663748f, -0.218809247f, -0.388053179f, 1.14209771f, + -0.0149565153f, -0.0850390941f, -0.662624538f, -1.19981551f, 0.0583523922f, -0.583323836f, 0.189747453f, + 0.00374309719f, -0.177360728f, -0.702828526f, 0.545279622f, -0.864403486f, 1.18916082f, -1.12617266f, + 0.190379173f, -0.524505258f, -1.17311966f, -0.890049219f, -0.760727167f, -1.22856367f, 1.00358129f, + -1.00081623f, 0.696871698f, 0.947187722f, 0.273085773f, -0.204268172f, 0.570266962f, 1.12098265f, + 1.11039031f, -0.156810522f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/output_ref_data.h new file mode 100644 index 00000000..bf1b6723 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/output_ref_data.h @@ -0,0 +1,21 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_large_f32_output_ref_data[128] = { + 1.89240444f, 8.96700096f, 1.43069029f, -3.52602839f, -3.63219762f, 1.66644192f, 6.4154706f, + -9.17818642f, -11.6753101f, -0.642855346f, -1.2984041f, -1.31892729f, 7.28839922f, 3.77010131f, + 12.2966127f, 2.00440931f, 1.4033035f, 7.94198179f, -6.24710083f, 7.32560444f, -8.48263359f, + -7.69412518f, 3.99196267f, 7.27900362f, 3.60374212f, 4.10496283f, 7.61136913f, 6.86027765f, + 5.89800596f, 15.7538843f, -0.201494485f, -6.40012169f, 7.90744209f, 5.28936958f, 3.1512506f, + 5.99412966f, -1.87528563f, 2.55593586f, -4.25500298f, -2.65375328f, 17.7581806f, 18.8415966f, + 2.814744f, 2.34367609f, -12.5188084f, 12.1812439f, -10.1996574f, -4.9096384f, 3.23509359f, + -3.67330909f, 5.86826229f, -7.35494471f, -2.37056041f, -2.70339799f, 6.22680712f, 7.99763441f, + 5.41027021f, 8.69497013f, 6.00098705f, -7.52873373f, -4.46602106f, -0.894450545f, 10.8049469f, + 1.8955375f, 1.7473563f, 5.24324846f, -6.42669964f, -0.665304303f, 1.90525031f, 1.49370909f, + 8.86610222f, -3.48158312f, -5.20642281f, 1.64412045f, -1.62413013f, 4.59876776f, 0.582727432f, + 7.85435963f, 11.0061636f, -3.23241711f, 9.37266445f, 2.91196704f, -4.32876062f, 2.98653722f, + -1.60078955f, -6.76068974f, 0.202817857f, 2.50257993f, -1.89448488f, 1.2761246f, -0.405326843f, + -1.64103401f, -11.5083103f, 9.3064518f, -0.761062264f, 0.648111343f, 3.57835674f, 2.82309794f, + -8.18990517f, 0.690326095f, -11.2797661f, 18.0345936f, 0.320282727f, 4.80477333f, 4.59509802f, + 9.29833317f, -1.9084872f, -18.7868443f, -8.32522488f, 1.64339554f, 6.98349571f, -0.651073098f, + -0.331544131f, 5.51195145f, -3.1680727f, 4.98951435f, -6.1320262f, -11.2397308f, -13.7148771f, + -12.9394388f, 3.35049105f, -1.74186254f, 10.3472176f, 9.12574196f, 14.1511211f, -6.27546263f, + 13.0179853f, 1.36167085f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/weights_data.h new file mode 100644 index 00000000..067ec50c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_large_f32/weights_data.h @@ -0,0 +1,5464 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_large_f32_weights_data[32768] = { + -0.324641556f, -0.073390089f, -0.0197349135f, 0.272344112f, -0.696290493f, 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b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/biases_data.h @@ -0,0 +1,7 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_basic_f16_biases_data[6] = {(float16_t)-0.428222656f, + (float16_t)-0.732910156f, + (float16_t)0.489257812f, + (float16_t)-0.588378906f, + (float16_t)-0.290771484f, + (float16_t)0.502929688f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/config_data.h new file mode 100644 index 00000000..b6202e09 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_BASIC_F16_OUT_CH 6 +#define FULLY_CONNECTED_MATCH_BASIC_F16_IN_CH 10 +#define FULLY_CONNECTED_MATCH_BASIC_F16_INPUT_W 2 +#define FULLY_CONNECTED_MATCH_BASIC_F16_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_BASIC_F16_DST_SIZE 18 +#define FULLY_CONNECTED_MATCH_BASIC_F16_INPUT_SIZE 20 +#define FULLY_CONNECTED_MATCH_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define FULLY_CONNECTED_MATCH_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define FULLY_CONNECTED_MATCH_BASIC_F16_INPUT_BATCHES 3 +#define FULLY_CONNECTED_MATCH_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_BASIC_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/input_data.h new file mode 100644 index 00000000..96724081 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/input_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_basic_f16_input_data[60] = { + (float16_t)-0.166992188f, (float16_t)0.767089844f, (float16_t)-1.11914062f, (float16_t)-0.146362305f, + (float16_t)-1.47558594f, (float16_t)-0.241088867f, (float16_t)-1.47363281f, (float16_t)0.875488281f, + (float16_t)-1.68554688f, (float16_t)0.403076172f, (float16_t)-0.126831055f, (float16_t)1.58300781f, + (float16_t)1.04785156f, (float16_t)-0.204223633f, (float16_t)-1.04296875f, (float16_t)1.53027344f, + (float16_t)1.60839844f, (float16_t)1.41113281f, (float16_t)0.0642089844f, (float16_t)1.16894531f, + (float16_t)1.27148438f, (float16_t)0.610839844f, (float16_t)-0.775878906f, (float16_t)1.82128906f, + (float16_t)0.489257812f, (float16_t)-0.93359375f, (float16_t)-0.7734375f, (float16_t)-0.287841797f, + (float16_t)-1.85351562f, (float16_t)1.22949219f, (float16_t)1.21875f, (float16_t)1.15332031f, + (float16_t)-0.856933594f, (float16_t)1.77441406f, (float16_t)-1.0859375f, (float16_t)1.8046875f, + (float16_t)1.05371094f, (float16_t)-0.640625f, (float16_t)-0.530273438f, (float16_t)-0.294433594f, + (float16_t)-1.07617188f, (float16_t)-1.33496094f, (float16_t)1.3203125f, (float16_t)-1.67578125f, + (float16_t)1.640625f, (float16_t)1.24023438f, (float16_t)1.40332031f, (float16_t)-1.95214844f, + (float16_t)1.56054688f, (float16_t)-1.27148438f, (float16_t)-1.9296875f, (float16_t)-0.79296875f, + (float16_t)-0.449462891f, (float16_t)-1.80078125f, (float16_t)1.87109375f, (float16_t)0.863769531f, + (float16_t)0.81640625f, (float16_t)1.4140625f, (float16_t)-1.72265625f, (float16_t)-1.06445312f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/output_ref_data.h new file mode 100644 index 00000000..76b3d677 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_basic_f16_output_ref_data[18] = {(float16_t)-4.28515625f, + (float16_t)2.8359375f, + (float16_t)-0.579589844f, + (float16_t)-1.72460938f, + (float16_t)-1.46582031f, + (float16_t)1.2421875f, + (float16_t)-3.46289062f, + (float16_t)1.77246094f, + (float16_t)-3.02929688f, + (float16_t)-7.3984375f, + (float16_t)-2.3984375f, + (float16_t)2.94335938f, + (float16_t)7.59765625f, + (float16_t)-1.97167969f, + (float16_t)4.5546875f, + (float16_t)-0.492919922f, + (float16_t)2.890625f, + (float16_t)3.91210938f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/weights_data.h new file mode 100644 index 00000000..56ac0caa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f16/weights_data.h @@ -0,0 +1,32 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_basic_f16_weights_data[120] = { + (float16_t)-0.853515625f, (float16_t)0.932617188f, (float16_t)-0.216552734f, (float16_t)-0.575195312f, + (float16_t)0.808105469f, (float16_t)0.610351562f, (float16_t)-1.25976562f, (float16_t)-0.795898438f, + (float16_t)0.846679688f, (float16_t)-0.0450134277f, (float16_t)1.39160156f, (float16_t)0.961914062f, + (float16_t)-1.42675781f, (float16_t)-0.905761719f, (float16_t)1.30078125f, (float16_t)-0.504882812f, + (float16_t)-1.15234375f, (float16_t)1.20410156f, (float16_t)-1.07128906f, (float16_t)-0.938476562f, + (float16_t)1.25585938f, (float16_t)-0.63671875f, (float16_t)-0.837890625f, (float16_t)-0.3203125f, + (float16_t)-0.908691406f, (float16_t)-0.645507812f, (float16_t)-0.288574219f, (float16_t)-0.625488281f, + (float16_t)0.1484375f, (float16_t)1.06347656f, (float16_t)-1.01171875f, (float16_t)1.28320312f, + (float16_t)1.07519531f, (float16_t)-0.663085938f, (float16_t)0.0316772461f, (float16_t)-0.0152359009f, + (float16_t)0.702148438f, (float16_t)-0.752929688f, (float16_t)0.194702148f, (float16_t)-1.46875f, + (float16_t)-1.12792969f, (float16_t)-0.184448242f, (float16_t)-1.4921875f, (float16_t)1.01464844f, + (float16_t)0.296142578f, (float16_t)-0.0250701904f, (float16_t)-0.415039062f, (float16_t)0.907226562f, + (float16_t)1.21679688f, (float16_t)-0.425048828f, (float16_t)0.0720214844f, (float16_t)-1.29199219f, + (float16_t)-0.440673828f, (float16_t)-0.748046875f, (float16_t)0.960449219f, (float16_t)0.228759766f, + (float16_t)0.219360352f, (float16_t)0.0566711426f, (float16_t)-1.28320312f, (float16_t)1.09082031f, + (float16_t)-1.3671875f, (float16_t)-1.24609375f, (float16_t)-0.486328125f, (float16_t)-0.420654297f, + (float16_t)-1.05664062f, (float16_t)-0.880371094f, (float16_t)1.34863281f, (float16_t)0.392578125f, + (float16_t)-0.635253906f, (float16_t)-1.3828125f, (float16_t)-0.912597656f, (float16_t)0.510253906f, + (float16_t)1.32910156f, (float16_t)-0.0443725586f, (float16_t)-0.381103516f, (float16_t)-1.09277344f, + (float16_t)-0.559570312f, (float16_t)-1.49316406f, (float16_t)-0.896484375f, (float16_t)0.307128906f, + (float16_t)1.11425781f, (float16_t)-1.21582031f, (float16_t)0.237060547f, (float16_t)-0.112915039f, + (float16_t)-0.54296875f, (float16_t)1.25195312f, (float16_t)1.18945312f, (float16_t)-0.187133789f, + (float16_t)0.51171875f, (float16_t)0.309570312f, (float16_t)0.678222656f, (float16_t)0.672363281f, + (float16_t)0.550292969f, (float16_t)-0.234619141f, (float16_t)0.140991211f, (float16_t)0.420654297f, + (float16_t)-0.0269165039f, (float16_t)0.797363281f, (float16_t)1.00292969f, (float16_t)-0.768066406f, + (float16_t)-0.261474609f, (float16_t)0.212280273f, (float16_t)-0.331054688f, (float16_t)-1.09765625f, + (float16_t)1.35839844f, (float16_t)-0.731445312f, (float16_t)0.720703125f, (float16_t)-0.641601562f, + (float16_t)0.110778809f, (float16_t)0.82421875f, (float16_t)1.19433594f, (float16_t)0.0977172852f, + (float16_t)-1.13769531f, (float16_t)-0.350830078f, (float16_t)-0.990722656f, (float16_t)-0.311523438f, + (float16_t)1.18359375f, (float16_t)1.484375f, (float16_t)0.314208984f, (float16_t)-0.129272461f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/biases_data.h new file mode 100644 index 00000000..253d7563 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_basic_f32_biases_data[6] = + {0.433437347f, -0.451486766f, 0.603065312f, 0.284521729f, 0.470644712f, 0.351996928f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/config_data.h new file mode 100644 index 00000000..271ee255 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_BASIC_F32_OUT_CH 6 +#define FULLY_CONNECTED_MATCH_BASIC_F32_IN_CH 10 +#define FULLY_CONNECTED_MATCH_BASIC_F32_INPUT_W 2 +#define FULLY_CONNECTED_MATCH_BASIC_F32_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_BASIC_F32_DST_SIZE 18 +#define FULLY_CONNECTED_MATCH_BASIC_F32_INPUT_SIZE 20 +#define FULLY_CONNECTED_MATCH_BASIC_F32_OUT_ACTIVATION_MIN -128.0f +#define FULLY_CONNECTED_MATCH_BASIC_F32_OUT_ACTIVATION_MAX 127.0f +#define FULLY_CONNECTED_MATCH_BASIC_F32_INPUT_BATCHES 3 +#define FULLY_CONNECTED_MATCH_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_BASIC_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/input_data.h new file mode 100644 index 00000000..9c4171bb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/input_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_basic_f32_input_data[60] = { + -0.628757536f, 1.27318609f, -0.086097084f, -1.09029138f, 1.84137237f, -0.0913173407f, -0.541851997f, + 1.9022702f, -0.363894641f, 1.61173773f, -0.48052904f, 0.396849185f, 0.816555798f, 1.08068788f, + -1.50636911f, 0.233147383f, 0.380517513f, -0.148600608f, -1.25891733f, -1.22891307f, -1.37013543f, + -1.39098275f, -1.46876514f, -1.35233092f, 0.680712998f, 0.79359597f, -1.93641984f, -0.307419419f, + 0.898739338f, 1.48283148f, 0.989973128f, 0.765282035f, -1.49720085f, 0.0592158474f, 0.223181203f, + 1.69119799f, -1.08335376f, -1.03914416f, 0.758421719f, -0.820158958f, -1.51147974f, 0.905619442f, + 1.99668789f, -0.858451605f, 0.361608624f, 0.525018871f, -0.476508856f, -0.166383132f, 1.93686211f, + 0.524681091f, -1.53133762f, -1.17515707f, 1.75358665f, 1.31434417f, 1.10694599f, -0.799817324f, + 1.2029835f, 1.62318802f, -0.108844861f, 1.70618904f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/output_ref_data.h new file mode 100644 index 00000000..ec77ee87 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/output_ref_data.h @@ -0,0 +1,19 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_basic_f32_output_ref_data[18] = {3.27312899f, + -6.50747108f, + 5.74798489f, + -4.01605082f, + 4.8135004f, + -3.85792065f, + -2.5293715f, + -4.53701687f, + -0.604919493f, + -0.964001656f, + 8.89540386f, + 0.595696807f, + 4.72256088f, + -3.76562834f, + 2.06780934f, + -8.06619453f, + -4.13030529f, + 8.26697063f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/weights_data.h new file mode 100644 index 00000000..f1faa3b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_basic_f32/weights_data.h @@ -0,0 +1,20 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_basic_f32_weights_data[120] = { + -0.63256079f, 0.758745849f, 1.49786568f, 0.777359962f, -0.634242475f, -0.26375556f, 0.484459192f, + 0.496889263f, 1.44554257f, 0.634121001f, 0.826227546f, 1.3730849f, -0.420397669f, 0.140391946f, + -0.637616515f, -0.056450095f, 0.86494571f, 0.207624242f, -1.22454047f, 0.265274465f, 1.08156812f, + 0.331207216f, -0.126974255f, 0.384931982f, -0.255607426f, -1.43061936f, 1.32275438f, 0.152342141f, + 0.751289427f, -0.306732923f, -0.132312775f, 0.51719445f, -1.41299009f, -0.466857642f, 0.184575826f, + 0.0145176388f, 0.00181112729f, 0.475079358f, 0.863522589f, 0.84057194f, -0.677794755f, 0.238932833f, + 0.640616119f, -0.350968808f, 0.434884369f, -1.44685078f, 0.119966917f, 1.27552688f, -0.338317156f, + -0.355297029f, 1.04007471f, -0.389499336f, 0.940027237f, 0.252601683f, 1.19567132f, 0.887008369f, + -0.552226603f, 1.04501367f, -0.95592159f, -1.31203806f, 0.83891952f, 0.405147642f, -0.098641105f, + -0.796411633f, 0.166387841f, -0.525337636f, 1.38304949f, 0.674137473f, -1.41839266f, -1.02864623f, + 0.142062888f, -0.134830877f, -1.47008061f, -1.1029892f, 0.821352839f, 1.48130047f, 0.361357868f, + 0.437099278f, 1.41920948f, -0.169960916f, 0.242316499f, -1.49703872f, -1.43959403f, -1.37284803f, + 1.41202021f, 1.32226682f, 0.266685277f, 1.14386427f, 0.351563931f, -0.125917733f, -0.976583779f, + -0.129306555f, -0.969722807f, 0.00827953871f, -0.701581597f, -0.126187354f, -0.955085814f, -0.589820385f, + -0.234736636f, 0.0664193407f, -1.26019406f, 0.370397061f, -0.322428644f, -0.980661213f, -0.972062469f, + -0.391570687f, 0.390103847f, -0.765518129f, 1.41701972f, -1.19387138f, -1.27593184f, -0.808385074f, + -0.904161632f, 0.729808331f, 1.09421098f, 0.0352688543f, 0.671503127f, 0.187859714f, 0.0530987382f, + -0.347784162f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/biases_data.h new file mode 100644 index 00000000..8d231a96 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/biases_data.h @@ -0,0 +1,8 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_fc_per_ch_f16_biases_data[22] = { + (float16_t)-0.181518555f, (float16_t)0.15637207f, (float16_t)-0.473144531f, (float16_t)-0.103149414f, + (float16_t)0.422607422f, (float16_t)0.493652344f, (float16_t)-0.225585938f, (float16_t)-0.273925781f, + (float16_t)0.128417969f, (float16_t)-0.430419922f, (float16_t)-0.406738281f, (float16_t)0.226806641f, + (float16_t)0.408447266f, (float16_t)-0.0538330078f, (float16_t)-0.467773438f, (float16_t)-0.474365234f, + (float16_t)-0.306640625f, (float16_t)0.235351562f, (float16_t)0.188232422f, (float16_t)0.0259094238f, + (float16_t)-0.0170440674f, (float16_t)-0.371826172f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/config_data.h new file mode 100644 index 00000000..20f9464d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_OUT_CH 22 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_IN_CH 89 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_DST_SIZE 22 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_INPUT_SIZE 89 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/input_data.h new file mode 100644 index 00000000..fc489e8c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/input_data.h @@ -0,0 +1,25 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_fc_per_ch_f16_input_data[89] = { + (float16_t)-0.249511719f, (float16_t)-0.418457031f, (float16_t)1.56738281f, (float16_t)-1.26757812f, + (float16_t)-1.70703125f, (float16_t)-0.474853516f, (float16_t)1.16113281f, (float16_t)0.377685547f, + (float16_t)-0.810058594f, (float16_t)0.147094727f, (float16_t)-1.35449219f, (float16_t)0.443115234f, + (float16_t)0.875976562f, (float16_t)-1.3671875f, (float16_t)-0.932128906f, (float16_t)0.291503906f, + (float16_t)1.92578125f, (float16_t)0.539550781f, (float16_t)1.18945312f, (float16_t)-1.85449219f, + (float16_t)-1.83886719f, (float16_t)1.67773438f, (float16_t)1.10742188f, (float16_t)-0.951660156f, + (float16_t)-1.69824219f, (float16_t)-1.05175781f, (float16_t)-0.350585938f, (float16_t)-0.0844726562f, + (float16_t)-0.568359375f, (float16_t)0.254394531f, (float16_t)1.68652344f, (float16_t)0.505371094f, + (float16_t)0.418945312f, (float16_t)0.175415039f, (float16_t)1.13769531f, (float16_t)-1.86425781f, + (float16_t)-1.71484375f, (float16_t)-1.17773438f, (float16_t)-0.603515625f, (float16_t)0.279785156f, + (float16_t)-0.618164062f, (float16_t)-0.936523438f, (float16_t)0.0570678711f, (float16_t)-0.438232422f, + (float16_t)-0.607421875f, (float16_t)-1.83984375f, (float16_t)1.29980469f, (float16_t)1.42089844f, + (float16_t)1.16113281f, (float16_t)1.15917969f, (float16_t)-1.5234375f, (float16_t)1.28710938f, + (float16_t)-0.184570312f, (float16_t)1.35351562f, (float16_t)0.921875f, (float16_t)-0.612304688f, + (float16_t)0.256591797f, (float16_t)-0.390136719f, (float16_t)1.55664062f, (float16_t)1.47753906f, + (float16_t)0.494628906f, (float16_t)-0.036529541f, (float16_t)0.603515625f, (float16_t)0.281494141f, + (float16_t)0.937988281f, (float16_t)-0.41796875f, (float16_t)1.65625f, (float16_t)1.76464844f, + (float16_t)0.823242188f, (float16_t)-1.4375f, (float16_t)-0.82421875f, (float16_t)1.16015625f, + (float16_t)-0.929199219f, (float16_t)0.15222168f, (float16_t)-1.78222656f, (float16_t)1.53417969f, + (float16_t)-1.66992188f, (float16_t)-0.374511719f, (float16_t)1.11328125f, (float16_t)1.09375f, + (float16_t)0.779296875f, (float16_t)-0.0802001953f, (float16_t)-1.7734375f, (float16_t)-1.81738281f, + (float16_t)-0.226318359f, (float16_t)0.203491211f, (float16_t)-1.81152344f, (float16_t)-1.7109375f, + (float16_t)1.18359375f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/output_ref_data.h new file mode 100644 index 00000000..aefd891e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/output_ref_data.h @@ -0,0 +1,8 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_fc_per_ch_f16_output_ref_data[22] = { + (float16_t)3.74023438f, (float16_t)0.337158203f, (float16_t)1.82226562f, (float16_t)7.2265625f, + (float16_t)0.9375f, (float16_t)-5.7890625f, (float16_t)-5.8359375f, (float16_t)2.56835938f, + (float16_t)-12.4453125f, (float16_t)0.195800781f, (float16_t)-6.6328125f, (float16_t)3.09179688f, + (float16_t)-5.70703125f, (float16_t)-13.84375f, (float16_t)1.76269531f, (float16_t)0.00709533691f, + (float16_t)-1.58007812f, (float16_t)6.6328125f, (float16_t)3.82226562f, (float16_t)-1.09472656f, + (float16_t)-0.697265625f, (float16_t)4.078125f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/weights_data.h new file mode 100644 index 00000000..157b47d5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f16/weights_data.h @@ -0,0 +1,492 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_fc_per_ch_f16_weights_data[1958] = { + (float16_t)-0.6875f, (float16_t)0.869140625f, (float16_t)0.853515625f, (float16_t)0.826171875f, + (float16_t)-0.654296875f, (float16_t)-0.933105469f, (float16_t)-0.371337891f, (float16_t)0.7265625f, + (float16_t)-0.115539551f, (float16_t)0.215454102f, (float16_t)-0.168945312f, (float16_t)0.541503906f, + (float16_t)-0.449707031f, (float16_t)0.284423828f, (float16_t)-0.147705078f, (float16_t)0.8515625f, + (float16_t)-0.151855469f, (float16_t)-0.852539062f, (float16_t)-0.508300781f, (float16_t)-0.40234375f, + (float16_t)0.0151062012f, (float16_t)-0.256103516f, (float16_t)0.786621094f, (float16_t)-0.509277344f, + (float16_t)0.94140625f, (float16_t)0.880371094f, (float16_t)-0.352539062f, (float16_t)0.77734375f, + (float16_t)0.772460938f, (float16_t)0.940917969f, (float16_t)0.419189453f, (float16_t)-0.596679688f, + (float16_t)-0.759765625f, (float16_t)-0.924804688f, (float16_t)-0.0193939209f, (float16_t)-0.8828125f, + (float16_t)-0.841796875f, (float16_t)-0.866210938f, (float16_t)0.205688477f, (float16_t)-0.460449219f, + (float16_t)-0.0289916992f, (float16_t)0.579101562f, (float16_t)-0.0786132812f, (float16_t)-0.0252990723f, + (float16_t)-0.485351562f, (float16_t)0.0275726318f, (float16_t)0.453613281f, (float16_t)0.721191406f, + (float16_t)-0.00229072571f, (float16_t)-0.903808594f, (float16_t)-0.719238281f, (float16_t)-0.783691406f, + (float16_t)-0.0880126953f, (float16_t)-0.470703125f, (float16_t)-0.383789062f, (float16_t)-0.158203125f, + (float16_t)-0.674804688f, (float16_t)0.0960693359f, (float16_t)-0.434082031f, (float16_t)-0.202636719f, + (float16_t)0.527832031f, (float16_t)0.770507812f, (float16_t)0.721679688f, (float16_t)-0.368652344f, + (float16_t)0.727539062f, (float16_t)0.10546875f, (float16_t)0.0927734375f, (float16_t)-0.785644531f, + (float16_t)0.930175781f, (float16_t)0.557128906f, (float16_t)-0.0477905273f, (float16_t)-0.0847167969f, + (float16_t)-0.0409545898f, (float16_t)0.810058594f, (float16_t)0.419677734f, (float16_t)0.210693359f, + (float16_t)-0.485351562f, (float16_t)-0.809570312f, (float16_t)-0.881347656f, (float16_t)0.13269043f, + (float16_t)-0.582519531f, (float16_t)-0.157958984f, (float16_t)-0.822753906f, (float16_t)-0.0682983398f, + (float16_t)0.122741699f, (float16_t)0.784179688f, (float16_t)0.175292969f, (float16_t)-0.481689453f, + (float16_t)0.725585938f, (float16_t)-0.7734375f, (float16_t)0.355957031f, (float16_t)-0.615722656f, + (float16_t)0.266113281f, (float16_t)-0.986816406f, (float16_t)-0.0422973633f, (float16_t)-0.495849609f, + (float16_t)-0.164428711f, (float16_t)0.501953125f, (float16_t)-0.55859375f, (float16_t)-0.151733398f, + (float16_t)0.903808594f, (float16_t)0.141235352f, (float16_t)0.944824219f, (float16_t)0.584472656f, + (float16_t)0.774902344f, (float16_t)0.108642578f, (float16_t)0.852050781f, (float16_t)-0.393066406f, + (float16_t)0.169433594f, (float16_t)0.6953125f, (float16_t)0.616699219f, (float16_t)-0.591796875f, + (float16_t)0.436279297f, (float16_t)-0.0762939453f, (float16_t)0.891601562f, (float16_t)-0.269775391f, + (float16_t)-0.638183594f, (float16_t)-0.343017578f, (float16_t)-0.126831055f, (float16_t)-0.716796875f, + (float16_t)0.456542969f, (float16_t)0.976074219f, (float16_t)0.253417969f, (float16_t)0.720214844f, + (float16_t)-0.741210938f, (float16_t)-0.955078125f, (float16_t)-0.209350586f, (float16_t)0.306152344f, + (float16_t)0.500488281f, (float16_t)-0.290039062f, (float16_t)-0.0952758789f, (float16_t)0.0165100098f, + (float16_t)0.284667969f, (float16_t)0.948242188f, (float16_t)-0.819335938f, (float16_t)0.0621643066f, + (float16_t)-0.383544922f, (float16_t)-0.278564453f, (float16_t)0.497802734f, (float16_t)0.356445312f, + (float16_t)-0.253417969f, (float16_t)0.850097656f, (float16_t)0.800292969f, (float16_t)-0.0399475098f, + (float16_t)-0.140625f, (float16_t)0.841796875f, (float16_t)0.388916016f, (float16_t)-0.565429688f, + (float16_t)-0.553710938f, (float16_t)-0.846679688f, (float16_t)-0.395263672f, (float16_t)-0.442871094f, + (float16_t)0.234619141f, (float16_t)0.103942871f, (float16_t)-0.934570312f, (float16_t)0.612792969f, + (float16_t)-0.557128906f, (float16_t)0.673828125f, (float16_t)-0.273193359f, (float16_t)-0.141967773f, + (float16_t)-0.943847656f, (float16_t)-0.890136719f, (float16_t)0.188964844f, (float16_t)0.299072266f, + (float16_t)-0.129394531f, (float16_t)-0.562988281f, (float16_t)0.938476562f, (float16_t)-0.34375f, + (float16_t)-0.291015625f, (float16_t)-0.448242188f, (float16_t)-0.826171875f, (float16_t)0.466064453f, + (float16_t)-0.484619141f, (float16_t)-0.688476562f, (float16_t)-0.952148438f, (float16_t)-0.831542969f, + (float16_t)0.178222656f, (float16_t)-0.0152282715f, (float16_t)-0.184570312f, (float16_t)0.959960938f, + (float16_t)-0.1875f, (float16_t)0.725097656f, (float16_t)0.343017578f, (float16_t)0.161621094f, + (float16_t)-0.334228516f, (float16_t)0.328369141f, (float16_t)-0.76953125f, (float16_t)0.491943359f, + (float16_t)0.109008789f, (float16_t)0.203613281f, (float16_t)0.908203125f, (float16_t)-0.489013672f, + (float16_t)-0.0365905762f, (float16_t)0.366943359f, (float16_t)0.903320312f, (float16_t)-0.480224609f, + (float16_t)-0.700683594f, (float16_t)-0.265380859f, (float16_t)0.702636719f, (float16_t)-0.20300293f, + (float16_t)-0.890136719f, (float16_t)-0.997070312f, (float16_t)0.975097656f, (float16_t)0.0631713867f, + (float16_t)0.458251953f, (float16_t)-0.555175781f, (float16_t)0.049407959f, (float16_t)0.0140228271f, + (float16_t)0.305175781f, (float16_t)0.079284668f, (float16_t)0.954101562f, (float16_t)-0.466552734f, + (float16_t)0.497558594f, (float16_t)-0.715332031f, (float16_t)-0.735839844f, (float16_t)-0.283691406f, + (float16_t)-0.0590820312f, (float16_t)0.595703125f, (float16_t)0.170043945f, (float16_t)-0.958496094f, + (float16_t)-0.936035156f, (float16_t)-0.371582031f, (float16_t)0.494384766f, (float16_t)0.184326172f, + (float16_t)0.134521484f, (float16_t)0.300292969f, (float16_t)-0.31640625f, (float16_t)-0.416503906f, + (float16_t)-0.637207031f, (float16_t)0.469726562f, (float16_t)-0.963867188f, (float16_t)-0.506835938f, + (float16_t)-0.112304688f, (float16_t)-0.148681641f, (float16_t)0.129150391f, (float16_t)0.225097656f, + (float16_t)-0.192138672f, (float16_t)0.120849609f, (float16_t)0.659179688f, (float16_t)0.480957031f, + (float16_t)-0.510253906f, (float16_t)-0.798339844f, (float16_t)-0.747558594f, (float16_t)0.186523438f, + (float16_t)0.688964844f, (float16_t)0.279785156f, (float16_t)-0.534667969f, (float16_t)0.760742188f, + (float16_t)-0.903320312f, (float16_t)0.103637695f, (float16_t)-0.926757812f, (float16_t)-0.115783691f, + (float16_t)-0.897460938f, (float16_t)0.278076172f, (float16_t)-0.330810547f, (float16_t)0.337158203f, + (float16_t)-0.682617188f, (float16_t)0.708496094f, (float16_t)-0.818359375f, (float16_t)0.194580078f, + (float16_t)0.854980469f, (float16_t)-0.515625f, (float16_t)-0.524902344f, (float16_t)-0.977539062f, + (float16_t)0.138305664f, (float16_t)0.917480469f, (float16_t)-0.564453125f, (float16_t)0.0331726074f, + (float16_t)-0.650390625f, (float16_t)-0.896484375f, (float16_t)-0.264160156f, (float16_t)-0.954589844f, + (float16_t)-0.942871094f, (float16_t)-0.356445312f, (float16_t)0.0374145508f, (float16_t)-0.767089844f, + (float16_t)-0.137329102f, (float16_t)-0.612304688f, (float16_t)-0.100036621f, (float16_t)0.485351562f, + (float16_t)0.706542969f, (float16_t)-0.798339844f, (float16_t)-0.465576172f, (float16_t)0.241943359f, + (float16_t)0.108459473f, 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(float16_t)-0.78125f, (float16_t)-0.309326172f, (float16_t)-0.407226562f, + (float16_t)0.272460938f, (float16_t)-0.150634766f, (float16_t)0.825195312f, (float16_t)0.433349609f, + (float16_t)-0.329345703f, (float16_t)-0.35546875f, (float16_t)-0.107177734f, (float16_t)0.125976562f, + (float16_t)0.884277344f, (float16_t)-0.336914062f, (float16_t)0.607421875f, (float16_t)-0.78125f, + (float16_t)-0.30078125f, (float16_t)0.62890625f, (float16_t)0.110473633f, (float16_t)-0.796386719f, + (float16_t)-0.564941406f, (float16_t)0.874023438f, (float16_t)-0.887207031f, (float16_t)0.659179688f, + (float16_t)-0.917480469f, (float16_t)-0.215332031f, (float16_t)-0.419921875f, (float16_t)0.891601562f, + (float16_t)-0.10925293f, (float16_t)0.642089844f, (float16_t)-0.0078125f, (float16_t)0.524414062f, + (float16_t)0.151245117f, (float16_t)-0.797851562f, (float16_t)0.822265625f, (float16_t)0.514160156f, + (float16_t)0.651367188f, (float16_t)0.853515625f, (float16_t)-0.854492188f, (float16_t)-0.324707031f, + (float16_t)-0.512695312f, (float16_t)0.838867188f, (float16_t)-0.702148438f, (float16_t)0.833984375f, + (float16_t)0.729492188f, (float16_t)0.711425781f, (float16_t)0.471435547f, (float16_t)-0.666503906f, + (float16_t)-0.752929688f, (float16_t)0.459472656f, (float16_t)0.419433594f, (float16_t)-0.729980469f, + (float16_t)0.790527344f, (float16_t)-0.0301055908f, (float16_t)0.37890625f, (float16_t)0.771484375f, + (float16_t)-0.826660156f, (float16_t)0.689453125f, (float16_t)-0.178710938f, (float16_t)-0.620605469f, + (float16_t)0.751953125f, (float16_t)-0.998535156f, (float16_t)-0.110351562f, (float16_t)0.94921875f, + (float16_t)-0.642089844f, (float16_t)0.495117188f, (float16_t)-0.5546875f, (float16_t)-0.319091797f, + (float16_t)-0.7734375f, (float16_t)-0.569335938f, (float16_t)-0.368652344f, (float16_t)-0.987304688f, + (float16_t)0.466308594f, (float16_t)0.663085938f, (float16_t)0.869628906f, (float16_t)0.218383789f, + (float16_t)0.596679688f, (float16_t)-0.540527344f, (float16_t)-0.243652344f, (float16_t)0.172119141f, + (float16_t)0.51953125f, (float16_t)-0.684082031f, (float16_t)-0.151733398f, (float16_t)-0.720703125f, + (float16_t)0.791503906f, (float16_t)-0.73046875f, (float16_t)0.316650391f, (float16_t)0.404785156f, + (float16_t)0.334960938f, (float16_t)-0.91796875f, (float16_t)-0.494384766f, (float16_t)-0.319824219f, + (float16_t)-0.338134766f, (float16_t)-0.142211914f, (float16_t)0.842773438f, (float16_t)0.200195312f, + (float16_t)0.403564453f, (float16_t)-0.376220703f, (float16_t)0.633789062f, (float16_t)0.602050781f, + (float16_t)-0.0203704834f, (float16_t)-0.773925781f, (float16_t)-0.53125f, (float16_t)0.288574219f, + (float16_t)-0.0314941406f, (float16_t)0.805664062f, (float16_t)0.666503906f, (float16_t)0.16003418f, + (float16_t)-0.00434494019f, (float16_t)-0.0921020508f, (float16_t)0.0612487793f, (float16_t)-0.288330078f, + (float16_t)0.584472656f, (float16_t)0.264160156f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/biases_data.h new file mode 100644 index 00000000..f20555c5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/biases_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_fc_per_ch_f32_biases_data[22] = { + 0.405404449f, 0.459099323f, -0.0951577723f, 0.320299268f, 0.367123842f, 0.428969771f, + -0.313517332f, 0.255975008f, -0.406960547f, -0.425913244f, 0.426121891f, 0.119579569f, + -0.0158243179f, -0.475943208f, 0.202934533f, 0.316405714f, 0.227577657f, 0.316273302f, + -0.151544794f, 0.357095093f, 0.195015132f, -0.281028897f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/config_data.h new file mode 100644 index 00000000..200af0fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_OUT_CH 22 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_IN_CH 89 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_DST_SIZE 22 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_INPUT_SIZE 89 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_OUT_ACTIVATION_MIN -128.0f +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_OUT_ACTIVATION_MAX 127.0f +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_FC_PER_CH_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/input_data.h new file mode 100644 index 00000000..77d77ec8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/input_data.h @@ -0,0 +1,15 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_fc_per_ch_f32_input_data[89] = { + -1.86413586f, 0.0797144547f, -1.23610806f, 1.20199263f, 1.33504534f, 0.895159185f, 1.02518272f, + -1.26451159f, 0.693256676f, 1.49680507f, 1.02796364f, 1.91780186f, -1.90190732f, -1.6189574f, + 0.45425719f, -0.833382189f, 1.31804907f, -1.48513997f, -0.167350635f, 0.978542805f, 0.742752314f, + 1.99446702f, 1.86922657f, -1.17501342f, -0.834812939f, -1.43383002f, -1.74873424f, 1.57761168f, + 1.85958838f, -1.40686667f, -0.509507298f, 1.09948647f, 1.61148393f, 0.208789632f, -0.189021885f, + -1.78976572f, 0.183947891f, -1.18157136f, 0.191779181f, 1.99204135f, 0.597566903f, 1.47060418f, + -1.86866021f, -1.41662967f, -0.46260038f, 1.68195236f, -0.41507256f, 1.93186378f, 0.978130639f, + 0.273339808f, -0.962331176f, 0.179401487f, -0.338182867f, -0.366595417f, 0.357013762f, -1.0931282f, + -1.7498697f, -0.142946005f, -1.78008389f, 1.13272119f, -1.67543316f, -1.07436979f, 0.951498687f, + 0.761842072f, -0.226072475f, 1.38141549f, 0.862939358f, -1.82664049f, 0.179440647f, 1.36899531f, + -1.66326523f, 0.952612221f, 0.03790107f, -0.827775955f, -1.17177057f, -0.324584365f, 1.75067461f, + 1.80790925f, 0.349630117f, -1.02950454f, -1.49576211f, -1.801283f, 0.0156544466f, -1.76025701f, + 1.49847615f, 1.08206487f, -1.9980762f, -0.292844892f, -1.93358624f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/output_ref_data.h new file mode 100644 index 00000000..6789e1a2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_fc_per_ch_f32_output_ref_data[22] = { + 4.29727077f, 13.0467911f, 4.23057604f, -2.69571877f, -7.58362103f, 10.6991224f, -4.78073597f, 4.6182375f, + -4.28369808f, -13.713129f, 13.4946442f, 5.12015486f, -5.57234335f, -7.40514183f, -4.83683872f, 7.4765687f, + 6.64471579f, -0.534159303f, -6.08563328f, 4.60722065f, -4.92709637f, 10.7864532f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/weights_data.h new file mode 100644 index 00000000..5d93fb1a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_fc_per_ch_f32/weights_data.h @@ -0,0 +1,329 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_fc_per_ch_f32_weights_data[1958] = { + 0.413938373f, -0.674366713f, 0.678850591f, -0.656136751f, 0.675597072f, 0.990479529f, + -0.268092632f, 0.498481691f, 0.316071361f, 0.225942582f, -0.989755511f, 0.852472425f, + -0.738149941f, -0.537091196f, 0.0857113376f, 0.0328314304f, -0.179089293f, 0.20412527f, + 0.428181112f, -0.692972243f, -0.981762826f, 0.414617866f, -0.583663762f, 0.40097183f, + -0.149148107f, -0.4323861f, -0.931847334f, -0.270249695f, 0.111312084f, 0.567312658f, + -0.57781136f, -0.471335679f, -0.478465497f, -0.941171288f, -0.528708279f, 0.150234118f, + 0.21680674f, -0.401310235f, 0.447593272f, 0.192366585f, 0.727195978f, 0.529705048f, + 0.735997021f, 0.921886802f, 0.0916434526f, 0.491173148f, 0.587719321f, 0.914933205f, + 0.294913471f, -0.111680754f, -0.853187919f, 0.932437778f, -0.221781939f, 0.0938773453f, + 0.738147736f, -0.192858532f, 0.945043623f, 0.574471951f, -0.287758857f, -0.9789837f, + 0.615690112f, 0.526565611f, 0.314904779f, 0.925007284f, -0.544416845f, -0.862697065f, + 0.880629897f, -0.955354452f, -0.402072638f, -0.619875908f, -0.00633222237f, -0.802511573f, + -0.678817451f, -0.131475344f, -0.888945937f, 0.149460435f, 0.111160755f, 0.393001288f, + 0.450916529f, 0.072675921f, -0.970638514f, 0.0749190301f, -0.0251962319f, 0.679401815f, + 0.925272048f, 0.994726896f, 0.446414918f, -0.833231986f, -0.17549777f, -0.174383357f, + -0.808285594f, -0.951770246f, 0.771723509f, 0.890621185f, 0.638625681f, -0.96139878f, + 0.333621979f, -0.0368996523f, 0.750554919f, 0.240218341f, 0.365350723f, 0.404631793f, + -0.889003575f, 0.141647503f, -0.0263922028f, 0.288512409f, 0.248975247f, -0.399914503f, + -0.869565368f, -0.653022766f, -0.291804135f, -0.885167599f, 0.516872704f, -0.362826377f, + 0.137321159f, 0.964980304f, -0.127084672f, 0.581159711f, 0.12505348f, 0.788025439f, + 0.740258157f, 0.920231342f, 0.0632227138f, 0.980843961f, 0.456733704f, 0.0246114749f, + 0.231717452f, 0.970822632f, -0.587543488f, 0.43783772f, 0.235074013f, 0.810337305f, + 0.0399075188f, 0.2281771f, 0.884287f, -0.532282591f, 0.679074347f, 0.21711275f, + 0.40251416f, 0.458739668f, 0.264513999f, -0.472194582f, 0.302984029f, -0.48299104f, + -0.922694087f, -0.553621948f, 0.62647897f, 0.201284945f, -0.501850963f, -0.813816607f, + 0.196347684f, -0.606102943f, -0.705045581f, 0.642344236f, 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0.524428189f, -0.676141322f, -0.0925887004f, 0.777634382f, + -0.594397545f, -0.918136001f, -0.922429442f, 0.930950701f, 0.164055765f, -0.63597244f, + 0.703546703f, 0.0936018229f, 0.125597015f, -0.624179959f, -0.00799053814f, -0.0882950351f, + -0.296075076f, -0.0308837909f, -0.894557893f, 0.743581951f, -0.801270664f, -0.0463849343f, + -0.124395639f, -0.510109842f, 0.21000728f, -0.0703669935f, 0.0522048026f, -0.532340169f, + -0.830062985f, -0.656295955f, -0.72616905f, 0.155779332f, -0.49608919f, -0.550248861f, + -0.506528795f, -0.70225215f, -0.870405853f, 0.929962695f, -0.573563159f, 0.0731452778f, + 0.739351332f, -0.938333571f, 0.68427211f, -0.350058645f, -0.580355465f, -0.430696934f, + 0.0732370317f, 0.35435608f, 0.0870239064f, 0.8922171f, -0.867493927f, 0.864442647f, + -0.00654258253f, 0.270354748f, -0.0992338508f, 0.248548463f, -0.720028222f, -0.566859901f, + -0.629230618f, 0.864102185f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/biases_data.h new file mode 100644 index 00000000..da4f03f5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/biases_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_0_f16_biases_data[9] = {(float16_t)0.411621094f, + (float16_t)-0.288085938f, + (float16_t)0.451171875f, + (float16_t)-0.288574219f, + (float16_t)0.328369141f, + (float16_t)0.669921875f, + (float16_t)-0.458007812f, + (float16_t)-0.400146484f, + (float16_t)-0.0784301758f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/config_data.h new file mode 100644 index 00000000..b4c80c53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_MVE_0_F16_OUT_CH 9 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_IN_CH 16 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_DST_SIZE 9 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_INPUT_SIZE 16 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define FULLY_CONNECTED_MATCH_MVE_0_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define FULLY_CONNECTED_MATCH_MVE_0_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_MVE_0_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/input_data.h new file mode 100644 index 00000000..f4b7bcec --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/input_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_0_f16_input_data[16] = {(float16_t)0.826660156f, + (float16_t)0.364257812f, + (float16_t)-0.382568359f, + (float16_t)-1.94824219f, + (float16_t)1.65625f, + (float16_t)1.52734375f, + (float16_t)-0.511230469f, + (float16_t)-1.69335938f, + (float16_t)-0.962402344f, + (float16_t)0.00852966309f, + (float16_t)-1.88671875f, + (float16_t)-0.837890625f, + (float16_t)0.770019531f, + (float16_t)-0.770996094f, + (float16_t)-1.91699219f, + (float16_t)-0.92578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/output_ref_data.h new file mode 100644 index 00000000..334d2aa3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_0_f16_output_ref_data[9] = {(float16_t)-2.4140625f, + (float16_t)-3.36132812f, + (float16_t)4.11328125f, + (float16_t)-5.00390625f, + (float16_t)0.8125f, + (float16_t)4.7890625f, + (float16_t)-2.32421875f, + (float16_t)-5.01171875f, + (float16_t)6.89453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/weights_data.h new file mode 100644 index 00000000..1ac8f780 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f16/weights_data.h @@ -0,0 +1,38 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_0_f16_weights_data[144] = { + (float16_t)-0.193359375f, (float16_t)-0.111938477f, (float16_t)-0.688476562f, (float16_t)-0.446044922f, + (float16_t)-0.206176758f, (float16_t)-1.02050781f, (float16_t)-1.06347656f, (float16_t)0.997070312f, + (float16_t)1.49707031f, (float16_t)-0.699707031f, (float16_t)0.175048828f, (float16_t)-0.1328125f, + (float16_t)-1.12011719f, (float16_t)1.16308594f, (float16_t)-0.693847656f, (float16_t)-1.4921875f, + (float16_t)-1.07519531f, (float16_t)0.76953125f, (float16_t)-0.0109863281f, (float16_t)1.171875f, + (float16_t)0.752441406f, (float16_t)-1.41308594f, (float16_t)-1.25683594f, (float16_t)-0.615722656f, + (float16_t)-0.861816406f, (float16_t)0.419189453f, (float16_t)0.748046875f, (float16_t)0.0272674561f, + (float16_t)-0.483398438f, (float16_t)-1.375f, (float16_t)-0.125366211f, (float16_t)1.39160156f, + (float16_t)0.446044922f, (float16_t)-0.444824219f, (float16_t)-1.41601562f, (float16_t)0.166748047f, + (float16_t)-1.00292969f, (float16_t)0.576660156f, (float16_t)-0.860839844f, (float16_t)-1.12207031f, + (float16_t)0.482910156f, (float16_t)0.830078125f, (float16_t)0.137451172f, (float16_t)-0.897460938f, + (float16_t)-0.410888672f, (float16_t)-0.198852539f, (float16_t)-1.10449219f, (float16_t)0.333496094f, + (float16_t)1.06542969f, (float16_t)0.662109375f, (float16_t)-0.613769531f, (float16_t)-0.505859375f, + (float16_t)-0.786621094f, (float16_t)-1.44140625f, (float16_t)-0.992675781f, (float16_t)0.199584961f, + (float16_t)0.243652344f, (float16_t)0.028717041f, (float16_t)1.14746094f, (float16_t)0.809082031f, + (float16_t)0.612304688f, (float16_t)1.15039062f, (float16_t)0.279296875f, (float16_t)-0.330078125f, + (float16_t)0.14855957f, (float16_t)-0.408935547f, (float16_t)1.22753906f, (float16_t)1.06054688f, + (float16_t)-0.0618591309f, (float16_t)0.784179688f, (float16_t)-0.592773438f, (float16_t)1.05273438f, + (float16_t)-0.128051758f, (float16_t)1.29394531f, (float16_t)-0.950683594f, (float16_t)-0.457519531f, + (float16_t)-0.328857422f, (float16_t)-0.6875f, (float16_t)-0.388427734f, (float16_t)-0.106689453f, + (float16_t)1.19921875f, (float16_t)1.30566406f, (float16_t)0.823242188f, (float16_t)-0.482177734f, + (float16_t)0.302001953f, (float16_t)0.877441406f, (float16_t)-0.519042969f, (float16_t)-0.200683594f, + (float16_t)-0.60546875f, (float16_t)-0.576660156f, (float16_t)1.24121094f, (float16_t)-1.15722656f, + (float16_t)0.299804688f, (float16_t)0.867675781f, (float16_t)-1.11035156f, (float16_t)1.41699219f, + (float16_t)-0.504882812f, (float16_t)-0.714355469f, (float16_t)0.9453125f, (float16_t)1.22558594f, + (float16_t)0.834472656f, (float16_t)-1.08300781f, (float16_t)0.0266418457f, (float16_t)-1.20117188f, + (float16_t)-0.655273438f, (float16_t)-0.587402344f, (float16_t)0.0118103027f, (float16_t)1.046875f, + (float16_t)-0.989746094f, (float16_t)-0.516113281f, (float16_t)-0.209350586f, (float16_t)-0.0508728027f, + (float16_t)-1.05859375f, (float16_t)-0.116333008f, (float16_t)1.19335938f, (float16_t)-0.355712891f, + (float16_t)-1.09863281f, (float16_t)0.948730469f, (float16_t)-0.351806641f, (float16_t)1.26269531f, + (float16_t)-0.294677734f, (float16_t)1.32226562f, (float16_t)-0.983886719f, (float16_t)0.948242188f, + (float16_t)-0.686035156f, (float16_t)1.49707031f, (float16_t)0.337402344f, (float16_t)0.678222656f, + (float16_t)-0.76953125f, (float16_t)1.20410156f, (float16_t)0.322753906f, (float16_t)-1.04003906f, + (float16_t)-1.36816406f, (float16_t)-0.415039062f, (float16_t)-0.896484375f, (float16_t)-1.40332031f, + (float16_t)0.173828125f, (float16_t)-0.842285156f, (float16_t)-1.42089844f, (float16_t)-1.46484375f, + (float16_t)1.18164062f, (float16_t)0.916015625f, (float16_t)-1.24414062f, (float16_t)1.06640625f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/biases_data.h new file mode 100644 index 00000000..2a525375 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/biases_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_0_f32_biases_data[9] = {-0.104332983f, + -0.116652645f, + 0.6125018f, + 0.159252644f, + -0.62134397f, + -0.389548987f, + -0.508505881f, + -0.502078652f, + -0.68410778f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/config_data.h new file mode 100644 index 00000000..cf57d095 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_MVE_0_F32_OUT_CH 9 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_IN_CH 16 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_DST_SIZE 9 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_INPUT_SIZE 16 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_OUT_ACTIVATION_MIN -128.0f +#define FULLY_CONNECTED_MATCH_MVE_0_F32_OUT_ACTIVATION_MAX 127.0f +#define FULLY_CONNECTED_MATCH_MVE_0_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_MVE_0_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_MVE_0_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/input_data.h new file mode 100644 index 00000000..76a1427e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/input_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_0_f32_input_data[16] = {-0.198858634f, + 1.17039013f, + 0.0252181441f, + 0.293829709f, + 0.600125015f, + 0.377994567f, + -0.031230364f, + 0.712100327f, + -1.34801018f, + 1.95853853f, + -0.421812296f, + -0.339538842f, + -1.33742738f, + 0.0932570323f, + -1.9313463f, + 0.0720222592f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/output_ref_data.h new file mode 100644 index 00000000..52320c1c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_0_f32_output_ref_data[9] = {-1.59747005f, + 0.725607693f, + 8.57967186f, + 4.67774343f, + -3.22920036f, + 0.155097991f, + 5.0174942f, + 5.19956541f, + -3.82436037f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/weights_data.h new file mode 100644 index 00000000..c8559758 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_0_f32/weights_data.h @@ -0,0 +1,23 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_0_f32_weights_data[144] = { + 1.28244627f, 1.15595829f, -0.366530955f, -1.41059661f, 0.60468334f, 0.240112007f, 0.0904906392f, + 0.00486538839f, 0.186505198f, -1.2045083f, -0.895272195f, -1.13758528f, -1.14233303f, 0.919814825f, + 1.22930074f, -0.199467495f, 0.499374479f, 1.242957f, 1.23410177f, -0.364771903f, -1.0091697f, + -0.112017132f, -0.630206048f, -0.92209971f, -0.991908312f, -0.0196791366f, -0.607599437f, 0.728552461f, + -1.3942821f, 0.567250788f, 1.19867194f, -0.864821553f, -1.15634072f, 1.48944962f, 0.869793952f, + -0.712649167f, 1.42553306f, 0.085063614f, 0.463716f, -1.15752053f, -1.30604219f, 1.29545438f, + -0.829261899f, -1.33717692f, -0.572487175f, 0.57471031f, -0.132351205f, -0.60947144f, 0.196987092f, + 1.44571209f, -0.158536285f, -0.89899689f, -0.977124631f, -1.06117702f, 1.07667792f, -0.473312497f, + 0.015703002f, 0.682159185f, 0.230471686f, -0.984815001f, -1.20434678f, 1.34987509f, -0.662615299f, + -1.06123304f, -0.915300906f, -1.23041344f, 0.788567543f, -0.674120069f, 0.749751031f, -1.47164392f, + 0.816163123f, 1.2793473f, 0.173063472f, -1.08658361f, -1.2469362f, 0.453493506f, 0.696846128f, + 0.92357564f, -0.506733358f, -1.30844176f, -0.485044181f, 0.276304454f, 1.15013707f, -0.996718407f, + -0.653440654f, 0.934533f, -1.00075245f, -0.352888614f, -1.04922521f, -0.932786107f, -1.1420275f, + 0.147755846f, 0.428844452f, -0.98638761f, -0.653194845f, 0.452279419f, 1.13019538f, 0.988487601f, + 1.16126728f, 1.32348824f, 0.43019399f, -0.928377092f, -0.650391042f, -1.42477024f, -1.26204407f, + 1.19561577f, 0.798103333f, 0.646442413f, -0.525144517f, 0.36161691f, -0.542183518f, -0.101204142f, + 0.929694772f, 0.864552617f, 0.476070762f, -0.333641112f, 0.226669192f, -0.453683376f, 0.418939531f, + 0.513088286f, -0.47983551f, 1.27334189f, 0.107796632f, -0.27793029f, -0.815129101f, -0.276012599f, + -0.201088071f, 0.0200269818f, 0.471933275f, -0.587747633f, 1.30875206f, 0.238819137f, -1.06192696f, + 1.47942805f, 0.297027826f, 1.22308457f, 0.126080215f, -0.567635953f, 0.625216603f, 1.33985364f, + -0.027134655f, 0.0589808114f, 0.629713833f, -0.979440033f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/biases_data.h new file mode 100644 index 00000000..24781384 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_1_f16_biases_data[4] = {(float16_t)-0.545410156f, + (float16_t)-0.536621094f, + (float16_t)-0.59375f, + (float16_t)-0.182617188f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/config_data.h new file mode 100644 index 00000000..75a2f6fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_CH 4 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_IN_CH 20 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_DST_SIZE 4 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_SIZE 20 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_ACTIVATION_MIN (float16_t) - 128.0f +#define FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_ACTIVATION_MAX (float16_t)127.0f +#define FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_MVE_1_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/input_data.h new file mode 100644 index 00000000..50472d4f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/input_data.h @@ -0,0 +1,7 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_1_f16_input_data[20] = { + (float16_t)-0.640136719f, (float16_t)1.1015625f, (float16_t)-0.826660156f, (float16_t)-1.12695312f, + (float16_t)-0.966796875f, (float16_t)1.62207031f, (float16_t)0.989746094f, (float16_t)-1.92578125f, + (float16_t)1.04394531f, (float16_t)1.50488281f, (float16_t)-0.0126266479f, (float16_t)1.41113281f, + (float16_t)-1.57617188f, (float16_t)-0.165405273f, (float16_t)1.58398438f, (float16_t)0.482666016f, + (float16_t)0.891601562f, (float16_t)-0.895019531f, (float16_t)-0.813964844f, (float16_t)-0.627929688f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/output_ref_data.h new file mode 100644 index 00000000..2a6fd28e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_1_f16_output_ref_data[4] = {(float16_t)1.5234375f, + (float16_t)3.13085938f, + (float16_t)1.0f, + (float16_t)5.28515625f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/weights_data.h new file mode 100644 index 00000000..e64f276f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f16/weights_data.h @@ -0,0 +1,22 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_match_mve_1_f16_weights_data[80] = { + (float16_t)0.501953125f, (float16_t)-1.10351562f, (float16_t)0.128295898f, (float16_t)-0.715332031f, + (float16_t)1.40722656f, (float16_t)1.33203125f, (float16_t)0.379638672f, (float16_t)-0.263183594f, + (float16_t)0.774414062f, (float16_t)0.149047852f, (float16_t)0.752441406f, (float16_t)-0.186645508f, + (float16_t)-1.13085938f, (float16_t)1.07617188f, (float16_t)-0.979980469f, (float16_t)-0.684082031f, + (float16_t)1.36328125f, (float16_t)-0.391845703f, (float16_t)0.948242188f, (float16_t)0.0862426758f, + (float16_t)-1.18945312f, (float16_t)-0.301757812f, (float16_t)-0.0143890381f, (float16_t)1.18652344f, + (float16_t)-0.526855469f, (float16_t)-0.222412109f, (float16_t)-0.475341797f, (float16_t)-0.770996094f, + (float16_t)0.0202484131f, (float16_t)0.707519531f, (float16_t)0.493652344f, (float16_t)0.196166992f, + (float16_t)-0.849609375f, (float16_t)-0.471679688f, (float16_t)0.488769531f, (float16_t)0.630371094f, + (float16_t)0.202270508f, (float16_t)0.634277344f, (float16_t)-0.482910156f, (float16_t)0.727539062f, + (float16_t)-1.10253906f, (float16_t)-1.26171875f, (float16_t)-1.33496094f, (float16_t)-0.437011719f, + (float16_t)-0.384765625f, (float16_t)-0.938476562f, (float16_t)-0.0411682129f, (float16_t)-1.3359375f, + (float16_t)-0.979980469f, (float16_t)0.699707031f, (float16_t)1.34375f, (float16_t)-0.489501953f, + (float16_t)-1.04785156f, (float16_t)-1.36523438f, (float16_t)-1.33984375f, (float16_t)-1.34667969f, + (float16_t)0.121459961f, (float16_t)-1.44921875f, (float16_t)1.20117188f, (float16_t)-0.7109375f, + (float16_t)-0.595214844f, (float16_t)0.975097656f, (float16_t)-1.41210938f, (float16_t)-0.717773438f, + (float16_t)0.343017578f, (float16_t)0.807617188f, (float16_t)1.38085938f, (float16_t)1.18164062f, + (float16_t)-0.357910156f, (float16_t)0.730957031f, (float16_t)0.885742188f, (float16_t)0.236450195f, + (float16_t)-0.0529785156f, (float16_t)-0.206665039f, (float16_t)1.34667969f, (float16_t)0.725585938f, + (float16_t)-0.424316406f, (float16_t)0.938964844f, (float16_t)-0.391113281f, (float16_t)1.24902344f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/biases_data.h new file mode 100644 index 00000000..34aa1f91 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/biases_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_1_f32_biases_data[4] = {0.485140294f, + -0.339216888f, + 0.715892017f, + 0.60747987f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/config_data.h new file mode 100644 index 00000000..0f32ddde --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_CH 4 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_IN_CH 20 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_W 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_H 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_DST_SIZE 4 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_SIZE 20 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_ACTIVATION_MIN -128.0f +#define FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_ACTIVATION_MAX 127.0f +#define FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MATCH_MVE_1_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MATCH_MVE_1_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/input_data.h new file mode 100644 index 00000000..5e33bf1e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/input_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_1_f32_input_data[20] = { + -1.15106368f, -1.85897899f, 0.647217512f, -1.60712028f, -1.25007951f, 1.32596564f, -0.854087532f, + 1.26931882f, -1.01968491f, -0.138275132f, -0.768471181f, 1.32637835f, -1.69400918f, 1.46766198f, + 1.22015655f, 1.07978451f, 1.75532126f, -0.559055209f, -0.245186582f, 0.353682101f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/output_ref_data.h new file mode 100644 index 00000000..ba67721c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_1_f32_output_ref_data[4] = {-2.03344059f, + -6.53915358f, + -0.130610645f, + 4.46209192f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/weights_data.h new file mode 100644 index 00000000..4e58670b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_match_mve_1_f32/weights_data.h @@ -0,0 +1,14 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_match_mve_1_f32_weights_data[80] = { + -1.23157811f, 0.0249196161f, 0.683974922f, -0.0440097377f, 0.222742185f, -0.563804448f, 0.832987189f, + -1.22502244f, -0.0865562484f, 0.779420078f, 1.16087246f, -0.101570256f, 0.260326922f, -0.906599939f, + 0.0527145304f, 1.47573447f, 0.393412828f, 0.934347332f, 1.18063414f, 0.48208648f, -0.676252067f, + 1.33613682f, 1.44487238f, 0.229300156f, -0.522359729f, -1.30363917f, -0.43988806f, -1.15295994f, + 0.500984073f, -0.578353643f, 0.417712808f, -0.818549693f, 0.701848447f, -1.06936312f, 0.745568871f, + -0.237280294f, 0.692733526f, 0.0871407837f, 1.03278279f, 0.369497657f, -0.23032251f, 1.42570651f, + -0.592593789f, 0.870944083f, -0.253009915f, 0.799138069f, -0.355267972f, -0.790730119f, -0.579743087f, + -0.0923256353f, 1.1915524f, -0.196109384f, 1.44562173f, 0.508516133f, 1.15672314f, 0.605758548f, + 1.11296976f, -1.3549757f, 0.541830838f, 0.783249438f, -0.416263312f, 0.126388907f, -0.794741631f, + -0.925303102f, -0.199041009f, 0.721854031f, -0.454388559f, -1.47777069f, -0.760766983f, 0.4760786f, + -0.317009509f, 1.11688578f, -1.17006123f, -0.573461175f, 0.833933949f, 0.116502248f, -0.656170547f, + 1.07884157f, 0.318089247f, 0.0950622708f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/biases_data.h new file mode 100644 index 00000000..4e002224 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/biases_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_medium_f16_biases_data[32] = { + (float16_t)-0.391845703f, (float16_t)0.0704956055f, (float16_t)0.28515625f, (float16_t)0.109436035f, + (float16_t)0.256347656f, (float16_t)0.0816040039f, (float16_t)0.277099609f, (float16_t)-0.411132812f, + (float16_t)-0.125732422f, (float16_t)0.00848388672f, (float16_t)-0.127929688f, (float16_t)-0.249511719f, + (float16_t)0.0876464844f, (float16_t)0.0687866211f, (float16_t)0.291748047f, (float16_t)0.0352172852f, + (float16_t)0.311279297f, (float16_t)-0.267333984f, (float16_t)0.0938720703f, (float16_t)0.341308594f, + (float16_t)-0.219726562f, (float16_t)-0.099609375f, (float16_t)-0.470703125f, (float16_t)0.269042969f, + (float16_t)-0.135375977f, (float16_t)0.0728149414f, (float16_t)-0.353271484f, (float16_t)-0.34375f, + (float16_t)-0.172119141f, (float16_t)0.284667969f, (float16_t)-0.465332031f, (float16_t)0.25f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/config_data.h new file mode 100644 index 00000000..e2242878 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MEDIUM_F16_OUT_CH 32 +#define FULLY_CONNECTED_MEDIUM_F16_IN_CH 1 +#define FULLY_CONNECTED_MEDIUM_F16_INPUT_W 16 +#define FULLY_CONNECTED_MEDIUM_F16_INPUT_H 16 +#define FULLY_CONNECTED_MEDIUM_F16_DST_SIZE 32 +#define FULLY_CONNECTED_MEDIUM_F16_INPUT_SIZE 256 +#define FULLY_CONNECTED_MEDIUM_F16_OUT_ACTIVATION_MIN (float16_t) - 100.0f +#define FULLY_CONNECTED_MEDIUM_F16_OUT_ACTIVATION_MAX (float16_t)100.0f +#define FULLY_CONNECTED_MEDIUM_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MEDIUM_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MEDIUM_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/input_data.h new file mode 100644 index 00000000..0f20f255 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/input_data.h @@ -0,0 +1,66 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_medium_f16_input_data[256] = { + (float16_t)1.0546875f, (float16_t)1.47363281f, (float16_t)-0.100708008f, (float16_t)0.289794922f, + (float16_t)-0.729980469f, (float16_t)-0.997558594f, (float16_t)1.12988281f, (float16_t)-0.955078125f, + (float16_t)-0.721679688f, (float16_t)1.00976562f, (float16_t)1.13378906f, (float16_t)-1.38183594f, + (float16_t)-0.290283203f, (float16_t)0.352783203f, (float16_t)1.18164062f, (float16_t)0.111450195f, + (float16_t)1.17480469f, (float16_t)-0.0668334961f, (float16_t)-0.96875f, (float16_t)-0.444335938f, + (float16_t)-0.266601562f, (float16_t)-0.602050781f, (float16_t)-1.36425781f, (float16_t)-0.0357971191f, + (float16_t)1.46289062f, (float16_t)0.141967773f, (float16_t)1.34570312f, (float16_t)-1.36816406f, + (float16_t)-0.559082031f, (float16_t)1.17285156f, (float16_t)1.09765625f, (float16_t)-1.18164062f, + (float16_t)1.33300781f, (float16_t)1.1015625f, (float16_t)-0.451660156f, (float16_t)1.44238281f, + (float16_t)1.16796875f, (float16_t)-1.21972656f, (float16_t)-1.14648438f, (float16_t)-0.365234375f, + (float16_t)0.263183594f, (float16_t)-1.21875f, (float16_t)1.31933594f, (float16_t)0.351318359f, + (float16_t)1.17773438f, (float16_t)0.478271484f, (float16_t)-0.00448608398f, (float16_t)-0.119934082f, + (float16_t)0.270751953f, (float16_t)-0.0149765015f, (float16_t)1.01953125f, (float16_t)-1.16210938f, + (float16_t)-0.0842895508f, (float16_t)-0.801757812f, (float16_t)-1.26171875f, (float16_t)1.05664062f, + (float16_t)0.688964844f, (float16_t)-1.07519531f, (float16_t)-0.333740234f, (float16_t)0.14074707f, + (float16_t)0.72265625f, (float16_t)0.672851562f, (float16_t)0.366455078f, (float16_t)0.967773438f, + (float16_t)1.49902344f, (float16_t)-0.100646973f, (float16_t)-1.24414062f, (float16_t)-0.5390625f, + (float16_t)1.22167969f, (float16_t)0.477294922f, (float16_t)0.1640625f, (float16_t)-0.0759277344f, + (float16_t)-0.982421875f, (float16_t)0.979980469f, (float16_t)-0.999511719f, (float16_t)-0.925292969f, + (float16_t)0.925292969f, (float16_t)0.194824219f, (float16_t)0.850585938f, (float16_t)-1.27441406f, + (float16_t)-0.883300781f, (float16_t)1.38964844f, (float16_t)0.649414062f, (float16_t)0.478759766f, + (float16_t)-0.560546875f, (float16_t)-0.591796875f, (float16_t)0.612304688f, (float16_t)-0.499511719f, + (float16_t)-0.196166992f, (float16_t)-0.458251953f, (float16_t)0.515136719f, (float16_t)-1.26171875f, + (float16_t)0.0198974609f, (float16_t)1.28125f, (float16_t)-0.520996094f, (float16_t)-0.418457031f, + (float16_t)-1.44140625f, (float16_t)1.12109375f, (float16_t)-1.32128906f, (float16_t)-0.995605469f, + (float16_t)0.0336303711f, (float16_t)0.700195312f, (float16_t)0.399169922f, (float16_t)-0.113647461f, + (float16_t)-1.49902344f, (float16_t)1.03027344f, (float16_t)1.20117188f, (float16_t)-0.889160156f, + (float16_t)-0.381347656f, (float16_t)0.829101562f, (float16_t)-0.490234375f, (float16_t)-0.17956543f, + (float16_t)-0.0724487305f, (float16_t)1.21777344f, (float16_t)-0.762695312f, (float16_t)0.657226562f, + (float16_t)-1.13085938f, (float16_t)0.401611328f, (float16_t)-0.258300781f, (float16_t)0.381103516f, + (float16_t)0.564453125f, (float16_t)1.26367188f, (float16_t)-1.03808594f, (float16_t)-0.562988281f, + (float16_t)-0.190429688f, (float16_t)0.424804688f, (float16_t)0.620117188f, (float16_t)0.0115814209f, + (float16_t)-0.348144531f, (float16_t)-0.853027344f, (float16_t)-0.150146484f, (float16_t)1.44042969f, + (float16_t)1.13867188f, (float16_t)0.849121094f, (float16_t)-1.45117188f, (float16_t)0.646484375f, + (float16_t)1.0078125f, (float16_t)0.575683594f, (float16_t)-0.973144531f, (float16_t)0.643066406f, + (float16_t)-0.31640625f, (float16_t)-0.961425781f, (float16_t)-0.56640625f, (float16_t)-0.306396484f, + (float16_t)-1.45410156f, (float16_t)-1.25585938f, (float16_t)-1.38476562f, (float16_t)-0.762695312f, + (float16_t)1.19140625f, (float16_t)1.34570312f, (float16_t)-0.883300781f, (float16_t)1.00390625f, + (float16_t)-0.59375f, (float16_t)0.856933594f, (float16_t)0.431640625f, (float16_t)1.3125f, + (float16_t)-1.00488281f, (float16_t)-1.29492188f, (float16_t)-0.0881347656f, (float16_t)0.132324219f, + (float16_t)1.04980469f, (float16_t)-0.687011719f, (float16_t)-1.06542969f, (float16_t)-0.171142578f, + (float16_t)1.265625f, (float16_t)0.771972656f, (float16_t)-1.49804688f, (float16_t)0.332519531f, + (float16_t)0.715820312f, (float16_t)0.789550781f, (float16_t)0.286376953f, (float16_t)0.906738281f, + (float16_t)-0.456542969f, (float16_t)0.854492188f, (float16_t)0.995117188f, (float16_t)-0.0190582275f, + (float16_t)-0.358398438f, (float16_t)0.205688477f, (float16_t)-0.22277832f, (float16_t)-1.38671875f, + (float16_t)-0.7265625f, (float16_t)-0.18762207f, (float16_t)-0.386474609f, (float16_t)0.0280914307f, + (float16_t)-1.0390625f, (float16_t)0.0220184326f, (float16_t)0.407958984f, (float16_t)-0.833984375f, + (float16_t)-0.582519531f, (float16_t)0.834960938f, (float16_t)0.364013672f, (float16_t)1.36425781f, + (float16_t)1.453125f, (float16_t)0.510742188f, (float16_t)0.984863281f, (float16_t)0.630859375f, + (float16_t)-1.40820312f, (float16_t)-0.183105469f, (float16_t)-1.16796875f, (float16_t)-0.883789062f, + (float16_t)0.387695312f, (float16_t)1.20214844f, (float16_t)0.338623047f, (float16_t)-1.43457031f, + (float16_t)0.610839844f, (float16_t)-1.40722656f, (float16_t)1.48242188f, (float16_t)-1.04394531f, + (float16_t)0.388427734f, (float16_t)1.37109375f, (float16_t)-0.923828125f, (float16_t)-1.1484375f, + (float16_t)1.125f, (float16_t)-0.375976562f, (float16_t)-0.276123047f, (float16_t)1.19042969f, + (float16_t)1.28125f, (float16_t)1.14257812f, (float16_t)-0.616210938f, (float16_t)1.17675781f, + (float16_t)0.604980469f, (float16_t)-0.20703125f, (float16_t)1.47070312f, (float16_t)-1.36523438f, + (float16_t)0.0570678711f, (float16_t)-0.396240234f, (float16_t)-1.40625f, (float16_t)0.696289062f, + (float16_t)-0.173583984f, (float16_t)-0.604492188f, (float16_t)1.17773438f, (float16_t)-0.75390625f, + (float16_t)-0.508789062f, (float16_t)-0.174682617f, (float16_t)0.501464844f, (float16_t)-1.19824219f, + (float16_t)0.901367188f, (float16_t)1.12207031f, (float16_t)-0.12512207f, (float16_t)-0.40234375f, + (float16_t)-0.694335938f, (float16_t)-0.696777344f, (float16_t)0.785644531f, (float16_t)-0.598144531f, + (float16_t)1.37890625f, (float16_t)0.163574219f, (float16_t)-0.3515625f, (float16_t)1.05175781f, + (float16_t)0.306396484f, (float16_t)-1.47167969f, (float16_t)0.845703125f, (float16_t)1.27539062f, + (float16_t)-1.05761719f, (float16_t)-1.04785156f, (float16_t)1.11523438f, (float16_t)0.92578125f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/output_ref_data.h new file mode 100644 index 00000000..0d4ff49b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_medium_f16_output_ref_data[32] = { + (float16_t)1.34863281f, (float16_t)-0.749023438f, (float16_t)9.5625f, (float16_t)-1.29980469f, + (float16_t)-6.71484375f, (float16_t)4.6796875f, (float16_t)3.08984375f, (float16_t)-14.6640625f, + (float16_t)-1.01660156f, (float16_t)3.13671875f, (float16_t)2.42773438f, (float16_t)-0.725585938f, + (float16_t)11.890625f, (float16_t)2.91992188f, (float16_t)-1.24609375f, (float16_t)-11.8984375f, + (float16_t)4.32421875f, (float16_t)0.538574219f, (float16_t)-4.578125f, (float16_t)-1.59863281f, + (float16_t)-13.3203125f, (float16_t)-2.30078125f, (float16_t)14.3359375f, (float16_t)2.05078125f, + (float16_t)-3.96484375f, (float16_t)-3.2734375f, (float16_t)-4.43359375f, (float16_t)-19.9375f, + (float16_t)-1.48046875f, (float16_t)3.68359375f, (float16_t)11.828125f, (float16_t)-6.171875f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/weights_data.h new file mode 100644 index 00000000..12345c8e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f16/weights_data.h @@ -0,0 +1,8193 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_medium_f16_weights_data[8192] = {(float16_t)0.022064209f, + (float16_t)-0.214233398f, + (float16_t)0.905273438f, + (float16_t)-0.3984375f, + (float16_t)-0.241699219f, + (float16_t)-0.602050781f, + (float16_t)0.148681641f, + (float16_t)0.202270508f, + (float16_t)-0.71875f, + (float16_t)0.476318359f, + (float16_t)-0.653320312f, + (float16_t)-0.114868164f, + (float16_t)-0.0224304199f, + (float16_t)0.626953125f, + (float16_t)0.787109375f, + (float16_t)-0.750976562f, + (float16_t)-0.519042969f, + (float16_t)0.645507812f, + (float16_t)0.00930023193f, + (float16_t)0.297363281f, + (float16_t)-0.0184020996f, + (float16_t)-0.463623047f, + (float16_t)0.791503906f, + (float16_t)0.9765625f, + (float16_t)-0.275634766f, + (float16_t)-0.802246094f, + (float16_t)-0.604003906f, + (float16_t)0.0547790527f, + (float16_t)-0.448730469f, + (float16_t)0.251953125f, + (float16_t)0.369628906f, + (float16_t)0.783691406f, + (float16_t)-0.567871094f, + (float16_t)-0.382080078f, + (float16_t)-0.599609375f, + (float16_t)-0.218017578f, + (float16_t)-0.659179688f, + (float16_t)0.591308594f, + (float16_t)0.763671875f, + (float16_t)-0.676269531f, + (float16_t)-0.0364074707f, + (float16_t)0.165161133f, + (float16_t)0.0314941406f, + (float16_t)0.895507812f, + (float16_t)-0.403564453f, + (float16_t)-0.440917969f, + (float16_t)-0.6796875f, + (float16_t)0.176513672f, + (float16_t)0.423339844f, + (float16_t)0.885253906f, + (float16_t)0.409667969f, + (float16_t)0.725585938f, + (float16_t)-0.560546875f, + (float16_t)0.857910156f, + (float16_t)0.0862426758f, + (float16_t)-0.791015625f, + (float16_t)-0.907226562f, + (float16_t)0.0908813477f, + (float16_t)-0.778808594f, + (float16_t)-0.90234375f, + (float16_t)-0.685546875f, + (float16_t)0.696777344f, + (float16_t)-0.487060547f, + (float16_t)0.157958984f, + (float16_t)0.255126953f, + (float16_t)-0.498535156f, + (float16_t)-0.937988281f, + (float16_t)-0.958984375f, + (float16_t)0.630371094f, + (float16_t)0.365478516f, + (float16_t)-0.548339844f, + (float16_t)0.0155334473f, + 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(float16_t)0.772460938f, + (float16_t)0.728515625f, + (float16_t)-0.741699219f, + (float16_t)0.0784912109f, + (float16_t)0.362304688f, + (float16_t)-0.999511719f, + (float16_t)0.517089844f, + (float16_t)-0.647949219f, + (float16_t)0.736328125f, + (float16_t)-0.412109375f, + (float16_t)0.813476562f, + (float16_t)0.182495117f, + (float16_t)-0.812011719f, + (float16_t)-0.275634766f, + (float16_t)-0.128051758f, + (float16_t)-0.0534057617f, + (float16_t)0.950195312f, + (float16_t)0.276123047f, + (float16_t)-0.811035156f, + (float16_t)0.530273438f, + (float16_t)0.762207031f, + (float16_t)0.130615234f, + (float16_t)0.523925781f, + (float16_t)0.108093262f, + (float16_t)-0.53125f, + (float16_t)-0.600585938f, + (float16_t)0.102661133f, + (float16_t)-0.0584106445f, + (float16_t)0.378662109f, + (float16_t)0.726074219f, + (float16_t)-0.479736328f, + (float16_t)-0.607421875f, + (float16_t)0.0575561523f, + (float16_t)-0.467285156f, + (float16_t)0.717773438f, + (float16_t)0.69140625f, + (float16_t)0.708007812f, + (float16_t)-0.566894531f, + (float16_t)-0.660644531f, + (float16_t)-0.108459473f, + (float16_t)-0.0545349121f, + (float16_t)0.949707031f, + (float16_t)0.343261719f, + (float16_t)0.976074219f, + (float16_t)-0.760253906f, + (float16_t)-0.859375f, + (float16_t)-0.849121094f, + (float16_t)0.856933594f, + (float16_t)0.403564453f, + (float16_t)0.999023438f, + (float16_t)-0.194335938f, + (float16_t)0.782226562f, + (float16_t)0.142089844f, + (float16_t)-0.234741211f, + (float16_t)0.170776367f, + (float16_t)-0.880371094f, + (float16_t)-0.733886719f, + (float16_t)-0.307128906f, + (float16_t)-0.561523438f, + (float16_t)-0.0293731689f, + (float16_t)-0.105041504f, + (float16_t)-0.500488281f, + (float16_t)0.079284668f, + (float16_t)0.778808594f, + (float16_t)-0.292236328f, + (float16_t)0.927246094f, + (float16_t)-0.73046875f, + (float16_t)0.402099609f, + (float16_t)0.923828125f, + (float16_t)0.834472656f, + (float16_t)0.267822266f, + (float16_t)0.690917969f, + (float16_t)0.541992188f, + (float16_t)0.0982666016f, + (float16_t)-0.763671875f, + (float16_t)-0.405761719f, + (float16_t)-0.739746094f, + (float16_t)-0.103393555f, + (float16_t)-0.408691406f, + (float16_t)-0.518066406f, + (float16_t)0.069152832f, + (float16_t)-0.657714844f, + (float16_t)0.948242188f, + (float16_t)-0.694335938f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/biases_data.h new file mode 100644 index 00000000..1a69c274 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/biases_data.h @@ -0,0 +1,7 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_medium_f32_biases_data[32] = { + -0.0259451736f, 0.45040223f, 0.323781967f, -0.379190415f, 0.0476855524f, -0.269589841f, -0.0277013388f, + -0.155062363f, -0.263597429f, 0.25069204f, -0.0561595559f, -0.153108925f, -0.153281868f, 0.355975926f, + -0.266405106f, 0.146307811f, 0.0508999415f, 0.100814037f, 0.207770333f, 0.323948234f, 0.136124671f, + 0.0916774571f, -0.102965899f, 0.254543424f, 0.0392383561f, -0.40168643f, -0.47765097f, 0.17509684f, + -0.445465177f, 0.00537755666f, -0.424988806f, 0.0871398523f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/config_data.h new file mode 100644 index 00000000..c877c91d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_MEDIUM_F32_OUT_CH 32 +#define FULLY_CONNECTED_MEDIUM_F32_IN_CH 1 +#define FULLY_CONNECTED_MEDIUM_F32_INPUT_W 16 +#define FULLY_CONNECTED_MEDIUM_F32_INPUT_H 16 +#define FULLY_CONNECTED_MEDIUM_F32_DST_SIZE 32 +#define FULLY_CONNECTED_MEDIUM_F32_INPUT_SIZE 256 +#define FULLY_CONNECTED_MEDIUM_F32_OUT_ACTIVATION_MIN -100.0f +#define FULLY_CONNECTED_MEDIUM_F32_OUT_ACTIVATION_MAX 100.0f +#define FULLY_CONNECTED_MEDIUM_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_MEDIUM_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_MEDIUM_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/input_data.h new file mode 100644 index 00000000..a7b3dcfe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/input_data.h @@ -0,0 +1,39 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_medium_f32_input_data[256] = { + -1.31319416f, 1.1594311f, 0.226137519f, 0.569754541f, -1.0348382f, 0.655975342f, 0.262139857f, + 0.149973094f, -0.286932558f, 1.47869444f, -1.13877201f, 0.36625728f, 0.881442904f, 0.706080377f, + -0.725565791f, -1.47052276f, -0.783531308f, 1.48424065f, -0.149814352f, -1.34927869f, 0.145474046f, + 1.03330553f, -0.738914728f, 0.804162621f, -0.708053708f, 0.89164257f, 0.611786187f, 1.39322495f, + 0.541086018f, 0.805660546f, 0.0372533686f, 0.239920303f, -0.21289508f, -1.19498289f, -0.939213037f, + 1.49112391f, 0.497160524f, -1.13179791f, 1.43810797f, -0.636342049f, -0.120212384f, 1.31865704f, + -0.201011255f, 0.815610707f, 0.844010055f, 1.22388887f, 0.303166956f, -0.516375482f, -1.33966351f, + -0.438102841f, 0.584615827f, -1.43321753f, -0.498333335f, 0.735739648f, 1.17692041f, 0.277731001f, + -0.379061967f, -0.60096246f, 1.07514405f, 1.42667437f, -0.790294647f, -0.03179387f, -0.864481032f, + -0.665007412f, -0.280742377f, -0.355825186f, 0.335223049f, -1.28772533f, 0.854135334f, -1.05412447f, + 0.613382995f, -0.199485615f, -1.17489874f, 0.905746818f, -0.435995758f, 0.175205722f, 0.956704557f, + 0.539545298f, -0.901046634f, 0.658643186f, -0.663078129f, 1.08315778f, -0.360496879f, -0.0998202041f, + -1.1561985f, 1.14732695f, 0.507407546f, 0.140379548f, 0.811922967f, 1.08326066f, -0.53186202f, + 1.11210632f, 1.06209838f, 0.080424495f, -0.472559184f, -0.899632096f, 0.588541269f, 0.179674968f, + -0.534482419f, 1.02449322f, 1.03117669f, -0.915589213f, -0.165724203f, -0.882192492f, -1.29142845f, + -0.879710734f, -0.761604369f, -0.364827335f, -1.0381366f, -1.01235044f, 0.279599488f, 1.43581057f, + -0.588215232f, 0.808934033f, -1.0846976f, -0.881879151f, 0.969172239f, 1.12412107f, 0.712288618f, + 0.844082057f, 1.16733909f, 0.0793480575f, -1.3889128f, 0.167555422f, 1.22781181f, -0.758880973f, + 0.867834747f, -0.894506156f, -0.371287376f, 1.36636412f, -0.901631236f, 1.09273791f, -1.10526347f, + -0.416624725f, 0.0237223487f, 0.806623399f, 0.950080335f, 1.42411184f, -0.507194936f, -0.466007322f, + 0.447679609f, 0.00863427576f, -1.40924549f, -0.0141021861f, -0.286520302f, -1.01269031f, 1.19486129f, + -1.04310584f, 0.835852385f, -0.997500658f, -0.0165502336f, -1.24669564f, 0.525394619f, -0.374882638f, + -1.08929586f, 1.13439691f, -0.684022844f, -0.705868602f, 0.834215581f, 0.785027862f, 0.814415336f, + 0.586352885f, 0.499830961f, 0.188494146f, 0.359295517f, -0.302218705f, -0.120673999f, -0.389565557f, + 0.872079849f, -0.0651970804f, 0.969489634f, -1.43517542f, 0.0016871572f, 1.44517577f, -0.0758213997f, + 0.0798867419f, -1.16477251f, 1.48271239f, -0.94522661f, -0.691075563f, -0.890127897f, 0.79012841f, + 0.412843019f, 0.479467958f, 1.25017846f, 1.45553148f, 0.949120104f, 0.949739933f, 1.18937778f, + -0.592147887f, 0.89069891f, 1.04095006f, 0.078122668f, 1.25575423f, 0.509488344f, -0.229131475f, + -1.22209907f, -1.46819615f, 0.518795073f, -1.30897403f, -0.429359853f, -0.158420131f, 1.05096018f, + 1.33288896f, 0.303184658f, -0.0670362562f, -0.384558856f, 1.28042877f, -0.992756724f, -0.758108556f, + 1.39696634f, -0.648428977f, -0.810029089f, 1.21061218f, -0.422482073f, -0.634675622f, -0.285769284f, + -1.29745567f, -0.672183156f, -0.842092454f, -0.196747616f, -1.42653954f, -0.0723002404f, -0.670527041f, + -0.117464349f, -1.22945976f, 0.34986642f, -1.2529006f, -0.163484082f, -1.30627561f, 0.827823579f, + 0.302097082f, 0.0936451852f, -1.25846469f, 0.124508992f, -1.08316934f, 1.35086775f, 1.47287321f, + -0.0011451171f, 0.119435698f, -0.976438105f, -1.47856319f, 1.18695045f, 1.07000673f, -1.11810994f, + 1.23814166f, -1.17425799f, -0.516111672f, 0.600676656f, -0.696706176f, 1.01712859f, -1.34758615f, + 0.636049628f, 1.00456572f, 1.09290814f, 0.225486472f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/output_ref_data.h new file mode 100644 index 00000000..5cd836b2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_medium_f32_output_ref_data[32] = { + 0.975145817f, -12.8451128f, -4.02449179f, -10.154151f, 15.0713701f, 10.5628986f, 2.09922481f, 4.02125692f, + 8.39992809f, -3.59506297f, -1.04826903f, -7.92972374f, -1.57304573f, -1.99998021f, -6.95991135f, 13.6815882f, + -9.27694511f, 11.1381159f, -12.7433023f, -21.609396f, 10.9267979f, -2.04292989f, 2.45480871f, 10.3315678f, + -12.8820782f, -0.826981306f, 0.85420382f, -4.0168519f, -2.80574512f, 8.67652321f, -3.2061522f, -4.52970314f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/weights_data.h new file mode 100644 index 00000000..2b03c407 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_medium_f32/weights_data.h @@ -0,0 +1,1368 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_medium_f32_weights_data[8192] = { + -0.0513185896f, 0.782830954f, 0.675493121f, 0.342312068f, -0.556506872f, 0.706623554f, + -0.311821818f, -0.466964602f, -0.414581746f, -0.81358254f, -0.197925702f, 0.675425053f, + -0.935634911f, -0.611784101f, -0.775887907f, -0.605666935f, -0.640828371f, 0.968658626f, + 0.837956429f, 0.073209241f, -0.615502536f, -0.228536233f, -0.545159638f, -0.893734753f, + 0.249702767f, -0.512761593f, 0.565882802f, -0.593787134f, -0.692548037f, 0.0743042529f, + -0.826642096f, -0.915614069f, -0.0221886076f, -0.104663596f, -0.274609387f, 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b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/biases_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_null_bias_f16_biases_data[5] = {(float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f, + (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/config_data.h new file mode 100644 index 00000000..1f6cd5a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_NULL_BIAS_F16_OUT_CH 5 +#define FULLY_CONNECTED_NULL_BIAS_F16_IN_CH 33 +#define FULLY_CONNECTED_NULL_BIAS_F16_INPUT_W 1 +#define FULLY_CONNECTED_NULL_BIAS_F16_INPUT_H 1 +#define FULLY_CONNECTED_NULL_BIAS_F16_DST_SIZE 10 +#define FULLY_CONNECTED_NULL_BIAS_F16_INPUT_SIZE 33 +#define FULLY_CONNECTED_NULL_BIAS_F16_OUT_ACTIVATION_MIN (float16_t) - 100.0f +#define FULLY_CONNECTED_NULL_BIAS_F16_OUT_ACTIVATION_MAX (float16_t)100.0f +#define FULLY_CONNECTED_NULL_BIAS_F16_INPUT_BATCHES 2 +#define FULLY_CONNECTED_NULL_BIAS_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_NULL_BIAS_F16_HAS_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/input_data.h new file mode 100644 index 00000000..ec46cd0e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/input_data.h @@ -0,0 +1,19 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_null_bias_f16_input_data[66] = { + (float16_t)0.376464844f, (float16_t)-1.90625f, (float16_t)-0.494873047f, (float16_t)1.99609375f, + (float16_t)-1.22851562f, (float16_t)1.31542969f, (float16_t)1.68261719f, (float16_t)1.20996094f, + (float16_t)0.750976562f, (float16_t)-1.61230469f, (float16_t)-0.267089844f, (float16_t)0.433349609f, + (float16_t)-1.27246094f, (float16_t)-1.90625f, (float16_t)-0.112182617f, (float16_t)-0.355712891f, + (float16_t)-0.738769531f, (float16_t)-0.393798828f, (float16_t)0.556640625f, (float16_t)0.588378906f, + (float16_t)0.00319671631f, (float16_t)1.86230469f, (float16_t)1.4765625f, (float16_t)-1.97167969f, + (float16_t)-0.848144531f, (float16_t)0.897460938f, (float16_t)-1.06054688f, (float16_t)-0.770507812f, + (float16_t)1.38964844f, (float16_t)-0.732910156f, (float16_t)-1.87792969f, (float16_t)-1.44238281f, + (float16_t)-0.5078125f, (float16_t)-1.45703125f, (float16_t)-1.99316406f, (float16_t)1.90039062f, + (float16_t)-1.35449219f, (float16_t)0.788574219f, (float16_t)-0.489990234f, (float16_t)-1.58007812f, + (float16_t)-1.27929688f, (float16_t)-1.61914062f, (float16_t)-1.81542969f, (float16_t)0.854003906f, + (float16_t)1.70019531f, (float16_t)-1.87988281f, (float16_t)1.43945312f, (float16_t)1.34667969f, + (float16_t)1.54003906f, (float16_t)-0.653320312f, (float16_t)1.72265625f, (float16_t)-1.74121094f, + (float16_t)-1.76367188f, (float16_t)1.29785156f, (float16_t)-1.6015625f, (float16_t)-0.9609375f, + (float16_t)-0.701171875f, (float16_t)-0.265625f, (float16_t)1.71484375f, (float16_t)0.119140625f, + (float16_t)-0.513671875f, (float16_t)-1.53710938f, (float16_t)-0.273925781f, (float16_t)0.462158203f, + (float16_t)1.75f, (float16_t)-0.148681641f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/output_ref_data.h new file mode 100644 index 00000000..532cfc17 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_null_bias_f16_output_ref_data[10] = {(float16_t)-1.46777344f, + (float16_t)-4.890625f, + (float16_t)-2.38867188f, + (float16_t)-2.09570312f, + (float16_t)-0.685546875f, + (float16_t)0.813964844f, + (float16_t)-2.16992188f, + (float16_t)-3.77929688f, + (float16_t)5.3359375f, + (float16_t)3.82421875f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/weights_data.h new file mode 100644 index 00000000..8060f1ec --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f16/weights_data.h @@ -0,0 +1,44 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_null_bias_f16_weights_data[165] = { + (float16_t)-0.285400391f, (float16_t)-0.130615234f, (float16_t)0.529785156f, (float16_t)-0.996582031f, + (float16_t)0.87109375f, (float16_t)0.202270508f, (float16_t)0.200195312f, (float16_t)0.76953125f, + (float16_t)-0.8828125f, (float16_t)-0.663574219f, (float16_t)-0.227539062f, (float16_t)0.352783203f, + (float16_t)0.762695312f, (float16_t)-0.505859375f, (float16_t)0.575195312f, (float16_t)-0.0582275391f, + (float16_t)0.185058594f, (float16_t)-0.346923828f, (float16_t)0.807617188f, (float16_t)0.078125f, + (float16_t)-0.451660156f, (float16_t)0.327880859f, (float16_t)-0.342529297f, (float16_t)-0.158203125f, + (float16_t)-0.823730469f, (float16_t)-0.231689453f, (float16_t)-0.135986328f, (float16_t)0.805175781f, + (float16_t)0.232543945f, (float16_t)0.268066406f, (float16_t)0.829101562f, (float16_t)0.225097656f, + (float16_t)-0.862304688f, (float16_t)-0.272705078f, (float16_t)0.896484375f, (float16_t)-0.842285156f, + (float16_t)-0.303222656f, (float16_t)-0.236328125f, (float16_t)-0.592773438f, (float16_t)-0.677246094f, + (float16_t)0.0217895508f, (float16_t)0.118286133f, (float16_t)0.983886719f, (float16_t)-0.597167969f, + (float16_t)0.254882812f, (float16_t)0.194702148f, (float16_t)-0.946289062f, (float16_t)0.856933594f, + (float16_t)0.0503845215f, (float16_t)-0.940429688f, (float16_t)0.327880859f, (float16_t)-0.785644531f, + (float16_t)-0.356445312f, (float16_t)0.694335938f, (float16_t)-0.260742188f, (float16_t)0.086730957f, + (float16_t)-0.102416992f, (float16_t)0.803222656f, (float16_t)-0.9375f, (float16_t)-0.730957031f, + (float16_t)0.846191406f, (float16_t)0.950195312f, (float16_t)-0.342529297f, (float16_t)0.0238494873f, + (float16_t)0.662597656f, (float16_t)0.868164062f, (float16_t)-0.652832031f, (float16_t)-0.434814453f, + (float16_t)-0.555664062f, (float16_t)-0.036315918f, (float16_t)-0.0977172852f, (float16_t)-0.979003906f, + (float16_t)-0.732910156f, (float16_t)0.96875f, (float16_t)-0.124511719f, (float16_t)0.0774536133f, + (float16_t)-0.0407714844f, (float16_t)-0.0665283203f, (float16_t)0.329101562f, (float16_t)0.850097656f, + (float16_t)0.0135726929f, (float16_t)-0.549804688f, (float16_t)0.418945312f, (float16_t)-0.686523438f, + (float16_t)0.929199219f, (float16_t)-0.279296875f, (float16_t)-0.908691406f, (float16_t)0.396728516f, + (float16_t)0.213745117f, (float16_t)-0.375732422f, (float16_t)0.7109375f, (float16_t)0.166503906f, + (float16_t)0.697265625f, (float16_t)0.266113281f, (float16_t)0.0132751465f, (float16_t)0.0124206543f, + (float16_t)0.430908203f, (float16_t)-0.251220703f, (float16_t)0.265869141f, (float16_t)-0.713378906f, + (float16_t)-0.319824219f, (float16_t)-0.459716797f, (float16_t)-0.824707031f, (float16_t)0.8046875f, + (float16_t)0.945800781f, (float16_t)0.773925781f, (float16_t)-0.903808594f, (float16_t)0.105224609f, + (float16_t)0.00619125366f, (float16_t)0.361816406f, (float16_t)-0.419433594f, (float16_t)-0.716796875f, + (float16_t)-0.6328125f, (float16_t)0.121643066f, (float16_t)0.0581359863f, (float16_t)-0.634277344f, + (float16_t)0.956054688f, (float16_t)0.0869750977f, (float16_t)-0.690429688f, (float16_t)0.00477981567f, + (float16_t)-0.575195312f, (float16_t)0.130737305f, (float16_t)0.0901489258f, (float16_t)0.094543457f, + (float16_t)-0.773925781f, (float16_t)0.772949219f, (float16_t)0.484130859f, (float16_t)-0.681152344f, + (float16_t)-0.864746094f, (float16_t)-0.14453125f, (float16_t)-0.165771484f, (float16_t)0.521972656f, + (float16_t)-0.0424804688f, (float16_t)0.125854492f, (float16_t)0.575683594f, (float16_t)-0.223022461f, + (float16_t)0.747558594f, (float16_t)-0.559570312f, (float16_t)-0.115783691f, (float16_t)-0.0362854004f, + (float16_t)0.464355469f, (float16_t)-0.919433594f, (float16_t)-0.262695312f, (float16_t)-0.791015625f, + (float16_t)0.0780029297f, (float16_t)-0.191650391f, (float16_t)0.95703125f, (float16_t)-0.376708984f, + (float16_t)-0.790527344f, (float16_t)-0.983886719f, (float16_t)0.329833984f, (float16_t)-0.549804688f, + (float16_t)0.147705078f, (float16_t)0.13269043f, (float16_t)-0.649414062f, (float16_t)-0.577148438f, + (float16_t)-0.274414062f, (float16_t)-0.37109375f, (float16_t)-0.379882812f, (float16_t)0.552246094f, + (float16_t)-0.802734375f, (float16_t)-0.0149078369f, (float16_t)-0.895507812f, (float16_t)0.983398438f, + (float16_t)-0.0923461914f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/biases_data.h new file mode 100644 index 00000000..1b37ff1e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/biases_data.h @@ -0,0 +1,2 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_null_bias_f32_biases_data[5] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/config_data.h new file mode 100644 index 00000000..d3bffaaf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_NULL_BIAS_F32_OUT_CH 5 +#define FULLY_CONNECTED_NULL_BIAS_F32_IN_CH 33 +#define FULLY_CONNECTED_NULL_BIAS_F32_INPUT_W 1 +#define FULLY_CONNECTED_NULL_BIAS_F32_INPUT_H 1 +#define FULLY_CONNECTED_NULL_BIAS_F32_DST_SIZE 10 +#define FULLY_CONNECTED_NULL_BIAS_F32_INPUT_SIZE 33 +#define FULLY_CONNECTED_NULL_BIAS_F32_OUT_ACTIVATION_MIN -100.0f +#define FULLY_CONNECTED_NULL_BIAS_F32_OUT_ACTIVATION_MAX 100.0f +#define FULLY_CONNECTED_NULL_BIAS_F32_INPUT_BATCHES 2 +#define FULLY_CONNECTED_NULL_BIAS_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_NULL_BIAS_F32_HAS_BIAS 0 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/input_data.h new file mode 100644 index 00000000..a4189e9f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/input_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_null_bias_f32_input_data[66] = { + -1.71936131f, -0.470790625f, 0.25557977f, -1.24807012f, 0.761352122f, -0.906047523f, -0.603944719f, + -1.06716895f, -0.500819325f, -0.516611099f, 0.695650458f, -0.516465545f, -0.688370407f, 0.479756266f, + -1.82535768f, -0.0433446579f, 0.622394204f, -0.723132968f, 1.18402886f, 1.61046028f, -0.831286967f, + 0.15074642f, 1.99357629f, 0.143922642f, 1.65472114f, 0.196746826f, -1.00242364f, 0.176264659f, + -1.3312372f, 0.208211184f, -1.95235741f, -1.75460756f, 0.397719443f, -1.87829745f, 1.48647594f, + -0.906985879f, 0.222397342f, -1.90009654f, 0.507798731f, 0.230334312f, -1.7908982f, 0.732966781f, + 1.07015276f, 0.778520226f, -1.33641422f, -1.21185732f, -1.21884787f, 1.91106009f, 0.73184675f, + -0.371192127f, 1.48873329f, 0.774017096f, -0.637849748f, -1.84705925f, -0.727693141f, 1.11923611f, + -1.8031925f, -1.3337121f, -1.0056411f, -1.18168914f, 1.01314151f, 1.62997258f, -0.341203243f, + -0.880394995f, -1.84632349f, -0.187750578f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/output_ref_data.h new file mode 100644 index 00000000..45453edf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_null_bias_f32_output_ref_data[10] = {1.21968091f, + 0.613622069f, + -3.91991735f, + 3.82579422f, + 5.64623737f, + -1.7434901f, + 3.51101136f, + 5.28429985f, + 2.36786151f, + 1.15221679f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/weights_data.h new file mode 100644 index 00000000..ad408358 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_null_bias_f32/weights_data.h @@ -0,0 +1,26 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_null_bias_f32_weights_data[165] = { + -0.821637154f, 0.554173589f, -0.190247789f, -0.640492082f, 0.0919120088f, 0.594427466f, -0.405335486f, + -0.615146816f, -0.551043808f, -0.759238005f, 0.0347884186f, 0.700146735f, 0.974237621f, -0.670096815f, + -0.731109321f, 0.760095f, 0.412066847f, 0.901791811f, 0.18210873f, 0.936259508f, 0.127818584f, + -0.645354748f, -0.55861944f, 0.451502949f, -0.934589744f, 0.148002028f, 0.764600158f, -0.345081389f, + -0.333533823f, 0.176562101f, -0.18747142f, 0.387693971f, 0.871798277f, 0.107282661f, -0.722705245f, + -0.278046489f, 0.00800652057f, -0.482687175f, -0.554436684f, 0.985086858f, -0.262681961f, 0.858318567f, + 0.181036279f, 0.00968046207f, 0.639251828f, 0.148316324f, -0.0301652495f, 0.732133031f, -0.219172686f, + 0.388374776f, 0.100862756f, 0.141891629f, 0.91104883f, -0.297789961f, -0.738680124f, 0.756587923f, + -0.321652681f, -0.145070449f, 0.389327943f, -0.498988688f, -0.754719734f, 0.540041506f, 0.192541927f, + -0.0464481637f, 0.152413696f, 0.650153518f, -0.592967391f, -0.777008832f, 0.627510071f, 0.282961637f, + 0.377684981f, 0.804846764f, -0.179771036f, -0.564168394f, -0.952719331f, 0.537221611f, -0.285115331f, + 0.41376543f, -0.55829674f, -0.127017662f, 0.207734719f, 0.575728416f, -0.962550282f, 0.749689102f, + 0.562645495f, -0.911169648f, 0.0661871433f, 0.622515738f, 0.451452255f, 0.351588786f, -0.671014249f, + -0.930208147f, -0.255070835f, 0.585807145f, 0.864455283f, 0.722826838f, 0.684917808f, 0.395592153f, + -0.425467998f, -0.463149726f, -0.610671997f, -0.56997329f, -0.992759347f, -0.268914223f, 0.648040354f, + -0.329216778f, -0.114742637f, 0.197137624f, -0.900124192f, -0.329722136f, 0.616018951f, -0.833208084f, + -0.728145838f, 0.778823793f, -0.836765587f, 0.66104275f, -0.409429848f, 0.876174271f, -0.545109034f, + 0.570608795f, -0.397448599f, 0.558363914f, 0.00942655653f, 0.905250251f, 0.37045905f, 0.397353739f, + 0.40475592f, 0.465341717f, 0.239210576f, -0.47894451f, -0.367598206f, -0.603224218f, -0.453964978f, + -0.869101644f, -0.367702454f, -0.61032486f, 0.449306309f, 0.441183001f, 0.00168233865f, -0.591072023f, + 0.609613597f, 0.595665395f, -0.579683363f, -0.140389115f, -0.303173035f, -0.613306284f, -0.823231876f, + 0.42123735f, 0.565472364f, 0.314308196f, -0.733778119f, 0.349676818f, -0.69752115f, 0.700304806f, + 0.207899809f, 0.470753819f, -0.836487114f, -0.0604469143f, 0.547396719f, -0.238904342f, -0.857231677f, + 0.357530683f, -0.745780408f, -0.655810952f, -0.1521018f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/biases_data.h new file mode 100644 index 00000000..df1629c5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/biases_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_out_activation_f16_biases_data[4] = {(float16_t)0.211791992f, + (float16_t)0.455810547f, + (float16_t)0.0715942383f, + (float16_t)0.588378906f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/config_data.h new file mode 100644 index 00000000..3306c60a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_OUT_CH 4 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_IN_CH 10 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_INPUT_W 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_INPUT_H 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_DST_SIZE 4 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_INPUT_SIZE 10 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_OUT_ACTIVATION_MIN (float16_t) - 0.700195312f +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_OUT_ACTIVATION_MAX (float16_t)1.0f +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_OUT_ACTIVATION_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/input_data.h new file mode 100644 index 00000000..3599dc8e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_out_activation_f16_input_data[10] = {(float16_t)1.76171875f, + (float16_t)-0.470214844f, + (float16_t)0.206298828f, + (float16_t)2.09765625f, + (float16_t)2.3671875f, + (float16_t)-2.7421875f, + (float16_t)-2.61132812f, + (float16_t)0.199584961f, + (float16_t)0.829589844f, + (float16_t)2.86132812f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/output_ref_data.h new file mode 100644 index 00000000..298a144b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_out_activation_f16_output_ref_data[4] = {(float16_t)-0.700195312f, + (float16_t)-0.700195312f, + (float16_t)-0.700195312f, + (float16_t)-0.700195312f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/weights_data.h new file mode 100644 index 00000000..9028d480 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f16/weights_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_out_activation_f16_weights_data[40] = { + (float16_t)-1.57714844f, (float16_t)-0.5078125f, (float16_t)0.895507812f, (float16_t)0.0718994141f, + (float16_t)-0.301513672f, (float16_t)0.0595092773f, (float16_t)-0.506347656f, (float16_t)-1.27636719f, + (float16_t)-0.992675781f, (float16_t)-1.93847656f, (float16_t)-1.68847656f, (float16_t)-1.31152344f, + (float16_t)-1.65527344f, (float16_t)-1.265625f, (float16_t)0.252929688f, (float16_t)-0.570800781f, + (float16_t)-1.68066406f, (float16_t)-1.16992188f, (float16_t)0.0457763672f, (float16_t)-1.50488281f, + (float16_t)-1.8515625f, (float16_t)0.4140625f, (float16_t)-0.9921875f, (float16_t)-1.53222656f, + (float16_t)1.20214844f, (float16_t)-1.83300781f, (float16_t)-0.759765625f, (float16_t)-1.11328125f, + (float16_t)0.0697021484f, (float16_t)-1.31640625f, (float16_t)-1.29394531f, (float16_t)0.139282227f, + (float16_t)1.26074219f, (float16_t)1.05761719f, (float16_t)1.6953125f, (float16_t)1.24316406f, + (float16_t)1.65332031f, (float16_t)-0.0662231445f, (float16_t)0.747558594f, (float16_t)-1.74804688f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/biases_data.h new file mode 100644 index 00000000..16adc9d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/biases_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_out_activation_f32_biases_data[4] = {-0.428294659f, + 0.598740995f, + -0.413622707f, + -0.693337917f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/config_data.h new file mode 100644 index 00000000..5967f434 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_OUT_CH 4 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_IN_CH 10 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_INPUT_W 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_INPUT_H 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_DST_SIZE 4 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_INPUT_SIZE 10 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_OUT_ACTIVATION_MIN -0.699999988f +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_OUT_ACTIVATION_MAX 1.0f +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_OUT_ACTIVATION_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/input_data.h new file mode 100644 index 00000000..46e58914 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/input_data.h @@ -0,0 +1,11 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_out_activation_f32_input_data[10] = {-2.56194592f, + -1.48222888f, + 2.710572f, + -0.896814466f, + 0.537100017f, + 2.06214929f, + -2.60774136f, + 0.626392245f, + -2.71927524f, + -0.706908584f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/output_ref_data.h new file mode 100644 index 00000000..d56fcfc7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/output_ref_data.h @@ -0,0 +1,5 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_out_activation_f32_output_ref_data[4] = {-0.699999988f, + -0.699999988f, + -0.699999988f, + -0.699999988f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/weights_data.h new file mode 100644 index 00000000..44e419ae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_out_activation_f32/weights_data.h @@ -0,0 +1,8 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_out_activation_f32_weights_data[40] = { + -1.3562175f, -1.59873879f, -1.60819972f, -1.5403527f, -0.012637062f, -0.891362846f, 1.83810711f, + -1.54217267f, -0.259809524f, -0.911165595f, 1.37063658f, 0.41132623f, 0.583709121f, -0.496940732f, + -0.415096402f, -1.21506941f, 1.53493345f, 1.486076f, 1.50104403f, 0.258086443f, 1.27805912f, + 1.21310055f, -0.354522765f, -0.336287737f, -0.590295315f, -1.30352962f, -1.36702871f, -0.587733328f, + 1.72242272f, -0.61341089f, -1.23218179f, 0.492253512f, -1.7588532f, -1.77914619f, -1.99272883f, + -0.582942724f, 0.0907165408f, 0.582335234f, -0.735775948f, -0.63677454f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/biases_data.h new file mode 100644 index 00000000..9ddec0a3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/biases_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_small_f16_biases_data[16] = {(float16_t)-0.405273438f, + (float16_t)-0.459228516f, + (float16_t)0.569824219f, + (float16_t)0.17590332f, + (float16_t)-0.348632812f, + (float16_t)-0.416259766f, + (float16_t)0.395507812f, + (float16_t)0.697265625f, + (float16_t)-0.58984375f, + (float16_t)-0.0624694824f, + (float16_t)0.606445312f, + (float16_t)0.541992188f, + (float16_t)0.244262695f, + (float16_t)0.012260437f, + (float16_t)0.349121094f, + (float16_t)0.629394531f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/config_data.h new file mode 100644 index 00000000..78024aa7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_SMALL_F16_OUT_CH 16 +#define FULLY_CONNECTED_SMALL_F16_IN_CH 1 +#define FULLY_CONNECTED_SMALL_F16_INPUT_W 8 +#define FULLY_CONNECTED_SMALL_F16_INPUT_H 8 +#define FULLY_CONNECTED_SMALL_F16_DST_SIZE 16 +#define FULLY_CONNECTED_SMALL_F16_INPUT_SIZE 64 +#define FULLY_CONNECTED_SMALL_F16_OUT_ACTIVATION_MIN (float16_t) - 100.0f +#define FULLY_CONNECTED_SMALL_F16_OUT_ACTIVATION_MAX (float16_t)100.0f +#define FULLY_CONNECTED_SMALL_F16_INPUT_BATCHES 1 +#define FULLY_CONNECTED_SMALL_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_SMALL_F16_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/input_data.h new file mode 100644 index 00000000..34110a29 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/input_data.h @@ -0,0 +1,18 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_small_f16_input_data[64] = { + (float16_t)-1.40625f, (float16_t)0.12512207f, (float16_t)0.473388672f, (float16_t)-0.332519531f, + (float16_t)-1.35839844f, (float16_t)1.59082031f, (float16_t)0.880859375f, (float16_t)0.932617188f, + (float16_t)-0.290771484f, (float16_t)1.68554688f, (float16_t)-0.790039062f, (float16_t)-1.53125f, + (float16_t)-0.731933594f, (float16_t)1.90722656f, (float16_t)-1.3828125f, (float16_t)-1.01660156f, + (float16_t)-1.30957031f, (float16_t)0.750976562f, (float16_t)-1.29296875f, (float16_t)-0.278076172f, + (float16_t)0.0905761719f, (float16_t)-1.55859375f, (float16_t)-1.62890625f, (float16_t)-1.09179688f, + (float16_t)-0.00682449341f, (float16_t)-0.142578125f, (float16_t)1.95410156f, (float16_t)-1.02148438f, + (float16_t)-0.0483093262f, (float16_t)1.32226562f, (float16_t)-0.712890625f, (float16_t)1.05371094f, + (float16_t)-0.982910156f, (float16_t)0.294189453f, (float16_t)-1.64746094f, (float16_t)0.389648438f, + (float16_t)1.23339844f, (float16_t)-1.52832031f, (float16_t)-1.36328125f, (float16_t)1.80761719f, + (float16_t)-1.64648438f, (float16_t)0.798339844f, (float16_t)1.22167969f, (float16_t)-1.44726562f, + (float16_t)-0.344238281f, (float16_t)1.63574219f, (float16_t)1.14648438f, (float16_t)-1.06640625f, + (float16_t)-0.859863281f, (float16_t)0.560546875f, (float16_t)-0.767578125f, (float16_t)1.12792969f, + (float16_t)-1.80664062f, (float16_t)-1.87109375f, (float16_t)-0.87109375f, (float16_t)1.88183594f, + (float16_t)-0.838867188f, (float16_t)-1.88964844f, (float16_t)0.916015625f, (float16_t)1.11621094f, + (float16_t)1.24023438f, (float16_t)1.90234375f, (float16_t)0.0223693848f, (float16_t)1.91210938f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/output_ref_data.h new file mode 100644 index 00000000..e02c9dda --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_small_f16_output_ref_data[16] = {(float16_t)-1.35058594f, + (float16_t)-0.410644531f, + (float16_t)-4.390625f, + (float16_t)5.5546875f, + (float16_t)-4.4296875f, + (float16_t)15.6484375f, + (float16_t)-4.9765625f, + (float16_t)-17.625f, + (float16_t)14.4765625f, + (float16_t)-10.34375f, + (float16_t)-2.09960938f, + (float16_t)3.41015625f, + (float16_t)-8.375f, + (float16_t)0.919433594f, + (float16_t)-4.8984375f, + (float16_t)1.30957031f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/weights_data.h new file mode 100644 index 00000000..4e6d5067 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f16/weights_data.h @@ -0,0 +1,258 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t fully_connected_small_f16_weights_data[1024] = { + (float16_t)-0.756347656f, (float16_t)0.553222656f, (float16_t)-1.45996094f, (float16_t)-0.690917969f, + (float16_t)-0.47265625f, (float16_t)1.35742188f, (float16_t)-1.07421875f, (float16_t)0.3515625f, + (float16_t)1.17480469f, (float16_t)-0.649902344f, (float16_t)-1.359375f, (float16_t)0.8984375f, + (float16_t)0.748535156f, (float16_t)0.708984375f, (float16_t)1.27734375f, (float16_t)0.526855469f, + (float16_t)-0.537597656f, (float16_t)-1.25585938f, (float16_t)-0.395751953f, (float16_t)-1.32910156f, + (float16_t)-0.399902344f, (float16_t)0.428222656f, (float16_t)-0.205322266f, (float16_t)-0.306884766f, + (float16_t)0.551757812f, (float16_t)0.335693359f, (float16_t)-1.28515625f, (float16_t)-0.0970458984f, + (float16_t)-0.0480957031f, (float16_t)-0.706542969f, (float16_t)-0.00161266327f, (float16_t)1.00292969f, + (float16_t)0.535644531f, (float16_t)1.31445312f, (float16_t)0.461181641f, (float16_t)-1.01464844f, + (float16_t)-1.34082031f, (float16_t)0.465332031f, (float16_t)-1.28125f, (float16_t)-0.00980377197f, + (float16_t)1.33886719f, (float16_t)1.31542969f, (float16_t)-0.304443359f, (float16_t)-1.27050781f, + (float16_t)0.790527344f, (float16_t)0.660644531f, (float16_t)-1.45214844f, (float16_t)-0.936035156f, + (float16_t)0.192260742f, (float16_t)0.657714844f, (float16_t)1.39941406f, (float16_t)0.544921875f, + (float16_t)-0.837890625f, (float16_t)1.29785156f, (float16_t)1.29980469f, (float16_t)1.20410156f, + (float16_t)-1.32421875f, (float16_t)0.130126953f, (float16_t)-1.37304688f, (float16_t)0.80859375f, + (float16_t)-1.25976562f, (float16_t)1.25097656f, (float16_t)1.08984375f, (float16_t)0.692382812f, + (float16_t)-0.504394531f, (float16_t)-0.607421875f, (float16_t)0.0173187256f, (float16_t)0.19140625f, + (float16_t)-1.47363281f, (float16_t)-1.11914062f, (float16_t)-0.132324219f, (float16_t)1.05566406f, + (float16_t)0.325683594f, (float16_t)-0.928710938f, (float16_t)-1.44628906f, (float16_t)-0.184692383f, + (float16_t)1.42871094f, (float16_t)0.457763672f, (float16_t)-0.268554688f, (float16_t)-1.09179688f, + (float16_t)0.811523438f, (float16_t)-0.650390625f, (float16_t)-0.274658203f, (float16_t)-0.200805664f, + (float16_t)1.46972656f, (float16_t)0.235717773f, (float16_t)1.27246094f, (float16_t)0.49609375f, + (float16_t)1.48242188f, (float16_t)-1.38964844f, (float16_t)-1.15234375f, (float16_t)-0.702636719f, + (float16_t)-0.551269531f, (float16_t)0.913085938f, (float16_t)0.809570312f, (float16_t)0.831542969f, + (float16_t)-0.165039062f, (float16_t)0.8515625f, (float16_t)0.453857422f, (float16_t)1.46484375f, + (float16_t)1.46484375f, (float16_t)-0.136474609f, (float16_t)0.930175781f, (float16_t)-1.3203125f, + (float16_t)0.60546875f, (float16_t)0.146972656f, (float16_t)-0.484619141f, (float16_t)0.270263672f, + (float16_t)0.277832031f, (float16_t)-0.874023438f, (float16_t)0.709472656f, (float16_t)1.04785156f, + (float16_t)0.58203125f, (float16_t)-0.22265625f, (float16_t)-1.38378906f, (float16_t)0.241455078f, + (float16_t)0.38671875f, (float16_t)-0.759765625f, (float16_t)-1.48730469f, (float16_t)1.2421875f, + (float16_t)-0.986328125f, (float16_t)1.43652344f, (float16_t)-0.642578125f, (float16_t)-1.24121094f, + (float16_t)0.190429688f, (float16_t)1.47460938f, (float16_t)0.367431641f, (float16_t)1.01953125f, + (float16_t)0.958007812f, (float16_t)-0.590332031f, (float16_t)-0.90234375f, (float16_t)-0.7421875f, + (float16_t)0.336914062f, (float16_t)1.17675781f, (float16_t)1.43359375f, (float16_t)0.403564453f, + (float16_t)-0.142089844f, (float16_t)0.277587891f, (float16_t)-0.794433594f, (float16_t)0.918945312f, + (float16_t)-0.101623535f, (float16_t)0.737304688f, (float16_t)1.25976562f, (float16_t)-0.591796875f, + (float16_t)0.186035156f, (float16_t)-1.39550781f, (float16_t)-0.670410156f, (float16_t)0.0472412109f, + (float16_t)0.584472656f, (float16_t)-1.41699219f, (float16_t)-0.852050781f, (float16_t)0.0911254883f, + (float16_t)0.579101562f, (float16_t)0.0313110352f, (float16_t)1.23046875f, (float16_t)1.26171875f, + (float16_t)-1.13769531f, (float16_t)-0.203857422f, (float16_t)1.375f, (float16_t)-0.495361328f, + (float16_t)1.18261719f, (float16_t)1.14355469f, (float16_t)1.42675781f, (float16_t)0.233398438f, + (float16_t)-0.336914062f, (float16_t)0.319335938f, (float16_t)0.778320312f, (float16_t)-1.29003906f, + (float16_t)0.625488281f, (float16_t)0.893554688f, (float16_t)0.368652344f, (float16_t)-0.768554688f, + (float16_t)0.137084961f, (float16_t)-0.536132812f, (float16_t)0.771484375f, (float16_t)-0.498779297f, + (float16_t)-0.444580078f, (float16_t)0.672363281f, (float16_t)0.961425781f, (float16_t)1.36230469f, + (float16_t)-0.175415039f, (float16_t)-0.192993164f, (float16_t)0.433349609f, (float16_t)-1.45214844f, + (float16_t)1.23925781f, (float16_t)1.28320312f, (float16_t)1.46972656f, (float16_t)0.609375f, + (float16_t)0.625976562f, (float16_t)-0.985839844f, (float16_t)-0.273193359f, (float16_t)0.0412597656f, + (float16_t)-0.93359375f, (float16_t)-0.341064453f, (float16_t)-0.944335938f, (float16_t)1.36523438f, + (float16_t)-0.518554688f, (float16_t)0.330566406f, (float16_t)1.0859375f, (float16_t)0.769042969f, + (float16_t)0.827148438f, (float16_t)-0.0686645508f, (float16_t)1.21972656f, (float16_t)0.555664062f, + (float16_t)-1.02734375f, (float16_t)0.975097656f, (float16_t)0.0635375977f, (float16_t)0.607910156f, + (float16_t)-0.419677734f, (float16_t)-1.25878906f, (float16_t)-1.40820312f, (float16_t)0.992675781f, + (float16_t)1.02148438f, (float16_t)-0.470947266f, (float16_t)0.472167969f, (float16_t)-0.826660156f, + (float16_t)-1.00195312f, (float16_t)1.21679688f, (float16_t)1.25390625f, (float16_t)-0.475585938f, + (float16_t)1.27929688f, (float16_t)-0.0367736816f, (float16_t)1.44726562f, (float16_t)0.317138672f, + (float16_t)-1.29199219f, (float16_t)0.875488281f, (float16_t)1.38476562f, (float16_t)0.425048828f, + (float16_t)-1.15332031f, (float16_t)-0.0119400024f, (float16_t)1.26464844f, (float16_t)-0.190795898f, + (float16_t)0.274414062f, (float16_t)-0.617675781f, (float16_t)1.07421875f, (float16_t)0.895996094f, + (float16_t)0.629882812f, (float16_t)-0.243041992f, (float16_t)-0.514160156f, (float16_t)-0.141113281f, + (float16_t)1.14648438f, (float16_t)-1.13476562f, (float16_t)0.0306243896f, (float16_t)-1.12988281f, + (float16_t)1.24804688f, (float16_t)-0.0694580078f, (float16_t)-0.805175781f, (float16_t)1.390625f, + (float16_t)-1.26074219f, (float16_t)-0.354003906f, (float16_t)-0.115905762f, (float16_t)1.06640625f, + (float16_t)0.985351562f, (float16_t)1.39648438f, (float16_t)-0.304443359f, (float16_t)-0.316162109f, + (float16_t)-0.811035156f, (float16_t)0.456787109f, (float16_t)-0.868652344f, (float16_t)0.0847167969f, + (float16_t)-0.931152344f, (float16_t)-0.546386719f, (float16_t)1.08007812f, (float16_t)0.13269043f, + (float16_t)1.39160156f, (float16_t)-1.24902344f, (float16_t)-1.05175781f, (float16_t)0.122375488f, + (float16_t)-1.04980469f, (float16_t)-1.21972656f, (float16_t)-0.80078125f, (float16_t)-0.440917969f, + (float16_t)1.38085938f, (float16_t)-1.37988281f, (float16_t)0.0661010742f, (float16_t)-0.961425781f, + (float16_t)-1.24121094f, (float16_t)0.161621094f, (float16_t)1.39160156f, (float16_t)-1.1796875f, + (float16_t)1.19824219f, (float16_t)0.149169922f, (float16_t)-1.21972656f, (float16_t)-1.27246094f, + (float16_t)-0.225952148f, (float16_t)-1.23242188f, (float16_t)-1.10351562f, (float16_t)-0.873046875f, + (float16_t)-1.38671875f, (float16_t)1.35546875f, (float16_t)-0.661132812f, (float16_t)1.37792969f, + (float16_t)1.01464844f, (float16_t)1.22558594f, (float16_t)-0.293945312f, (float16_t)-0.20690918f, + (float16_t)-1.22558594f, (float16_t)-1.24902344f, (float16_t)-0.346679688f, (float16_t)-0.641113281f, + (float16_t)-0.859375f, (float16_t)0.279785156f, (float16_t)-0.0672607422f, (float16_t)0.689453125f, + (float16_t)1.00585938f, (float16_t)0.233642578f, (float16_t)-0.420898438f, (float16_t)-1.03027344f, + (float16_t)-0.159301758f, (float16_t)-1.18261719f, (float16_t)0.456054688f, (float16_t)-0.541503906f, + (float16_t)1.18164062f, (float16_t)-1.47167969f, (float16_t)0.625488281f, (float16_t)-1.15136719f, + (float16_t)0.121032715f, (float16_t)-1.25976562f, (float16_t)-1.3515625f, (float16_t)-0.0742797852f, + (float16_t)-1.22363281f, (float16_t)-0.107666016f, (float16_t)0.712402344f, (float16_t)1.4765625f, + (float16_t)-0.611328125f, (float16_t)0.88671875f, (float16_t)1.06933594f, (float16_t)-1.11230469f, + (float16_t)1.43164062f, (float16_t)1.11230469f, (float16_t)0.457519531f, (float16_t)-1.04492188f, + (float16_t)-0.734863281f, (float16_t)0.920898438f, (float16_t)-1.28808594f, (float16_t)-0.140625f, + (float16_t)-0.829101562f, (float16_t)0.82421875f, (float16_t)-1.01367188f, (float16_t)0.425048828f, + (float16_t)0.34765625f, (float16_t)0.121582031f, (float16_t)0.935058594f, (float16_t)-0.611816406f, + (float16_t)-0.9609375f, (float16_t)-0.595703125f, (float16_t)1.15722656f, (float16_t)-0.895019531f, + (float16_t)-0.252197266f, (float16_t)0.432373047f, (float16_t)-0.33203125f, (float16_t)-1.22070312f, + (float16_t)0.733398438f, (float16_t)0.874511719f, 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(float16_t)0.375732422f, + (float16_t)-0.466064453f, (float16_t)0.410400391f, (float16_t)-1.39746094f, (float16_t)1.02734375f, + (float16_t)-1.36132812f, (float16_t)-0.487304688f, (float16_t)1.15332031f, (float16_t)0.645019531f, + (float16_t)0.60546875f, (float16_t)1.49414062f, (float16_t)1.03710938f, (float16_t)0.798828125f, + (float16_t)-0.129150391f, (float16_t)-1.39160156f, (float16_t)1.05957031f, (float16_t)0.247558594f, + (float16_t)0.341796875f, (float16_t)0.245727539f, (float16_t)-1.22363281f, (float16_t)1.15527344f, + (float16_t)0.133911133f, (float16_t)0.0401000977f, (float16_t)-0.249511719f, (float16_t)-0.985839844f, + (float16_t)-1.19628906f, (float16_t)0.417724609f, (float16_t)-0.916015625f, (float16_t)-0.0748901367f, + (float16_t)0.900390625f, (float16_t)0.639648438f, (float16_t)0.201416016f, (float16_t)-0.247070312f, + (float16_t)0.496826172f, (float16_t)1.03515625f, (float16_t)-0.860839844f, (float16_t)0.923828125f, + (float16_t)0.663574219f, (float16_t)-0.702148438f, (float16_t)0.709960938f, (float16_t)0.262207031f, + (float16_t)0.747558594f, (float16_t)0.886230469f, (float16_t)-0.521484375f, (float16_t)1.33886719f, + (float16_t)1.37890625f, (float16_t)-0.83203125f, (float16_t)-0.708007812f, (float16_t)0.876464844f, + (float16_t)-1.27246094f, (float16_t)1.06640625f, (float16_t)-0.422119141f, (float16_t)0.430664062f, + (float16_t)0.368408203f, (float16_t)-1.23730469f, (float16_t)-0.495849609f, (float16_t)0.556640625f, + (float16_t)0.880859375f, (float16_t)-0.472412109f, (float16_t)1.40234375f, (float16_t)1.47167969f, + (float16_t)0.718261719f, (float16_t)-0.634765625f, (float16_t)-0.63671875f, (float16_t)1.43359375f, + (float16_t)-0.776367188f, (float16_t)0.9609375f, (float16_t)0.58203125f, (float16_t)-0.507324219f, + (float16_t)0.516113281f, (float16_t)-0.308105469f, (float16_t)-1.38574219f, (float16_t)0.222290039f, + (float16_t)0.628417969f, (float16_t)1.05175781f, (float16_t)0.691894531f, (float16_t)0.0232849121f, + (float16_t)0.190917969f, (float16_t)0.616210938f, (float16_t)1.04882812f, (float16_t)-0.301025391f, + (float16_t)-0.387451172f, (float16_t)-1.40332031f, (float16_t)1.37402344f, (float16_t)-0.563964844f, + (float16_t)-0.973632812f, (float16_t)0.0762939453f, (float16_t)1.19140625f, (float16_t)-0.187988281f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/biases_data.h new file mode 100644 index 00000000..2f9fbee2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/biases_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_small_f32_biases_data[16] = {0.190492436f, + 0.0562157705f, + 0.402305812f, + -0.604363561f, + -0.503155649f, + 0.497153133f, + -0.236409917f, + -0.00646916265f, + 0.72396785f, + -0.297881544f, + -0.289365053f, + 0.655416369f, + -0.533433795f, + 0.393152744f, + 0.57486558f, + -0.226577893f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/config_data.h new file mode 100644 index 00000000..dcf526aa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/config_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#define FULLY_CONNECTED_SMALL_F32_OUT_CH 16 +#define FULLY_CONNECTED_SMALL_F32_IN_CH 1 +#define FULLY_CONNECTED_SMALL_F32_INPUT_W 8 +#define FULLY_CONNECTED_SMALL_F32_INPUT_H 8 +#define FULLY_CONNECTED_SMALL_F32_DST_SIZE 16 +#define FULLY_CONNECTED_SMALL_F32_INPUT_SIZE 64 +#define FULLY_CONNECTED_SMALL_F32_OUT_ACTIVATION_MIN -100.0f +#define FULLY_CONNECTED_SMALL_F32_OUT_ACTIVATION_MAX 100.0f +#define FULLY_CONNECTED_SMALL_F32_INPUT_BATCHES 1 +#define FULLY_CONNECTED_SMALL_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define FULLY_CONNECTED_SMALL_F32_HAS_BIAS 1 diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/input_data.h new file mode 100644 index 00000000..5e1d3f21 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/input_data.h @@ -0,0 +1,12 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_small_f32_input_data[64] = { + 1.67080605f, 1.31972742f, 1.17619574f, 0.878457904f, -1.73458362f, 0.731974185f, -0.128614023f, + -0.7168293f, -0.563592076f, -0.490510523f, -0.298322886f, -1.97338009f, 1.80437553f, 1.34246254f, + 0.878959537f, 1.85988963f, 1.92003918f, -0.000606339832f, -1.08608675f, 1.24772537f, -0.595834553f, + -0.488481134f, -0.0742153153f, 0.0801425651f, -1.33013439f, 1.62113929f, -1.23518276f, 1.81534994f, + -1.85293722f, 1.00865185f, -0.244608521f, -0.316287577f, 0.92168957f, -1.06928658f, -0.879282176f, + -1.21585846f, 1.71425498f, 0.207679108f, 1.7224009f, 1.35465288f, -1.74371076f, 1.59840751f, + -0.797658861f, 1.07507312f, 0.751907051f, 1.87805355f, -0.117002182f, -1.56848729f, -1.48607337f, + -0.619690359f, -1.34831321f, -0.930931151f, 0.381826937f, -0.566554487f, -1.84864604f, -1.62131727f, + -0.592525005f, -1.39146483f, 1.05704677f, 0.793807864f, -1.00447583f, -0.908020914f, -0.0937809125f, + -0.345740676f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/output_ref_data.h new file mode 100644 index 00000000..24cdd794 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_small_f32_output_ref_data[16] = {0.68022424f, + -8.78307629f, + 0.107000321f, + -6.89283466f, + -8.10632324f, + 6.35882282f, + -8.3527689f, + 12.9071026f, + -1.06352401f, + 4.63494158f, + 0.554975271f, + 6.75591278f, + -3.30435467f, + 2.45750856f, + 9.66902733f, + -15.3676052f}; diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/test_data.h new file mode 100644 index 00000000..3908fc86 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/weights_data.h new file mode 100644 index 00000000..c020d05a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/fully_connected_small_f32/weights_data.h @@ -0,0 +1,149 @@ +// Generated by fully_connected_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t fully_connected_small_f32_weights_data[1024] = { + 0.969343483f, 0.787882924f, 0.0933964998f, -0.813156486f, -1.11706638f, 0.153166741f, -1.29576409f, + 0.690574706f, 0.570161283f, -1.1600436f, 0.731717527f, 0.368146896f, -1.11154175f, -1.45291698f, + -0.478804082f, -0.488059491f, 0.453209877f, -0.761879861f, 1.17037189f, -1.11974061f, -1.17807937f, + 1.34928441f, -0.319231927f, 0.759591937f, -1.47306371f, 1.27870452f, 1.46447515f, 0.342033565f, + -0.0302898586f, 0.769047678f, -0.435805857f, 0.56237781f, -0.431598008f, 0.907495379f, -1.30701292f, + 1.23918962f, -1.12598586f, 0.688078701f, 0.36653167f, -0.309194893f, 1.03134513f, 0.7648983f, + -0.498409957f, -0.72574687f, -0.381571323f, 0.132387206f, 0.521074474f, 0.0261756647f, -0.511560738f, + -0.783134282f, 0.536099732f, 0.429393917f, -0.229903668f, -0.490435988f, -0.734646797f, -1.39054096f, + -0.15370743f, 0.590310872f, 0.658975959f, -1.0101558f, -1.38497245f, -1.20466173f, 1.49893212f, + 0.794564903f, 0.117435895f, -0.665292025f, -1.36645222f, -0.243158221f, -0.660228014f, 0.802951574f, + 0.645012796f, 0.535322189f, 0.205115482f, -0.022891283f, 1.42314982f, 1.10050297f, -1.08190501f, + 0.917150974f, 0.00350362994f, 0.414403766f, -1.48998654f, 1.4355123f, 0.232334942f, -1.01476538f, + -0.101921588f, -0.750308871f, 0.185549781f, 0.912301779f, -1.01806748f, -1.11392796f, -0.857619286f, + -0.796062708f, 1.2459259f, 0.800909996f, 1.38044703f, -0.809866786f, -0.059175767f, 0.290246189f, + 0.543522954f, -1.3359524f, -1.1462965f, -0.483036458f, 0.405485094f, 0.0549333543f, -0.919945121f, + -0.883941174f, 0.980230033f, -0.412362278f, -0.978422523f, 1.05529833f, 0.195651293f, 0.50623244f, + -0.325561583f, -0.870915115f, 0.70084691f, 1.26561093f, 0.782791674f, 1.17552209f, -0.48768127f, + 0.512484431f, -0.589580715f, -1.20954669f, -0.0162954181f, 0.880362809f, -1.43636012f, 0.559239805f, + 0.56223011f, -0.791688263f, -0.0474170409f, 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a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_bias_data.h new file mode 100644 index 00000000..e46e9717 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_cell_bias[24] = { + (float16_t)-0.101196289f, (float16_t)-0.0629272461f, (float16_t)0.165771484f, (float16_t)-0.245849609f, + (float16_t)-0.308349609f, (float16_t)-0.10369873f, (float16_t)-0.186767578f, (float16_t)-0.289306641f, + (float16_t)0.13671875f, (float16_t)0.124450684f, (float16_t)-0.108764648f, (float16_t)0.0615844727f, + (float16_t)-0.159545898f, (float16_t)0.0325012207f, (float16_t)-0.0323181152f, (float16_t)0.0881958008f, + (float16_t)0.157104492f, (float16_t)-0.00375556946f, (float16_t)-0.115234375f, (float16_t)-0.286865234f, + (float16_t)-0.0873413086f, (float16_t)-0.126586914f, (float16_t)0.237426758f, (float16_t)0.0204315186f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..a646af7a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_hidden_weights_data.h @@ -0,0 +1,146 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_cell_hidden_weights[576] = { + (float16_t)0.0563049316f, (float16_t)0.206176758f, (float16_t)-0.120483398f, (float16_t)-0.0290374756f, + (float16_t)0.100402832f, (float16_t)0.0765380859f, (float16_t)0.0332946777f, (float16_t)0.236206055f, + (float16_t)-0.165405273f, (float16_t)-0.141235352f, (float16_t)-0.23815918f, (float16_t)0.0909423828f, + (float16_t)0.349121094f, (float16_t)-0.122680664f, (float16_t)0.341308594f, (float16_t)-0.266357422f, + (float16_t)-0.284912109f, (float16_t)-0.112182617f, (float16_t)0.232299805f, (float16_t)-0.120910645f, + (float16_t)-0.0428771973f, (float16_t)-0.140136719f, (float16_t)0.0556945801f, (float16_t)-0.0834960938f, + (float16_t)-0.142456055f, (float16_t)0.087890625f, (float16_t)0.248413086f, (float16_t)0.319335938f, + (float16_t)0.0285339355f, (float16_t)0.19543457f, (float16_t)-0.333496094f, (float16_t)0.264160156f, + (float16_t)-0.241577148f, (float16_t)0.184082031f, (float16_t)-0.242553711f, (float16_t)0.286621094f, + (float16_t)0.0980224609f, (float16_t)0.283447266f, (float16_t)-0.000153899193f, (float16_t)0.258056641f, + (float16_t)-0.0299987793f, (float16_t)-0.0639038086f, (float16_t)0.327392578f, (float16_t)-0.336914062f, + (float16_t)0.217285156f, (float16_t)-0.0130386353f, (float16_t)-0.0018081665f, (float16_t)-0.336181641f, + (float16_t)0.0385742188f, (float16_t)-0.244873047f, (float16_t)0.083984375f, (float16_t)-0.285400391f, + (float16_t)-0.123596191f, (float16_t)-0.21496582f, (float16_t)0.197631836f, (float16_t)-0.102722168f, + 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(float16_t)-0.0605163574f, (float16_t)-0.21484375f, + (float16_t)0.193725586f, (float16_t)-0.345214844f, (float16_t)0.338623047f, (float16_t)0.230712891f, + (float16_t)-0.12286377f, (float16_t)0.0988769531f, (float16_t)0.10144043f, (float16_t)0.340332031f, + (float16_t)-0.172485352f, (float16_t)-0.00153827667f, (float16_t)0.107910156f, (float16_t)0.197998047f, + (float16_t)0.209594727f, (float16_t)0.145507812f, (float16_t)-0.23815918f, (float16_t)0.329833984f, + (float16_t)0.128662109f, (float16_t)-0.10949707f, (float16_t)0.181030273f, (float16_t)-0.040802002f, + (float16_t)0.218383789f, (float16_t)-0.330322266f, (float16_t)0.0526428223f, (float16_t)-0.249023438f, + (float16_t)-0.238037109f, (float16_t)0.0169372559f, (float16_t)-0.228393555f, (float16_t)-0.0892944336f, + (float16_t)-0.251220703f, (float16_t)-0.294433594f, (float16_t)0.249389648f, (float16_t)-0.335205078f, + (float16_t)0.255126953f, (float16_t)0.271484375f, (float16_t)0.320800781f, (float16_t)0.266845703f, + (float16_t)0.194946289f, (float16_t)0.0718383789f, (float16_t)-0.312011719f, (float16_t)-0.277832031f, + (float16_t)-0.274169922f, (float16_t)-0.128662109f, (float16_t)-0.114074707f, (float16_t)0.296386719f, + (float16_t)0.0850219727f, (float16_t)0.0540771484f, (float16_t)-0.291748047f, (float16_t)-0.122009277f, + (float16_t)0.0574951172f, (float16_t)0.0332336426f, (float16_t)0.0286865234f, (float16_t)-0.0707397461f, + (float16_t)-0.197509766f, (float16_t)0.255126953f, (float16_t)0.0806884766f, (float16_t)0.111206055f, + (float16_t)0.154052734f, (float16_t)-0.250976562f, (float16_t)-0.0770263672f, (float16_t)-0.335205078f, + (float16_t)0.0132369995f, (float16_t)-0.133422852f, (float16_t)0.134155273f, (float16_t)0.196777344f, + (float16_t)0.0889892578f, (float16_t)-0.143066406f, (float16_t)-0.213500977f, (float16_t)0.286132812f, + (float16_t)0.138793945f, (float16_t)0.130371094f, (float16_t)0.0581359863f, (float16_t)0.26171875f, + (float16_t)0.321044922f, (float16_t)-0.349853516f, (float16_t)0.00608062744f, (float16_t)-0.182617188f, + (float16_t)0.307128906f, (float16_t)0.177978516f, (float16_t)0.0710449219f, (float16_t)-0.167724609f, + (float16_t)-0.349853516f, (float16_t)0.0493469238f, (float16_t)0.205688477f, (float16_t)-0.298828125f, + (float16_t)0.328613281f, (float16_t)-0.0654296875f, (float16_t)-0.149658203f, (float16_t)0.195678711f, + (float16_t)0.314453125f, (float16_t)-0.149291992f, (float16_t)0.194580078f, (float16_t)-0.115783691f, + (float16_t)0.190307617f, (float16_t)-0.14855957f, (float16_t)-0.106567383f, (float16_t)-0.329345703f, + (float16_t)0.19909668f, (float16_t)0.141113281f, (float16_t)0.171386719f, (float16_t)-0.0237731934f, + (float16_t)0.0272674561f, (float16_t)-0.329101562f, (float16_t)0.276367188f, (float16_t)0.145019531f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_input_weights_data.h new file mode 100644 index 00000000..e5f55613 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/cell_input_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_cell_input_weights[384] = { + (float16_t)0.143920898f, (float16_t)0.240478516f, (float16_t)-0.22253418f, (float16_t)0.273193359f, + (float16_t)-0.327148438f, (float16_t)-0.162231445f, (float16_t)-0.189697266f, (float16_t)-0.118652344f, + (float16_t)0.0328979492f, (float16_t)0.192016602f, (float16_t)-0.322021484f, (float16_t)0.130859375f, + (float16_t)-0.305419922f, (float16_t)0.338134766f, (float16_t)-0.251464844f, (float16_t)0.160400391f, + (float16_t)-0.165649414f, (float16_t)-0.0547790527f, (float16_t)-0.0748291016f, (float16_t)0.338378906f, + (float16_t)0.147827148f, (float16_t)-0.0270690918f, (float16_t)0.0957641602f, (float16_t)0.0502624512f, + (float16_t)0.229370117f, (float16_t)-0.204956055f, (float16_t)0.229980469f, (float16_t)0.161499023f, + (float16_t)-0.134277344f, (float16_t)0.252685547f, (float16_t)0.165283203f, (float16_t)-0.331787109f, + (float16_t)0.198974609f, (float16_t)-0.0354919434f, (float16_t)0.162475586f, (float16_t)0.209106445f, + (float16_t)0.179321289f, (float16_t)-0.263427734f, (float16_t)-0.317138672f, (float16_t)0.332763672f, + (float16_t)-0.000140547752f, (float16_t)0.0843505859f, (float16_t)0.128417969f, (float16_t)-0.286132812f, + (float16_t)-0.0845947266f, (float16_t)0.261962891f, (float16_t)-0.247070312f, (float16_t)-0.301757812f, + (float16_t)0.285644531f, (float16_t)-0.0198059082f, (float16_t)-0.141113281f, (float16_t)-0.0738525391f, + (float16_t)0.192626953f, (float16_t)0.295654297f, (float16_t)0.212524414f, (float16_t)-0.253173828f, + (float16_t)-0.210327148f, (float16_t)-0.291748047f, (float16_t)-0.266357422f, (float16_t)0.299072266f, + (float16_t)0.271240234f, (float16_t)-0.137695312f, (float16_t)0.296386719f, (float16_t)-0.0259399414f, + (float16_t)-0.0708618164f, (float16_t)-0.0676879883f, (float16_t)-0.287353516f, (float16_t)0.11895752f, + (float16_t)0.188964844f, (float16_t)-0.0122528076f, (float16_t)0.321044922f, (float16_t)0.111572266f, + (float16_t)0.220581055f, (float16_t)-0.201416016f, (float16_t)0.0888671875f, (float16_t)-0.266845703f, + (float16_t)0.298583984f, (float16_t)0.349609375f, (float16_t)-0.23034668f, (float16_t)-0.291503906f, + (float16_t)-0.328613281f, (float16_t)0.177612305f, (float16_t)0.131225586f, (float16_t)0.119567871f, + (float16_t)-0.224243164f, (float16_t)-0.251464844f, (float16_t)0.107727051f, (float16_t)-0.325439453f, + (float16_t)-0.135498047f, (float16_t)-0.228027344f, (float16_t)0.245483398f, (float16_t)-0.309082031f, + (float16_t)-0.140380859f, (float16_t)-0.348876953f, (float16_t)-0.327636719f, (float16_t)-0.0710449219f, + (float16_t)0.0429992676f, (float16_t)0.301269531f, (float16_t)0.244750977f, (float16_t)0.00111865997f, + (float16_t)0.113464355f, (float16_t)-0.345703125f, (float16_t)-0.170532227f, (float16_t)-0.120178223f, + (float16_t)-0.0349121094f, (float16_t)0.0649414062f, (float16_t)-0.255615234f, (float16_t)-0.0124359131f, + (float16_t)0.0546875f, (float16_t)0.0934448242f, (float16_t)0.302734375f, (float16_t)-0.295898438f, + (float16_t)-0.0206451416f, (float16_t)-0.0104598999f, (float16_t)0.274902344f, (float16_t)-0.120422363f, + (float16_t)0.0745239258f, (float16_t)-0.18347168f, (float16_t)0.213623047f, (float16_t)-0.344482422f, + (float16_t)-0.247314453f, (float16_t)0.278808594f, (float16_t)-0.0526733398f, (float16_t)0.0272827148f, + (float16_t)0.220825195f, (float16_t)-0.0346069336f, (float16_t)-0.345458984f, (float16_t)-0.329101562f, + (float16_t)-0.297851562f, (float16_t)0.134643555f, (float16_t)-0.119628906f, (float16_t)-0.264160156f, + (float16_t)-0.129394531f, (float16_t)0.327636719f, (float16_t)0.130615234f, (float16_t)-0.268554688f, + (float16_t)0.29296875f, (float16_t)-0.124755859f, (float16_t)-0.060546875f, (float16_t)-0.298095703f, + (float16_t)0.190551758f, (float16_t)-0.0938110352f, (float16_t)0.310058594f, (float16_t)0.177001953f, + (float16_t)0.17980957f, (float16_t)-0.288085938f, (float16_t)0.248046875f, (float16_t)0.194580078f, + (float16_t)0.0180053711f, (float16_t)-0.0550231934f, (float16_t)-0.172363281f, (float16_t)-0.150146484f, + (float16_t)0.245361328f, (float16_t)0.272949219f, (float16_t)-0.308349609f, (float16_t)0.232910156f, + (float16_t)0.185424805f, (float16_t)0.251464844f, (float16_t)0.0210723877f, (float16_t)-0.305664062f, + (float16_t)-0.192260742f, (float16_t)-0.169433594f, (float16_t)0.177246094f, (float16_t)-0.317871094f, + (float16_t)-0.166625977f, (float16_t)0.273681641f, (float16_t)-0.121765137f, (float16_t)0.068359375f, + (float16_t)-0.042388916f, (float16_t)0.309082031f, (float16_t)-0.0508728027f, (float16_t)0.00914764404f, + (float16_t)-0.186767578f, (float16_t)-0.314208984f, (float16_t)0.195678711f, (float16_t)0.0179290771f, + (float16_t)0.235107422f, (float16_t)0.307617188f, (float16_t)-0.260253906f, (float16_t)0.022277832f, + (float16_t)-0.341552734f, (float16_t)-0.143066406f, (float16_t)0.224121094f, (float16_t)-0.110290527f, + (float16_t)0.117248535f, (float16_t)0.0501098633f, (float16_t)-0.202270508f, (float16_t)0.299072266f, + (float16_t)-0.209472656f, (float16_t)0.12097168f, (float16_t)0.221313477f, (float16_t)0.00623703003f, + (float16_t)0.0686035156f, (float16_t)-0.150756836f, (float16_t)0.236694336f, (float16_t)0.000356197357f, + (float16_t)-0.0582885742f, (float16_t)0.0156555176f, (float16_t)-0.169921875f, (float16_t)0.308105469f, + (float16_t)-0.149658203f, (float16_t)-0.241210938f, (float16_t)-0.322998047f, (float16_t)0.136230469f, + (float16_t)-0.345214844f, (float16_t)-0.159179688f, (float16_t)-0.137451172f, (float16_t)-0.142944336f, + (float16_t)-0.329589844f, (float16_t)0.108642578f, (float16_t)-0.0111846924f, (float16_t)-0.250244141f, + (float16_t)-0.190307617f, (float16_t)0.309082031f, (float16_t)-0.169433594f, (float16_t)-0.270751953f, + (float16_t)-0.244873047f, (float16_t)0.00315666199f, (float16_t)0.142578125f, (float16_t)-0.173217773f, + (float16_t)0.121765137f, (float16_t)0.113098145f, (float16_t)0.311523438f, (float16_t)0.248535156f, + (float16_t)-0.209838867f, (float16_t)-0.195800781f, (float16_t)-0.058013916f, (float16_t)-0.0289001465f, + (float16_t)-0.149902344f, (float16_t)-0.23425293f, (float16_t)0.0358886719f, (float16_t)0.183959961f, + (float16_t)-0.216918945f, (float16_t)0.0565490723f, (float16_t)-0.125732422f, (float16_t)0.065612793f, + (float16_t)-0.0695800781f, (float16_t)0.0244598389f, (float16_t)-0.237915039f, (float16_t)-0.131469727f, + (float16_t)-0.130493164f, (float16_t)0.134643555f, (float16_t)0.247558594f, (float16_t)-0.0497436523f, + (float16_t)-0.202880859f, (float16_t)0.138671875f, (float16_t)0.329833984f, (float16_t)-0.254882812f, + (float16_t)-0.216674805f, (float16_t)0.267333984f, (float16_t)0.224365234f, (float16_t)-0.0751953125f, + (float16_t)-0.0864257812f, (float16_t)-0.223510742f, (float16_t)0.00457000732f, (float16_t)-0.130371094f, + (float16_t)-0.0613098145f, (float16_t)-0.343994141f, (float16_t)0.272949219f, (float16_t)-0.192260742f, + (float16_t)0.101867676f, (float16_t)-0.281494141f, (float16_t)-0.302734375f, (float16_t)0.00854492188f, + (float16_t)0.333007812f, (float16_t)-0.102355957f, (float16_t)0.0317382812f, (float16_t)-0.176757812f, + (float16_t)-0.0224304199f, (float16_t)-0.325195312f, (float16_t)0.0138320923f, (float16_t)-0.341796875f, + (float16_t)0.349609375f, (float16_t)0.0330810547f, (float16_t)-0.155273438f, (float16_t)0.297119141f, + (float16_t)-0.00307273865f, (float16_t)-0.0742797852f, (float16_t)-0.317382812f, (float16_t)-0.247070312f, + (float16_t)-0.234985352f, (float16_t)0.213256836f, (float16_t)-0.224121094f, (float16_t)-0.16418457f, + (float16_t)-0.293212891f, (float16_t)-0.142333984f, (float16_t)0.161865234f, (float16_t)0.152099609f, + (float16_t)-0.082824707f, (float16_t)-0.157958984f, (float16_t)0.0869140625f, (float16_t)-0.286621094f, + (float16_t)0.275634766f, (float16_t)-0.038269043f, (float16_t)-0.162231445f, (float16_t)0.323242188f, + (float16_t)0.311279297f, (float16_t)-0.284667969f, (float16_t)0.0498962402f, (float16_t)-0.10723877f, + (float16_t)0.30859375f, (float16_t)-0.127929688f, (float16_t)-0.344482422f, (float16_t)0.322265625f, + (float16_t)0.214233398f, (float16_t)0.038482666f, (float16_t)-0.0900268555f, (float16_t)-0.0548400879f, + (float16_t)-0.152832031f, (float16_t)-0.0335388184f, (float16_t)-0.342529297f, (float16_t)-0.111816406f, + (float16_t)0.21496582f, (float16_t)-0.0354919434f, (float16_t)0.0444946289f, (float16_t)0.0811767578f, + (float16_t)0.168579102f, (float16_t)-0.00621795654f, (float16_t)0.279785156f, (float16_t)-0.12097168f, + (float16_t)-0.346679688f, (float16_t)-0.137329102f, (float16_t)-0.154785156f, (float16_t)-0.0062713623f, + (float16_t)-0.262451172f, (float16_t)-0.0578918457f, (float16_t)-0.334472656f, (float16_t)-0.324707031f, + (float16_t)0.00936889648f, (float16_t)0.306640625f, (float16_t)-0.303955078f, (float16_t)-0.101257324f, + (float16_t)0.152954102f, (float16_t)0.0393676758f, (float16_t)-0.298095703f, (float16_t)0.256591797f, + (float16_t)0.0803833008f, (float16_t)-0.320068359f, (float16_t)-0.0117721558f, (float16_t)0.128295898f, + (float16_t)-0.149169922f, (float16_t)-0.00936889648f, (float16_t)-0.0761108398f, (float16_t)-0.312011719f, + (float16_t)0.303466797f, (float16_t)-0.246582031f, (float16_t)-0.159667969f, (float16_t)-0.181762695f, + (float16_t)-0.115600586f, (float16_t)-0.273193359f, (float16_t)-0.270507812f, (float16_t)0.130859375f, + (float16_t)-0.264648438f, (float16_t)0.243408203f, (float16_t)0.0324401855f, (float16_t)-0.00196838379f, + (float16_t)-0.0151901245f, (float16_t)0.288330078f, (float16_t)0.15637207f, (float16_t)-0.0916137695f, + (float16_t)-0.126831055f, (float16_t)-0.136474609f, (float16_t)0.287841797f, (float16_t)-0.286865234f, + (float16_t)-0.25f, (float16_t)-0.109680176f, (float16_t)-0.143310547f, (float16_t)0.254882812f, + (float16_t)-0.134765625f, (float16_t)0.113830566f, (float16_t)0.143798828f, (float16_t)0.13269043f, + (float16_t)0.234375f, (float16_t)0.189697266f, (float16_t)0.187866211f, (float16_t)-0.173095703f, + (float16_t)-0.125976562f, (float16_t)-0.270263672f, (float16_t)0.033782959f, (float16_t)-0.239868164f, + (float16_t)0.185424805f, (float16_t)0.0267944336f, (float16_t)0.296142578f, (float16_t)0.176635742f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/config_data.h new file mode 100644 index 00000000..2b244268 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_LARGE_F16_TIME_MAJOR 1 +#define LSTM_LARGE_F16_TIME_STEPS 12 +#define LSTM_LARGE_F16_BATCH_SIZE 4 +#define LSTM_LARGE_F16_INPUT_SIZE 16 +#define LSTM_LARGE_F16_HIDDEN_SIZE 24 +#define LSTM_LARGE_F16_CELL_CLIP 0.0f +#define LSTM_LARGE_F16_CELL_STATE_SIZE 96 +#define LSTM_LARGE_F16_DST_SIZE 1152 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_bias_data.h new file mode 100644 index 00000000..7dc7079d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_forget_bias[24] = { + (float16_t)-0.399902344f, (float16_t)0.369873047f, (float16_t)0.306640625f, (float16_t)0.275390625f, + (float16_t)0.127563477f, (float16_t)-0.082824707f, (float16_t)-0.185791016f, (float16_t)-0.217407227f, + (float16_t)0.20300293f, (float16_t)-0.176879883f, (float16_t)0.0105438232f, (float16_t)0.0916748047f, + (float16_t)0.375976562f, (float16_t)-0.129272461f, (float16_t)-0.143188477f, (float16_t)-0.2890625f, + (float16_t)0.0292358398f, (float16_t)0.0731811523f, (float16_t)-0.430175781f, (float16_t)0.0185546875f, + (float16_t)-0.0469970703f, (float16_t)0.158203125f, (float16_t)0.0910644531f, (float16_t)0.118774414f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..77031e46 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_hidden_weights_data.h @@ -0,0 +1,146 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_forget_hidden_weights[576] = { + (float16_t)0.131835938f, (float16_t)-0.182983398f, (float16_t)0.203979492f, (float16_t)0.302490234f, + (float16_t)0.318603516f, (float16_t)-0.107055664f, (float16_t)0.210693359f, (float16_t)0.291015625f, + (float16_t)0.322021484f, (float16_t)0.166748047f, (float16_t)0.041595459f, (float16_t)0.0966186523f, + (float16_t)0.100708008f, (float16_t)0.332519531f, (float16_t)-0.189697266f, (float16_t)-0.157592773f, + (float16_t)-0.322509766f, (float16_t)0.129394531f, (float16_t)0.0988769531f, (float16_t)0.0212402344f, + (float16_t)-0.294433594f, (float16_t)0.324707031f, (float16_t)0.147583008f, (float16_t)0.0267791748f, + (float16_t)0.250976562f, (float16_t)0.348388672f, (float16_t)0.248901367f, (float16_t)-0.0113830566f, + (float16_t)-0.251464844f, (float16_t)-0.340576172f, (float16_t)0.334472656f, (float16_t)-0.0919799805f, + (float16_t)0.150512695f, (float16_t)-0.0420532227f, (float16_t)-0.121276855f, (float16_t)-0.162719727f, + (float16_t)-0.211303711f, (float16_t)-0.338134766f, (float16_t)-0.155151367f, (float16_t)0.0759887695f, + (float16_t)0.103637695f, (float16_t)-0.185058594f, (float16_t)-0.230957031f, (float16_t)-0.256103516f, + (float16_t)0.309082031f, (float16_t)0.0429992676f, (float16_t)0.330322266f, (float16_t)-0.0199279785f, + (float16_t)0.0980834961f, (float16_t)0.293457031f, (float16_t)0.218994141f, (float16_t)0.238525391f, + (float16_t)-0.301269531f, (float16_t)0.325195312f, (float16_t)-0.275634766f, (float16_t)-0.161743164f, + (float16_t)-0.199829102f, (float16_t)-0.0106811523f, (float16_t)-0.00406265259f, (float16_t)-0.157104492f, + (float16_t)0.264404297f, (float16_t)0.0942382812f, (float16_t)-0.139770508f, (float16_t)-0.0111999512f, + (float16_t)-0.29296875f, (float16_t)-0.273681641f, (float16_t)0.137817383f, (float16_t)-0.0566101074f, + (float16_t)0.322998047f, (float16_t)0.220336914f, (float16_t)0.280029297f, (float16_t)0.037109375f, + (float16_t)0.236816406f, (float16_t)0.339355469f, (float16_t)0.198608398f, (float16_t)0.00490570068f, + (float16_t)0.248046875f, (float16_t)-0.0668945312f, (float16_t)-0.0267944336f, (float16_t)0.00506973267f, + (float16_t)0.00390052795f, (float16_t)0.252929688f, (float16_t)-0.205688477f, (float16_t)-0.0767211914f, + (float16_t)0.344726562f, (float16_t)-0.14440918f, (float16_t)0.343017578f, (float16_t)-0.0694580078f, + (float16_t)0.344482422f, (float16_t)0.161254883f, (float16_t)-0.302001953f, (float16_t)-0.104858398f, + (float16_t)-0.213623047f, (float16_t)0.343017578f, (float16_t)-0.0309143066f, (float16_t)-0.018951416f, + (float16_t)0.213256836f, (float16_t)-0.0659790039f, (float16_t)-0.144165039f, (float16_t)0.219848633f, + (float16_t)0.292724609f, (float16_t)0.0100097656f, (float16_t)0.174926758f, (float16_t)-0.026763916f, + (float16_t)0.33203125f, (float16_t)-0.0132827759f, (float16_t)0.0852661133f, (float16_t)-0.311523438f, + (float16_t)-0.197875977f, (float16_t)0.179931641f, (float16_t)0.000329732895f, (float16_t)0.10760498f, + (float16_t)-0.281005859f, (float16_t)0.293701172f, (float16_t)0.183959961f, (float16_t)0.216430664f, + (float16_t)-0.0458374023f, (float16_t)-0.249389648f, (float16_t)0.248291016f, (float16_t)-0.299072266f, + (float16_t)0.302490234f, (float16_t)-0.00482940674f, (float16_t)-0.283203125f, (float16_t)-0.160522461f, + (float16_t)0.201293945f, (float16_t)-0.301513672f, (float16_t)0.060760498f, (float16_t)0.196533203f, + (float16_t)0.299804688f, (float16_t)-0.301025391f, (float16_t)0.0215301514f, (float16_t)0.00802612305f, + (float16_t)0.235717773f, (float16_t)-0.305908203f, (float16_t)0.0401306152f, (float16_t)0.347900391f, + (float16_t)-0.216064453f, (float16_t)0.155639648f, (float16_t)-0.0551757812f, (float16_t)0.250976562f, + (float16_t)-0.281494141f, (float16_t)-0.0737304688f, (float16_t)0.209228516f, (float16_t)0.263427734f, + (float16_t)0.288085938f, (float16_t)-0.0110092163f, (float16_t)-0.271484375f, (float16_t)0.0143890381f, + (float16_t)0.234619141f, (float16_t)0.0615539551f, (float16_t)-0.18359375f, (float16_t)0.104370117f, + (float16_t)-0.00130844116f, (float16_t)-0.301025391f, (float16_t)0.0442504883f, (float16_t)-0.0982055664f, + (float16_t)0.119384766f, (float16_t)0.159667969f, (float16_t)0.329345703f, (float16_t)0.2421875f, + (float16_t)-0.307373047f, (float16_t)-0.0423278809f, (float16_t)-0.239135742f, (float16_t)-0.0768432617f, + (float16_t)0.131835938f, (float16_t)-0.291992188f, (float16_t)0.299560547f, (float16_t)-0.252685547f, + (float16_t)-0.264160156f, (float16_t)-0.081237793f, (float16_t)0.123291016f, (float16_t)0.126342773f, + (float16_t)-0.162841797f, (float16_t)0.0565490723f, (float16_t)-0.303222656f, (float16_t)-0.0533447266f, + (float16_t)-0.347900391f, (float16_t)-0.300048828f, (float16_t)0.217651367f, (float16_t)0.349121094f, + (float16_t)-0.178955078f, (float16_t)0.236816406f, (float16_t)0.319335938f, (float16_t)0.209960938f, + (float16_t)0.150878906f, (float16_t)-0.0634765625f, (float16_t)0.141723633f, (float16_t)0.0853881836f, + (float16_t)0.155639648f, (float16_t)-0.291748047f, (float16_t)0.0616149902f, (float16_t)-0.328369141f, + (float16_t)-0.225219727f, (float16_t)-0.262451172f, (float16_t)-0.20324707f, (float16_t)0.205322266f, + (float16_t)0.288085938f, (float16_t)0.329589844f, (float16_t)-0.211669922f, (float16_t)-0.191772461f, + (float16_t)0.318847656f, (float16_t)0.0294494629f, (float16_t)0.226928711f, (float16_t)0.262451172f, + (float16_t)0.249389648f, (float16_t)-0.309814453f, (float16_t)0.226806641f, (float16_t)0.0539855957f, + (float16_t)0.168945312f, (float16_t)-0.0629272461f, (float16_t)-0.018081665f, (float16_t)-0.137451172f, + (float16_t)0.105712891f, (float16_t)-0.170654297f, (float16_t)-0.293212891f, (float16_t)0.246704102f, + (float16_t)0.207275391f, (float16_t)-0.301513672f, (float16_t)0.208129883f, (float16_t)0.162841797f, + (float16_t)-0.118713379f, (float16_t)0.338378906f, (float16_t)-0.349121094f, (float16_t)0.271240234f, + (float16_t)-0.0902099609f, (float16_t)0.163208008f, (float16_t)0.159667969f, (float16_t)0.211914062f, + (float16_t)0.147460938f, (float16_t)0.160888672f, (float16_t)0.168701172f, (float16_t)-0.204833984f, + (float16_t)-0.323974609f, (float16_t)-0.197021484f, (float16_t)-0.0958251953f, (float16_t)0.338623047f, + (float16_t)0.0129241943f, (float16_t)-0.0954589844f, (float16_t)0.272949219f, (float16_t)0.344238281f, + (float16_t)0.179443359f, (float16_t)0.0184173584f, (float16_t)0.343017578f, (float16_t)-0.0862426758f, + (float16_t)-0.319824219f, (float16_t)0.00978851318f, (float16_t)-0.23425293f, (float16_t)-0.205322266f, + (float16_t)-0.0535583496f, (float16_t)0.182128906f, (float16_t)0.128295898f, (float16_t)0.202026367f, + (float16_t)-0.3203125f, (float16_t)0.0339660645f, (float16_t)-0.292236328f, (float16_t)0.0497131348f, + (float16_t)0.00482559204f, (float16_t)-0.0173339844f, (float16_t)-0.340820312f, (float16_t)-0.182861328f, + (float16_t)-0.0178527832f, (float16_t)-0.0995483398f, (float16_t)-0.0841674805f, (float16_t)-0.269042969f, + (float16_t)0.198242188f, (float16_t)0.266601562f, (float16_t)-0.0468444824f, (float16_t)-0.0102539062f, + (float16_t)0.255371094f, (float16_t)-0.247314453f, (float16_t)0.293212891f, (float16_t)0.00221443176f, + (float16_t)0.100402832f, (float16_t)0.178222656f, (float16_t)-0.342041016f, (float16_t)-0.121459961f, + (float16_t)-0.325683594f, (float16_t)0.250244141f, (float16_t)0.117614746f, (float16_t)-0.332275391f, + (float16_t)-0.010017395f, (float16_t)-0.0128631592f, (float16_t)-0.0940551758f, (float16_t)-0.284667969f, + (float16_t)-0.324951172f, (float16_t)-0.180053711f, (float16_t)-0.303466797f, (float16_t)-0.268798828f, + (float16_t)-0.101379395f, (float16_t)0.241333008f, (float16_t)0.285888672f, (float16_t)0.272705078f, + (float16_t)-0.144042969f, (float16_t)-0.292480469f, (float16_t)-0.017364502f, (float16_t)0.264892578f, + (float16_t)0.101501465f, (float16_t)0.136230469f, (float16_t)-0.252197266f, (float16_t)-0.312011719f, + (float16_t)-0.053314209f, (float16_t)0.261474609f, (float16_t)0.253417969f, (float16_t)0.327636719f, + (float16_t)-0.123352051f, (float16_t)0.182739258f, (float16_t)0.0700073242f, (float16_t)0.164672852f, + (float16_t)0.0145721436f, (float16_t)-0.0791015625f, (float16_t)0.204101562f, (float16_t)-0.12109375f, + (float16_t)-0.300048828f, (float16_t)0.260986328f, (float16_t)0.183349609f, (float16_t)0.292724609f, + (float16_t)-0.145141602f, (float16_t)0.296142578f, 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(float16_t)-0.258056641f, (float16_t)-0.214111328f, (float16_t)-0.0948486328f, (float16_t)0.197143555f, + (float16_t)0.0673217773f, (float16_t)-0.226074219f, (float16_t)-0.0613098145f, (float16_t)0.181518555f, + (float16_t)0.00408554077f, (float16_t)0.118103027f, (float16_t)-0.146362305f, (float16_t)-0.207641602f, + (float16_t)0.0608520508f, (float16_t)0.0745849609f, (float16_t)0.14465332f, (float16_t)-0.104125977f, + (float16_t)-0.241699219f, (float16_t)0.261230469f, (float16_t)-0.161743164f, (float16_t)-0.0694580078f, + (float16_t)0.244628906f, (float16_t)0.244628906f, (float16_t)0.232421875f, (float16_t)0.241699219f, + (float16_t)0.00392913818f, (float16_t)0.17565918f, (float16_t)-0.180175781f, (float16_t)-0.340820312f, + (float16_t)-0.13659668f, (float16_t)0.0748291016f, (float16_t)-0.311767578f, (float16_t)-0.349853516f, + (float16_t)0.0974731445f, (float16_t)0.255126953f, (float16_t)-0.188110352f, (float16_t)-0.341064453f, + (float16_t)0.312011719f, (float16_t)0.0963134766f, (float16_t)-0.338134766f, (float16_t)0.307861328f, + (float16_t)-0.321044922f, (float16_t)0.237792969f, (float16_t)-0.0241088867f, (float16_t)0.289550781f, + (float16_t)0.0219573975f, (float16_t)-0.012008667f, (float16_t)-0.0765991211f, (float16_t)0.337890625f, + (float16_t)-0.0101470947f, (float16_t)0.102844238f, (float16_t)0.123779297f, (float16_t)0.056854248f, + (float16_t)-0.0164337158f, (float16_t)0.228881836f, (float16_t)-0.0325622559f, (float16_t)0.289550781f, + (float16_t)-0.237548828f, (float16_t)0.343994141f, (float16_t)0.133178711f, (float16_t)0.159790039f, + (float16_t)-0.298583984f, (float16_t)-0.249023438f, (float16_t)-0.321777344f, (float16_t)0.195556641f, + (float16_t)-0.248168945f, (float16_t)0.0444946289f, (float16_t)0.0372619629f, (float16_t)0.0794677734f, + (float16_t)-0.100463867f, (float16_t)0.141845703f, (float16_t)-0.224487305f, (float16_t)0.168457031f, + (float16_t)0.278320312f, (float16_t)0.0904541016f, (float16_t)0.170043945f, (float16_t)0.314697266f, + (float16_t)0.268310547f, (float16_t)0.0982666016f, (float16_t)0.342529297f, (float16_t)-0.301025391f, + (float16_t)0.202880859f, (float16_t)0.117919922f, (float16_t)-0.323974609f, (float16_t)-0.0559692383f, + (float16_t)-0.231079102f, (float16_t)0.248291016f, (float16_t)-0.334716797f, (float16_t)-0.0294036865f, + (float16_t)0.0885620117f, (float16_t)-0.266113281f, (float16_t)0.0431213379f, (float16_t)-0.20703125f, + (float16_t)0.16015625f, (float16_t)0.104431152f, (float16_t)-0.124450684f, (float16_t)-0.120544434f, + (float16_t)-0.222045898f, (float16_t)-0.335205078f, (float16_t)-0.0628662109f, (float16_t)0.118530273f, + (float16_t)0.233520508f, (float16_t)0.0696411133f, (float16_t)-0.0454101562f, (float16_t)0.0150146484f, + (float16_t)-0.0495300293f, (float16_t)-0.11151123f, (float16_t)0.304443359f, (float16_t)-0.20715332f, + (float16_t)-0.312011719f, (float16_t)0.0872192383f, (float16_t)-0.344238281f, (float16_t)-0.111816406f, + (float16_t)0.000587463379f, (float16_t)-0.194091797f, (float16_t)0.333740234f, (float16_t)0.215698242f, + (float16_t)-0.00170993805f, (float16_t)-0.0878295898f, (float16_t)-0.113220215f, (float16_t)-0.250244141f, + (float16_t)0.132202148f, (float16_t)0.0899658203f, (float16_t)0.290527344f, (float16_t)-0.137451172f, + (float16_t)-0.0811767578f, (float16_t)-0.230712891f, (float16_t)0.314453125f, (float16_t)-0.333251953f, + (float16_t)0.253417969f, (float16_t)0.30859375f, (float16_t)0.310546875f, (float16_t)-0.25f, + (float16_t)0.248657227f, (float16_t)0.253662109f, (float16_t)-0.339111328f, (float16_t)0.0960693359f, + (float16_t)0.195068359f, (float16_t)-0.278076172f, (float16_t)0.0606689453f, (float16_t)-0.278564453f, + (float16_t)0.199951172f, (float16_t)-0.339599609f, (float16_t)-0.208251953f, (float16_t)-0.289550781f, + (float16_t)0.215698242f, (float16_t)0.236816406f, (float16_t)0.270507812f, (float16_t)0.254394531f, + (float16_t)-0.339111328f, (float16_t)0.133911133f, (float16_t)0.185058594f, (float16_t)-0.301513672f, + (float16_t)0.266357422f, (float16_t)-0.220092773f, (float16_t)-0.138549805f, (float16_t)0.292236328f, + (float16_t)-0.0349731445f, (float16_t)0.185913086f, (float16_t)0.0243988037f, (float16_t)-0.105163574f, + (float16_t)-0.222167969f, (float16_t)-0.0507507324f, (float16_t)0.041229248f, (float16_t)-0.0989990234f, + (float16_t)-0.106811523f, (float16_t)-0.254638672f, (float16_t)-0.2265625f, (float16_t)0.0535888672f, + (float16_t)-0.323974609f, (float16_t)0.10357666f, (float16_t)-0.0727539062f, (float16_t)0.153930664f, + (float16_t)0.115722656f, (float16_t)0.0354003906f, (float16_t)-0.256591797f, (float16_t)0.0860595703f, + (float16_t)0.182128906f, (float16_t)-0.0815429688f, (float16_t)-0.0796508789f, (float16_t)0.0542907715f, + (float16_t)-0.314208984f, (float16_t)-0.0595397949f, (float16_t)-0.261474609f, (float16_t)-0.0706176758f, + (float16_t)-0.297607422f, (float16_t)0.297363281f, (float16_t)0.232543945f, (float16_t)-0.225952148f, + (float16_t)-0.168457031f, (float16_t)0.00956726074f, (float16_t)-0.144775391f, (float16_t)0.126953125f, + (float16_t)-0.00452423096f, (float16_t)-0.240112305f, (float16_t)-0.189941406f, (float16_t)0.049041748f, + (float16_t)-0.305908203f, (float16_t)0.212524414f, (float16_t)0.343261719f, (float16_t)0.0950927734f, + (float16_t)0.280517578f, (float16_t)0.0146560669f, (float16_t)0.0559997559f, (float16_t)-0.346191406f, + (float16_t)-0.0722045898f, (float16_t)-0.335205078f, (float16_t)0.212524414f, (float16_t)0.307861328f, + (float16_t)0.126220703f, (float16_t)-0.150512695f, (float16_t)-0.076965332f, (float16_t)0.336425781f, + (float16_t)-0.0553283691f, (float16_t)0.344482422f, (float16_t)0.261474609f, (float16_t)-0.196166992f, + (float16_t)0.267333984f, (float16_t)-0.221801758f, (float16_t)-0.0321655273f, (float16_t)0.349365234f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_input_weights_data.h new file mode 100644 index 00000000..35d1019d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/forget_input_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_forget_input_weights[384] = { + (float16_t)0.342285156f, (float16_t)-0.247924805f, (float16_t)-0.100158691f, (float16_t)-0.168701172f, + (float16_t)-0.304931641f, (float16_t)-0.284423828f, (float16_t)-0.0100479126f, (float16_t)-0.209106445f, + (float16_t)-0.0040435791f, (float16_t)0.191040039f, (float16_t)0.073059082f, (float16_t)-0.0203552246f, + (float16_t)0.301513672f, (float16_t)-0.101074219f, (float16_t)0.13671875f, (float16_t)-0.116943359f, + (float16_t)0.0736083984f, (float16_t)0.287353516f, (float16_t)-0.293457031f, (float16_t)-0.3046875f, + (float16_t)-0.0594787598f, (float16_t)0.272460938f, (float16_t)0.112609863f, (float16_t)-0.0547790527f, + (float16_t)-0.0515441895f, (float16_t)-0.285400391f, (float16_t)-0.0969238281f, (float16_t)-0.0145797729f, + (float16_t)-0.0274810791f, (float16_t)0.338378906f, (float16_t)0.255126953f, (float16_t)0.0765380859f, + (float16_t)-0.206787109f, (float16_t)-0.0966186523f, (float16_t)-0.000958919525f, (float16_t)0.057800293f, + (float16_t)-0.158081055f, (float16_t)-0.326171875f, (float16_t)0.297363281f, (float16_t)-0.122741699f, + (float16_t)0.264648438f, (float16_t)-0.0697631836f, (float16_t)0.149047852f, (float16_t)-0.241943359f, + (float16_t)-0.223266602f, (float16_t)-0.0871582031f, (float16_t)-0.263916016f, (float16_t)-0.00792694092f, + (float16_t)0.161499023f, (float16_t)-0.162231445f, (float16_t)-0.263916016f, (float16_t)-0.219726562f, + (float16_t)-0.268310547f, (float16_t)-0.330078125f, (float16_t)-0.217041016f, (float16_t)0.330078125f, + (float16_t)-0.126220703f, (float16_t)-0.280761719f, (float16_t)-0.265869141f, (float16_t)0.155639648f, + (float16_t)-0.136474609f, (float16_t)0.162841797f, (float16_t)0.00291824341f, (float16_t)0.337646484f, + (float16_t)-0.260009766f, (float16_t)-0.238037109f, (float16_t)-0.138183594f, (float16_t)0.140869141f, + (float16_t)0.0055847168f, (float16_t)0.211303711f, (float16_t)-0.252929688f, (float16_t)-0.205566406f, + (float16_t)-0.125976562f, (float16_t)-0.226318359f, (float16_t)0.104187012f, (float16_t)0.257568359f, + (float16_t)0.219116211f, (float16_t)-0.0811157227f, (float16_t)-0.0267333984f, (float16_t)-0.136962891f, + (float16_t)-0.0629272461f, (float16_t)0.0368652344f, (float16_t)-0.164428711f, (float16_t)0.334960938f, + (float16_t)-0.0304412842f, (float16_t)-0.0191345215f, (float16_t)-0.0072555542f, (float16_t)-0.0201721191f, + (float16_t)0.245727539f, (float16_t)0.305908203f, (float16_t)-0.0339355469f, (float16_t)-0.282470703f, + (float16_t)0.195068359f, (float16_t)-0.234130859f, (float16_t)0.184936523f, (float16_t)0.0823974609f, + (float16_t)-0.130859375f, (float16_t)0.0637817383f, (float16_t)0.3359375f, (float16_t)0.194702148f, + (float16_t)-0.0733642578f, (float16_t)0.319091797f, (float16_t)-0.23046875f, (float16_t)0.103088379f, + (float16_t)0.102539062f, (float16_t)0.279785156f, (float16_t)0.080871582f, (float16_t)-0.340087891f, + (float16_t)0.259521484f, (float16_t)-0.210693359f, (float16_t)-0.279785156f, (float16_t)0.285400391f, + (float16_t)-0.337646484f, (float16_t)-0.303466797f, (float16_t)-0.333251953f, (float16_t)-0.342285156f, + (float16_t)0.337402344f, (float16_t)-0.117004395f, (float16_t)0.172851562f, (float16_t)-0.133300781f, + (float16_t)0.116027832f, (float16_t)-0.344482422f, (float16_t)0.297119141f, (float16_t)-0.265380859f, + (float16_t)0.0892333984f, (float16_t)-0.102661133f, (float16_t)0.309082031f, (float16_t)0.0853271484f, + (float16_t)0.317382812f, (float16_t)0.327636719f, (float16_t)-0.196044922f, (float16_t)0.0993652344f, + (float16_t)-0.250488281f, (float16_t)-0.320556641f, (float16_t)0.199951172f, (float16_t)0.0052986145f, + (float16_t)0.211181641f, (float16_t)-0.294189453f, (float16_t)-0.257568359f, (float16_t)-0.283691406f, + (float16_t)-0.309082031f, (float16_t)-0.234619141f, (float16_t)-0.331787109f, (float16_t)0.048614502f, + (float16_t)-0.122131348f, (float16_t)-0.041015625f, (float16_t)-0.180297852f, (float16_t)0.223022461f, + (float16_t)0.043762207f, (float16_t)0.192504883f, (float16_t)-0.00925445557f, (float16_t)0.204833984f, + (float16_t)-0.0580749512f, (float16_t)0.161987305f, (float16_t)-0.121582031f, (float16_t)0.176391602f, + (float16_t)0.00476837158f, (float16_t)-0.216430664f, (float16_t)0.251953125f, (float16_t)-0.0269927979f, + (float16_t)0.0545654297f, (float16_t)-0.208007812f, (float16_t)-0.125854492f, (float16_t)-0.239257812f, + (float16_t)0.256347656f, (float16_t)-0.307128906f, (float16_t)0.333007812f, (float16_t)0.0770874023f, + (float16_t)0.187988281f, (float16_t)0.0321655273f, (float16_t)-0.200439453f, (float16_t)-0.125488281f, + (float16_t)0.179199219f, (float16_t)-0.264404297f, (float16_t)-0.246948242f, (float16_t)0.104431152f, + (float16_t)0.234130859f, (float16_t)-0.0475463867f, (float16_t)-0.253662109f, (float16_t)-0.043548584f, + (float16_t)0.309814453f, (float16_t)0.0729370117f, (float16_t)0.310791016f, (float16_t)0.0961303711f, + (float16_t)-0.110656738f, (float16_t)0.248291016f, (float16_t)-0.0912475586f, (float16_t)-0.2578125f, + (float16_t)0.176513672f, (float16_t)-0.000973701477f, (float16_t)-0.031829834f, (float16_t)0.096862793f, + (float16_t)0.277832031f, (float16_t)-0.120483398f, (float16_t)0.102539062f, (float16_t)0.253173828f, + (float16_t)0.329833984f, (float16_t)0.212158203f, (float16_t)-0.254150391f, (float16_t)0.146118164f, + (float16_t)0.0822753906f, (float16_t)-0.280761719f, (float16_t)-0.309814453f, (float16_t)-0.333007812f, + (float16_t)-0.344970703f, (float16_t)-0.0759277344f, (float16_t)0.10357666f, (float16_t)-0.136108398f, + (float16_t)0.263671875f, (float16_t)-0.0342407227f, (float16_t)-0.214233398f, (float16_t)-0.0182342529f, + (float16_t)-0.173706055f, (float16_t)-0.229492188f, (float16_t)-0.0939331055f, (float16_t)0.252197266f, + (float16_t)-0.00632858276f, (float16_t)0.283691406f, (float16_t)0.0577697754f, (float16_t)-0.16015625f, + (float16_t)0.241333008f, (float16_t)0.250732422f, (float16_t)0.124267578f, (float16_t)0.283691406f, + (float16_t)-0.212402344f, (float16_t)-0.173339844f, (float16_t)0.250488281f, (float16_t)-0.181518555f, + (float16_t)0.236083984f, (float16_t)0.164428711f, (float16_t)-0.0166168213f, (float16_t)0.346435547f, + (float16_t)0.303466797f, (float16_t)0.341796875f, (float16_t)-0.0681762695f, (float16_t)0.242797852f, + (float16_t)0.0740966797f, (float16_t)0.186767578f, (float16_t)0.185180664f, (float16_t)0.343017578f, + (float16_t)0.0963134766f, (float16_t)0.222900391f, (float16_t)-0.1953125f, (float16_t)0.190551758f, + (float16_t)0.0958251953f, (float16_t)0.17578125f, (float16_t)-0.244628906f, (float16_t)-0.233520508f, + (float16_t)0.150024414f, (float16_t)0.0339660645f, (float16_t)0.339111328f, (float16_t)0.267333984f, + (float16_t)0.0742797852f, (float16_t)-0.00531387329f, (float16_t)-0.196899414f, (float16_t)0.0259246826f, + (float16_t)-0.324462891f, (float16_t)0.280517578f, (float16_t)-0.334472656f, (float16_t)0.0954589844f, + (float16_t)0.0319519043f, (float16_t)0.161376953f, (float16_t)-0.279541016f, (float16_t)-0.0975952148f, + (float16_t)-0.0108032227f, (float16_t)0.287597656f, (float16_t)-0.0847167969f, (float16_t)0.0807495117f, + (float16_t)0.0135955811f, (float16_t)-0.286865234f, (float16_t)-0.281738281f, (float16_t)0.122619629f, + (float16_t)0.217041016f, (float16_t)0.286376953f, (float16_t)-0.235595703f, (float16_t)0.260742188f, + (float16_t)-0.162719727f, (float16_t)-0.24621582f, (float16_t)0.23034668f, (float16_t)-0.140258789f, + (float16_t)-0.325195312f, (float16_t)0.052947998f, (float16_t)0.0673217773f, (float16_t)-0.277832031f, + (float16_t)0.159790039f, (float16_t)-0.0977172852f, (float16_t)-0.307617188f, (float16_t)-0.221801758f, + (float16_t)0.0794067383f, (float16_t)-0.307373047f, (float16_t)0.323974609f, (float16_t)-0.146118164f, + (float16_t)0.0803833008f, (float16_t)0.313476562f, (float16_t)-0.0617980957f, (float16_t)-0.193481445f, + (float16_t)-0.133911133f, (float16_t)-0.346923828f, (float16_t)-0.249023438f, (float16_t)-0.121459961f, + (float16_t)0.289550781f, (float16_t)-0.105163574f, (float16_t)-0.109863281f, (float16_t)-0.0481872559f, + (float16_t)-0.210571289f, (float16_t)0.0659179688f, (float16_t)0.0464782715f, (float16_t)-0.0275268555f, + (float16_t)-0.258056641f, (float16_t)0.101501465f, (float16_t)0.0660400391f, (float16_t)0.129272461f, + (float16_t)0.161499023f, (float16_t)0.161132812f, (float16_t)-0.277099609f, (float16_t)0.188598633f, + (float16_t)-0.145385742f, (float16_t)-0.224853516f, (float16_t)0.0160369873f, (float16_t)-0.188964844f, + (float16_t)-0.0686645508f, (float16_t)0.149658203f, (float16_t)0.164550781f, (float16_t)0.337158203f, + (float16_t)-0.205078125f, (float16_t)0.283447266f, (float16_t)0.302001953f, (float16_t)-0.141357422f, + (float16_t)-0.176269531f, (float16_t)0.329833984f, (float16_t)0.0891113281f, (float16_t)-0.22644043f, + (float16_t)-0.291748047f, (float16_t)0.116638184f, (float16_t)0.260986328f, (float16_t)0.247558594f, + (float16_t)-0.180419922f, (float16_t)-0.0839233398f, (float16_t)0.0874023438f, (float16_t)-0.190673828f, + (float16_t)-0.213623047f, (float16_t)-0.0511169434f, (float16_t)-0.201660156f, (float16_t)0.0827026367f, + (float16_t)0.0464172363f, (float16_t)0.271240234f, (float16_t)-0.255615234f, (float16_t)-0.257568359f, + (float16_t)0.259277344f, (float16_t)0.271972656f, (float16_t)0.329833984f, (float16_t)-0.2890625f, + (float16_t)-0.258544922f, (float16_t)-0.127807617f, (float16_t)-0.234741211f, (float16_t)0.282958984f, + (float16_t)0.076171875f, (float16_t)0.275390625f, (float16_t)-0.260742188f, (float16_t)-0.024520874f, + (float16_t)0.155761719f, (float16_t)0.307373047f, (float16_t)-0.105651855f, (float16_t)-0.129516602f, + (float16_t)-0.15246582f, (float16_t)0.0357971191f, (float16_t)-0.1171875f, (float16_t)-0.0513000488f, + (float16_t)-0.291992188f, (float16_t)0.236083984f, (float16_t)-0.0132904053f, (float16_t)-0.171875f, + (float16_t)-0.0883178711f, (float16_t)-0.0753173828f, (float16_t)0.34765625f, (float16_t)-0.111938477f, + (float16_t)-0.190307617f, (float16_t)-0.203979492f, (float16_t)0.171020508f, (float16_t)0.0814819336f, + (float16_t)-0.260009766f, (float16_t)0.145141602f, (float16_t)0.310302734f, (float16_t)-0.0969238281f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_bias_data.h new file mode 100644 index 00000000..fce2e2c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_input_bias[24] = { + (float16_t)-0.491699219f, (float16_t)0.0233764648f, (float16_t)-0.142211914f, (float16_t)0.195678711f, + (float16_t)-0.00366210938f, (float16_t)-0.0117797852f, (float16_t)-0.013671875f, (float16_t)-0.19909668f, + (float16_t)0.2109375f, (float16_t)-0.313476562f, (float16_t)-0.164428711f, (float16_t)0.193115234f, + (float16_t)0.0548095703f, (float16_t)-0.193237305f, (float16_t)-0.215454102f, (float16_t)0.0483703613f, + (float16_t)0.334472656f, (float16_t)-0.36328125f, (float16_t)-0.138671875f, (float16_t)-0.044128418f, + (float16_t)0.414306641f, (float16_t)0.0562133789f, (float16_t)0.0679321289f, (float16_t)0.194091797f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_data.h new file mode 100644 index 00000000..957b8192 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_data.h @@ -0,0 +1,194 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_input[768] = { + (float16_t)-0.432861328f, (float16_t)-0.182861328f, (float16_t)0.949707031f, (float16_t)-0.23046875f, + (float16_t)-0.666992188f, (float16_t)0.436279297f, (float16_t)0.836914062f, (float16_t)0.0390625f, + (float16_t)0.603027344f, (float16_t)-0.402832031f, (float16_t)-0.287109375f, (float16_t)-0.963867188f, + (float16_t)0.519042969f, (float16_t)0.0744628906f, (float16_t)0.905273438f, (float16_t)-0.00340843201f, + (float16_t)0.524414062f, (float16_t)0.494873047f, (float16_t)-0.814453125f, (float16_t)-0.835449219f, + (float16_t)0.371582031f, (float16_t)0.466796875f, (float16_t)-0.626953125f, (float16_t)0.305664062f, + (float16_t)0.219604492f, (float16_t)0.955078125f, (float16_t)0.850585938f, (float16_t)0.307861328f, + (float16_t)0.213867188f, (float16_t)0.134521484f, (float16_t)0.805175781f, (float16_t)-0.496826172f, + (float16_t)-0.5546875f, (float16_t)0.845214844f, (float16_t)-0.175170898f, (float16_t)0.688964844f, + (float16_t)-0.204101562f, (float16_t)-0.91015625f, (float16_t)0.125244141f, (float16_t)-0.166625977f, + (float16_t)-0.918945312f, (float16_t)-0.958984375f, (float16_t)-0.681152344f, (float16_t)0.393798828f, + (float16_t)-0.318115234f, (float16_t)-0.0765991211f, (float16_t)-0.966796875f, (float16_t)0.834960938f, + (float16_t)-0.356933594f, (float16_t)0.00608825684f, (float16_t)0.784179688f, (float16_t)0.305664062f, + (float16_t)-0.738769531f, (float16_t)0.529785156f, (float16_t)-0.315673828f, (float16_t)0.573242188f, + (float16_t)-0.240356445f, (float16_t)-0.517089844f, (float16_t)-0.0953979492f, (float16_t)0.452636719f, + (float16_t)0.406005859f, (float16_t)-0.0720825195f, (float16_t)-0.612792969f, (float16_t)-0.636230469f, + (float16_t)-0.427734375f, (float16_t)-0.796875f, (float16_t)-0.175170898f, (float16_t)-0.395263672f, + (float16_t)0.293945312f, (float16_t)0.727539062f, (float16_t)-0.942382812f, (float16_t)0.577636719f, + (float16_t)0.913574219f, (float16_t)0.966308594f, (float16_t)0.246826172f, (float16_t)-0.224853516f, + (float16_t)0.895019531f, (float16_t)-0.8671875f, (float16_t)0.785644531f, (float16_t)0.474365234f, + (float16_t)0.488769531f, (float16_t)-0.361816406f, (float16_t)0.567382812f, (float16_t)0.595214844f, + (float16_t)0.637695312f, (float16_t)-0.292236328f, (float16_t)-0.837402344f, (float16_t)-0.798828125f, + (float16_t)0.463623047f, (float16_t)-0.130004883f, (float16_t)0.349609375f, (float16_t)0.82421875f, + (float16_t)0.337646484f, (float16_t)0.439208984f, (float16_t)0.934570312f, (float16_t)0.349609375f, + (float16_t)-0.439208984f, (float16_t)0.455322266f, (float16_t)0.756835938f, (float16_t)-0.135253906f, + (float16_t)0.732421875f, (float16_t)0.274169922f, (float16_t)-0.583496094f, (float16_t)0.126464844f, + (float16_t)0.978027344f, (float16_t)0.764160156f, (float16_t)-0.391357422f, (float16_t)-0.278564453f, + (float16_t)-0.213378906f, (float16_t)0.948242188f, (float16_t)0.396728516f, (float16_t)-0.633789062f, + (float16_t)-0.0738525391f, (float16_t)-0.15234375f, (float16_t)0.851074219f, (float16_t)-0.753417969f, + (float16_t)-0.838378906f, (float16_t)-0.784179688f, (float16_t)-0.773925781f, (float16_t)-0.477539062f, + (float16_t)-0.375f, (float16_t)-0.237182617f, (float16_t)-0.443359375f, (float16_t)-0.583007812f, + (float16_t)-0.188110352f, (float16_t)-0.728027344f, (float16_t)-0.838867188f, (float16_t)0.136230469f, + (float16_t)-0.743652344f, (float16_t)0.685546875f, (float16_t)-0.254150391f, (float16_t)-0.962890625f, + (float16_t)0.208984375f, (float16_t)-0.23425293f, (float16_t)0.000170469284f, (float16_t)-0.997070312f, + (float16_t)-0.270263672f, (float16_t)-0.753417969f, (float16_t)0.318359375f, (float16_t)-0.842773438f, + (float16_t)0.81640625f, (float16_t)0.382324219f, (float16_t)0.087097168f, (float16_t)0.169433594f, + (float16_t)-0.416748047f, (float16_t)-0.212524414f, (float16_t)0.0838012695f, (float16_t)0.0965576172f, + (float16_t)-0.147460938f, (float16_t)0.319824219f, (float16_t)-0.615722656f, (float16_t)-0.7265625f, + (float16_t)0.411621094f, (float16_t)-0.741699219f, (float16_t)0.402587891f, (float16_t)-0.176513672f, + (float16_t)0.00140666962f, (float16_t)0.43359375f, (float16_t)-0.210327148f, (float16_t)0.646972656f, + (float16_t)-0.300537109f, (float16_t)0.241699219f, (float16_t)0.272216797f, (float16_t)-0.147338867f, + (float16_t)0.911621094f, (float16_t)0.759765625f, (float16_t)-0.242797852f, (float16_t)-0.828613281f, + (float16_t)-0.0400695801f, (float16_t)-0.091796875f, (float16_t)-0.762695312f, (float16_t)0.913085938f, + (float16_t)0.17175293f, (float16_t)0.165893555f, (float16_t)-0.528320312f, (float16_t)0.076965332f, + (float16_t)0.632324219f, (float16_t)0.91015625f, (float16_t)0.107666016f, (float16_t)0.389648438f, + (float16_t)-0.0942382812f, (float16_t)0.417480469f, (float16_t)0.907714844f, (float16_t)-0.814453125f, + (float16_t)-0.752441406f, (float16_t)0.76171875f, (float16_t)-0.0560302734f, (float16_t)-0.826171875f, + (float16_t)0.781738281f, (float16_t)0.0400695801f, (float16_t)0.137084961f, (float16_t)-0.570800781f, + (float16_t)-0.336914062f, (float16_t)0.228515625f, (float16_t)0.0911254883f, (float16_t)-0.874511719f, + (float16_t)0.495605469f, (float16_t)0.358398438f, (float16_t)0.578125f, (float16_t)0.755371094f, + (float16_t)0.21496582f, (float16_t)0.275390625f, (float16_t)0.520996094f, (float16_t)-0.85546875f, + (float16_t)0.551757812f, (float16_t)-0.469726562f, (float16_t)-0.460693359f, (float16_t)-0.380615234f, + (float16_t)-0.139526367f, (float16_t)0.9296875f, (float16_t)0.065612793f, (float16_t)0.990722656f, + (float16_t)0.762695312f, (float16_t)0.573242188f, (float16_t)-0.287597656f, (float16_t)-0.00950622559f, + (float16_t)0.446533203f, (float16_t)0.286865234f, (float16_t)0.731933594f, (float16_t)-0.186767578f, + (float16_t)-0.231323242f, (float16_t)0.240234375f, (float16_t)0.300537109f, (float16_t)-0.284179688f, + (float16_t)-0.473388672f, (float16_t)-0.138305664f, (float16_t)-0.720214844f, (float16_t)-0.56640625f, + (float16_t)0.128540039f, (float16_t)0.0775756836f, (float16_t)-0.5546875f, (float16_t)0.0216369629f, + (float16_t)0.314941406f, (float16_t)0.116088867f, (float16_t)0.618164062f, (float16_t)0.494140625f, + (float16_t)-0.171142578f, (float16_t)-0.652832031f, (float16_t)-0.974121094f, (float16_t)-0.37890625f, + (float16_t)0.192871094f, (float16_t)-0.800292969f, (float16_t)-0.0411682129f, (float16_t)-0.17578125f, + (float16_t)-0.764648438f, (float16_t)-0.418212891f, (float16_t)-0.518554688f, (float16_t)0.625976562f, + (float16_t)-0.334716797f, (float16_t)-0.0717163086f, (float16_t)0.9296875f, (float16_t)-0.285644531f, + (float16_t)0.793457031f, (float16_t)0.278320312f, (float16_t)0.496826172f, (float16_t)-0.130737305f, + (float16_t)0.442138672f, (float16_t)0.849121094f, (float16_t)-0.327392578f, (float16_t)-0.708984375f, + (float16_t)-0.468261719f, (float16_t)0.355224609f, (float16_t)-0.486572266f, (float16_t)0.441894531f, + (float16_t)0.386474609f, (float16_t)0.462402344f, (float16_t)0.984375f, (float16_t)0.741699219f, + (float16_t)-0.961914062f, (float16_t)0.674804688f, (float16_t)-0.878417969f, (float16_t)0.572265625f, + (float16_t)-0.456054688f, (float16_t)-0.846679688f, (float16_t)0.0543823242f, (float16_t)-0.0956420898f, + (float16_t)0.6796875f, (float16_t)-0.590820312f, (float16_t)0.992675781f, (float16_t)-0.0316162109f, + (float16_t)-0.645996094f, (float16_t)-0.715820312f, (float16_t)0.593261719f, (float16_t)0.0943603516f, + (float16_t)0.0144271851f, (float16_t)0.215698242f, (float16_t)-0.805175781f, (float16_t)0.158569336f, + (float16_t)-0.654785156f, (float16_t)-0.557617188f, (float16_t)0.351074219f, (float16_t)-0.126220703f, + (float16_t)-0.612304688f, (float16_t)0.416992188f, (float16_t)-0.226928711f, (float16_t)0.194702148f, + (float16_t)-0.440673828f, (float16_t)-0.588378906f, (float16_t)0.0991821289f, (float16_t)-0.75390625f, + (float16_t)0.828613281f, (float16_t)0.314453125f, (float16_t)0.247436523f, (float16_t)-0.402587891f, + (float16_t)-0.15612793f, (float16_t)0.454589844f, (float16_t)0.829589844f, (float16_t)0.973144531f, + (float16_t)-0.146606445f, (float16_t)-0.345703125f, (float16_t)0.251220703f, (float16_t)0.00394058228f, + (float16_t)0.750488281f, (float16_t)-0.17980957f, (float16_t)-0.878417969f, (float16_t)0.327392578f, + (float16_t)-0.188964844f, (float16_t)-0.0674438477f, (float16_t)-0.13684082f, (float16_t)0.0409545898f, + (float16_t)-0.776855469f, (float16_t)-0.00213432312f, (float16_t)-0.748535156f, (float16_t)0.59765625f, + (float16_t)0.455566406f, (float16_t)-0.66796875f, (float16_t)0.0418395996f, (float16_t)-0.913085938f, + (float16_t)0.331054688f, (float16_t)0.11151123f, (float16_t)-0.504394531f, (float16_t)-0.438964844f, + (float16_t)0.458251953f, (float16_t)-0.938964844f, (float16_t)0.0356445312f, (float16_t)0.487548828f, + (float16_t)0.0166168213f, (float16_t)0.304443359f, (float16_t)0.223632812f, (float16_t)0.340576172f, + (float16_t)0.0556030273f, (float16_t)-0.00836181641f, (float16_t)-0.493164062f, (float16_t)0.810546875f, + (float16_t)-0.656738281f, 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(float16_t)-0.471679688f, (float16_t)-0.536132812f, (float16_t)-0.854003906f, + (float16_t)0.404541016f, (float16_t)0.298339844f, (float16_t)-0.0711669922f, (float16_t)0.952636719f, + (float16_t)-0.438964844f, (float16_t)0.908203125f, (float16_t)-0.00943756104f, (float16_t)-0.504882812f, + (float16_t)0.581054688f, (float16_t)-0.391113281f, (float16_t)-0.602539062f, (float16_t)0.197509766f, + (float16_t)-0.418457031f, (float16_t)0.973632812f, (float16_t)0.136352539f, (float16_t)-0.916503906f, + (float16_t)-0.378662109f, (float16_t)0.913574219f, (float16_t)0.590820312f, (float16_t)0.122802734f, + (float16_t)-0.9375f, (float16_t)0.583007812f, (float16_t)-0.730957031f, (float16_t)-0.00854492188f, + (float16_t)0.690429688f, (float16_t)0.772460938f, (float16_t)0.698242188f, (float16_t)0.955566406f, + (float16_t)0.830566406f, (float16_t)0.92578125f, (float16_t)0.694335938f, (float16_t)-0.75390625f, + (float16_t)0.45703125f, (float16_t)-0.0525512695f, (float16_t)-0.0387573242f, (float16_t)-0.875976562f, + (float16_t)-0.405273438f, (float16_t)0.529785156f, (float16_t)0.4375f, (float16_t)-0.584472656f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..78dd7d55 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_hidden_weights_data.h @@ -0,0 +1,146 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_input_hidden_weights[576] = { + (float16_t)0.0450134277f, (float16_t)-0.133422852f, (float16_t)-0.209228516f, (float16_t)-0.273193359f, + (float16_t)-0.12890625f, (float16_t)0.303222656f, (float16_t)0.213989258f, (float16_t)-0.0719604492f, + (float16_t)-0.332763672f, (float16_t)0.18359375f, (float16_t)-0.349121094f, (float16_t)-0.172363281f, + (float16_t)0.323730469f, (float16_t)0.147583008f, (float16_t)-0.209838867f, 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(float16_t)0.243896484f, (float16_t)0.138549805f, (float16_t)-0.332519531f, + (float16_t)0.0161895752f, (float16_t)0.11505127f, (float16_t)-0.156005859f, (float16_t)0.121704102f, + (float16_t)0.0178222656f, (float16_t)-0.182617188f, (float16_t)-0.228637695f, (float16_t)-0.336181641f, + (float16_t)0.094543457f, (float16_t)-0.0177612305f, (float16_t)0.259033203f, (float16_t)0.202514648f, + (float16_t)-0.220703125f, (float16_t)0.207519531f, (float16_t)0.31640625f, (float16_t)0.273925781f, + (float16_t)0.250976562f, (float16_t)-0.180664062f, (float16_t)0.132324219f, (float16_t)0.279296875f, + (float16_t)0.00276565552f, (float16_t)-0.0867919922f, (float16_t)-0.140258789f, (float16_t)0.0666503906f, + (float16_t)-0.0796508789f, (float16_t)-0.138671875f, (float16_t)0.0780639648f, (float16_t)0.295898438f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_input_weights_data.h new file mode 100644 index 00000000..39792684 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/input_input_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_input_input_weights[384] = { + (float16_t)-0.291748047f, (float16_t)0.205078125f, (float16_t)-0.0132446289f, (float16_t)0.174316406f, + (float16_t)0.34375f, (float16_t)-0.0592956543f, (float16_t)0.329345703f, (float16_t)0.065612793f, + (float16_t)0.057220459f, (float16_t)0.168212891f, (float16_t)0.000123500824f, (float16_t)-0.0661621094f, + (float16_t)-0.306640625f, (float16_t)-0.164672852f, (float16_t)-0.12890625f, (float16_t)-0.327636719f, + (float16_t)0.0718383789f, (float16_t)-0.100830078f, (float16_t)-0.252929688f, (float16_t)-0.13684082f, + (float16_t)0.344482422f, (float16_t)0.28125f, (float16_t)-0.0163574219f, (float16_t)0.101928711f, + (float16_t)-0.115112305f, (float16_t)0.033416748f, (float16_t)0.0354003906f, (float16_t)0.268066406f, + (float16_t)-0.267578125f, (float16_t)0.137084961f, (float16_t)-0.203613281f, (float16_t)0.0299987793f, + (float16_t)0.2578125f, (float16_t)-0.046875f, (float16_t)-0.0834960938f, (float16_t)-0.184204102f, + (float16_t)-0.194335938f, (float16_t)0.288085938f, (float16_t)0.00598526001f, (float16_t)0.320556641f, + (float16_t)0.171508789f, (float16_t)0.151123047f, (float16_t)-9.75728035e-05f, (float16_t)-0.186645508f, + (float16_t)-0.069152832f, (float16_t)-0.0959472656f, (float16_t)0.202636719f, (float16_t)-0.247192383f, + (float16_t)-0.094909668f, (float16_t)0.0812988281f, (float16_t)-0.313964844f, (float16_t)-0.112060547f, + (float16_t)0.09375f, (float16_t)-0.299072266f, (float16_t)0.130859375f, (float16_t)-0.0496826172f, + (float16_t)-0.228149414f, (float16_t)-0.0272216797f, (float16_t)0.14831543f, (float16_t)0.13684082f, + (float16_t)-0.188598633f, (float16_t)0.216064453f, (float16_t)0.0140762329f, (float16_t)-0.140136719f, + (float16_t)0.219238281f, (float16_t)0.0241241455f, (float16_t)-0.235351562f, (float16_t)-0.281738281f, + (float16_t)-0.0577087402f, (float16_t)0.17956543f, (float16_t)0.162231445f, (float16_t)-0.324462891f, + (float16_t)-0.032623291f, (float16_t)0.017288208f, (float16_t)-0.0163421631f, (float16_t)0.182128906f, + (float16_t)0.349365234f, (float16_t)0.336425781f, (float16_t)-0.0805053711f, (float16_t)0.0942993164f, + (float16_t)0.0835571289f, (float16_t)-0.147460938f, (float16_t)0.215820312f, (float16_t)-0.244628906f, + (float16_t)-0.273681641f, (float16_t)0.0468139648f, (float16_t)0.058807373f, (float16_t)0.18371582f, + (float16_t)-0.344238281f, (float16_t)-0.161865234f, (float16_t)-0.304199219f, (float16_t)-0.308105469f, + (float16_t)-0.331054688f, (float16_t)-0.0758666992f, (float16_t)-0.0399780273f, (float16_t)0.0377197266f, + (float16_t)0.320800781f, (float16_t)-0.00652313232f, (float16_t)-0.0612792969f, (float16_t)-0.203979492f, + (float16_t)0.195556641f, (float16_t)-0.00838470459f, (float16_t)0.253173828f, (float16_t)0.117614746f, + (float16_t)0.199829102f, (float16_t)-0.348144531f, (float16_t)0.303710938f, (float16_t)-0.0541687012f, + (float16_t)0.267578125f, (float16_t)-0.20715332f, (float16_t)0.186523438f, (float16_t)0.228027344f, + (float16_t)0.166137695f, (float16_t)0.260498047f, (float16_t)0.271240234f, (float16_t)0.295654297f, + (float16_t)-0.245361328f, (float16_t)-0.103881836f, (float16_t)0.284912109f, (float16_t)0.052520752f, + (float16_t)0.311523438f, (float16_t)-0.107727051f, (float16_t)-0.115966797f, (float16_t)0.331054688f, + (float16_t)0.233398438f, (float16_t)0.309326172f, (float16_t)0.318847656f, (float16_t)-0.329589844f, + (float16_t)0.120117188f, (float16_t)-0.259521484f, (float16_t)-0.267822266f, (float16_t)-0.240722656f, + (float16_t)0.0776367188f, (float16_t)0.220214844f, (float16_t)-0.263671875f, (float16_t)0.1640625f, + (float16_t)0.197265625f, (float16_t)-0.0889892578f, (float16_t)0.0883789062f, (float16_t)0.216796875f, + (float16_t)-0.320068359f, (float16_t)-0.0987548828f, (float16_t)0.248046875f, (float16_t)0.00907897949f, + (float16_t)0.255859375f, (float16_t)-0.314208984f, (float16_t)0.0531311035f, (float16_t)-0.105163574f, + (float16_t)0.233764648f, (float16_t)-0.0509643555f, (float16_t)-0.340332031f, (float16_t)-0.287841797f, + (float16_t)-0.160644531f, (float16_t)-0.249511719f, (float16_t)-0.0927734375f, (float16_t)-0.207519531f, + (float16_t)0.274169922f, (float16_t)0.328369141f, (float16_t)0.301025391f, (float16_t)-0.0994262695f, + (float16_t)-0.19543457f, (float16_t)-0.174072266f, (float16_t)0.177124023f, (float16_t)0.127807617f, + (float16_t)-0.0804443359f, (float16_t)-0.209594727f, (float16_t)-0.0667724609f, (float16_t)-0.325439453f, + (float16_t)0.342529297f, (float16_t)0.165039062f, (float16_t)-0.0350036621f, (float16_t)0.0481567383f, + (float16_t)-0.333007812f, (float16_t)-0.204101562f, (float16_t)-0.282958984f, (float16_t)0.153198242f, + (float16_t)-0.252197266f, (float16_t)-0.0712890625f, (float16_t)0.170043945f, (float16_t)0.111633301f, + (float16_t)0.17590332f, (float16_t)0.0650634766f, (float16_t)0.233154297f, (float16_t)-0.334716797f, + (float16_t)0.266845703f, (float16_t)0.296386719f, (float16_t)-0.312744141f, (float16_t)-0.120422363f, + (float16_t)0.0975952148f, (float16_t)0.138183594f, (float16_t)-0.232299805f, (float16_t)0.0839233398f, + (float16_t)0.105651855f, (float16_t)-0.275634766f, (float16_t)0.000824451447f, (float16_t)-0.00256347656f, + (float16_t)-0.169921875f, (float16_t)-0.248168945f, (float16_t)0.0579528809f, (float16_t)-0.285888672f, + (float16_t)-0.119934082f, (float16_t)-0.108642578f, (float16_t)0.0708618164f, (float16_t)-0.112182617f, + (float16_t)0.120422363f, (float16_t)-0.201538086f, (float16_t)-0.0252532959f, (float16_t)-0.229980469f, + (float16_t)-0.305664062f, (float16_t)-0.197021484f, (float16_t)0.315185547f, (float16_t)0.2421875f, + (float16_t)-0.203857422f, (float16_t)-0.277099609f, (float16_t)-0.274658203f, (float16_t)-0.169189453f, + (float16_t)0.253417969f, (float16_t)-0.324462891f, (float16_t)-0.263671875f, (float16_t)-0.0985717773f, + (float16_t)0.241455078f, (float16_t)0.0194244385f, (float16_t)-0.0621948242f, (float16_t)0.00709915161f, + (float16_t)0.0390319824f, (float16_t)0.0879516602f, (float16_t)0.264648438f, (float16_t)0.222412109f, + (float16_t)-0.0640869141f, (float16_t)0.270019531f, (float16_t)0.0744628906f, (float16_t)-0.162963867f, + (float16_t)-0.080871582f, (float16_t)-0.20324707f, (float16_t)0.347167969f, (float16_t)0.250976562f, + (float16_t)-0.117126465f, (float16_t)-0.330810547f, (float16_t)0.1796875f, (float16_t)0.345947266f, + (float16_t)-0.0171508789f, (float16_t)-0.313964844f, (float16_t)-0.149536133f, (float16_t)-0.0213165283f, + (float16_t)0.207275391f, (float16_t)-0.124023438f, (float16_t)0.310546875f, (float16_t)0.019317627f, + (float16_t)-0.0739746094f, (float16_t)0.201416016f, (float16_t)-0.232055664f, (float16_t)-0.00165653229f, + (float16_t)-0.252441406f, (float16_t)0.146484375f, (float16_t)-0.337402344f, (float16_t)0.105651855f, + (float16_t)0.266113281f, (float16_t)-0.01121521f, (float16_t)0.140258789f, (float16_t)0.0388793945f, + (float16_t)-0.274169922f, (float16_t)-0.101806641f, (float16_t)-0.0848388672f, (float16_t)-0.212402344f, + (float16_t)0.204589844f, (float16_t)0.0767211914f, (float16_t)-0.205810547f, (float16_t)0.205322266f, + (float16_t)-0.100463867f, (float16_t)-0.16809082f, (float16_t)-0.231445312f, (float16_t)-0.122314453f, + (float16_t)0.325195312f, (float16_t)0.119567871f, (float16_t)-0.087097168f, (float16_t)0.0542602539f, + (float16_t)-0.0364074707f, (float16_t)0.00653457642f, (float16_t)-0.284667969f, (float16_t)0.200927734f, + (float16_t)0.116699219f, (float16_t)-0.0408935547f, (float16_t)-0.150634766f, (float16_t)0.265625f, + (float16_t)-0.286376953f, (float16_t)-0.12902832f, (float16_t)-0.233276367f, (float16_t)-0.298583984f, + (float16_t)0.291015625f, (float16_t)-0.0832519531f, (float16_t)0.116027832f, (float16_t)-0.017364502f, + (float16_t)0.0749511719f, (float16_t)0.321044922f, (float16_t)0.18347168f, (float16_t)0.196533203f, + (float16_t)-0.190429688f, (float16_t)0.146484375f, (float16_t)0.174926758f, (float16_t)-0.0682373047f, + (float16_t)-0.286132812f, (float16_t)-0.118347168f, (float16_t)-0.291259766f, (float16_t)0.24609375f, + (float16_t)-0.039855957f, (float16_t)-0.0333251953f, (float16_t)-0.00782775879f, (float16_t)-0.0130157471f, + (float16_t)0.129272461f, (float16_t)0.205322266f, (float16_t)-0.088684082f, (float16_t)0.338378906f, + (float16_t)-0.0651245117f, (float16_t)0.279296875f, (float16_t)0.289306641f, (float16_t)-0.273925781f, + (float16_t)-0.264892578f, (float16_t)0.277832031f, (float16_t)-0.254882812f, (float16_t)0.188964844f, + (float16_t)0.269042969f, (float16_t)-0.235717773f, (float16_t)-0.278564453f, (float16_t)-0.0772705078f, + (float16_t)-0.273681641f, (float16_t)0.0461425781f, (float16_t)-0.000832080841f, (float16_t)0.303222656f, + (float16_t)-0.0327148438f, (float16_t)0.324707031f, (float16_t)-0.00923919678f, (float16_t)-0.0414428711f, + (float16_t)0.236938477f, (float16_t)0.00746536255f, (float16_t)0.216064453f, (float16_t)-0.296142578f, + (float16_t)-0.178222656f, (float16_t)0.152587891f, (float16_t)0.203735352f, (float16_t)-0.334472656f, + (float16_t)-0.249633789f, (float16_t)0.0582885742f, (float16_t)0.299560547f, (float16_t)0.0921630859f, + (float16_t)-0.0547485352f, (float16_t)-0.251464844f, (float16_t)0.0780639648f, (float16_t)0.275390625f, + (float16_t)-0.0317993164f, (float16_t)-0.0679931641f, (float16_t)0.229370117f, (float16_t)0.329833984f, + (float16_t)0.0765380859f, (float16_t)-0.282714844f, (float16_t)0.215087891f, (float16_t)0.309570312f, + (float16_t)0.0947265625f, (float16_t)-0.337890625f, (float16_t)0.233642578f, (float16_t)0.193725586f, + (float16_t)-0.107055664f, (float16_t)0.0137176514f, (float16_t)-0.0335083008f, (float16_t)0.0236968994f, + (float16_t)-0.331298828f, (float16_t)-0.0137405396f, (float16_t)-0.0263519287f, (float16_t)0.106262207f, + (float16_t)0.184204102f, (float16_t)0.0640869141f, (float16_t)0.261962891f, (float16_t)0.0321044922f, + (float16_t)-0.151611328f, (float16_t)-0.294433594f, (float16_t)-0.309570312f, (float16_t)0.289794922f, + (float16_t)-0.303222656f, (float16_t)0.3359375f, (float16_t)-0.283691406f, (float16_t)0.206542969f, + (float16_t)0.18737793f, (float16_t)-0.253662109f, (float16_t)-0.170898438f, (float16_t)-0.187866211f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_bias_data.h new file mode 100644 index 00000000..3fee7600 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_output_bias[24] = { + (float16_t)0.177978516f, (float16_t)0.0875854492f, (float16_t)0.169189453f, (float16_t)0.396240234f, + (float16_t)0.00784301758f, (float16_t)0.0780639648f, (float16_t)-0.156616211f, (float16_t)-0.127563477f, + (float16_t)-0.384277344f, (float16_t)-0.00075340271f, (float16_t)-0.0427856445f, (float16_t)0.135742188f, + (float16_t)-0.0346069336f, (float16_t)-0.252929688f, (float16_t)0.2578125f, (float16_t)0.0139846802f, + (float16_t)-0.202880859f, (float16_t)-0.180053711f, (float16_t)0.163208008f, (float16_t)-0.0137252808f, + (float16_t)0.409667969f, (float16_t)0.208984375f, (float16_t)0.110290527f, (float16_t)0.0103149414f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..a7f29c8a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_hidden_weights_data.h @@ -0,0 +1,146 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_output_hidden_weights[576] = { + (float16_t)0.262695312f, (float16_t)-0.109130859f, 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(float16_t)-0.303955078f, (float16_t)0.254150391f, + (float16_t)-0.281982422f, (float16_t)0.225708008f, (float16_t)0.220581055f, (float16_t)0.256103516f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_input_weights_data.h new file mode 100644 index 00000000..4168723b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_input_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_output_input_weights[384] = { + (float16_t)-0.246948242f, (float16_t)0.204101562f, (float16_t)-0.078918457f, (float16_t)-0.0900268555f, + (float16_t)-0.217895508f, (float16_t)0.0440063477f, (float16_t)-0.230957031f, (float16_t)-0.332275391f, + (float16_t)-0.270996094f, (float16_t)-0.229370117f, (float16_t)0.128540039f, (float16_t)-0.140258789f, + (float16_t)0.0541381836f, (float16_t)0.121398926f, (float16_t)-0.0897827148f, (float16_t)-0.0740966797f, + (float16_t)-0.324462891f, (float16_t)0.115661621f, (float16_t)-0.297363281f, (float16_t)-0.0974731445f, + (float16_t)0.0434875488f, (float16_t)0.039642334f, (float16_t)-0.209960938f, (float16_t)0.290283203f, + (float16_t)-0.163085938f, (float16_t)0.249145508f, (float16_t)0.0500793457f, (float16_t)-0.268066406f, + (float16_t)-0.0916748047f, (float16_t)0.295898438f, (float16_t)-0.339355469f, (float16_t)0.0317382812f, + (float16_t)0.338378906f, (float16_t)0.1328125f, (float16_t)0.00372505188f, (float16_t)0.156738281f, + (float16_t)-0.0903930664f, (float16_t)0.0808105469f, (float16_t)0.0947875977f, (float16_t)0.0787963867f, + (float16_t)-0.20690918f, (float16_t)-0.265136719f, (float16_t)-0.172729492f, (float16_t)-0.175170898f, + (float16_t)0.274414062f, (float16_t)-0.116882324f, (float16_t)-0.231933594f, (float16_t)-0.104858398f, + (float16_t)0.295654297f, (float16_t)0.313232422f, (float16_t)0.0643310547f, (float16_t)0.288574219f, + (float16_t)0.0400085449f, (float16_t)0.189331055f, (float16_t)0.167724609f, (float16_t)-0.0632324219f, + (float16_t)-0.276611328f, (float16_t)0.270996094f, (float16_t)0.286132812f, (float16_t)-0.0471191406f, + (float16_t)-0.0252838135f, (float16_t)-0.174072266f, (float16_t)-0.227172852f, (float16_t)0.334716797f, + (float16_t)-0.349853516f, (float16_t)0.213623047f, (float16_t)0.158081055f, (float16_t)-0.206787109f, + (float16_t)0.0842285156f, (float16_t)0.194335938f, (float16_t)0.0336608887f, (float16_t)0.165405273f, + (float16_t)0.0561828613f, (float16_t)-0.158569336f, (float16_t)-0.246582031f, (float16_t)0.110595703f, + (float16_t)0.333740234f, (float16_t)0.264648438f, (float16_t)-0.298339844f, (float16_t)0.314941406f, + (float16_t)-0.0108261108f, (float16_t)0.22253418f, (float16_t)-0.194091797f, (float16_t)-0.256591797f, + (float16_t)0.0360107422f, (float16_t)0.242431641f, (float16_t)-0.260742188f, (float16_t)-0.0690307617f, + (float16_t)-0.303222656f, (float16_t)0.153686523f, (float16_t)0.287109375f, (float16_t)-0.188476562f, + (float16_t)0.032989502f, (float16_t)-0.0072517395f, (float16_t)0.168579102f, (float16_t)-0.273681641f, + (float16_t)0.340576172f, (float16_t)-0.00875091553f, (float16_t)-0.309326172f, (float16_t)0.195800781f, + (float16_t)0.0423583984f, (float16_t)0.0221710205f, (float16_t)0.217895508f, (float16_t)0.0411376953f, + (float16_t)-0.287109375f, (float16_t)-0.167114258f, (float16_t)-0.159179688f, (float16_t)-0.320556641f, + (float16_t)0.0528564453f, (float16_t)0.237304688f, (float16_t)0.270263672f, (float16_t)0.0425415039f, + (float16_t)-0.109191895f, (float16_t)0.260009766f, (float16_t)-0.143432617f, (float16_t)-0.12878418f, + (float16_t)0.0604248047f, (float16_t)0.231079102f, (float16_t)0.313232422f, (float16_t)-0.103820801f, + (float16_t)-0.14465332f, (float16_t)-0.324707031f, (float16_t)-0.140136719f, (float16_t)-0.037109375f, + (float16_t)-0.0739746094f, (float16_t)0.333740234f, (float16_t)0.0552062988f, (float16_t)0.14465332f, + (float16_t)-0.169799805f, (float16_t)-0.161743164f, (float16_t)0.0332336426f, (float16_t)-0.20324707f, + (float16_t)-0.278808594f, (float16_t)0.260253906f, (float16_t)-0.227294922f, (float16_t)-0.0273132324f, + (float16_t)-0.293212891f, (float16_t)0.206542969f, (float16_t)0.182617188f, (float16_t)0.342529297f, + (float16_t)0.116638184f, (float16_t)-0.334716797f, (float16_t)-0.0472717285f, (float16_t)-0.209960938f, + (float16_t)-0.289550781f, (float16_t)-0.000911712646f, (float16_t)-0.113647461f, (float16_t)0.234741211f, + (float16_t)-0.263183594f, (float16_t)-0.00277900696f, (float16_t)-0.0277404785f, (float16_t)0.0324707031f, + (float16_t)-0.337646484f, (float16_t)0.25390625f, (float16_t)-0.0646362305f, (float16_t)-0.0101013184f, + (float16_t)-0.11151123f, (float16_t)0.0993041992f, (float16_t)-0.0731811523f, (float16_t)-0.147094727f, + (float16_t)0.107727051f, (float16_t)-0.0885620117f, (float16_t)0.235473633f, (float16_t)-0.27734375f, + (float16_t)0.270507812f, (float16_t)0.266845703f, (float16_t)-0.271728516f, (float16_t)-0.00777816772f, + (float16_t)0.0736083984f, (float16_t)0.260986328f, (float16_t)0.0165252686f, (float16_t)0.0991210938f, + (float16_t)0.0276489258f, (float16_t)-0.259277344f, (float16_t)0.221801758f, (float16_t)0.343261719f, + (float16_t)0.296386719f, (float16_t)0.00256156921f, (float16_t)-0.214233398f, (float16_t)-0.0455627441f, + (float16_t)0.321777344f, (float16_t)0.08984375f, (float16_t)-0.025100708f, (float16_t)-0.186401367f, + (float16_t)-0.280273438f, (float16_t)-0.168579102f, (float16_t)-0.291015625f, (float16_t)0.00465774536f, + (float16_t)-0.108459473f, (float16_t)-0.0484313965f, (float16_t)-0.0728759766f, (float16_t)-0.21472168f, + (float16_t)-0.344726562f, (float16_t)0.0119247437f, (float16_t)0.255615234f, (float16_t)0.302734375f, + (float16_t)0.194213867f, (float16_t)-0.0198059082f, (float16_t)0.162475586f, (float16_t)0.140380859f, + (float16_t)-0.0831298828f, (float16_t)0.194213867f, (float16_t)-0.0770263672f, (float16_t)-0.0588989258f, + (float16_t)-0.228881836f, (float16_t)0.333740234f, (float16_t)-0.188964844f, (float16_t)-0.133911133f, + (float16_t)-0.292236328f, (float16_t)0.263671875f, (float16_t)0.200683594f, (float16_t)0.259521484f, + (float16_t)0.283203125f, (float16_t)0.142700195f, (float16_t)0.038848877f, (float16_t)0.145263672f, + (float16_t)0.063293457f, (float16_t)-0.280517578f, (float16_t)-0.076965332f, (float16_t)0.0708007812f, + (float16_t)-0.0924072266f, (float16_t)-0.270751953f, (float16_t)0.265625f, (float16_t)0.141967773f, + (float16_t)0.032409668f, (float16_t)-0.266601562f, (float16_t)0.346923828f, (float16_t)-0.348632812f, + (float16_t)0.0216522217f, (float16_t)0.0832519531f, (float16_t)0.0797729492f, (float16_t)0.286132812f, + (float16_t)0.311279297f, (float16_t)0.313964844f, (float16_t)0.290039062f, (float16_t)0.112243652f, + (float16_t)0.0291137695f, (float16_t)0.336425781f, (float16_t)-0.260742188f, (float16_t)-0.347167969f, + (float16_t)-0.152587891f, (float16_t)0.310791016f, (float16_t)0.202636719f, (float16_t)-0.156860352f, + (float16_t)-0.265136719f, (float16_t)0.1328125f, (float16_t)-0.11126709f, (float16_t)0.0221557617f, + (float16_t)0.341308594f, (float16_t)0.244140625f, (float16_t)-0.217407227f, (float16_t)-0.231323242f, + (float16_t)-0.0117034912f, (float16_t)-0.126831055f, (float16_t)0.0655517578f, (float16_t)0.0973510742f, + (float16_t)-0.137939453f, (float16_t)0.0132293701f, (float16_t)-0.0944213867f, (float16_t)-0.218139648f, + (float16_t)0.299316406f, (float16_t)-0.0541992188f, (float16_t)-0.127197266f, (float16_t)-0.188476562f, + (float16_t)-0.0390014648f, (float16_t)0.156494141f, (float16_t)0.3125f, (float16_t)-0.334228516f, + (float16_t)-0.284667969f, (float16_t)-0.228515625f, (float16_t)-0.056640625f, (float16_t)-0.199951172f, + (float16_t)-0.0590820312f, (float16_t)0.0356750488f, (float16_t)-0.315185547f, (float16_t)0.046661377f, + (float16_t)0.298339844f, (float16_t)0.23425293f, (float16_t)-0.342041016f, (float16_t)-0.281982422f, + (float16_t)-0.215698242f, (float16_t)-0.140625f, (float16_t)0.0916137695f, (float16_t)0.0570678711f, + (float16_t)-0.233276367f, (float16_t)0.0959472656f, (float16_t)0.0289001465f, (float16_t)-0.328857422f, + (float16_t)0.258300781f, (float16_t)0.0196685791f, (float16_t)-0.0570983887f, (float16_t)0.305419922f, + (float16_t)-0.200195312f, (float16_t)-0.326660156f, (float16_t)0.197265625f, (float16_t)-0.256835938f, + (float16_t)0.00707626343f, (float16_t)-0.154541016f, (float16_t)0.299804688f, (float16_t)0.228393555f, + (float16_t)-0.318847656f, (float16_t)-0.0461730957f, (float16_t)-0.296875f, (float16_t)-0.128295898f, + (float16_t)0.124633789f, (float16_t)-0.0883789062f, (float16_t)0.190063477f, (float16_t)0.23449707f, + (float16_t)-0.0498657227f, (float16_t)0.272460938f, (float16_t)0.153076172f, (float16_t)0.252929688f, + (float16_t)0.00590896606f, (float16_t)-0.115844727f, (float16_t)0.232666016f, (float16_t)0.0246887207f, + (float16_t)0.325195312f, (float16_t)0.06640625f, (float16_t)-0.0986938477f, (float16_t)0.114135742f, + (float16_t)-0.280273438f, (float16_t)0.205322266f, (float16_t)-0.163330078f, (float16_t)-0.0951538086f, + (float16_t)0.158325195f, (float16_t)0.155151367f, (float16_t)0.25f, (float16_t)-0.321777344f, + (float16_t)-0.0603027344f, (float16_t)0.108093262f, (float16_t)0.27734375f, (float16_t)0.328369141f, + (float16_t)-0.310546875f, (float16_t)-0.315185547f, (float16_t)0.323242188f, (float16_t)0.113098145f, + (float16_t)-0.209716797f, (float16_t)0.171508789f, (float16_t)-0.23034668f, (float16_t)0.065246582f, + (float16_t)-0.321289062f, (float16_t)-0.262695312f, (float16_t)0.08984375f, (float16_t)-0.0619812012f, + (float16_t)-0.0395812988f, (float16_t)-0.129516602f, (float16_t)0.316650391f, (float16_t)-0.0988769531f, + (float16_t)-0.105407715f, (float16_t)0.128417969f, (float16_t)0.0950927734f, (float16_t)-0.213745117f, + (float16_t)-0.115600586f, (float16_t)-0.212036133f, (float16_t)-0.3046875f, (float16_t)0.105651855f, + (float16_t)0.185058594f, (float16_t)-0.291992188f, (float16_t)0.0607910156f, (float16_t)-0.059173584f, + (float16_t)-0.245605469f, (float16_t)0.218017578f, (float16_t)0.238647461f, (float16_t)0.197265625f, + (float16_t)-0.00310707092f, (float16_t)-0.290527344f, (float16_t)-0.187866211f, (float16_t)-0.0520935059f, + (float16_t)-0.277587891f, (float16_t)0.0797729492f, (float16_t)-0.0940551758f, (float16_t)-0.10949707f, + (float16_t)-0.308837891f, (float16_t)0.262451172f, (float16_t)0.349609375f, (float16_t)-0.157348633f, + (float16_t)-0.230712891f, (float16_t)0.0225524902f, (float16_t)-0.0478515625f, (float16_t)0.25f, + (float16_t)-0.170898438f, (float16_t)-0.239746094f, (float16_t)-0.170532227f, (float16_t)-0.0393371582f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_ref_data.h new file mode 100644 index 00000000..1d3c4b54 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/output_ref_data.h @@ -0,0 +1,290 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_large_f16_output_ref[1152] = { + (float16_t)-0.129394531f, (float16_t)-0.00605773926f, (float16_t)-0.106750488f, (float16_t)-0.0236206055f, + (float16_t)-0.00125789642f, (float16_t)0.0177459717f, (float16_t)-0.0167541504f, (float16_t)-0.142578125f, + (float16_t)0.123046875f, (float16_t)0.0286865234f, (float16_t)0.0567016602f, (float16_t)-0.01902771f, + (float16_t)-0.0939941406f, (float16_t)0.182739258f, (float16_t)-0.0993652344f, (float16_t)0.133178711f, + (float16_t)0.0824584961f, (float16_t)-0.0316772461f, (float16_t)0.000505447388f, (float16_t)-0.0631713867f, + (float16_t)-0.103637695f, (float16_t)0.020980835f, (float16_t)0.059387207f, (float16_t)0.134399414f, + (float16_t)-0.0688476562f, (float16_t)0.00089931488f, (float16_t)0.0703125f, (float16_t)-0.0369873047f, + (float16_t)-0.0930175781f, (float16_t)-0.20324707f, (float16_t)-0.00426483154f, (float16_t)-0.0941162109f, + (float16_t)0.11730957f, (float16_t)0.0251922607f, (float16_t)0.053527832f, (float16_t)0.0597229004f, + (float16_t)-0.0935668945f, (float16_t)0.0240478516f, (float16_t)-0.127563477f, (float16_t)0.00521087646f, + (float16_t)0.0211791992f, (float16_t)0.013420105f, (float16_t)-0.0630493164f, (float16_t)0.111328125f, + (float16_t)-0.274658203f, (float16_t)-0.204345703f, (float16_t)-0.0190429688f, (float16_t)-0.0759277344f, + (float16_t)0.211791992f, (float16_t)-0.0817871094f, (float16_t)-0.00479125977f, (float16_t)-0.132446289f, + (float16_t)-0.140869141f, (float16_t)0.143310547f, (float16_t)-0.0248565674f, (float16_t)-0.0456237793f, + (float16_t)-0.0377197266f, (float16_t)-0.0958251953f, (float16_t)-0.10760498f, (float16_t)0.136108398f, + (float16_t)0.094543457f, (float16_t)-0.0561523438f, (float16_t)0.0552978516f, (float16_t)-0.0284118652f, + (float16_t)-0.111816406f, (float16_t)0.0570983887f, (float16_t)-0.0275115967f, (float16_t)-0.126953125f, + (float16_t)0.0775756836f, (float16_t)0.0587158203f, (float16_t)0.23425293f, (float16_t)0.0592651367f, + (float16_t)-0.043182373f, (float16_t)0.0130004883f, (float16_t)0.122741699f, (float16_t)-0.0839233398f, + (float16_t)-0.0830078125f, (float16_t)0.0464782715f, (float16_t)-0.0350341797f, (float16_t)-0.0185089111f, + (float16_t)-0.0589599609f, (float16_t)0.080078125f, (float16_t)0.00359725952f, (float16_t)-0.0686645508f, + (float16_t)0.148925781f, (float16_t)-0.0218353271f, (float16_t)0.105651855f, (float16_t)0.0426330566f, + (float16_t)0.0619812012f, (float16_t)-0.12097168f, (float16_t)0.0166015625f, (float16_t)-0.0951538086f, + (float16_t)-0.0278320312f, (float16_t)-0.0409545898f, (float16_t)0.0416564941f, (float16_t)-0.0719604492f, + (float16_t)-0.133422852f, (float16_t)-0.125488281f, (float16_t)-0.0800170898f, (float16_t)-0.0813598633f, + (float16_t)-0.125488281f, (float16_t)-0.157592773f, (float16_t)-0.106018066f, (float16_t)-0.0883789062f, + (float16_t)0.311035156f, (float16_t)0.0932617188f, (float16_t)0.316894531f, (float16_t)-0.126098633f, + (float16_t)-0.105163574f, (float16_t)0.225219727f, (float16_t)-0.169311523f, (float16_t)-0.00222015381f, + (float16_t)0.269042969f, (float16_t)-0.041015625f, (float16_t)0.0718994141f, (float16_t)0.0980224609f, + (float16_t)0.0891723633f, (float16_t)-0.00756835938f, (float16_t)0.0664672852f, (float16_t)0.0321655273f, + (float16_t)-0.0580749512f, (float16_t)0.0366516113f, (float16_t)0.0560913086f, (float16_t)0.0368041992f, + (float16_t)-0.185424805f, (float16_t)-0.143676758f, (float16_t)0.0869750977f, (float16_t)-0.138549805f, + (float16_t)0.0127410889f, (float16_t)0.207641602f, (float16_t)-0.0672607422f, (float16_t)0.0656738281f, + (float16_t)-0.178100586f, (float16_t)0.0461120605f, (float16_t)-0.196044922f, (float16_t)-0.032623291f, + (float16_t)0.0875244141f, (float16_t)0.113098145f, (float16_t)-0.103210449f, (float16_t)0.128417969f, + (float16_t)-0.165283203f, (float16_t)-0.106262207f, (float16_t)-0.140991211f, (float16_t)0.037902832f, + (float16_t)0.0869750977f, (float16_t)0.0736083984f, (float16_t)0.155639648f, (float16_t)-0.180908203f, + (float16_t)-0.0577087402f, (float16_t)-0.0118713379f, (float16_t)0.0545654297f, (float16_t)-0.0188446045f, + (float16_t)0.0513305664f, (float16_t)0.133422852f, (float16_t)-0.0506286621f, (float16_t)-0.0101470947f, + (float16_t)0.0244140625f, (float16_t)-0.00988006592f, (float16_t)0.0452270508f, (float16_t)-0.0726928711f, + (float16_t)0.0705566406f, (float16_t)-0.0212097168f, (float16_t)-0.0176239014f, (float16_t)-0.0993652344f, + (float16_t)0.0241241455f, (float16_t)-0.0631713867f, (float16_t)0.0842285156f, (float16_t)0.00817871094f, + (float16_t)0.0151367188f, (float16_t)-0.127075195f, (float16_t)0.186279297f, (float16_t)-0.232666016f, + (float16_t)-0.198608398f, (float16_t)0.280029297f, (float16_t)-0.0219268799f, (float16_t)0.0259094238f, + (float16_t)-0.0188446045f, (float16_t)-0.0139694214f, (float16_t)0.109008789f, (float16_t)-0.0964355469f, + (float16_t)0.21472168f, (float16_t)0.0226898193f, (float16_t)0.133544922f, (float16_t)0.145263672f, + (float16_t)0.303955078f, (float16_t)-0.024810791f, (float16_t)0.0701293945f, (float16_t)-0.0776977539f, + (float16_t)0.139404297f, (float16_t)0.156005859f, (float16_t)0.119873047f, (float16_t)-0.168334961f, + (float16_t)-0.300048828f, (float16_t)-0.189331055f, (float16_t)-0.120666504f, (float16_t)-0.0362854004f, + (float16_t)0.00324630737f, (float16_t)0.151855469f, (float16_t)-0.1484375f, (float16_t)-0.204711914f, + (float16_t)0.18737793f, (float16_t)-0.145874023f, (float16_t)0.000854969025f, (float16_t)-0.178100586f, + (float16_t)-0.231445312f, (float16_t)0.262207031f, (float16_t)-0.032409668f, (float16_t)0.0778198242f, + (float16_t)0.145751953f, (float16_t)-0.1171875f, (float16_t)0.118896484f, (float16_t)0.0347900391f, + (float16_t)0.0850830078f, (float16_t)-0.174072266f, (float16_t)0.112731934f, (float16_t)0.162841797f, + (float16_t)-0.067565918f, (float16_t)0.0345458984f, (float16_t)0.00961303711f, (float16_t)-0.0953369141f, + (float16_t)-0.244995117f, (float16_t)0.0057144165f, (float16_t)-0.0357666016f, (float16_t)-0.214111328f, + (float16_t)0.129150391f, (float16_t)0.0893554688f, (float16_t)-0.107299805f, (float16_t)-0.00102996826f, + (float16_t)-0.215332031f, (float16_t)0.164672852f, (float16_t)-0.177612305f, (float16_t)-0.0440368652f, + (float16_t)0.107421875f, (float16_t)0.0863037109f, (float16_t)0.00481033325f, (float16_t)0.00833892822f, + (float16_t)0.00273132324f, (float16_t)0.0701293945f, (float16_t)0.0169525146f, (float16_t)0.0681152344f, + (float16_t)0.0379943848f, (float16_t)-0.0190124512f, (float16_t)0.0579528809f, (float16_t)0.0354919434f, + (float16_t)-0.224609375f, (float16_t)-0.0952758789f, (float16_t)0.00870513916f, (float16_t)0.0538635254f, + (float16_t)0.116699219f, (float16_t)0.165283203f, (float16_t)-0.0765380859f, (float16_t)0.00300979614f, + (float16_t)0.00843811035f, (float16_t)0.0413818359f, (float16_t)-0.0122146606f, (float16_t)-0.0238952637f, + (float16_t)-0.0157623291f, (float16_t)-0.0447387695f, (float16_t)-0.0228424072f, (float16_t)-0.126220703f, + (float16_t)0.102172852f, (float16_t)-0.0362548828f, (float16_t)0.135742188f, (float16_t)0.107788086f, + (float16_t)-0.0595092773f, (float16_t)-0.122741699f, (float16_t)0.225952148f, (float16_t)-0.018737793f, + (float16_t)0.00352478027f, (float16_t)0.349365234f, (float16_t)-0.0382995605f, (float16_t)0.203735352f, + (float16_t)0.0458984375f, (float16_t)-0.0512695312f, (float16_t)-0.0378723145f, (float16_t)0.03515625f, + (float16_t)-0.038482666f, (float16_t)0.0488586426f, (float16_t)-0.0283660889f, (float16_t)0.220581055f, + (float16_t)-0.0270385742f, (float16_t)-0.00860595703f, (float16_t)-0.179443359f, (float16_t)-0.141357422f, + (float16_t)0.0373535156f, (float16_t)-0.061340332f, (float16_t)0.157104492f, (float16_t)0.0909423828f, + (float16_t)-0.274169922f, (float16_t)-0.229492188f, (float16_t)0.0325012207f, (float16_t)-0.153320312f, + (float16_t)0.0647583008f, (float16_t)0.192993164f, (float16_t)-0.19519043f, (float16_t)-0.087097168f, + (float16_t)0.196411133f, (float16_t)-0.140258789f, (float16_t)0.0530700684f, (float16_t)-0.248657227f, + (float16_t)-0.205688477f, (float16_t)0.162353516f, (float16_t)0.0172729492f, (float16_t)0.181396484f, + (float16_t)0.255615234f, (float16_t)-0.150756836f, (float16_t)0.176269531f, (float16_t)-0.10534668f, + (float16_t)-0.193603516f, (float16_t)-0.236694336f, (float16_t)0.173950195f, (float16_t)0.176269531f, + (float16_t)-0.126098633f, (float16_t)0.198242188f, (float16_t)0.128540039f, (float16_t)-0.226806641f, + (float16_t)-0.0930175781f, (float16_t)-0.00490570068f, (float16_t)-0.0199890137f, (float16_t)-0.208496094f, + (float16_t)0.0530090332f, (float16_t)0.0575256348f, (float16_t)-0.140014648f, (float16_t)0.0283050537f, + (float16_t)-0.346679688f, (float16_t)0.139404297f, (float16_t)-0.246948242f, (float16_t)0.0217437744f, + (float16_t)0.0473632812f, (float16_t)0.130615234f, (float16_t)-0.0788574219f, (float16_t)-0.0293884277f, + (float16_t)-0.270996094f, (float16_t)-0.0667724609f, (float16_t)0.131347656f, (float16_t)0.0863647461f, + (float16_t)-0.0557250977f, (float16_t)-0.0134887695f, (float16_t)0.0891723633f, (float16_t)-0.151977539f, + (float16_t)-0.157226562f, (float16_t)0.00782775879f, (float16_t)-0.068359375f, (float16_t)0.00527191162f, + (float16_t)0.0210418701f, (float16_t)0.0703125f, (float16_t)-0.0312042236f, (float16_t)-0.0157012939f, + (float16_t)-0.00231170654f, (float16_t)-0.0253295898f, (float16_t)0.107177734f, (float16_t)0.0247039795f, + (float16_t)0.185913086f, (float16_t)-0.159790039f, (float16_t)0.106933594f, (float16_t)-0.129394531f, + (float16_t)0.129150391f, (float16_t)-0.012008667f, (float16_t)0.219970703f, (float16_t)-0.157836914f, + (float16_t)-0.101745605f, (float16_t)-0.0386352539f, (float16_t)0.248535156f, (float16_t)-0.143188477f, + (float16_t)-0.0346984863f, (float16_t)0.0789794922f, (float16_t)-0.08984375f, (float16_t)0.0171966553f, + (float16_t)0.0793457031f, (float16_t)0.0712890625f, (float16_t)-0.0126571655f, (float16_t)-0.0390319824f, + (float16_t)-0.0828857422f, (float16_t)0.0919799805f, (float16_t)0.0826416016f, (float16_t)0.0323791504f, + (float16_t)0.108947754f, (float16_t)-0.0599975586f, (float16_t)-0.0263214111f, (float16_t)-0.0329589844f, + (float16_t)0.00857543945f, (float16_t)-0.188720703f, (float16_t)0.0463867188f, (float16_t)0.0122375488f, + (float16_t)0.00478363037f, (float16_t)-0.268310547f, (float16_t)0.0911865234f, (float16_t)-0.438964844f, + (float16_t)-0.176147461f, (float16_t)-0.000118136406f, (float16_t)-0.166015625f, (float16_t)-0.114562988f, + (float16_t)0.0834350586f, (float16_t)-0.119140625f, (float16_t)0.0813598633f, (float16_t)0.0110015869f, + (float16_t)-0.0775146484f, (float16_t)0.0724487305f, (float16_t)0.121826172f, (float16_t)0.147827148f, + (float16_t)-0.051940918f, (float16_t)-0.199584961f, (float16_t)0.157104492f, (float16_t)0.00728988647f, + (float16_t)-0.163574219f, (float16_t)-0.327636719f, (float16_t)0.177001953f, (float16_t)-0.0397338867f, + (float16_t)-0.212524414f, (float16_t)0.152099609f, (float16_t)0.120788574f, (float16_t)-0.215209961f, + (float16_t)0.0149002075f, (float16_t)0.100158691f, (float16_t)-0.146728516f, (float16_t)-0.10723877f, + (float16_t)0.00313186646f, (float16_t)-0.0344543457f, (float16_t)-0.126464844f, (float16_t)-0.109619141f, + (float16_t)-0.302490234f, (float16_t)0.00859832764f, (float16_t)0.0306091309f, (float16_t)0.0494384766f, + (float16_t)0.0753173828f, (float16_t)-0.0281829834f, (float16_t)0.174682617f, (float16_t)-0.220336914f, + (float16_t)-0.251220703f, (float16_t)0.0173492432f, (float16_t)0.246948242f, (float16_t)0.161254883f, + (float16_t)-0.158081055f, (float16_t)0.0181884766f, (float16_t)0.205322266f, (float16_t)-0.131469727f, + (float16_t)-0.0481567383f, (float16_t)0.0753173828f, (float16_t)-0.197509766f, (float16_t)-0.0678100586f, + (float16_t)0.158569336f, (float16_t)0.0238952637f, (float16_t)-0.0197296143f, (float16_t)-0.134033203f, + (float16_t)0.0120620728f, (float16_t)0.141845703f, (float16_t)0.130615234f, (float16_t)0.0600280762f, + (float16_t)0.120544434f, (float16_t)-0.147460938f, (float16_t)0.0441894531f, (float16_t)-0.138916016f, + (float16_t)0.0810546875f, (float16_t)-0.0936889648f, (float16_t)0.141357422f, (float16_t)0.0587768555f, + (float16_t)0.0600585938f, (float16_t)0.0329284668f, (float16_t)0.237304688f, (float16_t)-0.167236328f, + (float16_t)-0.108032227f, (float16_t)0.0464172363f, (float16_t)0.0181884766f, (float16_t)-0.0600585938f, + (float16_t)0.036315918f, (float16_t)0.134399414f, (float16_t)-0.0809326172f, (float16_t)0.145263672f, + (float16_t)-0.0251159668f, (float16_t)-0.164306641f, (float16_t)0.0872192383f, (float16_t)0.0435791016f, + (float16_t)0.0768432617f, (float16_t)-0.0295257568f, (float16_t)-0.0721435547f, 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(float16_t)-0.0297546387f, (float16_t)-0.250488281f, + (float16_t)-0.127319336f, (float16_t)-0.156494141f, (float16_t)-0.218505859f, (float16_t)-0.150390625f, + (float16_t)-0.00265693665f, (float16_t)0.0110778809f, (float16_t)0.0837402344f, (float16_t)0.241577148f, + (float16_t)-0.157836914f, (float16_t)0.00415420532f, (float16_t)0.0354919434f, (float16_t)0.176635742f, + (float16_t)-0.22644043f, (float16_t)0.0908203125f, (float16_t)-0.381835938f, (float16_t)-0.198730469f, + (float16_t)0.017074585f, (float16_t)0.0349121094f, (float16_t)0.197753906f, (float16_t)-0.147338867f, + (float16_t)-0.040802002f, (float16_t)0.0970458984f, (float16_t)0.230957031f, (float16_t)0.0894165039f, + (float16_t)0.0358886719f, (float16_t)-0.10723877f, (float16_t)0.192260742f, (float16_t)0.00538635254f, + (float16_t)0.0589294434f, (float16_t)0.236450195f, (float16_t)-0.169555664f, (float16_t)0.355712891f, + (float16_t)-0.293212891f, (float16_t)-0.0103149414f, (float16_t)-0.252685547f, (float16_t)0.0961914062f, + (float16_t)-0.0760498047f, (float16_t)0.340820312f, (float16_t)-0.340576172f, (float16_t)-0.168823242f, + (float16_t)-0.214111328f, (float16_t)0.0237426758f, (float16_t)0.1015625f, (float16_t)0.0650024414f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_bias_data.h new file mode 100644 index 00000000..c66f16be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_bias_data.h @@ -0,0 +1,6 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_cell_bias[24] = { + -0.275247514f, -0.279356569f, 0.258129627f, -0.0318117104f, 0.0350841731f, -0.393345982f, + 0.226716593f, -0.235105067f, 0.0771873742f, 0.0342428386f, -0.0123274699f, -0.235995427f, + -0.0307703614f, -0.329160213f, 0.438301444f, -0.0968212783f, 0.259778053f, 0.0395539142f, + 0.374058664f, -0.0144720227f, -0.00795313716f, -0.31313616f, -0.229455978f, 0.00256547332f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..e6e733a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_hidden_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_cell_hidden_weights[576] = { + -0.1792548f, -0.0930259973f, -0.0259715784f, 0.243628949f, 0.278073877f, 0.0108130602f, + 0.00860362127f, -0.297983646f, -0.198428288f, 0.278800458f, 0.103350177f, 0.178979278f, + 0.140985146f, 0.237683803f, 0.336213201f, 0.270483762f, -0.203937173f, 0.131546959f, + 0.279566258f, -0.0163520128f, 0.250483483f, -0.0293353833f, -0.0192129649f, -0.0481267497f, + 0.0978709534f, -0.18618314f, -0.145478025f, 0.0140820919f, -0.241382867f, 0.329938561f, + -0.0462197065f, -0.00165844138f, 0.189146414f, -0.346977383f, -0.169812679f, -0.21344699f, + 0.110127345f, 0.00625717547f, -0.00510553131f, -0.194293901f, -0.227603301f, 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0.260555476f, 0.272933334f, 0.170740262f, -0.115933813f, 0.270700842f, + -0.199445277f, 0.145895064f, 0.17462559f, -0.223171309f, -0.223365918f, -0.330994129f, + -0.277045071f, 0.281360716f, 0.239452496f, 0.18602559f, -0.154999316f, 0.229240611f, + -0.252672613f, 0.0816923529f, 0.179310471f, 0.346882999f, 0.0771967396f, 0.268482774f, + 0.0873208046f, -0.08186315f, -0.245180964f, -0.0323968641f, -0.310718894f, -0.0250378363f, + -0.0926622152f, -0.223521426f, 0.0386265069f, 0.332798034f, 0.337333709f, 0.290120572f, + 0.1866218f, 0.103838131f, 0.135717258f, 0.0154421702f, 0.137163058f, -0.0112681603f, + -0.0867359489f, 0.26256007f, 0.341734231f, -0.295621932f, 0.33113125f, -0.128076583f, + -0.00745411031f, 0.319516987f, 0.259148747f, 0.330608308f, -0.22705631f, 0.0699935034f, + -0.190036908f, 0.0379652083f, -0.128006265f, 0.34549731f, -0.112605102f, 0.22137323f, + -0.0721018538f, -0.15086247f, 0.197546721f, 0.0595429428f, 0.300413072f, -0.230933145f, + -0.131665319f, 0.303625226f, -0.244418859f, 0.185764819f, 0.0948464945f, -0.126259685f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_input_weights_data.h new file mode 100644 index 00000000..97e48136 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/cell_input_weights_data.h @@ -0,0 +1,57 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_cell_input_weights[384] = { + -0.320769787f, 0.335195661f, 0.218383521f, -0.186908334f, -0.140014693f, 0.205692023f, 0.0555802844f, + -0.14658086f, -0.185727566f, 0.198626384f, 0.0266029779f, -0.315000504f, -0.21144478f, 0.237220973f, + -0.0957788825f, -0.185471535f, 0.225334451f, -0.328964472f, 0.327252507f, 0.265211135f, -0.0733108595f, + -0.306741387f, 0.0947513059f, 0.332555413f, 0.197666839f, 0.336485296f, -0.15987578f, -0.0679506063f, + -0.185706973f, -0.206901595f, 0.0969461799f, 0.135855362f, -0.0370220952f, 0.287299663f, 0.00693569146f, + 0.161164135f, -0.250049323f, -0.16632767f, 0.213369265f, 0.174638286f, 0.178332478f, -0.224121869f, + -0.0153211923f, -0.0292309243f, -0.0704306364f, 0.0486480296f, -0.324929178f, -0.160747185f, 0.04861819f, + -0.0134365447f, 0.250635117f, 0.270816624f, 0.264372528f, 0.271642923f, 0.061689008f, -0.315534234f, + -0.041667413f, 0.209851384f, 0.143979847f, -0.00381498015f, -0.117650263f, -0.190271646f, -0.255045325f, + -0.289200544f, 0.191948324f, 0.175040007f, -0.330807865f, 0.00325604971f, -0.103853397f, -0.0331597738f, + 0.0345383696f, 0.0586335957f, -0.00860924833f, -0.304528922f, -0.339645028f, 0.181857228f, -0.327897996f, + -0.305488974f, 0.26140967f, 0.0700546056f, -0.224168569f, -0.18058379f, -0.331134915f, -0.1255496f, + -0.128542721f, 0.0978604108f, -0.0873052999f, 0.0764496922f, 0.305923283f, 0.103945017f, -0.182649434f, + 0.0956532285f, -0.191911235f, -0.141129881f, -0.157096267f, -0.320232153f, -0.333945841f, -0.276962519f, + 0.102216981f, 0.199651778f, 0.333573997f, -0.0923532322f, -0.0624841154f, -0.325842649f, 0.0912859589f, + -0.304349154f, 0.21435076f, -0.0232280549f, 0.0701578856f, -0.230404466f, 0.175674006f, -0.0781090185f, + 0.332030714f, 0.123717241f, -0.198366672f, -0.0785997659f, -0.0690678954f, -0.101489045f, -0.163736582f, + 0.0602250285f, 0.0858476236f, 0.108732864f, -0.206577018f, -0.324689329f, -0.0668959022f, -0.281494945f, + 0.110083491f, -0.201102793f, 0.311420232f, 0.0662330389f, -0.304553419f, -0.21288164f, -0.273641467f, + 0.10436254f, -0.179320619f, 0.275460392f, 0.00119655079f, 0.172233075f, -0.0234818105f, 0.120111644f, + 0.0186648108f, 0.268284619f, 0.114569157f, 0.114521623f, 0.137847334f, 0.207339704f, 0.344428599f, + -0.122003809f, -0.0958512649f, -0.329493731f, -0.0591270663f, -0.319064289f, -0.214037582f, -0.0859058425f, + -0.0162740797f, -0.0602838248f, -0.349966854f, -0.203731537f, -0.113005586f, -0.0179730356f, -0.0164570659f, + -0.343255877f, -0.161418825f, 0.0510497838f, -0.244837895f, -0.073365353f, 0.119176552f, -0.163161412f, + -0.0737765282f, 0.176901802f, 0.0997277051f, 0.263828754f, -0.0509301834f, -0.158904225f, -0.126862779f, + 0.17058526f, 0.127952546f, 0.256273776f, -0.143987879f, 0.0337426253f, -0.0621125065f, 0.340148449f, + -0.225770622f, 0.0328566283f, -0.220842421f, -0.162896231f, 0.15938057f, 0.0573498569f, 0.267327219f, + -0.251927972f, -0.143186703f, -0.0131738996f, -0.31238097f, -0.320847631f, 0.305329055f, -0.181591973f, + 0.211119965f, -0.0245422181f, 0.276291102f, 0.347238898f, 0.0471385494f, 0.312800944f, 0.0487964563f, + -0.177373096f, 0.264817774f, -0.108691983f, -0.13212207f, -0.106906779f, -0.220388129f, -0.17220071f, + 0.00208081095f, -0.00556946825f, 0.0206144899f, -0.231769964f, -0.333985835f, -0.0934703723f, -0.171657741f, + 0.171072781f, -0.157896712f, 0.17315416f, -0.152206764f, 0.058206711f, -0.0566300042f, -0.300052196f, + 0.301955372f, 0.00562542491f, -0.256961554f, 0.0368582942f, -0.00756019028f, 0.243160233f, -0.0427270271f, + 0.103637345f, -0.0288110469f, 0.2698268f, -0.0903522745f, 0.297714889f, -0.0455552861f, -0.325645715f, + -0.304004371f, -0.287954003f, 0.105010897f, 0.125858694f, -0.209929779f, 0.174928486f, 0.303671449f, + 0.183140278f, 0.186846197f, -0.239670128f, 0.00610576244f, 0.181697726f, 0.0786417276f, 0.168031961f, + 0.213330135f, 0.00180036447f, -0.329326689f, 0.323918253f, 0.059975639f, 0.102239504f, -0.13941972f, + 0.3438164f, 0.0889175162f, 0.161536694f, 0.127029032f, -0.280804127f, 0.304869175f, -0.136551917f, + 0.246900782f, -0.225892261f, -0.327450156f, -0.338586718f, 0.337489843f, 0.0634268671f, 0.0798839629f, + -0.228914425f, -0.263530821f, -0.246097565f, -0.194064155f, 0.345560282f, -0.152277157f, -0.180907786f, + 0.0291988868f, 0.0469853617f, -0.137008086f, -0.32877025f, 0.0267098807f, -0.278728515f, 0.207433745f, + 0.347629249f, 0.316223711f, -0.144176334f, 0.0559893399f, -0.22522448f, -0.196545109f, 0.323254764f, + 0.153717324f, -0.311997265f, 0.0280215517f, 0.19096148f, -0.123750955f, -0.0087108342f, 0.00623671804f, + -0.0496664681f, -0.106774583f, 0.225582689f, -0.337429404f, -0.213078648f, -0.158034056f, -0.336294591f, + -0.154566363f, 0.161747187f, -0.0348734185f, 0.0358625762f, -0.238285467f, 0.162307426f, -0.180233449f, + 0.126883253f, 0.338768274f, -0.257562876f, -0.173990712f, 0.106606022f, 0.293964267f, -0.174133018f, + 0.0364425331f, -0.14209044f, 0.00999881793f, 0.0866602138f, -0.0907585248f, 0.194565609f, -0.131661505f, + 0.339695662f, 0.10991358f, 0.325159729f, -0.152059138f, 0.307255954f, 0.135624513f, -0.0036448515f, + -0.115885369f, 0.06124961f, 0.247403368f, 0.139657974f, 0.107180156f, 0.232754916f, 0.173055127f, + -0.0746013448f, 0.338447601f, -0.0539608039f, -0.292811155f, -0.289928913f, 0.237022161f, 0.0774185508f, + 0.264135808f, 0.275399506f, -0.0326549001f, -0.200750351f, -0.0319202282f, 0.256460577f, 0.126813725f, + -0.283965141f, -0.0898173228f, -0.172887966f, -0.05903605f, 0.147984684f, 0.11364992f, 0.205992222f, + 0.299821675f, 0.205119267f, 0.0876425505f, 0.110380456f, -0.174570933f, -0.0641396344f, 0.340771139f, + -0.0648596659f, -0.0655394271f, 0.0535697006f, 0.240072802f, -0.302031457f, 0.126489148f, -0.124549121f, + 0.344400793f, 0.183556914f, 0.0413099006f, -0.235798791f, 0.267628312f, -0.305772632f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/config_data.h new file mode 100644 index 00000000..8e23337d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_LARGE_F32_TIME_MAJOR 1 +#define LSTM_LARGE_F32_TIME_STEPS 12 +#define LSTM_LARGE_F32_BATCH_SIZE 4 +#define LSTM_LARGE_F32_INPUT_SIZE 16 +#define LSTM_LARGE_F32_HIDDEN_SIZE 24 +#define LSTM_LARGE_F32_CELL_CLIP 0.0f +#define LSTM_LARGE_F32_CELL_STATE_SIZE 96 +#define LSTM_LARGE_F32_DST_SIZE 1152 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_bias_data.h new file mode 100644 index 00000000..705a8f99 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_bias_data.h @@ -0,0 +1,6 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_forget_bias[24] = { + 0.177535146f, 0.0325826742f, 0.0370371155f, -0.108942188f, 0.0828021616f, -0.163678646f, + -0.300896555f, 0.0828549713f, 0.134673402f, -0.427880496f, -0.222125277f, -0.337266505f, + 0.0945300236f, -0.0410235673f, -0.00251658261f, -0.0808558241f, -0.339003235f, -0.319732398f, + -0.173601627f, 0.0631795749f, 0.254639924f, 0.195871294f, 0.0360061824f, 0.134412855f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..c3e7051a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_hidden_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_forget_hidden_weights[576] = { + -0.204378664f, 0.305310756f, -0.318264008f, 0.106026269f, 0.145476729f, 0.27995643f, + 0.000353683223f, -0.0596664175f, -0.161224321f, -0.0330269821f, 0.158858582f, 0.0887711793f, + 0.225049809f, -0.201812655f, 0.141829506f, 0.0951213762f, 0.261980355f, 0.299593329f, + 0.00942425709f, 0.110141799f, 0.219186068f, -0.258482665f, -0.331831187f, -0.00684741512f, + -0.314186931f, 0.0484422855f, 0.244554803f, -0.163938731f, -0.25973022f, 0.0685710683f, + 0.0847714171f, -0.0973946005f, -0.108740039f, 0.0502816588f, -0.300835729f, 0.211899087f, + 0.0303109176f, 0.193419963f, -0.271822661f, 0.190693066f, -0.311402738f, 0.326150894f, + 0.21327588f, 0.0158029627f, 0.327535391f, -0.192423761f, 0.24927339f, 0.168609589f, + 0.349538654f, 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--git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_input_weights_data.h new file mode 100644 index 00000000..6be23b5c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/forget_input_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_forget_input_weights[384] = { + 0.302235842f, -0.28990972f, -0.0385325737f, -0.294894248f, -0.178113446f, -0.318593979f, + 0.331942379f, -0.256581426f, 0.016970519f, 0.0916198939f, 0.239305079f, -0.0425473116f, + 0.107716307f, -0.167823225f, -0.0479411781f, -0.00425413949f, 0.074842833f, -0.0973824561f, + -0.0436185598f, -0.0484647714f, -0.312155008f, 0.0181788355f, -0.117770247f, -0.0432859808f, + 0.197147235f, -0.158706024f, 0.26934433f, 0.291833878f, -0.0103539471f, 0.0460506454f, + -0.322986335f, -0.247605592f, -0.0731544346f, -0.322960079f, -0.0240861084f, 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0.247081891f, + -0.334051311f, -0.110201195f, 0.340870351f, -0.00849210471f, 0.0962239429f, 0.263534129f, + -0.162894696f, -0.262657702f, 0.32488665f, -0.0566312149f, -0.055453945f, 0.194981635f, + -0.201761782f, 0.0337934159f, -0.289054453f, -0.0805069357f, 0.26477161f, 0.148998588f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_bias_data.h new file mode 100644 index 00000000..67980f01 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_bias_data.h @@ -0,0 +1,6 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_input_bias[24] = { + -0.27198267f, -0.456271321f, -0.194418892f, -0.202639699f, -0.330619186f, -0.0920842737f, + 0.170399904f, 0.0820278674f, -0.0701605082f, -0.167867333f, -0.0409303606f, 0.132525504f, + 0.0253695697f, 0.260245472f, -0.25312236f, -0.231304705f, -0.333095461f, -0.071423009f, + 0.00263228267f, 0.0478037894f, -0.292468667f, 0.0911770016f, 0.124635547f, 0.362224758f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_data.h new file mode 100644 index 00000000..c5966b29 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_data.h @@ -0,0 +1,112 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_input[768] = { + 0.911643624f, 0.820262671f, 0.743642688f, 0.705849946f, -0.641088545f, -0.91495502f, 0.180081859f, + 0.539016843f, 0.0846986696f, 0.656686068f, -0.686985493f, -0.769466817f, -0.717023611f, -0.337036401f, + -0.4036147f, -0.18057555f, 0.751643956f, 0.17849347f, 0.0166541301f, -0.0230489764f, 0.083085224f, + -0.491283298f, -0.609929502f, -0.129220918f, -0.209150761f, 0.686175108f, -0.297593981f, -0.988657355f, + 0.03534225f, -0.161102191f, 0.19665423f, -0.301506668f, 0.502913535f, 0.936307609f, 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-0.971186519f, -0.230717555f, -0.591428578f, 0.887882471f, 0.543180048f, 0.394945711f, 0.174475908f, + 0.929173112f, 0.221253157f, -0.381476253f, 0.34162575f, -0.496082395f, -0.441206604f, -0.296346873f, + -0.17950967f, 0.594575107f, 0.506579578f, 0.918812692f, 0.629126966f, 0.69960022f, -0.627285957f, + -0.742062092f, -0.346075952f, 0.802168012f, -0.985302269f, -0.415897697f, -0.147210389f, -0.795912921f, + 0.799043298f, 0.00822688919f, -0.415233374f, -0.619775057f, -0.367966175f, -0.954142511f, -0.208420977f, + -0.272749841f, 0.0650995746f, 0.0320054777f, 0.516136408f, -0.229000017f, -0.876039386f, 0.325717002f, + -0.365773588f, -0.832637012f, 0.0276829861f, -0.382720649f, -0.77737546f, -0.0574360453f, 0.857300758f, + -0.0125741949f, 0.335202873f, 0.216530472f, 0.382407367f, -0.316872776f, -0.90900445f, 0.917984843f, + -0.880088508f, 0.294064879f, 0.333627641f, 0.28757441f, -0.460256159f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..09824e74 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_hidden_weights_data.h @@ -0,0 +1,85 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_input_hidden_weights[576] = { + 0.0911052078f, 0.233859569f, 0.124388374f, 0.266772658f, 0.227752924f, -0.292766482f, -0.316796184f, + 0.191515014f, 0.122261025f, 0.0132127618f, -0.297804981f, -0.0886291564f, 0.117145188f, -0.132938743f, + 0.206688419f, 0.110828318f, -0.101714112f, -0.181962982f, 0.336609691f, 0.223537385f, 0.02909985f, + 0.321390808f, -0.0781570971f, -0.00831727963f, 0.11270079f, 0.333113343f, -0.100671075f, 0.2210875f, + 0.132110566f, 0.0340577662f, 0.188842848f, 0.0367926992f, -0.0589867085f, -0.0787324607f, -0.108485483f, + -0.108430222f, 0.243593052f, -0.24469395f, 0.32283622f, -0.0785149261f, 0.014500943f, -0.277758807f, + 0.238678142f, -0.254496217f, 0.303672224f, 0.167409748f, -0.247401834f, 0.0851740986f, 0.0384990312f, + 0.250150353f, 0.348840654f, 0.00100744562f, 0.0870423168f, -0.0381225571f, -0.180879116f, -0.229979232f, + -0.177766517f, -0.0530558191f, 0.0937026814f, -0.0472741053f, 0.141307816f, 0.31042558f, 0.268236846f, + 0.301811725f, 0.233457565f, 0.0393798053f, -0.111545034f, -0.188263848f, -0.269728065f, -0.343979836f, + -0.205608785f, 0.179186106f, 0.224004418f, -0.135049909f, 0.290259629f, 0.330384046f, -0.166998401f, + -0.225263432f, 0.182236239f, -0.0561628081f, 0.265883833f, 0.0871782377f, 0.152417466f, -0.0856537223f, + 0.235142186f, 0.300564975f, 0.283832818f, -0.142631471f, -0.144397542f, 0.275822341f, -0.248750582f, + 0.317438036f, -0.148306653f, 0.0983730927f, 0.186679095f, -0.280225843f, -0.299524844f, 0.0473769493f, + -0.156097591f, -0.0278473068f, 0.254236072f, -0.298007578f, 0.239526123f, 0.0594611242f, 0.161648393f, + 0.170155793f, 0.110394172f, 0.304384887f, 0.0231738631f, 0.0521754958f, -0.246428058f, -0.209821716f, + 0.128439233f, 0.108467616f, 0.259176612f, -0.218078569f, 0.165284395f, 0.287848502f, 0.159132138f, + 0.0329304896f, 0.294619352f, -0.0951730609f, 0.329981089f, -0.132405058f, -0.301395923f, 0.145755559f, + 0.0703556389f, 0.17951043f, -0.0903372839f, -0.0347189084f, 0.0926193073f, -0.340463966f, -0.167143628f, + -0.102288827f, 0.213126376f, -0.312083572f, 0.143652782f, 0.181859374f, 0.0907104835f, 0.0244658552f, + 0.102859437f, -0.0917631611f, 0.187361866f, 0.298590392f, -0.154402718f, -0.135927781f, 0.0874347091f, + -0.0828700736f, -0.0063941502f, -0.0248711836f, -0.0094946092f, 0.347849071f, 0.230771288f, -0.109796807f, + 0.331946492f, 0.307246447f, -0.0346539505f, -0.216619268f, -0.248097211f, 0.309210867f, 0.00260483404f, + -0.201720685f, -0.178973481f, 0.286523581f, -0.052873984f, -0.0832789987f, 0.301178664f, -0.0149197215f, + 0.295277625f, 0.322718799f, -0.147493705f, 0.243323207f, 0.302962691f, -0.259788811f, -0.0275565404f, + 0.0430020429f, 0.294344217f, -0.0976287946f, 0.174828991f, 0.320956081f, -0.0818583891f, -0.0501326695f, + 0.213834599f, 0.0591417886f, 0.248584405f, -0.295225948f, -0.111175984f, 0.0324859209f, -0.063434422f, + -0.307785928f, -0.0408154428f, 0.0687655136f, 0.140460819f, -0.231159329f, -0.104926147f, 0.338842332f, + -0.094902344f, -0.337432474f, 0.0661963969f, 0.315493584f, -0.152575791f, -0.157080069f, 0.10330002f, + -0.151202515f, -0.218701601f, 0.339506596f, -0.311703771f, -0.345043033f, 0.209832802f, -0.00779569894f, + -0.176760942f, -0.298672199f, 0.0472223274f, 0.326029271f, 0.208872393f, -0.314119756f, -0.167929858f, + -0.349539489f, 0.298422486f, -0.109320357f, 0.032820899f, 0.14558804f, 0.208932087f, -0.251823783f, + -0.301605791f, -0.254273087f, -0.0272009037f, 0.291277945f, -0.00063762645f, 0.249986157f, 0.283887327f, + 0.0938047841f, 0.182180986f, -0.330814898f, 0.289470553f, 0.0318887681f, -0.163091481f, -0.345733911f, + -0.0930474922f, -0.21349436f, -0.0438818783f, 0.00685790554f, -0.20991905f, 0.238450408f, 0.280234188f, + 0.19713293f, -0.237234622f, -0.253531486f, -0.302503318f, 0.112334356f, -0.109364599f, 0.106191009f, + -0.299829036f, 0.199422374f, -0.117871381f, 0.318531156f, 0.0328087136f, 0.0124849649f, 0.240515113f, + -0.0167286005f, 0.132925838f, 0.0360572897f, -0.321298331f, 0.0613342226f, -0.318021417f, -0.316126883f, + -0.141099319f, -0.345400512f, 0.224713907f, 0.250852525f, 0.0438593552f, -0.2969805f, -0.185811788f, + 0.0821653605f, -0.303009093f, -0.275459647f, -0.33382073f, 0.118396722f, 0.0789111927f, 0.340884626f, + -0.30913654f, 0.188330546f, 0.0464945808f, 0.250454396f, -0.0303373337f, 0.175322473f, -0.151703224f, + -0.13236773f, 0.294900119f, 0.292656332f, 0.124440238f, -0.229192868f, -0.334248453f, 0.238108292f, + 0.0449687466f, -0.264493704f, -0.14048782f, 0.221399412f, 0.0718915388f, 0.166421533f, -0.0667169467f, + 0.153019249f, -0.309363961f, -0.0513307229f, 0.204055637f, -0.103626147f, 0.266944349f, -0.342883766f, + 0.105763361f, 0.0510180071f, 0.2557033f, -0.0595090874f, -0.144838825f, -0.244933233f, -0.270811558f, + 0.138491735f, 0.109240972f, 0.224995524f, 0.244865537f, 0.00793776195f, -0.249383077f, 0.211169317f, + -0.0780941322f, -0.346178174f, 0.296862066f, 0.139593661f, -0.0124716312f, 0.164669558f, 0.169137061f, + -0.079639703f, -0.122171931f, -0.251679599f, 0.195376426f, 0.276502997f, 0.341722667f, -0.312600136f, + -0.0141125564f, -0.0858816579f, 0.155001163f, -0.0326925367f, 0.171069026f, 0.144465029f, -0.338594049f, + 0.0777787045f, -0.184601039f, -0.108539566f, -0.268221945f, -0.0598681495f, 0.0951219425f, 0.0888177827f, + 0.291407108f, 0.334761083f, 0.337716043f, -0.236395895f, 0.28080982f, 0.0974977985f, 0.154323786f, + 0.00498660747f, 0.134012207f, 0.0414886586f, 0.306974411f, 0.202727556f, -0.145478353f, 0.278733552f, + -0.169197053f, 0.0920910463f, 0.247024834f, -0.279455364f, 0.132426038f, 0.194351465f, 0.156450018f, + 0.0517202206f, -0.124290131f, 0.283487022f, 0.313562661f, 0.245372966f, 0.233408526f, -0.117377721f, + 0.293260485f, -0.119653627f, -0.205070883f, -0.273898959f, -0.289779305f, 0.321490109f, 0.22738339f, + 0.135462493f, -0.295432657f, 0.000580761232f, -0.223239824f, 0.298367053f, 0.101607367f, 0.0156004559f, + 0.171046346f, -0.277236968f, 0.105266146f, -0.0576682501f, -0.0172573496f, 0.138669923f, -0.292921633f, + -0.210235983f, 0.170132011f, 0.246520206f, 0.322675556f, -0.185173482f, -0.176454023f, -0.177773431f, + 0.344801426f, -0.316898584f, 0.218218207f, -0.116873905f, -0.123826861f, -0.135139525f, 0.216458619f, + -0.328618407f, -0.258820295f, -0.0493959002f, 0.114321135f, 0.308729917f, -0.117280915f, -0.10301163f, + 0.0815999135f, -0.0298190787f, -0.00152577576f, -0.0902697444f, 0.264784366f, -0.210080341f, -0.118259817f, + 0.166379362f, -0.158538356f, 0.119203009f, 0.0571686029f, 0.291899979f, -0.169073656f, 0.32224533f, + 0.136147559f, 0.0415285639f, 0.155644849f, -0.0671903118f, 0.30994153f, -0.335229307f, 0.0826316848f, + 0.247428313f, -0.147698715f, 0.0863014311f, 0.233637303f, -0.229541197f, -0.219629526f, 0.164477408f, + 0.318569064f, -0.136939839f, -0.0229258593f, 0.0506733879f, 0.219231427f, -0.143953234f, -0.0962117016f, + 0.123865791f, -0.0886069238f, 0.177179843f, 0.149879053f, -0.302236944f, 0.257898211f, -0.163587153f, + 0.160743043f, 0.111596659f, 0.205447495f, -0.346274406f, -0.322389305f, -0.221947566f, 0.0157518648f, + 0.0282686297f, -0.330732703f, 0.194718301f, 0.218469292f, 0.119256377f, 0.239507601f, -0.182189703f, + 0.214705348f, -0.227696538f, -0.0762988254f, 0.0708464906f, -0.267825335f, -0.0316731408f, 0.274918288f, + -0.0617472529f, -0.10381899f, 0.106687181f, -0.18841888f, -0.194550887f, 0.00199512369f, 0.0115605909f, + -0.00215407019f, 0.168159112f, 0.0891257599f, 0.163321465f, -0.196152434f, 0.0471439958f, -0.0374450311f, + -0.221228167f, -0.0550782643f, 0.219606847f, 0.264552802f, -0.00982465968f, 0.321337312f, 0.291349202f, + 0.265686095f, 0.343722671f, 0.095187068f, 0.210624635f, -0.302563697f, -0.14745003f, -0.196073189f, + -0.338984162f, -0.255912721f, -0.105392285f, -0.198135257f, 0.0193294287f, 0.0316423029f, 0.28339681f, + 0.293353319f, 0.322184682f, -0.121718287f, 0.128102601f, -0.160310745f, 0.0954938531f, 0.249509707f, + -0.0190293863f, 0.221893609f, -0.0116258394f, -0.0912312195f, -0.147213265f, 0.0516405031f, 0.174112827f, + -0.00997999776f, -0.0972122252f, -0.248914972f, 0.261537939f, -0.314115375f, -0.0953921229f, 0.170131579f, + 0.165389627f, 0.00812252238f, 0.0430771858f, 0.303236783f, 0.309148222f, 0.307303786f, 0.154182553f, + 0.338076025f, -0.295994073f, -0.223499909f, -0.148116589f, 0.242293492f, -0.141551569f, 0.186010092f, + 0.237095147f, -0.0552613176f, 0.0924955085f, 0.0336546637f, -0.253006965f, 0.0848258585f, 0.339254588f, + 0.349015206f, 0.0912476406f, -0.322510839f, -0.0735000372f, -0.0192862041f, -0.248489618f, 0.30478397f, + -0.295251697f, -0.171279475f, 0.122793764f, 0.100990519f, -0.107870519f, -0.0563524179f, -0.239595443f, + 0.0402964912f, 0.281089932f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_input_weights_data.h new file mode 100644 index 00000000..f98f968e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/input_input_weights_data.h @@ -0,0 +1,57 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_input_input_weights[384] = { + -0.235615388f, 0.34335196f, -0.288442224f, 0.0469962507f, 0.0017924587f, -0.0757574365f, -0.337536842f, + -0.338332146f, 0.328629524f, 0.0907760188f, -0.289031982f, 0.123759352f, -0.126217842f, 0.236910135f, + 0.263664126f, -0.0577641092f, -0.340590864f, -0.04918167f, -0.139209107f, 0.114205129f, 0.288428217f, + 0.315916181f, -0.211349785f, -0.326612592f, 0.0284972303f, 0.0995458812f, 0.0377816781f, 0.31489265f, + -0.345173031f, 0.0264251176f, -0.348293185f, 0.116562404f, 0.319817036f, -0.0906352699f, 0.33777678f, + -0.151802585f, 0.298006445f, -0.319652617f, -0.288003117f, -0.341985464f, 0.133942097f, 0.207292616f, + -0.343694806f, -0.240714297f, -0.11513155f, 0.28807202f, -0.14047502f, 0.341074973f, 0.0748296455f, + 0.256267011f, 0.242026061f, -0.305456758f, -0.267635226f, 0.315516412f, 0.0893556848f, 0.27114439f, + 0.133639902f, 0.0666603968f, 0.133655742f, -0.0856884122f, 0.0108281281f, 0.263504803f, -0.0305361915f, + 0.0932317749f, -0.118661113f, 0.317850173f, -0.2249742f, 0.222007796f, 0.0287151206f, 0.184282586f, + 0.191134214f, 0.00130432879f, 0.0468814075f, -0.279286683f, -0.230745435f, -0.202300966f, 0.0979921818f, + 0.0106131164f, 0.158166632f, -0.0370933339f, -0.15709959f, 0.22451587f, -0.120925039f, 0.228291914f, + -0.287230104f, 0.00802689232f, -0.107548222f, 0.145699963f, -0.0504598357f, -0.217708007f, 0.230216295f, + -0.11044459f, -0.00319897779f, -0.256985277f, -0.291994691f, 0.271884233f, -0.217879832f, 0.290725797f, + 0.123155944f, 0.0299257785f, 0.0811455026f, -0.135230258f, 0.339672387f, -0.32927677f, -0.166186675f, + 0.222985238f, 0.045521684f, -0.161145329f, -0.0755261332f, -0.18366684f, -0.251633793f, -0.310522288f, + -0.278033465f, 0.281338096f, -0.108683132f, 0.0212935507f, 0.0107571715f, -0.146304682f, 0.275950253f, + 0.279728204f, 0.153925657f, -0.0870844051f, 0.00628804788f, 0.216136917f, -0.154475227f, -0.270591974f, + -0.199962407f, 0.22298485f, 0.333782226f, 0.0633564219f, -0.134062827f, 0.198147818f, 0.233039171f, + 0.14838849f, 0.0597755983f, 0.0285950992f, 0.156001896f, 0.177765146f, -0.0602833219f, 0.234799281f, + -0.272301555f, -0.0103562158f, 0.0569562577f, 0.0591834821f, -0.30391565f, 0.330413669f, -0.178460479f, + -0.160064787f, -0.109224573f, -0.215775028f, -0.0121945217f, 0.0090902485f, 0.297428876f, -0.133120492f, + -0.0398492925f, 0.193651557f, -0.0321310349f, 0.0501160175f, -0.0469255745f, 0.111087777f, 0.0444176756f, + -0.29059726f, -0.251974672f, 0.105951741f, -0.186992168f, -0.283957422f, 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-0.345102251f, 0.336374551f, + 0.208213076f, -0.120260864f, 0.128645033f, -0.331071883f, 0.346413642f, 0.233204037f, 0.0935794562f, + -0.283192784f, 0.2015616f, -0.311014593f, -0.300929397f, 0.262357593f, -0.188425422f, -0.0210103244f, + 0.149233043f, 0.183328971f, 0.0965957642f, 0.269042939f, 0.155734375f, -0.0469313115f, -0.251621276f, + 0.0574872084f, -0.198980555f, 0.021276081f, 0.126734182f, 0.313085884f, 0.0536153838f, -0.3043046f, + -0.064974688f, 0.232742041f, -0.166760415f, -0.0495445803f, 0.186150625f, 0.218824148f, 0.0744704753f, + -0.170860186f, -0.250918269f, 0.11004281f, 0.00302627054f, -0.13247177f, -0.105661936f, 0.330006957f, + -0.252504438f, 0.308444381f, -0.320911825f, -0.163343668f, 0.120255567f, 0.213812903f, 0.120150015f, + -0.119491339f, -0.333924174f, -0.108103201f, 0.0932986438f, -0.0899537653f, -0.121869668f, -0.198238358f, + 0.303640455f, 0.289336354f, 0.285325468f, -0.328756273f, 0.163316295f, 0.32037577f, 0.0810383409f, + -0.144167632f, 0.0558349863f, -0.206585109f, 0.261068463f, -0.00346869836f, -0.152210161f, -0.200421542f, + -0.242991939f, 0.0994520485f, 0.344892859f, -0.301223516f, -0.345414788f, 0.0239309706f, 0.243414432f, + -0.339184821f, -0.20749411f, 0.255405039f, -0.0564889684f, -0.0841344669f, -0.0101958755f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_bias_data.h new file mode 100644 index 00000000..a074c104 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_bias_data.h @@ -0,0 +1,6 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_output_bias[24] = { + 0.321678251f, 0.452655852f, 0.013144955f, 0.237395063f, -0.181379989f, -0.333414257f, + -0.319228262f, 0.0664386004f, 0.0596641302f, 0.0796213746f, -0.314435422f, 0.220962897f, + -0.0848725215f, 0.242129385f, -0.00691461563f, -0.0197013617f, -0.0987781659f, 0.0484315008f, + -0.342258066f, -0.0799351037f, 0.0802617073f, -0.0496537983f, 0.22490485f, -0.388512343f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..0fa2ae61 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_hidden_weights_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_output_hidden_weights[576] = { + -0.341776192f, 0.120437317f, 0.054633908f, 0.0552400053f, -0.181495398f, -0.129840761f, + 0.31307292f, -0.20090954f, -0.148819059f, 0.0314821489f, -0.145402163f, 0.112419106f, + 0.224578783f, 0.170913279f, -0.045392327f, -0.0468778349f, 0.200562119f, 0.052853547f, + -0.179200679f, -0.00616837153f, 0.342514843f, 0.074048318f, 0.335298479f, 0.0352966785f, + 0.251850754f, 0.131012961f, -0.307853669f, 0.214981541f, -0.0511811301f, -0.243603751f, + 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0.0609252937f, + -0.181212798f, -0.208403096f, -0.234051615f, -0.291949183f, 0.340452045f, 0.334863394f, + -0.213435307f, -0.0492788963f, -0.115779154f, 0.298033297f, 0.169390887f, 0.109303311f, + 0.140874326f, 0.274953425f, -0.142957687f, -0.123224594f, 0.131856874f, 0.110870689f, + -0.0640268624f, -0.156151474f, -0.331537247f, 0.291130632f, -0.162025526f, -0.168286309f, + 0.0886018276f, 0.103465997f, 0.0391874611f, 0.180488825f, -0.09112598f, -0.322317481f, + -0.24774377f, 0.0989692584f, -0.175868377f, -0.0490469299f, 0.0865608156f, 0.3079696f, + -0.137774736f, 0.0423701853f, 0.227502897f, 0.325090408f, 0.162656978f, 0.0457035713f, + 0.00833706744f, 0.106718473f, -0.0415055342f, 0.158061981f, -0.276626289f, 0.280912042f, + 0.19882074f, -0.271168947f, -0.304121256f, -0.2211961f, 0.140563071f, 0.271903425f, + -0.159657538f, 0.249459922f, -0.348934442f, 0.082972683f, -0.27165553f, -0.334256709f, + 0.289049149f, -0.270368338f, 0.185575917f, -0.117736109f, 0.11963895f, -0.11367745f, + -0.0610283762f, 0.209369987f, -0.296650559f, 0.17245397f, -0.216343686f, -0.306601882f, + -0.244242877f, -0.120808288f, -0.151933134f, -0.054526642f, -0.206075042f, 0.0132254269f, + 0.00297266524f, 0.0844950378f, -0.164641753f, -0.287548631f, 0.166159168f, -0.0206551962f, + 0.34953475f, 0.122703522f, -0.261430174f, 0.228548855f, 0.194423884f, -0.0266253632f, + 0.348070174f, 0.295516104f, -0.089000605f, 0.240650475f, -0.0545711741f, -0.0139143076f, + -0.171075568f, 0.0252387021f, 0.136021182f, -0.119904362f, -0.0839146078f, 0.0811527893f, + 0.201568693f, 0.30088976f, -0.119276345f, -0.15936099f, 0.152819514f, -0.0505564325f, + 0.271197677f, -0.269038796f, -0.217803106f, 0.0529784933f, 0.322658747f, 0.307964355f, + 0.0769266859f, 0.262529969f, 0.182540178f, 0.232067809f, -0.00644107163f, -0.192452252f, + 0.043127656f, 0.0897162557f, 0.0505393073f, 0.30397743f, 0.184432849f, -0.251852155f, + 0.311514139f, -0.111243136f, 0.268085927f, 0.312596917f, 0.349623412f, -0.0106861405f, + 0.0479385071f, 0.0940047279f, 0.312984675f, -0.182997167f, 0.235281587f, 0.280423582f, + 0.342337668f, 0.325238436f, -0.240098715f, -0.307128459f, -0.150271699f, -0.221236214f, + 0.133567944f, -0.108182825f, -0.0389345512f, -0.263776779f, 0.334305704f, -0.193331093f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_input_weights_data.h new file mode 100644 index 00000000..3fbd9211 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_input_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_output_input_weights[384] = { + -0.00767656509f, -0.286407232f, -0.148220733f, 0.132956758f, -0.000956924399f, -0.239230067f, + -0.000386762782f, -0.0210145414f, 0.0631489754f, -0.137230575f, -0.143037736f, 0.0450525731f, + 0.316512585f, -0.0790831f, -0.0435399823f, -0.264918983f, -0.0980010852f, 0.00501024723f, + -0.0637069792f, 0.140183225f, -0.15042533f, -0.230191484f, 0.136477947f, -0.269288987f, + -0.17766051f, 0.296985745f, 0.10146305f, -0.202210113f, 0.211817145f, -0.275592715f, + 0.0932641849f, 0.0478518493f, -0.253548324f, -0.231709927f, -0.0160292871f, 0.201281115f, + 0.180366233f, 0.030735163f, -0.184033394f, 0.231230885f, 0.0566984862f, -0.0559456423f, + -0.0802137926f, 0.197593451f, 0.23338154f, 0.318183511f, -0.280573308f, -0.207330689f, + 0.00365934195f, 0.203379363f, 0.0285165738f, -0.0340641849f, -0.173002318f, 0.06831678f, + -0.169264928f, 0.068004556f, -0.0946892798f, 0.266770095f, -0.140309989f, 0.292604655f, + 0.319135696f, 0.311899334f, 0.31670469f, -0.0412641354f, -0.185742334f, 0.210713059f, + -0.0996432528f, 0.0934960619f, -0.261718631f, -0.181996077f, 0.294460773f, 0.0545237437f, + 0.127202347f, -0.307183057f, -0.139670908f, 0.297214746f, -0.121600047f, 0.0703061894f, + -0.285422653f, -0.215549454f, -0.22618036f, 0.295643896f, 0.274350643f, -0.101611987f, + -0.281851977f, -0.132044658f, -0.311450273f, 0.0572880507f, -0.337952524f, 0.0444375947f, + -0.344566762f, 0.0759503692f, -0.264119893f, 0.257405728f, 0.112526394f, -0.115635864f, + 0.317032874f, -0.346108705f, 0.0935615003f, -0.0521207228f, 0.199113816f, -0.074077718f, + 0.109584838f, 0.223931119f, -0.224110171f, -0.193300262f, -0.249999985f, -0.218001604f, + -0.208253011f, -0.294990331f, 0.253738582f, -0.0969347358f, 0.0142914094f, 0.201330438f, + -0.0879277885f, 0.154383183f, -0.259791911f, 0.13079752f, -0.306699067f, -0.222672775f, + -0.25959295f, 0.23908481f, 0.330857605f, -0.0696100965f, -0.283062249f, -0.225317344f, + -0.325590938f, -0.232872143f, -0.117898598f, 0.265771925f, -0.243992344f, -0.150502816f, + 0.225434572f, -0.266431242f, 0.292755365f, -0.069589667f, 0.103530057f, 0.0592808127f, + 0.3370184f, 0.000218742847f, -0.141189098f, -0.0522274897f, -0.261839271f, 0.130840406f, + -0.298884928f, -0.215111539f, -0.16296494f, 0.134332255f, -0.201069579f, -0.189547017f, + 0.294903487f, -0.162951872f, -0.00445815083f, -0.279805571f, 0.338943124f, 0.178487837f, + -0.208680227f, -0.347083986f, -0.0446315892f, 0.0773631334f, 0.0879681632f, -0.0130596831f, + -0.329405308f, 0.315184832f, -0.289463907f, -0.208867535f, 0.227369398f, 0.326342314f, + 0.114913993f, -0.0847215503f, 0.0534320772f, 0.101353474f, 0.342683405f, 0.268252641f, + -0.199370041f, -0.338994563f, 0.20227991f, 0.0248237289f, -0.300136447f, -0.275745869f, + 0.0456805304f, -0.20283401f, -0.0687296316f, -0.104772925f, -0.0319729038f, -0.328647226f, + -0.0394119993f, -0.167430758f, -0.301992923f, -0.205863491f, 0.177915871f, 0.048057396f, + 0.33559233f, -0.0530944169f, 0.134145245f, 0.0774373561f, 0.0821471885f, -0.210593656f, + 0.0917810127f, 0.126486287f, -0.296100587f, -0.327285856f, -0.183049902f, -0.320487857f, + -0.338383853f, -0.284476101f, -0.0477290228f, -0.293500334f, -0.106844462f, -0.196393907f, + -0.193062082f, -0.316991329f, 0.125288099f, -0.122937672f, -0.010922431f, -0.313544065f, + 0.231062099f, 0.103070706f, -0.24889113f, -0.00877074338f, 0.288794726f, -0.21156013f, + 0.0681463778f, 0.292339951f, -0.0171180498f, -0.022084197f, 0.157113612f, -0.0921685025f, + -0.132036269f, -0.219935626f, 0.136419341f, 0.324808508f, -0.130316213f, -0.115739584f, + -0.0737627149f, 0.34274289f, -0.0556983948f, 0.199763253f, -0.297927886f, 0.0293669309f, + 0.0635730252f, 0.209817186f, 0.191392392f, 0.210583642f, -0.185954064f, -0.110860601f, + 0.13020514f, -0.242417246f, 0.294415683f, 0.229343176f, -0.0357559659f, 0.201352865f, + 0.0223931558f, 0.00621988904f, 0.121024169f, -0.00458205026f, 0.13252075f, 0.230366483f, + 0.0934868753f, -0.0779224187f, -0.0287983958f, 0.137381271f, 0.0244049374f, -0.18960689f, + 0.229493007f, 0.0378708951f, -0.23534584f, -0.252000093f, -0.199132308f, -0.0789213181f, + 0.196140841f, -0.297847211f, -0.273342669f, -0.0202076063f, 0.0106378142f, -0.196563721f, + 0.170843005f, -0.305181086f, 0.208174303f, 0.0670947954f, 0.285485655f, 0.144674242f, + -0.107892528f, -0.324460089f, -0.202431291f, 0.328172535f, -0.320851028f, -0.0706819892f, + 0.110415861f, -0.215233207f, 0.0649592206f, -0.229611695f, -0.157986671f, -0.293885529f, + 0.172104403f, 0.247485057f, 0.311346918f, 0.177353188f, 0.153323174f, -0.206957638f, + 0.0743814036f, -0.133023664f, 0.0638604388f, 0.270488471f, -0.236730605f, 0.140930817f, + 0.3481341f, -0.346611857f, 0.081926845f, 0.344859302f, -0.334417701f, -0.295202911f, + -0.281130105f, -0.248870894f, -0.124233946f, 0.0982931629f, 0.319079727f, -0.299492985f, + 0.207659706f, -0.330192208f, 0.226366997f, 0.0702445582f, 0.0852765888f, 0.323035896f, + -0.319470555f, -0.311529398f, 0.286375135f, -0.254445821f, -0.0168645196f, -0.247713879f, + 0.329356968f, -0.235890821f, 0.213157028f, 0.2515634f, -0.199342072f, -0.0304851737f, + -0.192793608f, 0.211281806f, 0.226123363f, -0.0244833492f, -0.270605654f, -0.0241402704f, + -0.0632073209f, -0.217796326f, 0.0562989414f, 0.298910677f, -0.18778877f, 0.0192076657f, + 0.189075962f, 0.325382084f, 0.0542224608f, 0.198132262f, -0.0628155321f, 0.104416549f, + 0.12343429f, 0.241816357f, 0.258587539f, 0.253993124f, 0.114694022f, -0.192655116f, + -0.265813917f, 0.237794459f, 0.217998341f, 0.0318551064f, -0.00615135673f, -0.064047806f, + -0.2998088f, 0.252224773f, 0.170319647f, 0.1818721f, -0.188769549f, -0.10886471f, + -0.103447646f, 0.0380328f, -0.0364228338f, 0.251855999f, -0.0320848748f, 0.0460046306f, + -0.0989413708f, 0.160098687f, -0.271478176f, 0.0275623929f, 0.19606851f, -0.173621178f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_ref_data.h new file mode 100644 index 00000000..dd5eea8c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/output_ref_data.h @@ -0,0 +1,194 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_large_f32_output_ref[1152] = { + 0.0108457692f, 0.112922534f, 0.170529827f, 0.0367055759f, 0.0753424168f, -0.171232209f, + -0.221744776f, 0.219951153f, 0.0281218141f, 0.124885686f, -0.0714510009f, -0.20581241f, + -0.0389550366f, -0.0643041059f, 0.204003558f, -0.0972825885f, 0.0710492581f, -0.0935618728f, + 0.0808115751f, -0.0677195042f, -0.0218615811f, -0.0683050528f, -0.145229891f, -0.108941406f, + -0.0292855576f, 0.0329854228f, 0.000763052027f, 0.0162922442f, -0.00377335935f, -0.0992916748f, + -0.0297891945f, 0.134979397f, 0.059394557f, 0.0495861769f, -0.0640382543f, -0.111996152f, + -0.0287184436f, -0.0245812368f, 0.0986444205f, -0.0675941855f, 0.0700126961f, 0.0835485235f, + 0.120681778f, -0.0562801138f, 0.0107058408f, -0.0659881383f, -0.0451502837f, -0.13206616f, + -0.134555444f, -0.0291639809f, 0.219166264f, 0.0480270423f, -0.0531712286f, -0.099043332f, + 0.0147073837f, 0.0275178216f, 0.0143434247f, 0.0551951937f, -0.113198377f, 0.0901242867f, + 0.00218694401f, -0.0666349605f, 0.220646456f, 0.015536001f, 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-0.00234982278f, + 0.1574765f, -0.0781562179f, -0.236156225f, -0.101382457f, -0.105431385f, -0.0239969473f, + -0.192566276f, -0.036362648f, 0.0399799421f, -0.106266662f, 0.012297065f, -0.050578434f, + -0.0588556603f, 0.0282203518f, 0.10804832f, -0.0771313459f, 0.127692983f, -0.021470869f, + 0.0978558511f, -0.219798312f, 0.206394479f, 0.0128029548f, 0.00166830269f, 0.224495396f, + 0.386291623f, 0.0612966903f, -0.0363396257f, -0.0940088555f, -0.260346502f, -0.166107282f, + -0.00105006166f, -0.129096553f, 0.157385528f, 0.0683226883f, -0.1551494f, -0.134164244f, + 0.26310122f, -0.150128961f, -0.0874083191f, -0.0772527605f, -0.208253473f, -0.109043568f, + 0.240798309f, -0.0349785723f, -0.0375892781f, -0.121789724f, 0.0932908654f, -0.0215342231f, + 0.032835193f, -0.171988517f, -0.357850641f, -0.122135721f, -0.0670630857f, 0.0425153859f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_large_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_large_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_bias_data.h new file mode 100644 index 00000000..cdaa293d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_cell_bias[11] = {(float16_t)-0.139648438f, + (float16_t)-0.419189453f, + (float16_t)-0.267333984f, + (float16_t)0.0981445312f, + (float16_t)-0.105773926f, + (float16_t)0.395263672f, + (float16_t)-0.0853271484f, + (float16_t)0.240966797f, + (float16_t)0.134277344f, + (float16_t)-0.223754883f, + (float16_t)-0.321777344f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..9faaeb57 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_hidden_weights_data.h @@ -0,0 +1,33 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_cell_hidden_weights[121] = { + (float16_t)0.153198242f, (float16_t)-0.154907227f, (float16_t)0.295166016f, (float16_t)-0.231811523f, + (float16_t)-0.0247344971f, (float16_t)-0.0693359375f, (float16_t)-0.181640625f, (float16_t)-0.000930786133f, + (float16_t)-0.129638672f, (float16_t)-0.299804688f, (float16_t)0.186035156f, (float16_t)-0.0227050781f, + (float16_t)0.266845703f, (float16_t)0.0461425781f, (float16_t)-0.128051758f, (float16_t)-0.00058221817f, + (float16_t)0.163574219f, (float16_t)-0.100402832f, (float16_t)0.053894043f, (float16_t)0.143188477f, + (float16_t)0.0189208984f, (float16_t)0.00390625f, (float16_t)-0.0386047363f, (float16_t)0.296875f, + (float16_t)-0.273193359f, (float16_t)-0.183105469f, (float16_t)0.0864257812f, (float16_t)0.0283203125f, + (float16_t)-0.230957031f, (float16_t)0.189208984f, (float16_t)-0.254882812f, (float16_t)0.0326843262f, + (float16_t)0.288085938f, (float16_t)-0.0141983032f, (float16_t)-0.164794922f, (float16_t)0.0300292969f, + (float16_t)-0.309814453f, (float16_t)-0.288330078f, (float16_t)0.0423583984f, (float16_t)0.243286133f, + (float16_t)-0.293945312f, (float16_t)0.311767578f, (float16_t)0.221679688f, (float16_t)-0.108215332f, + (float16_t)0.104492188f, (float16_t)-0.276367188f, (float16_t)-0.229736328f, (float16_t)-0.0522766113f, + (float16_t)-0.331542969f, (float16_t)-0.276611328f, (float16_t)-0.0731811523f, (float16_t)0.0905151367f, + (float16_t)-0.0622558594f, (float16_t)0.228637695f, (float16_t)-0.118835449f, (float16_t)-0.0205841064f, + (float16_t)-0.0584106445f, (float16_t)-0.242919922f, (float16_t)-0.191894531f, (float16_t)0.280273438f, + (float16_t)-0.319091797f, (float16_t)0.0350036621f, (float16_t)0.0463562012f, (float16_t)-0.180419922f, + (float16_t)-0.121459961f, (float16_t)0.240600586f, (float16_t)0.214111328f, (float16_t)-0.0114212036f, + (float16_t)-0.197021484f, (float16_t)0.169189453f, (float16_t)-0.143432617f, (float16_t)-0.0859985352f, + (float16_t)0.311035156f, (float16_t)-0.257568359f, (float16_t)-0.239501953f, (float16_t)0.278564453f, + (float16_t)-0.2421875f, (float16_t)-0.324462891f, (float16_t)-0.320556641f, (float16_t)0.242553711f, + (float16_t)-0.266601562f, (float16_t)0.0668334961f, (float16_t)-0.160888672f, (float16_t)0.224853516f, + (float16_t)0.325439453f, (float16_t)-0.205688477f, (float16_t)0.322265625f, (float16_t)-0.263427734f, + (float16_t)0.146362305f, (float16_t)-0.0374145508f, (float16_t)0.347900391f, (float16_t)-0.274414062f, + (float16_t)-0.288574219f, (float16_t)-0.0844726562f, (float16_t)0.194702148f, (float16_t)-0.128173828f, + (float16_t)0.0234832764f, (float16_t)0.0794677734f, (float16_t)-0.101135254f, (float16_t)0.00818634033f, + (float16_t)-0.163330078f, (float16_t)0.327880859f, (float16_t)0.21496582f, (float16_t)0.0115814209f, + (float16_t)0.23059082f, (float16_t)0.0758056641f, (float16_t)0.162231445f, (float16_t)-0.0765991211f, + (float16_t)0.329833984f, (float16_t)-0.145751953f, (float16_t)0.0257568359f, (float16_t)-0.020980835f, + (float16_t)0.0885009766f, (float16_t)0.307373047f, (float16_t)-0.162475586f, (float16_t)0.0856323242f, + (float16_t)0.236083984f, (float16_t)0.329833984f, (float16_t)-0.273925781f, (float16_t)-0.15222168f, + (float16_t)0.257324219f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_input_weights_data.h new file mode 100644 index 00000000..a3ce8859 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/cell_input_weights_data.h @@ -0,0 +1,63 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_cell_input_weights[242] = { + (float16_t)0.082824707f, (float16_t)0.243164062f, (float16_t)-0.272949219f, (float16_t)-0.243652344f, + (float16_t)-0.303710938f, (float16_t)-0.229736328f, (float16_t)0.104064941f, (float16_t)0.181152344f, + (float16_t)-0.202758789f, (float16_t)-0.237182617f, (float16_t)0.338623047f, (float16_t)0.296875f, + (float16_t)-0.290283203f, (float16_t)-0.0279846191f, (float16_t)0.241088867f, (float16_t)0.0805664062f, + (float16_t)-0.111816406f, (float16_t)-0.18737793f, (float16_t)-0.144287109f, (float16_t)-0.24597168f, + (float16_t)0.212890625f, (float16_t)0.242797852f, (float16_t)0.272216797f, (float16_t)-0.0718994141f, + (float16_t)-0.306884766f, (float16_t)0.0726928711f, (float16_t)0.101745605f, (float16_t)-0.0429382324f, + (float16_t)-0.243164062f, (float16_t)0.246337891f, (float16_t)0.0117111206f, (float16_t)-0.180297852f, + (float16_t)-0.0750732422f, (float16_t)0.213012695f, (float16_t)0.348144531f, (float16_t)-0.0498657227f, + (float16_t)-0.164428711f, (float16_t)0.142944336f, (float16_t)-0.0927124023f, (float16_t)0.215820312f, + (float16_t)0.107849121f, (float16_t)0.276367188f, (float16_t)0.210449219f, (float16_t)0.108093262f, + (float16_t)-0.296875f, (float16_t)0.149414062f, (float16_t)-0.215454102f, (float16_t)-0.228637695f, + (float16_t)0.216796875f, (float16_t)0.0645141602f, (float16_t)0.0799560547f, (float16_t)0.0720214844f, + (float16_t)-0.346191406f, (float16_t)-0.161499023f, (float16_t)-0.0934448242f, (float16_t)-0.0364379883f, + (float16_t)-0.280517578f, (float16_t)0.209350586f, (float16_t)-0.0153808594f, (float16_t)-0.2890625f, + (float16_t)-0.294921875f, (float16_t)0.134155273f, (float16_t)-0.188354492f, (float16_t)-0.205932617f, + (float16_t)0.202514648f, (float16_t)0.102905273f, (float16_t)-0.281982422f, (float16_t)0.193237305f, + (float16_t)0.313476562f, (float16_t)-0.19128418f, (float16_t)-0.244018555f, (float16_t)0.0985107422f, + (float16_t)0.0963745117f, (float16_t)0.134155273f, (float16_t)-0.241699219f, (float16_t)-0.0698852539f, + (float16_t)0.0399169922f, (float16_t)-0.189575195f, (float16_t)0.223510742f, (float16_t)-0.172607422f, + (float16_t)0.331298828f, (float16_t)0.121337891f, (float16_t)0.17175293f, (float16_t)0.244018555f, + (float16_t)0.0904541016f, (float16_t)-0.263183594f, (float16_t)-0.0861206055f, (float16_t)0.201782227f, + (float16_t)0.0366210938f, (float16_t)-0.243530273f, (float16_t)-0.257080078f, (float16_t)0.204467773f, + (float16_t)0.24206543f, (float16_t)-0.132202148f, (float16_t)0.250488281f, (float16_t)0.0444335938f, + (float16_t)-0.332275391f, (float16_t)-0.224975586f, (float16_t)-0.017578125f, (float16_t)-0.204345703f, + (float16_t)-0.11932373f, (float16_t)-0.163208008f, (float16_t)0.122802734f, (float16_t)0.219116211f, + (float16_t)0.310058594f, (float16_t)0.308837891f, (float16_t)-0.305419922f, (float16_t)0.0830078125f, + (float16_t)0.209716797f, (float16_t)-0.305175781f, (float16_t)0.260498047f, (float16_t)-0.0176239014f, + (float16_t)-0.256835938f, (float16_t)0.192504883f, (float16_t)0.215576172f, (float16_t)0.0308227539f, + (float16_t)-0.189941406f, (float16_t)-0.334716797f, (float16_t)-0.228393555f, (float16_t)-0.223388672f, + (float16_t)0.197753906f, (float16_t)-0.115539551f, (float16_t)-0.336669922f, (float16_t)-0.103759766f, + (float16_t)-0.251708984f, (float16_t)0.259277344f, (float16_t)0.345214844f, (float16_t)0.117980957f, + (float16_t)-0.343994141f, (float16_t)-0.00884246826f, (float16_t)-0.322998047f, (float16_t)0.129638672f, + (float16_t)0.110229492f, (float16_t)-0.221923828f, (float16_t)-0.239624023f, (float16_t)-0.071105957f, + (float16_t)0.224243164f, (float16_t)0.213378906f, (float16_t)0.162109375f, (float16_t)-0.0697631836f, + (float16_t)-0.302978516f, (float16_t)0.00582504272f, (float16_t)0.276123047f, (float16_t)-0.154541016f, + (float16_t)0.0701904297f, (float16_t)-0.249267578f, (float16_t)-0.129638672f, (float16_t)-0.0372009277f, + (float16_t)-0.187744141f, (float16_t)0.315429688f, (float16_t)0.0844726562f, (float16_t)0.348388672f, + (float16_t)-0.298583984f, (float16_t)0.0367431641f, (float16_t)0.0146179199f, (float16_t)-0.306152344f, + (float16_t)0.271484375f, (float16_t)-0.02293396f, (float16_t)-0.201904297f, (float16_t)-0.323974609f, + (float16_t)0.235229492f, (float16_t)-0.178955078f, (float16_t)0.280517578f, (float16_t)0.0221405029f, + (float16_t)0.225463867f, (float16_t)-0.136230469f, (float16_t)-0.216552734f, (float16_t)-0.286376953f, + (float16_t)-0.0637817383f, (float16_t)0.0464477539f, (float16_t)0.155883789f, (float16_t)0.108581543f, + (float16_t)-0.00881958008f, (float16_t)-0.256835938f, (float16_t)-0.297607422f, (float16_t)-0.152709961f, + (float16_t)-0.255371094f, (float16_t)0.0608825684f, (float16_t)0.333496094f, (float16_t)-0.347167969f, + (float16_t)0.0197601318f, (float16_t)-0.233886719f, (float16_t)-0.0277709961f, (float16_t)0.182128906f, + (float16_t)0.343017578f, (float16_t)0.334716797f, (float16_t)0.0946044922f, (float16_t)-0.120117188f, + (float16_t)-0.0415344238f, (float16_t)0.251708984f, (float16_t)-0.0394897461f, (float16_t)0.0580749512f, + (float16_t)-0.107177734f, (float16_t)0.278564453f, (float16_t)0.0559997559f, (float16_t)0.165039062f, + (float16_t)-0.120727539f, (float16_t)0.0345458984f, (float16_t)-0.340332031f, (float16_t)0.0426330566f, + (float16_t)0.0397644043f, (float16_t)0.208862305f, (float16_t)0.0155334473f, (float16_t)0.272460938f, + (float16_t)0.15625f, (float16_t)-0.332763672f, (float16_t)-0.232543945f, (float16_t)-0.0732421875f, + (float16_t)0.0993041992f, (float16_t)0.137207031f, (float16_t)0.345947266f, (float16_t)0.0774536133f, + (float16_t)0.186157227f, (float16_t)-0.025970459f, (float16_t)-0.0563354492f, (float16_t)-0.287353516f, + (float16_t)0.303466797f, (float16_t)-0.103027344f, (float16_t)0.224609375f, (float16_t)-0.272949219f, + (float16_t)-0.0226593018f, (float16_t)-0.225463867f, (float16_t)-0.256835938f, (float16_t)0.137695312f, + (float16_t)0.270996094f, (float16_t)0.181518555f, (float16_t)-0.318603516f, (float16_t)0.0800170898f, + (float16_t)0.152709961f, (float16_t)-0.197631836f, (float16_t)0.308105469f, (float16_t)-0.116699219f, + (float16_t)-0.194702148f, (float16_t)0.347167969f, (float16_t)0.257568359f, (float16_t)-0.117370605f, + (float16_t)0.273681641f, (float16_t)-0.33984375f, (float16_t)0.00415802002f, (float16_t)0.0933837891f, + (float16_t)0.13671875f, (float16_t)-0.335693359f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/config_data.h new file mode 100644 index 00000000..5a24eafa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_1_F16_TIME_MAJOR 1 +#define LSTM_MATCH_1_F16_TIME_STEPS 10 +#define LSTM_MATCH_1_F16_BATCH_SIZE 1 +#define LSTM_MATCH_1_F16_INPUT_SIZE 22 +#define LSTM_MATCH_1_F16_HIDDEN_SIZE 11 +#define LSTM_MATCH_1_F16_CELL_CLIP 0.0f +#define LSTM_MATCH_1_F16_CELL_STATE_SIZE 11 +#define LSTM_MATCH_1_F16_DST_SIZE 110 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_bias_data.h new file mode 100644 index 00000000..c32382c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_forget_bias[11] = {(float16_t)0.091796875f, + (float16_t)-0.110839844f, + (float16_t)-0.212646484f, + (float16_t)-0.107055664f, + (float16_t)-0.391357422f, + (float16_t)0.284912109f, + (float16_t)0.272705078f, + (float16_t)-0.0389709473f, + (float16_t)0.475097656f, + (float16_t)0.0444946289f, + (float16_t)0.0791015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..95bdf866 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_hidden_weights_data.h @@ -0,0 +1,33 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_forget_hidden_weights[121] = { + (float16_t)-0.232299805f, (float16_t)0.318847656f, (float16_t)0.328857422f, (float16_t)-0.18359375f, + (float16_t)0.167236328f, (float16_t)-0.0990600586f, (float16_t)-0.256591797f, (float16_t)-0.104431152f, + (float16_t)-0.0658569336f, (float16_t)-0.108154297f, (float16_t)0.219604492f, (float16_t)0.250976562f, + (float16_t)0.0502929688f, (float16_t)-0.000183343887f, (float16_t)0.153930664f, (float16_t)-0.0176544189f, + (float16_t)-0.325439453f, (float16_t)0.137695312f, (float16_t)-0.0559082031f, (float16_t)0.273925781f, + (float16_t)0.293945312f, (float16_t)-0.315673828f, (float16_t)0.210693359f, (float16_t)0.153442383f, + (float16_t)0.202880859f, (float16_t)-0.0485534668f, (float16_t)-0.0293273926f, (float16_t)-0.310791016f, + (float16_t)-0.232666016f, (float16_t)-0.140136719f, (float16_t)-0.0218658447f, (float16_t)0.343261719f, + (float16_t)0.111328125f, (float16_t)0.0754394531f, (float16_t)0.339355469f, (float16_t)-0.255615234f, + (float16_t)0.223754883f, (float16_t)-0.285644531f, (float16_t)-0.258544922f, (float16_t)-0.272216797f, + (float16_t)0.208251953f, (float16_t)0.316894531f, (float16_t)-0.204589844f, (float16_t)-0.239257812f, + (float16_t)0.20324707f, (float16_t)0.10723877f, (float16_t)0.062286377f, (float16_t)-0.166625977f, + (float16_t)-0.301025391f, (float16_t)-0.112854004f, (float16_t)-0.331787109f, (float16_t)-0.268554688f, + (float16_t)0.349121094f, (float16_t)-0.101074219f, (float16_t)0.0436706543f, (float16_t)-0.20715332f, + (float16_t)-0.182495117f, (float16_t)0.196166992f, (float16_t)0.270263672f, (float16_t)0.317626953f, + (float16_t)-0.0370178223f, (float16_t)0.23828125f, (float16_t)-0.271484375f, (float16_t)0.324951172f, + (float16_t)-0.296875f, (float16_t)0.208496094f, (float16_t)0.229614258f, (float16_t)0.299072266f, + (float16_t)-0.054901123f, (float16_t)-0.248291016f, (float16_t)-0.0537719727f, (float16_t)0.184936523f, + (float16_t)0.341796875f, (float16_t)-0.0718994141f, (float16_t)-0.077331543f, (float16_t)-0.130615234f, + (float16_t)-0.250488281f, (float16_t)-0.239135742f, (float16_t)0.0817871094f, (float16_t)0.138427734f, + (float16_t)0.298339844f, (float16_t)-0.0641479492f, (float16_t)0.0568847656f, (float16_t)-0.295654297f, + (float16_t)0.163330078f, (float16_t)0.263427734f, (float16_t)-0.088684082f, (float16_t)0.0758666992f, + (float16_t)0.11126709f, (float16_t)-0.186767578f, (float16_t)-0.294189453f, (float16_t)-0.172973633f, + (float16_t)-0.0096282959f, (float16_t)0.206054688f, (float16_t)0.335693359f, (float16_t)-0.189453125f, + (float16_t)-0.0911254883f, (float16_t)-0.169433594f, (float16_t)-0.0914916992f, (float16_t)0.177490234f, + (float16_t)0.168945312f, (float16_t)0.134399414f, (float16_t)-0.0415039062f, (float16_t)-0.031829834f, + (float16_t)0.325195312f, (float16_t)0.249023438f, (float16_t)-0.199829102f, (float16_t)-0.239624023f, + (float16_t)-0.0835571289f, (float16_t)0.204101562f, (float16_t)0.13671875f, (float16_t)-0.25390625f, + (float16_t)0.00958251953f, (float16_t)-0.225708008f, (float16_t)-0.343994141f, (float16_t)-0.154907227f, + (float16_t)0.117614746f, (float16_t)0.0625f, (float16_t)0.327880859f, (float16_t)0.0714111328f, + (float16_t)0.270751953f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_input_weights_data.h new file mode 100644 index 00000000..f84173c4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/forget_input_weights_data.h @@ -0,0 +1,63 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_forget_input_weights[242] = { + (float16_t)-0.251464844f, (float16_t)-0.306396484f, (float16_t)0.0153427124f, (float16_t)-0.287109375f, + (float16_t)-0.348144531f, (float16_t)0.274414062f, (float16_t)0.0314025879f, (float16_t)0.290527344f, + (float16_t)0.00458145142f, (float16_t)0.0894775391f, (float16_t)-0.307617188f, (float16_t)-0.294921875f, + (float16_t)-0.0833129883f, (float16_t)0.263916016f, (float16_t)0.0346984863f, (float16_t)0.256103516f, + (float16_t)-0.0874633789f, (float16_t)-0.0966186523f, (float16_t)-0.0426330566f, (float16_t)-0.00593566895f, + (float16_t)-0.115356445f, (float16_t)-0.0552062988f, (float16_t)0.104675293f, (float16_t)-0.201782227f, + (float16_t)-0.335693359f, (float16_t)-0.0867919922f, (float16_t)0.0979614258f, (float16_t)0.121765137f, + (float16_t)0.307617188f, (float16_t)-0.346191406f, (float16_t)-0.158447266f, (float16_t)-0.190917969f, + (float16_t)-0.0877075195f, (float16_t)0.213012695f, (float16_t)-0.325195312f, (float16_t)-0.231933594f, + (float16_t)-0.0343017578f, (float16_t)0.294433594f, (float16_t)0.153564453f, (float16_t)-0.216552734f, + (float16_t)0.185546875f, (float16_t)0.0657958984f, (float16_t)-0.1875f, (float16_t)0.298583984f, + (float16_t)0.241210938f, (float16_t)-0.177124023f, (float16_t)-0.200561523f, (float16_t)0.0991210938f, + (float16_t)0.13659668f, (float16_t)0.343994141f, (float16_t)-0.298095703f, (float16_t)0.205688477f, + (float16_t)-0.185913086f, (float16_t)-0.297607422f, (float16_t)0.196289062f, (float16_t)0.326660156f, + (float16_t)0.298583984f, (float16_t)-0.200927734f, (float16_t)-0.0547790527f, (float16_t)-0.343505859f, + (float16_t)-0.33203125f, (float16_t)0.345458984f, (float16_t)-0.338623047f, (float16_t)-0.0919189453f, + (float16_t)-0.335693359f, (float16_t)0.0263519287f, (float16_t)0.275390625f, (float16_t)-0.13659668f, + (float16_t)-0.220703125f, (float16_t)-0.23425293f, (float16_t)0.119628906f, (float16_t)0.130859375f, + (float16_t)0.12286377f, (float16_t)-0.284179688f, (float16_t)0.309082031f, (float16_t)0.268066406f, + (float16_t)-0.218994141f, (float16_t)0.0551757812f, (float16_t)0.338623047f, (float16_t)-0.279785156f, + (float16_t)0.226806641f, (float16_t)-0.0515136719f, (float16_t)0.182128906f, (float16_t)0.154785156f, + (float16_t)0.223632812f, (float16_t)-0.335205078f, (float16_t)-0.249511719f, (float16_t)-0.0248413086f, + (float16_t)0.188964844f, (float16_t)0.283935547f, (float16_t)0.221191406f, (float16_t)0.0722045898f, + (float16_t)0.202514648f, (float16_t)0.207885742f, (float16_t)0.165161133f, (float16_t)-0.222900391f, + (float16_t)0.218994141f, (float16_t)-0.311523438f, (float16_t)0.0478515625f, (float16_t)-0.271972656f, + (float16_t)-0.25390625f, (float16_t)0.0416870117f, (float16_t)-0.0421142578f, (float16_t)0.125854492f, + (float16_t)0.172607422f, (float16_t)0.133056641f, (float16_t)-0.290283203f, (float16_t)-0.0852661133f, + (float16_t)-0.240966797f, (float16_t)-0.152587891f, (float16_t)-0.02394104f, (float16_t)-0.125244141f, + (float16_t)0.0936889648f, (float16_t)0.0569152832f, (float16_t)0.0704956055f, (float16_t)0.0762329102f, + (float16_t)-0.269775391f, (float16_t)-0.112304688f, (float16_t)-0.0823974609f, (float16_t)0.0520629883f, + (float16_t)0.0577392578f, (float16_t)-0.053527832f, (float16_t)0.327148438f, (float16_t)0.158691406f, + (float16_t)0.16003418f, (float16_t)0.158569336f, (float16_t)-0.156738281f, (float16_t)0.130493164f, + (float16_t)-0.158447266f, (float16_t)-0.217895508f, (float16_t)0.211425781f, (float16_t)-0.123962402f, + (float16_t)-0.00198364258f, (float16_t)-0.0833129883f, (float16_t)-0.302001953f, (float16_t)-0.136108398f, + (float16_t)-0.179443359f, (float16_t)0.218383789f, (float16_t)0.256103516f, (float16_t)0.103515625f, + (float16_t)0.113830566f, (float16_t)0.0648193359f, (float16_t)0.0285797119f, (float16_t)-0.185058594f, + (float16_t)-0.166625977f, (float16_t)0.0320739746f, (float16_t)0.190673828f, (float16_t)0.0397033691f, + (float16_t)-0.189331055f, (float16_t)0.0758666992f, (float16_t)0.0399780273f, (float16_t)0.0775146484f, + (float16_t)0.156616211f, (float16_t)0.209594727f, (float16_t)-0.179199219f, (float16_t)-0.135498047f, + (float16_t)0.0974121094f, (float16_t)-0.227905273f, (float16_t)-0.330810547f, (float16_t)0.314453125f, + (float16_t)0.205810547f, (float16_t)0.208374023f, (float16_t)-0.0251617432f, (float16_t)0.226318359f, + (float16_t)-0.234863281f, (float16_t)-0.0497131348f, (float16_t)0.146606445f, (float16_t)0.293457031f, + (float16_t)0.0168762207f, (float16_t)0.0228729248f, (float16_t)0.224853516f, (float16_t)0.102233887f, + (float16_t)-0.260498047f, (float16_t)0.183837891f, (float16_t)-0.321289062f, (float16_t)0.224243164f, + (float16_t)-0.23828125f, (float16_t)0.234863281f, (float16_t)-0.153198242f, (float16_t)-0.197265625f, + (float16_t)-0.314208984f, (float16_t)0.241577148f, (float16_t)0.0362243652f, (float16_t)-0.219848633f, + (float16_t)0.0901489258f, (float16_t)-0.340332031f, (float16_t)-0.139648438f, (float16_t)0.326416016f, + (float16_t)0.0833740234f, (float16_t)-0.177246094f, (float16_t)-0.00938415527f, (float16_t)-0.0375671387f, + (float16_t)-0.151977539f, (float16_t)0.00823974609f, (float16_t)0.284912109f, (float16_t)0.177734375f, + (float16_t)-0.105529785f, (float16_t)0.287841797f, (float16_t)0.283447266f, (float16_t)0.00522232056f, + (float16_t)-0.0390014648f, (float16_t)-0.215820312f, (float16_t)0.247070312f, (float16_t)0.182617188f, + (float16_t)-0.13671875f, (float16_t)0.1484375f, (float16_t)-0.338134766f, (float16_t)-0.143310547f, + (float16_t)0.267822266f, (float16_t)-0.0556945801f, (float16_t)0.315673828f, (float16_t)0.200439453f, + (float16_t)0.333251953f, (float16_t)0.0562438965f, (float16_t)0.109558105f, (float16_t)-0.232910156f, + (float16_t)-0.171020508f, (float16_t)0.298095703f, (float16_t)-0.0852050781f, (float16_t)0.0176086426f, + (float16_t)-0.0392150879f, (float16_t)-0.0113983154f, (float16_t)-0.0517578125f, (float16_t)-0.259521484f, + (float16_t)0.265869141f, (float16_t)0.19543457f, (float16_t)-0.0973510742f, (float16_t)0.208007812f, + (float16_t)-0.182617188f, (float16_t)0.235961914f, (float16_t)0.334960938f, (float16_t)-0.00849914551f, + (float16_t)0.274658203f, (float16_t)-0.076171875f, (float16_t)-0.0834350586f, (float16_t)-0.0986328125f, + (float16_t)-0.0466308594f, (float16_t)-0.194335938f, (float16_t)0.0411071777f, (float16_t)-0.299316406f, + (float16_t)-0.117614746f, (float16_t)0.160522461f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_bias_data.h new file mode 100644 index 00000000..fddd8d9b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_input_bias[11] = {(float16_t)0.311523438f, + (float16_t)-0.145019531f, + (float16_t)0.183837891f, + (float16_t)-0.062286377f, + (float16_t)0.15222168f, + (float16_t)-0.402099609f, + (float16_t)0.312011719f, + (float16_t)0.302490234f, + (float16_t)-0.100402832f, + (float16_t)0.0778808594f, + (float16_t)0.0491638184f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_data.h new file mode 100644 index 00000000..dfc30415 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_data.h @@ -0,0 +1,57 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_input[220] = { + (float16_t)-0.694824219f, (float16_t)-0.978515625f, (float16_t)0.576660156f, (float16_t)-0.0925292969f, + (float16_t)0.406005859f, (float16_t)-0.425048828f, (float16_t)0.822265625f, (float16_t)-0.0760498047f, + (float16_t)0.103088379f, (float16_t)-0.911621094f, (float16_t)-0.805175781f, (float16_t)0.231323242f, + (float16_t)-0.912597656f, (float16_t)0.565917969f, (float16_t)0.395751953f, (float16_t)0.203369141f, + (float16_t)0.0235748291f, (float16_t)0.647460938f, (float16_t)0.975585938f, (float16_t)-0.894042969f, + (float16_t)-0.300292969f, (float16_t)-0.0302124023f, (float16_t)0.059173584f, (float16_t)0.912109375f, + (float16_t)0.196166992f, (float16_t)-0.591796875f, (float16_t)-0.545410156f, (float16_t)-0.200073242f, + (float16_t)-0.10333252f, (float16_t)0.308105469f, (float16_t)-0.109069824f, (float16_t)0.791992188f, + (float16_t)-0.728515625f, (float16_t)0.302978516f, (float16_t)0.989257812f, (float16_t)-0.305419922f, + (float16_t)-0.127685547f, (float16_t)-0.812011719f, (float16_t)0.200195312f, (float16_t)-0.504394531f, + (float16_t)-0.0317382812f, (float16_t)-0.987792969f, (float16_t)0.771484375f, (float16_t)-0.142944336f, + (float16_t)0.902832031f, (float16_t)-0.839355469f, (float16_t)-0.633300781f, (float16_t)0.796386719f, + (float16_t)0.805175781f, (float16_t)-0.248657227f, (float16_t)-0.634277344f, (float16_t)0.259765625f, + (float16_t)0.313964844f, (float16_t)0.606445312f, (float16_t)-0.15234375f, (float16_t)0.873046875f, + (float16_t)0.544921875f, (float16_t)-0.123291016f, (float16_t)-0.607910156f, (float16_t)-0.79296875f, + (float16_t)0.0250549316f, (float16_t)-0.94140625f, (float16_t)0.421630859f, (float16_t)0.356933594f, + (float16_t)-0.0971069336f, (float16_t)0.751464844f, (float16_t)-0.676757812f, (float16_t)-0.688476562f, + (float16_t)0.103942871f, (float16_t)-0.413085938f, (float16_t)0.174194336f, (float16_t)-0.78125f, + (float16_t)0.527832031f, (float16_t)0.135253906f, (float16_t)0.634277344f, (float16_t)-0.174926758f, + (float16_t)0.486816406f, (float16_t)0.614746094f, (float16_t)-0.475341797f, (float16_t)-0.330810547f, + (float16_t)-0.112365723f, (float16_t)-0.0989990234f, (float16_t)-0.279785156f, (float16_t)0.711914062f, + (float16_t)0.897949219f, (float16_t)0.765136719f, (float16_t)-0.0988769531f, (float16_t)0.229614258f, + (float16_t)0.0598449707f, (float16_t)0.940429688f, (float16_t)-0.370605469f, (float16_t)-0.215087891f, + (float16_t)-0.09765625f, (float16_t)-0.114196777f, (float16_t)0.117675781f, (float16_t)-0.819824219f, + (float16_t)-0.0187988281f, (float16_t)-0.208251953f, (float16_t)0.18347168f, (float16_t)0.874023438f, + (float16_t)0.985351562f, (float16_t)-0.425048828f, (float16_t)0.497314453f, (float16_t)0.594238281f, + (float16_t)0.000763893127f, (float16_t)0.791992188f, (float16_t)0.542480469f, (float16_t)-0.138916016f, + (float16_t)-0.418701172f, (float16_t)0.231445312f, (float16_t)-0.476074219f, (float16_t)0.299072266f, + (float16_t)0.414306641f, (float16_t)0.790039062f, (float16_t)0.709960938f, (float16_t)-0.349609375f, + (float16_t)-0.987304688f, (float16_t)-0.0670776367f, (float16_t)-0.0376281738f, (float16_t)-0.794433594f, + (float16_t)0.20703125f, (float16_t)0.118713379f, (float16_t)-0.0338134766f, (float16_t)-0.247558594f, + (float16_t)0.458007812f, (float16_t)-0.253173828f, (float16_t)0.059173584f, (float16_t)0.00807952881f, + (float16_t)-0.704101562f, (float16_t)-0.323730469f, (float16_t)-0.911132812f, (float16_t)-0.272460938f, + (float16_t)-0.110046387f, (float16_t)0.424804688f, (float16_t)-0.3984375f, (float16_t)0.395996094f, + (float16_t)0.471679688f, (float16_t)-0.301025391f, (float16_t)-0.337890625f, (float16_t)-0.2578125f, + (float16_t)-0.485839844f, (float16_t)-0.80078125f, (float16_t)0.302490234f, (float16_t)0.80078125f, + (float16_t)0.595703125f, (float16_t)0.561523438f, (float16_t)0.346923828f, (float16_t)0.553222656f, + (float16_t)-0.142578125f, (float16_t)-0.894042969f, (float16_t)-0.344238281f, (float16_t)0.158691406f, + (float16_t)-0.65625f, (float16_t)0.0119476318f, (float16_t)0.721679688f, (float16_t)-0.404296875f, + (float16_t)-0.89453125f, (float16_t)-0.9453125f, (float16_t)0.738769531f, (float16_t)0.342529297f, + (float16_t)-0.131835938f, (float16_t)0.568359375f, (float16_t)-0.428955078f, (float16_t)-0.0378723145f, + (float16_t)0.922851562f, (float16_t)0.563476562f, (float16_t)-0.564453125f, (float16_t)-0.494140625f, + (float16_t)-0.301025391f, (float16_t)0.0745849609f, (float16_t)-0.159301758f, (float16_t)0.876464844f, + (float16_t)0.191162109f, (float16_t)-0.2265625f, (float16_t)-0.646972656f, (float16_t)-0.157104492f, + (float16_t)-0.689453125f, (float16_t)0.337402344f, (float16_t)0.205078125f, (float16_t)-0.552246094f, + (float16_t)0.148803711f, (float16_t)-0.309326172f, (float16_t)0.56640625f, (float16_t)-0.583496094f, + (float16_t)-0.175048828f, (float16_t)0.841308594f, (float16_t)-0.466308594f, (float16_t)-0.151855469f, + (float16_t)0.932128906f, (float16_t)-0.311035156f, (float16_t)-0.529296875f, (float16_t)-0.895996094f, + (float16_t)-0.84765625f, (float16_t)0.439941406f, (float16_t)-0.453125f, (float16_t)0.366943359f, + (float16_t)-0.231689453f, (float16_t)-0.671875f, (float16_t)-0.00164699554f, (float16_t)-0.568359375f, + (float16_t)0.407226562f, (float16_t)-0.427246094f, (float16_t)0.840332031f, (float16_t)-0.822265625f, + (float16_t)0.274169922f, (float16_t)-0.312988281f, (float16_t)0.301269531f, (float16_t)0.261962891f, + (float16_t)0.164306641f, (float16_t)-0.358398438f, (float16_t)0.0917358398f, (float16_t)0.144165039f, + (float16_t)-0.870605469f, (float16_t)0.886230469f, (float16_t)-0.212524414f, (float16_t)-0.543457031f, + (float16_t)0.263916016f, (float16_t)0.926269531f, (float16_t)-0.864746094f, (float16_t)-0.771484375f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..5bf6d9a4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_hidden_weights_data.h @@ -0,0 +1,33 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_input_hidden_weights[121] = { + (float16_t)0.0531616211f, (float16_t)0.224365234f, (float16_t)0.169921875f, (float16_t)-0.0297851562f, + (float16_t)0.100036621f, (float16_t)0.161132812f, (float16_t)-0.289306641f, (float16_t)0.110229492f, + (float16_t)0.293212891f, (float16_t)-0.287841797f, (float16_t)-0.135742188f, (float16_t)-0.146362305f, + (float16_t)-0.176147461f, (float16_t)0.157714844f, (float16_t)0.0784912109f, (float16_t)-0.307861328f, + (float16_t)0.0468139648f, (float16_t)-0.297607422f, (float16_t)0.310302734f, (float16_t)0.330566406f, + (float16_t)-0.319580078f, (float16_t)-0.329833984f, (float16_t)-0.164794922f, (float16_t)0.186035156f, + (float16_t)0.286865234f, (float16_t)-0.270019531f, (float16_t)0.104187012f, (float16_t)-0.0377197266f, + (float16_t)-0.168579102f, (float16_t)0.263427734f, (float16_t)-0.126586914f, (float16_t)-0.10760498f, + (float16_t)-0.345214844f, (float16_t)0.255615234f, (float16_t)-0.226074219f, (float16_t)0.295410156f, + (float16_t)-0.0625f, (float16_t)-0.0679931641f, (float16_t)0.151855469f, (float16_t)-0.342285156f, + (float16_t)0.236083984f, (float16_t)-0.272949219f, (float16_t)0.278808594f, (float16_t)0.0473327637f, + (float16_t)0.160644531f, (float16_t)-0.0119018555f, (float16_t)-0.132202148f, (float16_t)-0.321777344f, + (float16_t)-0.0940551758f, (float16_t)0.0786132812f, (float16_t)0.170043945f, (float16_t)0.181396484f, + (float16_t)-0.0511169434f, (float16_t)-0.127563477f, (float16_t)-0.0634765625f, (float16_t)-0.105041504f, + (float16_t)0.0855712891f, (float16_t)-0.245361328f, (float16_t)0.0928344727f, (float16_t)0.327880859f, + (float16_t)-0.149169922f, (float16_t)0.249511719f, (float16_t)0.318359375f, (float16_t)-0.313476562f, + (float16_t)-0.284423828f, (float16_t)-0.101928711f, (float16_t)0.0941162109f, (float16_t)-0.169677734f, + (float16_t)0.0841064453f, (float16_t)0.208862305f, (float16_t)0.194458008f, (float16_t)-0.205566406f, + (float16_t)0.311767578f, (float16_t)0.227294922f, (float16_t)0.0902709961f, (float16_t)-0.0138244629f, + (float16_t)-0.12286377f, (float16_t)-0.0598144531f, (float16_t)-0.133422852f, (float16_t)0.174072266f, + (float16_t)0.225708008f, (float16_t)-0.200927734f, (float16_t)0.197753906f, (float16_t)0.334472656f, + (float16_t)-0.0467224121f, (float16_t)0.259033203f, (float16_t)0.0859985352f, (float16_t)-0.239746094f, + (float16_t)-0.164428711f, (float16_t)-0.16784668f, (float16_t)-0.307373047f, (float16_t)-0.231323242f, + (float16_t)0.159790039f, (float16_t)0.16027832f, (float16_t)0.00574493408f, (float16_t)0.219238281f, + (float16_t)0.340576172f, (float16_t)0.122253418f, (float16_t)0.162719727f, (float16_t)0.0609741211f, + (float16_t)-0.258544922f, (float16_t)-0.322509766f, (float16_t)-0.186645508f, (float16_t)0.0772094727f, + (float16_t)-0.303222656f, (float16_t)0.202392578f, (float16_t)0.142822266f, (float16_t)-0.032623291f, + (float16_t)0.108764648f, (float16_t)0.0989379883f, (float16_t)-0.217041016f, (float16_t)0.15637207f, + (float16_t)0.173583984f, (float16_t)-0.324951172f, (float16_t)-0.284912109f, (float16_t)-0.123718262f, + (float16_t)-0.190673828f, (float16_t)-0.0494689941f, (float16_t)0.119812012f, (float16_t)-0.265380859f, + (float16_t)-0.21105957f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_input_weights_data.h new file mode 100644 index 00000000..eed0da40 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/input_input_weights_data.h @@ -0,0 +1,63 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_input_input_weights[242] = { + (float16_t)-0.203613281f, (float16_t)0.206054688f, (float16_t)-0.146240234f, (float16_t)0.333496094f, + (float16_t)-0.300537109f, (float16_t)0.243286133f, (float16_t)0.0256347656f, (float16_t)0.150878906f, + (float16_t)-0.0750732422f, (float16_t)0.0850219727f, (float16_t)0.182983398f, (float16_t)0.326416016f, + (float16_t)0.181762695f, (float16_t)-0.278808594f, (float16_t)0.0723876953f, (float16_t)-0.228393555f, + (float16_t)0.19934082f, (float16_t)-0.0438537598f, (float16_t)-0.316162109f, (float16_t)-0.337646484f, + (float16_t)-0.0341491699f, (float16_t)-0.0182342529f, (float16_t)0.106689453f, (float16_t)0.0144882202f, + (float16_t)-0.0955200195f, (float16_t)0.0556945801f, (float16_t)-0.270751953f, (float16_t)0.0253143311f, + (float16_t)-0.122070312f, (float16_t)-0.158569336f, (float16_t)0.232299805f, (float16_t)0.135009766f, + (float16_t)-0.182373047f, (float16_t)0.138061523f, (float16_t)0.155639648f, (float16_t)-0.205078125f, + (float16_t)-0.265380859f, (float16_t)-0.220825195f, (float16_t)0.303222656f, (float16_t)0.301025391f, + (float16_t)-0.111938477f, (float16_t)0.348388672f, (float16_t)-0.0134277344f, (float16_t)0.249389648f, + (float16_t)0.229492188f, (float16_t)0.2109375f, (float16_t)0.348632812f, (float16_t)-0.235473633f, + (float16_t)0.00899505615f, (float16_t)0.212524414f, (float16_t)-0.268310547f, (float16_t)-0.277099609f, + (float16_t)0.349121094f, (float16_t)0.117614746f, (float16_t)-0.258300781f, (float16_t)0.326904297f, + (float16_t)0.16418457f, (float16_t)-0.326416016f, (float16_t)-0.309326172f, (float16_t)0.345214844f, + (float16_t)0.103393555f, (float16_t)0.0152206421f, (float16_t)0.331542969f, (float16_t)-0.105529785f, + (float16_t)0.34375f, (float16_t)-0.0364685059f, (float16_t)0.173217773f, (float16_t)0.00203514099f, + (float16_t)0.0798950195f, (float16_t)-0.303710938f, (float16_t)0.103393555f, (float16_t)-0.0252990723f, + (float16_t)0.19128418f, (float16_t)-0.333496094f, (float16_t)0.227416992f, (float16_t)-0.118286133f, + (float16_t)-0.173706055f, (float16_t)0.260986328f, (float16_t)0.180419922f, (float16_t)-0.196044922f, + (float16_t)-0.0461120605f, (float16_t)-0.169067383f, (float16_t)-0.0234375f, (float16_t)0.0791015625f, + (float16_t)-0.170288086f, (float16_t)-0.0277404785f, (float16_t)0.195556641f, (float16_t)0.125f, + (float16_t)-0.339599609f, (float16_t)-0.129760742f, (float16_t)-0.0182800293f, (float16_t)-0.330566406f, + (float16_t)-0.234008789f, (float16_t)-0.129882812f, (float16_t)0.15612793f, (float16_t)0.07421875f, + (float16_t)0.169189453f, (float16_t)-0.30078125f, (float16_t)0.145751953f, (float16_t)-0.053894043f, + (float16_t)0.00434875488f, (float16_t)0.256591797f, (float16_t)-0.108154297f, (float16_t)-0.342529297f, + (float16_t)0.254882812f, (float16_t)-0.0418701172f, (float16_t)-0.162963867f, (float16_t)-0.221923828f, + (float16_t)0.323486328f, (float16_t)-0.17590332f, (float16_t)0.260498047f, (float16_t)-0.146728516f, + (float16_t)-0.271728516f, (float16_t)-0.166259766f, (float16_t)0.0405883789f, (float16_t)-0.239990234f, + (float16_t)0.225219727f, (float16_t)0.0111923218f, (float16_t)-0.125488281f, (float16_t)-0.0630493164f, + (float16_t)-0.234985352f, (float16_t)0.333740234f, (float16_t)0.0601806641f, (float16_t)0.118408203f, + (float16_t)-0.151855469f, (float16_t)-0.147094727f, (float16_t)0.250976562f, (float16_t)-0.310546875f, + (float16_t)-0.073059082f, (float16_t)-0.236083984f, (float16_t)0.188476562f, (float16_t)0.00714874268f, + (float16_t)-0.253417969f, (float16_t)-0.296386719f, (float16_t)0.28515625f, (float16_t)-0.133300781f, + (float16_t)-0.293945312f, (float16_t)-0.297363281f, (float16_t)0.0440673828f, (float16_t)-0.345703125f, + (float16_t)-0.0531616211f, (float16_t)-0.0551147461f, (float16_t)0.058013916f, (float16_t)-0.200195312f, + (float16_t)-0.131103516f, (float16_t)-0.121582031f, (float16_t)-0.107421875f, (float16_t)0.319335938f, + (float16_t)0.112731934f, (float16_t)-0.134277344f, (float16_t)0.11895752f, (float16_t)-0.243408203f, + (float16_t)0.130004883f, (float16_t)-0.135498047f, (float16_t)0.325683594f, (float16_t)-0.0983276367f, + (float16_t)0.24597168f, (float16_t)-0.199584961f, (float16_t)-0.192504883f, (float16_t)-0.269042969f, + (float16_t)0.321044922f, (float16_t)-0.145263672f, (float16_t)0.00662994385f, (float16_t)0.19128418f, + (float16_t)-0.177246094f, (float16_t)0.152099609f, (float16_t)0.0634155273f, (float16_t)-0.224121094f, + (float16_t)0.279541016f, (float16_t)0.269287109f, (float16_t)-0.14453125f, (float16_t)-0.310302734f, + (float16_t)0.348632812f, (float16_t)-0.039855957f, (float16_t)-0.0657958984f, (float16_t)-0.174682617f, + (float16_t)0.320556641f, (float16_t)-0.254394531f, (float16_t)-0.00721359253f, (float16_t)-0.00448989868f, + (float16_t)0.185424805f, (float16_t)-0.0806274414f, (float16_t)0.311767578f, (float16_t)0.126586914f, + (float16_t)0.124694824f, (float16_t)0.179321289f, (float16_t)0.139038086f, (float16_t)-0.0669555664f, + (float16_t)-0.00110530853f, (float16_t)0.344726562f, (float16_t)0.0748901367f, (float16_t)-0.0571289062f, + (float16_t)-0.311767578f, (float16_t)-0.275878906f, (float16_t)-0.319335938f, (float16_t)0.0802612305f, + (float16_t)0.160522461f, (float16_t)-0.0114898682f, (float16_t)-0.211181641f, (float16_t)-0.0464782715f, + (float16_t)-0.0999145508f, (float16_t)0.349121094f, (float16_t)-0.174438477f, (float16_t)0.165161133f, + (float16_t)-0.272460938f, (float16_t)-0.208618164f, (float16_t)0.205688477f, (float16_t)0.164794922f, + (float16_t)-0.206176758f, (float16_t)-0.0589599609f, (float16_t)-0.1328125f, (float16_t)0.162963867f, + (float16_t)0.0783691406f, (float16_t)0.307617188f, (float16_t)-0.000752925873f, (float16_t)-0.264160156f, + (float16_t)-0.154296875f, (float16_t)-0.0889892578f, (float16_t)0.305908203f, (float16_t)-0.332275391f, + (float16_t)-0.300292969f, (float16_t)0.126342773f, (float16_t)0.143676758f, (float16_t)-0.263427734f, + (float16_t)0.0413513184f, (float16_t)0.236450195f, (float16_t)0.232910156f, (float16_t)0.109985352f, + (float16_t)-0.265625f, (float16_t)0.301757812f, (float16_t)-0.017074585f, (float16_t)-0.0352478027f, + (float16_t)-0.246337891f, (float16_t)0.215820312f, (float16_t)0.311523438f, (float16_t)-0.105163574f, + (float16_t)-0.186889648f, (float16_t)-0.30859375f, (float16_t)-0.182128906f, (float16_t)-0.0351867676f, + (float16_t)-0.0138473511f, (float16_t)-0.105957031f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_bias_data.h new file mode 100644 index 00000000..b6361754 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_output_bias[11] = {(float16_t)0.0905151367f, + (float16_t)-0.193237305f, + (float16_t)-0.0345458984f, + (float16_t)0.43359375f, + (float16_t)-0.0293121338f, + (float16_t)-0.10559082f, + (float16_t)0.265869141f, + (float16_t)-0.06640625f, + (float16_t)-0.232543945f, + (float16_t)-0.0215454102f, + (float16_t)-0.132202148f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..7359b525 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_hidden_weights_data.h @@ -0,0 +1,33 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_output_hidden_weights[121] = { + (float16_t)-0.121459961f, (float16_t)0.235473633f, (float16_t)0.190551758f, (float16_t)0.0147247314f, + (float16_t)0.183837891f, (float16_t)-0.00748825073f, (float16_t)-0.0499267578f, (float16_t)0.203491211f, + (float16_t)0.275634766f, (float16_t)0.259277344f, (float16_t)-0.0857543945f, (float16_t)-0.182006836f, + (float16_t)-0.270019531f, (float16_t)0.0905761719f, (float16_t)-0.0101623535f, (float16_t)-0.054473877f, + (float16_t)-0.146484375f, (float16_t)0.276367188f, (float16_t)0.161499023f, (float16_t)-0.106750488f, + (float16_t)0.00251197815f, (float16_t)-0.135986328f, (float16_t)0.0305938721f, (float16_t)-0.296142578f, + (float16_t)-0.0751953125f, (float16_t)0.302001953f, (float16_t)0.0366821289f, (float16_t)-0.000613212585f, + (float16_t)-0.203125f, (float16_t)-0.0833740234f, (float16_t)0.122131348f, (float16_t)0.024017334f, + (float16_t)0.344726562f, (float16_t)-0.210693359f, (float16_t)0.170043945f, (float16_t)-0.340087891f, + (float16_t)-0.119689941f, (float16_t)0.286132812f, (float16_t)-0.144775391f, (float16_t)0.193237305f, + (float16_t)-0.0840454102f, (float16_t)0.148925781f, (float16_t)-0.0682373047f, (float16_t)-0.153320312f, + (float16_t)0.282226562f, (float16_t)0.31640625f, (float16_t)-0.0933837891f, (float16_t)0.045135498f, + (float16_t)-0.219482422f, (float16_t)0.317382812f, (float16_t)-0.150024414f, (float16_t)-0.0870361328f, + (float16_t)-0.0430908203f, (float16_t)-0.289794922f, (float16_t)0.146606445f, (float16_t)-0.0504760742f, + (float16_t)0.261474609f, (float16_t)0.0265350342f, (float16_t)-0.316650391f, (float16_t)0.0538635254f, + (float16_t)-0.0678100586f, (float16_t)0.0776977539f, (float16_t)-0.343261719f, (float16_t)0.312011719f, + (float16_t)0.160644531f, (float16_t)-0.197143555f, (float16_t)0.289306641f, (float16_t)-0.0916748047f, + (float16_t)0.314941406f, (float16_t)0.0799560547f, (float16_t)0.213256836f, (float16_t)-0.139038086f, + (float16_t)-0.228515625f, (float16_t)-0.331787109f, (float16_t)0.218017578f, (float16_t)0.316162109f, + (float16_t)0.306396484f, (float16_t)-0.315673828f, (float16_t)-0.137573242f, (float16_t)-0.208984375f, + (float16_t)0.283935547f, (float16_t)0.135131836f, (float16_t)-0.302734375f, (float16_t)0.103149414f, + (float16_t)0.23059082f, (float16_t)-0.285888672f, (float16_t)0.265869141f, (float16_t)0.153686523f, + (float16_t)-0.284667969f, (float16_t)0.0646972656f, (float16_t)-0.231811523f, (float16_t)0.218383789f, + (float16_t)0.135986328f, (float16_t)-0.236938477f, (float16_t)0.171630859f, (float16_t)-0.173461914f, + (float16_t)0.318359375f, (float16_t)-0.0664672852f, (float16_t)-0.125244141f, (float16_t)-0.0145950317f, + (float16_t)0.154052734f, (float16_t)0.0811767578f, (float16_t)-0.0376586914f, (float16_t)-0.209228516f, + (float16_t)0.180419922f, (float16_t)-0.327148438f, (float16_t)0.193237305f, (float16_t)0.111755371f, + (float16_t)-0.201538086f, (float16_t)0.0489501953f, (float16_t)0.12487793f, (float16_t)0.264648438f, + (float16_t)0.0717773438f, (float16_t)-0.284423828f, (float16_t)0.14465332f, (float16_t)0.314453125f, + (float16_t)0.106445312f, (float16_t)0.0971679688f, (float16_t)0.264404297f, (float16_t)0.0235900879f, + (float16_t)-0.0102081299f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_input_weights_data.h new file mode 100644 index 00000000..295928a4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_input_weights_data.h @@ -0,0 +1,63 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_output_input_weights[242] = { + (float16_t)-0.0147247314f, (float16_t)-0.0331726074f, (float16_t)0.313232422f, (float16_t)0.278564453f, + (float16_t)0.0657348633f, (float16_t)0.276123047f, (float16_t)0.296386719f, (float16_t)0.21496582f, + (float16_t)-0.19921875f, (float16_t)0.296142578f, (float16_t)-0.0492248535f, (float16_t)0.136108398f, + (float16_t)-0.265625f, (float16_t)0.0591430664f, (float16_t)0.286865234f, (float16_t)0.0489501953f, + (float16_t)-0.258544922f, (float16_t)0.21887207f, (float16_t)0.0720825195f, (float16_t)0.15234375f, + (float16_t)-0.288818359f, (float16_t)-0.201538086f, (float16_t)0.0306854248f, (float16_t)-0.333251953f, + (float16_t)-0.183105469f, (float16_t)0.108886719f, (float16_t)-0.173706055f, (float16_t)-0.256103516f, + (float16_t)-0.139526367f, (float16_t)0.157226562f, (float16_t)-0.0606689453f, (float16_t)0.202758789f, + (float16_t)0.107788086f, (float16_t)-0.255859375f, (float16_t)-0.123901367f, (float16_t)0.0404663086f, + (float16_t)0.223632812f, (float16_t)0.267822266f, (float16_t)0.192504883f, (float16_t)0.34765625f, + (float16_t)0.338134766f, (float16_t)0.328857422f, (float16_t)-0.145629883f, (float16_t)-0.243896484f, + (float16_t)-0.0412597656f, (float16_t)-0.200927734f, (float16_t)-0.329345703f, (float16_t)0.265136719f, + (float16_t)-0.0744018555f, (float16_t)0.00196838379f, (float16_t)-0.257080078f, (float16_t)-0.0819091797f, + (float16_t)-0.155029297f, (float16_t)-0.268066406f, (float16_t)0.216186523f, (float16_t)-0.289794922f, + (float16_t)0.0163421631f, (float16_t)-0.236572266f, (float16_t)0.174804688f, (float16_t)0.307373047f, + (float16_t)0.291992188f, (float16_t)-0.114746094f, (float16_t)0.0912475586f, (float16_t)0.232666016f, + (float16_t)-0.0664672852f, (float16_t)0.271728516f, (float16_t)0.285400391f, (float16_t)-0.0231323242f, + (float16_t)-0.0216369629f, (float16_t)0.142822266f, (float16_t)-0.112792969f, (float16_t)0.118103027f, + (float16_t)0.0080947876f, (float16_t)-0.135742188f, (float16_t)0.0642089844f, (float16_t)-0.347167969f, + (float16_t)-0.127075195f, (float16_t)-0.11517334f, (float16_t)0.0134048462f, (float16_t)-0.291259766f, + (float16_t)0.327148438f, (float16_t)0.239990234f, (float16_t)-0.325683594f, (float16_t)0.296142578f, + (float16_t)-0.230102539f, (float16_t)-0.0565490723f, (float16_t)0.100158691f, (float16_t)-0.153442383f, + (float16_t)0.293701172f, (float16_t)-0.321044922f, (float16_t)-0.243530273f, (float16_t)-0.267578125f, + (float16_t)-0.203369141f, (float16_t)0.102661133f, (float16_t)-0.296386719f, (float16_t)-0.338378906f, + (float16_t)0.307617188f, (float16_t)0.155151367f, (float16_t)0.0718383789f, (float16_t)0.109741211f, + (float16_t)-0.275878906f, (float16_t)0.0076675415f, (float16_t)-0.0525512695f, (float16_t)0.0562744141f, + (float16_t)-0.160522461f, (float16_t)0.159545898f, (float16_t)-0.150146484f, (float16_t)0.194946289f, + (float16_t)-0.158569336f, (float16_t)-0.224365234f, (float16_t)-0.181884766f, (float16_t)-0.0922241211f, + (float16_t)-0.248657227f, (float16_t)0.118225098f, (float16_t)-0.305419922f, (float16_t)0.0499572754f, + (float16_t)-0.319335938f, (float16_t)0.104614258f, (float16_t)-0.172851562f, (float16_t)0.159545898f, + (float16_t)0.0149917603f, (float16_t)-0.0609130859f, (float16_t)-0.0525817871f, (float16_t)0.0581054688f, + (float16_t)-0.332275391f, (float16_t)-0.0092010498f, (float16_t)0.0325012207f, (float16_t)0.339111328f, + (float16_t)-0.277587891f, (float16_t)0.21496582f, (float16_t)0.263183594f, (float16_t)0.0281066895f, + (float16_t)0.0409851074f, (float16_t)-0.315429688f, (float16_t)0.233520508f, (float16_t)-0.177124023f, + (float16_t)-0.187255859f, (float16_t)-0.156738281f, (float16_t)0.200439453f, (float16_t)0.080078125f, + (float16_t)-0.169311523f, (float16_t)-0.192749023f, (float16_t)-0.0146026611f, (float16_t)-0.12890625f, + (float16_t)-0.0748901367f, (float16_t)-0.313964844f, (float16_t)0.167480469f, (float16_t)0.338867188f, + (float16_t)-0.192871094f, (float16_t)-0.331298828f, (float16_t)0.10559082f, (float16_t)-0.273681641f, + (float16_t)-0.343994141f, (float16_t)-0.104858398f, (float16_t)0.176147461f, (float16_t)0.00259971619f, + (float16_t)-0.0162963867f, (float16_t)0.238037109f, (float16_t)-0.0665893555f, (float16_t)0.0384216309f, + (float16_t)-0.127075195f, (float16_t)0.0436096191f, (float16_t)0.0426940918f, (float16_t)-0.155151367f, + (float16_t)-0.102966309f, (float16_t)0.258300781f, (float16_t)-0.0864868164f, (float16_t)0.306396484f, + (float16_t)-0.0534667969f, (float16_t)-0.290283203f, (float16_t)0.299804688f, (float16_t)0.29296875f, + (float16_t)0.255126953f, (float16_t)-0.238037109f, (float16_t)-0.318847656f, (float16_t)-0.319091797f, + (float16_t)-0.239501953f, (float16_t)0.106262207f, (float16_t)0.0939941406f, (float16_t)-0.130371094f, + (float16_t)-0.11932373f, (float16_t)-0.183349609f, (float16_t)0.240356445f, (float16_t)0.280761719f, + (float16_t)0.0604858398f, (float16_t)-0.332763672f, (float16_t)-0.200439453f, (float16_t)-0.195068359f, + (float16_t)0.161499023f, (float16_t)-0.223144531f, (float16_t)0.330322266f, (float16_t)-0.077331543f, + (float16_t)-0.191772461f, (float16_t)0.0240478516f, (float16_t)0.299560547f, (float16_t)-0.222167969f, + (float16_t)-0.328369141f, (float16_t)-0.113708496f, (float16_t)-0.0902099609f, (float16_t)0.261962891f, + (float16_t)0.320068359f, (float16_t)-0.171630859f, (float16_t)0.12097168f, (float16_t)-0.293212891f, + (float16_t)0.103271484f, (float16_t)-0.130004883f, (float16_t)-0.139892578f, (float16_t)0.111633301f, + (float16_t)0.170166016f, (float16_t)0.332275391f, (float16_t)0.140869141f, (float16_t)-0.0690917969f, + (float16_t)-0.0677490234f, (float16_t)0.106628418f, (float16_t)-0.0325622559f, (float16_t)0.31640625f, + (float16_t)-0.0168151855f, (float16_t)-0.0768432617f, (float16_t)-0.0473632812f, (float16_t)-0.0686035156f, + (float16_t)-0.331787109f, (float16_t)-0.231689453f, (float16_t)-0.144897461f, (float16_t)-0.226806641f, + (float16_t)-0.243408203f, (float16_t)0.139770508f, (float16_t)0.209838867f, (float16_t)-0.231689453f, + (float16_t)-0.0708007812f, (float16_t)-0.274414062f, (float16_t)0.324462891f, (float16_t)-0.0459899902f, + (float16_t)-0.344482422f, (float16_t)-0.0733642578f, (float16_t)0.118713379f, (float16_t)0.326171875f, + (float16_t)-0.251464844f, (float16_t)-0.228637695f, (float16_t)-0.219116211f, (float16_t)-0.0251159668f, + (float16_t)-0.135620117f, (float16_t)-0.307373047f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_ref_data.h new file mode 100644 index 00000000..fb07b29c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/output_ref_data.h @@ -0,0 +1,30 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_1_f16_output_ref[110] = { + (float16_t)-0.0676269531f, (float16_t)-0.108398438f, (float16_t)0.0557250977f, (float16_t)0.153686523f, + (float16_t)0.08203125f, (float16_t)0.00671005249f, (float16_t)-0.240722656f, (float16_t)0.361083984f, + (float16_t)0.126220703f, (float16_t)-0.0121307373f, (float16_t)-0.126708984f, (float16_t)0.0354003906f, + (float16_t)-0.071472168f, (float16_t)-0.00592041016f, (float16_t)0.207519531f, (float16_t)-0.126342773f, + (float16_t)-0.191894531f, (float16_t)-0.281494141f, (float16_t)0.113342285f, (float16_t)0.0422058105f, + (float16_t)0.0901489258f, (float16_t)-0.122192383f, (float16_t)-0.1328125f, (float16_t)0.0806274414f, + (float16_t)-0.261962891f, (float16_t)-0.0722045898f, (float16_t)-0.136474609f, (float16_t)0.00475311279f, + (float16_t)-0.0977172852f, (float16_t)-0.0601806641f, (float16_t)-0.0631713867f, (float16_t)-0.0358276367f, + (float16_t)-0.0512084961f, (float16_t)-0.121948242f, (float16_t)0.0207672119f, (float16_t)-0.139770508f, + (float16_t)-0.0654907227f, (float16_t)-0.0857543945f, (float16_t)-0.0169677734f, (float16_t)0.0889282227f, + (float16_t)0.190185547f, (float16_t)0.0786132812f, (float16_t)-0.14050293f, (float16_t)-0.213623047f, + (float16_t)0.0158843994f, (float16_t)0.040435791f, (float16_t)-0.27734375f, (float16_t)0.23840332f, + (float16_t)-0.0853881836f, (float16_t)0.0537414551f, (float16_t)0.0921020508f, (float16_t)0.167480469f, + (float16_t)0.0638427734f, (float16_t)-0.00391387939f, (float16_t)-0.212646484f, (float16_t)-0.179931641f, + (float16_t)-0.0813598633f, (float16_t)-0.268554688f, (float16_t)0.144897461f, (float16_t)0.00109577179f, + (float16_t)0.128417969f, (float16_t)-0.0120391846f, (float16_t)0.16784668f, (float16_t)-0.0121917725f, + (float16_t)-0.0656738281f, (float16_t)-0.0615844727f, (float16_t)0.0304870605f, (float16_t)-0.052154541f, + (float16_t)-0.225219727f, (float16_t)-0.0261230469f, (float16_t)-0.0191192627f, (float16_t)0.176757812f, + (float16_t)-0.0667114258f, (float16_t)-0.0729370117f, (float16_t)-0.154785156f, (float16_t)0.0847167969f, + (float16_t)0.0548400879f, (float16_t)0.133789062f, (float16_t)-0.013458252f, (float16_t)-0.0772705078f, + (float16_t)-0.0707397461f, (float16_t)0.11920166f, (float16_t)0.283203125f, (float16_t)0.156860352f, + (float16_t)0.0614929199f, (float16_t)-0.0812988281f, (float16_t)-0.0552368164f, (float16_t)-0.00442886353f, + (float16_t)-0.195068359f, (float16_t)-0.108154297f, (float16_t)0.0103302002f, (float16_t)0.001329422f, + (float16_t)-0.116516113f, (float16_t)0.111816406f, (float16_t)0.17590332f, (float16_t)0.0723876953f, + (float16_t)0.0999145508f, (float16_t)0.0199890137f, (float16_t)-0.234375f, (float16_t)-0.205444336f, + (float16_t)-0.237792969f, (float16_t)-0.212036133f, (float16_t)-0.113586426f, (float16_t)-0.0556640625f, + (float16_t)0.106018066f, (float16_t)0.366210938f, (float16_t)0.243408203f, (float16_t)0.235839844f, + (float16_t)-0.0916748047f, (float16_t)-0.233642578f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_bias_data.h new file mode 100644 index 00000000..2a1b03e8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_cell_bias[11] = {0.367453396f, + 0.334282398f, + 0.0157604646f, + 0.0513678938f, + -0.0226461887f, + -0.144018799f, + -0.197210193f, + -0.158578768f, + -0.161302894f, + 0.177997932f, + -0.156867802f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..3018047e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_hidden_weights_data.h @@ -0,0 +1,20 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_cell_hidden_weights[121] = { + -0.345519394f, -0.337958813f, -0.0854757503f, -0.052027978f, 0.0373693742f, 0.324826807f, -0.190197468f, + 0.201161042f, 0.0815129131f, 0.266807288f, -0.269341409f, 0.308207601f, -0.347043663f, 0.332496017f, + -0.104911074f, -0.102297515f, 0.284773707f, -0.0768789649f, -0.143645778f, -0.204523847f, 0.169319406f, + 0.243698135f, -0.0276578516f, 0.260387629f, -0.193582445f, 0.00578611437f, -0.223910674f, 0.144224301f, + -0.265973806f, -0.183919221f, -0.126615152f, -0.0969786197f, -0.245263368f, -0.0992364809f, -0.0361597389f, + -0.188996956f, -0.0614193901f, -0.24303928f, 0.236613363f, 0.0928985924f, -0.140591592f, 0.265006989f, + 0.335418433f, 0.247740567f, 0.297240853f, -0.183201015f, 0.0546035096f, -0.279683769f, -0.265959084f, + -0.263966233f, -0.00775319804f, 0.0555191636f, 0.042541258f, 0.112153225f, -0.284237564f, -0.106992513f, + 0.292009324f, -0.0500887856f, -0.300256789f, -0.291314989f, -0.0132447975f, 0.165363088f, 0.161834523f, + -0.0351643264f, -0.0717492253f, -0.23118712f, 0.0250188429f, -0.180316284f, 0.0144086005f, 0.0183442105f, + -0.0751668736f, 0.26603511f, -0.0152046252f, -0.215609923f, -0.107803889f, -0.334771454f, -0.0597620346f, + 0.069550842f, -0.304754287f, 0.00792920403f, 0.133588493f, 0.152744055f, 0.195667207f, 0.243106678f, + -0.307059348f, 0.0758271664f, 0.0481374227f, -0.229956269f, -0.176880926f, 0.103955291f, -0.246883363f, + -0.126195863f, -0.348209739f, -0.192238465f, 0.290424258f, -0.234185204f, 0.276026577f, 0.295248717f, + 0.168285653f, -0.347986549f, -0.283300996f, 0.125958711f, -0.332622856f, -0.196730375f, -0.306736737f, + 0.341696531f, 0.277066231f, -0.0408032723f, 0.0732125714f, 0.243923083f, 0.156388775f, -0.0956505686f, + 0.133763447f, -0.344599843f, 0.184255451f, -0.2558586f, 0.168878287f, 0.118261687f, 0.142528757f, + -0.331403553f, -0.343389869f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_input_weights_data.h new file mode 100644 index 00000000..2e1c9920 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/cell_input_weights_data.h @@ -0,0 +1,37 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_cell_input_weights[242] = { + -0.00236753933f, 0.204886392f, -0.325549185f, 0.0915026367f, 0.119490668f, 0.205257133f, -0.27360788f, + 0.299568981f, 0.0790443197f, -0.0216409285f, -0.204272866f, -0.135833129f, 0.0554386228f, 0.236623764f, + -0.00621149736f, -0.130308628f, -0.00711957179f, -0.293167025f, -0.181404054f, 0.243779525f, 0.0633300021f, + -0.011121246f, 0.175347492f, -0.300871104f, 0.289852649f, -0.109161317f, -0.0782004446f, -0.264505267f, + -0.167784825f, 0.0877970159f, -0.227573842f, 0.229645878f, -0.157224298f, -0.240391538f, -0.0620884411f, + -0.0681765974f, -0.0626796708f, 0.242237628f, -0.224569142f, -0.121936783f, -0.226501837f, -0.18593739f, + 0.288324535f, 0.346530914f, 0.0804292038f, 0.195368975f, 0.0956976414f, -0.196748585f, 0.202391982f, + 0.220651135f, -0.336145967f, -0.278807014f, 0.255902201f, 0.096001856f, -0.0641119182f, 0.270839185f, + -0.0408596247f, -0.336772472f, -0.00927306525f, 0.264558822f, 0.216752157f, 0.00910598133f, -0.0773541182f, + 0.141298294f, 0.194442675f, -0.0382197946f, -0.176034242f, -0.223986432f, 0.0922381952f, 0.170327142f, + -0.193958417f, 0.055468116f, 0.316877067f, 0.176714778f, 0.104244627f, 0.173281461f, -0.251623243f, + 0.100986198f, 0.347888619f, -0.0664865002f, 0.181914955f, 0.235361263f, 0.0822178125f, -0.338579565f, + 0.0160955098f, -0.2067931f, 0.10509482f, 0.124553896f, 0.00481979921f, -0.262262672f, -0.0980501547f, + 0.18548657f, -0.0394526757f, -0.319580913f, -0.106282257f, -0.135683179f, -0.310421854f, -0.0749394223f, + -0.224711761f, 0.00590548571f, -0.250713408f, 0.18665351f, -0.325980812f, -0.293533862f, -0.257971883f, + 0.157868952f, 0.025972141f, -0.31045866f, 0.263622999f, 0.1473037f, -0.228252068f, -0.12450739f, + 0.116622142f, 0.142956913f, -0.336671889f, -0.178456128f, 0.341933966f, -0.22695531f, 0.27495569f, + -0.0151375169f, 0.342881471f, 0.0285924468f, 0.0803426951f, 0.334746629f, 0.271361768f, 0.10471338f, + 0.0753178224f, 0.0894096047f, -0.254603207f, -0.129490301f, 0.208427951f, 0.204607636f, -0.316225022f, + 0.158637732f, -0.0384656005f, 0.159493968f, -0.141591191f, -0.125106066f, -0.224656925f, -0.251071274f, + -0.143616691f, 0.167203128f, 0.280670226f, 0.225166574f, 0.289836407f, -0.215067431f, -0.104805656f, + 0.00921685994f, 0.0883506685f, -0.2494317f, 0.00623438414f, 0.310443521f, -0.167169914f, 0.239228711f, + -0.150924116f, -0.0473851934f, -0.195845768f, -0.226057887f, -0.0158009864f, 0.180213511f, -0.146357372f, + 0.207117587f, -0.0928096622f, -0.341377616f, 0.211608648f, -0.0708780587f, -0.113388211f, 0.344445169f, + -0.328207552f, -0.0970763266f, 0.242484286f, -0.347525835f, 0.0745198503f, -0.23069416f, 0.161174938f, + 0.0324622095f, -0.26045239f, -0.107719958f, -0.331522584f, 0.045955006f, -0.0293069333f, 0.142223522f, + -0.244048998f, 0.0176738165f, -0.153453439f, -0.00717704091f, -0.29860419f, 0.110542312f, -0.181519955f, + -0.332170486f, -0.190107986f, 0.191345677f, -0.0355452858f, -0.211152643f, 0.153325543f, -0.10343314f, + -0.0871289447f, 0.164048791f, -0.321784824f, -0.154472589f, -0.216779605f, -0.189754009f, 0.00480247894f, + -0.239442304f, 0.0967263505f, 0.0476985238f, 0.147850931f, 0.0760479197f, 0.0966907963f, -0.127596453f, + 0.142157152f, -0.00430071075f, 0.0883348957f, 0.136473745f, 0.212206081f, 0.240624085f, 0.219910994f, + -0.124370806f, 0.107124604f, -0.101785757f, -0.0223604031f, 0.23981379f, -0.312668115f, -0.222782731f, + -0.0689846352f, -0.349715292f, -0.255621195f, 0.0889437348f, -0.291241646f, -0.155066237f, 0.213191196f, + -0.182651609f, -0.212105721f, -0.087641798f, -0.19551152f, -0.24057886f, 0.173022464f, 0.316558689f, + 0.0345853977f, -0.102157064f, -0.17956306f, -0.192519426f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/config_data.h new file mode 100644 index 00000000..8048d51e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_1_F32_TIME_MAJOR 1 +#define LSTM_MATCH_1_F32_TIME_STEPS 10 +#define LSTM_MATCH_1_F32_BATCH_SIZE 1 +#define LSTM_MATCH_1_F32_INPUT_SIZE 22 +#define LSTM_MATCH_1_F32_HIDDEN_SIZE 11 +#define LSTM_MATCH_1_F32_CELL_CLIP 0.0f +#define LSTM_MATCH_1_F32_CELL_STATE_SIZE 11 +#define LSTM_MATCH_1_F32_DST_SIZE 110 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_bias_data.h new file mode 100644 index 00000000..6b574525 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_forget_bias[11] = {0.07326518f, + 0.0773076341f, + -0.0194033831f, + -0.243045479f, + 0.0540685728f, + -0.33674705f, + 0.0171036087f, + -0.0666771978f, + -0.129992083f, + 0.180498004f, + -0.219663337f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..37da8286 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_hidden_weights_data.h @@ -0,0 +1,20 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_forget_hidden_weights[121] = { + -0.0764595792f, 0.191497415f, -0.29516089f, 0.337379932f, 0.227851883f, -0.126773283f, -0.246638894f, + 0.270006806f, -0.0805091485f, 0.263850898f, -0.112854391f, -0.226268038f, -0.198371857f, -0.326265872f, + -0.203992441f, -0.0893559307f, 0.0284492522f, 0.27720198f, 0.278562397f, -0.0833182484f, 0.0698186755f, + 0.171744555f, 0.265336543f, -0.212388381f, 0.242690489f, 0.16312623f, -0.348398179f, 0.156282321f, + -0.310159236f, -0.184170231f, -0.237503082f, 0.270613164f, 0.229987249f, -0.289054573f, -0.0831634924f, + -0.249390811f, 0.0237512607f, 0.132013828f, -0.283534735f, 0.344270706f, 0.27989167f, 0.0186736807f, + 0.104144804f, -0.130743027f, 0.0589521453f, 0.151770294f, 0.307141781f, -0.116508119f, 0.013087214f, + -0.15820986f, 0.276327968f, 0.201102555f, -0.120225012f, 0.107921332f, -0.261375606f, -0.204064071f, + 0.0565006062f, -0.254581809f, -0.226054728f, -0.252668917f, 0.128801495f, -0.0365571789f, -0.216915518f, + 0.179009214f, -0.188715681f, -0.329327673f, -0.243165076f, 0.163870662f, 0.0556914993f, -0.239394203f, + -0.173705354f, -0.20676364f, 0.128018245f, -0.183320731f, 0.0840245038f, -0.209703535f, -0.131211996f, + -0.124773726f, 0.308876187f, 0.066868864f, -0.198544651f, 0.195183367f, 0.306511194f, -0.331379116f, + -0.0491280891f, 0.269515663f, 0.333439887f, 0.24957931f, -0.137779653f, 0.193625316f, -0.29276824f, + -0.245513976f, 0.050463818f, 0.257600963f, -0.129576012f, -0.16194573f, -0.127392352f, -0.320268035f, + 0.272002429f, -0.215323344f, -0.0415895581f, 0.302568972f, 0.20444034f, 0.0432122312f, 0.142293334f, + -0.19333531f, -0.0858237445f, 0.0233212616f, 0.346627414f, 0.129517972f, -0.235425904f, -0.184364796f, + 0.0118210027f, 0.224700108f, 0.0562230162f, 0.0158682633f, 0.199492991f, 0.0939024761f, 0.307490259f, + -0.182608172f, 0.287415534f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_input_weights_data.h new file mode 100644 index 00000000..ed801bf1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/forget_input_weights_data.h @@ -0,0 +1,37 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_forget_input_weights[242] = { + 0.333458722f, 0.00696294941f, -0.0917092413f, -0.0674061924f, -0.180505946f, -0.231115803f, 0.0717390776f, + -0.0375973098f, 0.149401858f, 0.28530544f, 0.30342862f, -0.247289747f, -0.332647175f, -0.219211787f, + 0.0108802542f, 0.194619f, 0.202461287f, -0.310651749f, -0.0558659248f, -0.0513946973f, -0.145368427f, + -0.308349609f, -0.178061992f, -0.168639928f, 0.253905147f, 0.327807516f, 0.325934947f, -0.138177782f, + -0.050679747f, -0.159098223f, -0.314741194f, 0.331720293f, -0.0838305503f, 0.191380858f, 0.153282151f, + 0.0693552867f, -0.196500987f, -0.209157556f, 0.293682724f, -0.226039544f, -0.273164839f, 0.158920452f, + -0.00835940707f, -0.149914116f, 0.295448035f, -0.080505088f, 0.269214451f, -0.139175192f, -0.228594914f, + -0.231907099f, 0.196626455f, -0.0216227602f, -0.143306285f, -0.0079436088f, -0.265313655f, 0.226213247f, + 0.225512713f, 0.135653853f, 0.185896322f, -0.283178985f, -0.16208671f, -0.0291442126f, -0.190100417f, + 0.345473856f, 0.247587308f, 0.0247012414f, 0.304065615f, 0.0914445147f, -0.274233192f, 0.198696747f, + 0.244635522f, 0.327440649f, 0.029323237f, 0.280980796f, -0.322001874f, -0.292378217f, 0.0792901814f, + 0.158390418f, -0.0864434764f, -0.302820474f, -0.0355558358f, -0.280543447f, 0.0296420231f, 0.0925697088f, + 0.270208627f, -0.217588037f, 0.0877129808f, -0.329513371f, -0.293371648f, -0.1023506f, -0.163619012f, + -0.202731431f, -0.0376107804f, 0.178272575f, -0.270925522f, 0.0676156804f, -0.314302772f, 0.104074351f, + 0.336705774f, 0.105583534f, -0.0533704385f, -0.0826091692f, 0.1376919f, -0.0273205545f, -0.325286955f, + 0.310771853f, -0.0874506757f, -0.220247075f, 0.227148965f, 0.271083653f, 0.248581812f, 0.202642798f, + 0.325889051f, -0.285394102f, -0.217177808f, 0.105492428f, -0.189421326f, 0.00718921656f, 0.230396956f, + -0.1612553f, -0.226683319f, 0.122752368f, 0.153800085f, -0.241069525f, -0.172765598f, 0.328293175f, + -0.153774574f, 0.261047691f, -0.0929526761f, 0.00194066495f, -0.0121054249f, 0.266758561f, -0.332057863f, + 0.319492489f, -0.165580854f, -0.262005746f, -0.120777369f, -0.329984516f, 0.0484601557f, -0.328797728f, + -0.289582163f, -0.0818559974f, 0.151456863f, -0.0538598448f, 0.200406045f, -0.212221682f, -0.232539192f, + -0.191780716f, 0.168341279f, -0.323283255f, -0.127770126f, 0.201554671f, 0.0110342558f, 0.115034766f, + -0.149957076f, -0.282324493f, 0.188264504f, -0.134912014f, 0.0924496725f, 0.183136046f, 0.103812262f, + -0.0564627573f, -0.307042807f, 0.130449191f, -0.299395978f, 0.0649517626f, -0.0500048809f, -0.256453812f, + -0.0190720279f, 0.119625241f, -0.00945916399f, -0.144995853f, -0.210069507f, 0.115109392f, 0.319297284f, + 0.0816161409f, -0.311659634f, 0.0964680761f, 0.138916001f, -0.313218117f, -0.195271254f, -0.0207941644f, + -0.189147994f, -0.269409716f, 0.0705467239f, 0.165717974f, -0.193198606f, 0.144201115f, -0.0914379954f, + -0.0726367012f, -0.320946872f, 0.0930585414f, -0.0954072997f, -0.13729398f, 0.322096139f, -0.317959249f, + -0.158323497f, -0.11445979f, -0.246607453f, -0.131725386f, -0.339770645f, -0.132626489f, -0.271468669f, + -0.193791673f, -0.250785619f, 0.0466314927f, -0.155845746f, -0.11703752f, 0.194007963f, 0.162880197f, + 0.0308001339f, -0.244246572f, 0.182196259f, -0.336815923f, -0.231357306f, -0.140434965f, -0.228601053f, + -0.0440316834f, 0.279430896f, 0.148718238f, -0.165386498f, -0.0117524015f, -0.194624707f, -0.27913025f, + 0.0369447432f, 0.290529191f, 0.083113037f, 0.304037005f, 0.273043573f, -0.290588737f, 0.228317842f, + 0.21044369f, 0.243557081f, 0.130249515f, 0.125179261f, -0.267276376f, 0.108386852f, 0.0452324003f, + 0.126102939f, 0.0633642301f, 0.075728856f, 0.171219885f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_bias_data.h new file mode 100644 index 00000000..b1776feb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_input_bias[11] = {-0.0296523571f, + -0.0872102678f, + 0.349362254f, + -0.0126139969f, + -0.198313937f, + -0.022658471f, + -0.227379307f, + 0.021823585f, + -0.0399556644f, + -0.407198131f, + 0.205341861f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_data.h new file mode 100644 index 00000000..52e2eb73 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_data.h @@ -0,0 +1,34 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_input[220] = { + -0.143625215f, -0.572229087f, -0.104646593f, -0.605853796f, 0.804576933f, -0.282846183f, 0.322513074f, + -0.194581434f, -0.547545969f, 0.313748628f, 0.321030408f, 0.228242055f, 0.949807763f, -0.517206609f, + -0.54901129f, 0.462729663f, -0.0550251007f, -0.463354975f, -0.72604394f, 0.185716659f, -0.675686061f, + 0.42984882f, 0.727802157f, 0.900162637f, -0.0498662591f, 0.525840759f, -0.958560169f, -0.872776568f, + -0.990961432f, 0.515599787f, 0.149546281f, -0.5353387f, -0.356145203f, -0.728315294f, 0.0995166227f, + 0.941168547f, -0.382523328f, -0.818244338f, 0.237650409f, 0.0465757474f, 0.819298506f, -0.0823062286f, + -0.805861592f, 0.0319414511f, 0.0893103257f, 0.142480418f, -0.929521203f, 0.523772895f, -0.9315207f, + 0.18004702f, 0.544418573f, -0.70601052f, -0.268122256f, -0.310837388f, 0.563920975f, 0.342436612f, + -0.868904412f, -0.590305209f, -0.73069936f, 0.0529513694f, -0.118420824f, -0.370941252f, -0.927681029f, + -0.477733195f, -0.377783209f, -0.0698794499f, -0.73826313f, 0.805007041f, 0.336760849f, 0.595688224f, + 0.854954541f, -0.358044147f, 0.136262342f, -0.570880115f, -0.881029665f, -0.063996084f, -0.0357650667f, + 0.246127203f, -0.855573356f, 0.293066978f, -0.749933481f, 0.959752679f, -0.74854809f, -0.0798366815f, + -0.660460889f, -0.0967148095f, 0.584452212f, -0.724812388f, 0.768621922f, -0.392600507f, 0.924699366f, + 0.230876327f, -0.359267533f, 0.0683251992f, 0.238064304f, 0.60730201f, 0.427342653f, 0.0891071633f, + -0.830668569f, 0.225109085f, 0.69818157f, -0.886891961f, -0.296676397f, 0.927424192f, 0.987403631f, + 0.228174552f, 0.507345617f, -0.771902263f, 0.535243571f, 0.759809375f, 0.163594246f, 0.262232691f, + 0.959139764f, 0.130858794f, 0.322568715f, 0.785580456f, -0.863520026f, -0.755631685f, -0.657288253f, + -0.289922446f, 0.0970787555f, 0.680300474f, 0.464011163f, 0.939481139f, -0.79949075f, 0.811942995f, + 0.0160722677f, 0.926482916f, 0.401810288f, 0.20641458f, -0.0857164934f, -0.210767314f, -0.195776522f, + 0.0183749329f, -0.830274045f, 0.750188649f, 0.893598139f, -0.00953605492f, 0.737374783f, 0.388616264f, + 0.675292552f, 0.911045313f, -0.60946548f, -0.249420792f, 0.877958417f, 0.275816709f, 0.880437374f, + 0.197084606f, -0.00310699386f, 0.875547767f, -0.367497802f, -0.416804075f, -0.143828258f, -0.182481512f, + -0.225531131f, 0.191882581f, -0.801461875f, 0.169878781f, 0.295127034f, -0.188503668f, 0.432001144f, + -0.658070862f, -0.0757716745f, 0.425015688f, 0.652151644f, -0.320350379f, -0.624190807f, -0.573808253f, + 0.77458477f, -0.00256113056f, -0.400026739f, -0.966123402f, 0.589792788f, -0.102737375f, 0.864050984f, + 0.205919608f, 0.07670407f, -0.599447429f, -0.599846959f, -0.21958527f, 0.951063097f, 0.0715794191f, + -0.627387762f, -0.550208688f, 0.826219797f, 0.590015709f, 0.444761634f, 0.286166131f, 0.49335438f, + -0.413181931f, 0.633784056f, 0.870626628f, 0.962957025f, 0.20306924f, 0.722407401f, 0.177291557f, + -0.157145306f, -0.678373039f, 0.271244287f, 0.00532455044f, -0.910318136f, -0.0575125441f, 0.515722096f, + -0.738108516f, -0.732367039f, 0.341256112f, -0.409553498f, 0.445913374f, 0.0256921481f, -0.964908242f, + -0.935139418f, -0.703140974f, 0.805253148f, 0.208096832f, -0.561276972f, 0.695583463f, 0.45876345f, + -0.676754415f, -0.813995659f, 0.442404807f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..8c5828a9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_hidden_weights_data.h @@ -0,0 +1,20 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_input_hidden_weights[121] = { + -0.131671056f, 0.270258456f, -0.118445151f, 0.119760774f, 0.111438572f, 0.278275192f, 0.173172295f, + 0.0635437518f, -0.0379421078f, 0.088807404f, 0.233038872f, -0.109820955f, -0.00137874403f, -0.0103450967f, + 0.167744428f, -0.242192581f, -0.14416492f, 0.306374609f, 0.0382273048f, 0.233448908f, -0.048991207f, + 0.343397647f, 0.345478833f, -0.21475257f, -0.225623384f, -0.0998589918f, 0.117713556f, 0.159221873f, + -0.217778176f, -0.23959969f, 0.23203665f, -0.145797357f, 0.107651673f, -0.304461777f, 0.314050645f, + -0.130258456f, 0.192850471f, -0.0847704113f, -0.112680577f, -0.300779819f, 0.302249432f, 0.334013641f, + -0.0359881632f, -0.172990456f, -0.030142013f, 0.046792753f, -0.208554804f, 0.254167259f, 0.073706679f, + -0.0970883891f, -0.0561238118f, -0.0569761209f, 0.304571867f, 0.345981747f, -0.293957651f, -0.349616259f, + 0.128715307f, 0.236187071f, -0.0794555917f, -0.0332556255f, 0.140836969f, 0.205080137f, -0.0755708963f, + 0.219467252f, 0.295015603f, -0.0634205118f, 0.265664816f, 0.0150504569f, -0.0940743759f, 0.0669438317f, + 0.0028882036f, -0.00451655034f, 0.217858568f, -0.258727163f, 0.253591686f, 0.206133753f, 0.221434653f, + 0.0806603283f, 0.0514091216f, 0.219938204f, 0.0310551189f, -0.345663339f, -0.00744615728f, 0.241151929f, + -0.0729374066f, 0.030020915f, 0.290580451f, 0.18271108f, 0.0360055491f, -0.0406181104f, -0.115380369f, + 0.1191305f, 0.0763697252f, -0.113539077f, -0.331200957f, 0.330840975f, -0.112959921f, -0.122058056f, + 0.0471469872f, 0.332061619f, -0.248966187f, 0.16126208f, -0.281971663f, 0.249379262f, -0.21012111f, + -0.214616284f, -0.0173143651f, -0.150350183f, -0.079069674f, -0.316660225f, 0.107522495f, -0.0254957024f, + 0.0343431085f, -0.195973381f, -0.322413504f, 0.0639409795f, -0.249847278f, -0.021028433f, -0.0779141784f, + -0.241171181f, 0.133351639f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_input_weights_data.h new file mode 100644 index 00000000..fd4716fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/input_input_weights_data.h @@ -0,0 +1,37 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_input_input_weights[242] = { + 0.221627265f, 0.246032864f, 0.202508166f, 0.0582529008f, -0.0722942129f, 0.15700452f, 0.104242168f, + -0.291113168f, 0.0151678817f, -0.0194211658f, -0.245560721f, 0.0448606089f, -0.138575703f, -0.314821661f, + -0.0910558701f, 0.233890906f, -0.243832305f, -0.11264874f, 0.19903633f, 0.105733477f, 0.156568155f, + -0.274908274f, -0.122487485f, 0.3286874f, -0.265319228f, 0.10446351f, 0.267274469f, -0.155386314f, + 0.0775656253f, -0.171157762f, 0.281396031f, -0.233258784f, -0.173447385f, 0.072407797f, 0.30272764f, + 0.277009904f, 0.0610065274f, 0.0183625817f, 0.289845526f, -0.298577607f, -0.0121311257f, 0.132440552f, + 0.133052289f, 0.281796366f, 0.234520942f, -0.177874148f, -0.0858597979f, 0.200741664f, -0.142240271f, + 0.0668479428f, -0.333413571f, -0.213098451f, 0.347118884f, -0.105006799f, -0.178493008f, 0.226068094f, + -0.154167116f, -0.0281501934f, -0.155402854f, -0.0483488813f, 0.181886539f, 0.128678247f, 0.321155131f, + -0.0425510295f, -0.0862134248f, -0.229687721f, 0.157972381f, 0.0820790157f, -0.0529439822f, 0.0696519837f, + 0.0257823169f, 0.323346615f, -0.22389248f, 0.124399059f, -0.166064844f, -0.0307753868f, 0.344821155f, + 0.0242528748f, 0.200868055f, 0.312805831f, -0.132039398f, -0.253226072f, 0.156030402f, 0.302161247f, + 0.10975302f, -0.333909452f, -0.153627768f, -0.103624664f, 0.0133907273f, 0.0438020304f, -0.23691164f, + -0.203539699f, 0.100026935f, -0.112259433f, -0.0299371593f, -0.256086558f, 0.112093255f, 0.213795573f, + 0.0829758048f, 0.0785316229f, 0.347577572f, 0.0405989401f, -0.0355364494f, -0.043815352f, -0.108230285f, + 0.332374871f, 0.314054489f, -0.323930651f, 0.299970329f, -0.0428140089f, -0.342254132f, 0.303924412f, + -0.121605612f, -0.328482717f, 0.114697844f, -0.00742785214f, 0.158141032f, 0.177922904f, -0.219202161f, + 0.267326504f, -0.0203607883f, 0.0873526037f, -0.0960332751f, -0.258462757f, 0.0657752231f, 0.336916715f, + -0.192943171f, 0.323097497f, -0.318412721f, -0.224231288f, 0.0908979326f, 0.347604305f, 0.323807627f, + 0.137227207f, 0.250440836f, -0.289617449f, 0.14550519f, -0.167249411f, -0.201622903f, -0.0830537528f, + -0.31962049f, 0.133162156f, -0.255796105f, 0.259757459f, 0.308559686f, 0.125019014f, -0.262315542f, + -0.27922672f, 0.282596171f, -0.11637909f, 0.221138299f, 0.04121539f, -0.145914122f, -0.302079976f, + -0.182689205f, 0.214218929f, 0.115961842f, 0.191560924f, -0.145267218f, -0.252705395f, 0.161096126f, + -0.0432391278f, 0.0366969444f, -0.102314629f, -0.335083246f, 0.190557852f, -0.133471221f, 0.255276799f, + -0.0394973233f, -0.133773521f, -0.129187211f, 0.212101668f, -0.157629743f, -0.0204013363f, 0.344389945f, + 0.198115885f, 0.0652429089f, -0.316131979f, -0.0657292008f, -0.0797528028f, 0.0724245384f, -0.171663389f, + 0.22500886f, -0.30827418f, -0.250310093f, 0.200460568f, -0.245206103f, -0.330953807f, -0.343006849f, + -0.321105003f, 0.0533272326f, 0.342482865f, 0.0540860966f, 0.307948709f, 0.106732175f, -0.194221422f, + 0.140908137f, 0.200017825f, -0.106338747f, 0.101241328f, -0.0602873899f, -0.0809041634f, 0.0813513622f, + 0.313982844f, -0.213262051f, -0.227098122f, 0.186628506f, 0.0657646134f, -0.0809633657f, 0.187002823f, + 0.167467177f, 0.04570207f, 0.298078954f, 0.299363405f, 0.225397661f, 0.144626781f, 0.19617404f, + -0.275870174f, 0.207986623f, 0.326103359f, 0.0090756109f, -0.019020373f, -0.142975867f, -0.169260159f, + 0.123615041f, 0.120577917f, -0.157770976f, 0.285249144f, -0.105934471f, 0.130945727f, -0.251531065f, + -0.344038486f, -0.0387038402f, -0.209102288f, -0.30825007f, 0.0802974254f, 0.227083594f, -0.209181175f, + 0.131328121f, 0.0584987067f, -0.143462151f, 0.00200534076f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_bias_data.h new file mode 100644 index 00000000..90079d31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_bias_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_output_bias[11] = {0.21179682f, + 0.0271224733f, + 0.0610890239f, + -0.00270882621f, + 0.0175702125f, + 0.327572525f, + 0.0448258594f, + 0.00832810998f, + -0.412203401f, + 0.0480898619f, + 0.308657616f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..071c8a8c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_hidden_weights_data.h @@ -0,0 +1,20 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_output_hidden_weights[121] = { + -0.341144204f, -0.234239832f, 0.0989037827f, 0.0427342765f, -0.19223167f, 0.205438152f, 0.271964729f, + -0.155236959f, -0.248487994f, 0.292099893f, 0.329870343f, 0.134931818f, -0.0992446095f, -0.0434610434f, + 0.229655579f, -0.321904927f, 0.319085181f, -0.119709611f, -0.314878017f, 0.133006752f, 0.247764185f, + -0.244122148f, 0.275535136f, 0.0899228156f, 0.237407342f, -0.11743103f, 0.123191059f, -0.349567413f, + 0.207711652f, -0.0123847276f, 0.056330014f, -0.322257161f, -0.222002223f, -0.30118981f, 0.0559124909f, + -0.319041431f, 0.30265522f, 0.00723363506f, -0.274954557f, 0.269664556f, 0.158908069f, -0.0358516723f, + -0.278182924f, -0.00472390046f, 0.175423861f, -0.343492478f, 0.31436944f, 0.292960703f, 0.181636333f, + 0.178767309f, -0.241388395f, -0.187938333f, 0.331157386f, 0.0314477831f, 0.250576228f, -0.0382930115f, + -0.307351083f, -0.0640077069f, -0.043007344f, 0.273126215f, -0.20959729f, 0.149182871f, 0.305659205f, + 0.282268405f, 0.223395973f, 0.00782448798f, 0.235125661f, 0.0972430333f, 0.0344492719f, 0.168350011f, + 0.110655621f, 0.30999428f, 0.042505499f, 0.00728064962f, -0.182170972f, -0.339009315f, -0.0601123869f, + 0.322911024f, -0.343649387f, -0.186553553f, 0.270729154f, 0.019230457f, 0.106140912f, -0.127850607f, + 0.209197462f, 0.158585891f, 0.0961723104f, -0.116905443f, 0.140083343f, 0.0669722483f, -0.280439407f, + -0.00846239924f, 0.067252554f, 0.183556139f, -0.0774819851f, -0.340098053f, -0.151333287f, 0.0206060447f, + -0.008017933f, -0.322764724f, 0.125034139f, -0.25354147f, -0.0869612917f, -0.278268129f, 0.184595525f, + 0.0874674842f, 0.249968827f, 0.288980335f, -0.256935656f, -0.128245816f, 0.21136272f, -0.233020365f, + -0.0734633803f, -0.127124146f, 0.311273038f, -0.157406539f, 0.184581906f, 0.326754481f, 0.32281357f, + -0.302669346f, -0.304480225f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_input_weights_data.h new file mode 100644 index 00000000..938f053c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_input_weights_data.h @@ -0,0 +1,37 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_output_input_weights[242] = { + 0.0721475035f, 0.136149034f, -0.214188159f, -0.23681289f, 0.193340883f, 0.330382913f, -0.163803592f, + -0.280184656f, -0.14087981f, -0.104254462f, 0.118843123f, -0.334708899f, 0.198956415f, -0.138660222f, + 0.29977724f, 0.00327272783f, -0.228352517f, -0.323462754f, -0.0820164755f, 0.0517767258f, -0.0650235787f, + 0.274567783f, -0.281456649f, -0.195080385f, 0.0595376156f, -0.328291774f, -0.102523781f, -0.0921282023f, + -0.0599757619f, -0.185452685f, 0.0867733285f, -0.236291409f, -0.326456308f, -0.00439882698f, 0.219351649f, + 0.281138629f, -0.209001914f, -0.0154161397f, -0.228622556f, 0.130072668f, 0.16702269f, -0.113762863f, + 0.0409618244f, -0.293195844f, 0.0750067458f, 0.13456066f, 0.191810012f, 0.125192255f, 0.0637206063f, + -0.0730145425f, 0.323621392f, -0.139034092f, 0.28746438f, -0.282533646f, 0.135534301f, -0.155519083f, + 0.0332536995f, -0.348478109f, 0.114097118f, -0.000162574943f, -0.217665851f, -0.0948751643f, -0.068823196f, + -0.211570278f, 0.220444173f, -0.0980414748f, 0.195033267f, 0.0468250364f, 0.191064015f, 0.337124676f, + 0.188780591f, 0.103110731f, 0.107576765f, -0.166899845f, 0.0670208856f, 0.338848978f, -0.193285331f, + 0.34761554f, -0.0499768183f, -0.130405962f, 0.150407031f, -0.0765830874f, -0.0217550937f, 0.159482449f, + 0.0412905775f, 0.131373599f, -0.0937807187f, -0.247350514f, 0.15363057f, 0.048975192f, 0.18919763f, + -0.0748020038f, 0.250784159f, -0.0200942419f, 0.24336946f, 0.175737604f, 0.123475753f, -0.0995202735f, + 0.338456333f, -0.0430026241f, 0.253680825f, -0.0310984738f, -0.0355742685f, -0.048792664f, -0.283068508f, + 0.188592687f, 0.160159871f, -0.261949867f, 0.223634422f, -0.269816577f, 0.0454852916f, -0.0589667931f, + 0.0388414785f, -0.151999667f, -0.25886178f, -0.2047396f, 0.314450175f, 0.179065257f, -0.242126718f, + -0.304449737f, -0.115716547f, -0.170288756f, 0.207072124f, 0.033027783f, 0.240878433f, 0.24375245f, + -0.110282831f, 0.0669271201f, -0.324036896f, 0.322781563f, -0.146131679f, -0.0916777775f, 0.235795528f, + -0.312085152f, 0.0490565002f, 0.2749255f, 0.174146414f, -0.227541715f, -0.0422609486f, -0.118130848f, + -0.28184855f, -0.214102179f, 0.302527547f, 0.068990387f, -0.0703021735f, -0.0491232239f, 0.311915666f, + -0.208882526f, -0.277712673f, 0.139999866f, 0.14613761f, 0.219164029f, -0.185808167f, 0.205730319f, + 0.128234655f, -0.296988398f, 0.262678415f, 0.169424698f, 0.148420468f, 0.311609447f, 0.210037366f, + -0.0394242033f, -0.332706273f, 0.0481379107f, 0.239581972f, 0.181039602f, 0.158410236f, 0.222378284f, + -0.324828535f, -0.0446393006f, -0.339348197f, 0.134201899f, -0.238165617f, -0.297078192f, 0.305425197f, + -0.324717164f, 0.176181272f, -0.345653385f, 0.14070341f, -0.168416977f, 0.190607473f, -0.217759356f, + -0.300551325f, 0.304003417f, -0.172422424f, 0.0613542423f, 0.320084363f, 0.176036522f, 0.265047014f, + -0.274965197f, 0.270426959f, -0.0017212868f, 0.14449653f, 0.33413744f, 0.212394387f, -0.0200959221f, + -0.17570211f, 0.110810593f, 0.337047368f, 0.0548609607f, 0.230749249f, -0.117792033f, -0.328251988f, + 0.171467528f, -0.0957514793f, 0.349467874f, 0.123691067f, -0.0152744856f, -0.323064953f, -0.118144736f, + 0.300524384f, 0.101081222f, 0.296817213f, -0.333764732f, -0.271748036f, -0.341851145f, -0.0129447402f, + 0.124213792f, 0.0135780601f, 0.283839643f, -0.174633563f, -0.108953193f, 0.230843127f, -0.0412625223f, + -0.312715471f, 0.21381861f, 0.0175991021f, 0.0222622678f, 0.263471425f, -0.0221462622f, -0.345185339f, + 0.228845626f, -0.0315760672f, 0.0882984698f, -0.240856409f, -0.0440409221f, -0.191557586f, -0.332403392f, + -0.145482033f, 0.0851914659f, 0.252923459f, -0.0776879266f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_ref_data.h new file mode 100644 index 00000000..9a8ab842 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/output_ref_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_1_f32_output_ref[110] = { + 0.0307480805f, 0.162283763f, 0.0272546802f, 0.0676472634f, -0.0816992298f, -0.163574293f, 0.171678439f, + -0.0563954264f, 0.0306690875f, 0.029763205f, -0.11059662f, 0.129922524f, 0.0425273553f, -0.197563589f, + -0.137497455f, 0.0718086511f, -0.114819795f, -0.0172095336f, 0.0431475788f, -0.0209262259f, 0.0104771778f, + 0.196540624f, 0.1146864f, 0.0185557492f, -0.185917199f, -0.119564369f, 0.0825134143f, 0.0309281964f, + 0.0101074334f, 0.0655726716f, 0.0443168953f, -0.0184913334f, 0.1855575f, 0.117317989f, 0.164833695f, + -0.00499320216f, -0.144103587f, 0.191033214f, -0.0861517861f, -0.0239591431f, -0.0703511089f, 0.0355766639f, + -0.0158501584f, 0.0192440897f, -0.0439082384f, 0.205166206f, 0.235803932f, 0.144299507f, 0.00752131501f, + -0.0961408615f, -0.103673995f, -0.1404576f, 0.108368084f, 0.108650632f, -0.303989559f, -0.0117301578f, + 0.211610779f, 0.211351603f, -0.21192421f, 0.0490084924f, -0.140116453f, -0.0615338311f, -0.133692533f, + -0.0187167171f, -0.0606355965f, -0.165311441f, 0.0804451108f, 0.0291060098f, 0.0978857651f, 0.11443723f, + -0.0964865908f, 0.10296037f, -0.0982649624f, -0.230249196f, -0.123354129f, 0.149303019f, -0.157250807f, + 0.22310105f, 0.109886348f, 0.12066859f, 0.0656848177f, -0.0372560099f, 0.0439852513f, -0.0592485741f, + -0.127405524f, -0.0044412287f, 0.133374318f, -0.158590868f, 0.136399418f, -0.0084346449f, 0.294841766f, + 0.0664035231f, -0.290093869f, -0.0382781252f, 0.00905382354f, -0.0854459703f, -0.0280607007f, 0.133612663f, + -0.129413977f, 0.149085462f, 0.0891038626f, 0.0215905365f, -0.0970410779f, -0.0645836517f, -0.265649647f, + -0.109217606f, -0.0784052908f, 0.237206757f, 0.222634315f, 0.0811926648f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_1_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_bias_data.h new file mode 100644 index 00000000..cb5ec5d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_cell_bias[7] = {(float16_t)-0.174072266f, + (float16_t)-0.0944824219f, + (float16_t)-0.063293457f, + (float16_t)0.0126190186f, + (float16_t)-0.0639648438f, + (float16_t)0.135742188f, + (float16_t)-0.186767578f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..2feb039b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_hidden_weights_data.h @@ -0,0 +1,15 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_cell_hidden_weights[49] = { + (float16_t)0.100891113f, (float16_t)0.305419922f, (float16_t)-0.281005859f, (float16_t)-0.192382812f, + (float16_t)0.262939453f, (float16_t)-0.177368164f, (float16_t)0.256347656f, (float16_t)-0.258300781f, + (float16_t)0.00282287598f, (float16_t)-0.00845336914f, (float16_t)-0.222045898f, (float16_t)-0.305419922f, + (float16_t)-0.13269043f, (float16_t)-0.30078125f, (float16_t)-0.138671875f, (float16_t)-0.129394531f, + (float16_t)0.0817871094f, (float16_t)0.107055664f, (float16_t)0.197998047f, (float16_t)0.0692138672f, + (float16_t)0.0534362793f, (float16_t)0.2421875f, (float16_t)0.00617599487f, (float16_t)-0.338623047f, + (float16_t)0.166748047f, (float16_t)-0.0500183105f, (float16_t)0.0518493652f, (float16_t)0.196655273f, + (float16_t)-0.0952758789f, (float16_t)0.0541687012f, (float16_t)0.0113983154f, (float16_t)-0.101074219f, + (float16_t)-0.244506836f, (float16_t)-0.094909668f, (float16_t)0.268554688f, (float16_t)0.229736328f, + (float16_t)-0.00652694702f, (float16_t)0.123474121f, (float16_t)0.30859375f, (float16_t)-0.0423583984f, + (float16_t)0.0980224609f, (float16_t)-0.254394531f, (float16_t)0.167480469f, (float16_t)0.138671875f, + (float16_t)-0.191772461f, (float16_t)0.150024414f, (float16_t)-0.109313965f, (float16_t)-0.249389648f, + (float16_t)0.306152344f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_input_weights_data.h new file mode 100644 index 00000000..a646dc66 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/cell_input_weights_data.h @@ -0,0 +1,13 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_cell_input_weights[42] = { + (float16_t)-0.250732422f, (float16_t)-0.159301758f, (float16_t)0.337402344f, (float16_t)-0.279541016f, + (float16_t)0.309814453f, (float16_t)-0.000255584717f, (float16_t)-0.175537109f, (float16_t)0.249145508f, + (float16_t)-0.0268859863f, (float16_t)-0.265380859f, (float16_t)-0.24230957f, (float16_t)0.2578125f, + (float16_t)-0.0124435425f, (float16_t)-0.176513672f, (float16_t)0.176025391f, (float16_t)-0.0114364624f, + (float16_t)0.286132812f, (float16_t)-0.0189208984f, (float16_t)0.270263672f, (float16_t)0.289550781f, + (float16_t)-0.271728516f, (float16_t)0.219360352f, (float16_t)-0.242797852f, (float16_t)0.174072266f, + (float16_t)0.109191895f, (float16_t)-0.291992188f, (float16_t)0.253417969f, (float16_t)-0.0689697266f, + (float16_t)-0.247924805f, (float16_t)-0.326416016f, (float16_t)0.108581543f, (float16_t)0.285644531f, + (float16_t)-0.0656738281f, (float16_t)-0.16015625f, (float16_t)-0.155639648f, (float16_t)0.0718383789f, + (float16_t)0.106079102f, (float16_t)0.130981445f, (float16_t)-0.0779418945f, (float16_t)0.044921875f, + (float16_t)-0.22253418f, (float16_t)0.105712891f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/config_data.h new file mode 100644 index 00000000..2e977ca2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_2_F16_TIME_MAJOR 0 +#define LSTM_MATCH_2_F16_TIME_STEPS 9 +#define LSTM_MATCH_2_F16_BATCH_SIZE 1 +#define LSTM_MATCH_2_F16_INPUT_SIZE 6 +#define LSTM_MATCH_2_F16_HIDDEN_SIZE 7 +#define LSTM_MATCH_2_F16_CELL_CLIP 0.0f +#define LSTM_MATCH_2_F16_CELL_STATE_SIZE 7 +#define LSTM_MATCH_2_F16_DST_SIZE 63 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_bias_data.h new file mode 100644 index 00000000..e40cf320 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_forget_bias[7] = {(float16_t)-0.0372924805f, + (float16_t)0.12512207f, + (float16_t)0.0423278809f, + (float16_t)0.178222656f, + (float16_t)-0.0850830078f, + (float16_t)0.06640625f, + (float16_t)-0.260986328f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..f299db39 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_hidden_weights_data.h @@ -0,0 +1,15 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_forget_hidden_weights[49] = { + (float16_t)0.208129883f, (float16_t)0.147949219f, (float16_t)-0.0701904297f, (float16_t)-0.0249633789f, + (float16_t)-0.243164062f, (float16_t)-0.0626831055f, (float16_t)0.220336914f, (float16_t)0.0952758789f, + (float16_t)-0.0321960449f, (float16_t)-0.0843505859f, (float16_t)-0.204589844f, (float16_t)0.177246094f, + (float16_t)0.346679688f, (float16_t)-0.110351562f, (float16_t)-0.202636719f, (float16_t)0.153320312f, + (float16_t)0.252929688f, (float16_t)0.333740234f, (float16_t)-0.035736084f, (float16_t)0.228271484f, + (float16_t)-0.114501953f, (float16_t)-0.0118713379f, (float16_t)0.205322266f, (float16_t)-0.316162109f, + (float16_t)-0.260009766f, (float16_t)0.121887207f, (float16_t)-0.120605469f, (float16_t)-0.0426940918f, + (float16_t)0.22644043f, (float16_t)0.150024414f, (float16_t)0.139892578f, (float16_t)0.143554688f, + (float16_t)-0.276855469f, (float16_t)0.0947265625f, (float16_t)-0.2578125f, (float16_t)-0.322998047f, + (float16_t)0.0505065918f, (float16_t)0.303466797f, (float16_t)0.0883178711f, (float16_t)-0.329833984f, + (float16_t)-0.0255737305f, (float16_t)0.310546875f, (float16_t)0.0415649414f, (float16_t)0.104492188f, + (float16_t)-0.152099609f, (float16_t)0.233764648f, (float16_t)0.291259766f, (float16_t)-0.0859985352f, + (float16_t)0.223510742f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_input_weights_data.h new file mode 100644 index 00000000..cf09572a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/forget_input_weights_data.h @@ -0,0 +1,13 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_forget_input_weights[42] = { + (float16_t)0.245239258f, (float16_t)0.348632812f, (float16_t)-0.216064453f, (float16_t)-0.0448303223f, + (float16_t)0.346679688f, (float16_t)0.131103516f, (float16_t)0.140258789f, (float16_t)0.0531616211f, + (float16_t)0.319580078f, (float16_t)-0.220947266f, (float16_t)-0.325927734f, (float16_t)0.136230469f, + (float16_t)-0.23059082f, (float16_t)-0.0791015625f, (float16_t)-0.161865234f, (float16_t)0.135375977f, + (float16_t)0.0316467285f, (float16_t)-0.145385742f, (float16_t)0.245117188f, (float16_t)0.0848999023f, + (float16_t)0.174072266f, (float16_t)0.000767230988f, (float16_t)-0.00543212891f, (float16_t)0.0244140625f, + (float16_t)-0.276367188f, (float16_t)-0.261962891f, (float16_t)-0.159423828f, (float16_t)0.00127315521f, + (float16_t)0.0957641602f, (float16_t)-0.148193359f, (float16_t)0.128051758f, (float16_t)-0.240234375f, + (float16_t)0.00688934326f, (float16_t)0.290527344f, (float16_t)-0.19543457f, (float16_t)0.173461914f, + (float16_t)0.248046875f, (float16_t)0.2109375f, (float16_t)-0.0618591309f, (float16_t)0.280517578f, + (float16_t)0.0614929199f, (float16_t)-0.0643310547f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_bias_data.h new file mode 100644 index 00000000..bd0b78bb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_input_bias[7] = {(float16_t)0.0979614258f, + (float16_t)-0.264648438f, + (float16_t)-0.049407959f, + (float16_t)0.0936889648f, + (float16_t)0.350585938f, + (float16_t)0.0824584961f, + (float16_t)-0.0574035645f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_data.h new file mode 100644 index 00000000..561836d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_input[54] = { + (float16_t)-0.626953125f, (float16_t)0.267089844f, (float16_t)0.22644043f, (float16_t)0.12878418f, + (float16_t)-0.500488281f, (float16_t)0.924804688f, (float16_t)0.766113281f, (float16_t)0.743652344f, + (float16_t)0.887207031f, (float16_t)0.946777344f, (float16_t)-0.772460938f, (float16_t)-0.0578613281f, + (float16_t)-0.314208984f, (float16_t)-0.798339844f, (float16_t)0.586425781f, (float16_t)0.614257812f, + (float16_t)0.475341797f, (float16_t)0.577148438f, (float16_t)0.332275391f, (float16_t)0.595214844f, + (float16_t)-0.254150391f, (float16_t)0.658203125f, (float16_t)0.621582031f, (float16_t)-0.672851562f, + (float16_t)0.292724609f, (float16_t)-0.884765625f, (float16_t)0.302734375f, (float16_t)-0.030960083f, + (float16_t)-0.146240234f, (float16_t)0.246459961f, (float16_t)0.69921875f, (float16_t)0.0455322266f, + (float16_t)-0.480957031f, (float16_t)0.973144531f, (float16_t)-0.629394531f, (float16_t)-0.837402344f, + (float16_t)0.486572266f, (float16_t)0.624511719f, (float16_t)-0.895996094f, (float16_t)-0.28515625f, + (float16_t)0.0674438477f, (float16_t)-0.8359375f, (float16_t)0.584472656f, (float16_t)-0.681152344f, + (float16_t)0.516113281f, (float16_t)0.528320312f, (float16_t)0.194458008f, (float16_t)-0.705078125f, + (float16_t)0.587402344f, (float16_t)0.741210938f, (float16_t)-0.142822266f, (float16_t)0.734863281f, + (float16_t)-0.755859375f, (float16_t)-0.388671875f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..8eac6de9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_hidden_weights_data.h @@ -0,0 +1,15 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_input_hidden_weights[49] = { + (float16_t)0.0449829102f, (float16_t)0.214111328f, (float16_t)0.095703125f, (float16_t)0.0418395996f, + (float16_t)0.141967773f, (float16_t)-0.0362243652f, (float16_t)0.262207031f, (float16_t)0.21081543f, + (float16_t)0.105834961f, (float16_t)0.0177459717f, (float16_t)-0.220703125f, (float16_t)0.127929688f, + (float16_t)-0.0234222412f, (float16_t)-0.258789062f, (float16_t)0.314941406f, (float16_t)-0.0735473633f, + (float16_t)-0.146972656f, (float16_t)0.0916748047f, (float16_t)0.325683594f, (float16_t)0.0866088867f, + (float16_t)0.340332031f, (float16_t)-0.0894165039f, (float16_t)-0.269775391f, (float16_t)-0.0618286133f, + (float16_t)0.00428771973f, (float16_t)0.235229492f, (float16_t)-0.281982422f, (float16_t)0.228027344f, + (float16_t)0.276123047f, (float16_t)0.246948242f, (float16_t)0.197021484f, (float16_t)0.324462891f, + (float16_t)0.141723633f, (float16_t)-0.00226020813f, (float16_t)0.138671875f, (float16_t)-0.189819336f, + (float16_t)-0.135253906f, (float16_t)0.0954589844f, (float16_t)0.307861328f, (float16_t)-0.00335121155f, + (float16_t)0.224365234f, (float16_t)0.203369141f, (float16_t)0.317138672f, (float16_t)0.0264434814f, + (float16_t)-0.0715332031f, (float16_t)0.322021484f, (float16_t)0.0247039795f, (float16_t)-0.196655273f, + (float16_t)0.286621094f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_input_weights_data.h new file mode 100644 index 00000000..53af75d3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/input_input_weights_data.h @@ -0,0 +1,13 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_input_input_weights[42] = { + (float16_t)-0.224243164f, (float16_t)0.0532226562f, (float16_t)0.295654297f, (float16_t)0.294189453f, + (float16_t)-0.24597168f, (float16_t)0.336181641f, (float16_t)-0.193237305f, (float16_t)0.0163269043f, + (float16_t)-0.237182617f, (float16_t)0.194946289f, (float16_t)-0.00817871094f, (float16_t)0.267822266f, + (float16_t)0.0272369385f, (float16_t)-0.12322998f, (float16_t)-0.083190918f, (float16_t)-0.345458984f, + (float16_t)0.0485534668f, (float16_t)-0.0685424805f, (float16_t)-0.190795898f, (float16_t)0.0842895508f, + (float16_t)-0.202514648f, (float16_t)-0.167114258f, (float16_t)-0.243774414f, (float16_t)0.177734375f, + (float16_t)-0.273925781f, (float16_t)0.325683594f, (float16_t)0.196533203f, (float16_t)0.210693359f, + (float16_t)0.220092773f, (float16_t)-0.0480651855f, (float16_t)0.241088867f, (float16_t)0.18359375f, + (float16_t)0.185668945f, (float16_t)0.145507812f, (float16_t)-0.285644531f, (float16_t)0.323974609f, + (float16_t)-0.322265625f, (float16_t)-0.228759766f, (float16_t)-0.14465332f, (float16_t)0.227783203f, + (float16_t)0.0760498047f, (float16_t)-0.161987305f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_bias_data.h new file mode 100644 index 00000000..c41ba21f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_bias_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_output_bias[7] = {(float16_t)0.118225098f, + (float16_t)0.0978393555f, + (float16_t)-0.313232422f, + (float16_t)-0.0643310547f, + (float16_t)-0.387207031f, + (float16_t)-0.166625977f, + (float16_t)-0.0728759766f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..fda26b1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_hidden_weights_data.h @@ -0,0 +1,15 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_output_hidden_weights[49] = { + (float16_t)-0.250488281f, (float16_t)-0.0325927734f, (float16_t)0.125732422f, (float16_t)-0.0267486572f, + (float16_t)-0.32421875f, (float16_t)0.19921875f, (float16_t)-0.231079102f, (float16_t)0.254882812f, + (float16_t)-0.0101928711f, (float16_t)0.104370117f, (float16_t)0.116821289f, (float16_t)-0.193237305f, + (float16_t)0.00773239136f, (float16_t)0.0861206055f, (float16_t)-0.138427734f, (float16_t)-0.333251953f, + (float16_t)-0.142089844f, (float16_t)0.0442199707f, (float16_t)-0.339599609f, (float16_t)0.295410156f, + (float16_t)-0.173461914f, (float16_t)-0.282714844f, (float16_t)0.00760650635f, (float16_t)-0.0523681641f, + (float16_t)-0.0017118454f, (float16_t)-0.101318359f, (float16_t)-0.129638672f, (float16_t)-0.197753906f, + (float16_t)0.029296875f, (float16_t)0.319091797f, (float16_t)0.185913086f, (float16_t)-0.188476562f, + (float16_t)-0.00373649597f, (float16_t)-0.075012207f, (float16_t)0.0883789062f, (float16_t)0.346923828f, + (float16_t)0.120361328f, (float16_t)-0.319824219f, (float16_t)-0.00319480896f, (float16_t)-0.293457031f, + (float16_t)-0.288330078f, (float16_t)-0.0888671875f, (float16_t)-0.288818359f, (float16_t)0.171020508f, + (float16_t)-0.31640625f, (float16_t)-0.287841797f, (float16_t)0.0220336914f, (float16_t)-0.00840759277f, + (float16_t)0.00494003296f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_input_weights_data.h new file mode 100644 index 00000000..5d3c47d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_input_weights_data.h @@ -0,0 +1,13 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_output_input_weights[42] = { + (float16_t)-0.185424805f, (float16_t)-0.282714844f, (float16_t)-0.282470703f, (float16_t)0.226806641f, + (float16_t)-0.158569336f, (float16_t)0.236816406f, (float16_t)-0.0174560547f, (float16_t)-0.0335998535f, + (float16_t)-0.341552734f, (float16_t)0.0586547852f, (float16_t)-0.312011719f, (float16_t)0.183227539f, + (float16_t)0.344726562f, (float16_t)-0.131958008f, (float16_t)-0.224243164f, (float16_t)0.273193359f, + (float16_t)0.220703125f, (float16_t)-0.0961303711f, (float16_t)0.0162353516f, (float16_t)0.157470703f, + (float16_t)-0.00168991089f, (float16_t)0.16394043f, (float16_t)-0.255126953f, (float16_t)0.0664672852f, + (float16_t)0.310302734f, (float16_t)0.255615234f, (float16_t)-0.313964844f, (float16_t)-0.214233398f, + (float16_t)-0.159179688f, (float16_t)-0.336669922f, (float16_t)-0.331542969f, (float16_t)0.339599609f, + (float16_t)-0.0583190918f, (float16_t)0.312744141f, (float16_t)0.0219573975f, (float16_t)-0.170654297f, + (float16_t)0.220214844f, (float16_t)0.0136642456f, (float16_t)0.189697266f, (float16_t)-0.127319336f, + (float16_t)-0.0314331055f, (float16_t)-0.060546875f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_ref_data.h new file mode 100644 index 00000000..4f860176 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/output_ref_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_2_f16_output_ref[63] = { + (float16_t)-0.0714111328f, (float16_t)0.115356445f, (float16_t)-0.0303497314f, (float16_t)0.0558166504f, + (float16_t)-0.0610046387f, (float16_t)0.0756225586f, (float16_t)-0.00440216064f, (float16_t)-0.201049805f, + (float16_t)0.0395202637f, (float16_t)-0.0634155273f, (float16_t)0.178344727f, (float16_t)0.0209960938f, + (float16_t)0.165283203f, (float16_t)0.0259094238f, (float16_t)-0.0527648926f, (float16_t)-0.0641479492f, + (float16_t)0.0320739746f, (float16_t)0.00717926025f, (float16_t)-0.00469970703f, (float16_t)0.0198669434f, + (float16_t)-0.0897216797f, (float16_t)-0.116394043f, (float16_t)-0.10949707f, (float16_t)0.0165252686f, + (float16_t)0.0425415039f, (float16_t)-0.0871582031f, (float16_t)0.0472717285f, (float16_t)-0.114257812f, + (float16_t)-0.108947754f, (float16_t)-0.110778809f, (float16_t)0.0326538086f, (float16_t)-0.0276794434f, + (float16_t)0.00807952881f, (float16_t)0.0110321045f, (float16_t)-0.115966797f, (float16_t)-0.245849609f, + (float16_t)-0.159423828f, (float16_t)-0.0492858887f, (float16_t)0.105712891f, (float16_t)0.0645751953f, + (float16_t)0.0487976074f, (float16_t)-0.0753173828f, (float16_t)-0.205566406f, (float16_t)-0.0930175781f, + (float16_t)-0.0836791992f, (float16_t)0.126953125f, (float16_t)-0.0249633789f, (float16_t)0.101928711f, + (float16_t)-0.0779418945f, (float16_t)-0.147216797f, (float16_t)-0.125366211f, (float16_t)0.0139770508f, + (float16_t)0.028717041f, (float16_t)0.0767211914f, (float16_t)0.0140838623f, (float16_t)-0.141479492f, + (float16_t)-0.264648438f, (float16_t)-0.110839844f, (float16_t)-0.0655517578f, (float16_t)0.190795898f, + (float16_t)0.0292358398f, (float16_t)0.135009766f, (float16_t)-0.0641479492f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_bias_data.h new file mode 100644 index 00000000..8c9641f9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_bias_data.h @@ -0,0 +1,3 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_cell_bias[7] = + {-0.226487935f, -0.140098155f, -0.121486284f, 0.0437849127f, 0.320686311f, 0.228260562f, -0.00593394041f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..b34671aa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_hidden_weights_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_cell_hidden_weights[49] = { + 0.178018048f, -0.113753103f, 0.031899821f, 0.184908837f, 0.316722184f, -0.239479393f, -0.143885955f, + 0.0605777986f, 0.317141801f, -0.342426836f, 0.326739281f, 0.298617005f, -0.218842119f, 0.293016553f, + -0.107516035f, -0.14007701f, 0.140042126f, 0.0563724525f, 0.336599708f, 0.33898139f, -0.0126153193f, + -0.0795672908f, -0.0715022013f, -0.042819526f, 0.0762938932f, 0.297282636f, -0.100398205f, 0.341860145f, + -0.150902703f, 0.0534465462f, 0.0984742045f, 0.110229105f, 0.127579376f, 0.172044471f, 0.25543955f, + -0.0899311751f, 0.263055474f, 0.0983815789f, -0.314794391f, 0.0515042059f, -0.293420464f, 0.202978775f, + -0.231259122f, 0.28239578f, 0.137889847f, -0.0646277517f, 0.273105234f, -0.275701463f, -0.342881739f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_input_weights_data.h new file mode 100644 index 00000000..9c29874a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/cell_input_weights_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_cell_input_weights[42] = { + 0.224792525f, 0.0328982361f, 0.151880398f, -0.170370519f, 0.13350527f, -0.139515728f, -0.0451501347f, + -0.0866701901f, 0.129734382f, -0.311050385f, 0.0567053519f, 0.257531434f, 0.092978619f, -0.200656652f, + -0.106261186f, -0.0281547084f, -0.122587964f, -0.102485739f, -0.303457648f, -0.0837018266f, 0.281866431f, + -0.336374342f, 0.321625948f, -0.249096274f, -0.313123226f, -0.0624568947f, -0.201077878f, -0.186172739f, + -0.0985492244f, -0.312802047f, 0.0162479132f, 0.258623481f, 0.0225739107f, 0.308190554f, 0.32619366f, + -0.0977654159f, -0.164183974f, 0.135699943f, -0.261328071f, 0.265135795f, 0.292162597f, -0.322081685f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/config_data.h new file mode 100644 index 00000000..0d889c39 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_2_F32_TIME_MAJOR 0 +#define LSTM_MATCH_2_F32_TIME_STEPS 9 +#define LSTM_MATCH_2_F32_BATCH_SIZE 1 +#define LSTM_MATCH_2_F32_INPUT_SIZE 6 +#define LSTM_MATCH_2_F32_HIDDEN_SIZE 7 +#define LSTM_MATCH_2_F32_CELL_CLIP 0.0f +#define LSTM_MATCH_2_F32_CELL_STATE_SIZE 7 +#define LSTM_MATCH_2_F32_DST_SIZE 63 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_bias_data.h new file mode 100644 index 00000000..e02681a5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_bias_data.h @@ -0,0 +1,3 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_forget_bias[7] = + {-0.0367999412f, 0.239391655f, 0.241278708f, -0.230712608f, 0.0918967426f, 0.322369218f, 0.170202538f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..3842407b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_hidden_weights_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_forget_hidden_weights[49] = { + -0.236690715f, -0.282249004f, 0.282708675f, 0.226947427f, 0.181631505f, 0.246695504f, -0.32072112f, + -0.153301403f, 0.0877558663f, -0.0949860513f, -0.304924995f, -0.127925873f, 0.0836837068f, -0.172800779f, + 0.13654387f, -0.125356182f, 0.200264215f, -0.189055443f, -0.0794292688f, -0.162598446f, 0.260964155f, + 0.0401290506f, 0.249771297f, 0.249208674f, -0.0399118625f, -0.000614161545f, -0.0369814076f, 0.045218803f, + -0.0820445865f, -0.0545194075f, -0.0766122341f, -0.334275991f, -0.193931982f, -0.258905083f, 0.00325229554f, + -0.217154771f, -0.233212352f, -0.192619771f, -0.191287667f, -0.192963764f, -0.167788222f, 0.0032386526f, + 0.277481139f, 0.246980131f, -0.106214888f, 0.0572817698f, 0.280929238f, 0.227691144f, 0.0978565812f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_input_weights_data.h new file mode 100644 index 00000000..abbde26d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/forget_input_weights_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_forget_input_weights[42] = { + 0.166301131f, 0.345778108f, 0.291240096f, -0.170349076f, 0.27239126f, 0.241571948f, 0.0474227816f, + -0.325037152f, -0.0715926811f, 0.19877021f, -0.046949394f, 0.0218362864f, -0.138640746f, -0.0522861443f, + -0.109115526f, 0.120982587f, -0.0512889698f, 0.132152766f, 0.27835688f, 0.223096952f, -0.0139917312f, + -0.323708385f, -0.194271073f, 0.288580507f, 0.0903282464f, 0.149380982f, -0.171066403f, 0.0875623748f, + 0.0715052038f, -0.0649058819f, -0.182330981f, -0.186650127f, -0.312422276f, 0.0255823359f, -0.318770796f, + 0.106817953f, -0.0328279026f, 0.0107934792f, -0.310694784f, 0.289763898f, 0.345925331f, -0.0599353611f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_bias_data.h new file mode 100644 index 00000000..9f8006b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_bias_data.h @@ -0,0 +1,3 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_input_bias[7] = + {0.140675694f, 0.195227027f, -0.0599374175f, -0.0801632181f, -0.104561523f, 0.258368373f, 0.293761671f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_data.h new file mode 100644 index 00000000..1268e9de --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_input[54] = { + -0.540875793f, -0.563643515f, 0.755660534f, 0.0989852697f, -0.434624255f, 0.569901884f, 0.112784117f, + 0.688014984f, -0.355962694f, 0.263872296f, 0.344186097f, -0.964283228f, 0.0471542701f, 0.528234005f, + 0.120493926f, 0.766386807f, -0.672832191f, 0.703954875f, -0.298451722f, 0.962363899f, -0.979394555f, + -0.438519061f, -0.415184617f, -0.741011381f, -0.0260340907f, -0.39812547f, 0.248252928f, 0.438123882f, + -0.542894483f, -0.712115526f, 0.904728591f, 0.508425593f, -0.26179418f, 0.587675512f, 0.609995544f, + -0.121299043f, 0.567767382f, 0.368377149f, 0.0390754975f, -0.1922874f, 0.549226284f, -0.797140419f, + -0.179377556f, -0.726238728f, -0.513277173f, -0.63337791f, 0.849707961f, -0.852432072f, -0.778126419f, + -0.0669338107f, 0.172574878f, -0.823479831f, -0.733853519f, -0.837146878f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..707b206e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_hidden_weights_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_input_hidden_weights[49] = { + 0.170788139f, 0.298436731f, -0.237186596f, -0.0981397107f, 0.244162545f, -0.209829837f, -0.223217338f, + -0.192057803f, 0.34121713f, -0.312561721f, 0.286083221f, 0.348446161f, -0.150566846f, -0.215342075f, + 0.0317455232f, 0.306212187f, -0.309765518f, -0.302971929f, 0.164528668f, -0.256719261f, -0.322535783f, + -0.0236839168f, 0.280857712f, 0.284254491f, -0.326449364f, -0.13267757f, -0.235817149f, 0.0588843189f, + -0.114166558f, 0.312375456f, 0.0564089157f, -0.132479534f, -0.0458241254f, 0.0638645291f, -0.0912068337f, + -0.0578389019f, -0.0626953021f, -0.200882912f, 0.224389464f, -0.0157506149f, -0.0862110928f, 0.209514692f, + -0.0608291738f, -0.238735199f, -0.0779278576f, -0.261884958f, 0.00370026147f, -0.273586512f, -0.251565427f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_input_weights_data.h new file mode 100644 index 00000000..00a81397 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/input_input_weights_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_input_input_weights[42] = { + 0.172436655f, 0.0532759093f, -0.326892465f, 0.0303683244f, 0.177190661f, 0.0480089411f, -0.0409517139f, + 0.0056059747f, 0.197783515f, 0.216596037f, -0.0139725003f, -0.0681057349f, 0.312057585f, -0.184510991f, + -0.238887608f, 0.13818866f, -0.112241589f, 0.0150301624f, -0.114829846f, -0.107581407f, -0.207768798f, + 0.232644618f, -0.238651812f, -0.273176432f, 0.1259408f, 0.16023317f, 0.0409130342f, 0.0689825416f, + 0.333058864f, 0.327922106f, 0.288361192f, -0.186690271f, 0.188033581f, -0.268244565f, -0.167520374f, + -0.13715221f, 0.18851088f, -0.00555094751f, 0.254012793f, 0.0487951301f, -0.0835962221f, 0.271177202f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_bias_data.h new file mode 100644 index 00000000..f283a761 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_bias_data.h @@ -0,0 +1,3 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_output_bias[7] = + {0.239603385f, -0.204275876f, 0.0466675311f, -0.393553823f, 0.173193783f, -0.213457257f, -0.172586948f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..fd63a714 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_hidden_weights_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_output_hidden_weights[49] = { + 0.333293796f, 0.225174516f, -0.137897342f, 0.298436582f, -0.167105973f, -0.24922809f, 0.20989655f, + 0.0361015275f, 0.158415645f, -0.308945447f, 0.0816668719f, 0.315234721f, -0.315538824f, 0.278256267f, + 0.183396623f, -0.329508871f, 0.312075764f, 0.0945049152f, 0.0982612148f, 0.115817085f, -0.330583364f, + 0.265932977f, -0.220728576f, 0.271525979f, 0.0649491996f, 0.0298531484f, 0.0548070706f, -0.296867788f, + -0.325614929f, -0.324731052f, -0.191652745f, 0.0136988899f, 0.272346824f, -0.23656255f, 0.0272825416f, + -0.180261895f, -0.124511048f, -0.124299616f, 0.115831316f, 0.183827683f, -0.33354336f, 0.311092496f, + -0.21435532f, 0.307342649f, -0.298631698f, -0.234335214f, 0.337233007f, 0.254191756f, 0.203075349f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_input_weights_data.h new file mode 100644 index 00000000..e4e82905 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_input_weights_data.h @@ -0,0 +1,8 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_output_input_weights[42] = { + -0.0206469372f, -0.0608360544f, 0.304530621f, -0.173160359f, -0.299776018f, 0.293223113f, -0.0614556968f, + 0.250719845f, -0.340722263f, 0.303230405f, -0.108986571f, -0.167127669f, -0.110963747f, -0.233327329f, + 0.223100781f, 0.324550599f, 0.00775054283f, -0.2597121f, -0.189986274f, -0.117255971f, 0.100690603f, + 0.0279872362f, 0.00660000043f, 0.13821736f, -0.338294089f, -0.149327174f, 0.26009047f, 0.117148235f, + 0.11431139f, 0.135357156f, 0.200033903f, 0.1168698f, -0.0958752483f, 0.322430044f, -0.240603343f, + -0.0105784759f, -0.223054796f, -0.165018976f, 0.237241104f, 0.195973024f, -0.346601129f, -0.0395279452f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_ref_data.h new file mode 100644 index 00000000..cb7c1721 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_2_f32_output_ref[63] = { + -0.113466039f, 0.0255285669f, -0.0367582366f, 0.0339609794f, 0.064836964f, -0.0184092019f, -0.163434491f, + -0.0490648746f, -0.145811483f, -0.0544738211f, 0.0255858991f, 0.130389616f, 0.13191165f, 0.0543192625f, + -0.173875228f, -0.150272936f, -0.0651336014f, -0.0838741213f, 0.0825128704f, 0.164726049f, -0.0473362878f, + -0.130258113f, -0.211731076f, -0.0275551509f, -0.033846315f, 0.157472104f, 0.134622291f, 0.0555442609f, + -0.122916467f, -0.215457857f, 0.0373738818f, 0.00541761937f, 0.203366235f, 0.121174656f, 0.0470691808f, + -0.0628216118f, -0.244913295f, 0.00733259227f, -0.0321525335f, 0.103207678f, 0.223828703f, 0.0864356607f, + -0.0115623688f, -0.19236654f, 0.00922121014f, 0.037096303f, 0.141382724f, 0.189952865f, 0.0897812471f, + -0.00842417311f, -0.129168272f, 0.0559588745f, 0.13114956f, 0.227101296f, 0.105049863f, 0.107333951f, + -0.042211853f, -0.075404346f, 0.0597937219f, 0.178716838f, 0.235407695f, 0.0102782939f, 0.034632314f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_2_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_bias_data.h new file mode 100644 index 00000000..aab772d3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_bias_data.h @@ -0,0 +1,4 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_cell_bias[3] = {(float16_t)-0.161865234f, + (float16_t)0.173095703f, + (float16_t)0.300048828f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..c683d920 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_cell_hidden_weights[9] = {(float16_t)-0.0135269165f, + (float16_t)-0.260986328f, + (float16_t)0.175170898f, + (float16_t)-0.254638672f, + (float16_t)0.283447266f, + (float16_t)0.217529297f, + (float16_t)-0.335693359f, + (float16_t)0.0269622803f, + (float16_t)0.179931641f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_input_weights_data.h new file mode 100644 index 00000000..bff3b528 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/cell_input_weights_data.h @@ -0,0 +1,19 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_cell_input_weights[66] = { + (float16_t)-0.284179688f, (float16_t)-0.0414123535f, (float16_t)0.164672852f, (float16_t)0.0824584961f, + (float16_t)-0.14453125f, (float16_t)0.0772705078f, (float16_t)-0.229736328f, (float16_t)0.278564453f, + (float16_t)-0.0107040405f, (float16_t)0.171386719f, (float16_t)0.0498962402f, (float16_t)0.303466797f, + (float16_t)0.0231933594f, (float16_t)0.107910156f, (float16_t)-0.334960938f, (float16_t)-0.0807495117f, + (float16_t)-0.256591797f, (float16_t)0.217651367f, (float16_t)-0.233642578f, (float16_t)-0.0421142578f, + (float16_t)0.250976562f, (float16_t)-0.194824219f, (float16_t)-0.0370178223f, (float16_t)-0.2578125f, + (float16_t)0.182373047f, (float16_t)0.135986328f, (float16_t)0.0908813477f, (float16_t)0.221069336f, + (float16_t)0.208618164f, (float16_t)0.065246582f, (float16_t)-0.123352051f, (float16_t)0.00511932373f, + (float16_t)-0.195556641f, (float16_t)-0.279785156f, (float16_t)-0.155639648f, (float16_t)0.257324219f, + (float16_t)0.192504883f, (float16_t)-0.142578125f, (float16_t)-0.346679688f, (float16_t)0.227294922f, + (float16_t)-0.216796875f, (float16_t)0.192260742f, (float16_t)-0.254394531f, (float16_t)0.0650634766f, + (float16_t)0.0477905273f, (float16_t)-0.24621582f, (float16_t)-0.250244141f, (float16_t)-0.230834961f, + (float16_t)0.272705078f, (float16_t)0.295654297f, (float16_t)-0.0692138672f, (float16_t)0.184692383f, + (float16_t)-0.141967773f, (float16_t)0.148071289f, (float16_t)-0.298095703f, (float16_t)-0.325927734f, + (float16_t)0.244384766f, (float16_t)-0.254150391f, (float16_t)-0.0966796875f, (float16_t)0.281982422f, + (float16_t)-0.0726318359f, (float16_t)-0.0352172852f, (float16_t)0.162841797f, (float16_t)0.026473999f, + (float16_t)-0.193481445f, (float16_t)0.238525391f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/config_data.h new file mode 100644 index 00000000..573c0369 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_ONE_TIME_STEP_F16_TIME_MAJOR 0 +#define LSTM_MATCH_ONE_TIME_STEP_F16_TIME_STEPS 1 +#define LSTM_MATCH_ONE_TIME_STEP_F16_BATCH_SIZE 3 +#define LSTM_MATCH_ONE_TIME_STEP_F16_INPUT_SIZE 22 +#define LSTM_MATCH_ONE_TIME_STEP_F16_HIDDEN_SIZE 3 +#define LSTM_MATCH_ONE_TIME_STEP_F16_CELL_CLIP 0.0f +#define LSTM_MATCH_ONE_TIME_STEP_F16_CELL_STATE_SIZE 9 +#define LSTM_MATCH_ONE_TIME_STEP_F16_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_bias_data.h new file mode 100644 index 00000000..3be5075e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_bias_data.h @@ -0,0 +1,4 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_forget_bias[3] = {(float16_t)0.0811157227f, + (float16_t)0.169189453f, + (float16_t)-0.260253906f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..f7a41b07 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_forget_hidden_weights[9] = {(float16_t)0.346679688f, + (float16_t)-0.220092773f, + (float16_t)0.265136719f, + (float16_t)-0.160644531f, + (float16_t)0.0973510742f, + (float16_t)-0.242553711f, + (float16_t)0.275634766f, + (float16_t)0.00457000732f, + (float16_t)-0.242797852f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_input_weights_data.h new file mode 100644 index 00000000..cd5f4572 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/forget_input_weights_data.h @@ -0,0 +1,19 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_forget_input_weights[66] = { + (float16_t)-0.337890625f, (float16_t)0.0718994141f, (float16_t)0.148193359f, (float16_t)0.302490234f, + (float16_t)0.165893555f, (float16_t)-0.159057617f, (float16_t)-0.118286133f, (float16_t)-0.338867188f, + (float16_t)0.0102539062f, (float16_t)-0.230224609f, (float16_t)0.0189819336f, (float16_t)-0.0634155273f, + (float16_t)0.216430664f, (float16_t)-0.128417969f, (float16_t)0.166625977f, (float16_t)0.0665283203f, + (float16_t)0.205688477f, (float16_t)-0.322265625f, (float16_t)-0.131347656f, (float16_t)0.309570312f, + (float16_t)-0.145385742f, (float16_t)0.0204467773f, (float16_t)0.181518555f, (float16_t)-0.120910645f, + (float16_t)-0.338623047f, (float16_t)0.233764648f, (float16_t)-0.223144531f, (float16_t)0.338623047f, + (float16_t)-0.109985352f, (float16_t)-0.147460938f, (float16_t)-0.229858398f, (float16_t)0.298828125f, + (float16_t)-0.0421142578f, (float16_t)-0.0100631714f, (float16_t)-0.0713500977f, (float16_t)-0.0272979736f, + (float16_t)-0.0256347656f, (float16_t)0.344726562f, (float16_t)-0.162353516f, (float16_t)-0.269042969f, + (float16_t)0.136108398f, (float16_t)-0.16003418f, (float16_t)0.227294922f, (float16_t)-0.217285156f, + (float16_t)0.126586914f, (float16_t)0.344238281f, (float16_t)-0.114257812f, (float16_t)0.104064941f, + (float16_t)0.150512695f, (float16_t)-0.310302734f, (float16_t)-0.0546569824f, (float16_t)0.138549805f, + (float16_t)0.221923828f, (float16_t)-0.292236328f, (float16_t)0.155273438f, (float16_t)-0.0363769531f, + (float16_t)-0.147705078f, (float16_t)-0.17578125f, (float16_t)0.0673828125f, (float16_t)-0.228637695f, + (float16_t)0.101135254f, (float16_t)0.211547852f, (float16_t)-0.0162811279f, (float16_t)-0.333984375f, + (float16_t)-0.0935058594f, (float16_t)0.0319824219f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_bias_data.h new file mode 100644 index 00000000..693f2f6f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_bias_data.h @@ -0,0 +1,4 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_input_bias[3] = {(float16_t)-0.20690918f, + (float16_t)-0.0510253906f, + (float16_t)0.224121094f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_data.h new file mode 100644 index 00000000..3b897852 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_data.h @@ -0,0 +1,19 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_input[66] = { + (float16_t)-0.752929688f, (float16_t)-0.612792969f, (float16_t)-0.472900391f, (float16_t)0.886230469f, + (float16_t)-0.104003906f, (float16_t)0.0447998047f, (float16_t)-0.276611328f, (float16_t)-0.357666016f, + (float16_t)-0.747558594f, (float16_t)-0.849609375f, (float16_t)0.37890625f, (float16_t)0.731445312f, + (float16_t)0.860839844f, (float16_t)0.571289062f, (float16_t)-0.0513000488f, (float16_t)0.5546875f, + (float16_t)0.983398438f, (float16_t)-0.297363281f, (float16_t)0.657714844f, (float16_t)-0.982421875f, + (float16_t)0.637695312f, (float16_t)-0.140258789f, (float16_t)-0.294189453f, (float16_t)0.0637207031f, + (float16_t)-0.492431641f, (float16_t)-0.355957031f, (float16_t)-0.5f, (float16_t)0.261474609f, + (float16_t)-0.546875f, (float16_t)-0.120300293f, (float16_t)0.251953125f, (float16_t)0.572265625f, + (float16_t)0.708007812f, (float16_t)-0.733886719f, (float16_t)-0.24597168f, (float16_t)0.0244140625f, + (float16_t)-0.755371094f, (float16_t)0.221191406f, (float16_t)-0.750488281f, (float16_t)0.816894531f, + (float16_t)0.8125f, (float16_t)0.488037109f, (float16_t)-0.942382812f, (float16_t)0.589355469f, + (float16_t)-0.0822753906f, (float16_t)0.763671875f, (float16_t)-0.670410156f, (float16_t)0.323486328f, + (float16_t)0.0723266602f, (float16_t)0.0940551758f, (float16_t)-0.513671875f, (float16_t)-0.960449219f, + (float16_t)0.875488281f, (float16_t)0.0104522705f, (float16_t)0.598632812f, (float16_t)-0.0704345703f, + (float16_t)-0.0592041016f, (float16_t)0.583984375f, (float16_t)0.21862793f, (float16_t)-0.66796875f, + (float16_t)-0.795898438f, (float16_t)-0.787109375f, (float16_t)-0.444824219f, (float16_t)0.75390625f, + (float16_t)-0.354003906f, (float16_t)0.190917969f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..03538500 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_input_hidden_weights[9] = {(float16_t)-0.0262145996f, + (float16_t)0.184204102f, + (float16_t)-0.239746094f, + (float16_t)-0.267089844f, + (float16_t)0.229858398f, + (float16_t)0.0948486328f, + (float16_t)-0.141967773f, + (float16_t)-0.116333008f, + (float16_t)-0.166748047f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_input_weights_data.h new file mode 100644 index 00000000..7e897629 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/input_input_weights_data.h @@ -0,0 +1,19 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_input_input_weights[66] = { + (float16_t)-0.210571289f, (float16_t)0.0914306641f, (float16_t)0.0500488281f, (float16_t)0.05859375f, + (float16_t)0.127441406f, (float16_t)0.297119141f, (float16_t)-0.266845703f, (float16_t)-0.150756836f, + (float16_t)0.309814453f, (float16_t)0.335693359f, (float16_t)-0.00262451172f, (float16_t)-0.0230560303f, + (float16_t)-0.144165039f, (float16_t)0.283203125f, (float16_t)-0.254394531f, (float16_t)-0.119384766f, + (float16_t)-0.0467834473f, (float16_t)0.168945312f, (float16_t)-0.0523071289f, (float16_t)0.108520508f, + (float16_t)-0.339111328f, (float16_t)-0.190307617f, (float16_t)-0.216308594f, (float16_t)0.346191406f, + (float16_t)0.0673828125f, (float16_t)-0.173339844f, (float16_t)-0.335205078f, (float16_t)0.227050781f, + (float16_t)-0.117492676f, (float16_t)-0.150390625f, (float16_t)0.0703735352f, (float16_t)-0.284667969f, + (float16_t)-0.147705078f, (float16_t)-0.0813598633f, (float16_t)0.111328125f, (float16_t)0.303222656f, + (float16_t)-0.221923828f, (float16_t)0.0752563477f, (float16_t)0.0475158691f, (float16_t)-0.272460938f, + (float16_t)0.17956543f, (float16_t)0.347412109f, (float16_t)-0.289550781f, (float16_t)-0.0728759766f, + (float16_t)-0.142456055f, (float16_t)-0.267822266f, (float16_t)-0.328125f, (float16_t)0.270263672f, + (float16_t)-0.195922852f, (float16_t)-0.208618164f, (float16_t)-0.233886719f, (float16_t)-0.127075195f, + (float16_t)0.224731445f, (float16_t)0.0536193848f, (float16_t)-0.0345153809f, (float16_t)0.0215148926f, + (float16_t)0.286376953f, (float16_t)0.0218963623f, (float16_t)-0.197387695f, (float16_t)0.107543945f, + (float16_t)-0.0321960449f, (float16_t)0.00939178467f, (float16_t)0.240722656f, (float16_t)-0.205932617f, + (float16_t)-0.0925292969f, (float16_t)0.170166016f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_bias_data.h new file mode 100644 index 00000000..7afa0733 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_bias_data.h @@ -0,0 +1,4 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_output_bias[3] = {(float16_t)0.253662109f, + (float16_t)-0.137817383f, + (float16_t)0.33984375f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..4d4a7634 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_output_hidden_weights[9] = {(float16_t)-0.160888672f, + (float16_t)0.265380859f, + (float16_t)-0.254150391f, + (float16_t)0.193725586f, + (float16_t)-0.0642089844f, + (float16_t)-0.138916016f, + (float16_t)-0.00296020508f, + (float16_t)-0.0197296143f, + (float16_t)0.153930664f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_input_weights_data.h new file mode 100644 index 00000000..e8cf7cca --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_input_weights_data.h @@ -0,0 +1,19 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_output_input_weights[66] = { + (float16_t)0.16418457f, (float16_t)-0.203735352f, (float16_t)-0.102355957f, (float16_t)-0.348876953f, + (float16_t)0.234375f, (float16_t)0.0653076172f, (float16_t)0.0556335449f, (float16_t)0.234741211f, + (float16_t)0.205078125f, (float16_t)-0.200805664f, (float16_t)0.290283203f, (float16_t)-0.095703125f, + (float16_t)0.262695312f, (float16_t)0.202758789f, (float16_t)-0.228881836f, (float16_t)0.288085938f, + (float16_t)-0.269287109f, (float16_t)-0.0679931641f, (float16_t)0.338623047f, (float16_t)-0.145751953f, + (float16_t)0.0109558105f, (float16_t)0.13659668f, (float16_t)0.268798828f, (float16_t)-0.0139389038f, + (float16_t)0.194824219f, (float16_t)0.0233612061f, (float16_t)-0.293701172f, (float16_t)0.142700195f, + (float16_t)-0.198608398f, (float16_t)0.343017578f, (float16_t)-0.279785156f, (float16_t)0.191772461f, + (float16_t)-0.249023438f, (float16_t)0.228515625f, (float16_t)0.0250091553f, (float16_t)0.0435791016f, + (float16_t)-0.200561523f, (float16_t)0.214599609f, (float16_t)0.0475769043f, (float16_t)-0.158081055f, + (float16_t)0.341796875f, (float16_t)-0.248535156f, (float16_t)-0.22265625f, (float16_t)-0.106018066f, + (float16_t)0.0841064453f, (float16_t)0.182006836f, (float16_t)-0.261230469f, (float16_t)-0.200195312f, + (float16_t)-0.115661621f, (float16_t)0.296142578f, (float16_t)-0.0693969727f, (float16_t)0.022354126f, + (float16_t)0.00247383118f, (float16_t)0.0883789062f, (float16_t)-0.0323791504f, (float16_t)0.208618164f, + (float16_t)0.132446289f, (float16_t)0.227661133f, (float16_t)0.173828125f, (float16_t)-0.338867188f, + (float16_t)0.210571289f, (float16_t)-0.280517578f, (float16_t)0.324951172f, (float16_t)-0.00135421753f, + (float16_t)-0.223999023f, (float16_t)-0.200073242f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_ref_data.h new file mode 100644 index 00000000..0bcdabb3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_match_one_time_step_f16_output_ref[9] = {(float16_t)-0.00536346436f, + (float16_t)-0.190185547f, + (float16_t)-0.00748825073f, + (float16_t)-0.0299072266f, + (float16_t)0.135864258f, + (float16_t)0.298095703f, + (float16_t)-0.125488281f, + (float16_t)0.0417785645f, + (float16_t)-0.16027832f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_bias_data.h new file mode 100644 index 00000000..59f2dcdc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_bias_data.h @@ -0,0 +1,2 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_cell_bias[3] = {-0.0987335593f, -0.272214502f, 0.0135283172f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..afea3a9d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_cell_hidden_weights[9] = {-0.337720931f, + 0.0425028093f, + -0.013403669f, + 0.209608719f, + 0.107653074f, + -0.276642174f, + 0.255400896f, + 0.107432522f, + 0.108269237f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_input_weights_data.h new file mode 100644 index 00000000..9db9c12f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/cell_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_cell_input_weights[66] = { + -0.239481747f, 0.252659321f, -0.121284112f, -0.0971978232f, -0.244902924f, -0.279958099f, -0.311089098f, + 0.303791136f, 2.54656807e-05f, 0.327767938f, -0.152771264f, -0.286900312f, 0.277662277f, -0.0779178515f, + 0.120706625f, -0.112071916f, -0.0799821243f, 0.277816594f, -0.335780978f, 0.264265597f, 0.257520258f, + -0.303019166f, 0.3482427f, 0.19958967f, -0.0902366266f, 0.163697228f, -0.240451157f, 0.296609133f, + -0.302626103f, 0.200388178f, -0.115121827f, 0.157766685f, 0.288011491f, -0.182019874f, -0.214196041f, + 0.335196733f, 0.124729313f, -0.348133683f, -0.278905421f, -0.0295807999f, 0.0139774363f, 0.0276400112f, + -0.0662332773f, 0.198187143f, 0.0549419783f, 0.113043912f, -0.186350793f, 0.0915749744f, 0.244178012f, + -0.13260442f, -0.216693416f, 0.33968243f, 0.0248396751f, -0.302930743f, -0.0335850604f, -0.296058655f, + 0.150517553f, 0.13634479f, -0.11823038f, -0.231665283f, -0.238951892f, 0.0179705489f, 0.226755336f, + -0.303477794f, 0.190425113f, 0.204250529f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/config_data.h new file mode 100644 index 00000000..c3b50890 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MATCH_ONE_TIME_STEP_F32_TIME_MAJOR 0 +#define LSTM_MATCH_ONE_TIME_STEP_F32_TIME_STEPS 1 +#define LSTM_MATCH_ONE_TIME_STEP_F32_BATCH_SIZE 3 +#define LSTM_MATCH_ONE_TIME_STEP_F32_INPUT_SIZE 22 +#define LSTM_MATCH_ONE_TIME_STEP_F32_HIDDEN_SIZE 3 +#define LSTM_MATCH_ONE_TIME_STEP_F32_CELL_CLIP 0.0f +#define LSTM_MATCH_ONE_TIME_STEP_F32_CELL_STATE_SIZE 9 +#define LSTM_MATCH_ONE_TIME_STEP_F32_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_bias_data.h new file mode 100644 index 00000000..37fb38df --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_bias_data.h @@ -0,0 +1,2 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_forget_bias[3] = {-0.31602329f, 0.0654003024f, -0.0747737288f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..4c3f6ed0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_forget_hidden_weights[9] = {0.316829979f, + 0.303915113f, + 0.181365564f, + 0.279291242f, + -0.226180315f, + -0.30044207f, + 0.338946372f, + 0.314945489f, + -0.125375122f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_input_weights_data.h new file mode 100644 index 00000000..f9793a32 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/forget_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_forget_input_weights[66] = { + -0.321300089f, 0.326674223f, -0.306664824f, 0.337759137f, -0.331098169f, 0.000200282986f, -0.113810979f, + 0.325152665f, -0.023282418f, 0.235930875f, 0.250080496f, 0.169470832f, 0.10549552f, 0.180197015f, + 0.329980791f, 0.04583266f, 0.137932777f, -0.0598937348f, -0.299016714f, 0.178755254f, -0.184217915f, + 0.113628559f, 0.0559094287f, 0.0189583935f, -0.177244529f, 0.00955157354f, 0.245382488f, -0.0203970224f, + -0.0806057826f, -0.31556797f, 0.0578781851f, -0.138012454f, 0.218845755f, -0.255354673f, -0.190101117f, + -0.0948104411f, 0.254946679f, -0.127958342f, 0.275727749f, -0.272797346f, -0.164916694f, -0.166337535f, + -0.28823328f, -0.182280228f, -0.0181956571f, 0.194978893f, 0.210093424f, 0.165083528f, -0.232702076f, + -0.200242206f, -0.167356089f, -0.237457857f, 0.180677637f, 0.111861967f, -0.0991776735f, -0.125791967f, + -0.162676468f, 0.0463615358f, -0.00893134624f, -0.29934147f, 0.083854124f, -0.159560427f, -0.0784547031f, + 0.0498659313f, -0.177256525f, -0.06495177f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_bias_data.h new file mode 100644 index 00000000..f4fd4a85 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_bias_data.h @@ -0,0 +1,2 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_input_bias[3] = {-0.142560869f, 0.0364502221f, 0.319348425f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_data.h new file mode 100644 index 00000000..b2a8ccd5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_input[66] = { + -0.610892475f, -0.687567532f, -0.408966273f, -0.326779366f, -0.179219037f, 0.314936668f, 0.410522431f, + 0.770236731f, -0.497456014f, 0.018544497f, 0.215349987f, 0.133410811f, 0.24328126f, -0.469182819f, + -0.426459134f, 0.278360546f, 0.0740393251f, -0.804174483f, -0.0353147201f, 0.464274287f, -0.444803357f, + 0.849718988f, 0.907513499f, -0.456156045f, 0.685613632f, 0.345633149f, 0.469240099f, 0.511864424f, + -0.594542801f, 0.94457525f, 0.356626272f, 0.695700526f, -0.805778623f, -0.50534755f, -0.774623871f, + 0.843126953f, 0.907968283f, -0.936181188f, 0.346237868f, -0.856484354f, -0.669396639f, -0.827822506f, + -0.843783438f, 0.135437846f, -0.876854956f, -0.729699433f, -0.875195742f, -0.128762886f, -0.6335693f, + -0.640542328f, -0.895530343f, 0.762443781f, 0.936155081f, -0.646185219f, 0.898226321f, 0.25159204f, + 0.604806483f, -0.00250766007f, 0.735725462f, 0.0675975531f, -0.481163383f, 0.0402602851f, -0.929414511f, + 0.216904312f, 0.342699796f, 0.263137579f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..de1e0c87 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_input_hidden_weights[9] = {-0.256600767f, + -0.342558533f, + -0.185401276f, + -0.342203498f, + -0.167991668f, + 0.109992482f, + 0.066665858f, + -0.146376982f, + -0.015817361f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_input_weights_data.h new file mode 100644 index 00000000..ef478941 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/input_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_input_input_weights[66] = { + 0.161210388f, 0.280676723f, -0.230559111f, -0.013079145f, -0.234351471f, 0.207725033f, -0.20676817f, + 0.33755964f, -0.169104233f, -0.22183384f, -0.0984308794f, -0.288101375f, 0.224215239f, 0.154955178f, + 0.135496154f, 0.107434459f, 0.284757316f, -0.154236913f, 0.139556125f, -0.245756507f, -0.14734f, + 0.0282603204f, 0.0940589011f, 0.192387551f, -0.192195967f, 0.297880083f, 0.338592976f, 0.182344481f, + 0.269582242f, -0.292002618f, -0.28928712f, 0.128810599f, -0.184741423f, 0.0129188243f, -0.312806666f, + 0.223161653f, -0.226922035f, -0.184507787f, 0.215631157f, 0.125365674f, -0.188674122f, 0.158873811f, + 0.077360563f, 0.248436362f, 0.130539641f, -0.205143303f, 0.242565975f, -0.259733081f, 0.349392384f, + 0.100714982f, 0.273746282f, 0.197761133f, 0.277317047f, 0.0307061467f, -0.0296776779f, -0.24104017f, + 0.0673104748f, -0.0774409547f, 0.102309473f, 0.105817266f, -0.237881556f, -0.296602339f, 0.00592885679f, + 0.343359977f, -0.150002331f, 0.329455435f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_bias_data.h new file mode 100644 index 00000000..de9bf4fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_bias_data.h @@ -0,0 +1,2 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_output_bias[3] = {0.147478789f, -0.00901256874f, -0.162585676f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..f4373b90 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_hidden_weights_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_output_hidden_weights[9] = {0.237836272f, + -0.0540235378f, + -0.0786147118f, + -0.0217899308f, + 0.244526252f, + 0.109344885f, + -0.286713481f, + -0.0474839397f, + -0.0513517521f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_input_weights_data.h new file mode 100644 index 00000000..e8d9d9f7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_output_input_weights[66] = { + -0.0982166156f, -0.0589632131f, -0.204577953f, -0.236441985f, -0.0641937479f, -0.291154981f, 0.0089231832f, + 0.234754995f, 0.195847929f, -0.0113118161f, -0.0779133216f, -0.163778335f, 0.324276268f, -0.13425225f, + -0.345678329f, -0.0521807112f, 0.182601795f, -0.249481648f, -0.15019764f, 0.274251431f, -0.0101285353f, + -0.139461175f, -0.335416198f, -0.283660978f, -0.213163361f, 0.228867397f, 0.0505279228f, 0.0987374932f, + -0.264137417f, -0.0665058568f, 0.092396915f, -0.26505816f, 0.305597991f, 0.304302543f, 0.220246688f, + 0.0903608873f, -0.0795850158f, -0.0389716513f, -0.18921937f, 0.318633258f, 0.119362369f, 0.272733539f, + 0.174418926f, 0.0243107378f, 0.216943413f, -0.126260519f, -0.172776461f, 0.200049773f, 0.232288718f, + -0.346625805f, 0.0430145413f, 0.0504067279f, -0.0619305223f, 0.263330102f, -0.077735804f, -0.0323099755f, + 0.309539944f, 0.285843313f, 0.262599379f, 0.313479483f, 0.306590348f, -0.0960812494f, -0.0307073742f, + -0.14216198f, 0.0790119022f, -0.282546818f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_ref_data.h new file mode 100644 index 00000000..5b371112 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_match_one_time_step_f32_output_ref[9] = {-0.161744192f, + -0.117305152f, + -0.0433944538f, + -0.102637619f, + 0.0759491548f, + 0.151019618f, + 0.263569236f, + -0.0263598394f, + 0.113580383f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_match_one_time_step_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_bias_data.h new file mode 100644 index 00000000..7011cceb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_cell_bias[16] = {(float16_t)-0.0922241211f, + (float16_t)0.278808594f, + (float16_t)-0.300048828f, + (float16_t)0.00943756104f, + (float16_t)0.127929688f, + (float16_t)0.367919922f, + (float16_t)-0.0385742188f, + (float16_t)0.0911254883f, + (float16_t)0.154663086f, + (float16_t)-0.0311431885f, + (float16_t)-0.113464355f, + (float16_t)0.0319824219f, + (float16_t)0.179443359f, + (float16_t)0.0790405273f, + (float16_t)-0.16394043f, + (float16_t)-0.129882812f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..b0c1fa9f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_hidden_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_cell_hidden_weights[256] = { + (float16_t)0.311035156f, (float16_t)-0.0487365723f, (float16_t)0.295166016f, (float16_t)0.163818359f, + (float16_t)0.130615234f, (float16_t)-0.0343933105f, (float16_t)0.0468139648f, (float16_t)0.0477600098f, + (float16_t)-0.348388672f, (float16_t)0.332763672f, (float16_t)0.130615234f, (float16_t)0.181152344f, + (float16_t)-0.322021484f, (float16_t)-0.310058594f, (float16_t)-0.208007812f, (float16_t)-0.0204925537f, + (float16_t)-0.273925781f, (float16_t)-0.284667969f, (float16_t)-0.166503906f, (float16_t)0.166381836f, + (float16_t)-0.0405578613f, (float16_t)0.289550781f, (float16_t)0.239013672f, (float16_t)0.081237793f, + (float16_t)0.301269531f, (float16_t)-0.117431641f, (float16_t)0.22265625f, (float16_t)0.0473937988f, + (float16_t)-0.345703125f, (float16_t)0.339355469f, (float16_t)0.338134766f, (float16_t)-0.227416992f, + (float16_t)0.212036133f, (float16_t)0.346679688f, (float16_t)-0.291503906f, (float16_t)0.00649261475f, + (float16_t)0.118041992f, (float16_t)0.31640625f, (float16_t)0.228271484f, (float16_t)-0.0925292969f, + (float16_t)0.216796875f, (float16_t)0.253417969f, (float16_t)-0.0797729492f, (float16_t)0.221191406f, + (float16_t)-0.233276367f, (float16_t)-0.0647583008f, (float16_t)0.349365234f, (float16_t)-0.285400391f, + (float16_t)-0.0758666992f, (float16_t)-0.242431641f, (float16_t)0.119689941f, (float16_t)-0.251708984f, + (float16_t)0.212280273f, (float16_t)-0.0466308594f, (float16_t)0.117614746f, (float16_t)-0.21875f, + (float16_t)-0.192382812f, (float16_t)-0.198120117f, (float16_t)-0.344238281f, (float16_t)-0.208862305f, + (float16_t)-0.315673828f, (float16_t)-0.310546875f, (float16_t)0.203735352f, (float16_t)0.0629272461f, + (float16_t)-0.323974609f, (float16_t)-0.178588867f, (float16_t)-0.295166016f, (float16_t)-0.095703125f, + (float16_t)0.262695312f, (float16_t)-0.249389648f, (float16_t)0.138793945f, (float16_t)-0.239501953f, + (float16_t)-0.194580078f, (float16_t)0.254150391f, (float16_t)-0.226318359f, (float16_t)-0.23828125f, + (float16_t)-0.0314941406f, (float16_t)0.190185547f, (float16_t)0.101806641f, (float16_t)0.225219727f, + (float16_t)-0.306640625f, (float16_t)0.0117721558f, (float16_t)-0.121826172f, (float16_t)-0.285400391f, + (float16_t)0.0264434814f, (float16_t)0.245605469f, (float16_t)0.110900879f, (float16_t)0.0208129883f, + (float16_t)0.113586426f, (float16_t)-0.0884399414f, (float16_t)-0.00249481201f, (float16_t)0.0609130859f, + (float16_t)0.0904541016f, (float16_t)0.333251953f, (float16_t)-0.172607422f, (float16_t)-0.210693359f, + (float16_t)-0.00176715851f, (float16_t)-0.00943756104f, (float16_t)-0.16784668f, (float16_t)0.17956543f, + (float16_t)0.166381836f, (float16_t)0.13659668f, (float16_t)-0.00592041016f, (float16_t)0.127563477f, + (float16_t)-0.0137786865f, (float16_t)0.0916748047f, (float16_t)0.285400391f, (float16_t)0.121826172f, + (float16_t)0.0510253906f, (float16_t)0.251220703f, (float16_t)0.290771484f, (float16_t)-0.0115509033f, + (float16_t)0.237060547f, (float16_t)0.170288086f, (float16_t)0.109619141f, (float16_t)-0.283447266f, + (float16_t)-0.110168457f, (float16_t)0.297851562f, (float16_t)-0.0853881836f, (float16_t)-0.0320739746f, + (float16_t)-0.0705566406f, (float16_t)0.322265625f, (float16_t)-0.14855957f, (float16_t)0.129760742f, + (float16_t)-0.0468139648f, (float16_t)0.243041992f, (float16_t)0.218994141f, (float16_t)0.177368164f, + (float16_t)0.200561523f, (float16_t)-0.0405883789f, (float16_t)0.329345703f, (float16_t)0.0626220703f, + (float16_t)-0.046875f, (float16_t)-0.0564880371f, (float16_t)0.321289062f, (float16_t)0.298095703f, + (float16_t)-0.300292969f, (float16_t)-0.100524902f, (float16_t)0.283447266f, (float16_t)0.00295448303f, + (float16_t)0.266601562f, (float16_t)-0.29296875f, (float16_t)-0.268066406f, (float16_t)-0.211303711f, + (float16_t)-0.0318908691f, (float16_t)-0.0460510254f, (float16_t)0.163818359f, (float16_t)-0.0858764648f, + (float16_t)0.182983398f, (float16_t)-0.329345703f, (float16_t)0.0512390137f, (float16_t)0.0544128418f, + (float16_t)-0.277587891f, (float16_t)-0.150268555f, (float16_t)-0.020690918f, (float16_t)-0.0108566284f, + (float16_t)-0.307373047f, (float16_t)0.218505859f, (float16_t)0.100769043f, (float16_t)-0.341552734f, + (float16_t)0.2421875f, (float16_t)-0.163696289f, (float16_t)-0.0527038574f, (float16_t)-0.329589844f, + (float16_t)-0.333740234f, (float16_t)0.167602539f, (float16_t)0.139892578f, (float16_t)0.315185547f, + (float16_t)-0.0276794434f, (float16_t)0.189208984f, (float16_t)-0.0264587402f, (float16_t)-0.106506348f, + (float16_t)0.062286377f, (float16_t)-0.151245117f, (float16_t)-0.301269531f, (float16_t)-0.0594787598f, + (float16_t)0.25390625f, (float16_t)0.317871094f, (float16_t)-0.283691406f, (float16_t)0.166259766f, + (float16_t)-0.323486328f, (float16_t)-0.334960938f, (float16_t)-0.165527344f, (float16_t)-0.164428711f, + (float16_t)-0.334716797f, (float16_t)0.309326172f, (float16_t)0.318359375f, (float16_t)-0.154541016f, + (float16_t)-0.0622253418f, (float16_t)0.337402344f, (float16_t)0.0471191406f, (float16_t)0.309082031f, + (float16_t)0.289550781f, (float16_t)-0.304931641f, (float16_t)0.0227966309f, (float16_t)0.306640625f, + (float16_t)0.263916016f, (float16_t)0.0338134766f, (float16_t)0.0692138672f, (float16_t)0.137939453f, + (float16_t)-0.0105056763f, (float16_t)0.0325317383f, (float16_t)-0.0107345581f, (float16_t)0.170043945f, + (float16_t)0.106201172f, (float16_t)0.345703125f, (float16_t)0.283935547f, (float16_t)0.171142578f, + (float16_t)0.0787963867f, (float16_t)0.0852050781f, (float16_t)-0.102539062f, (float16_t)0.0950927734f, + (float16_t)0.304931641f, (float16_t)-0.218139648f, (float16_t)0.332763672f, (float16_t)-0.162231445f, + (float16_t)0.203735352f, (float16_t)-0.104919434f, (float16_t)-0.0755615234f, (float16_t)0.174926758f, + (float16_t)-0.177612305f, (float16_t)0.0628051758f, (float16_t)0.205444336f, (float16_t)0.0590209961f, + (float16_t)-0.0452575684f, (float16_t)-0.201660156f, (float16_t)-0.160644531f, (float16_t)0.154418945f, + (float16_t)0.16027832f, (float16_t)-0.260986328f, (float16_t)-0.0823364258f, (float16_t)0.115783691f, + (float16_t)0.317626953f, (float16_t)-0.326904297f, (float16_t)0.213500977f, (float16_t)0.147338867f, + (float16_t)0.264404297f, (float16_t)0.299804688f, (float16_t)0.221679688f, (float16_t)-0.0626220703f, + (float16_t)0.135620117f, (float16_t)-0.329833984f, (float16_t)-0.00978851318f, (float16_t)-0.30078125f, + (float16_t)-0.197021484f, (float16_t)0.100280762f, (float16_t)-0.293212891f, (float16_t)-0.0823364258f, + (float16_t)0.267822266f, (float16_t)0.161621094f, (float16_t)0.348632812f, (float16_t)-0.23449707f, + (float16_t)0.348144531f, (float16_t)0.089050293f, (float16_t)-0.338378906f, (float16_t)-0.164916992f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_input_weights_data.h new file mode 100644 index 00000000..3695cf1c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/cell_input_weights_data.h @@ -0,0 +1,50 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_cell_input_weights[192] = { + (float16_t)0.314941406f, (float16_t)0.2890625f, (float16_t)-0.121643066f, (float16_t)0.0897827148f, + (float16_t)0.0491027832f, (float16_t)0.0838012695f, (float16_t)-0.142456055f, (float16_t)-0.043762207f, + (float16_t)-0.262207031f, (float16_t)-0.3125f, (float16_t)0.287841797f, (float16_t)0.246826172f, + (float16_t)0.0160369873f, (float16_t)-0.345214844f, (float16_t)-0.219360352f, (float16_t)0.142456055f, + (float16_t)0.0722045898f, (float16_t)0.134033203f, (float16_t)-0.0639648438f, (float16_t)-0.265136719f, + (float16_t)-0.0548095703f, (float16_t)0.265625f, (float16_t)-0.213256836f, (float16_t)0.338134766f, + (float16_t)-0.336669922f, (float16_t)0.297363281f, (float16_t)0.16003418f, (float16_t)0.0202178955f, + (float16_t)0.110839844f, (float16_t)-0.289550781f, (float16_t)-0.345703125f, (float16_t)-0.156005859f, + (float16_t)-0.255859375f, (float16_t)0.0763549805f, (float16_t)0.0362548828f, (float16_t)0.296386719f, + (float16_t)0.0834350586f, (float16_t)-0.135742188f, (float16_t)-0.3359375f, (float16_t)-0.256835938f, + (float16_t)0.0223999023f, (float16_t)-0.291748047f, (float16_t)0.12878418f, (float16_t)0.156005859f, + (float16_t)-0.262695312f, (float16_t)0.0378723145f, (float16_t)0.285644531f, (float16_t)-0.159545898f, + (float16_t)-0.234863281f, (float16_t)-0.110534668f, (float16_t)-0.211914062f, (float16_t)0.135131836f, + (float16_t)0.196899414f, (float16_t)0.328125f, (float16_t)-0.131347656f, (float16_t)0.274902344f, + (float16_t)0.262451172f, (float16_t)0.182861328f, (float16_t)0.0513305664f, (float16_t)0.0401611328f, + (float16_t)-0.196044922f, (float16_t)0.104309082f, (float16_t)0.167236328f, (float16_t)-0.226074219f, + (float16_t)-0.0797119141f, (float16_t)0.341796875f, (float16_t)-0.11920166f, (float16_t)0.165283203f, + (float16_t)-0.138183594f, (float16_t)0.00279808044f, (float16_t)0.315429688f, (float16_t)-0.170166016f, + (float16_t)0.197631836f, (float16_t)-0.182617188f, (float16_t)-0.0101928711f, (float16_t)-0.26171875f, + (float16_t)0.0392150879f, (float16_t)-0.284667969f, (float16_t)0.168945312f, (float16_t)-0.0107040405f, + (float16_t)-0.00354385376f, (float16_t)-0.0255584717f, (float16_t)0.103637695f, (float16_t)0.136474609f, + (float16_t)0.105102539f, (float16_t)-0.135498047f, (float16_t)-0.0547485352f, (float16_t)-0.250732422f, + (float16_t)-0.111083984f, (float16_t)0.338134766f, (float16_t)0.171630859f, (float16_t)0.336914062f, + (float16_t)-0.171020508f, (float16_t)0.134887695f, (float16_t)-0.309082031f, (float16_t)-0.142822266f, + (float16_t)0.163696289f, (float16_t)0.0899658203f, (float16_t)-0.196166992f, (float16_t)0.251220703f, + (float16_t)-0.203491211f, (float16_t)0.190673828f, (float16_t)-0.107727051f, (float16_t)-0.315917969f, + (float16_t)0.153564453f, (float16_t)0.307861328f, (float16_t)-0.234375f, (float16_t)0.295898438f, + (float16_t)0.304199219f, (float16_t)0.0831298828f, (float16_t)0.164550781f, (float16_t)0.156860352f, + (float16_t)0.251464844f, (float16_t)0.0911254883f, (float16_t)-0.320556641f, (float16_t)0.319091797f, + (float16_t)-0.101623535f, (float16_t)-0.235839844f, (float16_t)-0.0865478516f, (float16_t)-0.169067383f, + (float16_t)-0.109313965f, (float16_t)0.178588867f, (float16_t)-0.0225830078f, (float16_t)-0.188842773f, + (float16_t)-0.182250977f, (float16_t)0.154785156f, (float16_t)0.064453125f, (float16_t)0.322998047f, + (float16_t)0.0020160675f, (float16_t)-0.051940918f, (float16_t)-0.329833984f, (float16_t)0.118896484f, + (float16_t)0.346679688f, (float16_t)-0.0460510254f, (float16_t)0.284423828f, (float16_t)-0.118896484f, + (float16_t)0.0889892578f, (float16_t)0.05859375f, (float16_t)-0.0283508301f, (float16_t)-0.123962402f, + (float16_t)0.337402344f, (float16_t)0.187988281f, (float16_t)-0.207885742f, (float16_t)-0.0836791992f, + (float16_t)0.228271484f, (float16_t)-0.277832031f, (float16_t)0.0474243164f, (float16_t)-0.319824219f, + (float16_t)0.119750977f, (float16_t)-0.0450439453f, (float16_t)0.300292969f, (float16_t)-0.0448608398f, + (float16_t)0.270263672f, (float16_t)0.161621094f, (float16_t)-0.268554688f, (float16_t)-0.286621094f, + (float16_t)0.291992188f, (float16_t)0.114746094f, (float16_t)-0.252685547f, (float16_t)-0.172851562f, + (float16_t)0.0117950439f, (float16_t)-0.345458984f, (float16_t)0.307128906f, (float16_t)0.301025391f, + (float16_t)-0.14453125f, (float16_t)0.254150391f, (float16_t)0.00400161743f, (float16_t)0.333251953f, + (float16_t)-0.0542297363f, (float16_t)0.00859069824f, (float16_t)0.0165557861f, (float16_t)0.299316406f, + (float16_t)-0.042755127f, (float16_t)-0.293945312f, (float16_t)-0.130004883f, (float16_t)0.00144672394f, + (float16_t)-0.0567626953f, (float16_t)-0.174316406f, (float16_t)-0.0723266602f, (float16_t)0.331787109f, + (float16_t)-0.145996094f, (float16_t)0.0576782227f, (float16_t)0.0473327637f, (float16_t)-0.110534668f, + (float16_t)-0.232299805f, (float16_t)-0.0356445312f, (float16_t)-0.078918457f, (float16_t)0.289306641f, + (float16_t)-0.104492188f, (float16_t)-0.196899414f, (float16_t)0.07421875f, (float16_t)-0.145996094f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/config_data.h new file mode 100644 index 00000000..f08a8011 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MEDIUM_F16_TIME_MAJOR 1 +#define LSTM_MEDIUM_F16_TIME_STEPS 8 +#define LSTM_MEDIUM_F16_BATCH_SIZE 3 +#define LSTM_MEDIUM_F16_INPUT_SIZE 12 +#define LSTM_MEDIUM_F16_HIDDEN_SIZE 16 +#define LSTM_MEDIUM_F16_CELL_CLIP 0.0f +#define LSTM_MEDIUM_F16_CELL_STATE_SIZE 48 +#define LSTM_MEDIUM_F16_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_bias_data.h new file mode 100644 index 00000000..567cafc4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_forget_bias[16] = {(float16_t)-0.249145508f, + (float16_t)0.274902344f, + (float16_t)0.00445175171f, + (float16_t)0.037109375f, + (float16_t)0.182861328f, + (float16_t)-0.0617980957f, + (float16_t)-0.146362305f, + (float16_t)-0.104919434f, + (float16_t)-0.266601562f, + (float16_t)0.453857422f, + (float16_t)-0.0346069336f, + (float16_t)0.207763672f, + (float16_t)-0.362548828f, + (float16_t)-0.0906982422f, + (float16_t)-0.181762695f, + (float16_t)0.170654297f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..c0da56fd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_hidden_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_forget_hidden_weights[256] = { + (float16_t)-0.0173797607f, (float16_t)-0.298095703f, (float16_t)-0.0334777832f, (float16_t)-0.141845703f, + (float16_t)-0.200805664f, (float16_t)-0.311035156f, (float16_t)-0.178222656f, (float16_t)-0.0372314453f, + (float16_t)0.168457031f, (float16_t)0.0369262695f, (float16_t)-0.236816406f, (float16_t)0.127807617f, + (float16_t)-0.0215911865f, (float16_t)-0.281005859f, (float16_t)-0.310058594f, (float16_t)0.110534668f, + (float16_t)-0.0696411133f, (float16_t)0.150878906f, (float16_t)-0.323486328f, (float16_t)0.150512695f, + (float16_t)-0.324462891f, (float16_t)-0.309326172f, (float16_t)-0.258056641f, (float16_t)0.169311523f, + (float16_t)0.133422852f, (float16_t)0.215454102f, (float16_t)-0.0075378418f, (float16_t)-0.0320739746f, + (float16_t)-0.139160156f, (float16_t)-0.065612793f, (float16_t)0.148071289f, (float16_t)0.342773438f, + (float16_t)0.0368041992f, (float16_t)0.337646484f, (float16_t)0.197631836f, (float16_t)-0.107666016f, + (float16_t)0.317382812f, (float16_t)0.265625f, (float16_t)0.151611328f, (float16_t)0.180541992f, + (float16_t)0.0624084473f, (float16_t)-0.0828857422f, (float16_t)-0.101623535f, (float16_t)0.141723633f, + (float16_t)-0.0738525391f, (float16_t)0.104125977f, (float16_t)-0.152709961f, (float16_t)0.0619812012f, + (float16_t)0.171142578f, (float16_t)-0.0602416992f, (float16_t)-0.224975586f, (float16_t)-0.0218963623f, + (float16_t)0.131835938f, (float16_t)-0.0838623047f, (float16_t)0.103027344f, (float16_t)0.294921875f, + (float16_t)-0.223510742f, (float16_t)0.255859375f, (float16_t)0.204467773f, (float16_t)0.0114974976f, + (float16_t)0.313720703f, (float16_t)-0.012840271f, (float16_t)-0.150756836f, (float16_t)-0.196411133f, + (float16_t)0.0458984375f, (float16_t)-0.23449707f, (float16_t)0.149047852f, (float16_t)-0.11114502f, + (float16_t)-0.0344848633f, (float16_t)-0.283691406f, (float16_t)0.0938720703f, (float16_t)0.333740234f, + (float16_t)0.186279297f, (float16_t)0.341552734f, (float16_t)0.281494141f, (float16_t)-0.0480957031f, + (float16_t)-0.0494995117f, (float16_t)0.328369141f, (float16_t)-0.005859375f, (float16_t)0.235107422f, + (float16_t)-0.250976562f, (float16_t)-0.246582031f, (float16_t)0.265869141f, (float16_t)-0.111083984f, + (float16_t)0.0926513672f, (float16_t)-0.115844727f, (float16_t)-0.111938477f, (float16_t)0.156494141f, + (float16_t)-0.16809082f, (float16_t)0.292236328f, (float16_t)0.325439453f, (float16_t)0.114501953f, + (float16_t)-0.0787963867f, (float16_t)0.195068359f, (float16_t)0.153564453f, (float16_t)-0.227661133f, + (float16_t)-0.286376953f, (float16_t)0.314453125f, (float16_t)-0.151977539f, (float16_t)-0.0238037109f, + (float16_t)-0.179321289f, (float16_t)0.0195465088f, (float16_t)-0.255615234f, (float16_t)-0.123596191f, + (float16_t)0.155273438f, (float16_t)0.16784668f, (float16_t)-0.154663086f, (float16_t)-0.134643555f, + (float16_t)-0.155761719f, (float16_t)-0.098815918f, (float16_t)-0.293701172f, (float16_t)-0.328369141f, + (float16_t)0.256347656f, (float16_t)0.30859375f, (float16_t)0.079284668f, (float16_t)-0.0240936279f, + (float16_t)-0.0235748291f, (float16_t)-0.0913085938f, (float16_t)0.317382812f, (float16_t)0.286376953f, + (float16_t)-0.219360352f, (float16_t)0.288818359f, (float16_t)0.121276855f, (float16_t)0.133666992f, + (float16_t)0.331542969f, (float16_t)0.0459899902f, (float16_t)0.0509033203f, (float16_t)-0.182250977f, + (float16_t)0.241333008f, (float16_t)0.0485839844f, (float16_t)-0.285888672f, (float16_t)0.274658203f, + (float16_t)-0.304931641f, (float16_t)-0.214111328f, (float16_t)-0.304931641f, (float16_t)0.291015625f, + (float16_t)-0.338867188f, (float16_t)0.246948242f, (float16_t)0.324951172f, (float16_t)0.0280456543f, + (float16_t)0.197875977f, (float16_t)-0.182495117f, (float16_t)-0.177978516f, (float16_t)-0.138671875f, + (float16_t)0.102111816f, (float16_t)0.256835938f, (float16_t)0.212890625f, (float16_t)-0.303710938f, + (float16_t)0.149902344f, (float16_t)-0.228149414f, (float16_t)-0.137817383f, (float16_t)0.302734375f, + (float16_t)-0.262695312f, (float16_t)-0.228149414f, (float16_t)-0.337646484f, (float16_t)0.114685059f, + (float16_t)0.14453125f, (float16_t)0.12890625f, (float16_t)0.330566406f, (float16_t)-0.0927734375f, + (float16_t)-0.324951172f, (float16_t)0.104431152f, (float16_t)0.195678711f, (float16_t)0.117004395f, + (float16_t)0.281005859f, (float16_t)-0.198486328f, (float16_t)0.218994141f, (float16_t)0.240356445f, + (float16_t)0.0677490234f, (float16_t)0.193969727f, (float16_t)0.133789062f, (float16_t)-0.311279297f, + (float16_t)0.181274414f, (float16_t)0.0849609375f, (float16_t)0.0864257812f, (float16_t)0.0865478516f, + (float16_t)0.151733398f, (float16_t)0.0624389648f, (float16_t)-0.0303039551f, (float16_t)-0.246948242f, + (float16_t)-0.320800781f, (float16_t)-0.195068359f, (float16_t)0.128295898f, (float16_t)-0.284667969f, + (float16_t)-0.0548706055f, (float16_t)-0.260986328f, (float16_t)-0.318359375f, (float16_t)-0.0617980957f, + (float16_t)-0.0112915039f, (float16_t)-0.240356445f, (float16_t)0.316162109f, (float16_t)-0.19140625f, + (float16_t)0.135375977f, (float16_t)-0.232666016f, (float16_t)-0.26171875f, (float16_t)0.181274414f, + (float16_t)0.239379883f, (float16_t)-0.237304688f, (float16_t)-0.274658203f, (float16_t)-0.0302124023f, + (float16_t)0.349853516f, (float16_t)-0.257080078f, (float16_t)-0.129272461f, (float16_t)0.337158203f, + (float16_t)0.130859375f, (float16_t)-0.0397033691f, (float16_t)0.22265625f, (float16_t)0.130249023f, + (float16_t)0.275634766f, (float16_t)-0.0537719727f, (float16_t)0.189697266f, (float16_t)0.226196289f, + (float16_t)0.333984375f, (float16_t)0.035736084f, (float16_t)0.117614746f, (float16_t)-0.237060547f, + (float16_t)0.302734375f, (float16_t)-0.336425781f, (float16_t)0.242431641f, (float16_t)-0.0282897949f, + (float16_t)0.33984375f, (float16_t)0.198364258f, (float16_t)0.247802734f, (float16_t)-0.00557327271f, + (float16_t)-0.231445312f, (float16_t)0.348632812f, (float16_t)0.296386719f, (float16_t)0.211181641f, + (float16_t)-0.226074219f, (float16_t)0.13659668f, (float16_t)0.0788574219f, (float16_t)-0.162597656f, + (float16_t)0.176147461f, (float16_t)0.168945312f, (float16_t)-0.0762939453f, (float16_t)0.151733398f, + (float16_t)-0.113952637f, (float16_t)-0.197387695f, (float16_t)-0.301513672f, (float16_t)-0.243408203f, + (float16_t)0.312744141f, (float16_t)-0.155639648f, (float16_t)-0.321044922f, (float16_t)-0.221557617f, + (float16_t)-0.142456055f, (float16_t)-0.101623535f, (float16_t)-0.268554688f, (float16_t)-0.00367355347f, + (float16_t)-0.290771484f, (float16_t)0.0320739746f, (float16_t)0.281005859f, (float16_t)0.140625f, + (float16_t)0.334716797f, (float16_t)0.196655273f, (float16_t)0.0057220459f, (float16_t)-0.0453186035f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_input_weights_data.h new file mode 100644 index 00000000..cf96b16b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/forget_input_weights_data.h @@ -0,0 +1,50 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_forget_input_weights[192] = { + (float16_t)-0.262207031f, (float16_t)-0.228515625f, (float16_t)0.114501953f, (float16_t)-0.110412598f, + (float16_t)-0.22644043f, (float16_t)-0.267089844f, (float16_t)0.122375488f, (float16_t)-0.0875244141f, + (float16_t)0.054901123f, (float16_t)-0.345214844f, (float16_t)0.0925292969f, (float16_t)-0.197631836f, + (float16_t)-0.0284118652f, (float16_t)-0.274902344f, (float16_t)-0.0355224609f, (float16_t)-0.268310547f, + (float16_t)0.311279297f, (float16_t)0.318847656f, (float16_t)-0.285400391f, (float16_t)-0.175170898f, + (float16_t)-0.260253906f, (float16_t)0.254150391f, (float16_t)0.0725097656f, (float16_t)-0.0193634033f, + (float16_t)0.106933594f, (float16_t)0.325195312f, (float16_t)0.141235352f, (float16_t)0.11895752f, + (float16_t)-0.122619629f, (float16_t)0.338623047f, (float16_t)-0.291015625f, (float16_t)-0.021987915f, + (float16_t)0.223144531f, (float16_t)0.193603516f, (float16_t)0.122131348f, (float16_t)-0.209106445f, + (float16_t)-0.166137695f, (float16_t)0.201538086f, (float16_t)0.176757812f, (float16_t)0.23815918f, + (float16_t)0.253662109f, (float16_t)0.312744141f, (float16_t)-0.193603516f, (float16_t)0.0484924316f, + (float16_t)0.221923828f, (float16_t)0.325927734f, (float16_t)-0.0989990234f, (float16_t)0.188354492f, + (float16_t)0.243286133f, (float16_t)-0.177856445f, (float16_t)-0.294433594f, (float16_t)-0.268066406f, + (float16_t)0.0554199219f, (float16_t)-0.0858764648f, (float16_t)0.0899047852f, (float16_t)-0.0248718262f, + (float16_t)0.0872192383f, (float16_t)0.14453125f, (float16_t)-0.232543945f, (float16_t)-0.219238281f, + (float16_t)0.161865234f, (float16_t)-0.233886719f, (float16_t)0.341796875f, (float16_t)-0.170288086f, + (float16_t)-0.222167969f, (float16_t)-0.185913086f, (float16_t)-0.211303711f, (float16_t)-0.259521484f, + (float16_t)0.120788574f, (float16_t)0.0132751465f, (float16_t)0.0466308594f, (float16_t)-0.299560547f, + (float16_t)-0.0586242676f, (float16_t)0.309082031f, (float16_t)0.204345703f, (float16_t)-0.345214844f, + (float16_t)-0.276367188f, (float16_t)0.281005859f, (float16_t)0.182983398f, (float16_t)0.270263672f, + (float16_t)0.195800781f, (float16_t)0.158569336f, (float16_t)-0.159545898f, (float16_t)-0.118530273f, + (float16_t)0.0635986328f, (float16_t)-0.0620422363f, (float16_t)-0.254394531f, (float16_t)0.155761719f, + (float16_t)-0.219238281f, (float16_t)0.0534057617f, (float16_t)0.326416016f, (float16_t)-0.207519531f, + (float16_t)0.223510742f, (float16_t)0.0408630371f, (float16_t)0.157836914f, (float16_t)-0.282470703f, + (float16_t)0.0648193359f, (float16_t)0.0248260498f, (float16_t)0.0733032227f, (float16_t)-0.144042969f, + (float16_t)-0.0649414062f, (float16_t)0.256347656f, (float16_t)-0.277099609f, (float16_t)0.3203125f, + (float16_t)0.218017578f, (float16_t)0.105163574f, (float16_t)0.229492188f, (float16_t)-0.2890625f, + (float16_t)-0.0700073242f, (float16_t)-0.052520752f, (float16_t)-0.00179386139f, (float16_t)0.170288086f, + (float16_t)0.0640869141f, (float16_t)0.124755859f, (float16_t)0.221435547f, (float16_t)0.326171875f, + (float16_t)0.159301758f, (float16_t)0.159179688f, (float16_t)0.346923828f, (float16_t)0.161010742f, + (float16_t)-0.05078125f, (float16_t)-0.094909668f, (float16_t)-0.0702514648f, (float16_t)0.232910156f, + (float16_t)0.341308594f, (float16_t)0.182373047f, (float16_t)0.0454101562f, (float16_t)0.185424805f, + (float16_t)-0.210693359f, (float16_t)0.335449219f, (float16_t)-0.189208984f, (float16_t)0.0785522461f, + (float16_t)0.204467773f, (float16_t)-0.135009766f, (float16_t)0.274658203f, (float16_t)0.23828125f, + (float16_t)0.248901367f, (float16_t)0.204711914f, (float16_t)-0.0881958008f, (float16_t)0.344970703f, + (float16_t)-0.111938477f, (float16_t)-0.0781860352f, (float16_t)0.231079102f, (float16_t)-0.151855469f, + (float16_t)0.0700073242f, (float16_t)-0.296875f, (float16_t)-0.0203552246f, (float16_t)-0.036315918f, + (float16_t)0.286621094f, (float16_t)0.163696289f, (float16_t)-0.246704102f, (float16_t)0.264892578f, + (float16_t)0.0331115723f, (float16_t)0.221923828f, (float16_t)0.175537109f, (float16_t)-0.0853271484f, + (float16_t)0.0420532227f, (float16_t)0.155151367f, (float16_t)0.339599609f, (float16_t)0.139038086f, + (float16_t)0.295898438f, (float16_t)-0.148803711f, (float16_t)0.0161895752f, (float16_t)-0.0390625f, + (float16_t)-0.16784668f, (float16_t)0.312255859f, (float16_t)-0.215087891f, (float16_t)-0.192260742f, + (float16_t)0.26171875f, (float16_t)0.153320312f, (float16_t)0.300292969f, (float16_t)0.0561523438f, + (float16_t)0.304199219f, (float16_t)-0.160400391f, (float16_t)-0.11340332f, (float16_t)0.059753418f, + (float16_t)0.0703735352f, (float16_t)-0.0739135742f, (float16_t)-0.190185547f, (float16_t)-0.000716209412f, + (float16_t)0.0971679688f, (float16_t)0.27734375f, (float16_t)0.160766602f, (float16_t)0.081237793f, + (float16_t)-0.162231445f, (float16_t)0.132324219f, (float16_t)-0.240722656f, (float16_t)-0.263427734f, + (float16_t)-0.0371398926f, (float16_t)0.318847656f, (float16_t)-0.244384766f, (float16_t)0.229858398f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_bias_data.h new file mode 100644 index 00000000..99a0e23f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_input_bias[16] = {(float16_t)0.208496094f, + (float16_t)-0.0322875977f, + (float16_t)0.24230957f, + (float16_t)-0.0661010742f, + (float16_t)-0.216308594f, + (float16_t)0.25f, + (float16_t)0.00839996338f, + (float16_t)-0.0432434082f, + (float16_t)0.105285645f, + (float16_t)-0.292236328f, + (float16_t)0.315185547f, + (float16_t)0.110961914f, + (float16_t)-0.169311523f, + (float16_t)-0.211914062f, + (float16_t)-0.273193359f, + (float16_t)0.139282227f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_data.h new file mode 100644 index 00000000..2a692bd1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_data.h @@ -0,0 +1,74 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_input[288] = { + (float16_t)-0.0716552734f, (float16_t)-0.650390625f, (float16_t)-0.265380859f, (float16_t)-0.105102539f, + (float16_t)-0.776367188f, (float16_t)0.495117188f, (float16_t)-0.537597656f, (float16_t)0.386962891f, + (float16_t)-0.427490234f, (float16_t)-0.210571289f, (float16_t)0.545898438f, (float16_t)-0.92578125f, + (float16_t)0.142700195f, (float16_t)-0.931640625f, (float16_t)0.280761719f, (float16_t)0.261474609f, + (float16_t)0.277832031f, (float16_t)0.335205078f, (float16_t)0.029006958f, (float16_t)0.440429688f, + (float16_t)-0.557128906f, (float16_t)-0.544921875f, (float16_t)-0.970703125f, (float16_t)-0.658691406f, + (float16_t)0.792480469f, (float16_t)0.820800781f, (float16_t)-0.154052734f, (float16_t)0.0701293945f, + (float16_t)-0.301269531f, (float16_t)0.256835938f, (float16_t)0.690429688f, (float16_t)-0.241333008f, + (float16_t)0.635742188f, (float16_t)-0.530273438f, (float16_t)-0.578613281f, (float16_t)0.16394043f, + (float16_t)0.957519531f, (float16_t)0.984375f, (float16_t)0.653808594f, (float16_t)0.98828125f, + (float16_t)0.532714844f, (float16_t)0.603515625f, (float16_t)-0.454345703f, (float16_t)-0.595214844f, + (float16_t)-0.958007812f, (float16_t)-0.822265625f, (float16_t)0.729003906f, (float16_t)-0.0107574463f, + (float16_t)-0.825195312f, (float16_t)-0.832519531f, (float16_t)0.772460938f, (float16_t)-0.440185547f, + (float16_t)0.101318359f, (float16_t)0.831054688f, (float16_t)-0.923339844f, (float16_t)0.216796875f, + (float16_t)-0.44921875f, (float16_t)-0.101867676f, (float16_t)-0.634765625f, (float16_t)0.526855469f, + (float16_t)-0.778808594f, (float16_t)0.425048828f, (float16_t)0.353271484f, (float16_t)0.219848633f, + (float16_t)-0.596679688f, (float16_t)-0.144287109f, (float16_t)0.148803711f, (float16_t)0.547851562f, + (float16_t)-0.980957031f, (float16_t)0.375732422f, (float16_t)-0.598632812f, (float16_t)-0.642578125f, + (float16_t)-0.880859375f, (float16_t)-0.301025391f, (float16_t)-0.171020508f, (float16_t)-0.150024414f, + (float16_t)-0.652832031f, (float16_t)0.379638672f, (float16_t)-0.848632812f, (float16_t)0.774902344f, + (float16_t)0.5f, (float16_t)-0.677734375f, (float16_t)-0.829589844f, (float16_t)-0.476806641f, + (float16_t)0.569335938f, (float16_t)-0.464355469f, (float16_t)0.434082031f, (float16_t)-0.314453125f, + (float16_t)0.205444336f, (float16_t)0.543457031f, (float16_t)0.659667969f, (float16_t)-0.264404297f, + (float16_t)-0.953125f, (float16_t)-0.448486328f, (float16_t)-0.172363281f, (float16_t)-0.970214844f, + (float16_t)-0.300292969f, (float16_t)0.602539062f, (float16_t)-0.232299805f, (float16_t)0.5703125f, + (float16_t)-0.169067383f, (float16_t)-0.547851562f, (float16_t)0.927246094f, (float16_t)-0.0879516602f, + (float16_t)-1.0f, (float16_t)-0.884765625f, (float16_t)-0.268554688f, (float16_t)0.487792969f, + (float16_t)0.227783203f, (float16_t)0.00409317017f, (float16_t)0.998046875f, (float16_t)0.846679688f, + (float16_t)-0.673828125f, (float16_t)-0.423339844f, (float16_t)0.313720703f, (float16_t)-0.455322266f, + (float16_t)0.936035156f, (float16_t)-0.288085938f, (float16_t)-0.643066406f, (float16_t)0.307128906f, + (float16_t)-0.210083008f, (float16_t)0.509765625f, (float16_t)-0.286621094f, (float16_t)0.171630859f, + (float16_t)-0.179199219f, (float16_t)-0.624511719f, (float16_t)-0.899902344f, (float16_t)0.0352783203f, + (float16_t)0.829589844f, (float16_t)-0.205932617f, (float16_t)-0.801269531f, (float16_t)0.16003418f, + (float16_t)0.926757812f, (float16_t)0.886230469f, (float16_t)-0.212646484f, (float16_t)-0.802734375f, + (float16_t)0.66796875f, (float16_t)0.799804688f, (float16_t)0.599609375f, (float16_t)-0.210327148f, + (float16_t)0.350097656f, (float16_t)-0.272949219f, (float16_t)-0.115661621f, (float16_t)0.932617188f, + (float16_t)0.114013672f, (float16_t)0.267578125f, (float16_t)0.0119094849f, (float16_t)-0.981933594f, + (float16_t)0.889648438f, (float16_t)-0.459716797f, (float16_t)0.820800781f, (float16_t)0.392333984f, + (float16_t)0.875f, (float16_t)0.477294922f, (float16_t)-0.182006836f, (float16_t)0.306640625f, + (float16_t)-0.0661621094f, (float16_t)0.471679688f, (float16_t)-0.765625f, (float16_t)0.666015625f, + (float16_t)0.492919922f, (float16_t)-0.275634766f, (float16_t)0.548828125f, (float16_t)0.182861328f, + (float16_t)-0.162353516f, (float16_t)0.192016602f, (float16_t)-0.8515625f, (float16_t)-0.809570312f, + (float16_t)-0.179321289f, (float16_t)-0.743164062f, (float16_t)-0.158935547f, (float16_t)0.754882812f, + (float16_t)-0.905273438f, (float16_t)-0.100891113f, (float16_t)0.318847656f, (float16_t)0.650878906f, + (float16_t)0.940917969f, (float16_t)-0.364990234f, (float16_t)0.815429688f, (float16_t)0.578613281f, + (float16_t)0.360839844f, (float16_t)0.377441406f, (float16_t)0.983886719f, (float16_t)-0.633300781f, + (float16_t)0.859375f, (float16_t)0.966308594f, (float16_t)0.468017578f, (float16_t)0.561035156f, + (float16_t)-0.937011719f, (float16_t)0.193603516f, (float16_t)-0.373046875f, (float16_t)-0.921875f, + (float16_t)-0.356201172f, (float16_t)-0.833496094f, (float16_t)-0.310058594f, (float16_t)-0.0653076172f, + (float16_t)0.660644531f, (float16_t)0.275878906f, (float16_t)0.72265625f, (float16_t)-0.0525817871f, + (float16_t)-0.400878906f, (float16_t)-0.204345703f, (float16_t)0.840332031f, (float16_t)-0.12310791f, + (float16_t)-0.177124023f, (float16_t)-0.987304688f, (float16_t)0.311035156f, (float16_t)-0.929199219f, + (float16_t)0.486572266f, (float16_t)0.225708008f, (float16_t)0.418212891f, (float16_t)0.435302734f, + (float16_t)0.254882812f, (float16_t)0.766601562f, (float16_t)0.249023438f, (float16_t)0.104919434f, + (float16_t)-0.175415039f, (float16_t)-0.594238281f, (float16_t)-0.729980469f, (float16_t)-0.920898438f, + (float16_t)-0.0600280762f, (float16_t)-0.0123214722f, (float16_t)-0.53515625f, (float16_t)-0.908691406f, + (float16_t)0.0628662109f, (float16_t)-0.704589844f, (float16_t)-0.0814819336f, (float16_t)0.114685059f, + (float16_t)0.964355469f, (float16_t)0.130859375f, (float16_t)0.75f, (float16_t)-0.642089844f, + (float16_t)-0.535644531f, (float16_t)0.772949219f, (float16_t)0.162353516f, (float16_t)0.811035156f, + (float16_t)-0.013053894f, (float16_t)0.806640625f, (float16_t)0.983398438f, (float16_t)0.634277344f, + (float16_t)-0.905273438f, (float16_t)-0.65625f, (float16_t)-0.0270843506f, (float16_t)0.458740234f, + (float16_t)0.817382812f, (float16_t)0.478027344f, (float16_t)-0.834960938f, (float16_t)-0.741699219f, + (float16_t)0.245239258f, (float16_t)0.194824219f, (float16_t)0.575683594f, (float16_t)0.161865234f, + (float16_t)0.745117188f, (float16_t)-0.644042969f, (float16_t)0.704589844f, (float16_t)-0.840332031f, + (float16_t)-0.0648803711f, (float16_t)0.375f, (float16_t)-0.809570312f, (float16_t)-0.805175781f, + (float16_t)0.0510253906f, (float16_t)-0.585449219f, (float16_t)-0.603515625f, (float16_t)0.858398438f, + (float16_t)0.474121094f, (float16_t)-0.0664672852f, (float16_t)0.0491943359f, (float16_t)0.349609375f, + (float16_t)-0.106323242f, (float16_t)0.166015625f, (float16_t)-0.768554688f, (float16_t)-0.0795898438f, + (float16_t)0.721191406f, (float16_t)-0.511230469f, (float16_t)0.311767578f, (float16_t)0.731445312f, + (float16_t)-0.610839844f, (float16_t)-0.633300781f, (float16_t)0.877929688f, (float16_t)0.777832031f, + (float16_t)-0.194458008f, (float16_t)-0.750488281f, (float16_t)0.708007812f, (float16_t)-0.485107422f, + (float16_t)0.392822266f, (float16_t)0.755859375f, (float16_t)-0.930175781f, (float16_t)0.111694336f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..82a7f8e8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_hidden_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_input_hidden_weights[256] = { + (float16_t)-0.245361328f, (float16_t)0.349121094f, (float16_t)-0.257324219f, (float16_t)-0.3125f, + (float16_t)-0.343261719f, (float16_t)-0.297607422f, (float16_t)0.115783691f, (float16_t)0.0618896484f, + (float16_t)0.0553894043f, (float16_t)0.000987052917f, (float16_t)0.110046387f, (float16_t)0.235107422f, + (float16_t)-0.121520996f, (float16_t)-0.197143555f, (float16_t)0.0965576172f, (float16_t)0.10345459f, + (float16_t)0.225952148f, (float16_t)-0.102233887f, (float16_t)0.155639648f, (float16_t)0.035949707f, + (float16_t)0.272216797f, (float16_t)0.0598754883f, (float16_t)0.282470703f, (float16_t)-0.340820312f, + (float16_t)0.163574219f, (float16_t)-0.278320312f, (float16_t)-0.00937652588f, (float16_t)0.24621582f, + (float16_t)0.275146484f, (float16_t)-0.0148239136f, (float16_t)0.139282227f, (float16_t)-0.0669555664f, + (float16_t)-0.24987793f, (float16_t)-0.0794677734f, (float16_t)0.287597656f, (float16_t)0.304931641f, + (float16_t)0.289306641f, (float16_t)0.157104492f, (float16_t)0.00342750549f, (float16_t)0.0553283691f, + (float16_t)-0.181152344f, (float16_t)0.263427734f, (float16_t)-0.189453125f, (float16_t)-0.341552734f, + (float16_t)0.0407104492f, (float16_t)0.227172852f, (float16_t)-0.0887451172f, (float16_t)0.262451172f, + (float16_t)-0.279785156f, (float16_t)0.0405883789f, (float16_t)-0.146240234f, (float16_t)0.117919922f, + (float16_t)0.312988281f, (float16_t)-0.218139648f, (float16_t)-0.0242156982f, (float16_t)0.268310547f, + (float16_t)-0.0575256348f, (float16_t)0.208251953f, (float16_t)0.104309082f, (float16_t)-0.343994141f, + (float16_t)0.331054688f, (float16_t)-0.189208984f, (float16_t)-0.0453796387f, (float16_t)0.259521484f, + (float16_t)-0.0189361572f, (float16_t)-0.154541016f, (float16_t)-0.0523071289f, (float16_t)0.252685547f, + (float16_t)0.0267333984f, (float16_t)0.170898438f, (float16_t)0.100341797f, (float16_t)-0.151123047f, + (float16_t)0.343505859f, (float16_t)0.00203514099f, (float16_t)-0.019317627f, (float16_t)-0.302246094f, + (float16_t)-0.292480469f, (float16_t)0.181518555f, (float16_t)-0.197387695f, (float16_t)-0.0919799805f, + (float16_t)0.134521484f, (float16_t)0.0760498047f, (float16_t)0.123352051f, (float16_t)-0.239379883f, + (float16_t)-0.254150391f, (float16_t)0.196289062f, (float16_t)0.273193359f, (float16_t)-0.209960938f, + (float16_t)-0.0768432617f, (float16_t)-0.109436035f, (float16_t)-0.324462891f, (float16_t)0.118469238f, + (float16_t)0.056427002f, (float16_t)-0.191650391f, (float16_t)-0.225341797f, (float16_t)0.165649414f, + (float16_t)0.0320739746f, (float16_t)-0.16809082f, (float16_t)0.328857422f, (float16_t)0.0406494141f, + (float16_t)0.169311523f, (float16_t)0.0408935547f, (float16_t)-0.119689941f, (float16_t)0.155761719f, + (float16_t)0.271240234f, (float16_t)-0.260009766f, (float16_t)0.306640625f, (float16_t)0.331298828f, + (float16_t)0.00219917297f, (float16_t)-0.0196990967f, (float16_t)-0.160766602f, (float16_t)0.0389404297f, + (float16_t)-0.176513672f, (float16_t)-0.00881195068f, (float16_t)0.319580078f, (float16_t)0.145996094f, + (float16_t)0.172851562f, (float16_t)-0.200805664f, (float16_t)0.240112305f, (float16_t)0.159179688f, + (float16_t)-0.144042969f, (float16_t)0.260009766f, (float16_t)-0.329101562f, (float16_t)-0.0489501953f, + (float16_t)-0.225585938f, (float16_t)0.23059082f, (float16_t)-0.0786743164f, (float16_t)-0.0591125488f, + (float16_t)0.250244141f, (float16_t)0.0441894531f, (float16_t)-0.113220215f, (float16_t)0.108215332f, + (float16_t)0.307861328f, (float16_t)-0.0609436035f, (float16_t)-0.0922241211f, (float16_t)-0.186157227f, + (float16_t)-0.233764648f, (float16_t)0.129638672f, (float16_t)-0.271240234f, (float16_t)0.0297851562f, + (float16_t)-0.125854492f, (float16_t)0.349365234f, (float16_t)-0.349609375f, (float16_t)0.286376953f, + (float16_t)-0.196166992f, (float16_t)0.307617188f, (float16_t)0.0933837891f, (float16_t)0.169189453f, + (float16_t)0.328613281f, (float16_t)-0.0195007324f, (float16_t)0.182250977f, (float16_t)0.153808594f, + (float16_t)-0.194335938f, (float16_t)-0.281005859f, (float16_t)0.126831055f, (float16_t)0.151245117f, + (float16_t)0.309326172f, (float16_t)-0.0716552734f, (float16_t)-0.239379883f, (float16_t)0.229614258f, + (float16_t)-0.312744141f, (float16_t)0.332763672f, (float16_t)-0.0152053833f, (float16_t)0.101379395f, + (float16_t)0.180297852f, (float16_t)0.0151138306f, (float16_t)0.290039062f, (float16_t)0.044128418f, + (float16_t)0.30859375f, (float16_t)0.0476074219f, (float16_t)0.0451049805f, (float16_t)0.295898438f, + (float16_t)-0.348388672f, (float16_t)0.17956543f, (float16_t)-0.0786743164f, (float16_t)-0.193725586f, + (float16_t)-0.313232422f, (float16_t)0.232177734f, (float16_t)-0.213500977f, (float16_t)0.284423828f, + (float16_t)-0.325927734f, (float16_t)-0.000539302826f, (float16_t)0.200439453f, (float16_t)-0.307617188f, + (float16_t)-0.334472656f, (float16_t)0.0958862305f, (float16_t)0.349121094f, (float16_t)0.192016602f, + (float16_t)0.233642578f, (float16_t)0.226074219f, (float16_t)-0.330322266f, (float16_t)0.307861328f, + (float16_t)0.250976562f, (float16_t)0.299560547f, (float16_t)0.339111328f, (float16_t)-0.168334961f, + (float16_t)0.233398438f, (float16_t)0.212524414f, (float16_t)-0.0440368652f, (float16_t)-0.267089844f, + (float16_t)0.120849609f, (float16_t)-0.0116348267f, (float16_t)0.19128418f, (float16_t)-0.150390625f, + (float16_t)0.29296875f, (float16_t)-0.315917969f, (float16_t)0.0554199219f, (float16_t)-0.26953125f, + (float16_t)0.118774414f, (float16_t)0.278320312f, (float16_t)-0.329589844f, (float16_t)0.131225586f, + (float16_t)-0.117858887f, (float16_t)-0.115661621f, (float16_t)-0.343261719f, (float16_t)-0.265625f, + (float16_t)-0.135986328f, (float16_t)-0.0269317627f, (float16_t)0.101257324f, (float16_t)-0.0194091797f, + (float16_t)-0.270019531f, (float16_t)0.153686523f, (float16_t)-0.147338867f, (float16_t)0.223632812f, + (float16_t)-0.0152740479f, (float16_t)0.218383789f, (float16_t)0.151489258f, (float16_t)-0.140991211f, + (float16_t)-0.0979614258f, (float16_t)-0.126220703f, (float16_t)0.328369141f, (float16_t)-0.308349609f, + (float16_t)0.221435547f, (float16_t)0.0496826172f, (float16_t)-0.125366211f, (float16_t)-0.133544922f, + (float16_t)0.205932617f, (float16_t)-0.125366211f, (float16_t)0.20300293f, (float16_t)-0.162353516f, + (float16_t)0.0333557129f, (float16_t)-0.321777344f, (float16_t)-0.077331543f, (float16_t)-0.22253418f, + (float16_t)0.207519531f, (float16_t)0.194580078f, (float16_t)0.325927734f, (float16_t)-0.133666992f, + (float16_t)0.188110352f, (float16_t)0.269775391f, (float16_t)-0.278564453f, (float16_t)-0.162841797f, + (float16_t)-0.0659790039f, (float16_t)0.193725586f, (float16_t)0.211181641f, (float16_t)0.346435547f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_input_weights_data.h new file mode 100644 index 00000000..2c081ac8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/input_input_weights_data.h @@ -0,0 +1,50 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_input_input_weights[192] = { + (float16_t)0.238891602f, (float16_t)-0.0962524414f, (float16_t)-0.110046387f, (float16_t)0.109008789f, + (float16_t)0.173583984f, (float16_t)0.126098633f, (float16_t)-0.282958984f, (float16_t)0.314208984f, + (float16_t)0.272949219f, (float16_t)0.269775391f, (float16_t)-0.0744628906f, (float16_t)-0.0358276367f, + (float16_t)0.0574645996f, (float16_t)-0.0700073242f, (float16_t)0.339111328f, (float16_t)-0.272949219f, + (float16_t)-0.153442383f, (float16_t)-0.0829467773f, (float16_t)-0.219970703f, (float16_t)0.215820312f, + (float16_t)0.212280273f, (float16_t)0.154296875f, (float16_t)-0.034942627f, (float16_t)0.313720703f, + (float16_t)-0.0883789062f, (float16_t)-0.0460510254f, (float16_t)0.154052734f, (float16_t)0.251220703f, + (float16_t)0.0512695312f, (float16_t)0.198608398f, (float16_t)-0.277099609f, (float16_t)0.215942383f, + (float16_t)0.0149612427f, (float16_t)0.174194336f, (float16_t)0.0928344727f, (float16_t)0.10333252f, + (float16_t)-0.0460510254f, (float16_t)-0.284667969f, (float16_t)-0.131958008f, (float16_t)-0.260742188f, + (float16_t)-0.285644531f, (float16_t)-0.142089844f, (float16_t)-0.034362793f, (float16_t)0.2734375f, + (float16_t)0.132080078f, (float16_t)0.278076172f, (float16_t)0.026260376f, (float16_t)0.246826172f, + (float16_t)0.303955078f, (float16_t)0.302490234f, (float16_t)-0.306884766f, (float16_t)-0.24621582f, + (float16_t)-0.057434082f, (float16_t)0.159545898f, (float16_t)-0.338867188f, (float16_t)0.0930786133f, + (float16_t)-0.330322266f, (float16_t)-0.229003906f, (float16_t)0.349121094f, (float16_t)0.194458008f, + (float16_t)-0.123413086f, (float16_t)0.218994141f, (float16_t)-0.147460938f, (float16_t)-0.0855102539f, + (float16_t)0.228759766f, (float16_t)0.198974609f, (float16_t)0.289550781f, (float16_t)0.0109939575f, + (float16_t)-0.221557617f, (float16_t)0.052154541f, (float16_t)0.275390625f, (float16_t)-0.157104492f, + (float16_t)-0.162109375f, (float16_t)-0.333496094f, (float16_t)-0.124206543f, (float16_t)0.323730469f, + (float16_t)-0.0918579102f, (float16_t)0.196777344f, (float16_t)-0.155883789f, (float16_t)0.284667969f, + (float16_t)0.126220703f, (float16_t)0.0732421875f, (float16_t)-0.0260162354f, (float16_t)-0.147460938f, + (float16_t)-0.333496094f, (float16_t)-0.0551757812f, (float16_t)-0.00772476196f, (float16_t)-0.173706055f, + (float16_t)0.0816650391f, (float16_t)-0.050994873f, (float16_t)0.000517845154f, (float16_t)-0.264648438f, + (float16_t)0.0681762695f, (float16_t)0.235595703f, (float16_t)0.139160156f, (float16_t)-0.284667969f, + (float16_t)0.163452148f, (float16_t)-0.240844727f, (float16_t)-0.177490234f, (float16_t)0.328613281f, + (float16_t)-0.311767578f, (float16_t)-0.220336914f, (float16_t)-0.0196533203f, (float16_t)-0.231811523f, + (float16_t)0.156005859f, (float16_t)0.111999512f, (float16_t)0.221923828f, (float16_t)-0.341308594f, + (float16_t)-0.155395508f, (float16_t)-0.0102386475f, (float16_t)0.218383789f, (float16_t)0.257568359f, + (float16_t)-0.0509338379f, (float16_t)-0.0401916504f, (float16_t)-0.302490234f, (float16_t)0.256347656f, + (float16_t)0.19519043f, (float16_t)-0.333984375f, (float16_t)0.169921875f, (float16_t)-0.192626953f, + (float16_t)0.268310547f, (float16_t)-0.271728516f, (float16_t)0.307373047f, (float16_t)0.143554688f, + (float16_t)-0.0299987793f, (float16_t)0.212524414f, (float16_t)0.255371094f, (float16_t)0.0789794922f, + (float16_t)-0.199462891f, (float16_t)-0.322753906f, (float16_t)-0.146728516f, (float16_t)0.109985352f, + (float16_t)-0.233032227f, (float16_t)-0.269042969f, (float16_t)0.0433654785f, (float16_t)-0.187744141f, + (float16_t)-0.34375f, (float16_t)-0.324707031f, (float16_t)-0.12109375f, (float16_t)-0.284912109f, + (float16_t)-0.14831543f, (float16_t)-0.326904297f, (float16_t)-0.282226562f, (float16_t)0.0550537109f, + (float16_t)-0.234008789f, (float16_t)-0.160888672f, (float16_t)0.090637207f, (float16_t)0.297607422f, + (float16_t)-0.281982422f, (float16_t)-0.125366211f, (float16_t)-0.0426025391f, (float16_t)-0.0864868164f, + (float16_t)-0.263916016f, (float16_t)0.159423828f, (float16_t)-0.247924805f, (float16_t)-0.283935547f, + (float16_t)-0.270507812f, (float16_t)0.338134766f, (float16_t)0.102111816f, (float16_t)-0.142456055f, + (float16_t)0.028427124f, (float16_t)0.27734375f, (float16_t)0.0393981934f, (float16_t)-0.0844116211f, + (float16_t)0.0988769531f, (float16_t)0.127563477f, (float16_t)0.21105957f, (float16_t)0.281982422f, + (float16_t)0.307128906f, (float16_t)-0.034362793f, (float16_t)-0.0216064453f, (float16_t)-0.296142578f, + (float16_t)0.0491638184f, (float16_t)-0.299316406f, (float16_t)-0.231445312f, (float16_t)0.207519531f, + (float16_t)0.259033203f, (float16_t)-0.0958862305f, (float16_t)0.197875977f, (float16_t)-0.336914062f, + (float16_t)-0.151855469f, (float16_t)-0.0826416016f, (float16_t)-0.276123047f, (float16_t)-0.14074707f, + (float16_t)0.0115509033f, (float16_t)-0.251464844f, (float16_t)0.208618164f, (float16_t)-0.302490234f, + (float16_t)-0.229980469f, (float16_t)0.133911133f, (float16_t)0.189208984f, (float16_t)0.196289062f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_bias_data.h new file mode 100644 index 00000000..aae02928 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_output_bias[16] = {(float16_t)0.153198242f, + (float16_t)0.352539062f, + (float16_t)-0.347900391f, + (float16_t)-0.0693969727f, + (float16_t)0.132446289f, + (float16_t)0.0903320312f, + (float16_t)-0.0305480957f, + (float16_t)0.231933594f, + (float16_t)-0.146362305f, + (float16_t)-0.180053711f, + (float16_t)0.242675781f, + (float16_t)-0.0811157227f, + (float16_t)0.124023438f, + (float16_t)-0.124328613f, + (float16_t)0.0283355713f, + (float16_t)-0.00219154358f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..4fd23fb6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_hidden_weights_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_output_hidden_weights[256] = { + (float16_t)-0.193359375f, (float16_t)0.233154297f, (float16_t)-0.307128906f, (float16_t)-0.349853516f, + (float16_t)0.201782227f, (float16_t)0.347167969f, (float16_t)-0.311279297f, (float16_t)0.349609375f, + (float16_t)-0.10723877f, (float16_t)-0.300537109f, (float16_t)0.0303955078f, (float16_t)-0.2578125f, + (float16_t)0.271972656f, (float16_t)0.00473403931f, (float16_t)0.0816040039f, (float16_t)0.0530090332f, + (float16_t)0.19543457f, (float16_t)0.0377502441f, (float16_t)-0.299316406f, (float16_t)0.232421875f, + (float16_t)0.23828125f, (float16_t)-0.193969727f, (float16_t)-0.0583190918f, (float16_t)-0.1953125f, + (float16_t)-0.325195312f, (float16_t)-0.108398438f, (float16_t)-0.207397461f, (float16_t)-0.00637054443f, + (float16_t)-0.146972656f, (float16_t)0.0243225098f, (float16_t)-0.327636719f, (float16_t)0.247192383f, + (float16_t)0.154296875f, (float16_t)0.153564453f, (float16_t)0.185913086f, (float16_t)-0.143798828f, + (float16_t)0.222290039f, (float16_t)-0.200195312f, (float16_t)0.0574035645f, (float16_t)0.112060547f, + (float16_t)0.0556030273f, (float16_t)-0.0584106445f, (float16_t)-0.0711669922f, (float16_t)0.132202148f, + (float16_t)-0.225708008f, (float16_t)-0.0459899902f, (float16_t)0.257080078f, (float16_t)-0.288574219f, + (float16_t)-0.0657348633f, (float16_t)0.0872802734f, (float16_t)-0.238647461f, (float16_t)-0.261474609f, + (float16_t)-0.0530395508f, (float16_t)0.33203125f, (float16_t)-0.288818359f, (float16_t)0.259033203f, + (float16_t)0.300292969f, (float16_t)-0.233520508f, (float16_t)-0.243530273f, (float16_t)0.056640625f, + (float16_t)0.318115234f, (float16_t)0.291015625f, (float16_t)0.207885742f, (float16_t)0.162963867f, + (float16_t)0.148193359f, (float16_t)0.0956420898f, (float16_t)0.0739135742f, (float16_t)-0.11315918f, + (float16_t)-0.28125f, (float16_t)-0.235595703f, (float16_t)-0.205810547f, (float16_t)-0.348388672f, + (float16_t)-0.209838867f, (float16_t)-0.133789062f, (float16_t)0.291503906f, (float16_t)0.0823364258f, + (float16_t)-0.0840454102f, (float16_t)-0.305664062f, (float16_t)0.341796875f, (float16_t)-0.146728516f, + (float16_t)0.111633301f, (float16_t)0.0692138672f, (float16_t)0.346435547f, (float16_t)-0.105163574f, + (float16_t)0.246704102f, (float16_t)-0.0955200195f, (float16_t)-0.327880859f, (float16_t)0.251464844f, + (float16_t)-0.252441406f, (float16_t)0.196411133f, (float16_t)0.0880126953f, (float16_t)0.118774414f, + (float16_t)-0.0459899902f, (float16_t)-0.0646362305f, (float16_t)0.0637817383f, (float16_t)-0.0584106445f, + (float16_t)-0.294189453f, (float16_t)-0.275146484f, (float16_t)0.133300781f, (float16_t)0.0596923828f, + (float16_t)-0.344238281f, (float16_t)0.165283203f, (float16_t)0.15234375f, (float16_t)-0.229980469f, + (float16_t)-0.194946289f, (float16_t)0.246459961f, (float16_t)-0.233276367f, (float16_t)-0.0123291016f, + (float16_t)0.222045898f, (float16_t)-0.153930664f, (float16_t)0.207397461f, (float16_t)0.306396484f, + (float16_t)0.08203125f, (float16_t)-0.0207061768f, (float16_t)-0.0271759033f, (float16_t)-0.304199219f, + (float16_t)0.252441406f, (float16_t)0.104553223f, (float16_t)-0.335449219f, (float16_t)-0.155029297f, + (float16_t)-0.194213867f, (float16_t)-0.219726562f, (float16_t)-0.0569152832f, (float16_t)-0.0257110596f, + (float16_t)0.108093262f, (float16_t)0.125488281f, (float16_t)-0.269042969f, (float16_t)-0.220336914f, + (float16_t)-0.0997924805f, (float16_t)-0.0170288086f, (float16_t)-0.244018555f, (float16_t)-0.0427856445f, + (float16_t)-0.258300781f, (float16_t)0.187988281f, (float16_t)-0.0919799805f, (float16_t)0.317871094f, + (float16_t)0.131469727f, (float16_t)-0.0560913086f, (float16_t)-0.301269531f, (float16_t)-0.114013672f, + (float16_t)-0.262695312f, (float16_t)0.248168945f, (float16_t)0.0735473633f, (float16_t)0.314208984f, + (float16_t)-0.14465332f, (float16_t)-0.136962891f, (float16_t)-0.151489258f, (float16_t)0.0294799805f, + (float16_t)0.312255859f, (float16_t)0.155395508f, (float16_t)-0.214355469f, (float16_t)0.305908203f, + (float16_t)-0.0829467773f, (float16_t)-0.202148438f, (float16_t)-0.143432617f, (float16_t)0.176391602f, + (float16_t)-0.166625977f, (float16_t)0.193115234f, (float16_t)-0.182006836f, (float16_t)0.169311523f, + (float16_t)-0.133422852f, (float16_t)-0.283691406f, (float16_t)-0.0722045898f, (float16_t)0.182617188f, + (float16_t)0.00771713257f, (float16_t)0.0829467773f, (float16_t)-0.142211914f, (float16_t)-0.201904297f, + (float16_t)0.0101852417f, (float16_t)0.0471801758f, (float16_t)-0.342773438f, (float16_t)-0.320068359f, + (float16_t)-0.125366211f, (float16_t)-0.0154266357f, (float16_t)0.227050781f, (float16_t)-0.20690918f, + (float16_t)0.00600814819f, (float16_t)0.098449707f, (float16_t)0.323730469f, (float16_t)-0.340820312f, + (float16_t)-0.222167969f, (float16_t)0.117797852f, (float16_t)-0.217407227f, (float16_t)-0.146240234f, + (float16_t)0.0226287842f, (float16_t)0.139404297f, (float16_t)0.224975586f, (float16_t)0.187255859f, + (float16_t)-0.118408203f, (float16_t)0.255126953f, (float16_t)0.282470703f, (float16_t)-0.324462891f, + (float16_t)-0.0939331055f, (float16_t)-0.317138672f, (float16_t)0.255126953f, (float16_t)-0.0774536133f, + (float16_t)0.329589844f, (float16_t)0.091796875f, (float16_t)-0.269775391f, (float16_t)-0.038848877f, + (float16_t)0.121643066f, (float16_t)-0.279052734f, (float16_t)0.0710449219f, (float16_t)-0.16418457f, + (float16_t)0.304199219f, (float16_t)-0.112304688f, (float16_t)-0.0311737061f, (float16_t)0.243896484f, + (float16_t)0.282470703f, (float16_t)-0.0134048462f, (float16_t)0.224609375f, (float16_t)-0.0285949707f, + (float16_t)-0.125854492f, (float16_t)-0.239013672f, (float16_t)0.318115234f, (float16_t)0.340332031f, + (float16_t)0.186767578f, (float16_t)-0.134887695f, (float16_t)0.205688477f, (float16_t)-0.159057617f, + (float16_t)-0.229614258f, (float16_t)-0.268066406f, (float16_t)-0.267822266f, (float16_t)0.127929688f, + (float16_t)0.266601562f, (float16_t)-0.206054688f, (float16_t)0.152099609f, (float16_t)-0.186401367f, + (float16_t)0.345458984f, (float16_t)0.2421875f, (float16_t)-0.235351562f, (float16_t)0.322753906f, + (float16_t)0.197509766f, (float16_t)-0.0888061523f, (float16_t)-0.0171051025f, (float16_t)0.180541992f, + (float16_t)-0.247558594f, (float16_t)-0.0951538086f, (float16_t)-0.0197906494f, (float16_t)-0.221557617f, + (float16_t)0.239868164f, (float16_t)-0.184814453f, (float16_t)0.241210938f, (float16_t)0.124816895f, + (float16_t)-0.111938477f, (float16_t)0.0600585938f, (float16_t)-0.28515625f, (float16_t)-0.324707031f, + (float16_t)0.208251953f, (float16_t)-0.0624389648f, (float16_t)0.124023438f, (float16_t)0.0246276855f, + (float16_t)-0.326171875f, (float16_t)-0.072265625f, (float16_t)-0.159179688f, (float16_t)-0.192993164f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_input_weights_data.h new file mode 100644 index 00000000..7588cf50 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_input_weights_data.h @@ -0,0 +1,50 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_output_input_weights[192] = { + (float16_t)0.263671875f, (float16_t)-0.0167236328f, (float16_t)-0.107421875f, (float16_t)0.305419922f, + (float16_t)-0.177001953f, (float16_t)-0.223144531f, (float16_t)-0.150756836f, (float16_t)-0.0983276367f, + (float16_t)0.0289459229f, (float16_t)0.204101562f, (float16_t)-0.0586853027f, (float16_t)0.3046875f, + (float16_t)-0.193603516f, (float16_t)0.208618164f, (float16_t)-0.178344727f, (float16_t)-0.258789062f, + (float16_t)-0.302246094f, (float16_t)0.219604492f, (float16_t)0.204101562f, (float16_t)-0.238525391f, + (float16_t)-0.145019531f, (float16_t)-0.0300750732f, (float16_t)0.0541992188f, (float16_t)-0.274902344f, + (float16_t)-0.227539062f, (float16_t)0.101745605f, (float16_t)-0.0200653076f, (float16_t)-0.11517334f, + (float16_t)-0.349853516f, (float16_t)-0.161743164f, (float16_t)0.0131378174f, (float16_t)-0.213012695f, + (float16_t)-0.1171875f, (float16_t)-0.1328125f, (float16_t)-0.235595703f, (float16_t)-0.306640625f, + (float16_t)-0.124938965f, (float16_t)-0.0144195557f, (float16_t)-0.208618164f, (float16_t)-0.275878906f, + (float16_t)-0.339599609f, (float16_t)0.221435547f, (float16_t)0.0872192383f, (float16_t)0.289550781f, + (float16_t)-0.3125f, (float16_t)0.266845703f, (float16_t)0.337646484f, (float16_t)0.0358581543f, + (float16_t)-0.181274414f, (float16_t)0.0203704834f, (float16_t)-0.276123047f, (float16_t)-0.220825195f, + (float16_t)0.150634766f, (float16_t)0.236206055f, (float16_t)-0.161010742f, (float16_t)0.160400391f, + (float16_t)0.000879764557f, (float16_t)0.220947266f, (float16_t)0.234863281f, (float16_t)-0.114379883f, + (float16_t)-0.248657227f, (float16_t)0.191650391f, (float16_t)-0.213012695f, (float16_t)-0.207641602f, + (float16_t)0.0982666016f, (float16_t)0.321777344f, (float16_t)-0.0386047363f, (float16_t)-0.288085938f, + (float16_t)-0.289794922f, (float16_t)-0.0676879883f, (float16_t)0.237060547f, (float16_t)-0.311279297f, + (float16_t)0.137207031f, (float16_t)-0.0646362305f, (float16_t)-0.242675781f, (float16_t)-0.142578125f, + (float16_t)-0.239990234f, (float16_t)0.224243164f, (float16_t)-0.107421875f, (float16_t)0.254394531f, + (float16_t)-0.173339844f, (float16_t)-0.16809082f, (float16_t)0.00775146484f, (float16_t)-0.321533203f, + (float16_t)-0.282958984f, (float16_t)0.161376953f, (float16_t)-0.15637207f, (float16_t)0.223022461f, + (float16_t)0.133789062f, (float16_t)-0.255371094f, (float16_t)0.302001953f, (float16_t)0.310546875f, + (float16_t)0.0143203735f, (float16_t)-0.038482666f, (float16_t)0.299316406f, (float16_t)-0.310302734f, + (float16_t)0.0947265625f, (float16_t)0.00172710419f, (float16_t)-0.275146484f, (float16_t)-0.252929688f, + (float16_t)0.0574645996f, (float16_t)0.237915039f, (float16_t)0.0412902832f, (float16_t)0.0526428223f, + (float16_t)-0.227172852f, (float16_t)0.102233887f, (float16_t)-0.142456055f, (float16_t)-0.111816406f, + (float16_t)-0.113952637f, (float16_t)-0.0221252441f, (float16_t)0.0947265625f, (float16_t)0.179443359f, + (float16_t)-0.292236328f, (float16_t)0.25390625f, (float16_t)-0.102233887f, (float16_t)0.0967407227f, + (float16_t)-0.148681641f, (float16_t)-0.173339844f, (float16_t)-0.330078125f, (float16_t)0.100646973f, + (float16_t)0.02784729f, (float16_t)0.290283203f, (float16_t)-0.139038086f, (float16_t)-0.122070312f, + (float16_t)-0.178344727f, (float16_t)0.0737915039f, (float16_t)-0.113098145f, (float16_t)0.189697266f, + (float16_t)0.191894531f, (float16_t)-0.130981445f, (float16_t)-0.0175933838f, (float16_t)-0.340087891f, + (float16_t)-0.154418945f, (float16_t)-0.0445251465f, (float16_t)0.0989379883f, (float16_t)0.227661133f, + (float16_t)0.307617188f, (float16_t)0.213989258f, (float16_t)0.291015625f, (float16_t)0.0169830322f, + (float16_t)-0.299072266f, (float16_t)-0.0319213867f, (float16_t)-0.0913085938f, (float16_t)-0.0296936035f, + (float16_t)0.281738281f, (float16_t)-0.306396484f, (float16_t)0.19519043f, (float16_t)0.029083252f, + (float16_t)-0.293457031f, (float16_t)-0.341308594f, (float16_t)-0.163696289f, (float16_t)0.0163574219f, + (float16_t)0.0221557617f, (float16_t)-0.0114593506f, (float16_t)0.0692138672f, (float16_t)-0.227172852f, + (float16_t)-0.290283203f, (float16_t)0.338867188f, (float16_t)0.12878418f, (float16_t)-0.11315918f, + (float16_t)0.174316406f, (float16_t)0.0420837402f, (float16_t)-0.334716797f, (float16_t)0.313720703f, + (float16_t)0.291748047f, (float16_t)0.085144043f, (float16_t)-0.216796875f, (float16_t)0.00800323486f, + (float16_t)0.323974609f, (float16_t)0.0352478027f, (float16_t)0.133666992f, (float16_t)0.0784912109f, + (float16_t)0.199829102f, (float16_t)0.145141602f, (float16_t)-0.122253418f, (float16_t)-0.293457031f, + (float16_t)0.144775391f, (float16_t)-0.327880859f, (float16_t)-0.349609375f, (float16_t)-0.207275391f, + (float16_t)-0.346923828f, (float16_t)-0.106689453f, (float16_t)-0.187744141f, (float16_t)0.239501953f, + (float16_t)0.289794922f, (float16_t)0.343994141f, (float16_t)-0.208862305f, (float16_t)-0.229980469f, + (float16_t)-0.313232422f, (float16_t)0.243286133f, (float16_t)0.0195007324f, (float16_t)-0.108764648f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_ref_data.h new file mode 100644 index 00000000..941dbba8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/output_ref_data.h @@ -0,0 +1,98 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_medium_f16_output_ref[384] = { + (float16_t)-0.0289611816f, (float16_t)0.00792694092f, (float16_t)-0.182617188f, (float16_t)0.146362305f, + (float16_t)0.0972900391f, (float16_t)0.273193359f, (float16_t)-0.107543945f, (float16_t)0.142578125f, + (float16_t)-0.0797729492f, (float16_t)0.0503234863f, (float16_t)-0.0645141602f, (float16_t)-0.076171875f, + (float16_t)0.0146789551f, (float16_t)-0.060333252f, (float16_t)-0.113708496f, (float16_t)0.10748291f, + (float16_t)-0.106689453f, (float16_t)0.0847167969f, (float16_t)-0.170166016f, (float16_t)-0.0130996704f, + (float16_t)0.0205383301f, (float16_t)0.0657348633f, (float16_t)-0.0645141602f, (float16_t)0.142578125f, + (float16_t)-0.0645141602f, (float16_t)0.156860352f, (float16_t)0.0262451172f, (float16_t)0.0484313965f, + (float16_t)0.188476562f, (float16_t)-0.0384521484f, (float16_t)-0.0618286133f, (float16_t)0.00413894653f, + (float16_t)0.0631103516f, (float16_t)0.0255584717f, (float16_t)-0.130615234f, (float16_t)-0.0563659668f, + (float16_t)-0.0356445312f, (float16_t)-0.0132980347f, (float16_t)-0.00793457031f, (float16_t)0.0340270996f, + (float16_t)0.130249023f, (float16_t)-0.000716209412f, (float16_t)0.0978393555f, (float16_t)0.0883789062f, + (float16_t)0.073059082f, (float16_t)0.0617675781f, (float16_t)-0.0500183105f, (float16_t)-0.0430297852f, + (float16_t)0.228515625f, (float16_t)-0.0114059448f, (float16_t)-0.0466003418f, (float16_t)0.0152359009f, + (float16_t)-0.0756225586f, (float16_t)0.337890625f, (float16_t)-0.0871582031f, (float16_t)-0.0361328125f, + (float16_t)0.00539779663f, (float16_t)0.213500977f, (float16_t)-0.225341797f, (float16_t)-0.0884399414f, + (float16_t)-0.121398926f, (float16_t)-0.117004395f, (float16_t)-0.10559082f, (float16_t)0.000699520111f, + (float16_t)-0.173217773f, (float16_t)0.237426758f, (float16_t)-0.0300292969f, (float16_t)-0.202636719f, + (float16_t)0.117492676f, (float16_t)0.20690918f, (float16_t)-0.101928711f, (float16_t)0.109375f, + (float16_t)-0.0192871094f, (float16_t)0.125732422f, (float16_t)0.116394043f, (float16_t)0.0530700684f, + (float16_t)0.0632324219f, (float16_t)-0.202026367f, (float16_t)-0.0400695801f, (float16_t)0.0330505371f, + (float16_t)-0.0996704102f, (float16_t)0.00177955627f, (float16_t)-0.0532836914f, (float16_t)-0.0822143555f, + (float16_t)-0.0473022461f, (float16_t)0.201171875f, (float16_t)-0.117736816f, (float16_t)0.19152832f, + (float16_t)0.029586792f, (float16_t)-0.0170898438f, (float16_t)0.180053711f, (float16_t)-0.0594482422f, + (float16_t)0.00412368774f, (float16_t)0.0594787598f, (float16_t)-0.0215759277f, (float16_t)0.0819091797f, + (float16_t)-0.0194854736f, (float16_t)-0.0192260742f, (float16_t)-0.139648438f, (float16_t)-0.0681762695f, + (float16_t)0.0678710938f, (float16_t)0.246337891f, (float16_t)-0.297851562f, (float16_t)0.146362305f, + (float16_t)-0.0298614502f, (float16_t)0.172729492f, (float16_t)0.132568359f, (float16_t)-0.0783691406f, + (float16_t)-0.031829834f, (float16_t)-0.247192383f, (float16_t)-0.150268555f, (float16_t)0.105285645f, + (float16_t)-0.0853881836f, (float16_t)0.199707031f, (float16_t)-0.166503906f, (float16_t)-0.032043457f, + (float16_t)-0.0223236084f, (float16_t)0.390380859f, (float16_t)-0.0379333496f, (float16_t)0.250244141f, + (float16_t)-0.114501953f, (float16_t)0.0845336914f, (float16_t)0.000247478485f, (float16_t)0.0751342773f, + (float16_t)0.19519043f, (float16_t)-0.0803833008f, (float16_t)-0.217895508f, (float16_t)0.00479888916f, + (float16_t)0.0104141235f, (float16_t)0.0432434082f, (float16_t)-0.00168132782f, (float16_t)-0.0171813965f, + (float16_t)-0.105834961f, (float16_t)0.0994873047f, (float16_t)-0.0186767578f, (float16_t)0.0731201172f, + (float16_t)0.000268936157f, (float16_t)-0.0706787109f, (float16_t)0.131713867f, (float16_t)-0.275878906f, + (float16_t)-0.101379395f, (float16_t)0.0738525391f, (float16_t)0.0420532227f, (float16_t)0.0802001953f, + (float16_t)-0.122619629f, (float16_t)0.0386047363f, (float16_t)-0.149047852f, (float16_t)-0.113098145f, + (float16_t)-0.00471115112f, (float16_t)0.0659179688f, (float16_t)-0.0943603516f, (float16_t)0.0115432739f, + (float16_t)0.0955200195f, (float16_t)-0.0130615234f, (float16_t)0.0492553711f, (float16_t)0.106506348f, + (float16_t)0.0742797852f, (float16_t)-0.167480469f, (float16_t)-0.0602416992f, (float16_t)0.025894165f, + (float16_t)-0.163208008f, (float16_t)0.109924316f, (float16_t)-0.0407409668f, (float16_t)-0.124572754f, + (float16_t)0.00593566895f, (float16_t)0.131835938f, (float16_t)-0.0613708496f, (float16_t)0.071105957f, + (float16_t)0.0897827148f, (float16_t)0.0153884888f, (float16_t)0.096862793f, (float16_t)0.0404052734f, + (float16_t)0.0326538086f, (float16_t)-0.151245117f, (float16_t)-0.0734863281f, (float16_t)-0.000716209412f, + (float16_t)0.150878906f, (float16_t)0.142944336f, (float16_t)-0.071472168f, (float16_t)-0.0518188477f, + (float16_t)-0.0801391602f, (float16_t)0.131469727f, (float16_t)0.0198822021f, (float16_t)0.0662231445f, + (float16_t)0.0787963867f, (float16_t)-0.0292053223f, (float16_t)0.174926758f, (float16_t)-0.0219421387f, + (float16_t)-0.00100803375f, (float16_t)0.208374023f, (float16_t)-0.0807495117f, (float16_t)-0.0521850586f, + (float16_t)-0.208862305f, (float16_t)0.0458374023f, (float16_t)-0.100646973f, (float16_t)-0.0646362305f, + (float16_t)0.0391845703f, (float16_t)0.0806274414f, (float16_t)-0.00220298767f, (float16_t)0.0728759766f, + (float16_t)0.0446777344f, (float16_t)-0.044128418f, (float16_t)0.0707397461f, (float16_t)0.131713867f, + (float16_t)0.112304688f, (float16_t)0.102233887f, (float16_t)-0.132202148f, (float16_t)-0.0448913574f, + (float16_t)-0.185913086f, (float16_t)0.103088379f, (float16_t)-0.125854492f, (float16_t)-0.0601501465f, + (float16_t)0.0314025879f, (float16_t)0.0551757812f, (float16_t)-0.12298584f, (float16_t)0.157714844f, + (float16_t)0.107910156f, (float16_t)0.00951385498f, (float16_t)0.0806884766f, (float16_t)-0.0191040039f, + (float16_t)0.0853881836f, (float16_t)-0.0306549072f, (float16_t)-0.0952758789f, (float16_t)-0.0540466309f, + (float16_t)0.0120239258f, (float16_t)0.135375977f, (float16_t)-0.154541016f, (float16_t)-0.114318848f, + (float16_t)0.0407409668f, (float16_t)0.0886230469f, (float16_t)0.0574645996f, (float16_t)0.0377502441f, + (float16_t)0.0729370117f, (float16_t)-0.133911133f, (float16_t)0.0397949219f, (float16_t)-0.0424499512f, + (float16_t)-0.0329284668f, (float16_t)0.0680541992f, (float16_t)0.0262756348f, (float16_t)0.0198822021f, + (float16_t)-0.105773926f, (float16_t)-0.0139923096f, (float16_t)-0.180053711f, (float16_t)-0.0750732422f, + (float16_t)0.0249328613f, (float16_t)0.402832031f, (float16_t)-0.0433654785f, (float16_t)0.232177734f, + (float16_t)-0.0697021484f, (float16_t)0.0787353516f, (float16_t)0.13684082f, (float16_t)0.22277832f, + (float16_t)0.165893555f, (float16_t)0.0419311523f, (float16_t)-0.266357422f, (float16_t)-0.022857666f, + (float16_t)-0.127563477f, (float16_t)0.15246582f, (float16_t)-0.125488281f, (float16_t)0.0889892578f, + (float16_t)0.100708008f, (float16_t)0.286621094f, (float16_t)0.0182342529f, (float16_t)0.104187012f, + (float16_t)-0.0699462891f, (float16_t)-0.0503540039f, (float16_t)-0.0983276367f, (float16_t)-0.141235352f, + (float16_t)0.0770263672f, (float16_t)-0.0129318237f, (float16_t)-0.0815429688f, (float16_t)-0.0548400879f, + (float16_t)-0.211303711f, (float16_t)0.268554688f, (float16_t)-0.14831543f, (float16_t)0.0638427734f, + (float16_t)0.138671875f, (float16_t)0.223876953f, (float16_t)0.121154785f, (float16_t)0.183349609f, + (float16_t)-0.0149078369f, (float16_t)-0.116088867f, (float16_t)-0.00616073608f, (float16_t)0.0509338379f, + (float16_t)0.129150391f, (float16_t)0.134033203f, (float16_t)-0.0963745117f, (float16_t)-0.061920166f, + (float16_t)-0.0939331055f, (float16_t)0.166748047f, (float16_t)-0.147827148f, (float16_t)-0.0318908691f, + (float16_t)-0.0352783203f, (float16_t)0.385009766f, (float16_t)0.0721435547f, (float16_t)0.0927124023f, + (float16_t)0.0575866699f, (float16_t)-0.0734863281f, (float16_t)0.0323791504f, (float16_t)-0.0191345215f, + (float16_t)0.138916016f, (float16_t)-0.0595092773f, (float16_t)-0.168334961f, (float16_t)-0.00481033325f, + (float16_t)0.0220489502f, (float16_t)0.12097168f, (float16_t)-0.127319336f, (float16_t)0.102966309f, + (float16_t)0.117492676f, (float16_t)0.270263672f, (float16_t)0.0755615234f, (float16_t)0.129516602f, + (float16_t)0.00349807739f, (float16_t)-0.0505371094f, (float16_t)-0.0182037354f, (float16_t)-0.021774292f, + (float16_t)0.0700073242f, (float16_t)0.131591797f, (float16_t)-0.128173828f, (float16_t)-0.0291442871f, + (float16_t)-0.223510742f, (float16_t)0.329833984f, (float16_t)-0.0343322754f, (float16_t)-0.0692749023f, + (float16_t)0.19921875f, (float16_t)0.34765625f, (float16_t)-0.0253295898f, (float16_t)-0.0959472656f, + (float16_t)0.0740356445f, (float16_t)-0.148803711f, (float16_t)-0.149047852f, (float16_t)-0.0332641602f, + (float16_t)0.00752258301f, (float16_t)-0.112426758f, (float16_t)-0.0579833984f, (float16_t)-0.260498047f, + (float16_t)-0.0264434814f, (float16_t)0.278320312f, (float16_t)-0.0748291016f, (float16_t)-0.131225586f, + (float16_t)-0.102111816f, (float16_t)0.21484375f, (float16_t)0.0787963867f, (float16_t)0.0252990723f, + (float16_t)0.117431641f, (float16_t)-0.0431213379f, (float16_t)0.0305786133f, (float16_t)0.128662109f, + (float16_t)0.126831055f, (float16_t)-0.099609375f, (float16_t)-0.146972656f, (float16_t)-0.0563659668f, + (float16_t)0.151855469f, (float16_t)0.174316406f, (float16_t)-0.131347656f, (float16_t)-0.0778808594f, + (float16_t)0.101196289f, (float16_t)0.183105469f, (float16_t)0.0318603516f, (float16_t)-0.00773239136f, + (float16_t)0.115112305f, (float16_t)0.0212402344f, (float16_t)-0.083984375f, (float16_t)0.0191497803f, + (float16_t)-0.0018415451f, (float16_t)0.0169525146f, (float16_t)-0.0485229492f, (float16_t)-0.0670166016f, + (float16_t)-0.392089844f, (float16_t)0.282958984f, (float16_t)0.00647354126f, (float16_t)-0.108703613f, + (float16_t)0.0448303223f, (float16_t)0.080078125f, (float16_t)-0.0191497803f, (float16_t)-0.0914306641f, + (float16_t)0.0917358398f, (float16_t)-0.222045898f, (float16_t)-0.168701172f, (float16_t)0.0167388916f, + (float16_t)0.189208984f, (float16_t)-0.0658569336f, (float16_t)-0.021774292f, (float16_t)-0.231689453f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_bias_data.h new file mode 100644 index 00000000..d4ffa8b7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_cell_bias[16] = {-0.20815064f, + -0.332956433f, + 0.0429747254f, + 0.18109706f, + -0.164458483f, + -0.0203969777f, + 0.0888663977f, + 0.0852864385f, + 0.0245713964f, + 0.231951132f, + -0.040752016f, + 0.22246334f, + 0.225114584f, + -0.0689477772f, + -0.14960584f, + -0.243268877f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..c9972d49 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_hidden_weights_data.h @@ -0,0 +1,39 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_cell_hidden_weights[256] = { + 0.251648217f, -0.267154634f, -0.0971785784f, -0.33336693f, -0.0542725995f, 0.122602999f, 0.0517443381f, + -0.108177535f, 0.0251787193f, 0.282065332f, -0.212153971f, 0.180514917f, -0.161156401f, -0.251870304f, + -0.0618129484f, -0.0592603385f, -0.137066618f, -0.284005523f, -0.281697989f, -0.0994267315f, 0.00214902521f, + -0.115615293f, -0.343745589f, -0.247677997f, -0.141406611f, 0.313637137f, 0.251790851f, 0.172292039f, + -0.184892908f, 0.0956666023f, -0.264515013f, -0.143409178f, 0.100632407f, -0.134741694f, -0.00181851548f, + 0.255239993f, -0.244320184f, 0.296881437f, -0.30074048f, 0.170468733f, 0.234317631f, 0.0140926102f, + 0.207568184f, -0.101667859f, 0.209941775f, -0.318553567f, -0.0730004609f, -0.318790197f, 0.230531812f, + -0.308940113f, 0.0995616987f, -0.0315176919f, 0.163156152f, 0.034819942f, -0.265793115f, -0.130163938f, + 0.0965873227f, 0.290391535f, -0.0110870488f, -0.292616189f, 0.307960421f, -0.0961068198f, -0.329157382f, + 0.131606072f, 0.251532555f, 0.207540557f, 0.306089371f, -0.187608168f, 0.00748809753f, 0.189351395f, + 0.0685333014f, -0.290665448f, 0.323219806f, 0.24481608f, -0.0626329184f, 0.189852118f, -0.221164376f, + -0.281992137f, -0.160134047f, 0.0892827064f, -0.18614614f, -0.349932134f, -0.257972658f, -0.0824099258f, + -0.247653902f, 0.0765671656f, -0.179847911f, 0.183372363f, -0.29746595f, 0.0781302676f, -0.0221295618f, + -0.125525892f, -0.178551957f, -0.136900038f, -0.122366048f, -0.0492928699f, 0.184275731f, -0.0401914269f, + 0.306781322f, -0.13037701f, 0.193740755f, -0.331806809f, -0.108144663f, -0.0343219005f, -0.144780621f, + 0.0702413991f, 0.309296161f, 0.250397682f, -0.135806039f, 0.101073332f, 0.100054629f, 0.262928396f, + -0.249815613f, -0.0395675525f, -0.261401713f, 0.259058744f, -0.192275092f, -0.200937301f, 0.146851271f, + 0.113223918f, 0.164358065f, -0.21385397f, -0.0744568855f, 0.0426444784f, 0.0937625989f, -0.0208377931f, + -0.102654092f, 0.287735134f, -0.08945553f, 0.0518744029f, -0.0762880072f, 0.286156952f, -0.000162348937f, + -0.277907103f, 0.167080373f, -0.115334354f, 0.115127437f, 0.00650418783f, 0.260480493f, -0.0475251414f, + 0.302078158f, 0.193700656f, 0.279300749f, 0.190272018f, 0.2351062f, -0.0547724143f, -0.230646059f, + 0.260117114f, -0.00218842295f, -0.31730321f, 0.332773626f, -0.0809685811f, -0.278489292f, -0.126199931f, + -0.0803197697f, 0.0122593381f, 0.139313966f, 0.199409977f, -0.267204344f, -0.304186046f, -0.205095068f, + -0.275762409f, -0.240801066f, 0.288316041f, 0.00625458453f, -0.316020042f, -0.240668759f, 0.161540866f, + 0.0619344376f, 0.194421545f, -0.285410702f, 0.216485932f, 0.03471696f, -0.338484496f, 0.200218275f, + -0.0442535356f, -0.0407391787f, 0.209294468f, 0.265540183f, -0.155109003f, -0.301902473f, 0.186520174f, + -0.285259694f, -0.305542856f, -0.264229864f, -0.0448585637f, 0.201986283f, -0.0472738966f, -0.302057207f, + 0.217839897f, 0.313259006f, -0.100062281f, -0.160096034f, 0.285917521f, -0.256040812f, -0.0225441102f, + -0.302158654f, 0.271707296f, 0.249933749f, -0.275985777f, -0.119634278f, -0.06100576f, 0.201770231f, + 0.172040284f, 0.206510708f, 0.179296121f, 0.157156304f, -0.244321123f, -0.133550897f, -0.184508383f, + -0.0346279629f, 0.098248899f, 0.0054930253f, 0.0466855913f, -0.267257154f, -0.0565877371f, 0.0840843096f, + -0.0361484662f, 0.327025801f, 0.310748965f, -0.138089776f, 0.177168995f, -0.0489628054f, 0.176985756f, + -0.333339006f, -0.00456652045f, -0.297233731f, 0.22223714f, -0.210936099f, 0.0474980734f, -0.329281986f, + -0.0685706362f, -0.0283618644f, -0.0760032162f, -0.114103518f, 0.306014448f, -0.148302272f, -0.0320822559f, + -0.23020646f, -0.17027688f, 0.291974723f, 0.253020942f, -0.190677807f, -0.336754858f, -0.0628878102f, + 0.167181522f, 0.299857229f, -0.163505703f, 0.312809885f, -0.159165263f, 0.0461265855f, -0.20232439f, + -0.338576913f, -0.152654007f, 0.348115772f, -0.0141913462f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_input_weights_data.h new file mode 100644 index 00000000..ad80e623 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/cell_input_weights_data.h @@ -0,0 +1,30 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_cell_input_weights[192] = { + 0.0962230265f, -0.165447325f, -0.322198808f, -0.344180018f, -0.279970884f, 0.189433977f, -0.131369084f, + 0.101605527f, 0.232784435f, 0.12757273f, -0.239136174f, -0.139389202f, 0.00868832972f, -0.155172497f, + 0.182023659f, 0.156169176f, 0.165687069f, -0.311475933f, 0.0977161974f, -0.147089899f, 0.256650388f, + 0.320361316f, 0.216226265f, 0.192594647f, 0.276087314f, 0.13680239f, 0.14581345f, -0.300662249f, + 0.222433507f, 0.0561311804f, -0.144481987f, 0.204935119f, -0.200821117f, -0.339505702f, -0.0275127087f, + -0.0691178069f, -0.243343666f, 0.0534939282f, -0.308597088f, -0.0285286661f, 0.0287943054f, -0.282820046f, + -0.0877329409f, -0.245386913f, 0.220156923f, 0.0471260473f, 0.222106442f, 0.314504266f, -0.204864502f, + 0.125127211f, 0.066136077f, -0.016067002f, 0.0838070884f, -0.331306368f, 0.225021929f, -0.0378765501f, + -0.0191579349f, 0.170722261f, 0.109360568f, 0.231377482f, -0.0475639403f, -0.217285648f, 0.0705395639f, + -0.0766396746f, -0.296520174f, 0.126667023f, -0.294419587f, -0.0114682401f, -0.080701828f, 0.0207829773f, + 0.31360808f, 0.0211677738f, -0.0925733894f, -0.0210023206f, -0.204130217f, 0.0832507759f, -0.247754186f, + 0.307792187f, -0.0691699237f, -0.0672012269f, 0.228617668f, -0.00853962079f, -0.257390171f, -0.0363527164f, + 0.303744376f, 0.027151335f, 0.0786454976f, -0.111600526f, 0.224374026f, -0.0870104879f, -0.0413748696f, + 0.128522962f, 0.0343331434f, 0.157641456f, 0.305574924f, -0.0185594931f, 0.202910379f, 0.134596124f, + -0.272097349f, 0.148477688f, 0.229080796f, -0.228195176f, -0.14190273f, 0.0225165337f, 0.311145455f, + 0.310471207f, 0.221913129f, -0.115676746f, 0.131589219f, 0.130544856f, -0.228194654f, -0.0323090032f, + -0.297629893f, -0.106138557f, -0.268116087f, -0.0958881378f, 0.264787108f, -0.00683600688f, 0.226406276f, + 0.076041989f, 0.2596035f, -0.346048743f, 0.156528071f, 0.0918675214f, -0.153625578f, -0.0979067683f, + 0.145333827f, 0.265542865f, 0.236266702f, 0.227725387f, 0.165928066f, -0.142626554f, -0.0984521061f, + 0.18578136f, 0.347483158f, -0.244467959f, 0.0519346036f, 0.213062584f, 0.120078564f, -0.151749134f, + 0.200425312f, -0.0794311911f, 0.224717706f, 0.226975545f, -0.266847193f, 0.155197114f, -0.231614351f, + 0.08151941f, 0.344017029f, -0.107845753f, -0.317251503f, -0.0834122971f, -0.102806248f, -0.210820064f, + -0.230330601f, 0.0696294829f, 0.126269653f, -0.339397341f, -0.136495411f, 0.289097518f, 0.304596305f, + -0.0611360632f, -0.0748843327f, -0.189183727f, 0.0830389112f, 0.269915283f, 0.325482488f, -0.080834575f, + -0.0956204087f, -0.0169040356f, 0.191763818f, 0.0432765484f, 0.256479561f, 0.014661043f, 0.237840384f, + 0.0068048886f, -0.314316779f, 0.0574937314f, 0.218032703f, 0.177791268f, 0.335814983f, 0.114351146f, + 0.212745547f, -0.0451651551f, -0.297536701f, -0.142251298f, -0.296789646f, 0.330905706f, -0.101406805f, + 0.0252693165f, 0.0798490196f, 0.0913997293f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/config_data.h new file mode 100644 index 00000000..4ea22be8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_MEDIUM_F32_TIME_MAJOR 1 +#define LSTM_MEDIUM_F32_TIME_STEPS 8 +#define LSTM_MEDIUM_F32_BATCH_SIZE 3 +#define LSTM_MEDIUM_F32_INPUT_SIZE 12 +#define LSTM_MEDIUM_F32_HIDDEN_SIZE 16 +#define LSTM_MEDIUM_F32_CELL_CLIP 0.0f +#define LSTM_MEDIUM_F32_CELL_STATE_SIZE 48 +#define LSTM_MEDIUM_F32_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_bias_data.h new file mode 100644 index 00000000..1ea78a61 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_forget_bias[16] = {0.0426348262f, + 0.075036332f, + 0.0429688357f, + -0.315445542f, + 0.0600952357f, + 0.0535583533f, + -0.0529360324f, + -0.0947822258f, + -0.119968198f, + 0.0397104025f, + 0.0415237248f, + 0.225033268f, + -0.311384678f, + 0.20237869f, + 0.15059194f, + 0.11214906f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..73ed311d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_hidden_weights_data.h @@ -0,0 +1,39 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_forget_hidden_weights[256] = { + 0.239968851f, 0.344280303f, -0.273289323f, 0.0097827008f, 0.0248353481f, -0.129327551f, -0.335575819f, + -0.177099109f, 0.25398618f, -0.136532262f, -0.0327013172f, 0.279600233f, 0.109783389f, 0.161535487f, + -0.182194799f, 0.290897906f, 0.304229736f, 0.154871717f, 0.151577517f, 0.23443456f, -0.295304984f, + -0.329241008f, 0.153427348f, 0.282119155f, 0.0934941098f, -0.315797895f, -0.0702708438f, -0.130473495f, + -0.149795696f, 0.303925991f, -0.204190269f, 0.175594568f, 0.144140854f, 0.132756576f, -0.244070917f, + 0.1403943f, 0.253748238f, -0.171346158f, -0.339366794f, 0.254118025f, -0.129624858f, -0.109343715f, + -0.234725818f, -0.0860221088f, -0.0655702949f, -0.290879488f, 0.282562494f, 0.297410071f, -0.270124882f, + -0.219471827f, 0.216182455f, 0.297466815f, -0.127094194f, -0.326164633f, -0.273408711f, -0.270328671f, + 0.34807381f, -0.189598903f, 0.152480811f, 0.0068888166f, 0.135979921f, -0.103130676f, 0.113862269f, + 0.289796203f, 0.201403722f, -0.198680267f, 0.0571204945f, -0.0135413846f, -0.0504798181f, 0.0334445685f, + -0.0232135002f, 0.139266863f, -0.272154808f, -0.0993484482f, 0.186800301f, -0.063639842f, -0.335443437f, + 0.139515385f, -0.043621365f, -0.207848579f, 0.326530218f, -0.202790663f, 0.209675282f, -0.000265041512f, + 0.122009404f, 0.176855966f, -0.0524183586f, 0.169715151f, -0.313991308f, -0.318546504f, 0.14771083f, + 0.0691986158f, 0.143042535f, -0.170808733f, -0.217912748f, -0.340485483f, 0.00627779542f, 0.100241743f, + -0.255879521f, -0.00321329175f, -0.240265235f, -0.0326221511f, 0.210175872f, -0.162686676f, 0.284063965f, + 0.298532814f, 0.169374973f, 0.26356113f, 0.0388610624f, 0.222679287f, -0.0853942633f, -0.344339311f, + -0.0487916842f, 0.0134913027f, 0.162578627f, 0.267363667f, 0.0535565391f, -0.024190193f, -0.258914918f, + 0.123467825f, 0.033601258f, 0.1423067f, -0.223698065f, -0.0827035457f, -0.33182776f, -0.118573666f, + -0.184045851f, -0.0972566083f, 0.0208686516f, 0.0834748f, 0.0976005569f, -0.113125712f, -0.0161738861f, + 0.108793765f, 0.318347573f, 0.309569329f, 0.16062431f, 0.316076517f, 0.0183369201f, 0.261305064f, + -0.125493839f, -0.229456916f, -0.327646077f, 0.131802291f, 0.340851933f, -0.112102009f, 0.278926402f, + 0.045252908f, 0.157553867f, 0.0027671475f, 0.237781927f, -0.247290492f, 0.110137306f, -0.0132242348f, + -0.212648049f, -0.00171930355f, -0.0916377306f, -0.00128720794f, -0.268962085f, 0.19564867f, 0.212145686f, + -0.337195128f, 0.304481894f, -0.0858763903f, 0.341188788f, -0.0773457289f, 0.272710651f, 0.226521343f, + -0.287754118f, 0.178587884f, -0.00902105682f, -0.114773303f, -0.130696759f, -0.339900255f, 0.209018782f, + 0.0376387909f, 0.0410481095f, 0.2239649f, -0.0714648142f, 0.110661633f, 0.343933463f, -0.129885107f, + 0.0818105042f, 0.107363969f, 0.14548175f, 0.191278681f, 0.112324648f, 0.260501325f, 0.19244884f, + 0.22898683f, -0.114326239f, 0.00229311059f, -0.328452617f, 0.269351006f, -0.241264954f, 0.0906096175f, + 0.00441953633f, -0.0718416572f, 0.211353496f, 0.314744294f, 0.0251222327f, -0.14540495f, -0.289551437f, + 0.142928436f, -0.242928922f, 0.0370122269f, 0.172787592f, 0.113708705f, -0.240452543f, 0.138574168f, + 0.151128665f, -0.29839772f, 0.0762365162f, -0.118307576f, 0.078028582f, 0.263159961f, 0.286676913f, + 0.0551361181f, 0.0788103566f, 0.219234511f, -0.25578469f, 0.0927138701f, -0.113446914f, 0.330290318f, + -0.217397794f, 0.349958599f, -0.0788000673f, 0.316353977f, -0.293347657f, -0.177796796f, -0.142749131f, + 0.339028925f, 0.317232847f, 0.1376248f, 0.120445311f, 0.319560945f, 0.0584413931f, 0.0201252736f, + 0.00810844637f, -0.0597883426f, -0.176919773f, 0.038694296f, -0.269163311f, -0.0841181129f, 0.0796137676f, + 0.0704114512f, -0.235712528f, -0.0312624574f, 0.108558886f, 0.193208545f, -0.0692238957f, 0.186838493f, + 0.263775617f, -0.0471973978f, 0.149553239f, 0.289748967f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_input_weights_data.h new file mode 100644 index 00000000..e6ba4222 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/forget_input_weights_data.h @@ -0,0 +1,30 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_forget_input_weights[192] = { + -0.232639864f, -0.00537529774f, 0.132943884f, 0.313229173f, 0.134749159f, -0.249437377f, 0.0251161121f, + -0.340693057f, -0.077106446f, 0.100334771f, 0.342462331f, -0.184253782f, -0.0831676275f, -0.0806786865f, + 0.209988713f, -0.229772225f, -0.202109709f, -0.0804336593f, -0.085245125f, 0.153380752f, 0.183490247f, + -0.127767697f, -0.297180295f, 0.313264459f, 0.258948416f, -0.174015224f, -0.0223432593f, 0.0844035149f, + 0.224292025f, -0.263130486f, 0.0585069284f, -0.171277836f, 0.00720459782f, 0.179840997f, 0.262677252f, + -0.0704790726f, 0.125364423f, -0.0824543908f, 0.20226869f, 0.216371328f, -0.119218074f, -0.10774678f, + -0.249606758f, 0.109947249f, -0.0931248143f, -0.256150395f, -0.121320792f, -0.0680468455f, 0.0380478725f, + 0.179358512f, 0.0435975529f, 0.27754283f, 0.116280869f, -0.116927065f, -0.0320456773f, -0.294138789f, + -0.0538265146f, -0.145267904f, 0.026340846f, -0.0315504037f, -0.305011183f, 0.173664227f, 0.0215938408f, + -0.181371287f, 0.347037673f, 0.285064131f, 0.348653466f, -0.235671982f, 0.0600051023f, -0.277819753f, + -0.340864867f, 0.18243368f, -0.0289771147f, -0.153548211f, 0.0931163877f, 0.183317423f, 0.236080512f, + -0.257127345f, -0.305281192f, 0.262060672f, -0.0829059407f, -0.243517533f, 0.103521109f, -0.132732272f, + 0.150904179f, -0.00530994218f, 0.033180099f, 0.30916971f, 0.03997159f, 0.226951495f, -0.306833357f, + 0.243427023f, -0.11873854f, 0.19010213f, -0.198326245f, -0.0938251913f, -0.27764228f, 0.271758556f, + -0.204988018f, -0.0843603015f, 0.280830801f, 0.0652267486f, -0.0706103444f, 0.07273902f, -0.132194221f, + 0.347427458f, 0.178167954f, -0.0169663075f, -0.10843955f, 0.0698034391f, 0.224397749f, -0.00675453385f, + 0.225609526f, 0.252489328f, 0.0963395908f, -0.273025155f, 0.00359612494f, -0.117439851f, -0.184359506f, + 0.263633013f, 0.33567974f, -0.321462274f, 0.195526525f, 0.234571293f, -0.239659011f, -0.247679561f, + -0.198817819f, 0.0139676547f, 0.182109371f, 0.0169985425f, -0.108103305f, -0.190323964f, -0.00841998681f, + -0.279779971f, -0.130295202f, -0.147676468f, 0.214240506f, -0.25341773f, 0.00658771209f, -0.305840403f, + -0.322037935f, 0.312947899f, -0.222230062f, 0.235488757f, 0.212241992f, 0.10459429f, 0.0399330445f, + 0.0613136217f, 0.0742296055f, 0.272302866f, -0.116128042f, 0.309875429f, 0.211207107f, 0.011107469f, + -0.221395627f, 0.0967461169f, -0.268811613f, -0.291994363f, 0.280584663f, -0.0151464287f, 0.16255039f, + 0.185052097f, 0.348681897f, 0.0139599741f, 0.287942648f, 0.253897071f, -0.334193349f, -0.100920737f, + 0.0973276868f, -0.17952624f, 0.303919017f, 0.00688139303f, -0.344572812f, -0.246955305f, -0.076613903f, + 0.282503426f, 0.345838279f, -0.13430047f, -0.0769861415f, -0.264164418f, 0.100456409f, -0.345899463f, + -0.059587799f, -0.323744804f, 0.0983309299f, -0.118649244f, 0.332962573f, 0.337091655f, -0.0565823726f, + -0.304715067f, 0.0991090164f, 0.0663279369f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_bias_data.h new file mode 100644 index 00000000..025ceb38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_input_bias[16] = {-0.079425171f, + 0.0276038796f, + -0.254376531f, + 0.292876422f, + -0.183214664f, + 0.0292263702f, + 0.0504104048f, + -0.0329439789f, + 0.0265275836f, + 0.293287933f, + -0.439223707f, + -0.199884161f, + 0.414282322f, + 0.0952758342f, + -0.0710117072f, + 0.133840293f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_data.h new file mode 100644 index 00000000..9f58d24b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_data.h @@ -0,0 +1,44 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_input[288] = { + 0.37669307f, 0.782862008f, 0.699841082f, -0.107477657f, -0.380201191f, 0.175299138f, -0.71341759f, + -0.714202404f, -0.503317237f, 0.685704052f, -0.202584028f, 0.719929039f, -0.115324304f, -0.552036881f, + -0.574698508f, 0.36010918f, 0.548359036f, -0.691876233f, -0.579251766f, -0.115670525f, -0.903791487f, + 0.590255439f, -0.624595344f, -0.377749592f, -0.266754121f, -0.43648234f, -0.143465489f, 0.340453237f, + -0.782837272f, 0.513526618f, 0.714303195f, 0.181654185f, 0.549007595f, 0.207881913f, -0.0809846893f, + -0.581056118f, -0.747199953f, 0.493855745f, 0.324163914f, 0.100247279f, 0.332989126f, 0.266720444f, + 0.60377264f, 0.406039447f, 0.503874421f, 0.372484326f, -0.736671925f, -0.158211187f, -0.085114494f, + 0.414188236f, 0.224021748f, -0.745279014f, -0.358987719f, 0.126795024f, -0.312062144f, 0.775238633f, + -0.39009878f, -0.278976858f, -0.439552218f, -0.576933086f, -0.80513078f, 0.832672179f, 0.0797394291f, + -0.325463802f, -0.244648114f, -0.678676426f, 0.433022201f, -0.655232787f, -0.0592837706f, 0.0934946761f, + 0.53690064f, -0.0718831345f, -0.742497385f, -0.453610539f, 0.690350056f, 0.843775153f, -0.426858932f, + -0.124647886f, -0.148289949f, 0.105521739f, -0.303530902f, -0.847760856f, 0.48937732f, 0.683221221f, + -0.0558254048f, 0.291055232f, 0.786219537f, 0.46392107f, 0.599752665f, 0.960022926f, -0.109414481f, + 0.55146867f, 0.0342754945f, 0.035611935f, -0.768823504f, -0.210572019f, 0.951629281f, 0.65790689f, + 0.71778959f, -0.161184847f, -0.921794176f, -0.796840072f, 0.151721299f, 0.212792858f, 0.721641898f, + -0.735618532f, 0.434359431f, 0.331088245f, -0.778301775f, 0.382597506f, -0.92410928f, -0.0968932584f, + -0.314976662f, -0.188163176f, 0.602855265f, 0.890967488f, 0.678113282f, -0.0217321143f, -0.932614207f, + 0.364720404f, -0.529593468f, 0.385746509f, -0.953331053f, 0.178668693f, -0.0116343899f, -0.0526608564f, + -0.223699674f, 0.561988413f, 0.925765574f, 0.838565528f, -0.642331302f, 0.208084181f, -0.230615988f, + 0.887562335f, 0.0512199141f, 0.315372705f, 0.986984909f, -0.487471431f, 0.463277668f, 0.197769016f, + 0.903559983f, -0.0920435712f, -0.563794315f, 0.797702134f, -0.386511832f, -0.797145844f, 0.91392076f, + 0.692082107f, -0.439700246f, -0.251123428f, -0.850503981f, -0.35576573f, 0.484277785f, -0.198736012f, + -0.422011375f, -0.214723751f, 0.111860871f, 0.454457939f, 0.175407261f, 0.741669416f, 0.00169343594f, + 0.00144084694f, 0.771100819f, 0.844592214f, -0.803117096f, 0.481663108f, 0.870895088f, 0.860845685f, + 0.766552687f, 0.849951982f, -0.180262282f, 0.405169249f, -0.855207086f, -0.484913528f, 0.459144086f, + -0.329806596f, -0.853891313f, -0.407857358f, 0.433254391f, -0.382092029f, -0.574409187f, -0.462869316f, + -0.464062691f, 0.120173223f, -0.288553953f, -0.106146276f, -0.0596644096f, -0.732996166f, -0.0320977606f, + -0.736022711f, 0.931695998f, -0.442841709f, 0.091966249f, -0.463133126f, 0.816161096f, 0.270514786f, + 0.32538718f, 0.521503687f, -0.600632191f, -0.914980769f, -0.24548845f, -0.629641831f, -0.276222974f, + 0.988106549f, -0.531596303f, 0.398306191f, -0.766055644f, 0.291430593f, -0.750893414f, 0.0328400135f, + 0.845440745f, -0.585078895f, 0.772306979f, 0.309233993f, 0.18468377f, -0.357343286f, -0.174555719f, + 0.443519562f, -0.782244265f, 0.694132566f, -0.485416591f, -0.962288618f, 0.401941061f, 0.288750142f, + 0.165376782f, -0.548888445f, 0.308525264f, -0.86522162f, 0.6321913f, -0.599963427f, 0.627199709f, + 0.819253027f, 0.578999281f, 0.690315366f, -0.944411278f, -0.65220499f, -0.341164351f, 0.986820996f, + 0.652426481f, -0.921189606f, -0.409150541f, 0.879984021f, -0.697744846f, -0.57114321f, -0.664924443f, + -0.906654358f, -0.243359834f, 0.15101926f, -0.161436528f, 0.149524778f, 0.790425599f, -0.0783473179f, + -0.441304773f, 0.955536723f, -0.211116791f, 0.546228111f, -0.628704667f, -0.925074875f, 0.478541315f, + 0.0213507265f, -0.445720375f, -0.475331157f, 0.680044115f, -0.648946285f, -0.338716328f, 0.608782172f, + 0.847060859f, 0.144595653f, 0.672536552f, -0.148649648f, 0.941668272f, -0.389385641f, 0.576282501f, + -0.0734418258f, -0.806069732f, -0.126953676f, 0.257620752f, -0.606084228f, 0.151413038f, 0.999447644f, + 0.942948103f, -0.820956051f, -0.602242589f, 0.960408926f, -0.660287321f, 0.403804272f, -0.810776293f, + -0.271321237f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..e40efac5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_hidden_weights_data.h @@ -0,0 +1,39 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_input_hidden_weights[256] = { + -0.0833467916f, 0.175084963f, 0.324498832f, -0.198646322f, -0.319408149f, -0.264343292f, 0.242406741f, + -0.187170669f, 0.309966981f, -0.184632957f, -0.335673094f, -0.191106275f, 0.264356196f, 0.206727341f, + 0.127885222f, 0.162576482f, 0.129057631f, 0.296843231f, 0.178603247f, 0.229468957f, -0.08507929f, + 0.0199674908f, 0.271348327f, -0.048717808f, 0.0402370766f, 0.212103188f, -0.186382756f, 0.0212401804f, + 0.0860734358f, 0.00743180327f, 0.251972169f, 0.277051061f, -0.313347667f, -0.0447596908f, 0.0948933586f, + 0.19483985f, -0.321139127f, 0.215106502f, 0.25069657f, 0.0123121748f, 0.0914847106f, 0.29317981f, + -0.344694734f, -0.348648578f, -0.0541332699f, -0.342814475f, 0.0902792364f, -0.0277047846f, 0.316839099f, + -0.265476167f, 0.133182704f, -0.233700514f, -0.273185372f, -0.251989692f, 0.329766005f, 0.27346915f, + -0.0973369926f, -0.25090003f, 0.325951904f, -0.16825968f, 0.0226553213f, 0.330984592f, -0.0306786206f, + -0.188134745f, 0.28276816f, 0.0610953309f, -0.0790406168f, -0.309657544f, 0.109471112f, -0.186071932f, + -0.273884535f, 0.187479019f, 0.107300423f, 0.0671377778f, 0.172083005f, 0.224058792f, -0.213633984f, + 0.273459345f, 0.176242545f, 0.0428973548f, -0.0229261685f, 0.308081895f, 0.212969899f, 0.15092884f, + 0.0443720296f, -0.154044837f, -0.201080486f, 0.338701785f, -0.20611392f, 0.120767266f, -0.0561106578f, + -0.288842022f, -0.150619343f, -0.126992881f, 0.262050778f, 0.327029467f, -0.334599197f, 0.284843355f, + -0.119691044f, 0.121037252f, -0.0484835245f, 0.105022483f, 0.315243393f, -0.148413658f, 0.13575758f, + -0.000750381267f, 0.0258135702f, 0.0508932173f, -0.349485576f, 0.0155668221f, -0.00185055553f, -0.254985958f, + -0.142600715f, 0.309560686f, 0.275081038f, -0.199120358f, 0.182190269f, -0.140593767f, 0.0549068786f, + 0.271312356f, 0.294664294f, 0.127181172f, -0.0792347416f, -0.200880349f, 0.0388590135f, 0.0610912479f, + -0.302800655f, 0.122590631f, 0.144211099f, 0.342316568f, 0.01548668f, 0.0621903278f, 0.0996798724f, + -0.14119339f, -0.314803332f, 0.131727979f, -0.0861229151f, -0.318979383f, 0.000714620575f, 0.0436348096f, + 0.147525042f, 0.0393098444f, 0.227523506f, -0.0434595831f, 0.32870993f, -0.346420795f, -0.053874217f, + -0.130249336f, 0.269618869f, 0.0259611011f, -0.0315366574f, -0.22243166f, 0.315485388f, 0.285801798f, + 0.325900406f, 0.287086606f, 0.187141761f, 0.236686647f, 0.261195093f, 0.0767566934f, 0.134566262f, + 0.167880714f, 0.319404751f, 0.207215786f, -0.13460727f, -0.0228146352f, -0.198383361f, -0.20374842f, + -0.287330955f, -0.0454815961f, 0.192968071f, 0.129166439f, -0.348106682f, -0.238757357f, 0.0695742965f, + -0.0258259159f, -0.117839284f, -0.136245266f, 0.019363502f, -0.138416156f, -0.171260715f, -0.123808749f, + -0.257291049f, 0.138061464f, -0.0632692277f, -0.145689458f, 0.0894925594f, -0.348480225f, -0.262452424f, + 0.0801274106f, 0.142204255f, -0.213664427f, 0.328174561f, 0.265126377f, 0.30247587f, 0.000329661241f, + -0.216706008f, -0.320953965f, 0.176906288f, -0.173949555f, 0.263993323f, -0.00962918438f, -0.155664474f, + 0.217380613f, 0.153480679f, -0.334300041f, -0.222384974f, -0.0710563064f, 0.0233981851f, -0.294375092f, + -0.325438231f, -0.242669865f, -0.0245572329f, -0.193365559f, -0.033991728f, 0.289652318f, -0.336581439f, + -0.295348018f, -0.300954849f, -0.027567761f, 0.00664167386f, 0.221022189f, 0.0662347004f, 0.185844868f, + 0.136438072f, 0.327480465f, 0.292497307f, -0.20853579f, -0.226743937f, -0.0888736099f, 0.183972016f, + -0.305107832f, -0.0489366315f, -0.137089357f, -0.0433955342f, 0.243560418f, -0.303614169f, -0.0179398432f, + -0.269541174f, -0.23439306f, 0.333903968f, 0.277836949f, 0.186907783f, 0.152046412f, -0.0942380801f, + 0.293551236f, 0.0402528346f, -0.275656909f, 0.0573616214f, -0.299078852f, 0.163730115f, -0.0454154946f, + -0.285614908f, -0.19398433f, -0.0147728128f, -0.112987027f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_input_weights_data.h new file mode 100644 index 00000000..1da61863 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/input_input_weights_data.h @@ -0,0 +1,30 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_input_input_weights[192] = { + 0.206435427f, -0.147499725f, 0.198895589f, 0.0740213916f, 0.328931838f, 0.165485725f, 0.166561589f, + -0.330966026f, 0.255473912f, 0.249429017f, -0.100376122f, 0.0299803838f, 0.0820443481f, 0.331153572f, + 0.182124004f, 0.178378567f, -0.331660748f, -0.270480186f, -0.283291489f, -0.230060577f, 0.138532475f, + 0.0406448804f, -0.215939119f, -0.0562175512f, -0.0326452851f, -0.254500479f, -0.18962121f, 0.224092543f, + -0.233239308f, 0.183309242f, 0.074683398f, -0.000240221983f, 0.156654954f, -0.310835242f, -0.337691337f, + 0.347827911f, 0.109770298f, 0.0154170431f, 0.186560526f, 0.104231119f, 0.322767049f, -0.346931607f, + -0.164629221f, 0.0279667545f, -0.130333528f, -0.171692148f, -0.302328199f, 0.165315881f, 0.154330507f, + -0.271509916f, -0.191654801f, 0.170032233f, -0.174098507f, -0.110615961f, 0.318725377f, 0.0485487096f, + -0.0840594918f, -0.203918725f, 0.140648454f, -0.119322672f, -0.275819927f, -0.268224359f, -0.172047094f, + -0.172070727f, -0.339227408f, 0.155859128f, 0.33861205f, -0.342291147f, -0.195152119f, -0.247214109f, + 0.306493253f, 0.0354889967f, -0.20485349f, 0.142932296f, 0.141872242f, -0.0256056823f, -0.220129669f, + 0.243343607f, -0.119320974f, -0.223320007f, -0.270479113f, 0.219274536f, -0.240661383f, 0.0448559821f, + 0.29524529f, 0.0860927925f, 0.237299502f, 0.0477685928f, 0.156642258f, -0.0490036383f, 0.302056104f, + -0.106454149f, -0.252798885f, -0.0332270972f, -0.160406902f, 0.128932774f, 0.186900124f, 0.169046327f, + 0.207676455f, -0.233772248f, -0.171515137f, 0.0232318752f, -0.192796811f, 0.245127901f, -0.291084081f, + 0.166016221f, -0.336726129f, -0.0614439584f, 0.238008603f, -0.238102242f, -0.338553756f, -0.307215482f, + -0.0574856363f, 0.24002713f, -0.162520677f, -0.165968522f, 0.115159281f, 0.293426067f, -0.165394664f, + 0.312758535f, 0.189055562f, 0.333916634f, 0.139662623f, -0.190510467f, -0.188340724f, 0.238793686f, + -0.0623160563f, -0.219487831f, 0.27828896f, 0.2214894f, 0.23160173f, -0.0171488859f, -0.172033891f, + -0.106743023f, 0.194959983f, 0.00229135714f, -0.0704780743f, -0.103674121f, -0.107154347f, 0.0826911628f, + 0.180107147f, -0.148756742f, -0.294737339f, -0.20793134f, 0.0963201299f, 0.13923125f, -0.259308249f, + -0.124457985f, -0.10411223f, 0.0656079948f, -0.346056581f, 0.129403412f, -0.304323256f, 0.042546954f, + 0.0866209567f, 0.116356403f, -0.255973428f, -0.0149808759f, 0.0233478602f, -0.0819366053f, 0.135425061f, + 0.159993544f, 0.256707251f, -0.271827787f, 0.176126286f, 0.197451577f, -0.214286789f, 0.182115048f, + -0.185395911f, 0.149404541f, -0.265804589f, 0.123605818f, 0.273578286f, 0.289456606f, -0.213221923f, + 0.152963907f, -0.174312964f, 0.190098211f, 0.0992353335f, -0.0887413025f, -0.190549284f, 0.0153383724f, + -0.271089524f, 0.0298378468f, -0.19158259f, -0.00610481249f, -0.195123494f, -0.0146934651f, 0.348196656f, + -0.249585763f, 0.186509266f, 0.14584662f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_bias_data.h new file mode 100644 index 00000000..2adcff9b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_bias_data.h @@ -0,0 +1,17 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_output_bias[16] = {0.161083102f, + 0.136497378f, + -0.140612379f, + 0.175807774f, + 0.0885240287f, + 0.0331136137f, + 0.215115368f, + -0.071347639f, + 0.1613224f, + 0.160485238f, + -0.133611619f, + -0.355893016f, + 0.293039411f, + 0.00937993824f, + -0.128097504f, + 0.0508634448f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..ab27bf3d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_hidden_weights_data.h @@ -0,0 +1,39 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_output_hidden_weights[256] = { + -0.271232992f, 0.133879885f, 0.281532079f, -0.241667107f, 0.170357868f, -0.0665896907f, -0.268563449f, + -0.241552398f, 0.322226137f, 0.243572548f, -0.201615483f, -0.214516878f, -0.262061447f, -0.13302125f, + -0.13523525f, 0.258102328f, 0.184958428f, 0.345933646f, -0.196591631f, -0.0277982112f, -0.189620942f, + -0.00890249386f, 0.125764906f, -0.24184005f, -0.0456511304f, 0.0847475305f, 0.335362881f, 0.292670488f, + 0.157617882f, -0.0441517979f, -0.0729651377f, -0.158995092f, -0.0384796262f, 0.26887998f, -0.292765617f, + 0.276672155f, -0.221760452f, 0.0916996524f, 0.324562222f, -0.00177510013f, 0.17859754f, -0.081869863f, + -0.0653833374f, 0.26073432f, -0.00247848174f, -0.247673854f, -0.230571359f, 0.0273607634f, 0.189043269f, + -0.34944123f, 0.236073807f, 0.2142988f, -0.193673939f, 0.280441165f, -0.151929259f, -0.0859725922f, + -0.259396315f, 0.201091275f, 0.298167735f, -0.254952669f, 0.302234709f, 0.000660215272f, -0.343081176f, + 0.155572996f, 0.193647861f, -0.189915329f, 0.160710692f, 0.137998059f, -0.16481179f, -0.107122727f, + -0.181377113f, -0.1890935f, 0.241387129f, 0.0297424383f, 0.123920813f, 0.116733968f, -0.117606893f, + 0.32957685f, -0.0247058198f, 0.23550579f, 0.33230719f, -0.269900769f, 0.177087694f, 0.193509981f, + -0.139957905f, 0.287059724f, -0.0874130428f, 0.340363801f, -0.28208372f, 0.265989363f, 0.077620782f, + 0.198903561f, -0.150308371f, -0.126094297f, 0.344243944f, -0.0982449204f, 0.0780742094f, 0.263179034f, + 0.0485671163f, 0.0893181786f, 0.035140641f, 0.0549149178f, -0.173778981f, 0.253819466f, -0.334044307f, + 0.122996576f, -0.224501669f, -0.105673119f, 0.0502193868f, 0.296898782f, 0.135352388f, -0.0592963472f, + 0.0123087112f, -0.159348086f, 0.0648626611f, 0.288212031f, 0.235623732f, 0.102316864f, -0.160968959f, + -0.264585972f, 0.0522596836f, -0.250977665f, -0.137594327f, -0.0816448405f, 0.106281154f, 0.269581914f, + 0.0099997269f, 0.22781381f, -0.119869307f, 0.162351012f, 0.135638595f, -0.291158825f, -0.256957561f, + -0.349869817f, -0.10551881f, 0.0666627586f, -0.0723634437f, 0.146337241f, -0.273088574f, 0.13942413f, + -0.288190693f, 0.255763233f, 0.264513642f, 0.187637523f, 0.0745552182f, -0.169663474f, -0.12705557f, + -0.0811590925f, 0.182632148f, -0.28138122f, 0.0255446844f, 0.257613599f, 0.0802956223f, 0.171179712f, + -0.339472681f, -0.107394435f, 0.333357841f, -0.143213525f, 0.0451414734f, -0.175655812f, 0.0705066696f, + 0.30072692f, 0.303064674f, 0.309526384f, -0.0183716509f, 0.19695884f, 0.226460904f, -0.310865879f, + -0.170005694f, 0.260553479f, 0.0165319014f, -0.326778829f, -0.308974743f, -0.0289728586f, 0.217967317f, + 0.0888577923f, 0.0459058695f, 0.289315164f, -0.325964957f, -0.288319737f, -0.12413308f, -0.238033444f, + 0.338421077f, 0.314270586f, -0.0382255018f, 0.230141282f, 0.0480560064f, 0.194401979f, -0.289578974f, + 0.110240757f, -0.322305322f, -0.323237777f, 0.322171479f, 0.0749039277f, 0.21532625f, -0.326146603f, + -0.129929557f, 0.0423606783f, 0.0366881266f, 0.320382804f, 0.1883533f, -0.21175614f, 0.240163401f, + -0.319887727f, -0.0365346856f, 0.0662107319f, -0.194479927f, -0.0264315326f, -0.298678249f, -0.0973463878f, + 0.271325469f, 0.168574244f, -0.00857275352f, -0.197614655f, 0.281739593f, 0.134189829f, -0.08573699f, + 0.323562503f, 0.211905167f, 0.286358505f, 0.145578742f, 0.116619386f, -0.0376015268f, 0.0168045349f, + -0.0385721996f, -0.0998460725f, -0.208332539f, -0.12191388f, -0.283263326f, -0.205459476f, 0.197943076f, + 0.113275819f, 0.234280467f, -0.120197043f, 0.10040158f, 0.308123171f, -0.175176606f, 0.0141873918f, + 0.158466339f, 0.26410684f, 0.170220599f, 0.0182948094f, -0.337208152f, -0.279785156f, -0.204423308f, + 0.224821314f, 0.0363809913f, 0.2961905f, 0.0501699038f, -0.177482352f, 0.277393758f, 0.160049975f, + 0.250825346f, 0.148132846f, 0.00329095731f, -0.117242128f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_input_weights_data.h new file mode 100644 index 00000000..cb972d57 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_input_weights_data.h @@ -0,0 +1,30 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_output_input_weights[192] = { + 0.324796885f, -0.0412816331f, 0.117702059f, -0.0186976008f, -0.211198509f, -0.335274547f, 0.0380685404f, + -0.180204168f, 0.314445078f, 0.327008069f, 0.0876957774f, 0.297520876f, 0.183926702f, -0.0207649097f, + 0.269552469f, -0.0770850182f, 0.246788621f, 0.211485878f, -0.102563895f, 0.115942128f, -0.140818596f, + -0.239296511f, -0.333916306f, -0.31503132f, -0.14555946f, 0.294438094f, 0.167382553f, -0.325248271f, + 0.0208603069f, 0.334398538f, 0.0759483874f, 0.28461808f, 0.243080854f, -0.217534155f, -0.0243777782f, + 0.279160917f, 0.231449112f, -0.290892452f, 0.348054171f, 0.327466279f, -0.176967174f, 0.132827356f, + 0.337976873f, -0.211977661f, -0.33615464f, -0.0249644555f, -0.0875132009f, -0.00392633816f, -0.26857245f, + 0.234196976f, -0.143021092f, -0.117362842f, 0.277242094f, 0.265993774f, 0.0738230795f, 0.0895542875f, + 0.289065987f, -0.262883574f, 0.157209441f, 0.301505297f, 0.157490253f, -0.246301636f, -0.116662592f, + 0.290327817f, 0.141402602f, -0.220848039f, 0.25240463f, -0.14153108f, 0.124123462f, 0.0978116915f, + -0.0296294894f, 0.0611305684f, -0.29687804f, 0.246754512f, 0.160605982f, 0.304262012f, 0.207131967f, + 0.137045681f, 0.0163644385f, -0.213938192f, 0.157580599f, -0.0150183477f, -0.0546111353f, 0.240219682f, + -0.0463322774f, -0.263861418f, -0.222661987f, 0.275273025f, -0.136545613f, -0.0934810638f, 0.047351703f, + 0.306185901f, -0.148768216f, -0.231392115f, 0.246600121f, 0.183694944f, -0.122462615f, 0.299649239f, + -0.0355632976f, 0.00522823771f, -0.082547456f, 0.140193224f, -0.130873159f, -0.0546891391f, 0.197653696f, + -0.176905185f, -0.221384451f, 0.0429415479f, 0.0151399821f, -0.0379692353f, -0.321889192f, -0.222987562f, + -0.0810616463f, -0.302736312f, 0.124529488f, -0.306488097f, -0.236138806f, 0.284404993f, 0.0618140101f, + -0.0778819919f, 0.0222625304f, -0.220859692f, -0.314829022f, -0.34521842f, -0.335702538f, 0.126506209f, + -0.248947829f, -0.0329790749f, -0.134233147f, -0.182660535f, 0.201719254f, 0.041573897f, -0.198749617f, + 0.235139266f, -0.016082881f, 0.184757516f, 0.191594571f, -0.0881769434f, 0.265089184f, -0.339566857f, + 0.0792763531f, 0.228462756f, -0.00447088573f, 0.236145347f, 0.100646161f, -0.11964085f, -0.158790082f, + 0.254235774f, -0.156952322f, -0.27278915f, 0.246393621f, -0.203655988f, -0.170223773f, -0.342508376f, + -0.115273893f, -0.220739976f, 0.301380813f, 0.0989689529f, 0.325889885f, -0.0363861993f, -0.242486373f, + 0.0308086071f, 0.219400063f, -0.341460049f, -0.0270884633f, 0.0888664275f, -0.0476404466f, -0.100735195f, + -0.234622777f, 0.270445108f, 0.257373691f, 0.0578580983f, -0.172713012f, -0.0921606719f, 0.130802244f, + 0.0823055729f, -0.251059711f, 0.0447822958f, -0.0460470729f, 0.124441385f, -0.276722044f, 0.0334389396f, + -0.0565830842f, 0.239277288f, -0.0581698418f, 0.00249016471f, -0.320340574f, 0.0904811025f, -0.146206453f, + 0.0592451841f, -0.268999338f, -0.08511132f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_ref_data.h new file mode 100644 index 00000000..dda2eb28 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/output_ref_data.h @@ -0,0 +1,66 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_medium_f32_output_ref[384] = { + -0.136796802f, -0.0832417756f, 0.0167250279f, 0.0732575879f, -0.00604837248f, 0.0358269028f, + 0.0165125299f, 0.0269250292f, -0.044925373f, 0.15743123f, -0.109173059f, 0.101044782f, + 0.0661697313f, -0.11492189f, -0.0157785788f, 0.0744697899f, -0.0485415906f, -0.0795637369f, + -0.00771293649f, 0.079234682f, -0.0215691756f, -0.0380218513f, -0.0215463545f, 0.00507599115f, + 0.0759279951f, -0.0304023735f, -0.0192144327f, -0.121640511f, 0.286535949f, 0.0863478631f, + -0.02993498f, -0.104709186f, 0.0675765127f, -0.0903508365f, -0.121868268f, -0.0214008614f, + -0.0982345343f, 0.0198822729f, 0.148764312f, -0.0521789975f, -0.0259726234f, 0.0603389889f, + 0.0915006474f, 0.0204802938f, -0.116502792f, -0.022561064f, -0.0812829509f, -0.136971131f, + -0.0848114789f, -0.12831533f, -0.0425883271f, 0.0305382069f, 0.0133559676f, -0.0698637143f, + 0.114209138f, -0.00804266986f, -0.052010715f, 0.0104046697f, -0.0248297416f, 0.131790325f, + 0.0938936025f, -0.205001146f, -0.0193144754f, -0.126914635f, -0.00108081091f, -0.315948159f, + 0.13654232f, -0.0463497676f, -0.112500802f, -0.00713844644f, -0.00864883326f, 0.0176654942f, + -0.0669547915f, 0.0455796607f, -0.0367697924f, -0.0309660491f, 0.209268063f, -0.105710007f, + -0.11079172f, -0.026969241f, -0.0576018654f, -0.0260495152f, -0.100698054f, 0.15862821f, + 0.0450012721f, 0.00972153712f, 0.0003254365f, -0.017691385f, -0.0124439606f, 0.18499729f, + -0.0615798533f, 0.162187412f, 0.0965536013f, -0.105457552f, -0.0222047642f, -0.170000926f, + -0.164231285f, -0.0958142355f, -0.0439723171f, 0.143772587f, 0.000186801713f, 0.103113711f, + 0.035662055f, -0.0704246312f, -0.125342146f, 0.0456268415f, -0.0119211553f, 0.105952993f, + 0.15894644f, -0.135341272f, 0.0586312748f, -0.136797965f, -0.0565669388f, -0.345405042f, + 0.176630363f, -0.206510052f, -0.154817149f, -0.0350926071f, 0.088056691f, 0.00638393639f, + -0.139191985f, -0.0449082702f, -0.0152676692f, 0.0298204422f, 0.174616039f, -0.17702271f, + -0.0766637549f, -0.114186637f, -0.113670029f, -0.0298849791f, 0.0628229231f, 0.167015329f, + 0.0135512277f, 0.0210085083f, -0.0503468327f, 0.0870156288f, 0.0250867102f, 0.220619515f, + 0.0909922421f, 0.116087161f, -0.0653443485f, -0.180288762f, -0.0502871424f, 0.0296330173f, + 0.00516277133f, -0.160426185f, -0.107901268f, 0.113047883f, 0.000245273317f, -0.0404810421f, + 0.147212118f, -0.102197938f, -0.0641829818f, 0.1193427f, -0.00904542394f, 0.0407163948f, + 0.223135471f, -0.197403699f, -0.0658442155f, -0.277327746f, 0.115738049f, -0.144322246f, + -0.0554434173f, 0.0935998186f, -0.0865025818f, -0.0559221283f, 0.186529219f, -0.017515434f, + 0.0612076037f, 0.169926822f, -0.0112617221f, -0.0218303371f, 0.220902115f, -0.116057061f, + -0.185183659f, -0.236291289f, -0.22735998f, 0.0505967923f, 0.0695433691f, 0.205440864f, + 0.125087768f, -0.123245314f, -0.0270167589f, 0.0611841455f, 0.118791133f, 0.124306507f, + 0.00246581761f, 0.232650131f, 0.112738326f, -0.129947111f, -0.0122544533f, -0.138471976f, + -0.0421288796f, -0.107275851f, -0.06437397f, 0.192367956f, -0.0571496934f, 0.0673294961f, + 0.164859653f, -0.0957914814f, -0.0616698153f, 0.162914902f, 0.0475804284f, -0.00118661497f, + 0.210853845f, -0.182269111f, -0.109706491f, -0.206086114f, -0.101884596f, -0.0619692504f, + -0.00694432296f, 0.185752347f, 0.0633293167f, -0.029485289f, -0.049565468f, 0.144983351f, + 0.03824348f, 0.147863328f, 0.029010145f, -0.00121185009f, 0.0524796322f, -0.0870656371f, + 0.0880407915f, -0.0929471776f, -0.231062308f, -0.153693751f, 0.0619749352f, 0.16574648f, + 0.0136592174f, -0.0743990391f, -0.0488560349f, 0.0974609703f, 0.0628518313f, 0.211414024f, + 0.029213544f, 0.059761595f, 0.00896970648f, -0.124309234f, 0.00146230613f, -0.0734072402f, + -0.0660371259f, -0.0945814252f, -0.0402435064f, 0.234228924f, -0.111637071f, 0.201436445f, + 0.0719236657f, -0.0623627976f, -0.0246886704f, 0.284208775f, 0.0153174354f, 0.0117532406f, + 0.231111035f, -0.0161086842f, -0.0704588667f, -0.277094007f, -0.230407104f, -0.119694509f, + 0.106611341f, 0.192286745f, -0.0430636592f, 0.0559835397f, 0.00111082511f, 0.0240614563f, + -0.116894804f, 0.0850393102f, -0.0314150453f, 0.105889596f, 0.160678297f, -0.0541483387f, + 0.0592036508f, -0.154397041f, -0.116927698f, -0.0552683584f, -0.110984325f, 0.152468398f, + 0.0148122041f, -0.0822041854f, 0.15061745f, -0.0113282958f, 0.104744382f, 0.27832213f, + 0.0725696757f, 0.0741321519f, -0.0466904528f, -0.0526094623f, -0.0696103796f, -0.230445951f, + -0.079407461f, -0.127944231f, -0.0100459317f, 0.227396637f, -0.0762163699f, 0.0388158448f, + 0.0158023741f, -0.0538220517f, 0.070956327f, 0.225130334f, 0.0319263674f, -0.0742198899f, + 0.277207464f, -0.0245880894f, -0.11896231f, -0.306806058f, -0.289687216f, 0.00317044673f, + 0.0386367068f, -0.025881527f, -0.0860628858f, 0.132616967f, -0.0455491394f, 0.113208741f, + 0.0641415492f, 0.0929482654f, 0.0898344368f, 0.0524747409f, 0.0750738233f, 0.200593367f, + 0.0526407622f, -0.136073813f, -0.0658760741f, -0.0768450648f, -0.0549659207f, 0.131995529f, + 0.07229577f, 0.0355540216f, 0.00964461733f, 0.0382820591f, 0.168859079f, 0.374278069f, + 0.0108437417f, 0.0380591452f, 0.127636373f, -0.0762814954f, -0.100873366f, -0.122476384f, + -0.136697903f, -0.131574735f, 0.019639194f, 0.270306528f, -0.00762486504f, -0.00560910767f, + -0.0936516374f, -0.0335529372f, 0.0776731893f, 0.195649475f, 0.000211644961f, -0.0597940013f, + 0.220053285f, -0.0797736272f, -0.0855408832f, -0.249542758f, -0.353564054f, 0.0170720182f, + 0.0525403395f, -0.040429417f, -0.0737476349f, -0.0569547825f, -0.0435291603f, 0.013305638f, + 0.0173650328f, -0.0373940729f, -0.000591274176f, 0.2610524f, 0.135940939f, 0.0313767381f, + 0.0816994533f, -0.202886343f, -0.129126742f, -0.0574503578f, 0.0309457332f, 0.101297386f, + 0.00642049126f, -0.0269027613f, -0.0808912069f, 0.112555191f, 0.15283221f, 0.0279718135f, + 0.0236770231f, -0.11934638f, 0.279932737f, 0.0250771362f, -0.0374130942f, -0.110150762f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_medium_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_bias_data.h new file mode 100644 index 00000000..3b958210 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_cell_bias[8] = {(float16_t)-0.0487365723f, + (float16_t)-0.0910644531f, + (float16_t)-0.394775391f, + (float16_t)-0.0764770508f, + (float16_t)0.0546264648f, + (float16_t)0.0361633301f, + (float16_t)-0.0302124023f, + (float16_t)0.228271484f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_hidden_weights_data.h new file mode 100644 index 00000000..0c3f8b81 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_hidden_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_cell_hidden_weights[64] = { + (float16_t)0.290527344f, (float16_t)0.128417969f, (float16_t)-0.0911865234f, (float16_t)-0.289550781f, + (float16_t)-0.0681152344f, (float16_t)-0.00616455078f, (float16_t)0.12487793f, (float16_t)-0.0201416016f, + (float16_t)0.0878295898f, (float16_t)0.194335938f, (float16_t)-0.249023438f, (float16_t)0.045715332f, + (float16_t)0.0548400879f, (float16_t)-0.177124023f, (float16_t)0.329101562f, (float16_t)-0.150024414f, + (float16_t)-0.0914916992f, (float16_t)-0.296142578f, (float16_t)0.0925292969f, (float16_t)0.130615234f, + (float16_t)0.142944336f, (float16_t)-0.155639648f, (float16_t)-0.0198822021f, (float16_t)0.0856933594f, + (float16_t)-0.149414062f, (float16_t)0.338623047f, (float16_t)0.0554504395f, (float16_t)-0.221313477f, + (float16_t)0.0447998047f, (float16_t)-0.157836914f, (float16_t)-0.149047852f, (float16_t)-0.215454102f, + (float16_t)-0.332275391f, (float16_t)0.333007812f, (float16_t)0.061126709f, (float16_t)0.275634766f, + (float16_t)0.342529297f, (float16_t)-0.198364258f, (float16_t)0.226074219f, (float16_t)-0.0545349121f, + (float16_t)-0.204345703f, (float16_t)-0.0500488281f, (float16_t)-0.0438537598f, (float16_t)-0.209228516f, + (float16_t)0.268554688f, (float16_t)0.071472168f, (float16_t)0.348876953f, (float16_t)0.276611328f, + (float16_t)0.312011719f, (float16_t)0.269775391f, (float16_t)0.302734375f, (float16_t)-0.286865234f, + (float16_t)0.338867188f, (float16_t)0.106933594f, (float16_t)-0.0841064453f, (float16_t)0.0244903564f, + (float16_t)-0.194091797f, (float16_t)0.305908203f, (float16_t)0.0658569336f, (float16_t)0.223022461f, + (float16_t)-0.322265625f, (float16_t)-0.0276184082f, (float16_t)0.126464844f, (float16_t)0.204711914f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_input_weights_data.h new file mode 100644 index 00000000..0a443209 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/cell_input_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_cell_input_weights[64] = { + (float16_t)0.216918945f, (float16_t)-0.237548828f, (float16_t)-0.158569336f, (float16_t)-0.298828125f, + (float16_t)-0.251708984f, (float16_t)0.187866211f, (float16_t)0.203735352f, (float16_t)0.0927124023f, + (float16_t)-0.0956420898f, (float16_t)-0.0697631836f, (float16_t)0.016784668f, (float16_t)0.163208008f, + (float16_t)-0.275878906f, (float16_t)-0.24230957f, (float16_t)0.217163086f, (float16_t)0.104003906f, + (float16_t)-0.279541016f, (float16_t)-0.142822266f, (float16_t)0.0341491699f, (float16_t)0.172485352f, + (float16_t)-0.0606689453f, (float16_t)-0.227416992f, (float16_t)-0.0838012695f, (float16_t)-0.0322265625f, + (float16_t)0.159912109f, (float16_t)-0.114990234f, (float16_t)0.145629883f, (float16_t)0.29296875f, + (float16_t)0.256835938f, (float16_t)0.238647461f, (float16_t)0.164428711f, (float16_t)-0.1171875f, + (float16_t)-0.270751953f, (float16_t)-0.223510742f, (float16_t)-0.247680664f, (float16_t)0.0207519531f, + (float16_t)-0.155395508f, (float16_t)-0.0699462891f, (float16_t)-0.0986938477f, (float16_t)-0.0861816406f, + (float16_t)0.217407227f, (float16_t)-0.0349731445f, (float16_t)0.310302734f, (float16_t)0.0221862793f, + (float16_t)-0.251953125f, (float16_t)0.341796875f, (float16_t)0.051940918f, (float16_t)0.198364258f, + (float16_t)0.0863647461f, (float16_t)0.323730469f, (float16_t)-0.209106445f, (float16_t)0.216186523f, + (float16_t)-0.127319336f, (float16_t)-0.288330078f, (float16_t)0.297119141f, (float16_t)0.249267578f, + (float16_t)-0.136352539f, (float16_t)0.198730469f, (float16_t)-0.026260376f, (float16_t)-0.246459961f, + (float16_t)0.0504455566f, (float16_t)-0.0513305664f, (float16_t)0.039276123f, (float16_t)0.302734375f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/config_data.h new file mode 100644 index 00000000..b7ea945e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_SMALL_F16_TIME_MAJOR 1 +#define LSTM_SMALL_F16_TIME_STEPS 5 +#define LSTM_SMALL_F16_BATCH_SIZE 2 +#define LSTM_SMALL_F16_INPUT_SIZE 8 +#define LSTM_SMALL_F16_HIDDEN_SIZE 8 +#define LSTM_SMALL_F16_CELL_CLIP 0.0f +#define LSTM_SMALL_F16_CELL_STATE_SIZE 16 +#define LSTM_SMALL_F16_DST_SIZE 80 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_bias_data.h new file mode 100644 index 00000000..bb60b41e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_forget_bias[8] = {(float16_t)-0.0795288086f, + (float16_t)-0.0579833984f, + (float16_t)0.0495300293f, + (float16_t)-0.145263672f, + (float16_t)-0.272705078f, + (float16_t)0.341796875f, + (float16_t)0.218383789f, + (float16_t)-0.202026367f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_hidden_weights_data.h new file mode 100644 index 00000000..3bca0e7c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_hidden_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_forget_hidden_weights[64] = { + (float16_t)0.281738281f, (float16_t)-0.318847656f, (float16_t)-0.0020198822f, (float16_t)0.153320312f, + (float16_t)0.102050781f, (float16_t)-0.241943359f, (float16_t)0.138305664f, (float16_t)0.199829102f, + (float16_t)-0.301757812f, (float16_t)0.237060547f, (float16_t)-0.0135574341f, (float16_t)0.201782227f, + (float16_t)-0.0119552612f, (float16_t)0.218139648f, (float16_t)-0.161132812f, (float16_t)0.119018555f, + (float16_t)0.203613281f, (float16_t)0.0124511719f, (float16_t)-0.228027344f, (float16_t)-0.0715942383f, + (float16_t)0.155029297f, (float16_t)-0.00715637207f, (float16_t)0.293701172f, (float16_t)0.037689209f, + (float16_t)0.0335388184f, (float16_t)-0.256103516f, (float16_t)-0.128173828f, (float16_t)-0.264892578f, + (float16_t)0.0910644531f, (float16_t)0.110046387f, (float16_t)0.0904541016f, (float16_t)0.0941772461f, + (float16_t)0.133911133f, (float16_t)0.0817871094f, (float16_t)0.237304688f, (float16_t)-0.217407227f, + (float16_t)-0.319580078f, (float16_t)0.0280914307f, (float16_t)0.0767211914f, (float16_t)-0.0779418945f, + (float16_t)-0.239257812f, (float16_t)-0.240234375f, (float16_t)0.194702148f, (float16_t)-0.0513916016f, + (float16_t)-0.173217773f, (float16_t)0.0122833252f, (float16_t)0.0906982422f, (float16_t)-0.0631713867f, + (float16_t)-0.217163086f, (float16_t)-0.0726318359f, (float16_t)-0.0368041992f, (float16_t)-0.255615234f, + (float16_t)0.343994141f, (float16_t)-0.157104492f, (float16_t)-0.170043945f, (float16_t)-0.262451172f, + (float16_t)0.331298828f, (float16_t)0.337646484f, (float16_t)-0.140991211f, (float16_t)-0.0269927979f, + (float16_t)0.319824219f, (float16_t)-0.250976562f, (float16_t)0.140136719f, (float16_t)0.143188477f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_input_weights_data.h new file mode 100644 index 00000000..6781d79b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/forget_input_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_forget_input_weights[64] = { + (float16_t)-0.0891723633f, (float16_t)0.260742188f, (float16_t)0.34765625f, (float16_t)0.311523438f, + (float16_t)0.138916016f, (float16_t)0.17175293f, (float16_t)0.225341797f, (float16_t)-0.164672852f, + (float16_t)0.314208984f, (float16_t)-0.344238281f, (float16_t)0.0911865234f, (float16_t)0.330566406f, + (float16_t)0.181030273f, (float16_t)0.0856933594f, (float16_t)-0.305419922f, (float16_t)-0.267822266f, + (float16_t)0.338867188f, (float16_t)0.118530273f, (float16_t)-0.261230469f, (float16_t)0.110534668f, + (float16_t)0.0765991211f, (float16_t)-0.0986328125f, (float16_t)-0.159301758f, (float16_t)0.176269531f, + (float16_t)-0.132202148f, (float16_t)-0.119812012f, (float16_t)-0.251953125f, (float16_t)-0.344726562f, + (float16_t)-0.311523438f, (float16_t)-0.113891602f, (float16_t)-0.217529297f, (float16_t)0.00786590576f, + (float16_t)0.0458374023f, (float16_t)0.257324219f, (float16_t)-0.170654297f, (float16_t)-0.0642089844f, + (float16_t)-0.0733642578f, (float16_t)0.0055770874f, (float16_t)-0.315917969f, (float16_t)-0.22277832f, + (float16_t)-0.345214844f, (float16_t)0.0532531738f, (float16_t)-0.0172119141f, (float16_t)-0.134521484f, + (float16_t)-0.174560547f, (float16_t)0.333251953f, (float16_t)0.106384277f, (float16_t)-0.0262145996f, + (float16_t)0.277099609f, (float16_t)0.067565918f, (float16_t)0.318847656f, (float16_t)0.329101562f, + (float16_t)-0.262695312f, (float16_t)-0.159179688f, (float16_t)-0.270263672f, (float16_t)-0.325683594f, + (float16_t)-0.157714844f, (float16_t)-0.0145339966f, (float16_t)-0.24621582f, (float16_t)-0.295410156f, + (float16_t)-0.00290107727f, (float16_t)-0.222167969f, (float16_t)0.00917053223f, (float16_t)0.0212249756f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_bias_data.h new file mode 100644 index 00000000..290068ba --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_input_bias[8] = {(float16_t)0.0895385742f, + (float16_t)0.083984375f, + (float16_t)0.262939453f, + (float16_t)0.177856445f, + (float16_t)0.129150391f, + (float16_t)0.154785156f, + (float16_t)0.0285339355f, + (float16_t)-0.0446472168f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_data.h new file mode 100644 index 00000000..21deda7b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_data.h @@ -0,0 +1,22 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_input[80] = { + (float16_t)-0.379394531f, (float16_t)-0.796386719f, (float16_t)0.315185547f, (float16_t)-0.659179688f, + (float16_t)0.363037109f, (float16_t)-0.444580078f, (float16_t)-0.217041016f, (float16_t)-0.787597656f, + (float16_t)-0.290039062f, (float16_t)0.266113281f, (float16_t)-0.0986938477f, (float16_t)0.806152344f, + (float16_t)-0.576660156f, (float16_t)0.956054688f, (float16_t)0.874023438f, (float16_t)-0.669433594f, + (float16_t)-0.615234375f, (float16_t)-0.330810547f, (float16_t)0.788085938f, (float16_t)0.505859375f, + (float16_t)-0.645019531f, (float16_t)-0.950195312f, (float16_t)0.229492188f, (float16_t)-0.235473633f, + (float16_t)-0.727539062f, (float16_t)-0.0558166504f, (float16_t)-0.984863281f, (float16_t)-0.0501403809f, + (float16_t)0.611328125f, (float16_t)0.139770508f, (float16_t)-0.727539062f, (float16_t)-0.720703125f, + (float16_t)0.453125f, (float16_t)0.00247192383f, (float16_t)-0.249511719f, (float16_t)0.814941406f, + (float16_t)0.570800781f, (float16_t)0.189575195f, (float16_t)0.809570312f, (float16_t)-0.245727539f, + (float16_t)0.23815918f, (float16_t)-0.590332031f, (float16_t)-0.787109375f, (float16_t)-0.256347656f, + (float16_t)-0.054901123f, (float16_t)-0.580566406f, (float16_t)0.551269531f, (float16_t)0.278320312f, + (float16_t)-0.696289062f, (float16_t)0.42578125f, (float16_t)0.00675964355f, (float16_t)-0.324707031f, + (float16_t)-0.136108398f, (float16_t)-0.919433594f, (float16_t)0.133300781f, (float16_t)-0.844726562f, + (float16_t)-0.519042969f, (float16_t)-0.502929688f, (float16_t)-0.940917969f, (float16_t)-0.0461425781f, + (float16_t)-0.125f, (float16_t)-0.25390625f, (float16_t)-0.916503906f, (float16_t)-0.0211029053f, + (float16_t)0.688476562f, (float16_t)-0.890136719f, (float16_t)-0.90234375f, (float16_t)0.132324219f, + (float16_t)-0.742675781f, (float16_t)0.236572266f, (float16_t)-0.0579223633f, (float16_t)0.244140625f, + (float16_t)0.6171875f, (float16_t)0.247436523f, (float16_t)0.259033203f, (float16_t)0.257568359f, + (float16_t)0.666015625f, (float16_t)-0.781738281f, (float16_t)0.228149414f, (float16_t)-0.379882812f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_hidden_weights_data.h new file mode 100644 index 00000000..18a7dafb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_hidden_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_input_hidden_weights[64] = { + (float16_t)-0.109313965f, (float16_t)0.123352051f, (float16_t)0.143188477f, (float16_t)0.335449219f, + (float16_t)-0.00802612305f, (float16_t)-0.340576172f, (float16_t)0.13293457f, (float16_t)0.0806884766f, + (float16_t)-0.00448226929f, (float16_t)-0.24206543f, (float16_t)0.222412109f, (float16_t)0.0454406738f, + (float16_t)-0.311035156f, (float16_t)-0.348144531f, (float16_t)-0.245727539f, (float16_t)-0.30859375f, + (float16_t)0.229858398f, (float16_t)0.0765380859f, (float16_t)-0.104064941f, (float16_t)-0.168579102f, + (float16_t)0.0257415771f, (float16_t)0.22277832f, (float16_t)0.105163574f, (float16_t)0.278076172f, + (float16_t)0.181152344f, (float16_t)-0.30078125f, (float16_t)0.232421875f, (float16_t)0.0411682129f, + (float16_t)0.249389648f, (float16_t)-0.0360412598f, (float16_t)0.232543945f, (float16_t)0.327392578f, + (float16_t)0.078125f, (float16_t)0.147094727f, (float16_t)0.345214844f, (float16_t)-0.274414062f, + (float16_t)-0.0609741211f, (float16_t)0.210327148f, (float16_t)-0.0475158691f, (float16_t)-0.061706543f, + (float16_t)0.247314453f, (float16_t)0.141723633f, (float16_t)0.249755859f, (float16_t)0.134643555f, + (float16_t)-0.153930664f, (float16_t)-0.0790405273f, (float16_t)-0.230957031f, (float16_t)0.181152344f, + (float16_t)0.0996704102f, (float16_t)-0.16809082f, (float16_t)0.318359375f, (float16_t)-0.245239258f, + (float16_t)0.262939453f, (float16_t)-0.260742188f, (float16_t)-0.298583984f, (float16_t)-0.347412109f, + (float16_t)-0.251464844f, (float16_t)-0.291748047f, (float16_t)0.263183594f, (float16_t)-0.0919189453f, + (float16_t)0.251220703f, (float16_t)0.269287109f, (float16_t)0.273925781f, (float16_t)0.346923828f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_input_weights_data.h new file mode 100644 index 00000000..e93aff9a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/input_input_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_input_input_weights[64] = { + (float16_t)-0.309326172f, (float16_t)-0.199584961f, (float16_t)-0.312988281f, (float16_t)0.255859375f, + (float16_t)-0.0811157227f, (float16_t)0.272949219f, (float16_t)0.0961303711f, (float16_t)0.172119141f, + (float16_t)0.241333008f, (float16_t)0.291259766f, (float16_t)0.173583984f, (float16_t)0.340087891f, + (float16_t)0.1640625f, (float16_t)0.274658203f, (float16_t)0.346191406f, (float16_t)0.177368164f, + (float16_t)-0.141357422f, (float16_t)0.296875f, (float16_t)-0.122192383f, (float16_t)0.300292969f, + (float16_t)0.222167969f, (float16_t)-0.00863647461f, (float16_t)0.179931641f, (float16_t)-0.332519531f, + (float16_t)0.275878906f, (float16_t)-0.184936523f, (float16_t)-0.157836914f, (float16_t)-0.291748047f, + (float16_t)-0.0627441406f, (float16_t)0.0651855469f, (float16_t)0.16003418f, (float16_t)-0.048034668f, + (float16_t)-0.324951172f, (float16_t)0.0400390625f, (float16_t)-0.311035156f, (float16_t)0.181640625f, + (float16_t)-0.340087891f, (float16_t)0.0674438477f, (float16_t)-0.137939453f, (float16_t)-0.231079102f, + (float16_t)0.150878906f, (float16_t)-0.0199127197f, (float16_t)0.240478516f, (float16_t)-0.278564453f, + (float16_t)-0.141479492f, (float16_t)0.309814453f, (float16_t)0.220092773f, (float16_t)0.183959961f, + (float16_t)-0.260498047f, (float16_t)0.0690917969f, (float16_t)0.0403137207f, (float16_t)0.0170135498f, + (float16_t)0.0406494141f, (float16_t)-0.246704102f, (float16_t)-0.107055664f, (float16_t)0.299804688f, + (float16_t)-0.345458984f, (float16_t)-0.26171875f, (float16_t)0.174072266f, (float16_t)0.241821289f, + (float16_t)-0.174316406f, (float16_t)-0.223754883f, (float16_t)0.263183594f, (float16_t)-0.0869140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_bias_data.h new file mode 100644 index 00000000..2359b61c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_output_bias[8] = {(float16_t)0.338867188f, + (float16_t)-0.389648438f, + (float16_t)0.177490234f, + (float16_t)0.0235900879f, + (float16_t)0.0848999023f, + (float16_t)0.0776367188f, + (float16_t)-0.0282440186f, + (float16_t)-0.255859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_hidden_weights_data.h new file mode 100644 index 00000000..7ae086fe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_hidden_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_output_hidden_weights[64] = { + (float16_t)0.0729980469f, (float16_t)-0.0253448486f, (float16_t)-0.228515625f, (float16_t)-0.0780639648f, + (float16_t)0.284179688f, (float16_t)-0.0880737305f, (float16_t)-0.264160156f, (float16_t)0.10144043f, + (float16_t)-0.343017578f, (float16_t)0.300537109f, (float16_t)-0.14050293f, (float16_t)-0.116577148f, + (float16_t)-0.302001953f, (float16_t)-0.273193359f, (float16_t)-0.348144531f, (float16_t)0.316650391f, + (float16_t)0.254150391f, (float16_t)0.0787353516f, (float16_t)0.0133056641f, (float16_t)-0.0645751953f, + (float16_t)-0.106811523f, (float16_t)-0.0645141602f, (float16_t)0.0206298828f, (float16_t)0.165161133f, + (float16_t)-0.304199219f, (float16_t)0.216674805f, (float16_t)0.114868164f, (float16_t)0.256347656f, + (float16_t)-0.171630859f, (float16_t)-0.0432434082f, (float16_t)-0.329833984f, (float16_t)-0.227416992f, + (float16_t)-0.118530273f, (float16_t)0.301025391f, (float16_t)0.0915527344f, (float16_t)0.0113143921f, + (float16_t)-0.101013184f, (float16_t)-0.0587768555f, (float16_t)-0.219848633f, (float16_t)-0.152832031f, + (float16_t)0.110656738f, (float16_t)0.312744141f, (float16_t)-0.326416016f, (float16_t)-0.208984375f, + (float16_t)0.274414062f, (float16_t)0.12878418f, (float16_t)-0.00820922852f, (float16_t)-0.262695312f, + (float16_t)0.013458252f, (float16_t)0.221313477f, (float16_t)-0.0838623047f, (float16_t)0.212890625f, + (float16_t)0.0903930664f, (float16_t)0.0279693604f, (float16_t)-0.284667969f, (float16_t)0.161499023f, + (float16_t)0.0635986328f, (float16_t)-0.341308594f, (float16_t)0.282226562f, (float16_t)0.234008789f, + (float16_t)-0.242553711f, (float16_t)0.121276855f, (float16_t)0.268554688f, (float16_t)0.242431641f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_input_weights_data.h new file mode 100644 index 00000000..70c79440 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_input_weights_data.h @@ -0,0 +1,18 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_output_input_weights[64] = { + (float16_t)-0.305175781f, (float16_t)0.145507812f, (float16_t)0.318847656f, (float16_t)0.0354614258f, + (float16_t)0.240844727f, (float16_t)-0.19519043f, (float16_t)-0.172363281f, (float16_t)-0.237548828f, + (float16_t)0.247924805f, (float16_t)-0.329833984f, (float16_t)0.229003906f, (float16_t)-0.237915039f, + (float16_t)0.063293457f, (float16_t)0.0273742676f, (float16_t)0.144042969f, (float16_t)0.325683594f, + (float16_t)0.0483703613f, (float16_t)0.0289459229f, (float16_t)0.203979492f, (float16_t)-0.186767578f, + (float16_t)-0.301757812f, (float16_t)-0.0739746094f, (float16_t)0.0226593018f, (float16_t)0.0690917969f, + (float16_t)-0.2578125f, (float16_t)-0.281494141f, (float16_t)0.306884766f, (float16_t)-0.0936279297f, + (float16_t)0.044921875f, (float16_t)-0.294921875f, (float16_t)0.120605469f, (float16_t)-0.125366211f, + (float16_t)0.334716797f, (float16_t)0.0243835449f, (float16_t)0.317382812f, (float16_t)-0.326904297f, + (float16_t)-0.316894531f, (float16_t)-0.230712891f, (float16_t)0.0910644531f, (float16_t)-0.322021484f, + (float16_t)0.250732422f, (float16_t)-0.104003906f, (float16_t)-0.108337402f, (float16_t)-0.0790405273f, + (float16_t)0.266601562f, (float16_t)-0.0975341797f, (float16_t)-0.0347595215f, (float16_t)0.14050293f, + (float16_t)0.0905151367f, (float16_t)0.315917969f, (float16_t)-0.151245117f, (float16_t)-0.0626831055f, + (float16_t)-0.226074219f, (float16_t)-0.229858398f, (float16_t)0.0315551758f, (float16_t)0.306152344f, + (float16_t)0.151245117f, (float16_t)-0.231323242f, (float16_t)0.205688477f, (float16_t)-0.219238281f, + (float16_t)0.148681641f, (float16_t)0.192138672f, (float16_t)-0.179443359f, (float16_t)0.0850219727f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_ref_data.h new file mode 100644 index 00000000..eab7120f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/output_ref_data.h @@ -0,0 +1,22 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t lstm_small_f16_output_ref[80] = { + (float16_t)-0.0269012451f, (float16_t)-0.0317382812f, (float16_t)-0.0451660156f, (float16_t)-0.0574645996f, + (float16_t)0.0939941406f, (float16_t)-0.0870361328f, (float16_t)-0.107543945f, (float16_t)0.0197906494f, + (float16_t)0.0142440796f, (float16_t)0.0188140869f, (float16_t)-0.150512695f, (float16_t)0.0827636719f, + (float16_t)0.0354614258f, (float16_t)0.0717773438f, (float16_t)0.0198364258f, (float16_t)-0.0238494873f, + (float16_t)-0.140991211f, (float16_t)0.069519043f, (float16_t)0.0523071289f, (float16_t)-0.0791015625f, + (float16_t)0.129150391f, (float16_t)-0.0444946289f, (float16_t)-0.0645751953f, (float16_t)0.00542831421f, + (float16_t)-0.130859375f, (float16_t)-0.0272216797f, (float16_t)-0.125366211f, (float16_t)-0.00365829468f, + (float16_t)0.131713867f, (float16_t)-0.0588684082f, (float16_t)-0.078918457f, (float16_t)0.00701141357f, + (float16_t)-0.110961914f, (float16_t)0.0190124512f, (float16_t)-0.120788574f, (float16_t)0.127075195f, + (float16_t)0.022354126f, (float16_t)-0.0197296143f, (float16_t)0.0337524414f, (float16_t)-0.00994873047f, + (float16_t)0.0562438965f, (float16_t)0.0155029297f, (float16_t)-0.170166016f, (float16_t)-0.0770874023f, + (float16_t)0.151977539f, (float16_t)-0.0949707031f, (float16_t)0.027557373f, (float16_t)0.0531311035f, + (float16_t)-0.20324707f, (float16_t)0.0212249756f, (float16_t)-0.0921630859f, (float16_t)-0.0470581055f, + (float16_t)0.144897461f, (float16_t)-0.106079102f, (float16_t)0.0196228027f, (float16_t)0.0545043945f, + (float16_t)0.00993347168f, (float16_t)-0.000703811646f, (float16_t)-0.113769531f, (float16_t)-0.151733398f, + (float16_t)0.198364258f, (float16_t)-0.128662109f, (float16_t)-0.0434570312f, (float16_t)0.0573120117f, + (float16_t)0.095703125f, (float16_t)0.0430297852f, (float16_t)-0.148193359f, (float16_t)-0.0392150879f, + (float16_t)0.180664062f, (float16_t)0.0300292969f, (float16_t)0.0099105835f, (float16_t)0.0272064209f, + (float16_t)-0.0814819336f, (float16_t)-0.0174865723f, (float16_t)-0.1875f, (float16_t)-0.00292015076f, + (float16_t)0.0762939453f, (float16_t)-0.107299805f, (float16_t)0.0424804688f, (float16_t)0.0241394043f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f16/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_bias_data.h new file mode 100644 index 00000000..68e85f65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_cell_bias[8] = {0.230191752f, + 0.220425799f, + -0.189792126f, + 0.0313377231f, + 0.0875475705f, + 0.327914596f, + -0.280258566f, + -0.327337712f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_hidden_weights_data.h new file mode 100644 index 00000000..0cbf8bf4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_hidden_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_cell_hidden_weights[64] = { + -0.288872123f, 0.119263701f, 0.340033233f, 0.160303891f, -0.258720428f, -0.248144373f, 0.235644698f, + -0.104859829f, -0.196987197f, 0.0854487866f, -0.0417432226f, -0.134475887f, -0.18235755f, 0.112326428f, + -0.192151785f, 0.214705229f, 0.302411646f, -0.261031836f, 0.238166913f, 0.0968331993f, 0.269126087f, + -0.0235334374f, 0.224325791f, -0.0623765402f, -0.026884051f, -0.304813534f, 0.208873928f, 0.165861458f, + 0.189904392f, -0.146112531f, -0.0235381909f, 0.0976427272f, -0.269558281f, 0.343372941f, -0.278954625f, + 0.00492206914f, 0.246883616f, -0.215379149f, 0.145036653f, 0.193803921f, 0.120476656f, 0.246776789f, + -0.119126104f, 0.286890388f, -0.1210998f, 0.100434013f, 0.0569052584f, -0.100868359f, 0.0177196655f, + 0.00742395408f, -0.0384643674f, -0.0778852701f, -0.0902819559f, -0.271802127f, -0.273730755f, 0.0228586644f, + 0.24839814f, 0.0904428959f, -0.118917085f, -0.114071496f, 0.229400396f, -0.250829458f, -0.157281965f, + -0.341015518f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_input_weights_data.h new file mode 100644 index 00000000..0feafe03 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/cell_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_cell_input_weights[64] = { + 0.22135821f, 0.246764585f, -0.106330507f, 0.331626773f, -0.274927437f, -0.243807226f, 0.188898265f, + -0.170099258f, -0.0676003918f, 0.0257408768f, -0.110212125f, -0.196509093f, -0.00458696438f, 0.200002342f, + -0.102341115f, -0.186277255f, -0.224975809f, -0.323471934f, -0.188021958f, -0.0423319899f, -0.0511880703f, + -0.21620591f, 0.176081553f, -0.273899347f, -0.265003115f, 0.225382626f, 0.0578552634f, 0.0320335291f, + -0.324923456f, -0.187713772f, -0.280881524f, 0.0435552336f, 0.276854783f, 0.102765061f, 0.0767917857f, + 0.161510736f, 0.179550663f, -0.0335814729f, 0.289759666f, 0.0819121599f, 0.0541277565f, 0.298275918f, + -0.0139446175f, 0.310973734f, 0.134613857f, 0.180108562f, -0.26629436f, 0.0604459308f, 0.0017519308f, + -0.0493523143f, -0.198776171f, 0.0840804204f, 0.0882448927f, -0.218110666f, -0.216746345f, -0.139764011f, + -0.183733299f, 0.31010747f, -0.207315728f, 0.260068417f, 0.188067913f, 0.330117226f, -0.245406121f, + -0.302708387f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/config_data.h new file mode 100644 index 00000000..275c01fe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#define LSTM_SMALL_F32_TIME_MAJOR 1 +#define LSTM_SMALL_F32_TIME_STEPS 5 +#define LSTM_SMALL_F32_BATCH_SIZE 2 +#define LSTM_SMALL_F32_INPUT_SIZE 8 +#define LSTM_SMALL_F32_HIDDEN_SIZE 8 +#define LSTM_SMALL_F32_CELL_CLIP 0.0f +#define LSTM_SMALL_F32_CELL_STATE_SIZE 16 +#define LSTM_SMALL_F32_DST_SIZE 80 diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_bias_data.h new file mode 100644 index 00000000..14984263 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_forget_bias[8] = {0.237394065f, + -0.113466352f, + 0.267885447f, + 0.281396568f, + 0.0399351493f, + 0.280820549f, + 0.225407809f, + -0.0104173273f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_hidden_weights_data.h new file mode 100644 index 00000000..1a8bd1e6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_hidden_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_forget_hidden_weights[64] = { + 0.316060275f, -0.322344273f, -0.257937849f, -0.0417401716f, 0.143973321f, -0.23870109f, 0.0255193301f, + -0.160253197f, -0.235275909f, 0.108128168f, 0.0491987914f, -0.339159787f, -0.00650616642f, 0.0451955236f, + -0.20163697f, -0.211353704f, 0.31978026f, -0.180026919f, -0.0948277414f, -0.0752587244f, 0.0481877774f, + -0.251564145f, -0.118140243f, -0.255010277f, 0.342369586f, 0.316700757f, -0.0121500771f, 0.322332919f, + 0.113475867f, -0.080712162f, 0.343854636f, -0.0289531872f, -0.0822319314f, -0.305208892f, -0.313166112f, + 0.031572137f, 0.0135687739f, -0.320171207f, 0.198492646f, 0.262940526f, -0.328311384f, 0.167684674f, + -0.0549110249f, -0.0365660749f, 0.314283043f, 0.279126227f, -0.308616757f, -0.26518774f, 0.246156767f, + -0.154794246f, 0.0681352541f, 0.0175396632f, 0.295259625f, -0.290988714f, 0.0376146957f, -0.22339265f, + -0.125242636f, -0.0499938242f, -0.162302792f, 0.151668489f, -0.169307292f, 0.0920724571f, -0.0405208506f, + -0.178646177f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_input_weights_data.h new file mode 100644 index 00000000..7f47116f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/forget_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_forget_input_weights[64] = { + -0.0900837705f, -0.127352893f, 0.243179277f, 0.162056059f, 0.107422173f, -0.124860264f, 0.0746088326f, + -0.278909206f, 0.0910320729f, 0.0279602036f, 0.131179631f, -0.0864019468f, 0.077883333f, 0.176318794f, + -0.349653423f, -0.00631744415f, 0.0386116728f, 0.198927701f, -0.210816205f, 0.223313749f, -0.176082f, + -0.0979457498f, -0.000778831018f, 0.0905534178f, 0.011399338f, -0.328178525f, 0.295545071f, -0.090727888f, + 0.342374086f, -0.268979013f, -0.0696922094f, 0.0549148321f, 0.112781286f, 0.234881908f, -0.0339938477f, + -0.103770897f, -0.171800449f, -0.271281928f, -0.00805742387f, 0.105046384f, 0.26069802f, 0.0354426913f, + 0.213598058f, 0.292240381f, -0.135589689f, -0.109527349f, -0.1828174f, -0.0766545534f, -0.338454127f, + -0.298255861f, -0.300676197f, -0.162393451f, -0.237862304f, 0.115597121f, 0.198312059f, 0.226754144f, + -0.15388152f, -0.211266607f, -0.035325408f, 0.0826638639f, -0.0200085472f, 0.112301841f, -0.342131317f, + 0.342020094f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_bias_data.h new file mode 100644 index 00000000..43834692 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_input_bias[8] = {-0.41971314f, + -0.0608372092f, + 0.190660983f, + 0.21183449f, + 0.0506335385f, + 0.0598877072f, + 0.346913338f, + -0.143105775f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_data.h new file mode 100644 index 00000000..78556508 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_data.h @@ -0,0 +1,14 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_input[80] = { + -0.319120377f, -0.831746876f, -0.973515689f, -0.189940959f, -0.730559945f, 0.196476772f, -0.393241197f, + 0.144869491f, -0.629279613f, -0.515817583f, 0.465858102f, 0.697098017f, -0.768282473f, 0.556637049f, + 0.405371785f, 0.956647098f, -0.710141718f, 0.0244361646f, 0.73199904f, 0.679469228f, 0.405670613f, + -0.374672025f, -0.275225163f, -0.613611579f, 0.831756532f, 0.254597187f, 0.108631022f, -0.689429462f, + -0.294614851f, 0.122909397f, -0.626239657f, 0.319649041f, 0.977791786f, -0.252631098f, -0.948293626f, + 0.644998014f, 0.315606505f, 0.863892138f, 0.622153878f, -0.435943186f, -0.600050271f, 0.565352917f, + -0.434495956f, -0.476250648f, -0.783539355f, 0.413490832f, -0.0533833764f, -0.6027897f, -0.413319468f, + -0.618175447f, 0.44903329f, -0.984417915f, 0.825043857f, 0.975994706f, 0.793708622f, 0.82157433f, + 0.0426176824f, -0.168631077f, 0.783026278f, 0.683851063f, -0.601929665f, 0.275846481f, 0.272892982f, + 0.401278287f, -0.3763704f, 0.102506034f, -0.484934896f, 0.264877439f, 0.527251601f, 0.460729301f, + 0.777454376f, 0.354816943f, -0.034462437f, -0.308916003f, 0.218109131f, -0.706833184f, -0.71475786f, + -0.400883734f, -0.287317842f, -0.263916165f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_hidden_weights_data.h new file mode 100644 index 00000000..fec81b68 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_hidden_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_input_hidden_weights[64] = { + 0.0153357321f, -0.265234709f, -0.230192319f, 0.0869162306f, 0.291041642f, 0.222540662f, -0.167171523f, + -0.290925831f, 0.0203119311f, -0.340529293f, 0.265637487f, 0.00143744971f, 0.105593443f, 0.286769241f, + 0.122121513f, -0.29742381f, 0.0366669036f, -0.085027054f, -0.32952553f, 0.23697488f, -0.238968775f, + -0.11214634f, -0.0807557479f, 0.21581243f, 0.247517198f, -0.29324472f, -0.0892358944f, -0.220004916f, + 0.0990761891f, 0.105966993f, -0.254254252f, 0.152101189f, -0.127018228f, 0.175959036f, 0.152488843f, + -0.176895872f, -0.0908197835f, 0.262113929f, 0.0413203947f, 0.30006516f, 0.282050878f, -0.346841782f, + -0.325272262f, 0.0451661423f, 0.233190313f, -0.337236077f, 0.0388513654f, 0.0438603647f, 0.270602793f, + 0.254045278f, 0.000513770909f, 0.162416384f, -0.0114339646f, 0.12563996f, -0.2033225f, 0.255421042f, + -0.26119256f, 0.193249494f, -0.181035265f, 0.132855758f, -0.164084449f, 0.234488308f, -0.325881958f, + -0.27142784f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_input_weights_data.h new file mode 100644 index 00000000..6552981b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/input_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_input_input_weights[64] = { + -0.0114953723f, -0.050989449f, 0.189440683f, -0.264418542f, -0.338703364f, 0.125013605f, 0.0956771523f, + -0.122388981f, 0.0988767818f, -0.0030344422f, 0.349552304f, -0.2151068f, 0.343473107f, 0.140586242f, + 0.112497114f, 0.317667812f, 0.0417767949f, -0.210876778f, -0.0670325831f, -0.271888494f, -0.208228752f, + -0.129128456f, 0.177272186f, 0.171234623f, -0.0234854128f, 0.0552568547f, -0.0739175528f, -0.199142858f, + -0.00224291161f, -0.212769255f, -0.342538774f, -0.0194943026f, 0.20432581f, -0.00537520787f, 0.172034398f, + -0.196654648f, -0.260753989f, -0.167160571f, -0.252236545f, 0.334410399f, 0.0343310088f, -0.251048833f, + 0.13699618f, 0.0835162625f, -0.334393829f, -0.257337958f, 0.188557297f, 0.0951970667f, 0.294515133f, + -0.0906501263f, -0.0133232074f, -0.193674996f, 0.00023948634f, -0.319470316f, 0.13330701f, -0.162477881f, + -0.148414403f, -0.138143137f, 0.163570791f, 0.112029158f, 0.213723972f, 0.326269239f, 0.117956802f, + -0.00880203396f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_bias_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_bias_data.h new file mode 100644 index 00000000..ceb88b79 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_bias_data.h @@ -0,0 +1,9 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_output_bias[8] = {0.0584857911f, + 0.139860123f, + 0.288155317f, + -0.357741117f, + -0.0796674937f, + -0.108601421f, + -0.0784846917f, + -0.19089067f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_hidden_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_hidden_weights_data.h new file mode 100644 index 00000000..2607de0e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_hidden_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_output_hidden_weights[64] = { + 0.239049256f, 0.157853737f, 0.0135364216f, -0.211908951f, 0.074353531f, -0.294962347f, 0.198911741f, + 0.181894287f, 0.259374738f, -0.117579378f, 0.22913231f, -0.209332198f, 0.198378339f, 0.329017341f, + -0.153121173f, 0.197271779f, -0.0466069058f, -0.263758868f, -0.011898539f, -0.203318104f, -0.207767397f, + -0.130910575f, 0.0677610189f, 0.161043301f, -0.2480001f, -0.238114268f, 0.147468075f, -0.0487619378f, + -0.260480702f, -0.0699481741f, -0.25497362f, -0.0161911491f, 0.0875137821f, 0.268130153f, 0.136945918f, + -0.125734687f, -0.119822115f, 0.262080759f, 0.326298714f, -0.346177906f, 0.0469799526f, -0.225083768f, + -0.0491676629f, -0.057313107f, 0.234658137f, -0.0555984229f, 0.306501329f, 0.0214951448f, -0.181798384f, + -0.102802031f, 0.22140044f, -0.0319670886f, 0.0389786586f, 0.0836588889f, 0.191726342f, 0.113752067f, + -0.169132948f, 0.296198875f, -0.299185365f, -0.268041641f, -0.124750756f, 0.271799743f, -0.17143248f, + -0.170219794f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_input_weights_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_input_weights_data.h new file mode 100644 index 00000000..69dd294d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_input_weights_data.h @@ -0,0 +1,12 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_output_input_weights[64] = { + -0.101575665f, 0.301177472f, 1.73320743e-07f, 0.0631079078f, 0.265743971f, -0.1900879f, -0.10211426f, + 0.207201436f, -0.276668876f, 0.01798537f, -0.281256884f, -0.232942089f, -0.0275955144f, -0.283096164f, + -0.00138054579f, -0.0285333805f, -0.139786422f, 0.0665458739f, -0.106286757f, -0.0235774051f, -0.334258169f, + 0.0085173836f, -0.0437361673f, -0.238770679f, 0.0167490542f, -0.259021491f, -0.0603890233f, 0.0229397155f, + 0.0872341171f, -0.106164694f, -0.117686994f, -0.226890355f, -0.25141257f, -0.134730324f, 0.319418252f, + 0.256966263f, 0.270416766f, -0.158112258f, 0.0537358224f, 0.0411791392f, 0.1878362f, -0.344954461f, + 0.218236744f, 0.0473732539f, 0.00291312858f, 0.20979701f, -0.0682938918f, 0.127081871f, 0.31208089f, + 0.349561065f, 0.0992599204f, 0.163775459f, 0.234102353f, -0.328683615f, -0.311506003f, 0.103878349f, + 0.296864152f, -0.0172175746f, 0.00887024775f, 0.202123135f, 0.0720962137f, -0.110108204f, -0.300535321f, + 0.0595972352f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_ref_data.h new file mode 100644 index 00000000..4d28c699 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/output_ref_data.h @@ -0,0 +1,14 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t lstm_small_f32_output_ref[80] = { + 0.00871653948f, 0.086322546f, 0.0916965902f, 0.0491194613f, -0.0822262019f, 0.0187133271f, -0.0193896797f, + -0.0686048865f, 0.0274761971f, -0.0103086364f, -0.0887795538f, 0.0304044075f, 0.0146776959f, 0.104478173f, + -0.0885134861f, -0.121615238f, 0.068849653f, 0.0637566224f, 0.0334740579f, 0.0944700688f, -0.0710793883f, + 0.116876386f, -0.045285888f, -0.0273112096f, 0.0236368012f, 0.0779691637f, -0.224672183f, 0.0383286029f, + 0.00236119283f, 0.156418413f, -0.183448777f, -0.187251434f, 0.0995391682f, 0.082541123f, -0.024632711f, + -0.0819350034f, 0.0468629152f, 0.187882289f, -0.0889240205f, 0.00910643581f, 0.0571067668f, 0.164956316f, + -0.220422894f, 0.0870648399f, -0.057223957f, 0.113998234f, -0.124497741f, -0.0311287344f, -0.102740049f, + 0.18057549f, -0.0842797011f, -0.139126912f, 0.0690308064f, 0.0781212002f, -0.145566553f, -0.129747808f, + 0.099952817f, 0.0495745577f, -0.23908262f, 0.0503158979f, 0.0442698374f, 0.255583733f, -0.195651919f, + -0.153825447f, -0.043957863f, 0.148527995f, -0.123605527f, -0.139075339f, 0.105424382f, 0.100872204f, + -0.193493038f, -0.0886232182f, 0.0459942147f, 0.123654082f, -0.158311129f, 0.0991785079f, -0.0611353442f, + 0.140557885f, -0.176646069f, -0.189982191f}; diff --git a/Tests/UnitTest/TestCases/TestData/lstm_small_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/test_data.h new file mode 100644 index 00000000..c58a6aac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/lstm_small_f32/test_data.h @@ -0,0 +1,16 @@ +// Generated by lstm_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "cell_bias_data.h" +#include "cell_hidden_weights_data.h" +#include "cell_input_weights_data.h" +#include "config_data.h" +#include "forget_bias_data.h" +#include "forget_hidden_weights_data.h" +#include "forget_input_weights_data.h" +#include "input_bias_data.h" +#include "input_data.h" +#include "input_hidden_weights_data.h" +#include "input_input_weights_data.h" +#include "output_bias_data.h" +#include "output_hidden_weights_data.h" +#include "output_input_weights_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/config_data.h new file mode 100644 index 00000000..5e3cc391 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_BATCH_F16_BATCH_1 2 +#define MAXIMUM_BROADCAST_BATCH_F16_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_BATCH_F16_WIDTH_1 6 +#define MAXIMUM_BROADCAST_BATCH_F16_CHANNEL_1 21 +#define MAXIMUM_BROADCAST_BATCH_F16_BATCH_2 1 +#define MAXIMUM_BROADCAST_BATCH_F16_HEIGHT_2 1 +#define MAXIMUM_BROADCAST_BATCH_F16_WIDTH_2 6 +#define MAXIMUM_BROADCAST_BATCH_F16_CHANNEL_2 21 +#define MAXIMUM_BROADCAST_BATCH_F16_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_BATCH_F16_OUTPUT_HEIGHT 1 +#define MAXIMUM_BROADCAST_BATCH_F16_OUTPUT_WIDTH 6 +#define MAXIMUM_BROADCAST_BATCH_F16_OUTPUT_CHANNEL 21 +#define MAXIMUM_BROADCAST_BATCH_F16_DST_SIZE 252 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_1_data.h new file mode 100644 index 00000000..e451e51d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_1_data.h @@ -0,0 +1,65 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_batch_f16_input_tensor_1[252] = { + (float16_t)1.6484375f, (float16_t)-1.234375f, (float16_t)-0.603027344f, (float16_t)0.0302581787f, + (float16_t)4.80859375f, (float16_t)-5.7890625f, (float16_t)0.277099609f, (float16_t)4.98828125f, + (float16_t)-3.4765625f, (float16_t)-5.22265625f, (float16_t)-2.25585938f, (float16_t)5.5546875f, + (float16_t)-1.47558594f, (float16_t)-3.52539062f, (float16_t)4.42578125f, (float16_t)-2.3515625f, + (float16_t)-4.3125f, (float16_t)1.46679688f, (float16_t)4.26953125f, (float16_t)-4.74609375f, + (float16_t)2.06835938f, (float16_t)5.046875f, (float16_t)-2.08984375f, (float16_t)5.93359375f, + (float16_t)1.30371094f, (float16_t)-3.83398438f, (float16_t)1.65332031f, (float16_t)-1.53515625f, + (float16_t)4.671875f, (float16_t)-5.83203125f, (float16_t)3.75976562f, (float16_t)-5.07421875f, + (float16_t)1.57617188f, (float16_t)2.52734375f, (float16_t)4.6640625f, (float16_t)2.62695312f, + (float16_t)-5.14453125f, (float16_t)4.27734375f, (float16_t)0.126953125f, (float16_t)3.08007812f, + (float16_t)5.65625f, (float16_t)3.00195312f, (float16_t)-2.97265625f, (float16_t)-3.67578125f, + (float16_t)1.9140625f, (float16_t)-0.0344543457f, (float16_t)-3.47460938f, (float16_t)1.63085938f, + (float16_t)2.45898438f, (float16_t)-1.47851562f, (float16_t)5.8125f, (float16_t)-2.8515625f, + (float16_t)1.92773438f, (float16_t)3.5859375f, (float16_t)-2.6953125f, (float16_t)4.29296875f, + (float16_t)-5.12109375f, (float16_t)3.15820312f, (float16_t)3.40234375f, (float16_t)-1.32128906f, + (float16_t)2.53710938f, (float16_t)3.69335938f, (float16_t)4.64453125f, (float16_t)-0.47265625f, + (float16_t)-1.11328125f, (float16_t)4.68359375f, (float16_t)0.244262695f, (float16_t)-1.16601562f, + (float16_t)-4.078125f, (float16_t)-2.98046875f, (float16_t)0.750976562f, (float16_t)-0.0377807617f, + (float16_t)4.9609375f, (float16_t)1.27148438f, (float16_t)-3.29101562f, (float16_t)-4.8203125f, + (float16_t)3.04296875f, (float16_t)4.0625f, (float16_t)-3.625f, (float16_t)4.4765625f, + (float16_t)5.3359375f, (float16_t)5.5703125f, (float16_t)2.296875f, (float16_t)-5.68359375f, + (float16_t)1.92675781f, (float16_t)4.06640625f, (float16_t)-1.22949219f, (float16_t)-2.42773438f, + (float16_t)3.76367188f, (float16_t)-3.86328125f, (float16_t)1.88671875f, (float16_t)-1.06445312f, + (float16_t)-4.34375f, (float16_t)-5.96484375f, (float16_t)4.25f, (float16_t)0.812988281f, + (float16_t)-0.0682983398f, (float16_t)3.95507812f, (float16_t)0.595214844f, (float16_t)3.65820312f, + (float16_t)1.83984375f, (float16_t)-2.44140625f, (float16_t)5.4375f, (float16_t)-3.0078125f, + (float16_t)-3.8515625f, (float16_t)-1.56640625f, (float16_t)4.1484375f, (float16_t)-4.77734375f, + (float16_t)-2.2578125f, (float16_t)5.796875f, (float16_t)4.0078125f, (float16_t)4.75f, + (float16_t)-0.0523071289f, (float16_t)-5.55859375f, (float16_t)4.47265625f, (float16_t)-5.2109375f, + (float16_t)5.10546875f, (float16_t)5.5546875f, (float16_t)4.26953125f, (float16_t)-2.51367188f, + (float16_t)-2.7265625f, (float16_t)2.34179688f, (float16_t)1.38183594f, (float16_t)-3.59765625f, + (float16_t)3.41992188f, (float16_t)-3.8671875f, (float16_t)2.75390625f, (float16_t)-1.01269531f, + (float16_t)1.73144531f, (float16_t)-0.9765625f, (float16_t)-0.632324219f, (float16_t)3.796875f, + (float16_t)3.69140625f, (float16_t)4.7265625f, (float16_t)1.93554688f, (float16_t)5.61328125f, + (float16_t)-3.59179688f, (float16_t)-5.12109375f, (float16_t)0.264160156f, (float16_t)4.95703125f, + (float16_t)0.820800781f, (float16_t)1.33105469f, (float16_t)2.88476562f, (float16_t)-1.01074219f, + (float16_t)-2.52539062f, (float16_t)3.47460938f, (float16_t)2.18554688f, (float16_t)2.02734375f, + (float16_t)1.21386719f, (float16_t)1.86621094f, (float16_t)2.18359375f, (float16_t)1.07617188f, + (float16_t)3.27734375f, (float16_t)0.755371094f, (float16_t)-5.4609375f, (float16_t)3.11523438f, + (float16_t)0.389404297f, (float16_t)-2.1328125f, (float16_t)-1.2578125f, (float16_t)-5.5546875f, + (float16_t)-3.71679688f, (float16_t)3.15429688f, (float16_t)-0.859375f, (float16_t)3.17578125f, + (float16_t)1.68261719f, (float16_t)-4.86328125f, (float16_t)1.51953125f, (float16_t)-0.136230469f, + (float16_t)-2.76367188f, (float16_t)-5.83203125f, (float16_t)-0.702148438f, (float16_t)4.0703125f, + (float16_t)-4.12890625f, (float16_t)-1.70605469f, (float16_t)-0.166503906f, (float16_t)5.9296875f, + (float16_t)-2.234375f, (float16_t)-3.13085938f, (float16_t)-0.377685547f, (float16_t)1.11132812f, + (float16_t)4.1171875f, (float16_t)-3.95507812f, (float16_t)-1.87695312f, (float16_t)3.18359375f, + (float16_t)-3.91210938f, (float16_t)-2.40820312f, (float16_t)2.83203125f, (float16_t)4.5546875f, + (float16_t)-4.734375f, (float16_t)5.39453125f, (float16_t)-1.30566406f, (float16_t)-5.0f, + (float16_t)-2.79101562f, (float16_t)3.94140625f, (float16_t)3.45117188f, (float16_t)1.48632812f, + (float16_t)-5.60546875f, (float16_t)-4.70703125f, (float16_t)4.1875f, (float16_t)2.15429688f, + (float16_t)1.73242188f, (float16_t)-4.08203125f, (float16_t)5.86328125f, (float16_t)1.13964844f, + (float16_t)3.47265625f, (float16_t)-2.0f, (float16_t)-5.48828125f, (float16_t)-0.0395507812f, + (float16_t)-4.1640625f, (float16_t)-5.82421875f, (float16_t)-5.2890625f, (float16_t)-5.140625f, + (float16_t)5.6171875f, (float16_t)-3.76757812f, (float16_t)4.828125f, (float16_t)2.546875f, + (float16_t)4.04296875f, (float16_t)-2.7578125f, (float16_t)1.359375f, (float16_t)-4.8984375f, + (float16_t)-5.99609375f, (float16_t)5.00390625f, (float16_t)-0.931152344f, (float16_t)-1.8046875f, + (float16_t)5.56640625f, (float16_t)-5.58984375f, (float16_t)-4.88671875f, (float16_t)0.520019531f, + (float16_t)1.83886719f, (float16_t)-1.97558594f, (float16_t)-1.08105469f, (float16_t)-4.15234375f, + (float16_t)3.87695312f, (float16_t)-0.961914062f, (float16_t)-1.03515625f, (float16_t)2.70703125f, + (float16_t)3.88671875f, (float16_t)4.66796875f, (float16_t)1.57519531f, (float16_t)3.3046875f, + (float16_t)-4.5078125f, (float16_t)-4.5234375f, (float16_t)3.72265625f, (float16_t)-3.99804688f, + (float16_t)4.16015625f, (float16_t)0.78125f, (float16_t)5.42578125f, (float16_t)-4.24609375f, + (float16_t)-3.72265625f, (float16_t)-4.6875f, (float16_t)4.33984375f, (float16_t)-0.772949219f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_2_data.h new file mode 100644 index 00000000..6bc82932 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/input_tensor_2_data.h @@ -0,0 +1,34 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_batch_f16_input_tensor_2[126] = { + (float16_t)-5.4375f, (float16_t)-0.240356445f, (float16_t)1.8671875f, (float16_t)1.17871094f, + (float16_t)-5.35546875f, (float16_t)-3.02929688f, (float16_t)-3.35546875f, (float16_t)-2.14648438f, + (float16_t)-1.78320312f, (float16_t)3.0390625f, (float16_t)2.15429688f, (float16_t)0.682128906f, + (float16_t)-5.234375f, (float16_t)-5.94921875f, (float16_t)1.51367188f, (float16_t)5.4453125f, + (float16_t)-0.614746094f, (float16_t)2.43359375f, (float16_t)2.44140625f, (float16_t)2.13867188f, + (float16_t)-2.54101562f, (float16_t)-2.3359375f, (float16_t)4.24609375f, (float16_t)-3.74414062f, + (float16_t)-0.103942871f, (float16_t)-5.296875f, (float16_t)-3.1875f, (float16_t)-4.9765625f, + (float16_t)-1.69726562f, (float16_t)-4.03125f, (float16_t)-1.88867188f, (float16_t)1.87011719f, + (float16_t)-4.4296875f, (float16_t)-5.98828125f, (float16_t)0.672363281f, (float16_t)-4.22265625f, + (float16_t)1.84667969f, (float16_t)0.328369141f, (float16_t)0.711425781f, (float16_t)5.87109375f, + (float16_t)5.83203125f, (float16_t)5.7421875f, (float16_t)-5.6015625f, (float16_t)-4.1484375f, + (float16_t)1.45898438f, (float16_t)-4.984375f, (float16_t)4.8125f, (float16_t)-1.31835938f, + (float16_t)-1.1796875f, (float16_t)-5.1015625f, (float16_t)-4.1953125f, (float16_t)3.29101562f, + (float16_t)-0.598632812f, (float16_t)1.29589844f, (float16_t)0.530761719f, (float16_t)3.2109375f, + (float16_t)4.38671875f, (float16_t)-5.75390625f, (float16_t)4.70703125f, (float16_t)-0.090637207f, + (float16_t)0.280517578f, (float16_t)-0.338623047f, (float16_t)-3.39257812f, (float16_t)-0.982910156f, + (float16_t)-5.6640625f, (float16_t)-3.84179688f, (float16_t)5.12109375f, (float16_t)-2.56054688f, + (float16_t)-4.3828125f, (float16_t)5.15234375f, (float16_t)-1.16210938f, (float16_t)-5.5703125f, + (float16_t)-1.47558594f, (float16_t)3.25f, (float16_t)-1.6875f, (float16_t)2.70117188f, + (float16_t)3.796875f, (float16_t)-2.00585938f, (float16_t)4.296875f, (float16_t)5.35546875f, + (float16_t)-0.935058594f, (float16_t)-5.92578125f, (float16_t)-1.35546875f, (float16_t)-5.1640625f, + (float16_t)5.67578125f, (float16_t)0.986328125f, (float16_t)-0.736816406f, (float16_t)4.703125f, + (float16_t)-1.74023438f, (float16_t)5.8828125f, (float16_t)-1.6328125f, (float16_t)5.7734375f, + (float16_t)-5.58984375f, (float16_t)5.78515625f, (float16_t)1.06542969f, (float16_t)-4.421875f, + (float16_t)-3.93164062f, (float16_t)-1.09765625f, (float16_t)-4.11328125f, (float16_t)-5.91015625f, + (float16_t)5.71875f, (float16_t)-2.39648438f, (float16_t)1.15039062f, (float16_t)-4.046875f, + (float16_t)-4.26171875f, (float16_t)-4.4453125f, (float16_t)1.953125f, (float16_t)-2.375f, + (float16_t)4.75f, (float16_t)3.06640625f, (float16_t)-1.63769531f, (float16_t)-0.0238800049f, + (float16_t)2.21289062f, (float16_t)5.4296875f, (float16_t)5.79296875f, (float16_t)3.67578125f, + (float16_t)-2.02734375f, (float16_t)0.71484375f, (float16_t)0.21496582f, (float16_t)4.2734375f, + (float16_t)-3.38476562f, (float16_t)-2.0859375f, (float16_t)4.40234375f, (float16_t)4.31640625f, + (float16_t)3.828125f, (float16_t)4.19140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/output_ref_data.h new file mode 100644 index 00000000..9f2ee0bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/output_ref_data.h @@ -0,0 +1,65 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_batch_f16_output_ref[252] = { + (float16_t)1.6484375f, (float16_t)-0.240356445f, (float16_t)1.8671875f, (float16_t)1.17871094f, + (float16_t)4.80859375f, (float16_t)-3.02929688f, (float16_t)0.277099609f, (float16_t)4.98828125f, + (float16_t)-1.78320312f, (float16_t)3.0390625f, (float16_t)2.15429688f, (float16_t)5.5546875f, + (float16_t)-1.47558594f, (float16_t)-3.52539062f, (float16_t)4.42578125f, (float16_t)5.4453125f, + (float16_t)-0.614746094f, (float16_t)2.43359375f, (float16_t)4.26953125f, (float16_t)2.13867188f, + (float16_t)2.06835938f, (float16_t)5.046875f, (float16_t)4.24609375f, (float16_t)5.93359375f, + (float16_t)1.30371094f, (float16_t)-3.83398438f, (float16_t)1.65332031f, (float16_t)-1.53515625f, + (float16_t)4.671875f, (float16_t)-4.03125f, (float16_t)3.75976562f, (float16_t)1.87011719f, + (float16_t)1.57617188f, (float16_t)2.52734375f, (float16_t)4.6640625f, (float16_t)2.62695312f, + (float16_t)1.84667969f, (float16_t)4.27734375f, (float16_t)0.711425781f, (float16_t)5.87109375f, + (float16_t)5.83203125f, (float16_t)5.7421875f, (float16_t)-2.97265625f, (float16_t)-3.67578125f, + (float16_t)1.9140625f, (float16_t)-0.0344543457f, (float16_t)4.8125f, (float16_t)1.63085938f, + (float16_t)2.45898438f, (float16_t)-1.47851562f, (float16_t)5.8125f, (float16_t)3.29101562f, + (float16_t)1.92773438f, (float16_t)3.5859375f, (float16_t)0.530761719f, (float16_t)4.29296875f, + (float16_t)4.38671875f, (float16_t)3.15820312f, (float16_t)4.70703125f, (float16_t)-0.090637207f, + (float16_t)2.53710938f, (float16_t)3.69335938f, (float16_t)4.64453125f, (float16_t)-0.47265625f, + (float16_t)-1.11328125f, (float16_t)4.68359375f, (float16_t)5.12109375f, (float16_t)-1.16601562f, + (float16_t)-4.078125f, (float16_t)5.15234375f, (float16_t)0.750976562f, (float16_t)-0.0377807617f, + (float16_t)4.9609375f, (float16_t)3.25f, (float16_t)-1.6875f, (float16_t)2.70117188f, + (float16_t)3.796875f, (float16_t)4.0625f, (float16_t)4.296875f, (float16_t)5.35546875f, + (float16_t)5.3359375f, (float16_t)5.5703125f, (float16_t)2.296875f, (float16_t)-5.1640625f, + (float16_t)5.67578125f, (float16_t)4.06640625f, (float16_t)-0.736816406f, (float16_t)4.703125f, + (float16_t)3.76367188f, (float16_t)5.8828125f, (float16_t)1.88671875f, (float16_t)5.7734375f, + (float16_t)-4.34375f, (float16_t)5.78515625f, (float16_t)4.25f, (float16_t)0.812988281f, + (float16_t)-0.0682983398f, (float16_t)3.95507812f, (float16_t)0.595214844f, (float16_t)3.65820312f, + (float16_t)5.71875f, (float16_t)-2.39648438f, (float16_t)5.4375f, (float16_t)-3.0078125f, + (float16_t)-3.8515625f, (float16_t)-1.56640625f, (float16_t)4.1484375f, (float16_t)-2.375f, + (float16_t)4.75f, (float16_t)5.796875f, (float16_t)4.0078125f, (float16_t)4.75f, + (float16_t)2.21289062f, (float16_t)5.4296875f, (float16_t)5.79296875f, (float16_t)3.67578125f, + (float16_t)5.10546875f, (float16_t)5.5546875f, (float16_t)4.26953125f, (float16_t)4.2734375f, + (float16_t)-2.7265625f, (float16_t)2.34179688f, (float16_t)4.40234375f, (float16_t)4.31640625f, + (float16_t)3.828125f, (float16_t)4.19140625f, (float16_t)2.75390625f, (float16_t)-0.240356445f, + (float16_t)1.8671875f, (float16_t)1.17871094f, (float16_t)-0.632324219f, (float16_t)3.796875f, + (float16_t)3.69140625f, (float16_t)4.7265625f, (float16_t)1.93554688f, (float16_t)5.61328125f, + (float16_t)2.15429688f, (float16_t)0.682128906f, (float16_t)0.264160156f, (float16_t)4.95703125f, + (float16_t)1.51367188f, (float16_t)5.4453125f, (float16_t)2.88476562f, (float16_t)2.43359375f, + (float16_t)2.44140625f, (float16_t)3.47460938f, (float16_t)2.18554688f, (float16_t)2.02734375f, + (float16_t)4.24609375f, (float16_t)1.86621094f, (float16_t)2.18359375f, (float16_t)1.07617188f, + (float16_t)3.27734375f, (float16_t)0.755371094f, (float16_t)-1.69726562f, (float16_t)3.11523438f, + (float16_t)0.389404297f, (float16_t)1.87011719f, (float16_t)-1.2578125f, (float16_t)-5.5546875f, + (float16_t)0.672363281f, (float16_t)3.15429688f, (float16_t)1.84667969f, (float16_t)3.17578125f, + (float16_t)1.68261719f, (float16_t)5.87109375f, (float16_t)5.83203125f, (float16_t)5.7421875f, + (float16_t)-2.76367188f, (float16_t)-4.1484375f, (float16_t)1.45898438f, (float16_t)4.0703125f, + (float16_t)4.8125f, (float16_t)-1.31835938f, (float16_t)-0.166503906f, (float16_t)5.9296875f, + (float16_t)-2.234375f, (float16_t)3.29101562f, (float16_t)-0.377685547f, (float16_t)1.29589844f, + (float16_t)4.1171875f, (float16_t)3.2109375f, (float16_t)4.38671875f, (float16_t)3.18359375f, + (float16_t)4.70703125f, (float16_t)-0.090637207f, (float16_t)2.83203125f, (float16_t)4.5546875f, + (float16_t)-3.39257812f, (float16_t)5.39453125f, (float16_t)-1.30566406f, (float16_t)-3.84179688f, + (float16_t)5.12109375f, (float16_t)3.94140625f, (float16_t)3.45117188f, (float16_t)5.15234375f, + (float16_t)-1.16210938f, (float16_t)-4.70703125f, (float16_t)4.1875f, (float16_t)3.25f, + (float16_t)1.73242188f, (float16_t)2.70117188f, (float16_t)5.86328125f, (float16_t)1.13964844f, + (float16_t)4.296875f, (float16_t)5.35546875f, (float16_t)-0.935058594f, (float16_t)-0.0395507812f, + (float16_t)-1.35546875f, (float16_t)-5.1640625f, (float16_t)5.67578125f, (float16_t)0.986328125f, + (float16_t)5.6171875f, (float16_t)4.703125f, (float16_t)4.828125f, (float16_t)5.8828125f, + (float16_t)4.04296875f, (float16_t)5.7734375f, (float16_t)1.359375f, (float16_t)5.78515625f, + (float16_t)1.06542969f, (float16_t)5.00390625f, (float16_t)-0.931152344f, (float16_t)-1.09765625f, + (float16_t)5.56640625f, (float16_t)-5.58984375f, (float16_t)5.71875f, (float16_t)0.520019531f, + (float16_t)1.83886719f, (float16_t)-1.97558594f, (float16_t)-1.08105469f, (float16_t)-4.15234375f, + (float16_t)3.87695312f, (float16_t)-0.961914062f, (float16_t)4.75f, (float16_t)3.06640625f, + (float16_t)3.88671875f, (float16_t)4.66796875f, (float16_t)2.21289062f, (float16_t)5.4296875f, + (float16_t)5.79296875f, (float16_t)3.67578125f, (float16_t)3.72265625f, (float16_t)0.71484375f, + (float16_t)4.16015625f, (float16_t)4.2734375f, (float16_t)5.42578125f, (float16_t)-2.0859375f, + (float16_t)4.40234375f, (float16_t)4.31640625f, (float16_t)4.33984375f, (float16_t)4.19140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/config_data.h new file mode 100644 index 00000000..63cdd759 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_BATCH_F32_BATCH_1 2 +#define MAXIMUM_BROADCAST_BATCH_F32_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_BATCH_F32_WIDTH_1 6 +#define MAXIMUM_BROADCAST_BATCH_F32_CHANNEL_1 21 +#define MAXIMUM_BROADCAST_BATCH_F32_BATCH_2 1 +#define MAXIMUM_BROADCAST_BATCH_F32_HEIGHT_2 1 +#define MAXIMUM_BROADCAST_BATCH_F32_WIDTH_2 6 +#define MAXIMUM_BROADCAST_BATCH_F32_CHANNEL_2 21 +#define MAXIMUM_BROADCAST_BATCH_F32_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_BATCH_F32_OUTPUT_HEIGHT 1 +#define MAXIMUM_BROADCAST_BATCH_F32_OUTPUT_WIDTH 6 +#define MAXIMUM_BROADCAST_BATCH_F32_OUTPUT_CHANNEL 21 +#define MAXIMUM_BROADCAST_BATCH_F32_DST_SIZE 252 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_1_data.h new file mode 100644 index 00000000..4ec97b09 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_1_data.h @@ -0,0 +1,38 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_batch_f32_input_tensor_1[252] = { + -3.89020491f, -2.34340882f, 0.235927597f, -0.932157278f, -0.896355271f, 0.93340075f, -0.992572308f, + -0.925303817f, -0.543866515f, -5.4918766f, -3.14202261f, -4.77924919f, -1.34899497f, -1.03973699f, + 2.8744061f, -4.60905933f, -4.23203468f, 5.34513712f, 2.3958509f, 2.83377051f, 0.752108991f, + -4.10280943f, -4.38537979f, -4.48815393f, -1.23369622f, -1.06672084f, 1.73737645f, -3.26018333f, + -3.53009319f, 2.91890621f, -3.48479915f, -3.69669533f, -0.596043408f, 1.45522606f, 3.14343977f, + 0.943932772f, -2.71256375f, 1.25922167f, -0.756281734f, 5.3507514f, 1.31585228f, -5.10634661f, + -0.780170977f, 0.379496574f, -2.49842262f, 2.70329785f, 0.362587184f, 3.3068285f, -4.12018061f, + -3.43921733f, -3.01427722f, 0.813560367f, -1.76846707f, -3.87114429f, -3.67675662f, -4.67871189f, + -4.55503082f, -3.82054663f, -4.69966412f, 5.86293602f, 3.21333122f, 1.47398806f, -5.35755634f, + -2.22697067f, 5.15910387f, -1.13975847f, -4.24815416f, 3.94829535f, 3.26573706f, -4.73497725f, + 5.24929619f, -1.04320741f, 0.724264383f, -3.53937197f, 2.57190299f, 2.62385869f, -2.2189374f, + -4.02175236f, 1.65617752f, 3.73808718f, 0.796897888f, -3.57343173f, -5.08137131f, -0.44814989f, + 1.28084397f, -4.8410058f, -2.39694595f, 2.33501673f, -4.46222639f, 4.37415791f, 3.06122088f, + 4.21424627f, -5.73658657f, -5.32744646f, 2.00865746f, 2.62278199f, -5.04051399f, -2.99914217f, + -5.67134142f, 4.68149996f, -0.361203164f, 3.81403971f, -4.76054573f, -2.10023689f, -4.11576605f, + 5.84980345f, 5.87938118f, 0.0508162566f, -3.3453598f, 5.09210968f, 3.36546659f, -3.12634492f, + 0.336866438f, -4.1682477f, -5.36685085f, -4.72642231f, -3.84537101f, -1.64190054f, -4.21291876f, + -5.15124512f, -2.12995124f, 5.84704399f, 2.97440696f, -2.24303603f, -3.7217958f, -4.6558218f, + -5.52887917f, 0.977583408f, -5.4900527f, 0.132094711f, 4.39649487f, 4.14352512f, 2.48436022f, + -5.44278145f, 0.800169408f, -3.3678813f, 2.45196486f, -0.578519404f, -2.81112814f, 0.321295857f, + 3.55285001f, -0.542277455f, -4.5852952f, -2.84996724f, 2.15834236f, 4.40226936f, -2.59640551f, + -0.856803596f, 5.78809643f, 5.98048878f, -4.32741213f, -1.54485524f, 0.841110885f, -5.21666574f, + -0.39862892f, 0.408298612f, 0.300475359f, 0.887066782f, 5.88000965f, -3.44856119f, 1.62395012f, + 1.87353671f, -2.16198874f, -3.77239442f, 4.79820871f, -0.637339771f, -4.65500212f, -1.59509385f, + -2.91450477f, -2.78559065f, -2.83921146f, 3.0232079f, 0.376998216f, -2.06751561f, 4.08777952f, + 2.3528204f, 3.23181581f, 3.90872478f, -0.713514805f, -3.83183861f, -2.29222298f, 3.6493113f, + -1.43509889f, 4.76715565f, -3.02399945f, 2.29225397f, -5.48311663f, -5.77992296f, 4.92589045f, + -1.67181778f, -2.15448356f, -0.309278786f, -4.88169193f, 4.55816269f, 1.33139873f, -1.90110219f, + -2.64001942f, 1.91902804f, -4.026896f, -2.8991015f, 1.89324057f, 3.83865285f, -1.1165545f, + -0.359923244f, -1.59768033f, -3.83392668f, -5.15672159f, -3.16808462f, 2.64043689f, 4.92339706f, + 3.93019176f, 3.85062575f, -5.98793221f, -1.31706369f, -2.76034641f, -2.01376772f, -3.22300625f, + 2.113307f, -0.488925487f, -0.14490287f, 5.53942013f, -5.61371994f, 0.43211025f, 3.80504727f, + 4.19385719f, 2.47384453f, -1.96652639f, 3.14264607f, 4.61331081f, 2.39256167f, -4.26337957f, + -3.2865746f, 0.855261624f, 4.24240732f, 2.23182297f, 5.81700087f, 1.03320932f, -5.14296246f, + -0.749368966f, 2.75336766f, -4.53027105f, 2.89115429f, -3.33746195f, -1.76271975f, -3.1968677f, + -4.61339188f, 0.0210243687f, 0.298938602f, -5.06834698f, 0.637515664f, 5.4099021f, -1.15406406f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_2_data.h new file mode 100644 index 00000000..ef5bed73 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/input_tensor_2_data.h @@ -0,0 +1,18 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_batch_f32_input_tensor_2[126] = { + 1.87550342f, 0.68271631f, 5.52706766f, -4.275033f, -2.05451155f, 5.37083864f, 4.28143263f, 4.48661518f, + 0.862548113f, 4.51229286f, 1.96613419f, 2.34727383f, 5.07087421f, 3.5214653f, -4.93070984f, 2.58662844f, + -3.96151137f, -4.79027128f, -1.88365829f, 3.52483416f, 5.94709921f, -4.47171926f, 4.74157333f, 1.31877291f, + -2.72319198f, 2.41001201f, 4.16614294f, -1.43777537f, -3.71816063f, 5.64602852f, 5.03518534f, 3.94848871f, + -2.87214017f, 1.06977379f, 1.65868449f, -3.30371666f, 1.07361925f, -2.61527491f, 2.64231181f, 2.03001404f, + -5.86262035f, 4.07391262f, -0.516260922f, 0.74434936f, 1.70365226f, 5.04304409f, -5.60890484f, -3.65526104f, + 2.70941496f, 1.27847171f, 4.54282999f, 4.91200876f, -1.94123828f, 0.116904333f, 3.26405382f, -5.04636621f, + 1.35589409f, 2.03741193f, 1.74992228f, -3.1139009f, -3.30946136f, 4.75391436f, -4.81672335f, -5.82831049f, + -2.21025181f, 1.97516453f, -3.82486987f, 2.29107213f, -4.88095522f, 3.10901213f, 1.59208655f, -4.29528093f, + -0.134034276f, -5.89308834f, 1.48895156f, 1.22243834f, -3.99888778f, 4.1981163f, -2.65067458f, -4.40800142f, + -1.85520077f, -3.43339825f, 2.0822401f, 1.32402527f, 0.879095435f, -3.97485018f, -1.05328012f, -4.48295975f, + 2.86206937f, 1.60542619f, -2.63001633f, 4.58636713f, -4.49965811f, -1.29900825f, 2.33631754f, 4.66109514f, + 5.65495443f, 1.15594995f, 3.38300323f, 4.43909788f, -0.773263752f, 3.25528884f, -3.4962194f, 5.20889187f, + 3.80801964f, 0.747231185f, 1.94890475f, -1.08402753f, 4.54263639f, -5.65433073f, -2.06671524f, -5.95984268f, + -0.0842600763f, -0.150296226f, -2.46189094f, 2.25747037f, 0.170792952f, -2.43174243f, -5.2219348f, -3.38525152f, + -1.08149636f, 5.66920471f, -3.54900289f, -4.9859972f, 3.30293107f, -5.79396343f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/output_ref_data.h new file mode 100644 index 00000000..3df9777b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/output_ref_data.h @@ -0,0 +1,38 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_batch_f32_output_ref[252] = { + 1.87550342f, 0.68271631f, 5.52706766f, -0.932157278f, -0.896355271f, 5.37083864f, 4.28143263f, + 4.48661518f, 0.862548113f, 4.51229286f, 1.96613419f, 2.34727383f, 5.07087421f, 3.5214653f, + 2.8744061f, 2.58662844f, -3.96151137f, 5.34513712f, 2.3958509f, 3.52483416f, 5.94709921f, + -4.10280943f, 4.74157333f, 1.31877291f, -1.23369622f, 2.41001201f, 4.16614294f, -1.43777537f, + -3.53009319f, 5.64602852f, 5.03518534f, 3.94848871f, -0.596043408f, 1.45522606f, 3.14343977f, + 0.943932772f, 1.07361925f, 1.25922167f, 2.64231181f, 5.3507514f, 1.31585228f, 4.07391262f, + -0.516260922f, 0.74434936f, 1.70365226f, 5.04304409f, 0.362587184f, 3.3068285f, 2.70941496f, + 1.27847171f, 4.54282999f, 4.91200876f, -1.76846707f, 0.116904333f, 3.26405382f, -4.67871189f, + 1.35589409f, 2.03741193f, 1.74992228f, 5.86293602f, 3.21333122f, 4.75391436f, -4.81672335f, + -2.22697067f, 5.15910387f, 1.97516453f, -3.82486987f, 3.94829535f, 3.26573706f, 3.10901213f, + 5.24929619f, -1.04320741f, 0.724264383f, -3.53937197f, 2.57190299f, 2.62385869f, -2.2189374f, + 4.1981163f, 1.65617752f, 3.73808718f, 0.796897888f, -3.43339825f, 2.0822401f, 1.32402527f, + 1.28084397f, -3.97485018f, -1.05328012f, 2.33501673f, 2.86206937f, 4.37415791f, 3.06122088f, + 4.58636713f, -4.49965811f, -1.29900825f, 2.33631754f, 4.66109514f, 5.65495443f, 1.15594995f, + 3.38300323f, 4.68149996f, -0.361203164f, 3.81403971f, -3.4962194f, 5.20889187f, 3.80801964f, + 5.84980345f, 5.87938118f, 0.0508162566f, 4.54263639f, 5.09210968f, 3.36546659f, -3.12634492f, + 0.336866438f, -0.150296226f, -2.46189094f, 2.25747037f, 0.170792952f, -1.64190054f, -4.21291876f, + -3.38525152f, -1.08149636f, 5.84704399f, 2.97440696f, -2.24303603f, 3.30293107f, -4.6558218f, + 1.87550342f, 0.977583408f, 5.52706766f, 0.132094711f, 4.39649487f, 5.37083864f, 4.28143263f, + 4.48661518f, 0.862548113f, 4.51229286f, 2.45196486f, 2.34727383f, 5.07087421f, 3.5214653f, + 3.55285001f, 2.58662844f, -3.96151137f, -2.84996724f, 2.15834236f, 4.40226936f, 5.94709921f, + -0.856803596f, 5.78809643f, 5.98048878f, -2.72319198f, 2.41001201f, 4.16614294f, -1.43777537f, + -0.39862892f, 5.64602852f, 5.03518534f, 3.94848871f, 5.88000965f, 1.06977379f, 1.65868449f, + 1.87353671f, 1.07361925f, -2.61527491f, 4.79820871f, 2.03001404f, -4.65500212f, 4.07391262f, + -0.516260922f, 0.74434936f, 1.70365226f, 5.04304409f, 0.376998216f, -2.06751561f, 4.08777952f, + 2.3528204f, 4.54282999f, 4.91200876f, -0.713514805f, 0.116904333f, 3.26405382f, 3.6493113f, + 1.35589409f, 4.76715565f, 1.74992228f, 2.29225397f, -3.30946136f, 4.75391436f, 4.92589045f, + -1.67181778f, -2.15448356f, 1.97516453f, -3.82486987f, 4.55816269f, 1.33139873f, 3.10901213f, + 1.59208655f, 1.91902804f, -0.134034276f, -2.8991015f, 1.89324057f, 3.83865285f, -1.1165545f, + 4.1981163f, -1.59768033f, -3.83392668f, -1.85520077f, -3.16808462f, 2.64043689f, 4.92339706f, + 3.93019176f, 3.85062575f, -1.05328012f, -1.31706369f, 2.86206937f, 1.60542619f, -2.63001633f, + 4.58636713f, -0.488925487f, -0.14490287f, 5.53942013f, 4.66109514f, 5.65495443f, 3.80504727f, + 4.19385719f, 4.43909788f, -0.773263752f, 3.25528884f, 4.61331081f, 5.20889187f, 3.80801964f, + 0.747231185f, 1.94890475f, 4.24240732f, 4.54263639f, 5.81700087f, 1.03320932f, -5.14296246f, + -0.0842600763f, 2.75336766f, -2.46189094f, 2.89115429f, 0.170792952f, -1.76271975f, -3.1968677f, + -3.38525152f, 0.0210243687f, 5.66920471f, -3.54900289f, 0.637515664f, 5.4099021f, -1.15406406f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_batch_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/config_data.h new file mode 100644 index 00000000..ae9c24ab --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_CH_F16_BATCH_1 2 +#define MAXIMUM_BROADCAST_CH_F16_HEIGHT_1 2 +#define MAXIMUM_BROADCAST_CH_F16_WIDTH_1 4 +#define MAXIMUM_BROADCAST_CH_F16_CHANNEL_1 1 +#define MAXIMUM_BROADCAST_CH_F16_BATCH_2 2 +#define MAXIMUM_BROADCAST_CH_F16_HEIGHT_2 2 +#define MAXIMUM_BROADCAST_CH_F16_WIDTH_2 4 +#define MAXIMUM_BROADCAST_CH_F16_CHANNEL_2 24 +#define MAXIMUM_BROADCAST_CH_F16_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_CH_F16_OUTPUT_HEIGHT 2 +#define MAXIMUM_BROADCAST_CH_F16_OUTPUT_WIDTH 4 +#define MAXIMUM_BROADCAST_CH_F16_OUTPUT_CHANNEL 24 +#define MAXIMUM_BROADCAST_CH_F16_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_1_data.h new file mode 100644 index 00000000..84e664c9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_1_data.h @@ -0,0 +1,17 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_ch_f16_input_tensor_1[16] = {(float16_t)-4.328125f, + (float16_t)-2.0078125f, + (float16_t)1.23730469f, + (float16_t)-4.9375f, + (float16_t)-1.40917969f, + (float16_t)-4.20703125f, + (float16_t)1.43847656f, + (float16_t)-1.71972656f, + (float16_t)1.046875f, + (float16_t)3.40429688f, + (float16_t)-5.4609375f, + (float16_t)2.78320312f, + (float16_t)2.9609375f, + (float16_t)-3.88867188f, + (float16_t)-0.138793945f, + (float16_t)2.96875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_2_data.h new file mode 100644 index 00000000..3bb7e20f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/input_tensor_2_data.h @@ -0,0 +1,98 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_ch_f16_input_tensor_2[384] = { + (float16_t)5.41796875f, (float16_t)0.685058594f, (float16_t)5.9609375f, (float16_t)-5.40625f, + (float16_t)4.22265625f, (float16_t)0.128662109f, (float16_t)4.6015625f, (float16_t)3.60546875f, + (float16_t)-0.0541687012f, (float16_t)5.04296875f, (float16_t)-5.76953125f, (float16_t)-1.69238281f, + (float16_t)-2.48242188f, (float16_t)3.94726562f, (float16_t)-0.28515625f, (float16_t)2.94140625f, + (float16_t)3.47070312f, (float16_t)-4.94140625f, (float16_t)3.328125f, (float16_t)5.0625f, + (float16_t)-0.974121094f, (float16_t)4.07421875f, (float16_t)1.72070312f, (float16_t)-4.125f, + (float16_t)-4.90625f, (float16_t)5.08984375f, (float16_t)-0.601074219f, (float16_t)-5.88671875f, + (float16_t)1.04003906f, (float16_t)-5.77734375f, (float16_t)3.71289062f, (float16_t)-5.76953125f, + (float16_t)3.94726562f, (float16_t)2.35351562f, (float16_t)-2.6484375f, (float16_t)5.87890625f, + (float16_t)-5.84765625f, (float16_t)5.33203125f, (float16_t)4.8125f, (float16_t)-4.66015625f, + (float16_t)-2.74414062f, (float16_t)1.984375f, (float16_t)0.471435547f, (float16_t)-5.98828125f, + (float16_t)1.11621094f, (float16_t)2.2734375f, (float16_t)4.3359375f, (float16_t)-5.47265625f, + (float16_t)2.17578125f, (float16_t)-5.84375f, (float16_t)-3.75195312f, (float16_t)-3.70703125f, + (float16_t)5.7109375f, (float16_t)-0.865234375f, (float16_t)-1.42285156f, (float16_t)-1.98535156f, + (float16_t)1.14453125f, (float16_t)-0.494873047f, (float16_t)1.00976562f, (float16_t)-1.07128906f, + (float16_t)-0.403320312f, (float16_t)0.644042969f, (float16_t)-5.73828125f, (float16_t)0.231933594f, + (float16_t)5.7578125f, (float16_t)4.22265625f, (float16_t)-1.69140625f, (float16_t)0.601074219f, + (float16_t)-2.94140625f, (float16_t)-5.1796875f, (float16_t)-1.83007812f, (float16_t)5.38671875f, + (float16_t)-1.35058594f, (float16_t)0.304443359f, (float16_t)-1.02539062f, (float16_t)0.770996094f, + (float16_t)-0.320556641f, (float16_t)2.796875f, (float16_t)-5.2890625f, (float16_t)-4.44921875f, + (float16_t)-3.5859375f, (float16_t)4.03515625f, (float16_t)-0.665039062f, (float16_t)5.62890625f, + (float16_t)-5.91015625f, (float16_t)-5.8984375f, (float16_t)-5.7109375f, (float16_t)1.30664062f, + (float16_t)0.872558594f, (float16_t)2.76953125f, (float16_t)1.95214844f, (float16_t)-0.392822266f, + (float16_t)-4.16015625f, (float16_t)4.6640625f, (float16_t)-4.69921875f, (float16_t)0.211425781f, + (float16_t)3.32226562f, (float16_t)-0.768554688f, (float16_t)-5.63671875f, (float16_t)-4.1171875f, + (float16_t)4.30859375f, (float16_t)4.41015625f, (float16_t)-5.84765625f, (float16_t)-5.49609375f, + (float16_t)-3.25585938f, (float16_t)-3.28320312f, (float16_t)-4.6875f, (float16_t)3.66601562f, + (float16_t)-3.97851562f, (float16_t)-4.06640625f, (float16_t)3.83398438f, (float16_t)-3.34179688f, + (float16_t)0.194702148f, (float16_t)5.4296875f, (float16_t)3.86328125f, (float16_t)2.62304688f, + (float16_t)1.16113281f, (float16_t)-4.76171875f, (float16_t)1.40722656f, (float16_t)1.75683594f, + (float16_t)0.884277344f, (float16_t)0.766601562f, (float16_t)4.3046875f, (float16_t)-3.48046875f, + (float16_t)-5.54296875f, (float16_t)1.66699219f, (float16_t)-0.546386719f, (float16_t)-4.7109375f, + (float16_t)-4.0703125f, (float16_t)3.4375f, (float16_t)-2.15429688f, (float16_t)-4.44140625f, + (float16_t)-2.58203125f, (float16_t)5.93359375f, (float16_t)-5.40625f, (float16_t)-1.97558594f, + (float16_t)-3.21289062f, (float16_t)4.73828125f, (float16_t)3.84960938f, (float16_t)-5.390625f, + (float16_t)2.47460938f, (float16_t)-1.97167969f, (float16_t)3.96679688f, (float16_t)-3.08984375f, + (float16_t)1.29882812f, (float16_t)-0.493164062f, (float16_t)-2.68359375f, (float16_t)1.24609375f, + (float16_t)3.29101562f, (float16_t)2.04101562f, (float16_t)-2.11914062f, (float16_t)-1.96777344f, + (float16_t)-0.577148438f, (float16_t)-4.26953125f, (float16_t)5.7265625f, (float16_t)-4.2890625f, + (float16_t)0.00396728516f, (float16_t)-4.8984375f, (float16_t)-5.8046875f, (float16_t)-5.265625f, + (float16_t)-5.2578125f, (float16_t)1.74902344f, (float16_t)-2.80078125f, (float16_t)-3.00976562f, + (float16_t)1.17578125f, (float16_t)1.125f, (float16_t)-0.779785156f, (float16_t)-2.54296875f, + (float16_t)5.75f, (float16_t)-1.51757812f, (float16_t)-3.62109375f, (float16_t)-0.694335938f, + (float16_t)0.206298828f, (float16_t)-1.9296875f, (float16_t)-4.0546875f, (float16_t)-4.19140625f, + (float16_t)-0.980957031f, (float16_t)-3.99804688f, (float16_t)-4.4765625f, (float16_t)2.64453125f, + (float16_t)1.27636719f, (float16_t)-0.794433594f, (float16_t)-4.60546875f, (float16_t)-4.7109375f, + (float16_t)-2.29882812f, (float16_t)-2.04492188f, (float16_t)-4.5078125f, (float16_t)3.15429688f, + (float16_t)-2.91015625f, (float16_t)0.0889282227f, (float16_t)-3.6875f, (float16_t)3.32617188f, + (float16_t)-4.4296875f, (float16_t)-0.15612793f, (float16_t)-1.99121094f, (float16_t)-0.592285156f, + (float16_t)-1.17480469f, (float16_t)0.810058594f, (float16_t)2.1171875f, (float16_t)1.12207031f, + (float16_t)-0.249755859f, (float16_t)-3.67578125f, (float16_t)-2.52148438f, (float16_t)-2.47265625f, + (float16_t)1.30859375f, (float16_t)-5.984375f, (float16_t)-3.3671875f, (float16_t)4.49609375f, + (float16_t)-0.242553711f, (float16_t)-2.90625f, (float16_t)-3.62890625f, (float16_t)4.3671875f, + (float16_t)5.67578125f, (float16_t)1.40625f, (float16_t)2.19921875f, (float16_t)0.180786133f, + (float16_t)2.52734375f, (float16_t)0.866210938f, (float16_t)-3.61914062f, (float16_t)0.814453125f, + (float16_t)-5.58203125f, (float16_t)4.4375f, (float16_t)1.25292969f, (float16_t)3.78515625f, + (float16_t)-3.38476562f, (float16_t)-2.84570312f, (float16_t)-0.0251617432f, (float16_t)3.82421875f, + (float16_t)-5.75f, (float16_t)-4.1015625f, (float16_t)-2.79101562f, (float16_t)3.75f, + (float16_t)5.21875f, (float16_t)-1.15820312f, (float16_t)3.43359375f, (float16_t)0.628417969f, + (float16_t)4.05859375f, (float16_t)-0.662109375f, (float16_t)4.26171875f, (float16_t)-3.07226562f, + (float16_t)3.40234375f, (float16_t)-3.9296875f, (float16_t)-0.553710938f, (float16_t)0.687988281f, + (float16_t)-0.0600891113f, (float16_t)0.669433594f, (float16_t)4.39453125f, (float16_t)-0.291259766f, + (float16_t)5.140625f, (float16_t)1.57910156f, (float16_t)1.6015625f, (float16_t)-1.49609375f, + (float16_t)-5.54296875f, (float16_t)-1.5703125f, (float16_t)5.01953125f, (float16_t)5.45703125f, + (float16_t)4.7421875f, (float16_t)-1.91699219f, (float16_t)-5.52734375f, (float16_t)0.431640625f, + (float16_t)-5.734375f, (float16_t)5.453125f, (float16_t)0.153320312f, (float16_t)-5.57421875f, + (float16_t)4.328125f, (float16_t)0.537109375f, (float16_t)4.453125f, (float16_t)-1.12011719f, + (float16_t)-2.90039062f, (float16_t)4.3828125f, (float16_t)1.45800781f, (float16_t)-2.80273438f, + (float16_t)4.65625f, (float16_t)1.33007812f, (float16_t)-2.56835938f, (float16_t)-0.208007812f, + (float16_t)-2.74609375f, (float16_t)-3.99414062f, (float16_t)4.453125f, (float16_t)-5.2890625f, + (float16_t)1.96679688f, (float16_t)-3.85351562f, (float16_t)-4.33203125f, (float16_t)2.88476562f, + (float16_t)-5.82421875f, (float16_t)-5.14453125f, (float16_t)2.24609375f, (float16_t)-4.1484375f, + (float16_t)3.48046875f, (float16_t)4.17578125f, (float16_t)-1.55078125f, (float16_t)2.97460938f, + (float16_t)3.12109375f, (float16_t)-0.227539062f, (float16_t)-3.8046875f, (float16_t)5.57421875f, + (float16_t)3.19335938f, (float16_t)-4.40625f, (float16_t)-2.22265625f, (float16_t)-3.5703125f, + (float16_t)1.35742188f, (float16_t)-4.86328125f, (float16_t)2.19335938f, (float16_t)3.12890625f, + (float16_t)-4.828125f, (float16_t)-0.214233398f, (float16_t)0.193237305f, (float16_t)-5.59375f, + (float16_t)-1.93945312f, (float16_t)1.54101562f, (float16_t)4.2265625f, (float16_t)5.9765625f, + (float16_t)4.8671875f, (float16_t)-3.0390625f, (float16_t)-3.31054688f, (float16_t)5.30859375f, + (float16_t)-2.98046875f, (float16_t)0.770996094f, (float16_t)-1.57714844f, (float16_t)-5.1328125f, + (float16_t)4.70703125f, (float16_t)-3.7890625f, (float16_t)-1.1015625f, (float16_t)-1.31152344f, + (float16_t)-5.09375f, (float16_t)-4.58203125f, (float16_t)1.578125f, (float16_t)5.1328125f, + (float16_t)0.634277344f, (float16_t)4.640625f, (float16_t)4.77734375f, (float16_t)4.3203125f, + (float16_t)5.15625f, (float16_t)-3.31640625f, (float16_t)-0.414794922f, (float16_t)-1.9375f, + (float16_t)1.57519531f, (float16_t)1.68457031f, (float16_t)-5.42578125f, (float16_t)4.84375f, + (float16_t)-0.383544922f, (float16_t)2.92578125f, (float16_t)-3.08007812f, (float16_t)-0.984375f, + (float16_t)-3.70507812f, (float16_t)1.35546875f, (float16_t)-2.75390625f, (float16_t)-3.81640625f, + (float16_t)4.640625f, (float16_t)2.5546875f, (float16_t)3.9296875f, (float16_t)4.60546875f, + (float16_t)0.490966797f, (float16_t)1.50585938f, (float16_t)-2.57421875f, (float16_t)0.608398438f, + (float16_t)-3.04492188f, (float16_t)-3.18359375f, (float16_t)-1.55175781f, (float16_t)-0.790039062f, + (float16_t)5.015625f, (float16_t)5.6015625f, (float16_t)-4.59375f, (float16_t)4.35546875f, + (float16_t)-0.541015625f, (float16_t)-1.39355469f, (float16_t)-0.415771484f, (float16_t)-3.87304688f, + (float16_t)4.74609375f, (float16_t)1.09765625f, (float16_t)-2.6640625f, (float16_t)-4.2734375f, + (float16_t)-1.37597656f, (float16_t)-1.4296875f, (float16_t)0.21472168f, (float16_t)-3.52929688f, + (float16_t)-5.640625f, (float16_t)-3.80664062f, (float16_t)2.67382812f, (float16_t)4.0703125f, + (float16_t)-2.875f, (float16_t)5.171875f, (float16_t)5.10546875f, (float16_t)-1.984375f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/output_ref_data.h new file mode 100644 index 00000000..7d958de5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/output_ref_data.h @@ -0,0 +1,98 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_ch_f16_output_ref[384] = { + (float16_t)5.41796875f, (float16_t)0.685058594f, (float16_t)5.9609375f, (float16_t)-4.328125f, + (float16_t)4.22265625f, (float16_t)0.128662109f, (float16_t)4.6015625f, (float16_t)3.60546875f, + (float16_t)-0.0541687012f, (float16_t)5.04296875f, (float16_t)-4.328125f, (float16_t)-1.69238281f, + (float16_t)-2.48242188f, (float16_t)3.94726562f, (float16_t)-0.28515625f, (float16_t)2.94140625f, + (float16_t)3.47070312f, (float16_t)-4.328125f, (float16_t)3.328125f, (float16_t)5.0625f, + (float16_t)-0.974121094f, (float16_t)4.07421875f, (float16_t)1.72070312f, (float16_t)-4.125f, + (float16_t)-2.0078125f, (float16_t)5.08984375f, (float16_t)-0.601074219f, (float16_t)-2.0078125f, + (float16_t)1.04003906f, (float16_t)-2.0078125f, (float16_t)3.71289062f, (float16_t)-2.0078125f, + (float16_t)3.94726562f, (float16_t)2.35351562f, (float16_t)-2.0078125f, (float16_t)5.87890625f, + (float16_t)-2.0078125f, (float16_t)5.33203125f, (float16_t)4.8125f, (float16_t)-2.0078125f, + (float16_t)-2.0078125f, (float16_t)1.984375f, (float16_t)0.471435547f, (float16_t)-2.0078125f, + (float16_t)1.11621094f, (float16_t)2.2734375f, (float16_t)4.3359375f, (float16_t)-2.0078125f, + (float16_t)2.17578125f, (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, + (float16_t)5.7109375f, (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, + (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, + (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, + (float16_t)5.7578125f, (float16_t)4.22265625f, (float16_t)1.23730469f, (float16_t)1.23730469f, + (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)5.38671875f, + (float16_t)-1.35058594f, (float16_t)0.304443359f, (float16_t)-1.02539062f, (float16_t)0.770996094f, + (float16_t)-0.320556641f, (float16_t)2.796875f, (float16_t)-4.9375f, (float16_t)-4.44921875f, + (float16_t)-3.5859375f, (float16_t)4.03515625f, (float16_t)-0.665039062f, (float16_t)5.62890625f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)1.30664062f, + (float16_t)0.872558594f, (float16_t)2.76953125f, (float16_t)1.95214844f, (float16_t)-0.392822266f, + (float16_t)-4.16015625f, (float16_t)4.6640625f, (float16_t)-4.69921875f, (float16_t)0.211425781f, + (float16_t)3.32226562f, (float16_t)-0.768554688f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, + (float16_t)4.30859375f, (float16_t)4.41015625f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, + (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)3.66601562f, + (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)3.83398438f, (float16_t)-1.40917969f, + (float16_t)0.194702148f, (float16_t)5.4296875f, (float16_t)3.86328125f, (float16_t)2.62304688f, + (float16_t)1.16113281f, (float16_t)-1.40917969f, (float16_t)1.40722656f, (float16_t)1.75683594f, + (float16_t)0.884277344f, (float16_t)0.766601562f, (float16_t)4.3046875f, (float16_t)-3.48046875f, + (float16_t)-4.20703125f, (float16_t)1.66699219f, (float16_t)-0.546386719f, (float16_t)-4.20703125f, + (float16_t)-4.0703125f, (float16_t)3.4375f, (float16_t)-2.15429688f, (float16_t)-4.20703125f, + (float16_t)-2.58203125f, (float16_t)5.93359375f, (float16_t)-4.20703125f, (float16_t)-1.97558594f, + (float16_t)-3.21289062f, (float16_t)4.73828125f, (float16_t)3.84960938f, (float16_t)-4.20703125f, + (float16_t)2.47460938f, (float16_t)-1.97167969f, (float16_t)3.96679688f, (float16_t)-3.08984375f, + (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, + (float16_t)3.29101562f, (float16_t)2.04101562f, (float16_t)1.43847656f, (float16_t)1.43847656f, + (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)5.7265625f, (float16_t)1.43847656f, + (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, + (float16_t)1.43847656f, (float16_t)1.74902344f, (float16_t)1.43847656f, (float16_t)1.43847656f, + (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)1.43847656f, + (float16_t)5.75f, (float16_t)-1.51757812f, (float16_t)-1.71972656f, (float16_t)-0.694335938f, + (float16_t)0.206298828f, (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)-1.71972656f, + (float16_t)-0.980957031f, (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)2.64453125f, + (float16_t)1.27636719f, (float16_t)-0.794433594f, (float16_t)-1.71972656f, (float16_t)-1.71972656f, + (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)3.15429688f, + (float16_t)-1.71972656f, (float16_t)0.0889282227f, (float16_t)-1.71972656f, (float16_t)3.32617188f, + (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, + (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)2.1171875f, (float16_t)1.12207031f, + (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, + (float16_t)1.30859375f, (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)4.49609375f, + (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)4.3671875f, + (float16_t)5.67578125f, (float16_t)1.40625f, (float16_t)2.19921875f, (float16_t)1.046875f, + (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.40429688f, + (float16_t)3.40429688f, (float16_t)4.4375f, (float16_t)3.40429688f, (float16_t)3.78515625f, + (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.82421875f, + (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.40429688f, (float16_t)3.75f, + (float16_t)5.21875f, (float16_t)3.40429688f, (float16_t)3.43359375f, (float16_t)3.40429688f, + (float16_t)4.05859375f, (float16_t)3.40429688f, (float16_t)4.26171875f, (float16_t)3.40429688f, + (float16_t)3.40234375f, (float16_t)-3.9296875f, (float16_t)-0.553710938f, (float16_t)0.687988281f, + (float16_t)-0.0600891113f, (float16_t)0.669433594f, (float16_t)4.39453125f, (float16_t)-0.291259766f, + (float16_t)5.140625f, (float16_t)1.57910156f, (float16_t)1.6015625f, (float16_t)-1.49609375f, + (float16_t)-5.4609375f, (float16_t)-1.5703125f, (float16_t)5.01953125f, (float16_t)5.45703125f, + (float16_t)4.7421875f, (float16_t)-1.91699219f, (float16_t)-5.4609375f, (float16_t)0.431640625f, + (float16_t)-5.4609375f, (float16_t)5.453125f, (float16_t)0.153320312f, (float16_t)-5.4609375f, + (float16_t)4.328125f, (float16_t)2.78320312f, (float16_t)4.453125f, (float16_t)2.78320312f, + (float16_t)2.78320312f, (float16_t)4.3828125f, (float16_t)2.78320312f, (float16_t)2.78320312f, + (float16_t)4.65625f, (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)2.78320312f, + (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)4.453125f, (float16_t)2.78320312f, + (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)2.88476562f, + (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)2.78320312f, (float16_t)2.78320312f, + (float16_t)3.48046875f, (float16_t)4.17578125f, (float16_t)2.9609375f, (float16_t)2.97460938f, + (float16_t)3.12109375f, (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)5.57421875f, + (float16_t)3.19335938f, (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)2.9609375f, + (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)3.12890625f, + (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)2.9609375f, + (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)4.2265625f, (float16_t)5.9765625f, + (float16_t)4.8671875f, (float16_t)-3.0390625f, (float16_t)-3.31054688f, (float16_t)5.30859375f, + (float16_t)-2.98046875f, (float16_t)0.770996094f, (float16_t)-1.57714844f, (float16_t)-3.88867188f, + (float16_t)4.70703125f, (float16_t)-3.7890625f, (float16_t)-1.1015625f, (float16_t)-1.31152344f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)1.578125f, (float16_t)5.1328125f, + (float16_t)0.634277344f, (float16_t)4.640625f, (float16_t)4.77734375f, (float16_t)4.3203125f, + (float16_t)5.15625f, (float16_t)-3.31640625f, (float16_t)-0.414794922f, (float16_t)-1.9375f, + (float16_t)1.57519531f, (float16_t)1.68457031f, (float16_t)-0.138793945f, (float16_t)4.84375f, + (float16_t)-0.138793945f, (float16_t)2.92578125f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, + (float16_t)-0.138793945f, (float16_t)1.35546875f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, + (float16_t)4.640625f, (float16_t)2.5546875f, (float16_t)3.9296875f, (float16_t)4.60546875f, + (float16_t)0.490966797f, (float16_t)1.50585938f, (float16_t)-0.138793945f, (float16_t)0.608398438f, + (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, + (float16_t)5.015625f, (float16_t)5.6015625f, (float16_t)2.96875f, (float16_t)4.35546875f, + (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, + (float16_t)4.74609375f, (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, + (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, + (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)4.0703125f, + (float16_t)2.96875f, (float16_t)5.171875f, (float16_t)5.10546875f, (float16_t)2.96875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/config_data.h new file mode 100644 index 00000000..dfe528ec --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_CH_F32_BATCH_1 2 +#define MAXIMUM_BROADCAST_CH_F32_HEIGHT_1 2 +#define MAXIMUM_BROADCAST_CH_F32_WIDTH_1 4 +#define MAXIMUM_BROADCAST_CH_F32_CHANNEL_1 1 +#define MAXIMUM_BROADCAST_CH_F32_BATCH_2 2 +#define MAXIMUM_BROADCAST_CH_F32_HEIGHT_2 2 +#define MAXIMUM_BROADCAST_CH_F32_WIDTH_2 4 +#define MAXIMUM_BROADCAST_CH_F32_CHANNEL_2 24 +#define MAXIMUM_BROADCAST_CH_F32_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_CH_F32_OUTPUT_HEIGHT 2 +#define MAXIMUM_BROADCAST_CH_F32_OUTPUT_WIDTH 4 +#define MAXIMUM_BROADCAST_CH_F32_OUTPUT_CHANNEL 24 +#define MAXIMUM_BROADCAST_CH_F32_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_1_data.h new file mode 100644 index 00000000..1d3ae5a8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_1_data.h @@ -0,0 +1,17 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_ch_f32_input_tensor_1[16] = {-2.79946804f, + 3.99777508f, + 4.7493329f, + -5.07016373f, + 0.0300274566f, + 2.40418911f, + 3.73551178f, + -5.0873518f, + 2.60367012f, + 0.385420024f, + 0.468389601f, + 5.47010231f, + 5.08810282f, + 4.48744392f, + -5.41141224f, + 0.519257128f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_2_data.h new file mode 100644 index 00000000..8ccfc2cf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/input_tensor_2_data.h @@ -0,0 +1,57 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_ch_f32_input_tensor_2[384] = { + -5.81857395f, 3.53431511f, -3.43794775f, -5.9171896f, -4.02916431f, 3.92857432f, 3.08190894f, + -5.28822565f, 5.22775507f, -3.0644803f, 0.530017316f, -4.46785212f, -1.01627815f, 5.09683037f, + 5.95009279f, -1.06094968f, 4.61344671f, 5.40496969f, -5.03422785f, 1.639323f, -2.96253419f, + -1.90082014f, 1.01374424f, -2.55639839f, 1.58507955f, 2.49705005f, 0.249409929f, -0.0348887406f, + -2.89857435f, -0.0334655084f, 1.3208797f, -5.96453905f, -4.07583237f, -4.73021078f, -4.28760862f, + -3.04700613f, -1.76338267f, 1.72121751f, -2.82083559f, -0.0825804323f, 1.52107918f, 0.313556969f, + 0.204173967f, 5.04898643f, 4.94273233f, -4.03286743f, 2.67828941f, -2.33016348f, 5.42924309f, + 0.428456903f, 5.1180191f, 1.75475061f, -2.00222397f, 1.98119354f, 4.511343f, 5.1502614f, + -1.71982896f, -1.97188973f, 4.44592905f, 0.13798216f, 4.70152807f, -0.993848681f, -5.47357607f, + 3.4820888f, 5.44882917f, -4.24970531f, -2.15164828f, -1.70661306f, -5.46865845f, -1.82973289f, + -1.85645282f, -4.57259369f, 3.57256079f, 4.78576183f, 5.99715662f, 0.904260993f, -5.46233654f, + 0.706884801f, 2.22314954f, -3.85930896f, -5.47886658f, 4.83492184f, 2.8507092f, 2.26734591f, + 1.48972201f, -4.01050806f, 1.03246188f, -2.83248115f, 0.504711092f, 4.53716326f, 5.901752f, + 1.29180527f, -4.08421421f, -4.28865576f, 3.54089308f, 5.02479076f, -3.86136222f, -2.2738049f, + -3.1324358f, 4.04458475f, 4.14922571f, 4.94081879f, 2.29100823f, 4.80190563f, -4.19971228f, + -4.42897606f, 0.842420995f, -2.82134199f, 3.61959147f, -3.58570981f, 4.07256699f, -2.56117225f, + 5.9185853f, 4.94481802f, 0.788578749f, -2.02079344f, 0.798386693f, -4.98145437f, 0.718806624f, + -3.1369555f, -1.33506536f, 0.86048466f, 5.46794987f, 1.7890321f, 5.5152216f, 0.739144087f, + -0.518388808f, 5.35301304f, -2.88631177f, -2.90389919f, -0.892657638f, -2.42383909f, -1.87559426f, + 5.49149323f, 0.516366422f, 5.91928959f, -4.62314701f, -4.76797247f, -4.60599518f, -1.95736444f, + -0.832552612f, 2.28296232f, -2.90118241f, -4.90500116f, 0.862293124f, -4.51998568f, 4.86690187f, + 4.29681969f, 2.49267292f, -3.96154785f, -2.29110456f, -5.60855103f, -1.13048565f, -0.010748635f, + 1.21851254f, -3.0851903f, 5.04861927f, -3.34025097f, 2.01621222f, -5.31638145f, -2.90726972f, + -5.82931423f, 2.83189225f, -1.99061084f, 4.46657372f, 1.88980019f, 5.52644444f, -2.03232408f, + 2.26003933f, 3.57091999f, -5.84511423f, 5.37788057f, 2.7429285f, 5.44246864f, -3.48870587f, + -3.5988121f, -0.0197519846f, 5.20903635f, 5.51451159f, -3.85487938f, 5.84751558f, 5.60741901f, + 1.10836673f, -3.78942752f, -0.333450317f, -3.03335524f, -3.94971299f, 0.520921171f, 3.1513195f, + 1.45204759f, 1.53168178f, 2.47778535f, -2.13027263f, 3.16780257f, -1.18628061f, 0.0473567806f, + -5.67637491f, 2.78433776f, -2.04120827f, -3.09532642f, 1.19804502f, 0.281121045f, 2.65411663f, + 1.70114625f, 0.488126844f, -2.23086333f, 2.00305915f, -0.159159377f, -4.32881355f, 2.92691135f, + -5.48137999f, 2.30685282f, 3.91632938f, -2.46610713f, 0.617998421f, -5.16215611f, 2.99449587f, + -3.0749104f, 3.37036562f, 1.17670083f, -3.78520393f, 0.547186732f, 1.02675366f, -3.06451941f, + 3.12815475f, 3.77494121f, 5.92309141f, -1.46927381f, 0.194556773f, -4.34782696f, -0.362680972f, + 0.625895202f, -1.36787188f, -3.45114899f, 0.534722567f, 0.544402659f, -1.43822634f, -5.75769281f, + -3.98651624f, -2.41333675f, 3.44800067f, -1.30595577f, 3.86567545f, -0.164896145f, 4.82536936f, + 0.808961511f, -1.3541007f, 4.03132868f, -1.16697598f, -1.33790064f, 4.99936295f, 2.08136392f, + 0.469598144f, -4.95266676f, 4.30390692f, 1.07291138f, -3.43018198f, 0.554416895f, -5.04606915f, + 3.81235099f, 0.426200092f, 1.12604046f, -2.23211622f, 3.41484809f, 4.46840286f, 2.4824574f, + -3.6813519f, -0.8883003f, 0.574476182f, 5.36865616f, -2.53047442f, -2.13165665f, 5.5733614f, + -0.576437354f, 3.77873683f, 0.765248656f, -4.6694231f, 5.93600082f, 3.48022342f, 4.88055944f, + -2.81772208f, -0.666741133f, 5.23013449f, 4.88224077f, -1.8256669f, 4.22875547f, 5.51502514f, + 2.66109276f, -4.75895882f, -3.533108f, 2.93441272f, 5.50977659f, -0.343114585f, 3.00083137f, + -5.61721611f, 0.488634169f, -3.40017986f, 3.70441246f, 5.17440939f, -1.61307359f, 1.49967337f, + -3.11282516f, 4.33862638f, -3.11676121f, 0.918532968f, 1.55540657f, -0.487199336f, 2.42873263f, + 3.23270988f, 2.01436639f, 0.143748552f, -5.83948708f, 0.548818171f, 3.85125375f, 4.84875393f, + -5.1284771f, -5.99992418f, 3.84876227f, 3.69624925f, 5.1339426f, -1.04546225f, 1.94597697f, + 1.07050776f, -1.86275923f, 1.96258783f, 3.57243371f, -4.99783897f, 0.111679569f, -5.0894227f, + 3.11141491f, 4.1813035f, 1.0789907f, -3.65423322f, 4.68895626f, 1.60659707f, -3.76159954f, + -1.35881317f, 3.06219959f, 1.66579783f, 5.5488224f, 5.08280373f, -1.5901885f, -5.05826521f, + 1.31841838f, 4.58983517f, 2.70450354f, 1.44673026f, 3.86599016f, 3.99965858f, -1.10586941f, + 0.511591136f, 0.359958321f, -5.49746513f, 1.49873412f, 5.32756376f, 1.75317168f, 4.4250946f, + -1.33294952f, -2.27021098f, -5.44089699f, -0.506264865f, 2.85474324f, 3.92870903f, 4.35894823f, + 3.83739567f, 1.90712202f, 2.56696916f, 5.16120529f, -5.1342721f, 4.22690821f, -1.71033311f, + 4.97142172f, 3.69499302f, -2.18889523f, -0.172188058f, -2.74337077f, -3.99706244f, -1.58633614f, + -4.98113585f, 5.40567493f, 3.65765643f, -5.29397917f, 4.55982637f, 4.98912573f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/output_ref_data.h new file mode 100644 index 00000000..ab1e5890 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/output_ref_data.h @@ -0,0 +1,57 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_ch_f32_output_ref[384] = { + -2.79946804f, 3.53431511f, -2.79946804f, -2.79946804f, -2.79946804f, 3.92857432f, 3.08190894f, + -2.79946804f, 5.22775507f, -2.79946804f, 0.530017316f, -2.79946804f, -1.01627815f, 5.09683037f, + 5.95009279f, -1.06094968f, 4.61344671f, 5.40496969f, -2.79946804f, 1.639323f, -2.79946804f, + -1.90082014f, 1.01374424f, -2.55639839f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, + 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, + 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, 3.99777508f, + 3.99777508f, 5.04898643f, 4.94273233f, 3.99777508f, 3.99777508f, 3.99777508f, 5.42924309f, + 4.7493329f, 5.1180191f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, 5.1502614f, + 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, + 4.7493329f, 5.44882917f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, 4.7493329f, + 4.7493329f, 4.7493329f, 3.57256079f, 4.78576183f, 5.99715662f, 0.904260993f, -5.07016373f, + 0.706884801f, 2.22314954f, -3.85930896f, -5.07016373f, 4.83492184f, 2.8507092f, 2.26734591f, + 1.48972201f, -4.01050806f, 1.03246188f, -2.83248115f, 0.504711092f, 4.53716326f, 5.901752f, + 1.29180527f, -4.08421421f, -4.28865576f, 3.54089308f, 5.02479076f, 0.0300274566f, 0.0300274566f, + 0.0300274566f, 4.04458475f, 4.14922571f, 4.94081879f, 2.29100823f, 4.80190563f, 0.0300274566f, + 0.0300274566f, 0.842420995f, 0.0300274566f, 3.61959147f, 0.0300274566f, 4.07256699f, 0.0300274566f, + 5.9185853f, 4.94481802f, 0.788578749f, 0.0300274566f, 0.798386693f, 0.0300274566f, 0.718806624f, + 0.0300274566f, 2.40418911f, 2.40418911f, 5.46794987f, 2.40418911f, 5.5152216f, 2.40418911f, + 2.40418911f, 5.35301304f, 2.40418911f, 2.40418911f, 2.40418911f, 2.40418911f, 2.40418911f, + 5.49149323f, 2.40418911f, 5.91928959f, 2.40418911f, 2.40418911f, 2.40418911f, 2.40418911f, + 2.40418911f, 2.40418911f, 2.40418911f, 2.40418911f, 3.73551178f, 3.73551178f, 4.86690187f, + 4.29681969f, 3.73551178f, 3.73551178f, 3.73551178f, 3.73551178f, 3.73551178f, 3.73551178f, + 3.73551178f, 3.73551178f, 5.04861927f, 3.73551178f, 3.73551178f, 3.73551178f, 3.73551178f, + 3.73551178f, 3.73551178f, 3.73551178f, 4.46657372f, 3.73551178f, 5.52644444f, 3.73551178f, + 2.26003933f, 3.57091999f, -5.0873518f, 5.37788057f, 2.7429285f, 5.44246864f, -3.48870587f, + -3.5988121f, -0.0197519846f, 5.20903635f, 5.51451159f, -3.85487938f, 5.84751558f, 5.60741901f, + 1.10836673f, -3.78942752f, -0.333450317f, -3.03335524f, -3.94971299f, 0.520921171f, 3.1513195f, + 1.45204759f, 1.53168178f, 2.47778535f, 2.60367012f, 3.16780257f, 2.60367012f, 2.60367012f, + 2.60367012f, 2.78433776f, 2.60367012f, 2.60367012f, 2.60367012f, 2.60367012f, 2.65411663f, + 2.60367012f, 2.60367012f, 2.60367012f, 2.60367012f, 2.60367012f, 2.60367012f, 2.92691135f, + 2.60367012f, 2.60367012f, 3.91632938f, 2.60367012f, 2.60367012f, 2.60367012f, 2.99449587f, + 0.385420024f, 3.37036562f, 1.17670083f, 0.385420024f, 0.547186732f, 1.02675366f, 0.385420024f, + 3.12815475f, 3.77494121f, 5.92309141f, 0.385420024f, 0.385420024f, 0.385420024f, 0.385420024f, + 0.625895202f, 0.385420024f, 0.385420024f, 0.534722567f, 0.544402659f, 0.385420024f, 0.385420024f, + 0.385420024f, 0.385420024f, 3.44800067f, 0.468389601f, 3.86567545f, 0.468389601f, 4.82536936f, + 0.808961511f, 0.468389601f, 4.03132868f, 0.468389601f, 0.468389601f, 4.99936295f, 2.08136392f, + 0.469598144f, 0.468389601f, 4.30390692f, 1.07291138f, 0.468389601f, 0.554416895f, 0.468389601f, + 3.81235099f, 0.468389601f, 1.12604046f, 0.468389601f, 3.41484809f, 5.47010231f, 5.47010231f, + 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.5733614f, + 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.93600082f, 5.47010231f, 5.47010231f, + 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.47010231f, 5.51502514f, + 5.47010231f, 5.08810282f, 5.08810282f, 5.08810282f, 5.50977659f, 5.08810282f, 5.08810282f, + 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, 5.17440939f, 5.08810282f, 5.08810282f, + 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, + 5.08810282f, 5.08810282f, 5.08810282f, 5.08810282f, 4.48744392f, 4.48744392f, 4.84875393f, + 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, 5.1339426f, 4.48744392f, 4.48744392f, + 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, + 4.48744392f, 4.48744392f, 4.48744392f, 4.48744392f, 4.68895626f, 4.48744392f, 4.48744392f, + -1.35881317f, 3.06219959f, 1.66579783f, 5.5488224f, 5.08280373f, -1.5901885f, -5.05826521f, + 1.31841838f, 4.58983517f, 2.70450354f, 1.44673026f, 3.86599016f, 3.99965858f, -1.10586941f, + 0.511591136f, 0.359958321f, -5.41141224f, 1.49873412f, 5.32756376f, 1.75317168f, 4.4250946f, + -1.33294952f, -2.27021098f, -5.41141224f, 0.519257128f, 2.85474324f, 3.92870903f, 4.35894823f, + 3.83739567f, 1.90712202f, 2.56696916f, 5.16120529f, 0.519257128f, 4.22690821f, 0.519257128f, + 4.97142172f, 3.69499302f, 0.519257128f, 0.519257128f, 0.519257128f, 0.519257128f, 0.519257128f, + 0.519257128f, 5.40567493f, 3.65765643f, 0.519257128f, 4.55982637f, 4.98912573f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_ch_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/config_data.h new file mode 100644 index 00000000..117ffdf5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_HEIGHT_F16_BATCH_1 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_HEIGHT_F16_WIDTH_1 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_CHANNEL_1 17 +#define MAXIMUM_BROADCAST_HEIGHT_F16_BATCH_2 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_HEIGHT_2 4 +#define MAXIMUM_BROADCAST_HEIGHT_F16_WIDTH_2 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_CHANNEL_2 17 +#define MAXIMUM_BROADCAST_HEIGHT_F16_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_OUTPUT_HEIGHT 4 +#define MAXIMUM_BROADCAST_HEIGHT_F16_OUTPUT_WIDTH 2 +#define MAXIMUM_BROADCAST_HEIGHT_F16_OUTPUT_CHANNEL 17 +#define MAXIMUM_BROADCAST_HEIGHT_F16_DST_SIZE 272 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_1_data.h new file mode 100644 index 00000000..7382a394 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_1_data.h @@ -0,0 +1,19 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_height_f16_input_tensor_1[68] = { + (float16_t)1.87597656f, (float16_t)0.682617188f, (float16_t)5.52734375f, (float16_t)-4.2734375f, + (float16_t)-2.0546875f, (float16_t)5.37109375f, (float16_t)4.28125f, (float16_t)4.48828125f, + (float16_t)0.862304688f, (float16_t)4.51171875f, (float16_t)1.96582031f, (float16_t)2.34765625f, + (float16_t)5.0703125f, (float16_t)3.52148438f, (float16_t)-4.9296875f, (float16_t)2.5859375f, + (float16_t)-3.9609375f, (float16_t)-4.7890625f, (float16_t)-1.88378906f, (float16_t)3.52539062f, + (float16_t)5.9453125f, (float16_t)-4.47265625f, (float16_t)4.7421875f, (float16_t)1.31835938f, + (float16_t)-2.72265625f, (float16_t)2.41015625f, (float16_t)4.16796875f, (float16_t)-1.4375f, + (float16_t)-3.71875f, (float16_t)5.64453125f, (float16_t)5.03515625f, (float16_t)3.94921875f, + (float16_t)-2.87304688f, (float16_t)1.06933594f, (float16_t)1.65820312f, (float16_t)-3.3046875f, + (float16_t)1.07324219f, (float16_t)-2.61523438f, (float16_t)2.64257812f, (float16_t)2.02929688f, + (float16_t)-5.86328125f, (float16_t)4.07421875f, (float16_t)-0.516113281f, (float16_t)0.744140625f, + (float16_t)1.70410156f, (float16_t)5.04296875f, (float16_t)-5.609375f, (float16_t)-3.65429688f, + (float16_t)2.70898438f, (float16_t)1.27832031f, (float16_t)4.54296875f, (float16_t)4.91015625f, + (float16_t)-1.94140625f, (float16_t)0.116882324f, (float16_t)3.26367188f, (float16_t)-5.046875f, + (float16_t)1.35546875f, (float16_t)2.03710938f, (float16_t)1.75f, (float16_t)-3.11328125f, + (float16_t)-3.30859375f, (float16_t)4.75390625f, (float16_t)-4.81640625f, (float16_t)-5.828125f, + (float16_t)-2.2109375f, (float16_t)1.97558594f, (float16_t)-3.82421875f, (float16_t)2.29101562f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_2_data.h new file mode 100644 index 00000000..23e962af --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/input_tensor_2_data.h @@ -0,0 +1,70 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_height_f16_input_tensor_2[272] = { + (float16_t)-4.33203125f, (float16_t)-1.83984375f, (float16_t)-2.89257812f, (float16_t)-4.6328125f, + (float16_t)-1.78613281f, (float16_t)3.84570312f, (float16_t)-5.52734375f, (float16_t)-3.4296875f, + (float16_t)4.38671875f, (float16_t)-3.57226562f, (float16_t)-0.512695312f, (float16_t)-0.875f, + (float16_t)-0.341064453f, (float16_t)3.37695312f, (float16_t)-1.16113281f, (float16_t)2.25585938f, + (float16_t)-2.3515625f, (float16_t)4.6640625f, (float16_t)2.67773438f, (float16_t)3.78710938f, + (float16_t)5.54296875f, (float16_t)-2.20898438f, (float16_t)0.431396484f, (float16_t)4.484375f, + (float16_t)-3.76367188f, (float16_t)-3.28320312f, (float16_t)-1.41796875f, (float16_t)1.66894531f, + (float16_t)-3.9609375f, (float16_t)-2.28320312f, (float16_t)4.67578125f, (float16_t)5.9921875f, + (float16_t)-1.95703125f, (float16_t)-2.39257812f, (float16_t)-4.375f, (float16_t)-5.5234375f, + (float16_t)-4.97265625f, (float16_t)-0.891601562f, (float16_t)-1.078125f, (float16_t)3.890625f, + (float16_t)-4.8984375f, (float16_t)-0.882324219f, (float16_t)0.413574219f, (float16_t)-0.607910156f, + (float16_t)-5.41796875f, (float16_t)-2.23242188f, (float16_t)-2.12109375f, (float16_t)0.956542969f, + (float16_t)-5.30078125f, (float16_t)0.806152344f, (float16_t)1.85351562f, (float16_t)1.58691406f, + (float16_t)4.07421875f, (float16_t)-2.86914062f, (float16_t)-1.59863281f, (float16_t)2.84179688f, + (float16_t)-1.71972656f, (float16_t)5.4375f, (float16_t)0.353515625f, (float16_t)-1.29003906f, + (float16_t)-5.30859375f, (float16_t)0.0581054688f, (float16_t)2.6328125f, (float16_t)0.427734375f, + (float16_t)0.166015625f, (float16_t)2.6875f, (float16_t)5.484375f, (float16_t)-1.53808594f, + (float16_t)-2.21289062f, (float16_t)-5.92578125f, (float16_t)3.85351562f, (float16_t)5.6875f, + (float16_t)5.984375f, (float16_t)-1.43847656f, (float16_t)2.4453125f, (float16_t)-0.134277344f, + (float16_t)3.796875f, (float16_t)-1.37109375f, (float16_t)0.709472656f, (float16_t)-0.339355469f, + (float16_t)-0.94140625f, (float16_t)-3.796875f, (float16_t)-2.20898438f, (float16_t)-2.34375f, + (float16_t)1.3203125f, (float16_t)-5.359375f, (float16_t)-3.18554688f, (float16_t)-2.95507812f, + (float16_t)2.10546875f, (float16_t)2.85546875f, (float16_t)-2.58203125f, (float16_t)0.882324219f, + (float16_t)-0.126953125f, (float16_t)2.76953125f, (float16_t)2.49609375f, (float16_t)-3.24414062f, + (float16_t)0.154785156f, (float16_t)3.17578125f, (float16_t)0.874023438f, (float16_t)-1.63085938f, + (float16_t)3.36523438f, (float16_t)-0.468261719f, (float16_t)-2.63671875f, (float16_t)1.0625f, + (float16_t)-2.41796875f, (float16_t)0.334716797f, (float16_t)-4.37109375f, (float16_t)2.61328125f, + (float16_t)1.22851562f, (float16_t)0.90625f, (float16_t)3.87304688f, (float16_t)5.87109375f, + (float16_t)-2.47265625f, (float16_t)-5.78515625f, (float16_t)-5.390625f, (float16_t)-3.59375f, + (float16_t)3.67382812f, (float16_t)-1.84375f, (float16_t)3.5546875f, (float16_t)-3.72851562f, + (float16_t)3.4453125f, (float16_t)1.08203125f, (float16_t)-0.387939453f, (float16_t)4.9140625f, + (float16_t)-5.92578125f, (float16_t)2.99804688f, (float16_t)-2.56445312f, (float16_t)5.12109375f, + (float16_t)2.09375f, (float16_t)0.447021484f, (float16_t)-1.35644531f, (float16_t)0.155273438f, + (float16_t)-1.87695312f, (float16_t)-1.83886719f, (float16_t)-0.466064453f, (float16_t)-0.555664062f, + (float16_t)-5.125f, (float16_t)-2.046875f, (float16_t)-4.703125f, (float16_t)-5.28515625f, + (float16_t)-0.368652344f, (float16_t)-2.953125f, (float16_t)3.91210938f, (float16_t)5.80078125f, + (float16_t)2.72851562f, (float16_t)-5.93359375f, (float16_t)-4.37890625f, (float16_t)4.6875f, + (float16_t)-0.555175781f, (float16_t)-1.6953125f, (float16_t)-1.84179688f, (float16_t)5.2109375f, + (float16_t)-2.57421875f, (float16_t)4.53125f, (float16_t)-2.36914062f, (float16_t)4.453125f, + (float16_t)5.25390625f, (float16_t)0.694824219f, (float16_t)-5.875f, (float16_t)-5.9453125f, + (float16_t)4.65234375f, (float16_t)0.567382812f, (float16_t)0.508300781f, (float16_t)-4.94140625f, + (float16_t)-1.23925781f, (float16_t)4.70703125f, (float16_t)2.56835938f, (float16_t)2.86328125f, + (float16_t)-0.384521484f, (float16_t)1.48144531f, (float16_t)0.891113281f, (float16_t)-1.95117188f, + (float16_t)1.98046875f, (float16_t)2.61328125f, (float16_t)-4.28515625f, (float16_t)4.10546875f, + (float16_t)0.333496094f, (float16_t)-3.8984375f, (float16_t)-1.42675781f, (float16_t)-5.23046875f, + (float16_t)5.53515625f, (float16_t)4.58203125f, (float16_t)2.3671875f, (float16_t)-3.61523438f, + (float16_t)-4.234375f, (float16_t)-1.63964844f, (float16_t)-0.430419922f, (float16_t)4.42578125f, + (float16_t)5.8203125f, (float16_t)-3.04882812f, (float16_t)-4.26953125f, (float16_t)-1.62011719f, + (float16_t)-4.8984375f, (float16_t)-2.77734375f, (float16_t)-3.69335938f, (float16_t)3.47070312f, + (float16_t)5.734375f, (float16_t)-1.66894531f, (float16_t)4.4375f, (float16_t)4.609375f, + (float16_t)-3.3359375f, (float16_t)2.9296875f, (float16_t)5.69921875f, (float16_t)-4.546875f, + (float16_t)0.8515625f, (float16_t)2.07226562f, (float16_t)-0.936523438f, (float16_t)0.34375f, + (float16_t)4.03515625f, (float16_t)4.97265625f, (float16_t)-3.22460938f, (float16_t)5.67578125f, + (float16_t)3.15039062f, (float16_t)4.2734375f, (float16_t)5.61328125f, (float16_t)-0.101135254f, + (float16_t)4.453125f, (float16_t)-0.356933594f, (float16_t)1.35058594f, (float16_t)1.29882812f, + (float16_t)2.88867188f, (float16_t)-0.223022461f, (float16_t)4.65234375f, (float16_t)-3.78125f, + (float16_t)3.58789062f, (float16_t)-1.98925781f, (float16_t)-5.76171875f, (float16_t)-3.671875f, + (float16_t)4.26953125f, (float16_t)-4.0234375f, (float16_t)3.1171875f, (float16_t)5.02734375f, + (float16_t)0.682128906f, (float16_t)1.51757812f, (float16_t)0.703613281f, (float16_t)2.6796875f, + (float16_t)-5.796875f, (float16_t)-3.36328125f, (float16_t)-4.578125f, (float16_t)3.61523438f, + (float16_t)3.30664062f, (float16_t)2.18359375f, (float16_t)-3.90039062f, (float16_t)4.02734375f, + (float16_t)4.796875f, (float16_t)4.69921875f, (float16_t)-5.546875f, (float16_t)5.8515625f, + (float16_t)0.545898438f, (float16_t)2.44140625f, (float16_t)1.46289062f, (float16_t)0.8359375f, + (float16_t)-0.307373047f, (float16_t)2.29296875f, (float16_t)3.390625f, (float16_t)0.451416016f, + (float16_t)-5.7265625f, (float16_t)2.61523438f, (float16_t)-2.7734375f, (float16_t)-2.921875f, + (float16_t)-1.88378906f, (float16_t)-5.33203125f, (float16_t)-3.1875f, (float16_t)-0.233886719f, + (float16_t)0.457275391f, (float16_t)-4.90625f, (float16_t)-3.71484375f, (float16_t)4.25f, + (float16_t)-5.16796875f, (float16_t)-4.70703125f, (float16_t)-3.98632812f, (float16_t)4.1015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/output_ref_data.h new file mode 100644 index 00000000..340afdfe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/output_ref_data.h @@ -0,0 +1,70 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_height_f16_output_ref[272] = { + (float16_t)1.87597656f, (float16_t)0.682617188f, (float16_t)5.52734375f, (float16_t)-4.2734375f, + (float16_t)-1.78613281f, (float16_t)5.37109375f, (float16_t)4.28125f, (float16_t)4.48828125f, + (float16_t)4.38671875f, (float16_t)4.51171875f, (float16_t)1.96582031f, (float16_t)2.34765625f, + (float16_t)5.0703125f, (float16_t)3.52148438f, (float16_t)-1.16113281f, (float16_t)2.5859375f, + (float16_t)-2.3515625f, (float16_t)4.6640625f, (float16_t)2.67773438f, (float16_t)3.78710938f, + (float16_t)5.9453125f, (float16_t)-2.20898438f, (float16_t)4.7421875f, (float16_t)4.484375f, + (float16_t)-2.72265625f, (float16_t)2.41015625f, (float16_t)4.16796875f, (float16_t)1.66894531f, + (float16_t)-3.71875f, (float16_t)5.64453125f, (float16_t)5.03515625f, (float16_t)5.9921875f, + (float16_t)-1.95703125f, (float16_t)1.06933594f, (float16_t)1.87597656f, (float16_t)0.682617188f, + (float16_t)5.52734375f, (float16_t)-0.891601562f, (float16_t)-1.078125f, (float16_t)5.37109375f, + (float16_t)4.28125f, (float16_t)4.48828125f, (float16_t)0.862304688f, (float16_t)4.51171875f, + (float16_t)1.96582031f, (float16_t)2.34765625f, (float16_t)5.0703125f, (float16_t)3.52148438f, + (float16_t)-4.9296875f, (float16_t)2.5859375f, (float16_t)1.85351562f, (float16_t)1.58691406f, + (float16_t)4.07421875f, (float16_t)3.52539062f, (float16_t)5.9453125f, (float16_t)2.84179688f, + (float16_t)4.7421875f, (float16_t)5.4375f, (float16_t)0.353515625f, (float16_t)2.41015625f, + (float16_t)4.16796875f, (float16_t)0.0581054688f, (float16_t)2.6328125f, (float16_t)5.64453125f, + (float16_t)5.03515625f, (float16_t)3.94921875f, (float16_t)5.484375f, (float16_t)1.06933594f, + (float16_t)1.87597656f, (float16_t)0.682617188f, (float16_t)5.52734375f, (float16_t)5.6875f, + (float16_t)5.984375f, (float16_t)5.37109375f, (float16_t)4.28125f, (float16_t)4.48828125f, + (float16_t)3.796875f, (float16_t)4.51171875f, (float16_t)1.96582031f, (float16_t)2.34765625f, + (float16_t)5.0703125f, (float16_t)3.52148438f, (float16_t)-2.20898438f, (float16_t)2.5859375f, + (float16_t)1.3203125f, (float16_t)-4.7890625f, (float16_t)-1.88378906f, (float16_t)3.52539062f, + (float16_t)5.9453125f, (float16_t)2.85546875f, (float16_t)4.7421875f, (float16_t)1.31835938f, + (float16_t)-0.126953125f, (float16_t)2.76953125f, (float16_t)4.16796875f, (float16_t)-1.4375f, + (float16_t)0.154785156f, (float16_t)5.64453125f, (float16_t)5.03515625f, (float16_t)3.94921875f, + (float16_t)3.36523438f, (float16_t)1.06933594f, (float16_t)1.87597656f, (float16_t)1.0625f, + (float16_t)5.52734375f, (float16_t)0.334716797f, (float16_t)-2.0546875f, (float16_t)5.37109375f, + (float16_t)4.28125f, (float16_t)4.48828125f, (float16_t)3.87304688f, (float16_t)5.87109375f, + (float16_t)1.96582031f, (float16_t)2.34765625f, (float16_t)5.0703125f, (float16_t)3.52148438f, + (float16_t)3.67382812f, (float16_t)2.5859375f, (float16_t)3.5546875f, (float16_t)-3.72851562f, + (float16_t)3.4453125f, (float16_t)3.52539062f, (float16_t)5.9453125f, (float16_t)4.9140625f, + (float16_t)4.7421875f, (float16_t)2.99804688f, (float16_t)-2.56445312f, (float16_t)5.12109375f, + (float16_t)4.16796875f, (float16_t)0.447021484f, (float16_t)-1.35644531f, (float16_t)5.64453125f, + (float16_t)5.03515625f, (float16_t)3.94921875f, (float16_t)-0.466064453f, (float16_t)1.06933594f, + (float16_t)1.65820312f, (float16_t)-2.046875f, (float16_t)1.07324219f, (float16_t)-2.61523438f, + (float16_t)2.64257812f, (float16_t)2.02929688f, (float16_t)3.91210938f, (float16_t)5.80078125f, + (float16_t)2.72851562f, (float16_t)0.744140625f, (float16_t)1.70410156f, (float16_t)5.04296875f, + (float16_t)-0.555175781f, (float16_t)-1.6953125f, (float16_t)2.70898438f, (float16_t)5.2109375f, + (float16_t)4.54296875f, (float16_t)4.91015625f, (float16_t)-1.94140625f, (float16_t)4.453125f, + (float16_t)5.25390625f, (float16_t)0.694824219f, (float16_t)1.35546875f, (float16_t)2.03710938f, + (float16_t)4.65234375f, (float16_t)0.567382812f, (float16_t)0.508300781f, (float16_t)4.75390625f, + (float16_t)-1.23925781f, (float16_t)4.70703125f, (float16_t)2.56835938f, (float16_t)2.86328125f, + (float16_t)-0.384521484f, (float16_t)2.29101562f, (float16_t)1.65820312f, (float16_t)-1.95117188f, + (float16_t)1.98046875f, (float16_t)2.61328125f, (float16_t)2.64257812f, (float16_t)4.10546875f, + (float16_t)0.333496094f, (float16_t)4.07421875f, (float16_t)-0.516113281f, (float16_t)0.744140625f, + (float16_t)5.53515625f, (float16_t)5.04296875f, (float16_t)2.3671875f, (float16_t)-3.61523438f, + (float16_t)2.70898438f, (float16_t)1.27832031f, (float16_t)4.54296875f, (float16_t)4.91015625f, + (float16_t)5.8203125f, (float16_t)0.116882324f, (float16_t)3.26367188f, (float16_t)-1.62011719f, + (float16_t)1.35546875f, (float16_t)2.03710938f, (float16_t)1.75f, (float16_t)3.47070312f, + (float16_t)5.734375f, (float16_t)4.75390625f, (float16_t)4.4375f, (float16_t)4.609375f, + (float16_t)-2.2109375f, (float16_t)2.9296875f, (float16_t)5.69921875f, (float16_t)2.29101562f, + (float16_t)1.65820312f, (float16_t)2.07226562f, (float16_t)1.07324219f, (float16_t)0.34375f, + (float16_t)4.03515625f, (float16_t)4.97265625f, (float16_t)-3.22460938f, (float16_t)5.67578125f, + (float16_t)3.15039062f, (float16_t)4.2734375f, (float16_t)5.61328125f, (float16_t)5.04296875f, + (float16_t)4.453125f, (float16_t)-0.356933594f, (float16_t)2.70898438f, (float16_t)1.29882812f, + (float16_t)4.54296875f, (float16_t)4.91015625f, (float16_t)4.65234375f, (float16_t)0.116882324f, + (float16_t)3.58789062f, (float16_t)-1.98925781f, (float16_t)1.35546875f, (float16_t)2.03710938f, + (float16_t)4.26953125f, (float16_t)-3.11328125f, (float16_t)3.1171875f, (float16_t)5.02734375f, + (float16_t)0.682128906f, (float16_t)1.51757812f, (float16_t)0.703613281f, (float16_t)2.6796875f, + (float16_t)-3.82421875f, (float16_t)2.29101562f, (float16_t)1.65820312f, (float16_t)3.61523438f, + (float16_t)3.30664062f, (float16_t)2.18359375f, (float16_t)2.64257812f, (float16_t)4.02734375f, + (float16_t)4.796875f, (float16_t)4.69921875f, (float16_t)-0.516113281f, (float16_t)5.8515625f, + (float16_t)1.70410156f, (float16_t)5.04296875f, (float16_t)1.46289062f, (float16_t)0.8359375f, + (float16_t)2.70898438f, (float16_t)2.29296875f, (float16_t)4.54296875f, (float16_t)4.91015625f, + (float16_t)-1.94140625f, (float16_t)2.61523438f, (float16_t)3.26367188f, (float16_t)-2.921875f, + (float16_t)1.35546875f, (float16_t)2.03710938f, (float16_t)1.75f, (float16_t)-0.233886719f, + (float16_t)0.457275391f, (float16_t)4.75390625f, (float16_t)-3.71484375f, (float16_t)4.25f, + (float16_t)-2.2109375f, (float16_t)1.97558594f, (float16_t)-3.82421875f, (float16_t)4.1015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/config_data.h new file mode 100644 index 00000000..34a5f079 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_HEIGHT_F32_BATCH_1 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_HEIGHT_F32_WIDTH_1 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_CHANNEL_1 17 +#define MAXIMUM_BROADCAST_HEIGHT_F32_BATCH_2 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_HEIGHT_2 4 +#define MAXIMUM_BROADCAST_HEIGHT_F32_WIDTH_2 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_CHANNEL_2 17 +#define MAXIMUM_BROADCAST_HEIGHT_F32_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_OUTPUT_HEIGHT 4 +#define MAXIMUM_BROADCAST_HEIGHT_F32_OUTPUT_WIDTH 2 +#define MAXIMUM_BROADCAST_HEIGHT_F32_OUTPUT_CHANNEL 17 +#define MAXIMUM_BROADCAST_HEIGHT_F32_DST_SIZE 272 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_1_data.h new file mode 100644 index 00000000..11e16517 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_1_data.h @@ -0,0 +1,11 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_height_f32_input_tensor_1[68] = { + 3.47351384f, 4.80259657f, 3.53741288f, 0.419778824f, 2.00192738f, 3.01079535f, 2.32117105f, 3.71429944f, + 1.57537222f, -5.33611202f, 0.462889761f, 2.82877207f, -2.47482491f, 5.28843069f, -4.27755737f, 3.09172058f, + -4.22368145f, 5.52119684f, 2.02633309f, 0.5782305f, 2.41745877f, -5.0151577f, 0.678871512f, -0.867281556f, + -3.36905575f, 3.61806726f, -4.31763172f, -1.76629686f, 1.52691019f, -2.3601613f, 4.90055847f, -2.8335371f, + -3.8926878f, 4.03069782f, 3.65231347f, -2.17047858f, -3.90910792f, -0.559841454f, -4.87714386f, -3.39814925f, + -3.77397299f, 0.577274084f, -4.96852636f, -0.731654048f, 0.0965373665f, 3.43956685f, 5.99938393f, 0.780567706f, + 0.402634799f, 4.12220669f, 0.076542452f, -3.4154036f, -1.7341789f, -5.34286928f, -4.65397263f, -1.24971056f, + -4.09150505f, -4.71824408f, 1.62730765f, 4.06203556f, -1.30110705f, -4.68837166f, 0.961123347f, -0.70858562f, + -5.10217285f, 1.09769106f, 1.02084768f, 0.38991034f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_2_data.h new file mode 100644 index 00000000..a193e9f1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/input_tensor_2_data.h @@ -0,0 +1,41 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_height_f32_input_tensor_2[272] = { + 5.99916744f, -0.558638692f, -0.410337716f, 2.61993837f, -2.56251502f, -3.74399614f, 4.75914621f, + 1.99581099f, 1.20465505f, -3.98378134f, 0.199769154f, -4.9906044f, 4.48663568f, 1.22842491f, + -1.4426986f, -3.78419948f, 0.0543402731f, 5.48222923f, 5.18688965f, 5.39868402f, 4.84459543f, + 1.95429671f, -3.37382174f, 2.26324415f, 0.731634021f, -3.03866005f, 1.77108753f, 2.17121673f, + 2.68789387f, 1.75871813f, 0.240108013f, 4.25185823f, -3.42614007f, 2.96636248f, 1.34845722f, + -3.3797605f, -5.23072338f, -0.484633297f, -1.37728834f, -5.7080102f, -2.66574907f, 3.1910212f, + -3.37098312f, 1.53011608f, -2.92441368f, -4.52515697f, -2.1343286f, 2.81578422f, -2.68048739f, + -2.57961845f, -4.38411236f, -4.91851711f, 0.303323597f, -2.03369212f, -1.79894042f, -3.55140948f, + 3.78897119f, -0.598503947f, 5.2398653f, 1.58089876f, 2.74547958f, 3.64880919f, 1.68920982f, + -2.52018738f, 1.52753139f, 3.81733704f, -4.56635809f, -4.16392422f, -0.743269503f, 1.1294539f, + 4.91911364f, -4.72998333f, -5.80859184f, 4.64957142f, 0.0315940566f, -3.83376241f, -3.51480269f, + 3.27261281f, 3.5119164f, -0.179977134f, -1.78023529f, 1.18950224f, -4.77444601f, 1.56217444f, + 2.91743898f, 4.31399775f, -3.80754733f, 5.04363918f, -4.70095015f, -0.684813917f, 5.45226192f, + 1.64039111f, -0.0894357637f, 5.74309015f, 5.00561619f, 0.898507893f, 3.14885139f, -4.32917881f, + 4.26522732f, 3.06201792f, -4.97807932f, -3.5765698f, -3.08296061f, 2.13073516f, 0.0994178727f, + -1.57907212f, 5.38049269f, 1.55963659f, 0.384851635f, 2.20956135f, 2.98811626f, 0.419100136f, + 5.04293871f, 0.978068411f, 0.855557382f, -0.0823808908f, -1.89334512f, 5.43488169f, -0.92661494f, + 1.31700671f, 4.041749f, -3.35992146f, -1.74092817f, -4.28433704f, 4.36335468f, 5.0748415f, + -0.124468021f, 3.19889665f, -3.12557721f, -5.36447048f, 2.854599f, -0.7912485f, -0.86292547f, + -2.74308848f, 1.26041508f, -3.42368269f, 2.78720593f, 2.42831135f, 1.92243016f, -0.714776635f, + -5.40177011f, -3.32516909f, -0.2303866f, 0.482332259f, -5.11730671f, -3.02454233f, -1.40968156f, + -2.2198894f, 2.85761738f, 5.97991753f, -0.810782015f, 1.10585105f, 2.02300978f, 5.37282467f, + -1.27639103f, 3.09191298f, -0.233792335f, -1.72103059f, -3.72919679f, 5.446383f, 2.82199621f, + -2.90703487f, -2.35722733f, 5.09082365f, -4.11147165f, 3.00180459f, 2.85362482f, -3.43482065f, + -1.03137052f, 3.42322588f, 0.0902925357f, 3.98842335f, 3.22511339f, 3.57067037f, -0.655524194f, + 5.23435354f, 4.38254023f, -3.76337361f, -2.44550586f, -4.77990055f, 3.4826088f, -5.0515542f, + 3.57134748f, -5.39843035f, 5.00375938f, -1.77877581f, 4.17503166f, -5.47433615f, -0.623244941f, + 4.82289839f, -2.71706605f, -4.03740788f, -5.36289692f, 0.544187903f, 0.278381377f, 0.0860661939f, + -3.89292955f, 4.48523045f, 3.69429231f, 2.20803857f, -3.71398258f, 3.26196098f, 3.11023927f, + -1.02396655f, -3.86045313f, -0.586388707f, 2.80014014f, -3.70953989f, -4.71591854f, -5.6087842f, + -2.19708824f, 3.72728443f, -5.91473722f, 4.59176064f, -3.57918906f, 2.29640508f, -0.228720054f, + 2.78964972f, 2.92447424f, 5.36446524f, -0.259355694f, 5.97965384f, -3.86203241f, -4.87940454f, + -5.9310174f, -3.90473557f, -3.2123847f, -3.85786414f, 5.50049925f, 1.33119535f, 2.32632399f, + -5.45771551f, -1.66731846f, 2.71455693f, -5.56299686f, -3.51601291f, 2.53673315f, 1.53367102f, + 5.47524786f, -5.56260395f, 2.91291213f, 0.463025481f, -1.55778229f, -3.94566512f, 3.81586313f, + -2.59917521f, 5.74641657f, -0.705747545f, 1.51510179f, 2.41129589f, -3.87306046f, -3.53183699f, + 1.15185785f, -4.67194605f, -2.8368125f, 5.48901939f, -0.591653764f, -4.25708294f, 5.98635864f, + -3.91140389f, -3.46124911f, -1.36470068f, -5.49520206f, -0.792065859f, 2.22560525f, 5.91025019f, + -0.292450786f, -2.7891252f, 5.73723078f, -0.54810822f, 0.756070435f, 0.920532942f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/output_ref_data.h new file mode 100644 index 00000000..8c43af48 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/output_ref_data.h @@ -0,0 +1,41 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_height_f32_output_ref[272] = { + 5.99916744f, 4.80259657f, 3.53741288f, 2.61993837f, 2.00192738f, 3.01079535f, 4.75914621f, + 3.71429944f, 1.57537222f, -3.98378134f, 0.462889761f, 2.82877207f, 4.48663568f, 5.28843069f, + -1.4426986f, 3.09172058f, 0.0543402731f, 5.52119684f, 5.18688965f, 5.39868402f, 4.84459543f, + 1.95429671f, 0.678871512f, 2.26324415f, 0.731634021f, 3.61806726f, 1.77108753f, 2.17121673f, + 2.68789387f, 1.75871813f, 4.90055847f, 4.25185823f, -3.42614007f, 4.03069782f, 3.47351384f, + 4.80259657f, 3.53741288f, 0.419778824f, 2.00192738f, 3.01079535f, 2.32117105f, 3.71429944f, + 1.57537222f, 1.53011608f, 0.462889761f, 2.82877207f, -2.1343286f, 5.28843069f, -2.68048739f, + 3.09172058f, -4.22368145f, 5.52119684f, 2.02633309f, 0.5782305f, 2.41745877f, -3.55140948f, + 3.78897119f, -0.598503947f, 5.2398653f, 3.61806726f, 2.74547958f, 3.64880919f, 1.68920982f, + -2.3601613f, 4.90055847f, 3.81733704f, -3.8926878f, 4.03069782f, 3.47351384f, 4.80259657f, + 4.91911364f, 0.419778824f, 2.00192738f, 4.64957142f, 2.32117105f, 3.71429944f, 1.57537222f, + 3.27261281f, 3.5119164f, 2.82877207f, -1.78023529f, 5.28843069f, -4.27755737f, 3.09172058f, + 2.91743898f, 5.52119684f, 2.02633309f, 5.04363918f, 2.41745877f, -0.684813917f, 5.45226192f, + 1.64039111f, -0.0894357637f, 5.74309015f, 5.00561619f, 0.898507893f, 3.14885139f, -2.3601613f, + 4.90055847f, 3.06201792f, -3.8926878f, 4.03069782f, 3.47351384f, 4.80259657f, 3.53741288f, + 0.419778824f, 5.38049269f, 3.01079535f, 2.32117105f, 3.71429944f, 2.98811626f, 0.419100136f, + 5.04293871f, 2.82877207f, 0.855557382f, 5.28843069f, -1.89334512f, 5.43488169f, -0.92661494f, + 5.52119684f, 4.041749f, 0.5782305f, 2.41745877f, -4.28433704f, 4.36335468f, 5.0748415f, + -0.124468021f, 3.61806726f, -3.12557721f, -1.76629686f, 2.854599f, -0.7912485f, 4.90055847f, + -2.74308848f, 1.26041508f, 4.03069782f, 3.65231347f, 2.42831135f, 1.92243016f, -0.559841454f, + -4.87714386f, -3.32516909f, -0.2303866f, 0.577274084f, -4.96852636f, -0.731654048f, 0.0965373665f, + 3.43956685f, 5.99938393f, 5.97991753f, 0.402634799f, 4.12220669f, 2.02300978f, 5.37282467f, + -1.27639103f, 3.09191298f, -0.233792335f, -1.24971056f, -3.72919679f, 5.446383f, 2.82199621f, + 4.06203556f, -1.30110705f, 5.09082365f, 0.961123347f, 3.00180459f, 2.85362482f, 1.09769106f, + 1.02084768f, 3.42322588f, 3.65231347f, 3.98842335f, 3.22511339f, 3.57067037f, -0.655524194f, + 5.23435354f, 4.38254023f, 0.577274084f, -2.44550586f, -0.731654048f, 3.4826088f, 3.43956685f, + 5.99938393f, 0.780567706f, 5.00375938f, 4.12220669f, 4.17503166f, -3.4154036f, -0.623244941f, + 4.82289839f, -2.71706605f, -1.24971056f, -4.09150505f, 0.544187903f, 1.62730765f, 4.06203556f, + -1.30110705f, 4.48523045f, 3.69429231f, 2.20803857f, -3.71398258f, 3.26196098f, 3.11023927f, + 0.38991034f, 3.65231347f, -0.586388707f, 2.80014014f, -0.559841454f, -4.71591854f, -3.39814925f, + -2.19708824f, 3.72728443f, -4.96852636f, 4.59176064f, 0.0965373665f, 3.43956685f, 5.99938393f, + 2.78964972f, 2.92447424f, 5.36446524f, 0.076542452f, 5.97965384f, -1.7341789f, -4.87940454f, + -4.65397263f, -1.24971056f, -3.2123847f, -3.85786414f, 5.50049925f, 4.06203556f, 2.32632399f, + -4.68837166f, 0.961123347f, 2.71455693f, -5.10217285f, 1.09769106f, 2.53673315f, 1.53367102f, + 5.47524786f, -2.17047858f, 2.91291213f, 0.463025481f, -1.55778229f, -3.39814925f, 3.81586313f, + 0.577274084f, 5.74641657f, -0.705747545f, 1.51510179f, 3.43956685f, 5.99938393f, 0.780567706f, + 1.15185785f, 4.12220669f, 0.076542452f, 5.48901939f, -0.591653764f, -4.25708294f, 5.98635864f, + -1.24971056f, -3.46124911f, -1.36470068f, 1.62730765f, 4.06203556f, 2.22560525f, 5.91025019f, + 0.961123347f, -0.70858562f, 5.73723078f, 1.09769106f, 1.02084768f, 0.920532942f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_height_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/config_data.h new file mode 100644 index 00000000..bdc38963 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_WIDTH_F16_BATCH_1 2 +#define MAXIMUM_BROADCAST_WIDTH_F16_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_WIDTH_F16_WIDTH_1 4 +#define MAXIMUM_BROADCAST_WIDTH_F16_CHANNEL_1 19 +#define MAXIMUM_BROADCAST_WIDTH_F16_BATCH_2 2 +#define MAXIMUM_BROADCAST_WIDTH_F16_HEIGHT_2 1 +#define MAXIMUM_BROADCAST_WIDTH_F16_WIDTH_2 1 +#define MAXIMUM_BROADCAST_WIDTH_F16_CHANNEL_2 19 +#define MAXIMUM_BROADCAST_WIDTH_F16_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_WIDTH_F16_OUTPUT_HEIGHT 1 +#define MAXIMUM_BROADCAST_WIDTH_F16_OUTPUT_WIDTH 4 +#define MAXIMUM_BROADCAST_WIDTH_F16_OUTPUT_CHANNEL 19 +#define MAXIMUM_BROADCAST_WIDTH_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_1_data.h new file mode 100644 index 00000000..cbaf97ef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_1_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_width_f16_input_tensor_1[152] = { + (float16_t)6.0f, (float16_t)-0.55859375f, (float16_t)-0.410400391f, (float16_t)2.61914062f, + (float16_t)-2.5625f, (float16_t)-3.74414062f, (float16_t)4.7578125f, (float16_t)1.99609375f, + (float16_t)1.20507812f, (float16_t)-3.984375f, (float16_t)0.199829102f, (float16_t)-4.9921875f, + (float16_t)4.48828125f, (float16_t)1.22851562f, (float16_t)-1.44238281f, (float16_t)-3.78515625f, + (float16_t)0.0543518066f, (float16_t)5.48046875f, (float16_t)5.1875f, (float16_t)5.3984375f, + (float16_t)4.84375f, (float16_t)1.95410156f, (float16_t)-3.37304688f, (float16_t)2.26367188f, + (float16_t)0.731445312f, (float16_t)-3.0390625f, (float16_t)1.77148438f, (float16_t)2.171875f, + (float16_t)2.6875f, (float16_t)1.75878906f, (float16_t)0.240112305f, (float16_t)4.25f, + (float16_t)-3.42578125f, (float16_t)2.96679688f, (float16_t)1.34863281f, (float16_t)-3.37890625f, + (float16_t)-5.23046875f, (float16_t)-0.484619141f, (float16_t)-1.37695312f, (float16_t)-5.70703125f, + (float16_t)-2.66601562f, (float16_t)3.19140625f, (float16_t)-3.37109375f, (float16_t)1.53027344f, + (float16_t)-2.92382812f, (float16_t)-4.5234375f, (float16_t)-2.13476562f, (float16_t)2.81640625f, + (float16_t)-2.6796875f, (float16_t)-2.58007812f, (float16_t)-4.3828125f, (float16_t)-4.91796875f, + (float16_t)0.303222656f, (float16_t)-2.03320312f, (float16_t)-1.79882812f, (float16_t)-3.55078125f, + (float16_t)3.7890625f, (float16_t)-0.598632812f, (float16_t)5.23828125f, (float16_t)1.58105469f, + (float16_t)2.74609375f, (float16_t)3.6484375f, (float16_t)1.68945312f, (float16_t)-2.51953125f, + (float16_t)1.52734375f, (float16_t)3.81640625f, (float16_t)-4.56640625f, (float16_t)-4.1640625f, + (float16_t)-0.743164062f, (float16_t)1.12988281f, (float16_t)4.91796875f, (float16_t)-4.73046875f, + (float16_t)-5.80859375f, (float16_t)4.6484375f, (float16_t)0.0315856934f, (float16_t)-3.83398438f, + (float16_t)-3.515625f, (float16_t)3.2734375f, (float16_t)3.51171875f, (float16_t)-0.179931641f, + (float16_t)-1.78027344f, (float16_t)1.18945312f, (float16_t)-4.7734375f, (float16_t)1.5625f, + (float16_t)2.91796875f, (float16_t)4.3125f, (float16_t)-3.80664062f, (float16_t)5.04296875f, + (float16_t)-4.69921875f, (float16_t)-0.684570312f, (float16_t)5.453125f, (float16_t)1.640625f, + (float16_t)-0.0894165039f, (float16_t)5.7421875f, (float16_t)5.00390625f, (float16_t)0.8984375f, + (float16_t)3.1484375f, (float16_t)-4.328125f, (float16_t)4.265625f, (float16_t)3.0625f, + (float16_t)-4.9765625f, (float16_t)-3.57617188f, (float16_t)-3.08203125f, (float16_t)2.13085938f, + (float16_t)0.0994262695f, (float16_t)-1.57910156f, (float16_t)5.37890625f, (float16_t)1.55957031f, + (float16_t)0.384765625f, (float16_t)2.20898438f, (float16_t)2.98828125f, (float16_t)0.419189453f, + (float16_t)5.04296875f, (float16_t)0.978027344f, (float16_t)0.85546875f, (float16_t)-0.0823974609f, + (float16_t)-1.89355469f, (float16_t)5.43359375f, (float16_t)-0.926757812f, (float16_t)1.31738281f, + (float16_t)4.04296875f, (float16_t)-3.359375f, (float16_t)-1.74121094f, (float16_t)-4.28515625f, + (float16_t)4.36328125f, (float16_t)5.07421875f, (float16_t)-0.124450684f, (float16_t)3.19921875f, + (float16_t)-3.125f, (float16_t)-5.36328125f, (float16_t)2.85546875f, (float16_t)-0.791015625f, + (float16_t)-0.862792969f, (float16_t)-2.7421875f, (float16_t)1.26074219f, (float16_t)-3.42382812f, + (float16_t)2.78710938f, (float16_t)2.42773438f, (float16_t)1.92285156f, (float16_t)-0.71484375f, + (float16_t)-5.40234375f, (float16_t)-3.32421875f, (float16_t)-0.23034668f, (float16_t)0.482421875f, + (float16_t)-5.1171875f, (float16_t)-3.02539062f, (float16_t)-1.41015625f, (float16_t)-2.22070312f, + (float16_t)2.85742188f, (float16_t)5.98046875f, (float16_t)-0.810546875f, (float16_t)1.10546875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_2_data.h new file mode 100644 index 00000000..b90b3f4a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/input_tensor_2_data.h @@ -0,0 +1,12 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_width_f16_input_tensor_2[38] = { + (float16_t)-4.578125f, (float16_t)-1.17089844f, (float16_t)1.56054688f, (float16_t)1.10058594f, + (float16_t)5.07421875f, (float16_t)-0.922851562f, (float16_t)-3.35742188f, (float16_t)3.51757812f, + (float16_t)5.91796875f, (float16_t)0.639160156f, (float16_t)-4.1171875f, (float16_t)-4.72265625f, + (float16_t)-4.59765625f, (float16_t)-3.87890625f, (float16_t)3.62890625f, (float16_t)2.4296875f, + (float16_t)3.02929688f, (float16_t)-0.222167969f, (float16_t)-2.21289062f, (float16_t)-2.80859375f, + (float16_t)1.13574219f, (float16_t)4.33203125f, (float16_t)-2.10742188f, (float16_t)4.34765625f, + (float16_t)-1.04882812f, (float16_t)2.89453125f, (float16_t)-1.02148438f, (float16_t)4.3828125f, + (float16_t)-1.35839844f, (float16_t)4.57421875f, (float16_t)0.728027344f, (float16_t)-4.6875f, + (float16_t)2.62109375f, (float16_t)-5.41015625f, (float16_t)5.078125f, (float16_t)-4.66015625f, + (float16_t)1.82226562f, (float16_t)-5.30859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/output_ref_data.h new file mode 100644 index 00000000..81c98adf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_broadcast_width_f16_output_ref[152] = { + (float16_t)6.0f, (float16_t)-0.55859375f, (float16_t)1.56054688f, (float16_t)2.61914062f, + (float16_t)5.07421875f, (float16_t)-0.922851562f, (float16_t)4.7578125f, (float16_t)3.51757812f, + (float16_t)5.91796875f, (float16_t)0.639160156f, (float16_t)0.199829102f, (float16_t)-4.72265625f, + (float16_t)4.48828125f, (float16_t)1.22851562f, (float16_t)3.62890625f, (float16_t)2.4296875f, + (float16_t)3.02929688f, (float16_t)5.48046875f, (float16_t)5.1875f, (float16_t)5.3984375f, + (float16_t)4.84375f, (float16_t)1.95410156f, (float16_t)1.10058594f, (float16_t)5.07421875f, + (float16_t)0.731445312f, (float16_t)-3.0390625f, (float16_t)3.51757812f, (float16_t)5.91796875f, + (float16_t)2.6875f, (float16_t)1.75878906f, (float16_t)0.240112305f, (float16_t)4.25f, + (float16_t)-3.42578125f, (float16_t)3.62890625f, (float16_t)2.4296875f, (float16_t)3.02929688f, + (float16_t)-0.222167969f, (float16_t)-0.484619141f, (float16_t)-1.37695312f, (float16_t)-1.17089844f, + (float16_t)1.56054688f, (float16_t)3.19140625f, (float16_t)5.07421875f, (float16_t)1.53027344f, + (float16_t)-2.92382812f, (float16_t)3.51757812f, (float16_t)5.91796875f, (float16_t)2.81640625f, + (float16_t)-2.6796875f, (float16_t)-2.58007812f, (float16_t)-4.3828125f, (float16_t)-3.87890625f, + (float16_t)3.62890625f, (float16_t)2.4296875f, (float16_t)3.02929688f, (float16_t)-0.222167969f, + (float16_t)3.7890625f, (float16_t)-0.598632812f, (float16_t)5.23828125f, (float16_t)1.58105469f, + (float16_t)2.74609375f, (float16_t)5.07421875f, (float16_t)1.68945312f, (float16_t)-2.51953125f, + (float16_t)3.51757812f, (float16_t)5.91796875f, (float16_t)0.639160156f, (float16_t)-4.1171875f, + (float16_t)-0.743164062f, (float16_t)1.12988281f, (float16_t)4.91796875f, (float16_t)3.62890625f, + (float16_t)2.4296875f, (float16_t)4.6484375f, (float16_t)0.0315856934f, (float16_t)-2.21289062f, + (float16_t)-2.80859375f, (float16_t)3.2734375f, (float16_t)4.33203125f, (float16_t)-0.179931641f, + (float16_t)4.34765625f, (float16_t)1.18945312f, (float16_t)2.89453125f, (float16_t)1.5625f, + (float16_t)4.3828125f, (float16_t)4.3125f, (float16_t)4.57421875f, (float16_t)5.04296875f, + (float16_t)-4.6875f, (float16_t)2.62109375f, (float16_t)5.453125f, (float16_t)5.078125f, + (float16_t)-0.0894165039f, (float16_t)5.7421875f, (float16_t)5.00390625f, (float16_t)0.8984375f, + (float16_t)3.1484375f, (float16_t)4.33203125f, (float16_t)4.265625f, (float16_t)4.34765625f, + (float16_t)-1.04882812f, (float16_t)2.89453125f, (float16_t)-1.02148438f, (float16_t)4.3828125f, + (float16_t)0.0994262695f, (float16_t)4.57421875f, (float16_t)5.37890625f, (float16_t)1.55957031f, + (float16_t)2.62109375f, (float16_t)2.20898438f, (float16_t)5.078125f, (float16_t)0.419189453f, + (float16_t)5.04296875f, (float16_t)0.978027344f, (float16_t)0.85546875f, (float16_t)1.13574219f, + (float16_t)4.33203125f, (float16_t)5.43359375f, (float16_t)4.34765625f, (float16_t)1.31738281f, + (float16_t)4.04296875f, (float16_t)-1.02148438f, (float16_t)4.3828125f, (float16_t)-1.35839844f, + (float16_t)4.57421875f, (float16_t)5.07421875f, (float16_t)-0.124450684f, (float16_t)3.19921875f, + (float16_t)-3.125f, (float16_t)5.078125f, (float16_t)2.85546875f, (float16_t)1.82226562f, + (float16_t)-0.862792969f, (float16_t)-2.7421875f, (float16_t)1.26074219f, (float16_t)4.33203125f, + (float16_t)2.78710938f, (float16_t)4.34765625f, (float16_t)1.92285156f, (float16_t)2.89453125f, + (float16_t)-1.02148438f, (float16_t)4.3828125f, (float16_t)-0.23034668f, (float16_t)4.57421875f, + (float16_t)0.728027344f, (float16_t)-3.02539062f, (float16_t)2.62109375f, (float16_t)-2.22070312f, + (float16_t)5.078125f, (float16_t)5.98046875f, (float16_t)1.82226562f, (float16_t)1.10546875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/config_data.h new file mode 100644 index 00000000..9816f927 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_BROADCAST_WIDTH_F32_BATCH_1 2 +#define MAXIMUM_BROADCAST_WIDTH_F32_HEIGHT_1 1 +#define MAXIMUM_BROADCAST_WIDTH_F32_WIDTH_1 4 +#define MAXIMUM_BROADCAST_WIDTH_F32_CHANNEL_1 19 +#define MAXIMUM_BROADCAST_WIDTH_F32_BATCH_2 2 +#define MAXIMUM_BROADCAST_WIDTH_F32_HEIGHT_2 1 +#define MAXIMUM_BROADCAST_WIDTH_F32_WIDTH_2 1 +#define MAXIMUM_BROADCAST_WIDTH_F32_CHANNEL_2 19 +#define MAXIMUM_BROADCAST_WIDTH_F32_OUTPUT_BATCH 2 +#define MAXIMUM_BROADCAST_WIDTH_F32_OUTPUT_HEIGHT 1 +#define MAXIMUM_BROADCAST_WIDTH_F32_OUTPUT_WIDTH 4 +#define MAXIMUM_BROADCAST_WIDTH_F32_OUTPUT_CHANNEL 19 +#define MAXIMUM_BROADCAST_WIDTH_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_1_data.h new file mode 100644 index 00000000..420e76b6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_1_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_width_f32_input_tensor_1[152] = { + -1.15488911f, -0.766639471f, -5.11683559f, 5.72913885f, 5.50783443f, -3.15459538f, -5.49169397f, + 4.19145584f, -1.63117409f, 0.361239105f, 0.29814294f, -0.550040424f, -2.85840917f, 2.59882402f, + 1.18606985f, 3.05807424f, 5.03918123f, 4.73934507f, -1.30687177f, -2.15744615f, -2.60060549f, + -2.08618379f, 4.9535656f, 4.29568243f, 5.10192347f, 3.84195375f, 1.97685158f, -4.42658472f, + -5.20641088f, -2.45479226f, -2.77429295f, -1.05544245f, -2.34083891f, -5.90689993f, 0.684295356f, + -3.70651913f, 3.72191119f, 0.0442053638f, 1.64866841f, -3.89817357f, 2.61895967f, -1.44277596f, + -2.69061756f, -3.59258628f, 1.41181839f, -4.36192894f, 4.76783562f, -5.87606478f, 2.33501506f, + 3.46706343f, -0.875079155f, -3.68199778f, -1.55246544f, -0.550904036f, -3.62081337f, -4.52544022f, + 1.35972655f, 1.27638257f, -5.91330004f, 0.957579494f, 0.599104643f, 2.25359416f, -1.03884423f, + -0.357206196f, -1.42529285f, -2.12161565f, -3.45474601f, 0.362913549f, -3.28513098f, -1.95913923f, + 0.798217475f, -0.0369582213f, -1.46015084f, 5.62090731f, -3.07798743f, -0.854619503f, 5.51013517f, + 2.93422651f, 0.906709194f, 5.07127619f, 5.24610424f, 2.98853421f, -4.49895048f, 0.365762025f, + 0.616637707f, 3.61022949f, -1.84079361f, 3.28096008f, -1.88569856f, 4.67051458f, 0.299018294f, + -3.35452938f, 2.92276883f, -0.624852777f, -0.398369282f, -2.19472885f, 3.58462119f, -5.83771467f, + 4.07468271f, -4.69792366f, -0.0790890008f, -1.98215866f, 0.917364299f, -5.679317f, 2.23546958f, + 2.28170681f, 2.74448228f, -1.70383191f, 3.86907864f, -4.65257359f, -0.354852974f, 2.137712f, + 5.93039083f, -2.57386494f, 5.46465206f, -2.94817638f, 2.67286444f, -1.50875568f, 0.214210868f, + 3.24048519f, -5.58417463f, 4.20520449f, 2.30899405f, -2.88141561f, 2.28554606f, -5.39753437f, + 4.21850157f, -5.27150393f, 2.26024246f, 0.955751061f, 3.28911328f, -2.70307565f, -0.102202892f, + 2.01300979f, -1.42466474f, -4.85392952f, -4.44143486f, 2.94270587f, 2.60656881f, -4.24597359f, + -0.901760638f, -1.58985269f, 0.328746766f, 0.160811976f, 1.60033703f, 4.35619974f, 2.01416254f, + 0.266076028f, 2.48327899f, -3.86477876f, -0.987839699f, 0.992649436f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_2_data.h new file mode 100644 index 00000000..037d70e6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/input_tensor_2_data.h @@ -0,0 +1,7 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_width_f32_input_tensor_2[38] = { + -4.32866287f, -2.00825858f, 1.23698986f, -4.93670082f, -1.40911126f, -4.2084794f, 1.43887436f, -1.71943676f, + 1.04715466f, 3.40381026f, -5.46089363f, 2.78261137f, 2.96077704f, -3.88828516f, -0.138854384f, 2.96879935f, + -2.91716194f, 1.23262358f, 5.22867346f, -0.694068015f, -3.00308847f, 5.78685188f, -5.07802534f, 5.12143421f, + -0.335914761f, -5.85000706f, -5.33463717f, 1.9125936f, -2.4347291f, 4.47531509f, -5.04852724f, 2.8412118f, + -4.90123987f, -1.08357072f, 5.21622944f, -5.00559521f, 2.84628773f, 2.30211282f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/output_ref_data.h new file mode 100644 index 00000000..64be4271 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/output_ref_data.h @@ -0,0 +1,21 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_broadcast_width_f32_output_ref[152] = { + -1.15488911f, -0.766639471f, 1.23698986f, 5.72913885f, 5.50783443f, -3.15459538f, 1.43887436f, 4.19145584f, + 1.04715466f, 3.40381026f, 0.29814294f, 2.78261137f, 2.96077704f, 2.59882402f, 1.18606985f, 3.05807424f, + 5.03918123f, 4.73934507f, 5.22867346f, -2.15744615f, -2.00825858f, 1.23698986f, 4.9535656f, 4.29568243f, + 5.10192347f, 3.84195375f, 1.97685158f, 1.04715466f, 3.40381026f, -2.45479226f, 2.78261137f, 2.96077704f, + -2.34083891f, -0.138854384f, 2.96879935f, -2.91716194f, 3.72191119f, 5.22867346f, 1.64866841f, -2.00825858f, + 2.61895967f, -1.44277596f, -1.40911126f, -3.59258628f, 1.43887436f, -1.71943676f, 4.76783562f, 3.40381026f, + 2.33501506f, 3.46706343f, 2.96077704f, -3.68199778f, -0.138854384f, 2.96879935f, -2.91716194f, 1.23262358f, + 5.22867346f, 1.27638257f, -2.00825858f, 1.23698986f, 0.599104643f, 2.25359416f, -1.03884423f, 1.43887436f, + -1.42529285f, 1.04715466f, 3.40381026f, 0.362913549f, 2.78261137f, 2.96077704f, 0.798217475f, -0.0369582213f, + 2.96879935f, 5.62090731f, 1.23262358f, 5.22867346f, 5.51013517f, 2.93422651f, 5.78685188f, 5.07127619f, + 5.24610424f, 2.98853421f, -4.49895048f, 0.365762025f, 1.9125936f, 3.61022949f, 4.47531509f, 3.28096008f, + 2.8412118f, 4.67051458f, 0.299018294f, 5.21622944f, 2.92276883f, 2.84628773f, 2.30211282f, -0.694068015f, + 3.58462119f, 5.78685188f, 4.07468271f, 5.12143421f, -0.0790890008f, -1.98215866f, 0.917364299f, 1.9125936f, + 2.23546958f, 4.47531509f, 2.74448228f, 2.8412118f, 3.86907864f, -1.08357072f, 5.21622944f, 2.137712f, + 5.93039083f, 2.30211282f, 5.46465206f, -2.94817638f, 5.78685188f, -1.50875568f, 5.12143421f, 3.24048519f, + -5.58417463f, 4.20520449f, 2.30899405f, -2.4347291f, 4.47531509f, -5.04852724f, 4.21850157f, -4.90123987f, + 2.26024246f, 5.21622944f, 3.28911328f, 2.84628773f, 2.30211282f, 2.01300979f, -1.42466474f, 5.78685188f, + -4.44143486f, 5.12143421f, 2.60656881f, -4.24597359f, -0.901760638f, 1.9125936f, 0.328746766f, 4.47531509f, + 1.60033703f, 4.35619974f, 2.01416254f, 0.266076028f, 5.21622944f, -3.86477876f, 2.84628773f, 2.30211282f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_broadcast_width_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/config_data.h new file mode 100644 index 00000000..0b217bd4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_NO_BROADCAST_F16_BATCH_1 2 +#define MAXIMUM_NO_BROADCAST_F16_HEIGHT_1 2 +#define MAXIMUM_NO_BROADCAST_F16_WIDTH_1 3 +#define MAXIMUM_NO_BROADCAST_F16_CHANNEL_1 18 +#define MAXIMUM_NO_BROADCAST_F16_BATCH_2 2 +#define MAXIMUM_NO_BROADCAST_F16_HEIGHT_2 2 +#define MAXIMUM_NO_BROADCAST_F16_WIDTH_2 3 +#define MAXIMUM_NO_BROADCAST_F16_CHANNEL_2 18 +#define MAXIMUM_NO_BROADCAST_F16_OUTPUT_BATCH 2 +#define MAXIMUM_NO_BROADCAST_F16_OUTPUT_HEIGHT 2 +#define MAXIMUM_NO_BROADCAST_F16_OUTPUT_WIDTH 3 +#define MAXIMUM_NO_BROADCAST_F16_OUTPUT_CHANNEL 18 +#define MAXIMUM_NO_BROADCAST_F16_DST_SIZE 216 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_1_data.h new file mode 100644 index 00000000..764af8ab --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_1_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_no_broadcast_f16_input_tensor_1[216] = { + (float16_t)-4.8671875f, (float16_t)1.69335938f, (float16_t)1.41992188f, (float16_t)-2.07421875f, + (float16_t)0.375f, (float16_t)5.2109375f, (float16_t)-5.63671875f, (float16_t)-3.63085938f, + (float16_t)0.715332031f, (float16_t)-4.73046875f, (float16_t)-0.0118026733f, (float16_t)-0.588867188f, + (float16_t)-0.860839844f, (float16_t)-0.9453125f, (float16_t)-2.18164062f, (float16_t)3.94335938f, + (float16_t)2.390625f, (float16_t)-4.421875f, (float16_t)0.791992188f, (float16_t)-4.875f, + (float16_t)-3.41796875f, (float16_t)4.28125f, (float16_t)-1.37011719f, (float16_t)-2.23828125f, + (float16_t)-3.00585938f, (float16_t)-1.33007812f, (float16_t)-4.765625f, (float16_t)-4.53515625f, + (float16_t)-1.59765625f, (float16_t)5.578125f, (float16_t)2.8984375f, (float16_t)5.4765625f, + (float16_t)-0.344726562f, (float16_t)-2.63085938f, (float16_t)-4.42578125f, (float16_t)2.1484375f, + (float16_t)-1.38574219f, (float16_t)-3.17578125f, (float16_t)0.227416992f, (float16_t)-0.0990600586f, + (float16_t)5.57421875f, (float16_t)0.173339844f, (float16_t)-3.65625f, (float16_t)-2.66210938f, + (float16_t)-0.603515625f, (float16_t)-1.42773438f, (float16_t)2.68359375f, (float16_t)-4.4296875f, + (float16_t)3.78515625f, (float16_t)-5.92578125f, (float16_t)-4.39453125f, (float16_t)5.12890625f, + (float16_t)3.703125f, (float16_t)1.734375f, (float16_t)-0.916503906f, (float16_t)-3.42382812f, + (float16_t)-1.56054688f, (float16_t)5.53125f, (float16_t)-5.890625f, (float16_t)-2.38671875f, + (float16_t)5.70703125f, (float16_t)5.3203125f, (float16_t)2.73828125f, (float16_t)-4.09375f, + (float16_t)-5.9453125f, (float16_t)-1.98828125f, (float16_t)1.9765625f, (float16_t)-3.8671875f, + (float16_t)0.563476562f, (float16_t)-1.96484375f, (float16_t)-3.56445312f, (float16_t)4.2421875f, + (float16_t)-3.79101562f, (float16_t)4.13671875f, (float16_t)-5.70703125f, (float16_t)-2.26367188f, + (float16_t)-0.0217132568f, (float16_t)4.0390625f, (float16_t)-3.83398438f, (float16_t)4.58984375f, + (float16_t)-2.41210938f, (float16_t)-1.53417969f, (float16_t)-0.106201172f, (float16_t)-1.58300781f, + (float16_t)-0.923339844f, (float16_t)2.8984375f, (float16_t)-3.38867188f, (float16_t)3.55859375f, + (float16_t)0.707519531f, (float16_t)-1.76855469f, (float16_t)-0.686035156f, (float16_t)0.21105957f, + (float16_t)-5.12890625f, (float16_t)4.80859375f, (float16_t)5.33203125f, (float16_t)5.4609375f, + (float16_t)3.4296875f, (float16_t)-5.21875f, (float16_t)1.20996094f, (float16_t)3.04492188f, + (float16_t)-5.14453125f, (float16_t)-2.67773438f, (float16_t)1.46582031f, (float16_t)5.82421875f, + (float16_t)2.36914062f, (float16_t)-0.153442383f, (float16_t)-2.421875f, (float16_t)0.160400391f, + (float16_t)-3.59375f, (float16_t)3.265625f, (float16_t)0.94921875f, (float16_t)0.440429688f, + (float16_t)2.7421875f, (float16_t)-5.89453125f, (float16_t)2.8046875f, (float16_t)-5.12109375f, + (float16_t)5.41015625f, (float16_t)-0.995117188f, (float16_t)1.87011719f, (float16_t)1.03417969f, + (float16_t)-5.61328125f, (float16_t)0.348876953f, (float16_t)2.17773438f, (float16_t)-5.43359375f, + (float16_t)-3.97851562f, (float16_t)1.33886719f, (float16_t)-2.7265625f, (float16_t)-3.43164062f, + (float16_t)2.7265625f, (float16_t)-3.2890625f, (float16_t)-5.74609375f, (float16_t)1.59472656f, + (float16_t)-5.04296875f, (float16_t)-1.94335938f, (float16_t)-1.19433594f, (float16_t)3.87695312f, + (float16_t)-2.32617188f, (float16_t)0.864257812f, (float16_t)-4.59765625f, (float16_t)1.07421875f, + (float16_t)-3.51171875f, (float16_t)-1.78417969f, (float16_t)-5.640625f, (float16_t)1.35644531f, + (float16_t)-2.42578125f, (float16_t)5.31640625f, (float16_t)-3.41601562f, (float16_t)5.72265625f, + (float16_t)1.53417969f, (float16_t)-0.0532226562f, (float16_t)-1.62207031f, (float16_t)-1.98535156f, + (float16_t)0.267578125f, (float16_t)4.90234375f, (float16_t)-0.143554688f, (float16_t)-5.6328125f, + (float16_t)-2.30273438f, (float16_t)-5.44140625f, (float16_t)1.12597656f, (float16_t)2.61328125f, + (float16_t)5.79296875f, (float16_t)1.98925781f, (float16_t)-4.37890625f, (float16_t)-1.73144531f, + (float16_t)-4.7734375f, (float16_t)1.18164062f, (float16_t)-0.19909668f, (float16_t)3.12695312f, + (float16_t)-5.9921875f, (float16_t)-2.09765625f, (float16_t)-0.934570312f, (float16_t)-3.203125f, + (float16_t)2.24414062f, (float16_t)6.0f, (float16_t)-3.75195312f, (float16_t)5.73046875f, + (float16_t)-3.50195312f, (float16_t)-5.2734375f, (float16_t)-0.321044922f, (float16_t)5.8359375f, + (float16_t)-5.421875f, (float16_t)0.546386719f, (float16_t)-0.924316406f, (float16_t)4.828125f, + (float16_t)-0.913085938f, (float16_t)1.61230469f, (float16_t)-2.50195312f, (float16_t)-2.02539062f, + (float16_t)-5.99609375f, (float16_t)0.180786133f, (float16_t)-0.848144531f, (float16_t)-3.421875f, + (float16_t)-0.510742188f, (float16_t)4.58203125f, (float16_t)-4.14453125f, (float16_t)0.440917969f, + (float16_t)-3.71875f, (float16_t)2.78320312f, (float16_t)-5.83203125f, (float16_t)-4.70703125f, + (float16_t)1.11914062f, (float16_t)-2.5703125f, (float16_t)2.61132812f, (float16_t)-2.86523438f, + (float16_t)2.25585938f, (float16_t)2.98242188f, (float16_t)1.34179688f, (float16_t)-4.43359375f, + (float16_t)1.75976562f, (float16_t)-3.8046875f, (float16_t)3.828125f, (float16_t)-3.8828125f, + (float16_t)0.516113281f, (float16_t)-0.728027344f, (float16_t)0.443847656f, (float16_t)-3.8359375f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_2_data.h new file mode 100644 index 00000000..fd64c3d8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/input_tensor_2_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_no_broadcast_f16_input_tensor_2[216] = { + (float16_t)4.66015625f, (float16_t)1.38476562f, (float16_t)-2.46875f, (float16_t)4.828125f, + (float16_t)-5.703125f, (float16_t)-0.319580078f, (float16_t)1.37011719f, (float16_t)5.6796875f, + (float16_t)-4.06640625f, (float16_t)-3.5625f, (float16_t)3.47070312f, (float16_t)4.8203125f, + (float16_t)1.72070312f, (float16_t)5.54296875f, (float16_t)5.390625f, (float16_t)2.91796875f, + (float16_t)4.203125f, (float16_t)-2.59375f, (float16_t)4.625f, (float16_t)2.84375f, + (float16_t)0.0674438477f, (float16_t)0.859375f, (float16_t)5.828125f, (float16_t)5.87109375f, + (float16_t)5.83984375f, (float16_t)2.01367188f, (float16_t)-1.01269531f, (float16_t)3.21875f, + (float16_t)1.984375f, (float16_t)-3.93554688f, (float16_t)-0.596191406f, (float16_t)-3.32421875f, + (float16_t)3.72851562f, (float16_t)-3.953125f, (float16_t)-0.0460510254f, (float16_t)-0.134277344f, + (float16_t)-4.35546875f, (float16_t)4.625f, (float16_t)-1.62304688f, (float16_t)-5.0625f, + (float16_t)3.82226562f, (float16_t)-5.48828125f, (float16_t)5.70703125f, (float16_t)-3.20703125f, + (float16_t)-3.375f, (float16_t)5.80859375f, (float16_t)1.01269531f, (float16_t)0.00531005859f, + (float16_t)1.15429688f, (float16_t)5.515625f, (float16_t)4.46484375f, (float16_t)5.1328125f, + (float16_t)-1.45605469f, (float16_t)-1.86132812f, (float16_t)-1.78417969f, (float16_t)-1.6328125f, + (float16_t)-1.390625f, (float16_t)5.25390625f, (float16_t)2.18359375f, (float16_t)4.66796875f, + (float16_t)-3.84179688f, (float16_t)-4.94921875f, (float16_t)5.640625f, (float16_t)-0.994140625f, + (float16_t)-0.755859375f, (float16_t)-4.65234375f, (float16_t)-5.64453125f, (float16_t)-2.30664062f, + (float16_t)2.63085938f, (float16_t)-5.9453125f, (float16_t)-1.77734375f, (float16_t)-5.390625f, + (float16_t)1.703125f, (float16_t)4.98046875f, (float16_t)-5.54296875f, (float16_t)-0.662597656f, + (float16_t)3.89257812f, (float16_t)2.36914062f, (float16_t)-3.5078125f, (float16_t)-2.03320312f, + (float16_t)-3.0234375f, (float16_t)-1.19238281f, (float16_t)0.553710938f, (float16_t)-0.381103516f, + (float16_t)-5.18359375f, (float16_t)-1.625f, (float16_t)-4.60546875f, (float16_t)-1.09667969f, + (float16_t)2.73242188f, (float16_t)0.293945312f, (float16_t)-5.87890625f, (float16_t)-4.80078125f, + (float16_t)2.99804688f, (float16_t)3.74023438f, (float16_t)1.65917969f, (float16_t)3.24023438f, + (float16_t)-4.01953125f, (float16_t)2.71484375f, (float16_t)3.17382812f, (float16_t)-2.26367188f, + (float16_t)-4.2109375f, (float16_t)-5.921875f, (float16_t)0.673828125f, (float16_t)-2.73632812f, + (float16_t)-1.10644531f, (float16_t)5.41796875f, (float16_t)-0.0107040405f, (float16_t)4.05078125f, + (float16_t)-3.63867188f, (float16_t)4.35546875f, (float16_t)1.31738281f, (float16_t)2.22070312f, + (float16_t)-0.67578125f, (float16_t)-1.83789062f, (float16_t)2.51367188f, (float16_t)-2.18945312f, + (float16_t)-5.58203125f, (float16_t)3.84765625f, (float16_t)2.50195312f, (float16_t)5.44921875f, + (float16_t)-0.456298828f, (float16_t)2.57617188f, (float16_t)3.83789062f, (float16_t)5.640625f, + (float16_t)-1.0f, (float16_t)0.0395202637f, (float16_t)-3.171875f, (float16_t)-1.046875f, + (float16_t)1.62988281f, (float16_t)-3.0703125f, (float16_t)0.54296875f, (float16_t)1.10839844f, + (float16_t)4.4609375f, (float16_t)-5.28515625f, (float16_t)1.49804688f, (float16_t)-5.40625f, + (float16_t)-0.23828125f, (float16_t)-5.265625f, (float16_t)4.07421875f, (float16_t)-2.34765625f, + (float16_t)-1.73339844f, (float16_t)4.35546875f, (float16_t)5.4921875f, (float16_t)-2.50976562f, + (float16_t)5.27734375f, (float16_t)-1.87207031f, (float16_t)-2.13476562f, (float16_t)5.8515625f, + (float16_t)-3.06445312f, (float16_t)-5.3203125f, (float16_t)1.23925781f, (float16_t)0.362304688f, + (float16_t)4.23828125f, (float16_t)2.9375f, (float16_t)-1.76464844f, (float16_t)-3.29492188f, + (float16_t)-5.453125f, (float16_t)1.02734375f, (float16_t)0.335693359f, (float16_t)-1.3984375f, + (float16_t)-1.57128906f, (float16_t)2.96875f, (float16_t)0.298095703f, (float16_t)4.23828125f, + (float16_t)-1.83984375f, (float16_t)-5.90625f, (float16_t)3.46875f, (float16_t)-0.815917969f, + (float16_t)0.499755859f, (float16_t)-5.3046875f, (float16_t)4.59375f, (float16_t)-1.49804688f, + (float16_t)-2.99023438f, (float16_t)1.125f, (float16_t)-4.5390625f, (float16_t)-2.03710938f, + (float16_t)0.146118164f, (float16_t)4.91015625f, (float16_t)-5.65234375f, (float16_t)-1.91113281f, + (float16_t)-0.872070312f, (float16_t)-1.85253906f, (float16_t)2.0f, (float16_t)-4.1484375f, + (float16_t)2.51953125f, (float16_t)3.890625f, (float16_t)-1.33984375f, (float16_t)3.00195312f, + (float16_t)2.71875f, (float16_t)-0.284179688f, (float16_t)3.8671875f, (float16_t)-1.8125f, + (float16_t)5.3125f, (float16_t)0.90234375f, (float16_t)5.0546875f, (float16_t)3.18359375f, + (float16_t)1.41210938f, (float16_t)-0.141357422f, (float16_t)-5.16015625f, (float16_t)3.96484375f, + (float16_t)-5.0703125f, (float16_t)-2.01171875f, (float16_t)0.973144531f, (float16_t)-5.55859375f, + (float16_t)5.1015625f, (float16_t)-2.03710938f, (float16_t)-3.41992188f, (float16_t)-2.47265625f, + (float16_t)-4.00390625f, (float16_t)3.83007812f, (float16_t)4.5703125f, (float16_t)-1.87011719f, + (float16_t)3.3671875f, (float16_t)-2.82226562f, (float16_t)-1.74511719f, (float16_t)-3.36523438f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/output_ref_data.h new file mode 100644 index 00000000..076b6f61 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_no_broadcast_f16_output_ref[216] = { + (float16_t)4.66015625f, (float16_t)1.69335938f, (float16_t)1.41992188f, (float16_t)4.828125f, + (float16_t)0.375f, (float16_t)5.2109375f, (float16_t)1.37011719f, (float16_t)5.6796875f, + (float16_t)0.715332031f, (float16_t)-3.5625f, (float16_t)3.47070312f, (float16_t)4.8203125f, + (float16_t)1.72070312f, (float16_t)5.54296875f, (float16_t)5.390625f, (float16_t)3.94335938f, + (float16_t)4.203125f, (float16_t)-2.59375f, (float16_t)4.625f, (float16_t)2.84375f, + (float16_t)0.0674438477f, (float16_t)4.28125f, (float16_t)5.828125f, (float16_t)5.87109375f, + (float16_t)5.83984375f, (float16_t)2.01367188f, (float16_t)-1.01269531f, (float16_t)3.21875f, + (float16_t)1.984375f, (float16_t)5.578125f, (float16_t)2.8984375f, (float16_t)5.4765625f, + (float16_t)3.72851562f, (float16_t)-2.63085938f, (float16_t)-0.0460510254f, (float16_t)2.1484375f, + (float16_t)-1.38574219f, (float16_t)4.625f, (float16_t)0.227416992f, (float16_t)-0.0990600586f, + (float16_t)5.57421875f, (float16_t)0.173339844f, (float16_t)5.70703125f, (float16_t)-2.66210938f, + (float16_t)-0.603515625f, (float16_t)5.80859375f, (float16_t)2.68359375f, (float16_t)0.00531005859f, + (float16_t)3.78515625f, (float16_t)5.515625f, (float16_t)4.46484375f, (float16_t)5.1328125f, + (float16_t)3.703125f, (float16_t)1.734375f, (float16_t)-0.916503906f, (float16_t)-1.6328125f, + (float16_t)-1.390625f, (float16_t)5.53125f, (float16_t)2.18359375f, (float16_t)4.66796875f, + (float16_t)5.70703125f, (float16_t)5.3203125f, (float16_t)5.640625f, (float16_t)-0.994140625f, + (float16_t)-0.755859375f, (float16_t)-1.98828125f, (float16_t)1.9765625f, (float16_t)-2.30664062f, + (float16_t)2.63085938f, (float16_t)-1.96484375f, (float16_t)-1.77734375f, (float16_t)4.2421875f, + (float16_t)1.703125f, (float16_t)4.98046875f, (float16_t)-5.54296875f, (float16_t)-0.662597656f, + (float16_t)3.89257812f, (float16_t)4.0390625f, (float16_t)-3.5078125f, (float16_t)4.58984375f, + (float16_t)-2.41210938f, (float16_t)-1.19238281f, (float16_t)0.553710938f, (float16_t)-0.381103516f, + (float16_t)-0.923339844f, (float16_t)2.8984375f, (float16_t)-3.38867188f, (float16_t)3.55859375f, + (float16_t)2.73242188f, (float16_t)0.293945312f, (float16_t)-0.686035156f, (float16_t)0.21105957f, + (float16_t)2.99804688f, (float16_t)4.80859375f, (float16_t)5.33203125f, (float16_t)5.4609375f, + (float16_t)3.4296875f, (float16_t)2.71484375f, (float16_t)3.17382812f, (float16_t)3.04492188f, + (float16_t)-4.2109375f, (float16_t)-2.67773438f, (float16_t)1.46582031f, (float16_t)5.82421875f, + (float16_t)2.36914062f, (float16_t)5.41796875f, (float16_t)-0.0107040405f, (float16_t)4.05078125f, + (float16_t)-3.59375f, (float16_t)4.35546875f, (float16_t)1.31738281f, (float16_t)2.22070312f, + (float16_t)2.7421875f, (float16_t)-1.83789062f, (float16_t)2.8046875f, (float16_t)-2.18945312f, + (float16_t)5.41015625f, (float16_t)3.84765625f, (float16_t)2.50195312f, (float16_t)5.44921875f, + (float16_t)-0.456298828f, (float16_t)2.57617188f, (float16_t)3.83789062f, (float16_t)5.640625f, + (float16_t)-1.0f, (float16_t)1.33886719f, (float16_t)-2.7265625f, (float16_t)-1.046875f, + (float16_t)2.7265625f, (float16_t)-3.0703125f, (float16_t)0.54296875f, (float16_t)1.59472656f, + (float16_t)4.4609375f, (float16_t)-1.94335938f, (float16_t)1.49804688f, (float16_t)3.87695312f, + (float16_t)-0.23828125f, (float16_t)0.864257812f, (float16_t)4.07421875f, (float16_t)1.07421875f, + (float16_t)-1.73339844f, (float16_t)4.35546875f, (float16_t)5.4921875f, (float16_t)1.35644531f, + (float16_t)5.27734375f, (float16_t)5.31640625f, (float16_t)-2.13476562f, (float16_t)5.8515625f, + (float16_t)1.53417969f, (float16_t)-0.0532226562f, (float16_t)1.23925781f, (float16_t)0.362304688f, + (float16_t)4.23828125f, (float16_t)4.90234375f, (float16_t)-0.143554688f, (float16_t)-3.29492188f, + (float16_t)-2.30273438f, (float16_t)1.02734375f, (float16_t)1.12597656f, (float16_t)2.61328125f, + (float16_t)5.79296875f, (float16_t)2.96875f, (float16_t)0.298095703f, (float16_t)4.23828125f, + (float16_t)-1.83984375f, (float16_t)1.18164062f, (float16_t)3.46875f, (float16_t)3.12695312f, + (float16_t)0.499755859f, (float16_t)-2.09765625f, (float16_t)4.59375f, (float16_t)-1.49804688f, + (float16_t)2.24414062f, (float16_t)6.0f, (float16_t)-3.75195312f, (float16_t)5.73046875f, + (float16_t)0.146118164f, (float16_t)4.91015625f, (float16_t)-0.321044922f, (float16_t)5.8359375f, + (float16_t)-0.872070312f, (float16_t)0.546386719f, (float16_t)2.0f, (float16_t)4.828125f, + (float16_t)2.51953125f, (float16_t)3.890625f, (float16_t)-1.33984375f, (float16_t)3.00195312f, + (float16_t)2.71875f, (float16_t)0.180786133f, (float16_t)3.8671875f, (float16_t)-1.8125f, + (float16_t)5.3125f, (float16_t)4.58203125f, (float16_t)5.0546875f, (float16_t)3.18359375f, + (float16_t)1.41210938f, (float16_t)2.78320312f, (float16_t)-5.16015625f, (float16_t)3.96484375f, + (float16_t)1.11914062f, (float16_t)-2.01171875f, (float16_t)2.61132812f, (float16_t)-2.86523438f, + (float16_t)5.1015625f, (float16_t)2.98242188f, (float16_t)1.34179688f, (float16_t)-2.47265625f, + (float16_t)1.75976562f, (float16_t)3.83007812f, (float16_t)4.5703125f, (float16_t)-1.87011719f, + (float16_t)3.3671875f, (float16_t)-0.728027344f, (float16_t)0.443847656f, (float16_t)-3.36523438f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/config_data.h new file mode 100644 index 00000000..377f4908 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_NO_BROADCAST_F32_BATCH_1 2 +#define MAXIMUM_NO_BROADCAST_F32_HEIGHT_1 2 +#define MAXIMUM_NO_BROADCAST_F32_WIDTH_1 3 +#define MAXIMUM_NO_BROADCAST_F32_CHANNEL_1 18 +#define MAXIMUM_NO_BROADCAST_F32_BATCH_2 2 +#define MAXIMUM_NO_BROADCAST_F32_HEIGHT_2 2 +#define MAXIMUM_NO_BROADCAST_F32_WIDTH_2 3 +#define MAXIMUM_NO_BROADCAST_F32_CHANNEL_2 18 +#define MAXIMUM_NO_BROADCAST_F32_OUTPUT_BATCH 2 +#define MAXIMUM_NO_BROADCAST_F32_OUTPUT_HEIGHT 2 +#define MAXIMUM_NO_BROADCAST_F32_OUTPUT_WIDTH 3 +#define MAXIMUM_NO_BROADCAST_F32_OUTPUT_CHANNEL 18 +#define MAXIMUM_NO_BROADCAST_F32_DST_SIZE 216 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_1_data.h new file mode 100644 index 00000000..5d5d146c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_1_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_no_broadcast_f32_input_tensor_1[216] = { + 3.54859233f, -5.28985357f, -2.51128197f, 2.70166564f, 4.94923925f, 4.82310581f, 2.16511559f, + 0.981965423f, -5.58695507f, -2.40365553f, 0.0524275973f, -2.19667792f, -1.87678695f, 5.40697145f, + -4.71175385f, -1.56301522f, -0.920281589f, -4.81922865f, 1.8713007f, 3.75665832f, -4.88382912f, + -1.23323369f, 1.71281111f, -3.50359178f, 5.6102972f, -4.28991318f, -4.90166187f, -3.30401325f, + -1.62029207f, -3.31031895f, 1.01608908f, -3.35113597f, 3.74018764f, 3.36774468f, 5.4252491f, + -0.785462618f, -4.77387333f, 5.8336544f, -1.23996305f, -2.40769744f, 2.89907932f, 4.21094894f, + 0.89725256f, -4.79522324f, 2.08457208f, 5.41486788f, 5.13064432f, 1.40777779f, 2.96150303f, + 3.59984541f, 3.21541214f, 1.40501428f, 5.09268093f, 0.440592855f, -5.05080128f, -0.192876115f, + 4.12421274f, 2.77585912f, 5.2063942f, 2.52588582f, 1.44121981f, -0.269237578f, -0.320019692f, + 3.43214774f, 1.5963949f, 5.5356679f, 1.52252316f, 5.1000762f, 3.03940749f, -2.547544f, + -0.114425145f, 0.553405643f, -3.4035027f, 0.878228724f, -2.21927595f, -2.85408902f, 3.35580373f, + 1.9274497f, -2.59424734f, -5.39505768f, -1.43656182f, 3.85419321f, 3.98124409f, 4.01516056f, + 2.07432699f, 0.820593715f, -4.56682825f, 4.86053276f, -4.54668808f, -2.75101614f, -5.31336975f, + 3.70922637f, -1.19157362f, 4.82865095f, -0.823588371f, -3.1468513f, 2.88950896f, 3.42099786f, + 0.826356709f, -5.37847281f, 0.321523786f, -0.899172246f, -2.99052095f, -3.49646044f, 0.593757451f, + 3.45233393f, -1.53338265f, -1.95324016f, 4.60194254f, 0.515294492f, 4.64196157f, 5.67987537f, + -0.65898639f, 3.33762002f, 2.32736015f, 5.93743849f, -1.22947121f, -0.447180688f, 3.96796775f, + -5.29747248f, 4.21306467f, 0.0926673338f, 2.77063251f, -3.87053275f, 2.05644011f, 0.449764907f, + 2.00947928f, 4.29181004f, -3.56461143f, -0.324009448f, -2.66336775f, -4.09587049f, -5.52904034f, + -2.9178803f, 2.6352818f, -3.94158435f, 0.888885081f, 3.96802044f, -3.09868932f, 5.10338402f, + 2.33758497f, -1.0298084f, 0.682799041f, 0.639222562f, 2.28235793f, 4.37771082f, 4.74231815f, + 1.13132274f, 0.557658553f, -1.43524301f, -1.5479573f, -2.47179675f, -1.52044046f, 4.72074175f, + 2.65523434f, -5.45556927f, 4.86872292f, 5.73550129f, 3.25457716f, 0.79691422f, -4.13919973f, + 5.57994461f, -5.59512568f, 5.30571699f, -2.47375441f, -0.241560891f, 3.13949013f, -3.91538501f, + 5.00667667f, 3.39120007f, -4.83491755f, 3.90737414f, -2.16226339f, -5.67984056f, 1.27654409f, + 0.763260663f, -5.33619356f, -2.22619557f, 5.02738714f, 3.00979495f, 0.614150584f, -4.82739639f, + 4.68154621f, 2.88412523f, 0.915246606f, -4.77843332f, -2.74277258f, -0.578148186f, -2.21171713f, + 0.774448097f, -0.861319959f, 2.33596659f, -1.70530677f, -3.41349864f, 2.19503593f, 2.75131822f, + 4.96433735f, 5.64155579f, -5.43999147f, -2.15311599f, -3.29872584f, 0.793684125f, -5.74038792f, + 0.899059772f, -4.11331129f, -5.86711645f, -2.85813212f, -1.16106951f, -1.99973321f, 0.0278032254f, + 2.29012489f, 0.470339894f, -3.77278948f, -3.94610071f, -2.41746235f, -2.84770894f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_2_data.h new file mode 100644 index 00000000..e2283d81 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/input_tensor_2_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_no_broadcast_f32_input_tensor_2[216] = { + 1.64868045f, -1.23483014f, -0.603016376f, 0.0302653424f, 4.80920267f, -5.78733635f, 0.277170986f, + 4.98945665f, -3.47682691f, -5.22103977f, -2.25548577f, 5.55512428f, -1.47584951f, -3.52635789f, + 4.42706442f, -2.35115767f, -4.31297874f, 1.46667027f, 4.27084017f, -4.74502754f, 2.06833124f, + 5.04725122f, -2.09016705f, 5.93409204f, 1.30367827f, -3.83405805f, 1.65284073f, -1.53480768f, + 4.67023563f, -5.83335543f, 3.75960088f, -5.07280207f, 1.57655847f, 2.52710843f, 4.66214371f, + 2.62744451f, -5.14495611f, 4.27641058f, 0.126966253f, 3.08029842f, 5.65467215f, 3.00247359f, + -2.97293901f, -3.67507553f, 1.91392922f, -0.0344589278f, -3.4736495f, 1.63074458f, 2.458565f, + -1.47876894f, 5.81185484f, -2.85187244f, 1.92804575f, 3.58561015f, -2.69492435f, 4.29240799f, + -5.12135792f, 3.15814757f, 3.40263367f, -1.32095861f, 2.53715348f, 3.69428015f, 4.64482021f, + -0.472669095f, -1.11361098f, 4.68285847f, 0.244310915f, -1.16603792f, -4.07679367f, -2.9807775f, + 0.751075983f, -0.0377908796f, 4.96147776f, 1.27174842f, -3.29075718f, -4.82068491f, 3.04203486f, + 4.06237125f, -3.62405396f, 4.47682571f, 5.33655357f, 5.56847239f, 2.29663014f, -5.68255615f, + 1.92700255f, 4.06727886f, -1.22926569f, -2.42786264f, 3.76449275f, -3.86243963f, 1.88661289f, + -1.06397855f, -4.34354687f, -5.96328783f, 4.24912167f, 0.812966049f, -0.0683127344f, 3.95533967f, + 0.5952636f, 3.65764165f, 1.83937132f, -2.44050574f, 5.43586397f, -3.00853562f, -3.85244465f, + -1.56594837f, 4.14728451f, -4.77647161f, -2.25760794f, 5.79724264f, 4.00587702f, 4.7503953f, + -0.052314017f, -5.55797291f, 4.47104979f, -5.21061039f, 5.10644579f, 5.55317116f, 4.26860285f, + -2.51352048f, -2.72639322f, 2.3423965f, 1.38179469f, -3.59772587f, 3.42076635f, -3.86810708f, + 2.75421023f, -1.01249564f, 1.73188424f, -0.976615489f, -0.632246912f, 3.79739285f, 3.69075036f, + 4.72792482f, 1.93570542f, 5.61335325f, -3.59094572f, -5.12004185f, 0.264076889f, 4.95587063f, + 0.820922136f, 1.33108222f, 2.88518643f, -1.01031649f, -2.52455568f, 3.47558236f, 2.1862762f, + 2.02685189f, 1.2136935f, 1.86666501f, 2.18381214f, 1.07662475f, 3.27757907f, 0.755448759f, + -5.46132278f, 3.11508441f, 0.389392763f, -2.13262653f, -1.25827265f, -5.55324984f, -3.7158885f, + 3.15487814f, -0.859482169f, 3.17575741f, 1.68295002f, -4.86285591f, 1.51954186f, -0.136206329f, + -2.76408339f, -5.8322525f, -0.702260375f, 4.07164717f, -4.12932348f, -1.70647979f, -0.166517183f, + 5.93039036f, -2.23390603f, -3.13046122f, -0.37762329f, 1.11165845f, 4.11828852f, -3.95584273f, + -1.87651229f, 3.18288994f, -3.91119719f, -2.40843892f, 2.8318944f, 4.55374575f, -4.73549318f, + 5.39617491f, -1.30608118f, -4.99970055f, -2.79176402f, 3.94070745f, 3.45115185f, 1.48599923f, + -5.60478306f, -4.70619297f, 4.18609476f, 2.15375543f, 1.73208523f, -4.08324862f, 5.86208868f, + 1.14004147f, 3.47199082f, -1.9995327f, -5.48905373f, -0.039547462f, -4.16275978f, -5.82250071f, + -5.28897762f, -5.13984585f, 5.61706352f, -3.76725245f, 4.82698345f, 2.54777336f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/output_ref_data.h new file mode 100644 index 00000000..db6bd578 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/output_ref_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_no_broadcast_f32_output_ref[216] = { + 3.54859233f, -1.23483014f, -0.603016376f, 2.70166564f, 4.94923925f, 4.82310581f, 2.16511559f, + 4.98945665f, -3.47682691f, -2.40365553f, 0.0524275973f, 5.55512428f, -1.47584951f, 5.40697145f, + 4.42706442f, -1.56301522f, -0.920281589f, 1.46667027f, 4.27084017f, 3.75665832f, 2.06833124f, + 5.04725122f, 1.71281111f, 5.93409204f, 5.6102972f, -3.83405805f, 1.65284073f, -1.53480768f, + 4.67023563f, -3.31031895f, 3.75960088f, -3.35113597f, 3.74018764f, 3.36774468f, 5.4252491f, + 2.62744451f, -4.77387333f, 5.8336544f, 0.126966253f, 3.08029842f, 5.65467215f, 4.21094894f, + 0.89725256f, -3.67507553f, 2.08457208f, 5.41486788f, 5.13064432f, 1.63074458f, 2.96150303f, + 3.59984541f, 5.81185484f, 1.40501428f, 5.09268093f, 3.58561015f, -2.69492435f, 4.29240799f, + 4.12421274f, 3.15814757f, 5.2063942f, 2.52588582f, 2.53715348f, 3.69428015f, 4.64482021f, + 3.43214774f, 1.5963949f, 5.5356679f, 1.52252316f, 5.1000762f, 3.03940749f, -2.547544f, + 0.751075983f, 0.553405643f, 4.96147776f, 1.27174842f, -2.21927595f, -2.85408902f, 3.35580373f, + 4.06237125f, -2.59424734f, 4.47682571f, 5.33655357f, 5.56847239f, 3.98124409f, 4.01516056f, + 2.07432699f, 4.06727886f, -1.22926569f, 4.86053276f, 3.76449275f, -2.75101614f, 1.88661289f, + 3.70922637f, -1.19157362f, 4.82865095f, 4.24912167f, 0.812966049f, 2.88950896f, 3.95533967f, + 0.826356709f, 3.65764165f, 1.83937132f, -0.899172246f, 5.43586397f, -3.00853562f, 0.593757451f, + 3.45233393f, 4.14728451f, -1.95324016f, 4.60194254f, 5.79724264f, 4.64196157f, 5.67987537f, + -0.052314017f, 3.33762002f, 4.47104979f, 5.93743849f, 5.10644579f, 5.55317116f, 4.26860285f, + -2.51352048f, 4.21306467f, 2.3423965f, 2.77063251f, -3.59772587f, 3.42076635f, 0.449764907f, + 2.75421023f, 4.29181004f, 1.73188424f, -0.324009448f, -0.632246912f, 3.79739285f, 3.69075036f, + 4.72792482f, 2.6352818f, 5.61335325f, 0.888885081f, 3.96802044f, 0.264076889f, 5.10338402f, + 2.33758497f, 1.33108222f, 2.88518643f, 0.639222562f, 2.28235793f, 4.37771082f, 4.74231815f, + 2.02685189f, 1.2136935f, 1.86666501f, 2.18381214f, 1.07662475f, 3.27757907f, 4.72074175f, + 2.65523434f, 3.11508441f, 4.86872292f, 5.73550129f, 3.25457716f, 0.79691422f, -3.7158885f, + 5.57994461f, -0.859482169f, 5.30571699f, 1.68295002f, -0.241560891f, 3.13949013f, -0.136206329f, + 5.00667667f, 3.39120007f, -0.702260375f, 4.07164717f, -2.16226339f, -1.70647979f, 1.27654409f, + 5.93039036f, -2.23390603f, -2.22619557f, 5.02738714f, 3.00979495f, 4.11828852f, -3.95584273f, + 4.68154621f, 3.18288994f, 0.915246606f, -2.40843892f, 2.8318944f, 4.55374575f, -2.21171713f, + 5.39617491f, -0.861319959f, 2.33596659f, -1.70530677f, 3.94070745f, 3.45115185f, 2.75131822f, + 4.96433735f, 5.64155579f, 4.18609476f, 2.15375543f, 1.73208523f, 0.793684125f, 5.86208868f, + 1.14004147f, 3.47199082f, -1.9995327f, -2.85813212f, -0.039547462f, -1.99973321f, 0.0278032254f, + 2.29012489f, 0.470339894f, 5.61706352f, -3.76725245f, 4.82698345f, 2.54777336f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_no_broadcast_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/config_data.h new file mode 100644 index 00000000..a713997e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_SCALAR_1_F16_BATCH_1 1 +#define MAXIMUM_SCALAR_1_F16_HEIGHT_1 1 +#define MAXIMUM_SCALAR_1_F16_WIDTH_1 1 +#define MAXIMUM_SCALAR_1_F16_CHANNEL_1 1 +#define MAXIMUM_SCALAR_1_F16_BATCH_2 1 +#define MAXIMUM_SCALAR_1_F16_HEIGHT_2 2 +#define MAXIMUM_SCALAR_1_F16_WIDTH_2 4 +#define MAXIMUM_SCALAR_1_F16_CHANNEL_2 19 +#define MAXIMUM_SCALAR_1_F16_OUTPUT_BATCH 1 +#define MAXIMUM_SCALAR_1_F16_OUTPUT_HEIGHT 2 +#define MAXIMUM_SCALAR_1_F16_OUTPUT_WIDTH 4 +#define MAXIMUM_SCALAR_1_F16_OUTPUT_CHANNEL 19 +#define MAXIMUM_SCALAR_1_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_1_data.h new file mode 100644 index 00000000..6433f721 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_1_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_1_f16_input_tensor_1[1] = {(float16_t)3.7890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_2_data.h new file mode 100644 index 00000000..350e914d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/input_tensor_2_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_1_f16_input_tensor_2[152] = { + (float16_t)-3.81835938f, (float16_t)-5.078125f, (float16_t)-4.58203125f, (float16_t)-2.87109375f, + (float16_t)-5.08984375f, (float16_t)3.71679688f, (float16_t)-5.2421875f, (float16_t)-1.89746094f, + (float16_t)5.94140625f, (float16_t)4.1640625f, (float16_t)4.6875f, (float16_t)-1.52246094f, + (float16_t)-3.47460938f, (float16_t)-3.30273438f, (float16_t)-0.9609375f, (float16_t)3.4453125f, + (float16_t)-4.84375f, (float16_t)-3.91601562f, (float16_t)-5.046875f, (float16_t)4.921875f, + (float16_t)-3.72851562f, (float16_t)-0.461669922f, (float16_t)0.561035156f, (float16_t)4.88671875f, + (float16_t)3.2109375f, (float16_t)2.90625f, (float16_t)-2.31054688f, (float16_t)3.5546875f, + (float16_t)-2.40429688f, (float16_t)-2.0625f, (float16_t)3.31054688f, (float16_t)-3.79492188f, + (float16_t)-2.47070312f, (float16_t)-0.967773438f, (float16_t)-3.03515625f, (float16_t)0.397216797f, + (float16_t)5.1484375f, (float16_t)5.48828125f, (float16_t)2.16796875f, (float16_t)3.6796875f, + (float16_t)0.378662109f, (float16_t)5.390625f, (float16_t)4.734375f, (float16_t)-5.984375f, + (float16_t)-2.375f, (float16_t)3.03320312f, (float16_t)4.78515625f, (float16_t)5.74609375f, + (float16_t)-2.7109375f, (float16_t)-3.66796875f, (float16_t)-2.00195312f, (float16_t)-1.1640625f, + (float16_t)0.877441406f, (float16_t)-3.57617188f, (float16_t)-2.56640625f, (float16_t)-1.28613281f, + (float16_t)-1.07226562f, (float16_t)-1.078125f, (float16_t)-1.87792969f, (float16_t)4.94921875f, + (float16_t)-1.27539062f, (float16_t)1.05273438f, (float16_t)5.48828125f, (float16_t)4.4296875f, + (float16_t)-1.19042969f, (float16_t)4.27734375f, (float16_t)-1.41699219f, (float16_t)1.27929688f, + (float16_t)-5.26953125f, (float16_t)-4.68359375f, (float16_t)-5.60546875f, (float16_t)0.746582031f, + (float16_t)3.81835938f, (float16_t)-3.54296875f, (float16_t)2.80859375f, (float16_t)-2.6328125f, + (float16_t)0.470458984f, (float16_t)-2.36328125f, (float16_t)5.07421875f, (float16_t)5.0390625f, + (float16_t)-1.72558594f, (float16_t)3.59375f, (float16_t)-1.01464844f, (float16_t)-2.30859375f, + (float16_t)-5.3125f, (float16_t)-0.561035156f, (float16_t)-4.19140625f, (float16_t)-1.94042969f, + (float16_t)-5.6953125f, (float16_t)-1.86230469f, (float16_t)0.603027344f, (float16_t)5.17578125f, + (float16_t)-2.66796875f, (float16_t)4.515625f, (float16_t)5.90234375f, (float16_t)-0.298828125f, + (float16_t)4.8984375f, (float16_t)5.015625f, (float16_t)3.0859375f, (float16_t)-1.88964844f, + (float16_t)5.5859375f, (float16_t)3.9296875f, (float16_t)-5.66796875f, (float16_t)1.83691406f, + (float16_t)1.07324219f, (float16_t)-1.90136719f, (float16_t)-2.35351562f, (float16_t)4.35546875f, + (float16_t)1.375f, (float16_t)3.80859375f, (float16_t)-5.31640625f, (float16_t)0.383056641f, + (float16_t)-2.64453125f, (float16_t)0.34375f, (float16_t)-0.781738281f, (float16_t)0.635253906f, + (float16_t)3.19335938f, (float16_t)1.24316406f, (float16_t)3.5546875f, (float16_t)-2.68945312f, + (float16_t)-1.56542969f, (float16_t)2.26171875f, (float16_t)0.468261719f, (float16_t)-3.26171875f, + (float16_t)-5.15234375f, (float16_t)1.33105469f, (float16_t)-1.9375f, (float16_t)5.58984375f, + (float16_t)5.89453125f, (float16_t)-3.9453125f, (float16_t)-5.91015625f, (float16_t)-5.67578125f, + (float16_t)-1.74316406f, (float16_t)1.81738281f, (float16_t)2.3671875f, (float16_t)3.44140625f, + (float16_t)-4.9453125f, (float16_t)2.25976562f, (float16_t)5.32421875f, (float16_t)-2.58984375f, + (float16_t)-0.644042969f, (float16_t)-3.40039062f, (float16_t)4.77734375f, (float16_t)-5.71875f, + (float16_t)-0.463134766f, (float16_t)-5.21484375f, (float16_t)-0.178955078f, (float16_t)5.33203125f, + (float16_t)4.58984375f, (float16_t)-2.26953125f, (float16_t)-3.87695312f, (float16_t)1.89355469f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/output_ref_data.h new file mode 100644 index 00000000..10b7c8a7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_1_f16_output_ref[152] = { + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)5.94140625f, (float16_t)4.1640625f, (float16_t)4.6875f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.921875f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.88671875f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)5.1484375f, (float16_t)5.48828125f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)5.390625f, (float16_t)4.734375f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.78515625f, (float16_t)5.74609375f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.94921875f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.48828125f, (float16_t)4.4296875f, + (float16_t)3.7890625f, (float16_t)4.27734375f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.81835938f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.07421875f, (float16_t)5.0390625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.17578125f, + (float16_t)3.7890625f, (float16_t)4.515625f, (float16_t)5.90234375f, (float16_t)3.7890625f, + (float16_t)4.8984375f, (float16_t)5.015625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)5.5859375f, (float16_t)3.9296875f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.35546875f, + (float16_t)3.7890625f, (float16_t)3.80859375f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.58984375f, + (float16_t)5.89453125f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.32421875f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)4.77734375f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)5.33203125f, + (float16_t)4.58984375f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/config_data.h new file mode 100644 index 00000000..0c10f0ab --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_SCALAR_1_F32_BATCH_1 1 +#define MAXIMUM_SCALAR_1_F32_HEIGHT_1 1 +#define MAXIMUM_SCALAR_1_F32_WIDTH_1 1 +#define MAXIMUM_SCALAR_1_F32_CHANNEL_1 1 +#define MAXIMUM_SCALAR_1_F32_BATCH_2 1 +#define MAXIMUM_SCALAR_1_F32_HEIGHT_2 2 +#define MAXIMUM_SCALAR_1_F32_WIDTH_2 4 +#define MAXIMUM_SCALAR_1_F32_CHANNEL_2 19 +#define MAXIMUM_SCALAR_1_F32_OUTPUT_BATCH 1 +#define MAXIMUM_SCALAR_1_F32_OUTPUT_HEIGHT 2 +#define MAXIMUM_SCALAR_1_F32_OUTPUT_WIDTH 4 +#define MAXIMUM_SCALAR_1_F32_OUTPUT_CHANNEL 19 +#define MAXIMUM_SCALAR_1_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_1_data.h new file mode 100644 index 00000000..59660402 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_1_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_1_f32_input_tensor_1[1] = {-1.33102453f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_2_data.h new file mode 100644 index 00000000..26f605b6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/input_tensor_2_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_1_f32_input_tensor_2[152] = { + -2.33778191f, 2.41181374f, 4.50241995f, 3.90808606f, 4.82162666f, -4.37790203f, -5.99091196f, + -5.76504517f, -4.10262251f, -0.232303366f, -3.18678761f, -2.26306176f, -4.18038368f, 3.54770184f, + -0.083846584f, 3.17593169f, -4.90483618f, 5.53488922f, -5.41985416f, 0.852559626f, -3.27907586f, + 0.104315661f, 3.18602991f, 0.374441832f, 5.01691723f, -5.00389528f, 2.2601037f, 3.68813944f, + 4.46874666f, -4.74341869f, -0.0778963342f, -4.89908075f, 3.95771408f, -5.82906437f, 1.60777771f, + 3.25474477f, 4.23021936f, -0.916315556f, -5.8493619f, 4.13712692f, 0.606094241f, 4.42138147f, + 1.52516508f, 4.64856339f, -1.80117357f, 2.71755433f, -2.25655293f, 5.14123201f, 2.61878705f, + -1.96027207f, 3.90796041f, 0.0553211309f, -1.96053982f, 0.657846272f, -0.888796091f, 0.276572526f, + -5.59448242f, 1.66659868f, -1.91583514f, -0.421893299f, 5.67003775f, -2.143852f, 4.01850843f, + -0.107471697f, -2.41714859f, -2.6935029f, 2.56869054f, -1.95466328f, 5.8743372f, 5.63287449f, + 4.9808445f, -1.55709898f, 4.25145149f, -4.20448923f, -3.54530525f, -3.21836424f, 5.94866133f, + -5.81591415f, 3.33156276f, -1.57384706f, -5.73466253f, 0.178171813f, -2.59080482f, 3.28741193f, + -4.63642693f, 2.49446678f, 1.00580609f, -0.525342703f, -3.21013904f, -2.91663074f, -3.64932847f, + -2.73275614f, 1.60295427f, 2.9646976f, -0.616162777f, -0.886478901f, -5.8759079f, -5.42675447f, + 1.45218182f, -0.7907148f, 0.586581469f, 4.96738482f, 0.894516647f, -5.79615068f, 3.20638323f, + -2.15334439f, -5.83860826f, -1.68949926f, -3.01636457f, -1.91108048f, -0.750714719f, -5.01797915f, + 5.87076712f, 2.70632625f, -0.258777946f, -2.81812167f, 0.336809576f, 0.902599394f, 4.61446619f, + -3.74116731f, -3.84269691f, 0.310189217f, -1.07934666f, 3.19508886f, 2.19408035f, -3.17076492f, + 3.87453151f, -1.13749635f, -0.96239084f, 4.42107582f, -3.72210145f, -4.72606087f, -0.0133705167f, + -5.26969194f, -3.79380083f, -0.327241808f, 4.83841848f, 5.45226192f, 5.99788666f, -0.853680253f, + 4.88839293f, -5.24139977f, 4.04919863f, -3.61354518f, -5.89492702f, -2.86836815f, -5.63141441f, + 4.53382397f, 5.6887455f, 0.557972372f, -4.69218493f, -0.825421333f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/output_ref_data.h new file mode 100644 index 00000000..ed1ba664 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_1_f32_output_ref[152] = { + -1.33102453f, 2.41181374f, 4.50241995f, 3.90808606f, 4.82162666f, -1.33102453f, -1.33102453f, + -1.33102453f, -1.33102453f, -0.232303366f, -1.33102453f, -1.33102453f, -1.33102453f, 3.54770184f, + -0.083846584f, 3.17593169f, -1.33102453f, 5.53488922f, -1.33102453f, 0.852559626f, -1.33102453f, + 0.104315661f, 3.18602991f, 0.374441832f, 5.01691723f, -1.33102453f, 2.2601037f, 3.68813944f, + 4.46874666f, -1.33102453f, -0.0778963342f, -1.33102453f, 3.95771408f, -1.33102453f, 1.60777771f, + 3.25474477f, 4.23021936f, -0.916315556f, -1.33102453f, 4.13712692f, 0.606094241f, 4.42138147f, + 1.52516508f, 4.64856339f, -1.33102453f, 2.71755433f, -1.33102453f, 5.14123201f, 2.61878705f, + -1.33102453f, 3.90796041f, 0.0553211309f, -1.33102453f, 0.657846272f, -0.888796091f, 0.276572526f, + -1.33102453f, 1.66659868f, -1.33102453f, -0.421893299f, 5.67003775f, -1.33102453f, 4.01850843f, + -0.107471697f, -1.33102453f, -1.33102453f, 2.56869054f, -1.33102453f, 5.8743372f, 5.63287449f, + 4.9808445f, -1.33102453f, 4.25145149f, -1.33102453f, -1.33102453f, -1.33102453f, 5.94866133f, + -1.33102453f, 3.33156276f, -1.33102453f, -1.33102453f, 0.178171813f, -1.33102453f, 3.28741193f, + -1.33102453f, 2.49446678f, 1.00580609f, -0.525342703f, -1.33102453f, -1.33102453f, -1.33102453f, + -1.33102453f, 1.60295427f, 2.9646976f, -0.616162777f, -0.886478901f, -1.33102453f, -1.33102453f, + 1.45218182f, -0.7907148f, 0.586581469f, 4.96738482f, 0.894516647f, -1.33102453f, 3.20638323f, + -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -0.750714719f, -1.33102453f, + 5.87076712f, 2.70632625f, -0.258777946f, -1.33102453f, 0.336809576f, 0.902599394f, 4.61446619f, + -1.33102453f, -1.33102453f, 0.310189217f, -1.07934666f, 3.19508886f, 2.19408035f, -1.33102453f, + 3.87453151f, -1.13749635f, -0.96239084f, 4.42107582f, -1.33102453f, -1.33102453f, -0.0133705167f, + -1.33102453f, -1.33102453f, -0.327241808f, 4.83841848f, 5.45226192f, 5.99788666f, -0.853680253f, + 4.88839293f, -1.33102453f, 4.04919863f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, + 4.53382397f, 5.6887455f, 0.557972372f, -1.33102453f, -0.825421333f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_1_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/config_data.h new file mode 100644 index 00000000..b65231d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_SCALAR_2_F16_BATCH_1 1 +#define MAXIMUM_SCALAR_2_F16_HEIGHT_1 2 +#define MAXIMUM_SCALAR_2_F16_WIDTH_1 4 +#define MAXIMUM_SCALAR_2_F16_CHANNEL_1 19 +#define MAXIMUM_SCALAR_2_F16_BATCH_2 1 +#define MAXIMUM_SCALAR_2_F16_HEIGHT_2 1 +#define MAXIMUM_SCALAR_2_F16_WIDTH_2 1 +#define MAXIMUM_SCALAR_2_F16_CHANNEL_2 1 +#define MAXIMUM_SCALAR_2_F16_OUTPUT_BATCH 1 +#define MAXIMUM_SCALAR_2_F16_OUTPUT_HEIGHT 2 +#define MAXIMUM_SCALAR_2_F16_OUTPUT_WIDTH 4 +#define MAXIMUM_SCALAR_2_F16_OUTPUT_CHANNEL 19 +#define MAXIMUM_SCALAR_2_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_1_data.h new file mode 100644 index 00000000..f366a301 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_1_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_2_f16_input_tensor_1[152] = { + (float16_t)-2.33789062f, (float16_t)2.41210938f, (float16_t)4.50390625f, (float16_t)3.90820312f, + (float16_t)4.8203125f, (float16_t)-4.37890625f, (float16_t)-5.9921875f, (float16_t)-5.765625f, + (float16_t)-4.1015625f, (float16_t)-0.232299805f, (float16_t)-3.1875f, (float16_t)-2.26367188f, + (float16_t)-4.1796875f, (float16_t)3.546875f, (float16_t)-0.0838623047f, (float16_t)3.17578125f, + (float16_t)-4.90625f, (float16_t)5.53515625f, (float16_t)-5.41796875f, (float16_t)0.852539062f, + (float16_t)-3.27929688f, (float16_t)0.104309082f, (float16_t)3.18554688f, (float16_t)0.374511719f, + (float16_t)5.015625f, (float16_t)-5.00390625f, (float16_t)2.25976562f, (float16_t)3.6875f, + (float16_t)4.46875f, (float16_t)-4.7421875f, (float16_t)-0.0778808594f, (float16_t)-4.8984375f, + (float16_t)3.95703125f, (float16_t)-5.828125f, (float16_t)1.60742188f, (float16_t)3.25390625f, + (float16_t)4.23046875f, (float16_t)-0.916503906f, (float16_t)-5.84765625f, (float16_t)4.13671875f, + (float16_t)0.605957031f, (float16_t)4.421875f, (float16_t)1.52539062f, (float16_t)4.6484375f, + (float16_t)-1.80078125f, (float16_t)2.71679688f, (float16_t)-2.25585938f, (float16_t)5.140625f, + (float16_t)2.61914062f, (float16_t)-1.95996094f, (float16_t)3.90820312f, (float16_t)0.0553283691f, + (float16_t)-1.9609375f, (float16_t)0.657714844f, (float16_t)-0.888671875f, (float16_t)0.276611328f, + (float16_t)-5.59375f, (float16_t)1.66699219f, (float16_t)-1.91601562f, (float16_t)-0.421875f, + (float16_t)5.671875f, (float16_t)-2.14453125f, (float16_t)4.01953125f, (float16_t)-0.10748291f, + (float16_t)-2.41796875f, (float16_t)-2.69335938f, (float16_t)2.56835938f, (float16_t)-1.95507812f, + (float16_t)5.875f, (float16_t)5.6328125f, (float16_t)4.98046875f, (float16_t)-1.55664062f, + (float16_t)4.25f, (float16_t)-4.203125f, (float16_t)-3.54492188f, (float16_t)-3.21875f, + (float16_t)5.94921875f, (float16_t)-5.81640625f, (float16_t)3.33203125f, (float16_t)-1.57421875f, + (float16_t)-5.734375f, (float16_t)0.178222656f, (float16_t)-2.58984375f, (float16_t)3.28710938f, + (float16_t)-4.63671875f, (float16_t)2.49414062f, (float16_t)1.00585938f, (float16_t)-0.525390625f, + (float16_t)-3.2109375f, (float16_t)-2.91601562f, (float16_t)-3.6484375f, (float16_t)-2.73242188f, + (float16_t)1.60253906f, (float16_t)2.96484375f, (float16_t)-0.616210938f, (float16_t)-0.88671875f, + (float16_t)-5.875f, (float16_t)-5.42578125f, (float16_t)1.45214844f, (float16_t)-0.790527344f, + (float16_t)0.586425781f, (float16_t)4.96875f, (float16_t)0.89453125f, (float16_t)-5.796875f, + (float16_t)3.20703125f, (float16_t)-2.15429688f, (float16_t)-5.83984375f, (float16_t)-1.68945312f, + (float16_t)-3.015625f, (float16_t)-1.91113281f, (float16_t)-0.750488281f, (float16_t)-5.01953125f, + (float16_t)5.87109375f, (float16_t)2.70703125f, (float16_t)-0.258789062f, (float16_t)-2.81835938f, + (float16_t)0.336914062f, (float16_t)0.902832031f, (float16_t)4.61328125f, (float16_t)-3.74023438f, + (float16_t)-3.84179688f, (float16_t)0.310302734f, (float16_t)-1.07910156f, (float16_t)3.1953125f, + (float16_t)2.19335938f, (float16_t)-3.16992188f, (float16_t)3.875f, (float16_t)-1.13769531f, + (float16_t)-0.962402344f, (float16_t)4.421875f, (float16_t)-3.72265625f, (float16_t)-4.7265625f, + (float16_t)-0.0133743286f, (float16_t)-5.26953125f, (float16_t)-3.79296875f, (float16_t)-0.327148438f, + (float16_t)4.83984375f, (float16_t)5.453125f, (float16_t)5.99609375f, (float16_t)-0.853515625f, + (float16_t)4.88671875f, (float16_t)-5.2421875f, (float16_t)4.05078125f, (float16_t)-3.61328125f, + (float16_t)-5.89453125f, (float16_t)-2.86914062f, (float16_t)-5.6328125f, (float16_t)4.53515625f, + (float16_t)5.6875f, (float16_t)0.558105469f, (float16_t)-4.69140625f, (float16_t)-0.825195312f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_2_data.h new file mode 100644 index 00000000..54dd5224 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/input_tensor_2_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_2_f16_input_tensor_2[1] = {(float16_t)-3.98242188f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/output_ref_data.h new file mode 100644 index 00000000..0793c608 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maximum_scalar_2_f16_output_ref[152] = { + (float16_t)-2.33789062f, (float16_t)2.41210938f, (float16_t)4.50390625f, (float16_t)3.90820312f, + (float16_t)4.8203125f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-0.232299805f, (float16_t)-3.1875f, (float16_t)-2.26367188f, + (float16_t)-3.98242188f, (float16_t)3.546875f, (float16_t)-0.0838623047f, (float16_t)3.17578125f, + (float16_t)-3.98242188f, (float16_t)5.53515625f, (float16_t)-3.98242188f, (float16_t)0.852539062f, + (float16_t)-3.27929688f, (float16_t)0.104309082f, (float16_t)3.18554688f, (float16_t)0.374511719f, + (float16_t)5.015625f, (float16_t)-3.98242188f, (float16_t)2.25976562f, (float16_t)3.6875f, + (float16_t)4.46875f, (float16_t)-3.98242188f, (float16_t)-0.0778808594f, (float16_t)-3.98242188f, + (float16_t)3.95703125f, (float16_t)-3.98242188f, (float16_t)1.60742188f, (float16_t)3.25390625f, + (float16_t)4.23046875f, (float16_t)-0.916503906f, (float16_t)-3.98242188f, (float16_t)4.13671875f, + (float16_t)0.605957031f, (float16_t)4.421875f, (float16_t)1.52539062f, (float16_t)4.6484375f, + (float16_t)-1.80078125f, (float16_t)2.71679688f, (float16_t)-2.25585938f, (float16_t)5.140625f, + (float16_t)2.61914062f, (float16_t)-1.95996094f, (float16_t)3.90820312f, (float16_t)0.0553283691f, + (float16_t)-1.9609375f, (float16_t)0.657714844f, (float16_t)-0.888671875f, (float16_t)0.276611328f, + (float16_t)-3.98242188f, (float16_t)1.66699219f, (float16_t)-1.91601562f, (float16_t)-0.421875f, + (float16_t)5.671875f, (float16_t)-2.14453125f, (float16_t)4.01953125f, (float16_t)-0.10748291f, + (float16_t)-2.41796875f, (float16_t)-2.69335938f, (float16_t)2.56835938f, (float16_t)-1.95507812f, + (float16_t)5.875f, (float16_t)5.6328125f, (float16_t)4.98046875f, (float16_t)-1.55664062f, + (float16_t)4.25f, (float16_t)-3.98242188f, (float16_t)-3.54492188f, (float16_t)-3.21875f, + (float16_t)5.94921875f, (float16_t)-3.98242188f, (float16_t)3.33203125f, (float16_t)-1.57421875f, + (float16_t)-3.98242188f, (float16_t)0.178222656f, (float16_t)-2.58984375f, (float16_t)3.28710938f, + (float16_t)-3.98242188f, (float16_t)2.49414062f, (float16_t)1.00585938f, (float16_t)-0.525390625f, + (float16_t)-3.2109375f, (float16_t)-2.91601562f, (float16_t)-3.6484375f, (float16_t)-2.73242188f, + (float16_t)1.60253906f, (float16_t)2.96484375f, (float16_t)-0.616210938f, (float16_t)-0.88671875f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)1.45214844f, (float16_t)-0.790527344f, + (float16_t)0.586425781f, (float16_t)4.96875f, (float16_t)0.89453125f, (float16_t)-3.98242188f, + (float16_t)3.20703125f, (float16_t)-2.15429688f, (float16_t)-3.98242188f, (float16_t)-1.68945312f, + (float16_t)-3.015625f, (float16_t)-1.91113281f, (float16_t)-0.750488281f, (float16_t)-3.98242188f, + (float16_t)5.87109375f, (float16_t)2.70703125f, (float16_t)-0.258789062f, (float16_t)-2.81835938f, + (float16_t)0.336914062f, (float16_t)0.902832031f, (float16_t)4.61328125f, (float16_t)-3.74023438f, + (float16_t)-3.84179688f, (float16_t)0.310302734f, (float16_t)-1.07910156f, (float16_t)3.1953125f, + (float16_t)2.19335938f, (float16_t)-3.16992188f, (float16_t)3.875f, (float16_t)-1.13769531f, + (float16_t)-0.962402344f, (float16_t)4.421875f, (float16_t)-3.72265625f, (float16_t)-3.98242188f, + (float16_t)-0.0133743286f, (float16_t)-3.98242188f, (float16_t)-3.79296875f, (float16_t)-0.327148438f, + (float16_t)4.83984375f, (float16_t)5.453125f, (float16_t)5.99609375f, (float16_t)-0.853515625f, + (float16_t)4.88671875f, (float16_t)-3.98242188f, (float16_t)4.05078125f, (float16_t)-3.61328125f, + (float16_t)-3.98242188f, (float16_t)-2.86914062f, (float16_t)-3.98242188f, (float16_t)4.53515625f, + (float16_t)5.6875f, (float16_t)0.558105469f, (float16_t)-3.98242188f, (float16_t)-0.825195312f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/config_data.h new file mode 100644 index 00000000..aa8ab53f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXIMUM_SCALAR_2_F32_BATCH_1 1 +#define MAXIMUM_SCALAR_2_F32_HEIGHT_1 2 +#define MAXIMUM_SCALAR_2_F32_WIDTH_1 4 +#define MAXIMUM_SCALAR_2_F32_CHANNEL_1 19 +#define MAXIMUM_SCALAR_2_F32_BATCH_2 1 +#define MAXIMUM_SCALAR_2_F32_HEIGHT_2 1 +#define MAXIMUM_SCALAR_2_F32_WIDTH_2 1 +#define MAXIMUM_SCALAR_2_F32_CHANNEL_2 1 +#define MAXIMUM_SCALAR_2_F32_OUTPUT_BATCH 1 +#define MAXIMUM_SCALAR_2_F32_OUTPUT_HEIGHT 2 +#define MAXIMUM_SCALAR_2_F32_OUTPUT_WIDTH 4 +#define MAXIMUM_SCALAR_2_F32_OUTPUT_CHANNEL 19 +#define MAXIMUM_SCALAR_2_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_1_data.h new file mode 100644 index 00000000..a9aee8b4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_1_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_2_f32_input_tensor_1[152] = { + 4.21651506f, -2.44645262f, -2.58304834f, -1.00068307f, 1.71813881f, 0.229273468f, -1.32676303f, + -5.77569151f, -2.8990159f, -0.519005716f, -3.57659769f, 3.76164603f, -3.56612134f, -1.95359755f, + -2.09227586f, 1.60295153f, -5.26404715f, 0.855953693f, 3.37189221f, -4.4846859f, 1.06080365f, + 1.22572792f, 1.83382714f, 4.36309814f, 4.2877326f, -4.29909468f, -1.5225569f, 1.66976035f, + -2.90561366f, 5.75992727f, 0.269500077f, -0.296420217f, -5.91729784f, -5.40399265f, 5.14160442f, + -1.67769504f, -3.3278234f, 2.47849607f, -1.12402809f, -0.848127484f, -5.17140722f, -4.32199478f, + 4.58439922f, 0.692196608f, 1.94302189f, 1.94695878f, 5.45770121f, 5.34246206f, -5.76576233f, + -1.64440763f, -4.21322203f, 5.94327784f, -2.95788717f, -4.55879545f, 0.640772879f, -2.38353634f, + 1.83990824f, -5.49134874f, 3.56541514f, -1.19899225f, -4.9643693f, -3.67932487f, -1.2503525f, + 4.635993f, -2.05336976f, 2.46690202f, -2.13392019f, 3.44667435f, 4.79914713f, -5.53693724f, + 1.81337965f, 0.54110378f, -5.34813309f, 4.34579659f, 4.94211006f, 1.13815951f, 0.444551766f, + -0.516891301f, -1.43879521f, -1.8839252f, -5.48385143f, -1.4392699f, -4.77348852f, 3.65590119f, + 1.22632003f, 3.10053086f, 5.39067936f, 5.59765053f, 0.0950766951f, 3.0594244f, -3.6377964f, + -1.81328011f, 2.27910781f, -1.79949868f, -3.71277642f, 3.98877907f, 2.53513765f, -4.95961475f, + -5.23175526f, -4.81570244f, 3.48046589f, 2.47036457f, 5.9871664f, -4.1907835f, 4.46137905f, + 2.32326365f, -2.44084239f, 3.61053324f, 1.84585333f, 1.0879482f, -5.75145864f, -0.545061409f, + 2.0519383f, 0.912212133f, -5.49078751f, 4.44412899f, -1.25164509f, -2.78041625f, -3.41571951f, + 0.710473895f, -4.77362061f, -5.39968777f, -5.24263191f, 3.81943154f, 3.05806017f, 4.42284298f, + -0.992328644f, 0.672959805f, 3.50049853f, -3.66966677f, -0.235395417f, 5.1538763f, -1.54643023f, + 5.34219217f, -0.0929627866f, 1.08004832f, 4.36181021f, -5.76473093f, -0.570274293f, 2.73397088f, + 2.5359509f, 1.74922264f, -4.56324244f, 1.32867968f, 4.02708912f, 4.80426931f, -1.80577505f, + -2.12380052f, 5.92663383f, -0.64609313f, -1.7343936f, -1.41045737f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_2_data.h new file mode 100644 index 00000000..470051a8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/input_tensor_2_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_2_f32_input_tensor_2[1] = {-4.86829948f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/output_ref_data.h new file mode 100644 index 00000000..f73e2c49 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maximum_scalar_2_f32_output_ref[152] = { + 4.21651506f, -2.44645262f, -2.58304834f, -1.00068307f, 1.71813881f, 0.229273468f, -1.32676303f, + -4.86829948f, -2.8990159f, -0.519005716f, -3.57659769f, 3.76164603f, -3.56612134f, -1.95359755f, + -2.09227586f, 1.60295153f, -4.86829948f, 0.855953693f, 3.37189221f, -4.4846859f, 1.06080365f, + 1.22572792f, 1.83382714f, 4.36309814f, 4.2877326f, -4.29909468f, -1.5225569f, 1.66976035f, + -2.90561366f, 5.75992727f, 0.269500077f, -0.296420217f, -4.86829948f, -4.86829948f, 5.14160442f, + -1.67769504f, -3.3278234f, 2.47849607f, -1.12402809f, -0.848127484f, -4.86829948f, -4.32199478f, + 4.58439922f, 0.692196608f, 1.94302189f, 1.94695878f, 5.45770121f, 5.34246206f, -4.86829948f, + -1.64440763f, -4.21322203f, 5.94327784f, -2.95788717f, -4.55879545f, 0.640772879f, -2.38353634f, + 1.83990824f, -4.86829948f, 3.56541514f, -1.19899225f, -4.86829948f, -3.67932487f, -1.2503525f, + 4.635993f, -2.05336976f, 2.46690202f, -2.13392019f, 3.44667435f, 4.79914713f, -4.86829948f, + 1.81337965f, 0.54110378f, -4.86829948f, 4.34579659f, 4.94211006f, 1.13815951f, 0.444551766f, + -0.516891301f, -1.43879521f, -1.8839252f, -4.86829948f, -1.4392699f, -4.77348852f, 3.65590119f, + 1.22632003f, 3.10053086f, 5.39067936f, 5.59765053f, 0.0950766951f, 3.0594244f, -3.6377964f, + -1.81328011f, 2.27910781f, -1.79949868f, -3.71277642f, 3.98877907f, 2.53513765f, -4.86829948f, + -4.86829948f, -4.81570244f, 3.48046589f, 2.47036457f, 5.9871664f, -4.1907835f, 4.46137905f, + 2.32326365f, -2.44084239f, 3.61053324f, 1.84585333f, 1.0879482f, -4.86829948f, -0.545061409f, + 2.0519383f, 0.912212133f, -4.86829948f, 4.44412899f, -1.25164509f, -2.78041625f, -3.41571951f, + 0.710473895f, -4.77362061f, -4.86829948f, -4.86829948f, 3.81943154f, 3.05806017f, 4.42284298f, + -0.992328644f, 0.672959805f, 3.50049853f, -3.66966677f, -0.235395417f, 5.1538763f, -1.54643023f, + 5.34219217f, -0.0929627866f, 1.08004832f, 4.36181021f, -4.86829948f, -0.570274293f, 2.73397088f, + 2.5359509f, 1.74922264f, -4.56324244f, 1.32867968f, 4.02708912f, 4.80426931f, -1.80577505f, + -2.12380052f, 5.92663383f, -0.64609313f, -1.7343936f, -1.41045737f}; diff --git a/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maximum_scalar_2_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/config_data.h new file mode 100644 index 00000000..e7e54695 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K2S2_CLIP_F16_BATCH_SIZE 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_INPUT_N 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_INPUT_W 4 +#define MAXPOOLING_1D_K2S2_CLIP_F16_INPUT_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_INPUT_C 4 +#define MAXPOOLING_1D_K2S2_CLIP_F16_FILTER_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F16_FILTER_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_STRIDE_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F16_STRIDE_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_ACTIVATION_MIN (float16_t)0.0f +#define MAXPOOLING_1D_K2S2_CLIP_F16_ACTIVATION_MAX (float16_t)6.0f +#define MAXPOOLING_1D_K2S2_CLIP_F16_OUTPUT_C 4 +#define MAXPOOLING_1D_K2S2_CLIP_F16_OUTPUT_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F16_OUTPUT_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F16_PADDING_H 0 +#define MAXPOOLING_1D_K2S2_CLIP_F16_PADDING_W 0 +#define MAXPOOLING_1D_K2S2_CLIP_F16_DST_SIZE 8 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/input_tensor_data.h new file mode 100644 index 00000000..b341faaa --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/input_tensor_data.h @@ -0,0 +1,16 @@ +const float16_t maxpooling_1d_k2s2_clip_f16_input_tensor[16] = {(float16_t)-1.0f, + (float16_t)8.0f, + (float16_t)4.0f, + (float16_t)-7.0f, + (float16_t)2.0f, + (float16_t)3.0f, + (float16_t)10.0f, + (float16_t)-2.0f, + (float16_t)5.0f, + (float16_t)-4.0f, + (float16_t)1.0f, + (float16_t)9.0f, + (float16_t)7.0f, + (float16_t)12.0f, + (float16_t)-3.0f, + (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/output_ref_data.h new file mode 100644 index 00000000..5d5ac1a0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/output_ref_data.h @@ -0,0 +1,8 @@ +const float16_t maxpooling_1d_k2s2_clip_f16_output_ref[8] = {(float16_t)2.0f, + (float16_t)6.0f, + (float16_t)6.0f, + (float16_t)0.0f, + (float16_t)6.0f, + (float16_t)6.0f, + (float16_t)1.0f, + (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/config_data.h new file mode 100644 index 00000000..009151e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K2S2_CLIP_F32_BATCH_SIZE 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_INPUT_N 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_INPUT_W 4 +#define MAXPOOLING_1D_K2S2_CLIP_F32_INPUT_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_INPUT_C 4 +#define MAXPOOLING_1D_K2S2_CLIP_F32_FILTER_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F32_FILTER_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_STRIDE_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F32_STRIDE_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_ACTIVATION_MIN 0.0f +#define MAXPOOLING_1D_K2S2_CLIP_F32_ACTIVATION_MAX 6.0f +#define MAXPOOLING_1D_K2S2_CLIP_F32_OUTPUT_C 4 +#define MAXPOOLING_1D_K2S2_CLIP_F32_OUTPUT_W 2 +#define MAXPOOLING_1D_K2S2_CLIP_F32_OUTPUT_H 1 +#define MAXPOOLING_1D_K2S2_CLIP_F32_PADDING_H 0 +#define MAXPOOLING_1D_K2S2_CLIP_F32_PADDING_W 0 +#define MAXPOOLING_1D_K2S2_CLIP_F32_DST_SIZE 8 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/input_tensor_data.h new file mode 100644 index 00000000..d7c91048 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/input_tensor_data.h @@ -0,0 +1,2 @@ +const float32_t maxpooling_1d_k2s2_clip_f32_input_tensor[16] = + {-1.0f, 8.0f, 4.0f, -7.0f, 2.0f, 3.0f, 10.0f, -2.0f, 5.0f, -4.0f, 1.0f, 9.0f, 7.0f, 12.0f, -3.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/output_ref_data.h new file mode 100644 index 00000000..27ae0881 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/output_ref_data.h @@ -0,0 +1 @@ +const float32_t maxpooling_1d_k2s2_clip_f32_output_ref[8] = {2.0f, 6.0f, 6.0f, 0.0f, 6.0f, 6.0f, 1.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_clip_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/config_data.h new file mode 100644 index 00000000..3236f886 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_BATCH_SIZE 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_INPUT_N 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_INPUT_W 6 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_INPUT_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_INPUT_C 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_FILTER_W 2 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_FILTER_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_STRIDE_W 2 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_STRIDE_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_OUTPUT_C 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_OUTPUT_W 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_OUTPUT_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_PADDING_H 0 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_PADDING_W 0 +#define MAXPOOLING_1D_K2S2_NOCLIP_F16_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/input_tensor_data.h new file mode 100644 index 00000000..f541ac17 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/input_tensor_data.h @@ -0,0 +1,18 @@ +const float16_t maxpooling_1d_k2s2_noclip_f16_input_tensor[18] = {(float16_t)1.0f, + (float16_t)-2.0f, + (float16_t)0.0f, + (float16_t)3.0f, + (float16_t)-4.0f, + (float16_t)5.0f, + (float16_t)-1.0f, + (float16_t)6.0f, + (float16_t)2.0f, + (float16_t)4.0f, + (float16_t)1.0f, + (float16_t)-3.0f, + (float16_t)7.0f, + (float16_t)-8.0f, + (float16_t)9.0f, + (float16_t)0.0f, + (float16_t)2.0f, + (float16_t)-5.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/output_ref_data.h new file mode 100644 index 00000000..f9091139 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/output_ref_data.h @@ -0,0 +1,9 @@ +const float16_t maxpooling_1d_k2s2_noclip_f16_output_ref[9] = {(float16_t)3.0f, + (float16_t)-2.0f, + (float16_t)5.0f, + (float16_t)4.0f, + (float16_t)6.0f, + (float16_t)2.0f, + (float16_t)7.0f, + (float16_t)2.0f, + (float16_t)9.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/config_data.h new file mode 100644 index 00000000..c8c66cc2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_BATCH_SIZE 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_INPUT_N 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_INPUT_W 6 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_INPUT_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_INPUT_C 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_FILTER_W 2 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_FILTER_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_STRIDE_W 2 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_STRIDE_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_OUTPUT_C 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_OUTPUT_W 3 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_OUTPUT_H 1 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_PADDING_H 0 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_PADDING_W 0 +#define MAXPOOLING_1D_K2S2_NOCLIP_F32_DST_SIZE 9 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/input_tensor_data.h new file mode 100644 index 00000000..d23e9cb5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/input_tensor_data.h @@ -0,0 +1,2 @@ +const float32_t maxpooling_1d_k2s2_noclip_f32_input_tensor[18] = + {1.0f, -2.0f, 0.0f, 3.0f, -4.0f, 5.0f, -1.0f, 6.0f, 2.0f, 4.0f, 1.0f, -3.0f, 7.0f, -8.0f, 9.0f, 0.0f, 2.0f, -5.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/output_ref_data.h new file mode 100644 index 00000000..6beac0d3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/output_ref_data.h @@ -0,0 +1 @@ +const float32_t maxpooling_1d_k2s2_noclip_f32_output_ref[9] = {3.0f, -2.0f, 5.0f, 4.0f, 6.0f, 2.0f, 7.0f, 2.0f, 9.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k2s2_noclip_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/config_data.h new file mode 100644 index 00000000..49279ac9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_BATCH_SIZE 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_INPUT_N 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_INPUT_W 9 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_INPUT_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_INPUT_C 2 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_FILTER_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_FILTER_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_STRIDE_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_STRIDE_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_OUTPUT_C 2 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_OUTPUT_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_OUTPUT_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_PADDING_H 0 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_PADDING_W 0 +#define MAXPOOLING_1D_K3S3_NOCLIP_F16_DST_SIZE 6 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/input_tensor_data.h new file mode 100644 index 00000000..0d5259e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/input_tensor_data.h @@ -0,0 +1,18 @@ +const float16_t maxpooling_1d_k3s3_noclip_f16_input_tensor[18] = {(float16_t)1.0f, + (float16_t)-1.0f, + (float16_t)3.0f, + (float16_t)2.0f, + (float16_t)0.0f, + (float16_t)5.0f, + (float16_t)-4.0f, + (float16_t)7.0f, + (float16_t)6.0f, + (float16_t)1.0f, + (float16_t)2.0f, + (float16_t)8.0f, + (float16_t)9.0f, + (float16_t)-3.0f, + (float16_t)4.0f, + (float16_t)0.0f, + (float16_t)5.0f, + (float16_t)10.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/output_ref_data.h new file mode 100644 index 00000000..6d535847 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/output_ref_data.h @@ -0,0 +1,2 @@ +const float16_t maxpooling_1d_k3s3_noclip_f16_output_ref[6] = + {(float16_t)3.0f, (float16_t)5.0f, (float16_t)6.0f, (float16_t)8.0f, (float16_t)9.0f, (float16_t)10.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/config_data.h new file mode 100644 index 00000000..200b0c46 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/config_data.h @@ -0,0 +1,17 @@ +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_BATCH_SIZE 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_INPUT_N 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_INPUT_W 9 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_INPUT_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_INPUT_C 2 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_FILTER_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_FILTER_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_STRIDE_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_STRIDE_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_OUTPUT_C 2 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_OUTPUT_W 3 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_OUTPUT_H 1 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_PADDING_H 0 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_PADDING_W 0 +#define MAXPOOLING_1D_K3S3_NOCLIP_F32_DST_SIZE 6 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/input_tensor_data.h new file mode 100644 index 00000000..82130479 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/input_tensor_data.h @@ -0,0 +1,2 @@ +const float32_t maxpooling_1d_k3s3_noclip_f32_input_tensor[18] = + {1.0f, -1.0f, 3.0f, 2.0f, 0.0f, 5.0f, -4.0f, 7.0f, 6.0f, 1.0f, 2.0f, 8.0f, 9.0f, -3.0f, 4.0f, 0.0f, 5.0f, 10.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/output_ref_data.h new file mode 100644 index 00000000..99925c85 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/output_ref_data.h @@ -0,0 +1 @@ +const float32_t maxpooling_1d_k3s3_noclip_f32_output_ref[6] = {3.0f, 5.0f, 6.0f, 8.0f, 9.0f, 10.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_1d_k3s3_noclip_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/config_data.h new file mode 100644 index 00000000..dbadd232 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F16_BATCH_SIZE 2 +#define MAXPOOLING_F16_INPUT_N 2 +#define MAXPOOLING_F16_INPUT_W 22 +#define MAXPOOLING_F16_INPUT_H 12 +#define MAXPOOLING_F16_INPUT_C 8 +#define MAXPOOLING_F16_FILTER_W 6 +#define MAXPOOLING_F16_FILTER_H 5 +#define MAXPOOLING_F16_STRIDE_W 9 +#define MAXPOOLING_F16_STRIDE_H 5 +#define MAXPOOLING_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_F16_OUTPUT_C 8 +#define MAXPOOLING_F16_OUTPUT_W 3 +#define MAXPOOLING_F16_OUTPUT_H 3 +#define MAXPOOLING_F16_PADDING_H 1 +#define MAXPOOLING_F16_PADDING_W 1 +#define MAXPOOLING_F16_DST_SIZE 144 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/input_tensor_data.h new file mode 100644 index 00000000..8f972e77 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/input_tensor_data.h @@ -0,0 +1,4225 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_input_tensor[4224] = {(float16_t)-5.63671875f, + (float16_t)4.2421875f, + (float16_t)-3.73828125f, + (float16_t)-5.65234375f, + (float16_t)-4.53125f, + (float16_t)4.7265625f, + (float16_t)4.0546875f, + (float16_t)3.5390625f, + (float16_t)-2.40234375f, + (float16_t)-4.8359375f, + (float16_t)-2.375f, + (float16_t)-3.16796875f, + (float16_t)2.8828125f, + (float16_t)-0.491943359f, + (float16_t)2.39257812f, + (float16_t)-2.83984375f, + (float16_t)-3.07617188f, + (float16_t)0.90234375f, + (float16_t)-3.59179688f, + (float16_t)-3.31640625f, + (float16_t)-3.265625f, + (float16_t)-4.30078125f, + (float16_t)5.0546875f, + (float16_t)-5.89453125f, + (float16_t)-3.23242188f, + (float16_t)3.35546875f, + (float16_t)3.83203125f, + (float16_t)4.86328125f, + (float16_t)-4.33984375f, + (float16_t)-2.19921875f, + (float16_t)5.390625f, + (float16_t)-1.15917969f, + (float16_t)0.355224609f, + (float16_t)-3.34570312f, + (float16_t)-3.49804688f, + (float16_t)4.3671875f, + (float16_t)-2.40429688f, + (float16_t)1.73730469f, + (float16_t)3.89257812f, + (float16_t)-5.53125f, + (float16_t)3.32421875f, + (float16_t)-4.40625f, + (float16_t)4.609375f, + (float16_t)3.3046875f, + (float16_t)2.25f, + (float16_t)3.00585938f, + (float16_t)0.0522766113f, + (float16_t)-5.640625f, + (float16_t)-3.42382812f, + (float16_t)-5.08203125f, + (float16_t)-2.6171875f, + (float16_t)4.61328125f, + (float16_t)4.77734375f, + (float16_t)-2.01367188f, + (float16_t)-2.24414062f, + (float16_t)0.505859375f, + (float16_t)4.98828125f, + (float16_t)-3.296875f, + (float16_t)-5.81640625f, + (float16_t)5.1640625f, + (float16_t)1.06933594f, + (float16_t)-5.6953125f, + (float16_t)1.04980469f, + (float16_t)-4.078125f, + (float16_t)1.59667969f, + (float16_t)2.24414062f, + (float16_t)-0.573242188f, + (float16_t)2.41210938f, + (float16_t)-4.140625f, + (float16_t)5.2421875f, + (float16_t)0.293457031f, + (float16_t)-0.451416016f, + (float16_t)-4.92578125f, + (float16_t)0.409179688f, + (float16_t)2.7421875f, + (float16_t)1.20507812f, + (float16_t)-2.85546875f, + (float16_t)2.76171875f, + (float16_t)2.89257812f, + (float16_t)4.921875f, + (float16_t)0.276123047f, + (float16_t)0.586425781f, + (float16_t)4.83203125f, + (float16_t)-5.671875f, + (float16_t)-1.27441406f, + (float16_t)-0.85546875f, + (float16_t)-2.58984375f, + 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(float16_t)-5.8671875f, + (float16_t)-4.08203125f, + (float16_t)-4.02734375f, + (float16_t)-4.109375f, + (float16_t)-3.45703125f, + (float16_t)-0.555664062f, + (float16_t)-1.49023438f, + (float16_t)1.296875f, + (float16_t)1.92089844f, + (float16_t)-0.649414062f, + (float16_t)4.1484375f, + (float16_t)4.453125f, + (float16_t)2.50195312f, + (float16_t)-0.991699219f, + (float16_t)4.21484375f, + (float16_t)2.43945312f, + (float16_t)2.45898438f, + (float16_t)4.8671875f, + (float16_t)3.48242188f, + (float16_t)-0.516601562f, + (float16_t)-4.67578125f, + (float16_t)3.47265625f, + (float16_t)0.0609741211f, + (float16_t)3.27539062f, + (float16_t)-2.6171875f, + (float16_t)-3.015625f, + (float16_t)1.88671875f, + (float16_t)-2.34960938f, + (float16_t)-2.12109375f, + (float16_t)1.01757812f, + (float16_t)-2.50585938f, + (float16_t)-3.74023438f, + (float16_t)0.607910156f, + (float16_t)5.703125f, + (float16_t)-4.9765625f, + (float16_t)-2.18945312f, + (float16_t)3.5390625f, + (float16_t)0.5390625f, + (float16_t)-1.84277344f, + (float16_t)5.9296875f, + (float16_t)4.4296875f, + (float16_t)3.3984375f, + (float16_t)3.11523438f, + (float16_t)-0.705566406f, + (float16_t)0.604003906f, + (float16_t)-5.0625f, + (float16_t)-1.64746094f, + (float16_t)1.49121094f, + (float16_t)5.984375f, + (float16_t)5.0859375f, + (float16_t)0.8203125f, + (float16_t)-4.11328125f, + (float16_t)0.787597656f, + (float16_t)-1.23828125f, + (float16_t)3.90429688f, + (float16_t)1.9375f, + (float16_t)1.265625f, + (float16_t)-4.8984375f, + (float16_t)-2.96875f, + (float16_t)3.67773438f, + (float16_t)-4.37890625f, + (float16_t)3.625f, + (float16_t)3.38671875f, + (float16_t)-1.74609375f, + (float16_t)-0.728027344f, + (float16_t)-3.9453125f, + (float16_t)5.75390625f, + (float16_t)-0.213867188f, + (float16_t)3.88671875f, + (float16_t)4.11328125f, + (float16_t)-4.484375f, + (float16_t)5.2421875f, + (float16_t)1.80078125f, + (float16_t)-5.1875f, + (float16_t)5.13671875f, + (float16_t)-2.99804688f, + (float16_t)0.22668457f, + (float16_t)0.7421875f, + (float16_t)-4.8046875f, + (float16_t)3.04882812f, + (float16_t)-1.20605469f, + (float16_t)-4.21484375f, + (float16_t)-4.02734375f, + (float16_t)0.306884766f, + (float16_t)-0.696777344f, + (float16_t)-2.83203125f, + (float16_t)5.21484375f, + (float16_t)-0.291259766f, + (float16_t)3.71289062f, + (float16_t)2.36914062f, + (float16_t)-5.36328125f, + (float16_t)-2.76171875f, + (float16_t)0.192382812f, + (float16_t)0.6171875f, + (float16_t)2.58789062f, + (float16_t)4.25f, + (float16_t)1.58886719f, + (float16_t)-0.968261719f, + (float16_t)-1.89550781f, + (float16_t)2.50390625f, + (float16_t)3.06445312f, + (float16_t)0.178710938f, + (float16_t)-1.48535156f, + (float16_t)-1.84277344f, + (float16_t)0.342773438f, + (float16_t)-2.48242188f, + (float16_t)-0.24597168f, + (float16_t)-1.78320312f, + (float16_t)3.37304688f, + (float16_t)-0.108520508f, + (float16_t)-1.82128906f, + (float16_t)-3.4140625f, + (float16_t)5.51953125f, + (float16_t)1.22265625f, + (float16_t)-4.19921875f, + (float16_t)-1.02246094f, + (float16_t)-2.32617188f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/output_ref_data.h new file mode 100644 index 00000000..4c9150a6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/output_ref_data.h @@ -0,0 +1,38 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_output_ref[144] = { + (float16_t)5.64453125f, (float16_t)5.64453125f, (float16_t)4.94921875f, (float16_t)4.86328125f, + (float16_t)5.23828125f, (float16_t)5.78125f, (float16_t)5.98046875f, (float16_t)3.75976562f, + (float16_t)5.9140625f, (float16_t)5.43359375f, (float16_t)5.78125f, (float16_t)5.265625f, + (float16_t)5.24609375f, (float16_t)5.99609375f, (float16_t)5.47265625f, (float16_t)5.890625f, + (float16_t)4.98046875f, (float16_t)5.984375f, (float16_t)5.40234375f, (float16_t)5.65625f, + (float16_t)5.359375f, (float16_t)5.5625f, (float16_t)5.15625f, (float16_t)4.91015625f, + (float16_t)5.0859375f, (float16_t)5.10546875f, (float16_t)5.69921875f, (float16_t)5.90625f, + (float16_t)5.984375f, (float16_t)5.85546875f, (float16_t)5.62109375f, (float16_t)4.7890625f, + (float16_t)5.2734375f, (float16_t)5.9765625f, (float16_t)5.8359375f, (float16_t)5.609375f, + (float16_t)5.87109375f, (float16_t)5.73046875f, (float16_t)5.7734375f, (float16_t)5.86328125f, + (float16_t)5.3671875f, (float16_t)5.6953125f, (float16_t)5.51171875f, (float16_t)5.94140625f, + (float16_t)5.35546875f, (float16_t)5.28125f, (float16_t)5.61328125f, (float16_t)5.84375f, + (float16_t)5.83203125f, (float16_t)5.421875f, (float16_t)5.953125f, (float16_t)4.484375f, + (float16_t)5.63671875f, (float16_t)5.85546875f, (float16_t)4.8828125f, (float16_t)5.90625f, + (float16_t)5.41015625f, (float16_t)5.34765625f, (float16_t)5.62890625f, (float16_t)5.203125f, + (float16_t)5.74609375f, (float16_t)4.6015625f, (float16_t)4.765625f, (float16_t)4.1328125f, + (float16_t)5.640625f, (float16_t)5.5625f, (float16_t)5.2734375f, (float16_t)3.17773438f, + (float16_t)5.2578125f, (float16_t)5.8046875f, (float16_t)5.91796875f, (float16_t)5.828125f, + (float16_t)5.5234375f, (float16_t)5.5234375f, (float16_t)5.92578125f, (float16_t)5.1484375f, + (float16_t)5.22265625f, (float16_t)5.56640625f, (float16_t)5.97265625f, (float16_t)5.67578125f, + (float16_t)5.93359375f, (float16_t)5.73046875f, (float16_t)5.30078125f, (float16_t)5.86328125f, + (float16_t)5.7109375f, (float16_t)5.79296875f, (float16_t)5.81640625f, (float16_t)5.703125f, + (float16_t)4.47265625f, (float16_t)5.88671875f, (float16_t)5.0546875f, (float16_t)5.97265625f, + (float16_t)5.734375f, (float16_t)5.84765625f, (float16_t)5.21484375f, (float16_t)5.74609375f, + (float16_t)5.91796875f, (float16_t)4.75390625f, (float16_t)5.62109375f, (float16_t)4.7578125f, + (float16_t)5.8203125f, (float16_t)5.91796875f, (float16_t)5.171875f, (float16_t)5.5f, + (float16_t)5.59375f, (float16_t)5.09765625f, (float16_t)5.4375f, (float16_t)5.953125f, + (float16_t)5.171875f, (float16_t)5.72265625f, (float16_t)5.453125f, (float16_t)5.62109375f, + (float16_t)5.8359375f, (float16_t)5.23046875f, (float16_t)4.75390625f, (float16_t)5.78515625f, + (float16_t)5.46484375f, (float16_t)5.87109375f, (float16_t)3.54296875f, (float16_t)5.9609375f, + (float16_t)5.796875f, (float16_t)5.97265625f, (float16_t)5.96484375f, (float16_t)5.76171875f, + (float16_t)4.9453125f, (float16_t)5.80078125f, (float16_t)5.6953125f, (float16_t)5.5859375f, + (float16_t)3.51171875f, (float16_t)4.91015625f, (float16_t)5.9296875f, (float16_t)5.984375f, + (float16_t)5.703125f, (float16_t)5.99609375f, (float16_t)4.453125f, (float16_t)3.6796875f, + (float16_t)5.40234375f, (float16_t)5.21484375f, (float16_t)4.25f, (float16_t)5.51953125f, + (float16_t)5.53125f, (float16_t)5.40234375f, (float16_t)5.3515625f, (float16_t)5.8671875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/config_data.h new file mode 100644 index 00000000..e91859f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F16_1_BATCH_SIZE 1 +#define MAXPOOLING_F16_1_INPUT_N 1 +#define MAXPOOLING_F16_1_INPUT_W 9 +#define MAXPOOLING_F16_1_INPUT_H 5 +#define MAXPOOLING_F16_1_INPUT_C 3 +#define MAXPOOLING_F16_1_FILTER_W 9 +#define MAXPOOLING_F16_1_FILTER_H 5 +#define MAXPOOLING_F16_1_STRIDE_W 1 +#define MAXPOOLING_F16_1_STRIDE_H 2 +#define MAXPOOLING_F16_1_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_F16_1_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_F16_1_OUTPUT_C 3 +#define MAXPOOLING_F16_1_OUTPUT_W 1 +#define MAXPOOLING_F16_1_OUTPUT_H 1 +#define MAXPOOLING_F16_1_PADDING_H 0 +#define MAXPOOLING_F16_1_PADDING_W 0 +#define MAXPOOLING_F16_1_DST_SIZE 3 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/input_tensor_data.h new file mode 100644 index 00000000..464b5fde --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/input_tensor_data.h @@ -0,0 +1,36 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_1_input_tensor[135] = { + (float16_t)0.111572266f, (float16_t)2.1640625f, (float16_t)-3.18554688f, (float16_t)0.824707031f, + (float16_t)2.8671875f, (float16_t)-3.07617188f, (float16_t)-4.27734375f, (float16_t)-4.0859375f, + (float16_t)-5.27734375f, (float16_t)-2.01953125f, (float16_t)5.7734375f, (float16_t)-3.8984375f, + (float16_t)1.31933594f, (float16_t)4.921875f, (float16_t)2.91015625f, (float16_t)-4.2734375f, + (float16_t)-5.36328125f, (float16_t)2.82226562f, (float16_t)-1.75292969f, (float16_t)2.78125f, + (float16_t)-0.546875f, (float16_t)-2.46484375f, (float16_t)5.4453125f, (float16_t)-0.489501953f, + (float16_t)3.32617188f, (float16_t)4.84765625f, (float16_t)5.56640625f, (float16_t)-0.630371094f, + (float16_t)3.04296875f, (float16_t)-0.874023438f, (float16_t)4.828125f, (float16_t)0.0602416992f, + (float16_t)-2.56054688f, (float16_t)4.55859375f, (float16_t)1.35644531f, (float16_t)5.78515625f, + (float16_t)3.15234375f, (float16_t)1.36425781f, (float16_t)-1.20605469f, (float16_t)4.890625f, + (float16_t)5.359375f, (float16_t)-4.78515625f, (float16_t)-2.58007812f, (float16_t)1.09570312f, + (float16_t)-3.40625f, (float16_t)-4.53125f, (float16_t)3.38867188f, (float16_t)-0.784179688f, + (float16_t)-1.984375f, (float16_t)5.87890625f, (float16_t)5.40625f, (float16_t)3.10742188f, + (float16_t)-0.712402344f, (float16_t)-3.15429688f, (float16_t)0.977539062f, (float16_t)-3.91796875f, + (float16_t)5.8046875f, (float16_t)-2.43164062f, (float16_t)4.609375f, (float16_t)-3.75585938f, + (float16_t)-0.178955078f, (float16_t)0.228637695f, (float16_t)0.905761719f, (float16_t)4.2265625f, + (float16_t)-1.39453125f, (float16_t)-4.14453125f, (float16_t)2.09570312f, (float16_t)0.538574219f, + (float16_t)-3.29492188f, (float16_t)3.4140625f, (float16_t)-1.05078125f, (float16_t)-5.3125f, + (float16_t)1.19238281f, (float16_t)1.07226562f, (float16_t)0.779785156f, (float16_t)-5.25f, + (float16_t)5.03125f, (float16_t)2.85546875f, (float16_t)-5.265625f, (float16_t)1.59863281f, + (float16_t)-2.8828125f, (float16_t)1.89746094f, (float16_t)3.34375f, (float16_t)-0.323242188f, + (float16_t)-1.47363281f, (float16_t)-4.42578125f, (float16_t)0.184082031f, (float16_t)2.7109375f, + (float16_t)1.89941406f, (float16_t)3.19726562f, (float16_t)1.92773438f, (float16_t)0.0425109863f, + (float16_t)1.01367188f, (float16_t)0.625488281f, (float16_t)-4.16015625f, (float16_t)-1.98339844f, + (float16_t)-2.24414062f, (float16_t)-3.34179688f, (float16_t)4.37109375f, (float16_t)0.0744628906f, + (float16_t)-0.687011719f, (float16_t)4.7421875f, (float16_t)-4.21875f, (float16_t)4.3828125f, + (float16_t)-2.0625f, (float16_t)3.81445312f, (float16_t)2.02148438f, (float16_t)5.38671875f, + (float16_t)-0.579589844f, (float16_t)-2.90625f, (float16_t)1.70117188f, (float16_t)-1.80371094f, + (float16_t)-0.467041016f, (float16_t)1.04785156f, (float16_t)-4.76953125f, (float16_t)1.68457031f, + (float16_t)-1.02050781f, (float16_t)-5.515625f, (float16_t)2.50585938f, (float16_t)-2.65820312f, + (float16_t)-2.03125f, (float16_t)3.72851562f, (float16_t)-4.78125f, (float16_t)5.01171875f, + (float16_t)-4.1640625f, (float16_t)5.33984375f, (float16_t)5.80859375f, (float16_t)-2.234375f, + (float16_t)-1.4140625f, (float16_t)-3.72460938f, (float16_t)0.956542969f, (float16_t)4.5859375f, + (float16_t)2.40039062f, (float16_t)0.284179688f, (float16_t)-2.3125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/output_ref_data.h new file mode 100644 index 00000000..787f7de2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/output_ref_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_1_output_ref[3] = {(float16_t)5.80859375f, + (float16_t)5.87890625f, + (float16_t)5.8046875f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_1/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/config_data.h new file mode 100644 index 00000000..6f765768 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F16_2_BATCH_SIZE 1 +#define MAXPOOLING_F16_2_INPUT_N 1 +#define MAXPOOLING_F16_2_INPUT_W 1 +#define MAXPOOLING_F16_2_INPUT_H 5 +#define MAXPOOLING_F16_2_INPUT_C 17 +#define MAXPOOLING_F16_2_FILTER_W 3 +#define MAXPOOLING_F16_2_FILTER_H 4 +#define MAXPOOLING_F16_2_STRIDE_W 1 +#define MAXPOOLING_F16_2_STRIDE_H 3 +#define MAXPOOLING_F16_2_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_F16_2_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_F16_2_OUTPUT_C 17 +#define MAXPOOLING_F16_2_OUTPUT_W 1 +#define MAXPOOLING_F16_2_OUTPUT_H 2 +#define MAXPOOLING_F16_2_PADDING_H 1 +#define MAXPOOLING_F16_2_PADDING_W 1 +#define MAXPOOLING_F16_2_DST_SIZE 34 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/input_tensor_data.h new file mode 100644 index 00000000..eded67bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/input_tensor_data.h @@ -0,0 +1,24 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_2_input_tensor[85] = { + (float16_t)1.92480469f, (float16_t)-4.36328125f, (float16_t)-4.921875f, (float16_t)-2.7265625f, + (float16_t)0.965332031f, (float16_t)1.20410156f, (float16_t)-1.34960938f, (float16_t)-0.473632812f, + (float16_t)-1.94140625f, (float16_t)-4.6171875f, (float16_t)-2.41601562f, (float16_t)-4.76953125f, + (float16_t)-5.17578125f, (float16_t)2.60742188f, (float16_t)1.99316406f, (float16_t)-2.93945312f, + (float16_t)1.37011719f, (float16_t)0.250976562f, (float16_t)-3.57226562f, (float16_t)-2.65234375f, + (float16_t)-4.17578125f, (float16_t)-4.42578125f, (float16_t)5.0390625f, (float16_t)3.859375f, + (float16_t)-2.83398438f, (float16_t)5.48046875f, (float16_t)5.984375f, (float16_t)-1.58300781f, + (float16_t)-0.984375f, (float16_t)-2.40820312f, (float16_t)-3.62109375f, (float16_t)-1.83886719f, + (float16_t)-5.265625f, (float16_t)-0.797851562f, (float16_t)3.75585938f, (float16_t)2.40625f, + (float16_t)-0.180786133f, (float16_t)1.40429688f, (float16_t)4.109375f, (float16_t)0.361083984f, + (float16_t)-0.959960938f, (float16_t)1.14648438f, (float16_t)2.89257812f, (float16_t)4.703125f, + (float16_t)-0.246337891f, (float16_t)5.1640625f, (float16_t)0.063659668f, (float16_t)-1.609375f, + (float16_t)4.671875f, (float16_t)0.128417969f, (float16_t)4.57421875f, (float16_t)2.70703125f, + (float16_t)-3.97070312f, (float16_t)2.95117188f, (float16_t)-4.8671875f, (float16_t)5.73046875f, + (float16_t)-5.6171875f, (float16_t)1.81347656f, (float16_t)3.46679688f, (float16_t)3.2578125f, + (float16_t)3.95117188f, (float16_t)0.982421875f, (float16_t)-5.19921875f, (float16_t)1.88183594f, + (float16_t)1.74121094f, (float16_t)-3.75585938f, (float16_t)1.78515625f, (float16_t)-5.4921875f, + (float16_t)-5.0078125f, (float16_t)-3.21679688f, (float16_t)3.4453125f, (float16_t)-0.951660156f, + (float16_t)-1.05566406f, (float16_t)-4.26171875f, (float16_t)-2.42382812f, (float16_t)-5.890625f, + (float16_t)0.944824219f, (float16_t)1.13671875f, (float16_t)-3.88671875f, (float16_t)-3.93359375f, + (float16_t)-2.85742188f, (float16_t)-0.396972656f, (float16_t)2.09570312f, (float16_t)3.46679688f, + (float16_t)5.75390625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/output_ref_data.h new file mode 100644 index 00000000..ba2e3d48 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_2_output_ref[34] = { + (float16_t)3.75585938f, (float16_t)2.40625f, (float16_t)-0.180786133f, (float16_t)1.40429688f, + (float16_t)4.109375f, (float16_t)5.0390625f, (float16_t)3.859375f, (float16_t)1.14648438f, + (float16_t)5.48046875f, (float16_t)5.984375f, (float16_t)-0.246337891f, (float16_t)5.1640625f, + (float16_t)0.063659668f, (float16_t)2.60742188f, (float16_t)4.671875f, (float16_t)0.128417969f, + (float16_t)4.57421875f, (float16_t)3.75585938f, (float16_t)2.40625f, (float16_t)3.4453125f, + (float16_t)1.40429688f, (float16_t)5.73046875f, (float16_t)0.361083984f, (float16_t)1.81347656f, + (float16_t)3.46679688f, (float16_t)3.2578125f, (float16_t)4.703125f, (float16_t)0.982421875f, + (float16_t)5.1640625f, (float16_t)1.88183594f, (float16_t)1.74121094f, (float16_t)4.671875f, + (float16_t)3.46679688f, (float16_t)5.75390625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_2/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/config_data.h new file mode 100644 index 00000000..52bdd631 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F16_3_BATCH_SIZE 1 +#define MAXPOOLING_F16_3_INPUT_N 1 +#define MAXPOOLING_F16_3_INPUT_W 4 +#define MAXPOOLING_F16_3_INPUT_H 2 +#define MAXPOOLING_F16_3_INPUT_C 1 +#define MAXPOOLING_F16_3_FILTER_W 2 +#define MAXPOOLING_F16_3_FILTER_H 2 +#define MAXPOOLING_F16_3_STRIDE_W 2 +#define MAXPOOLING_F16_3_STRIDE_H 2 +#define MAXPOOLING_F16_3_ACTIVATION_MIN (float16_t)0.0f +#define MAXPOOLING_F16_3_ACTIVATION_MAX (float16_t)6.0f +#define MAXPOOLING_F16_3_OUTPUT_C 1 +#define MAXPOOLING_F16_3_OUTPUT_W 2 +#define MAXPOOLING_F16_3_OUTPUT_H 1 +#define MAXPOOLING_F16_3_PADDING_H 0 +#define MAXPOOLING_F16_3_PADDING_W 0 +#define MAXPOOLING_F16_3_DST_SIZE 2 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/input_tensor_data.h new file mode 100644 index 00000000..e57efd05 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/input_tensor_data.h @@ -0,0 +1,9 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_3_input_tensor[8] = {(float16_t)-0.858398438f, + (float16_t)8.9453125f, + (float16_t)6.63671875f, + (float16_t)-3.48242188f, + (float16_t)7.71875f, + (float16_t)8.9921875f, + (float16_t)6.1953125f, + (float16_t)9.3828125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/output_ref_data.h new file mode 100644 index 00000000..0cb40754 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/output_ref_data.h @@ -0,0 +1,2 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_f16_3_output_ref[2] = {(float16_t)6.0f, (float16_t)6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f16_3/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/config_data.h new file mode 100644 index 00000000..873d2199 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F32_BATCH_SIZE 2 +#define MAXPOOLING_F32_INPUT_N 2 +#define MAXPOOLING_F32_INPUT_W 22 +#define MAXPOOLING_F32_INPUT_H 12 +#define MAXPOOLING_F32_INPUT_C 8 +#define MAXPOOLING_F32_FILTER_W 6 +#define MAXPOOLING_F32_FILTER_H 5 +#define MAXPOOLING_F32_STRIDE_W 9 +#define MAXPOOLING_F32_STRIDE_H 5 +#define MAXPOOLING_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_F32_OUTPUT_C 8 +#define MAXPOOLING_F32_OUTPUT_W 3 +#define MAXPOOLING_F32_OUTPUT_H 3 +#define MAXPOOLING_F32_PADDING_H 1 +#define MAXPOOLING_F32_PADDING_W 1 +#define MAXPOOLING_F32_DST_SIZE 144 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/input_tensor_data.h new file mode 100644 index 00000000..5c376836 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/input_tensor_data.h @@ -0,0 +1,606 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_input_tensor[4224] = { + -0.66147095f, -3.62303662f, -2.98898983f, -1.06241488f, 0.296315491f, 1.04554522f, 0.78252399f, + 5.96178913f, -2.06167722f, -3.82644796f, 3.15265965f, 3.13839197f, -4.48438931f, 0.824839413f, + -0.6876809f, 0.747116327f, 2.76974463f, -5.83220434f, -3.91617346f, 0.884690106f, 1.57261455f, + -2.91467595f, -1.90860772f, -4.36361742f, -5.19084215f, -1.21641302f, -2.49996734f, -2.08509493f, + -4.81437874f, 5.65867805f, -0.201533675f, -4.00338936f, -0.734911501f, 2.8581779f, 5.57018709f, + 1.34985757f, 5.31228113f, 3.38731623f, -2.97612286f, 4.46287394f, -3.86104417f, 3.33056211f, + 4.37248516f, -0.277916819f, 0.502791464f, 4.40149736f, 1.29973376f, -3.78910279f, -5.31262207f, + -4.80066586f, -2.92460203f, -2.69982958f, -4.88867378f, 2.61295033f, 4.21112204f, -2.64463997f, + -3.61392689f, -1.00795805f, -3.2965045f, -4.08283758f, 5.08793163f, 3.096977f, -1.84429228f, + 4.34468222f, -4.25738955f, -3.56990671f, -1.01112795f, -1.71498728f, 4.13075972f, -4.81174326f, + 1.80728078f, 4.98661041f, 1.4194113f, 2.77602482f, 3.85272694f, -2.2305789f, 4.09513521f, + -2.63630152f, 1.04829311f, 5.1189127f, -5.05583048f, 2.96423221f, -3.73971057f, -0.998247743f, + -0.31620577f, 2.38999462f, -0.122550495f, 5.90152216f, 1.9525497f, 0.584718227f, -4.22372293f, + -4.48478365f, 3.22441626f, -5.13858223f, 2.80790091f, -1.99092793f, -0.875893593f, 0.176528677f, + -4.70755196f, -3.04817724f, -0.966426671f, 0.673288345f, -3.30491853f, -0.251539618f, 2.43385005f, + 3.33402181f, 4.68625593f, 3.59014821f, -4.44851732f, -0.825363517f, -3.92915583f, -4.01847506f, + 0.0511924252f, -4.95371771f, -3.70221806f, -3.36798048f, -2.92628431f, -4.90194607f, -4.50612259f, + 3.76635742f, 1.17759812f, 2.26940536f, 1.77039218f, -2.02916169f, 2.29870486f, 5.73157692f, + -5.56985617f, -1.77198458f, -1.59620333f, 0.709095955f, -3.52604699f, 4.83041573f, -1.00969827f, + -1.92136121f, -5.31263256f, -4.12358665f, -1.65706003f, -3.83253336f, -2.54993749f, 5.96328115f, + 0.367374122f, -0.846823931f, 0.53019464f, -4.36811876f, -3.92224145f, -0.677461922f, -1.16902745f, + -1.16345668f, 5.00961828f, -0.962601125f, 4.23888063f, -2.13840723f, -2.84980297f, 0.893573523f, + -2.08267164f, 3.60970592f, 4.40777254f, -5.66445923f, -1.43835139f, -4.45328236f, 0.215612292f, + 4.61073494f, -1.03548777f, -3.56079555f, 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4.16685724f, -3.83822131f, + -1.65351558f, -3.49019504f, -2.910007f, 1.10453105f, 5.90101242f, 4.04841137f, 5.01299334f, + 5.97058916f, -0.016623456f, 2.18749046f, 2.04075742f, 1.42628407f, -3.7260108f, 3.01318884f, + -4.72555351f, 0.703639567f, 4.31225109f, 3.18818879f, -0.586811483f, -2.0075922f, -2.96420765f, + -1.94846153f, -1.11831379f, 3.66707373f, -0.300525278f, 2.21013904f, 0.608429372f, -2.42897034f, + -0.850799978f, -2.30069089f, -0.436454147f, -2.5081501f, 3.52022767f, -5.31832027f, -3.86467409f, + 3.45879936f, -5.52257299f, -3.53734159f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/output_ref_data.h new file mode 100644 index 00000000..61ce8945 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/output_ref_data.h @@ -0,0 +1,20 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_output_ref[144] = { + 5.28300142f, 4.25061655f, 5.90282917f, 5.66255808f, 5.31228113f, 5.68862534f, 5.87328529f, 5.96178913f, + 5.73714638f, 5.88087702f, 5.79510975f, 4.2870841f, 5.45243406f, 5.74628401f, 5.84894848f, 5.90152216f, + 5.35087204f, 5.07975769f, 5.221066f, 5.96328115f, 5.04070902f, 5.36935568f, 5.71930695f, 5.26421213f, + 5.80846071f, 5.98792028f, 5.89592218f, 5.61253166f, 5.9431529f, 5.72236919f, 4.7175827f, 5.90204382f, + 5.66485119f, 5.51900053f, 5.7177515f, 5.62998438f, 4.96134758f, 5.96659756f, 4.55789852f, 5.65874195f, + 5.46423006f, 3.87505341f, 5.90841341f, 5.57260895f, 4.80750084f, 5.9191885f, 5.97243834f, 5.26570749f, + 4.64995766f, 4.87136126f, 5.76356268f, 0.204082727f, 5.79068947f, 5.89815044f, 5.52538109f, 4.85542202f, + 3.25646973f, 5.65873909f, 5.49607372f, 5.75057554f, 4.15993595f, 4.70781469f, 5.25918484f, 5.94465446f, + 5.25688219f, 5.19382668f, 5.93013144f, 4.61236858f, 5.79528904f, 5.17740726f, 5.22379446f, 4.34248257f, + 4.97203159f, 5.29849243f, 4.81650019f, 5.40666437f, 5.93542671f, 5.67744684f, 4.72189236f, 5.31350756f, + 4.22788191f, 5.78901911f, 5.38345194f, 5.97306299f, 5.83648348f, 5.88501883f, 5.12233639f, 4.6973176f, + 5.32390738f, 5.90966988f, 5.57328081f, 5.52819824f, 5.08821726f, 5.97710562f, 5.99333477f, 4.16096163f, + 5.33586788f, 5.3566165f, 5.31283426f, 5.96905804f, 5.98171997f, 4.28115082f, 4.72321606f, 5.47538614f, + 5.93494701f, 5.78687572f, 5.99029112f, 5.64055204f, 5.82844114f, 5.56414223f, 5.84132242f, 5.51486111f, + 3.81738687f, 5.75025892f, 5.24420977f, 4.47727489f, 4.22823048f, 5.45711851f, 5.83905506f, 5.35251474f, + 5.68789959f, 4.54309511f, 5.63289642f, 5.92938566f, 4.02508068f, 5.23129034f, 3.66656685f, 4.75283337f, + 5.88834095f, 5.72624826f, 5.42821598f, 5.59745312f, 5.67020893f, 4.0921998f, 3.59061623f, 5.40856314f, + 5.37507772f, 5.97058916f, 5.40217304f, 5.99309921f, 5.61763477f, 4.85393476f, 5.90101242f, 4.6940012f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/config_data.h new file mode 100644 index 00000000..b3dfc157 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F32_1_BATCH_SIZE 1 +#define MAXPOOLING_F32_1_INPUT_N 1 +#define MAXPOOLING_F32_1_INPUT_W 9 +#define MAXPOOLING_F32_1_INPUT_H 5 +#define MAXPOOLING_F32_1_INPUT_C 3 +#define MAXPOOLING_F32_1_FILTER_W 9 +#define MAXPOOLING_F32_1_FILTER_H 5 +#define MAXPOOLING_F32_1_STRIDE_W 1 +#define MAXPOOLING_F32_1_STRIDE_H 2 +#define MAXPOOLING_F32_1_ACTIVATION_MIN -100.0f +#define MAXPOOLING_F32_1_ACTIVATION_MAX 100.0f +#define MAXPOOLING_F32_1_OUTPUT_C 3 +#define MAXPOOLING_F32_1_OUTPUT_W 1 +#define MAXPOOLING_F32_1_OUTPUT_H 1 +#define MAXPOOLING_F32_1_PADDING_H 0 +#define MAXPOOLING_F32_1_PADDING_W 0 +#define MAXPOOLING_F32_1_DST_SIZE 3 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/input_tensor_data.h new file mode 100644 index 00000000..f49f52f0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_1_input_tensor[135] = { + -5.44640732f, -1.86775732f, -5.26060677f, -3.45273757f, 4.41348362f, -1.96135235f, -5.39657688f, -2.87777162f, + 4.83113289f, 3.9097085f, -4.67808819f, -4.4013834f, -2.17692804f, -2.61867428f, -5.29760695f, -5.53239727f, + 4.07496309f, 2.30850601f, -2.86192369f, 0.954541445f, 3.8203845f, 0.99767518f, 3.91651845f, -5.24546051f, + 1.70867682f, -5.3023386f, 5.75887871f, -3.84214687f, 5.11830473f, -1.71394467f, 1.76722991f, 1.46392715f, + -3.93120575f, -3.7775085f, -2.63945985f, 5.8056736f, 2.39172292f, -3.8751862f, 3.05879784f, 0.628692746f, + -1.40079331f, -1.24746597f, 3.41683698f, -1.76160157f, -3.66165996f, 2.24337506f, 1.79971468f, -3.12393737f, + 0.128023818f, -0.291398257f, -5.67102289f, -1.55159795f, -0.768615007f, 2.85676694f, -1.40816545f, 2.19554281f, + 0.967093885f, 2.01640391f, 1.27177632f, 1.31134892f, -2.73640156f, -1.48578703f, -4.28479338f, 5.24869347f, + 0.775922477f, -2.53009915f, 4.44874048f, -3.95328522f, 5.21288157f, 1.12149143f, -4.68459415f, -4.33872747f, + 0.386401802f, -4.97388315f, 1.01851916f, 3.07360244f, -0.29483372f, 3.64561367f, -0.650221467f, 2.53985071f, + -1.11862576f, -4.41341209f, 4.43521452f, -4.11208868f, 5.65034628f, -2.70044041f, 1.06383479f, 2.0679934f, + 2.02093816f, 4.1091466f, 0.847356319f, 4.80972672f, 5.80015278f, 4.59113979f, 1.36339009f, -1.54060054f, + 5.86718178f, -5.11593342f, 2.20924997f, 2.15648532f, 4.55953979f, 4.36020803f, 0.118602186f, -2.9845221f, + 2.69881248f, -4.42780876f, -3.76384449f, 1.76452887f, 3.50468564f, 5.25239944f, -4.01214933f, -5.2747221f, + -5.77141523f, -1.28948212f, -3.22602963f, 0.834862292f, -1.30304074f, 0.500254452f, 4.60299206f, 3.33607292f, + 5.35820961f, -0.0456807315f, 4.21797895f, 1.43266141f, 3.62207389f, -4.37273788f, -3.69986105f, -1.28450942f, + -2.18056297f, -4.83209038f, -4.0013299f, -2.03749084f, -4.58052826f, 3.46261764f, 5.96844864f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/output_ref_data.h new file mode 100644 index 00000000..3d0d4b80 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/output_ref_data.h @@ -0,0 +1,2 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_1_output_ref[3] = {5.86718178f, 5.25239944f, 5.96844864f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_1/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/config_data.h new file mode 100644 index 00000000..ec993188 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F32_2_BATCH_SIZE 1 +#define MAXPOOLING_F32_2_INPUT_N 1 +#define MAXPOOLING_F32_2_INPUT_W 1 +#define MAXPOOLING_F32_2_INPUT_H 5 +#define MAXPOOLING_F32_2_INPUT_C 17 +#define MAXPOOLING_F32_2_FILTER_W 3 +#define MAXPOOLING_F32_2_FILTER_H 4 +#define MAXPOOLING_F32_2_STRIDE_W 1 +#define MAXPOOLING_F32_2_STRIDE_H 3 +#define MAXPOOLING_F32_2_ACTIVATION_MIN -100.0f +#define MAXPOOLING_F32_2_ACTIVATION_MAX 100.0f +#define MAXPOOLING_F32_2_OUTPUT_C 17 +#define MAXPOOLING_F32_2_OUTPUT_W 1 +#define MAXPOOLING_F32_2_OUTPUT_H 2 +#define MAXPOOLING_F32_2_PADDING_H 1 +#define MAXPOOLING_F32_2_PADDING_W 1 +#define MAXPOOLING_F32_2_DST_SIZE 34 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/input_tensor_data.h new file mode 100644 index 00000000..91e9db6d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/input_tensor_data.h @@ -0,0 +1,15 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_2_input_tensor[85] = { + -5.2737937f, 1.21951389f, 0.129755437f, 5.62027645f, 0.423457861f, -2.05828238f, 5.91367626f, + -3.35168958f, -4.16511536f, -3.46499157f, -4.91262007f, 2.34822702f, 1.1309433f, 1.60827219f, + 0.321655393f, -2.30897164f, -1.87565434f, 3.35656452f, 0.152360991f, -5.39310932f, 5.26878595f, + -0.805354536f, 1.14563119f, -5.59421539f, 0.590422094f, 3.61573887f, -4.69212103f, -4.87954235f, + 3.34263778f, 2.13694406f, -2.22837687f, -0.704135776f, 4.52036524f, 0.684025109f, -4.59712601f, + -0.851927698f, -3.16070843f, -5.68438435f, -3.87033391f, -4.39828777f, 5.8438797f, -3.60687256f, + 0.555889666f, -3.08525801f, 0.366813451f, -0.921043098f, -4.50193834f, 3.46301699f, -0.102896176f, + 3.59462357f, -5.29894257f, 0.0134411305f, -0.641151369f, 5.37236786f, -0.0981087014f, 1.04538512f, + 5.12808084f, 0.213290215f, 0.423031092f, -5.06596088f, 2.89631367f, -2.29160094f, 0.659356952f, + -5.65973282f, 2.23795295f, 4.14968395f, 0.936739266f, 2.09833765f, -4.11261082f, -1.06783319f, + -5.01846504f, -4.94796562f, 3.93139172f, -2.78891826f, -1.34507477f, 4.37425423f, -2.99443531f, + 0.700306654f, 5.35279417f, 5.07919693f, 4.78457069f, -5.6952157f, -5.11987114f, -2.71204185f, + -5.72116089f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/output_ref_data.h new file mode 100644 index 00000000..3071e052 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_2_output_ref[34] = { + 3.35656452f, 1.21951389f, 0.129755437f, 5.62027645f, 0.423457861f, 1.14563119f, 5.91367626f, + 0.590422094f, 3.61573887f, -3.08525801f, 0.366813451f, 3.34263778f, 2.13694406f, 3.46301699f, + 0.321655393f, 4.52036524f, 0.684025109f, 0.0134411305f, -0.641151369f, 5.37236786f, -0.0981087014f, + 3.93139172f, 5.12808084f, 5.8438797f, 4.37425423f, 0.555889666f, 2.89631367f, 5.35279417f, + 5.07919693f, 4.78457069f, 3.46301699f, 4.14968395f, 3.59462357f, 2.09833765f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_2/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/config_data.h new file mode 100644 index 00000000..f1ab53a0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_F32_3_BATCH_SIZE 1 +#define MAXPOOLING_F32_3_INPUT_N 1 +#define MAXPOOLING_F32_3_INPUT_W 4 +#define MAXPOOLING_F32_3_INPUT_H 2 +#define MAXPOOLING_F32_3_INPUT_C 1 +#define MAXPOOLING_F32_3_FILTER_W 2 +#define MAXPOOLING_F32_3_FILTER_H 2 +#define MAXPOOLING_F32_3_STRIDE_W 2 +#define MAXPOOLING_F32_3_STRIDE_H 2 +#define MAXPOOLING_F32_3_ACTIVATION_MIN 0.0f +#define MAXPOOLING_F32_3_ACTIVATION_MAX 6.0f +#define MAXPOOLING_F32_3_OUTPUT_C 1 +#define MAXPOOLING_F32_3_OUTPUT_W 2 +#define MAXPOOLING_F32_3_OUTPUT_H 1 +#define MAXPOOLING_F32_3_PADDING_H 0 +#define MAXPOOLING_F32_3_PADDING_W 0 +#define MAXPOOLING_F32_3_DST_SIZE 2 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/input_tensor_data.h new file mode 100644 index 00000000..ba5570f4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/input_tensor_data.h @@ -0,0 +1,3 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_3_input_tensor[8] = + {5.5887661f, -1.79883885f, 7.83963394f, 2.05810666f, 8.88128853f, -0.263135225f, -2.66723323f, 9.48214912f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/output_ref_data.h new file mode 100644 index 00000000..ced0b143 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/output_ref_data.h @@ -0,0 +1,2 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_f32_3_output_ref[2] = {6.0f, 6.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_f32_3/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/config_data.h new file mode 100644 index 00000000..7e64b2b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_2_F16_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_2_F16_INPUT_N 1 +#define MAXPOOLING_MATCH_2_F16_INPUT_W 12 +#define MAXPOOLING_MATCH_2_F16_INPUT_H 1 +#define MAXPOOLING_MATCH_2_F16_INPUT_C 5 +#define MAXPOOLING_MATCH_2_F16_FILTER_W 3 +#define MAXPOOLING_MATCH_2_F16_FILTER_H 1 +#define MAXPOOLING_MATCH_2_F16_STRIDE_W 1 +#define MAXPOOLING_MATCH_2_F16_STRIDE_H 2 +#define MAXPOOLING_MATCH_2_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_MATCH_2_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_MATCH_2_F16_OUTPUT_C 5 +#define MAXPOOLING_MATCH_2_F16_OUTPUT_W 12 +#define MAXPOOLING_MATCH_2_F16_OUTPUT_H 1 +#define MAXPOOLING_MATCH_2_F16_PADDING_H 0 +#define MAXPOOLING_MATCH_2_F16_PADDING_W 1 +#define MAXPOOLING_MATCH_2_F16_DST_SIZE 60 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/input_tensor_data.h new file mode 100644 index 00000000..01db8b85 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/input_tensor_data.h @@ -0,0 +1,17 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_2_f16_input_tensor[60] = { + (float16_t)5.94921875f, (float16_t)4.41015625f, (float16_t)-2.7265625f, (float16_t)-1.00292969f, + (float16_t)-1.95898438f, (float16_t)-2.76171875f, (float16_t)-3.29882812f, (float16_t)3.0234375f, + (float16_t)-4.44140625f, (float16_t)-3.76757812f, (float16_t)-0.802734375f, (float16_t)2.09765625f, + (float16_t)-3.453125f, (float16_t)-2.66601562f, (float16_t)-3.40039062f, (float16_t)4.625f, + (float16_t)-2.90039062f, (float16_t)3.1328125f, (float16_t)-0.965820312f, (float16_t)-5.2578125f, + (float16_t)5.8203125f, (float16_t)-4.1171875f, (float16_t)2.23828125f, (float16_t)0.94921875f, + (float16_t)-4.74609375f, (float16_t)-3.18945312f, (float16_t)-0.023651123f, (float16_t)-4.65625f, + (float16_t)-0.37109375f, (float16_t)-1.89453125f, (float16_t)2.8125f, (float16_t)-2.11132812f, + (float16_t)-3.37304688f, (float16_t)5.65234375f, (float16_t)3.29882812f, (float16_t)-1.16796875f, + (float16_t)4.56640625f, (float16_t)3.33203125f, (float16_t)5.33984375f, (float16_t)-2.19726562f, + (float16_t)-2.26953125f, (float16_t)4.78125f, (float16_t)1.68457031f, (float16_t)-2.04882812f, + (float16_t)-4.6015625f, (float16_t)4.61328125f, (float16_t)-2.296875f, (float16_t)1.23828125f, + (float16_t)-5.0859375f, (float16_t)1.92773438f, (float16_t)-5.2734375f, (float16_t)3.91601562f, + (float16_t)4.3125f, (float16_t)3.23828125f, (float16_t)-3.5078125f, (float16_t)-4.3671875f, + (float16_t)3.07421875f, (float16_t)-3.20117188f, (float16_t)5.55859375f, (float16_t)-4.1015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/output_ref_data.h new file mode 100644 index 00000000..901de33c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_2_f16_output_ref[60] = { + (float16_t)5.94921875f, (float16_t)4.41015625f, (float16_t)3.0234375f, (float16_t)-1.00292969f, + (float16_t)-1.95898438f, (float16_t)5.94921875f, (float16_t)4.41015625f, (float16_t)3.0234375f, + (float16_t)-1.00292969f, (float16_t)-1.95898438f, (float16_t)4.625f, (float16_t)2.09765625f, + (float16_t)3.1328125f, (float16_t)-0.965820312f, (float16_t)-3.40039062f, (float16_t)5.8203125f, + (float16_t)2.09765625f, (float16_t)3.1328125f, (float16_t)0.94921875f, (float16_t)-3.40039062f, + (float16_t)5.8203125f, (float16_t)-0.023651123f, (float16_t)3.1328125f, (float16_t)0.94921875f, + (float16_t)-1.89453125f, (float16_t)5.8203125f, (float16_t)-0.023651123f, (float16_t)2.23828125f, + (float16_t)5.65234375f, (float16_t)3.29882812f, (float16_t)2.8125f, (float16_t)4.56640625f, + (float16_t)3.33203125f, (float16_t)5.65234375f, (float16_t)3.29882812f, (float16_t)2.8125f, + (float16_t)4.78125f, (float16_t)3.33203125f, (float16_t)5.65234375f, (float16_t)3.29882812f, + (float16_t)4.61328125f, (float16_t)4.78125f, (float16_t)3.33203125f, (float16_t)5.33984375f, + (float16_t)1.92773438f, (float16_t)4.61328125f, (float16_t)4.78125f, (float16_t)4.3125f, + (float16_t)3.23828125f, (float16_t)1.92773438f, (float16_t)4.61328125f, (float16_t)3.91601562f, + (float16_t)4.3125f, (float16_t)5.55859375f, (float16_t)1.92773438f, (float16_t)-4.3671875f, + (float16_t)3.91601562f, (float16_t)4.3125f, (float16_t)5.55859375f, (float16_t)-3.5078125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/config_data.h new file mode 100644 index 00000000..bf7e5bc0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_2_F32_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_2_F32_INPUT_N 1 +#define MAXPOOLING_MATCH_2_F32_INPUT_W 12 +#define MAXPOOLING_MATCH_2_F32_INPUT_H 1 +#define MAXPOOLING_MATCH_2_F32_INPUT_C 5 +#define MAXPOOLING_MATCH_2_F32_FILTER_W 3 +#define MAXPOOLING_MATCH_2_F32_FILTER_H 1 +#define MAXPOOLING_MATCH_2_F32_STRIDE_W 1 +#define MAXPOOLING_MATCH_2_F32_STRIDE_H 2 +#define MAXPOOLING_MATCH_2_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_MATCH_2_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_MATCH_2_F32_OUTPUT_C 5 +#define MAXPOOLING_MATCH_2_F32_OUTPUT_W 12 +#define MAXPOOLING_MATCH_2_F32_OUTPUT_H 1 +#define MAXPOOLING_MATCH_2_F32_PADDING_H 0 +#define MAXPOOLING_MATCH_2_F32_PADDING_W 1 +#define MAXPOOLING_MATCH_2_F32_DST_SIZE 60 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/input_tensor_data.h new file mode 100644 index 00000000..1e6c35ac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/input_tensor_data.h @@ -0,0 +1,10 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_2_f32_input_tensor[60] = { + 3.63730955f, -4.4162364f, 3.19308186f, -4.33730602f, 3.79478145f, -2.34458995f, -5.51951885f, 3.1380024f, + -3.54618502f, 3.47303748f, -4.88753557f, -2.79209518f, 3.7505095f, -5.38159084f, -5.52353382f, -4.50383759f, + -4.5911808f, 5.05831194f, -3.15086055f, 1.27153397f, 0.641606748f, 2.29677963f, -4.7145648f, -2.75334287f, + 5.93520498f, 3.78086805f, 0.830027163f, 5.72221804f, 1.84007001f, -0.509328067f, 5.87033224f, 1.51863945f, + -3.32067823f, 1.48391783f, 1.58454204f, 2.80094671f, 0.0966754407f, -5.599473f, -0.208082065f, -0.141234249f, + -0.524533331f, -5.63432503f, 5.13321781f, -5.18920994f, 0.355199635f, -4.44938898f, -3.04079032f, -0.247557998f, + -3.15931225f, 5.68614197f, -5.84732533f, -1.25749993f, 5.48551273f, -2.41204166f, -0.355560482f, 4.77101564f, + 1.57302868f, -5.97774935f, 0.260169387f, -5.14116478f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/output_ref_data.h new file mode 100644 index 00000000..ae35a83e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/output_ref_data.h @@ -0,0 +1,10 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_2_f32_output_ref[60] = { + 3.63730955f, -4.4162364f, 3.19308186f, -3.54618502f, 3.79478145f, 3.63730955f, -2.79209518f, 3.7505095f, + -3.54618502f, 3.79478145f, -2.34458995f, -2.79209518f, 5.05831194f, -3.15086055f, 3.47303748f, 0.641606748f, + 2.29677963f, 5.05831194f, -2.75334287f, 5.93520498f, 3.78086805f, 2.29677963f, 5.72221804f, 1.84007001f, + 5.93520498f, 5.87033224f, 2.29677963f, 5.72221804f, 1.84007001f, 5.93520498f, 5.87033224f, 1.51863945f, + 5.72221804f, 1.84007001f, 1.58454204f, 5.87033224f, 1.51863945f, 5.13321781f, 1.48391783f, 1.58454204f, + 2.80094671f, 0.0966754407f, 5.13321781f, -0.208082065f, 5.68614197f, -0.524533331f, -1.25749993f, 5.48551273f, + -2.41204166f, 5.68614197f, 4.77101564f, 1.57302868f, 5.48551273f, 0.260169387f, 5.68614197f, 4.77101564f, + 1.57302868f, 5.48551273f, 0.260169387f, -0.355560482f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_2_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/config_data.h new file mode 100644 index 00000000..dec79d3e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_3_F16_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_3_F16_INPUT_N 1 +#define MAXPOOLING_MATCH_3_F16_INPUT_W 9 +#define MAXPOOLING_MATCH_3_F16_INPUT_H 1 +#define MAXPOOLING_MATCH_3_F16_INPUT_C 2 +#define MAXPOOLING_MATCH_3_F16_FILTER_W 1 +#define MAXPOOLING_MATCH_3_F16_FILTER_H 1 +#define MAXPOOLING_MATCH_3_F16_STRIDE_W 2 +#define MAXPOOLING_MATCH_3_F16_STRIDE_H 1 +#define MAXPOOLING_MATCH_3_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_MATCH_3_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_MATCH_3_F16_OUTPUT_C 2 +#define MAXPOOLING_MATCH_3_F16_OUTPUT_W 5 +#define MAXPOOLING_MATCH_3_F16_OUTPUT_H 1 +#define MAXPOOLING_MATCH_3_F16_PADDING_H 0 +#define MAXPOOLING_MATCH_3_F16_PADDING_W 0 +#define MAXPOOLING_MATCH_3_F16_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/input_tensor_data.h new file mode 100644 index 00000000..6a30b333 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_3_f16_input_tensor[18] = {(float16_t)-4.94921875f, + (float16_t)-3.2421875f, + (float16_t)0.588867188f, + (float16_t)2.13085938f, + (float16_t)1.98632812f, + (float16_t)-5.5703125f, + (float16_t)5.6328125f, + (float16_t)-3.49414062f, + (float16_t)-1.77734375f, + (float16_t)3.51953125f, + (float16_t)1.80273438f, + (float16_t)-5.3125f, + (float16_t)-1.08007812f, + (float16_t)5.4375f, + (float16_t)0.376464844f, + (float16_t)1.87695312f, + (float16_t)5.35546875f, + (float16_t)-3.57226562f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/output_ref_data.h new file mode 100644 index 00000000..4c779308 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_3_f16_output_ref[10] = {(float16_t)-4.94921875f, + (float16_t)-3.2421875f, + (float16_t)1.98632812f, + (float16_t)-5.5703125f, + (float16_t)-1.77734375f, + (float16_t)3.51953125f, + (float16_t)-1.08007812f, + (float16_t)5.4375f, + (float16_t)5.35546875f, + (float16_t)-3.57226562f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/config_data.h new file mode 100644 index 00000000..f7687fc5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_3_F32_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_3_F32_INPUT_N 1 +#define MAXPOOLING_MATCH_3_F32_INPUT_W 9 +#define MAXPOOLING_MATCH_3_F32_INPUT_H 1 +#define MAXPOOLING_MATCH_3_F32_INPUT_C 2 +#define MAXPOOLING_MATCH_3_F32_FILTER_W 1 +#define MAXPOOLING_MATCH_3_F32_FILTER_H 1 +#define MAXPOOLING_MATCH_3_F32_STRIDE_W 2 +#define MAXPOOLING_MATCH_3_F32_STRIDE_H 1 +#define MAXPOOLING_MATCH_3_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_MATCH_3_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_MATCH_3_F32_OUTPUT_C 2 +#define MAXPOOLING_MATCH_3_F32_OUTPUT_W 5 +#define MAXPOOLING_MATCH_3_F32_OUTPUT_H 1 +#define MAXPOOLING_MATCH_3_F32_PADDING_H 0 +#define MAXPOOLING_MATCH_3_F32_PADDING_W 0 +#define MAXPOOLING_MATCH_3_F32_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/input_tensor_data.h new file mode 100644 index 00000000..9ca39207 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/input_tensor_data.h @@ -0,0 +1,19 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_3_f32_input_tensor[18] = {-3.89307833f, + 4.61016512f, + 1.59076071f, + 5.87301779f, + -5.60490513f, + 3.29377055f, + -1.30082762f, + -0.940929174f, + -2.76508212f, + 4.99265099f, + -5.4262886f, + 2.09028506f, + 0.341757447f, + 0.936909616f, + 1.52199817f, + 4.86541224f, + 3.62441826f, + -0.83713603f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/output_ref_data.h new file mode 100644 index 00000000..5331b8d0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_3_f32_output_ref[10] = {-3.89307833f, + 4.61016512f, + -5.60490513f, + 3.29377055f, + -2.76508212f, + 4.99265099f, + 0.341757447f, + 0.936909616f, + 3.62441826f, + -0.83713603f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_3_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/config_data.h new file mode 100644 index 00000000..e5c1873b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_4_F16_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_4_F16_INPUT_N 1 +#define MAXPOOLING_MATCH_4_F16_INPUT_W 1 +#define MAXPOOLING_MATCH_4_F16_INPUT_H 20 +#define MAXPOOLING_MATCH_4_F16_INPUT_C 2 +#define MAXPOOLING_MATCH_4_F16_FILTER_W 1 +#define MAXPOOLING_MATCH_4_F16_FILTER_H 3 +#define MAXPOOLING_MATCH_4_F16_STRIDE_W 1 +#define MAXPOOLING_MATCH_4_F16_STRIDE_H 3 +#define MAXPOOLING_MATCH_4_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_MATCH_4_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_MATCH_4_F16_OUTPUT_C 2 +#define MAXPOOLING_MATCH_4_F16_OUTPUT_W 1 +#define MAXPOOLING_MATCH_4_F16_OUTPUT_H 7 +#define MAXPOOLING_MATCH_4_F16_PADDING_H 0 +#define MAXPOOLING_MATCH_4_F16_PADDING_W 0 +#define MAXPOOLING_MATCH_4_F16_DST_SIZE 14 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/input_tensor_data.h new file mode 100644 index 00000000..c7c6d587 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/input_tensor_data.h @@ -0,0 +1,12 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_4_f16_input_tensor[40] = { + (float16_t)2.79101562f, (float16_t)2.57617188f, (float16_t)-0.80078125f, (float16_t)1.80957031f, + (float16_t)-0.485839844f, (float16_t)3.44335938f, (float16_t)2.9296875f, (float16_t)5.91796875f, + (float16_t)-0.15625f, (float16_t)5.90234375f, (float16_t)-1.76660156f, (float16_t)4.578125f, + (float16_t)-5.37109375f, (float16_t)-2.83007812f, (float16_t)3.5f, (float16_t)-4.79296875f, + (float16_t)-2.35546875f, (float16_t)-3.02539062f, (float16_t)3.94335938f, (float16_t)2.76757812f, + (float16_t)0.059173584f, (float16_t)5.140625f, (float16_t)4.58203125f, (float16_t)-1.02636719f, + (float16_t)2.30664062f, (float16_t)-3.3671875f, (float16_t)2.65234375f, (float16_t)-5.54296875f, + (float16_t)0.638671875f, (float16_t)-3.08203125f, (float16_t)-1.6796875f, (float16_t)4.55078125f, + (float16_t)0.888183594f, (float16_t)-5.8828125f, (float16_t)-0.966796875f, (float16_t)5.25f, + (float16_t)0.505859375f, (float16_t)5.3828125f, (float16_t)0.8984375f, (float16_t)-2.484375f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/output_ref_data.h new file mode 100644 index 00000000..fe77f8a3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/output_ref_data.h @@ -0,0 +1,15 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_4_f16_output_ref[14] = {(float16_t)2.79101562f, + (float16_t)3.44335938f, + (float16_t)2.9296875f, + (float16_t)5.91796875f, + (float16_t)3.5f, + (float16_t)-2.83007812f, + (float16_t)4.58203125f, + (float16_t)5.140625f, + (float16_t)2.65234375f, + (float16_t)-3.08203125f, + (float16_t)0.888183594f, + (float16_t)5.25f, + (float16_t)0.8984375f, + (float16_t)5.3828125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/config_data.h new file mode 100644 index 00000000..11d0cc24 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_4_F32_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_4_F32_INPUT_N 1 +#define MAXPOOLING_MATCH_4_F32_INPUT_W 1 +#define MAXPOOLING_MATCH_4_F32_INPUT_H 20 +#define MAXPOOLING_MATCH_4_F32_INPUT_C 2 +#define MAXPOOLING_MATCH_4_F32_FILTER_W 1 +#define MAXPOOLING_MATCH_4_F32_FILTER_H 3 +#define MAXPOOLING_MATCH_4_F32_STRIDE_W 1 +#define MAXPOOLING_MATCH_4_F32_STRIDE_H 3 +#define MAXPOOLING_MATCH_4_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_MATCH_4_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_MATCH_4_F32_OUTPUT_C 2 +#define MAXPOOLING_MATCH_4_F32_OUTPUT_W 1 +#define MAXPOOLING_MATCH_4_F32_OUTPUT_H 7 +#define MAXPOOLING_MATCH_4_F32_PADDING_H 0 +#define MAXPOOLING_MATCH_4_F32_PADDING_W 0 +#define MAXPOOLING_MATCH_4_F32_DST_SIZE 14 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/input_tensor_data.h new file mode 100644 index 00000000..5e6a886d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/input_tensor_data.h @@ -0,0 +1,7 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_4_f32_input_tensor[40] = { + -3.86728573f, -0.632937908f, 3.46533942f, -5.22560215f, -5.65849686f, 1.74015796f, -1.75443995f, -1.93146181f, + 4.14305162f, 4.16029263f, 2.05301905f, 3.38968563f, -0.787204027f, 3.14939165f, 1.98812437f, -3.45690441f, + -1.13777924f, 3.10909534f, 3.56214118f, -5.02772379f, -5.63566494f, -4.79251051f, 2.27802658f, -5.11665678f, + -5.33432817f, 0.580448508f, 0.361050844f, 1.83646429f, -4.7531414f, -4.49428844f, 3.91708446f, -0.0334849954f, + -3.28662634f, -5.86511087f, -3.63890982f, 5.14079475f, -3.35028577f, -1.35828745f, -2.53676391f, -4.18930149f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/output_ref_data.h new file mode 100644 index 00000000..25bf1da8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/output_ref_data.h @@ -0,0 +1,15 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_4_f32_output_ref[14] = {3.46533942f, + 1.74015796f, + 4.14305162f, + 4.16029263f, + 1.98812437f, + 3.14939165f, + 3.56214118f, + -4.79251051f, + 0.361050844f, + 1.83646429f, + 3.91708446f, + 5.14079475f, + -2.53676391f, + -1.35828745f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_4_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/config_data.h new file mode 100644 index 00000000..9937102a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_5_F16_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_5_F16_INPUT_N 1 +#define MAXPOOLING_MATCH_5_F16_INPUT_W 3 +#define MAXPOOLING_MATCH_5_F16_INPUT_H 3 +#define MAXPOOLING_MATCH_5_F16_INPUT_C 20 +#define MAXPOOLING_MATCH_5_F16_FILTER_W 1 +#define MAXPOOLING_MATCH_5_F16_FILTER_H 1 +#define MAXPOOLING_MATCH_5_F16_STRIDE_W 1 +#define MAXPOOLING_MATCH_5_F16_STRIDE_H 1 +#define MAXPOOLING_MATCH_5_F16_ACTIVATION_MIN (float16_t) - 100.0f +#define MAXPOOLING_MATCH_5_F16_ACTIVATION_MAX (float16_t)100.0f +#define MAXPOOLING_MATCH_5_F16_OUTPUT_C 20 +#define MAXPOOLING_MATCH_5_F16_OUTPUT_W 3 +#define MAXPOOLING_MATCH_5_F16_OUTPUT_H 3 +#define MAXPOOLING_MATCH_5_F16_PADDING_H 0 +#define MAXPOOLING_MATCH_5_F16_PADDING_W 0 +#define MAXPOOLING_MATCH_5_F16_DST_SIZE 180 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/input_tensor_data.h new file mode 100644 index 00000000..fc17dfb4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/input_tensor_data.h @@ -0,0 +1,47 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_5_f16_input_tensor[180] = { + (float16_t)4.51953125f, (float16_t)-5.8515625f, (float16_t)3.72851562f, (float16_t)-0.222290039f, + (float16_t)-1.88574219f, (float16_t)-5.1953125f, (float16_t)-4.78515625f, (float16_t)5.42578125f, + (float16_t)3.31054688f, (float16_t)1.97363281f, (float16_t)2.52734375f, (float16_t)1.24414062f, + (float16_t)-0.798339844f, (float16_t)-1.38378906f, (float16_t)0.107849121f, (float16_t)-2.44335938f, + (float16_t)-2.38476562f, (float16_t)-5.58203125f, (float16_t)5.37109375f, (float16_t)0.428466797f, + (float16_t)-3.32421875f, (float16_t)-0.810546875f, (float16_t)5.23828125f, (float16_t)0.0328979492f, + (float16_t)-3.70703125f, (float16_t)3.1015625f, (float16_t)-2.390625f, (float16_t)-0.697753906f, + (float16_t)3.890625f, (float16_t)-2.015625f, (float16_t)-5.86328125f, (float16_t)4.234375f, + (float16_t)4.109375f, (float16_t)2.55273438f, (float16_t)-0.647949219f, (float16_t)2.47851562f, + (float16_t)-0.264160156f, (float16_t)5.953125f, (float16_t)1.14453125f, (float16_t)1.69628906f, + (float16_t)2.74023438f, (float16_t)-3.0625f, (float16_t)4.76953125f, (float16_t)-1.9765625f, + (float16_t)2.95703125f, (float16_t)4.48828125f, (float16_t)-5.03125f, (float16_t)-2.70507812f, + (float16_t)-0.0908813477f, (float16_t)1.15625f, (float16_t)5.47265625f, (float16_t)1.48925781f, + (float16_t)2.390625f, (float16_t)-4.3359375f, (float16_t)-5.06640625f, (float16_t)-0.946289062f, + (float16_t)-2.00195312f, (float16_t)-0.586425781f, (float16_t)4.74609375f, (float16_t)-0.0110015869f, + (float16_t)-0.474609375f, (float16_t)0.29296875f, (float16_t)0.738769531f, (float16_t)-1.84667969f, + (float16_t)3.671875f, (float16_t)-5.91015625f, (float16_t)1.09472656f, (float16_t)5.22265625f, + (float16_t)2.04882812f, (float16_t)-5.921875f, (float16_t)0.3515625f, (float16_t)-3.83007812f, + (float16_t)-4.6015625f, (float16_t)2.82226562f, (float16_t)4.3515625f, (float16_t)-1.203125f, + (float16_t)-2.16796875f, (float16_t)-3.26367188f, (float16_t)0.0346679688f, (float16_t)1.47070312f, + (float16_t)-4.375f, (float16_t)-0.248413086f, (float16_t)-4.33984375f, (float16_t)4.81640625f, + (float16_t)-5.25390625f, (float16_t)4.84375f, (float16_t)-0.839355469f, (float16_t)-5.5703125f, + (float16_t)-1.75f, (float16_t)-2.00390625f, (float16_t)5.57421875f, (float16_t)4.8515625f, + (float16_t)-4.8125f, (float16_t)1.93457031f, (float16_t)3.515625f, (float16_t)-2.56835938f, + (float16_t)-0.888183594f, (float16_t)-5.78515625f, (float16_t)-1.35839844f, (float16_t)5.265625f, + (float16_t)3.82226562f, (float16_t)3.265625f, (float16_t)1.94726562f, (float16_t)-5.20703125f, + (float16_t)5.61328125f, (float16_t)-3.8203125f, (float16_t)4.30078125f, (float16_t)3.09375f, + (float16_t)-3.66210938f, (float16_t)1.52636719f, (float16_t)2.65234375f, (float16_t)2.30664062f, + (float16_t)-1.359375f, (float16_t)-3.33984375f, (float16_t)-5.91015625f, (float16_t)-2.9921875f, + (float16_t)-5.96875f, (float16_t)-2.56054688f, (float16_t)5.03515625f, (float16_t)0.715332031f, + (float16_t)-1.99609375f, (float16_t)2.95898438f, (float16_t)3.78515625f, (float16_t)2.1875f, + (float16_t)2.73242188f, (float16_t)1.8359375f, (float16_t)0.118652344f, (float16_t)3.46679688f, + (float16_t)-1.41894531f, (float16_t)-1.44140625f, (float16_t)-4.83984375f, (float16_t)1.28320312f, + (float16_t)1.82421875f, (float16_t)1.97363281f, (float16_t)5.11328125f, (float16_t)0.83203125f, + (float16_t)5.515625f, (float16_t)4.44140625f, (float16_t)0.721191406f, (float16_t)0.454345703f, + (float16_t)-3.33398438f, (float16_t)2.12695312f, (float16_t)-4.98046875f, (float16_t)-1.91113281f, + (float16_t)1.67578125f, (float16_t)-0.135375977f, (float16_t)-1.64257812f, (float16_t)-1.37207031f, + (float16_t)-3.47851562f, (float16_t)-1.79785156f, (float16_t)3.09765625f, (float16_t)-2.00390625f, + (float16_t)-1.44042969f, (float16_t)0.682128906f, (float16_t)1.04785156f, (float16_t)4.8203125f, + (float16_t)-1.12011719f, (float16_t)2.15820312f, (float16_t)5.00390625f, (float16_t)1.95117188f, + (float16_t)3.26367188f, (float16_t)-5.9609375f, (float16_t)-1.2734375f, (float16_t)1.84375f, + (float16_t)-2.41796875f, (float16_t)-4.3359375f, (float16_t)-2.64648438f, (float16_t)3.29492188f, + (float16_t)2.55664062f, (float16_t)-4.35546875f, (float16_t)0.962890625f, (float16_t)1.40917969f, + (float16_t)4.83203125f, (float16_t)-4.5625f, (float16_t)1.26855469f, (float16_t)-3.984375f, + (float16_t)-5.140625f, (float16_t)-1.046875f, (float16_t)-5.62890625f, (float16_t)-3.64453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/output_ref_data.h new file mode 100644 index 00000000..5837ec00 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/output_ref_data.h @@ -0,0 +1,47 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t maxpooling_match_5_f16_output_ref[180] = { + (float16_t)4.51953125f, (float16_t)-5.8515625f, (float16_t)3.72851562f, (float16_t)-0.222290039f, + (float16_t)-1.88574219f, (float16_t)-5.1953125f, (float16_t)-4.78515625f, (float16_t)5.42578125f, + (float16_t)3.31054688f, (float16_t)1.97363281f, (float16_t)2.52734375f, (float16_t)1.24414062f, + (float16_t)-0.798339844f, (float16_t)-1.38378906f, (float16_t)0.107849121f, (float16_t)-2.44335938f, + (float16_t)-2.38476562f, (float16_t)-5.58203125f, (float16_t)5.37109375f, (float16_t)0.428466797f, + (float16_t)-3.32421875f, (float16_t)-0.810546875f, (float16_t)5.23828125f, (float16_t)0.0328979492f, + (float16_t)-3.70703125f, (float16_t)3.1015625f, (float16_t)-2.390625f, (float16_t)-0.697753906f, + (float16_t)3.890625f, (float16_t)-2.015625f, (float16_t)-5.86328125f, (float16_t)4.234375f, + (float16_t)4.109375f, (float16_t)2.55273438f, (float16_t)-0.647949219f, (float16_t)2.47851562f, + (float16_t)-0.264160156f, (float16_t)5.953125f, (float16_t)1.14453125f, (float16_t)1.69628906f, + (float16_t)2.74023438f, (float16_t)-3.0625f, (float16_t)4.76953125f, (float16_t)-1.9765625f, + (float16_t)2.95703125f, (float16_t)4.48828125f, (float16_t)-5.03125f, (float16_t)-2.70507812f, + (float16_t)-0.0908813477f, (float16_t)1.15625f, (float16_t)5.47265625f, (float16_t)1.48925781f, + (float16_t)2.390625f, (float16_t)-4.3359375f, (float16_t)-5.06640625f, (float16_t)-0.946289062f, + (float16_t)-2.00195312f, (float16_t)-0.586425781f, (float16_t)4.74609375f, (float16_t)-0.0110015869f, + (float16_t)-0.474609375f, (float16_t)0.29296875f, (float16_t)0.738769531f, (float16_t)-1.84667969f, + (float16_t)3.671875f, (float16_t)-5.91015625f, (float16_t)1.09472656f, (float16_t)5.22265625f, + (float16_t)2.04882812f, (float16_t)-5.921875f, (float16_t)0.3515625f, (float16_t)-3.83007812f, + (float16_t)-4.6015625f, (float16_t)2.82226562f, (float16_t)4.3515625f, (float16_t)-1.203125f, + (float16_t)-2.16796875f, (float16_t)-3.26367188f, (float16_t)0.0346679688f, (float16_t)1.47070312f, + (float16_t)-4.375f, (float16_t)-0.248413086f, (float16_t)-4.33984375f, (float16_t)4.81640625f, + (float16_t)-5.25390625f, (float16_t)4.84375f, (float16_t)-0.839355469f, (float16_t)-5.5703125f, + (float16_t)-1.75f, (float16_t)-2.00390625f, (float16_t)5.57421875f, (float16_t)4.8515625f, + (float16_t)-4.8125f, (float16_t)1.93457031f, (float16_t)3.515625f, (float16_t)-2.56835938f, + (float16_t)-0.888183594f, (float16_t)-5.78515625f, (float16_t)-1.35839844f, (float16_t)5.265625f, + (float16_t)3.82226562f, (float16_t)3.265625f, (float16_t)1.94726562f, (float16_t)-5.20703125f, + (float16_t)5.61328125f, (float16_t)-3.8203125f, (float16_t)4.30078125f, (float16_t)3.09375f, + (float16_t)-3.66210938f, (float16_t)1.52636719f, (float16_t)2.65234375f, (float16_t)2.30664062f, + (float16_t)-1.359375f, (float16_t)-3.33984375f, (float16_t)-5.91015625f, (float16_t)-2.9921875f, + (float16_t)-5.96875f, (float16_t)-2.56054688f, (float16_t)5.03515625f, (float16_t)0.715332031f, + (float16_t)-1.99609375f, (float16_t)2.95898438f, (float16_t)3.78515625f, (float16_t)2.1875f, + (float16_t)2.73242188f, (float16_t)1.8359375f, (float16_t)0.118652344f, (float16_t)3.46679688f, + (float16_t)-1.41894531f, (float16_t)-1.44140625f, (float16_t)-4.83984375f, (float16_t)1.28320312f, + (float16_t)1.82421875f, (float16_t)1.97363281f, (float16_t)5.11328125f, (float16_t)0.83203125f, + (float16_t)5.515625f, (float16_t)4.44140625f, (float16_t)0.721191406f, (float16_t)0.454345703f, + (float16_t)-3.33398438f, (float16_t)2.12695312f, (float16_t)-4.98046875f, (float16_t)-1.91113281f, + (float16_t)1.67578125f, (float16_t)-0.135375977f, (float16_t)-1.64257812f, (float16_t)-1.37207031f, + (float16_t)-3.47851562f, (float16_t)-1.79785156f, (float16_t)3.09765625f, (float16_t)-2.00390625f, + (float16_t)-1.44042969f, (float16_t)0.682128906f, (float16_t)1.04785156f, (float16_t)4.8203125f, + (float16_t)-1.12011719f, (float16_t)2.15820312f, (float16_t)5.00390625f, (float16_t)1.95117188f, + (float16_t)3.26367188f, (float16_t)-5.9609375f, (float16_t)-1.2734375f, (float16_t)1.84375f, + (float16_t)-2.41796875f, (float16_t)-4.3359375f, (float16_t)-2.64648438f, (float16_t)3.29492188f, + (float16_t)2.55664062f, (float16_t)-4.35546875f, (float16_t)0.962890625f, (float16_t)1.40917969f, + (float16_t)4.83203125f, (float16_t)-4.5625f, (float16_t)1.26855469f, (float16_t)-3.984375f, + (float16_t)-5.140625f, (float16_t)-1.046875f, (float16_t)-5.62890625f, (float16_t)-3.64453125f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/config_data.h new file mode 100644 index 00000000..a112092d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/config_data.h @@ -0,0 +1,18 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MAXPOOLING_MATCH_5_F32_BATCH_SIZE 1 +#define MAXPOOLING_MATCH_5_F32_INPUT_N 1 +#define MAXPOOLING_MATCH_5_F32_INPUT_W 3 +#define MAXPOOLING_MATCH_5_F32_INPUT_H 3 +#define MAXPOOLING_MATCH_5_F32_INPUT_C 20 +#define MAXPOOLING_MATCH_5_F32_FILTER_W 1 +#define MAXPOOLING_MATCH_5_F32_FILTER_H 1 +#define MAXPOOLING_MATCH_5_F32_STRIDE_W 1 +#define MAXPOOLING_MATCH_5_F32_STRIDE_H 1 +#define MAXPOOLING_MATCH_5_F32_ACTIVATION_MIN -100.0f +#define MAXPOOLING_MATCH_5_F32_ACTIVATION_MAX 100.0f +#define MAXPOOLING_MATCH_5_F32_OUTPUT_C 20 +#define MAXPOOLING_MATCH_5_F32_OUTPUT_W 3 +#define MAXPOOLING_MATCH_5_F32_OUTPUT_H 3 +#define MAXPOOLING_MATCH_5_F32_PADDING_H 0 +#define MAXPOOLING_MATCH_5_F32_PADDING_W 0 +#define MAXPOOLING_MATCH_5_F32_DST_SIZE 180 diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/input_tensor_data.h new file mode 100644 index 00000000..43f73363 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/input_tensor_data.h @@ -0,0 +1,28 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_5_f32_input_tensor[180] = { + -0.234675586f, 2.96011019f, 5.5664463f, 5.49696636f, 5.7308836f, -0.345864803f, -3.37546444f, + -2.08841586f, -5.70357275f, -1.55274463f, 1.78811276f, 3.80934405f, 5.29434776f, -2.6278038f, + 3.77437282f, 4.47306156f, -0.641940594f, 0.413283199f, -1.26459384f, 5.25005579f, 3.26350713f, + 2.16697311f, 3.99836969f, -5.60057831f, -4.81719542f, -2.48228312f, 3.92936945f, 4.43297243f, + 3.74359798f, -5.28621054f, -3.8314352f, -3.97459221f, -4.26741266f, -5.75766516f, -5.97815514f, + -3.84720397f, -5.36448956f, 2.69292068f, 5.41744518f, 0.790804267f, -0.930429757f, -2.12539268f, + 0.348666608f, -4.3743515f, 3.89659858f, -2.4746604f, 2.8405726f, -1.93215525f, -0.139327273f, + -4.24524736f, 4.9784627f, 1.88621294f, -0.901601791f, 4.36523438f, -3.68780994f, 4.09387493f, + -1.41513717f, 1.440871f, 5.05647898f, 3.41486311f, 2.64438462f, -4.55405569f, -5.47300625f, + 5.04331923f, 1.9913528f, -2.67450118f, 1.68191588f, 2.57510376f, -1.84016919f, -3.84858871f, + -0.535933852f, -2.45731497f, 2.89955926f, 2.00991774f, 2.69898486f, -1.54942405f, -4.41345549f, + 4.15013504f, -4.46109915f, 1.21138561f, 0.346929133f, 0.0659828782f, 1.39743924f, 2.50589943f, + -1.05312014f, 1.97743952f, 3.48327065f, 5.59584618f, -0.558271706f, -1.51818836f, 4.68869209f, + 4.31101084f, 3.86270642f, -1.77272594f, 3.45278478f, 5.90437317f, 1.56849575f, -2.08663964f, + 2.20863438f, -3.81678271f, 0.488244027f, 0.155435473f, 2.95062637f, 2.91756463f, -5.2433362f, + 0.81156069f, -0.51367718f, 0.617070735f, -2.05658436f, -5.52570915f, -0.247643843f, 2.47609544f, + 0.413474888f, 5.26285028f, -3.35237098f, -3.06713653f, 3.08566976f, -2.50087976f, -5.01760769f, + -4.88636589f, -3.31256032f, 2.59800816f, 2.21200442f, -4.31350803f, -5.98820591f, -4.27068377f, + 1.71399081f, 2.8276341f, 2.59036517f, 3.12148309f, -5.18588066f, -1.65401304f, 2.63387609f, + 2.72305036f, -4.65929174f, -1.95411301f, 1.46508348f, -4.12389612f, 3.05865002f, 4.1602664f, + -0.181480557f, 4.09759712f, 2.06945801f, 0.0133925416f, 4.52067995f, 2.21249151f, -3.41918468f, + 3.40595746f, 4.67214918f, -3.86374688f, 3.14401579f, 1.18274784f, -1.35293126f, 5.90673304f, + -1.8348186f, -0.498951912f, -4.2645669f, 1.43258953f, 4.41544437f, 3.83987093f, 0.511433005f, + 2.8248148f, -0.0858237371f, 2.10662246f, -0.681359351f, -4.59287882f, -3.97347474f, 2.50380087f, + -5.85688782f, 3.34171534f, 2.912184f, -3.40470695f, 2.90939021f, -3.22265387f, 1.05965912f, + -4.1335001f, -5.250453f, -1.54531002f, 1.23795354f, -0.649799287f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/output_ref_data.h new file mode 100644 index 00000000..14d44273 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/output_ref_data.h @@ -0,0 +1,28 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t maxpooling_match_5_f32_output_ref[180] = { + -0.234675586f, 2.96011019f, 5.5664463f, 5.49696636f, 5.7308836f, -0.345864803f, -3.37546444f, + -2.08841586f, -5.70357275f, -1.55274463f, 1.78811276f, 3.80934405f, 5.29434776f, -2.6278038f, + 3.77437282f, 4.47306156f, -0.641940594f, 0.413283199f, -1.26459384f, 5.25005579f, 3.26350713f, + 2.16697311f, 3.99836969f, -5.60057831f, -4.81719542f, -2.48228312f, 3.92936945f, 4.43297243f, + 3.74359798f, -5.28621054f, -3.8314352f, -3.97459221f, -4.26741266f, -5.75766516f, -5.97815514f, + -3.84720397f, -5.36448956f, 2.69292068f, 5.41744518f, 0.790804267f, -0.930429757f, -2.12539268f, + 0.348666608f, -4.3743515f, 3.89659858f, -2.4746604f, 2.8405726f, -1.93215525f, -0.139327273f, + -4.24524736f, 4.9784627f, 1.88621294f, -0.901601791f, 4.36523438f, -3.68780994f, 4.09387493f, + -1.41513717f, 1.440871f, 5.05647898f, 3.41486311f, 2.64438462f, -4.55405569f, -5.47300625f, + 5.04331923f, 1.9913528f, -2.67450118f, 1.68191588f, 2.57510376f, -1.84016919f, -3.84858871f, + -0.535933852f, -2.45731497f, 2.89955926f, 2.00991774f, 2.69898486f, -1.54942405f, -4.41345549f, + 4.15013504f, -4.46109915f, 1.21138561f, 0.346929133f, 0.0659828782f, 1.39743924f, 2.50589943f, + -1.05312014f, 1.97743952f, 3.48327065f, 5.59584618f, -0.558271706f, -1.51818836f, 4.68869209f, + 4.31101084f, 3.86270642f, -1.77272594f, 3.45278478f, 5.90437317f, 1.56849575f, -2.08663964f, + 2.20863438f, -3.81678271f, 0.488244027f, 0.155435473f, 2.95062637f, 2.91756463f, -5.2433362f, + 0.81156069f, -0.51367718f, 0.617070735f, -2.05658436f, -5.52570915f, -0.247643843f, 2.47609544f, + 0.413474888f, 5.26285028f, -3.35237098f, -3.06713653f, 3.08566976f, -2.50087976f, -5.01760769f, + -4.88636589f, -3.31256032f, 2.59800816f, 2.21200442f, -4.31350803f, -5.98820591f, -4.27068377f, + 1.71399081f, 2.8276341f, 2.59036517f, 3.12148309f, -5.18588066f, -1.65401304f, 2.63387609f, + 2.72305036f, -4.65929174f, -1.95411301f, 1.46508348f, -4.12389612f, 3.05865002f, 4.1602664f, + -0.181480557f, 4.09759712f, 2.06945801f, 0.0133925416f, 4.52067995f, 2.21249151f, -3.41918468f, + 3.40595746f, 4.67214918f, -3.86374688f, 3.14401579f, 1.18274784f, -1.35293126f, 5.90673304f, + -1.8348186f, -0.498951912f, -4.2645669f, 1.43258953f, 4.41544437f, 3.83987093f, 0.511433005f, + 2.8248148f, -0.0858237371f, 2.10662246f, -0.681359351f, -4.59287882f, -3.97347474f, 2.50380087f, + -5.85688782f, 3.34171534f, 2.912184f, -3.40470695f, 2.90939021f, -3.22265387f, 1.05965912f, + -4.1335001f, -5.250453f, -1.54531002f, 1.23795354f, -0.649799287f}; diff --git a/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/test_data.h new file mode 100644 index 00000000..6b017f38 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/maxpooling_match_5_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pooling_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/config_data.h new file mode 100644 index 00000000..5decf9d8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_BATCH_F16_BATCH_1 2 +#define MINIMUM_BROADCAST_BATCH_F16_HEIGHT_1 1 +#define MINIMUM_BROADCAST_BATCH_F16_WIDTH_1 6 +#define MINIMUM_BROADCAST_BATCH_F16_CHANNEL_1 21 +#define MINIMUM_BROADCAST_BATCH_F16_BATCH_2 1 +#define MINIMUM_BROADCAST_BATCH_F16_HEIGHT_2 1 +#define MINIMUM_BROADCAST_BATCH_F16_WIDTH_2 6 +#define MINIMUM_BROADCAST_BATCH_F16_CHANNEL_2 21 +#define MINIMUM_BROADCAST_BATCH_F16_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_BATCH_F16_OUTPUT_HEIGHT 1 +#define MINIMUM_BROADCAST_BATCH_F16_OUTPUT_WIDTH 6 +#define MINIMUM_BROADCAST_BATCH_F16_OUTPUT_CHANNEL 21 +#define MINIMUM_BROADCAST_BATCH_F16_DST_SIZE 252 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_1_data.h new file mode 100644 index 00000000..1968e3cb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_1_data.h @@ -0,0 +1,65 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_batch_f16_input_tensor_1[252] = { + (float16_t)1.6484375f, (float16_t)-1.234375f, (float16_t)-0.603027344f, (float16_t)0.0302581787f, + (float16_t)4.80859375f, (float16_t)-5.7890625f, (float16_t)0.277099609f, (float16_t)4.98828125f, + (float16_t)-3.4765625f, (float16_t)-5.22265625f, (float16_t)-2.25585938f, (float16_t)5.5546875f, + (float16_t)-1.47558594f, (float16_t)-3.52539062f, (float16_t)4.42578125f, (float16_t)-2.3515625f, + (float16_t)-4.3125f, (float16_t)1.46679688f, (float16_t)4.26953125f, (float16_t)-4.74609375f, + (float16_t)2.06835938f, (float16_t)5.046875f, (float16_t)-2.08984375f, (float16_t)5.93359375f, + (float16_t)1.30371094f, (float16_t)-3.83398438f, (float16_t)1.65332031f, (float16_t)-1.53515625f, + (float16_t)4.671875f, (float16_t)-5.83203125f, (float16_t)3.75976562f, (float16_t)-5.07421875f, + (float16_t)1.57617188f, (float16_t)2.52734375f, (float16_t)4.6640625f, (float16_t)2.62695312f, + (float16_t)-5.14453125f, (float16_t)4.27734375f, (float16_t)0.126953125f, (float16_t)3.08007812f, + (float16_t)5.65625f, (float16_t)3.00195312f, (float16_t)-2.97265625f, (float16_t)-3.67578125f, + (float16_t)1.9140625f, (float16_t)-0.0344543457f, (float16_t)-3.47460938f, (float16_t)1.63085938f, + (float16_t)2.45898438f, (float16_t)-1.47851562f, (float16_t)5.8125f, (float16_t)-2.8515625f, + (float16_t)1.92773438f, (float16_t)3.5859375f, (float16_t)-2.6953125f, (float16_t)4.29296875f, + (float16_t)-5.12109375f, (float16_t)3.15820312f, (float16_t)3.40234375f, (float16_t)-1.32128906f, + (float16_t)2.53710938f, (float16_t)3.69335938f, (float16_t)4.64453125f, (float16_t)-0.47265625f, + (float16_t)-1.11328125f, (float16_t)4.68359375f, (float16_t)0.244262695f, (float16_t)-1.16601562f, + (float16_t)-4.078125f, (float16_t)-2.98046875f, (float16_t)0.750976562f, (float16_t)-0.0377807617f, + (float16_t)4.9609375f, (float16_t)1.27148438f, (float16_t)-3.29101562f, (float16_t)-4.8203125f, + (float16_t)3.04296875f, (float16_t)4.0625f, (float16_t)-3.625f, (float16_t)4.4765625f, + (float16_t)5.3359375f, (float16_t)5.5703125f, (float16_t)2.296875f, (float16_t)-5.68359375f, + (float16_t)1.92675781f, (float16_t)4.06640625f, (float16_t)-1.22949219f, (float16_t)-2.42773438f, + (float16_t)3.76367188f, (float16_t)-3.86328125f, (float16_t)1.88671875f, (float16_t)-1.06445312f, + (float16_t)-4.34375f, (float16_t)-5.96484375f, (float16_t)4.25f, (float16_t)0.812988281f, + (float16_t)-0.0682983398f, (float16_t)3.95507812f, (float16_t)0.595214844f, (float16_t)3.65820312f, + (float16_t)1.83984375f, (float16_t)-2.44140625f, (float16_t)5.4375f, (float16_t)-3.0078125f, + (float16_t)-3.8515625f, (float16_t)-1.56640625f, (float16_t)4.1484375f, (float16_t)-4.77734375f, + (float16_t)-2.2578125f, (float16_t)5.796875f, (float16_t)4.0078125f, (float16_t)4.75f, + (float16_t)-0.0523071289f, (float16_t)-5.55859375f, (float16_t)4.47265625f, (float16_t)-5.2109375f, + (float16_t)5.10546875f, (float16_t)5.5546875f, (float16_t)4.26953125f, (float16_t)-2.51367188f, + (float16_t)-2.7265625f, (float16_t)2.34179688f, (float16_t)1.38183594f, (float16_t)-3.59765625f, + (float16_t)3.41992188f, (float16_t)-3.8671875f, (float16_t)2.75390625f, (float16_t)-1.01269531f, + (float16_t)1.73144531f, (float16_t)-0.9765625f, (float16_t)-0.632324219f, (float16_t)3.796875f, + (float16_t)3.69140625f, (float16_t)4.7265625f, (float16_t)1.93554688f, (float16_t)5.61328125f, + (float16_t)-3.59179688f, (float16_t)-5.12109375f, (float16_t)0.264160156f, (float16_t)4.95703125f, + (float16_t)0.820800781f, (float16_t)1.33105469f, (float16_t)2.88476562f, (float16_t)-1.01074219f, + (float16_t)-2.52539062f, (float16_t)3.47460938f, (float16_t)2.18554688f, (float16_t)2.02734375f, + (float16_t)1.21386719f, (float16_t)1.86621094f, (float16_t)2.18359375f, (float16_t)1.07617188f, + (float16_t)3.27734375f, (float16_t)0.755371094f, (float16_t)-5.4609375f, (float16_t)3.11523438f, + (float16_t)0.389404297f, (float16_t)-2.1328125f, (float16_t)-1.2578125f, (float16_t)-5.5546875f, + (float16_t)-3.71679688f, (float16_t)3.15429688f, (float16_t)-0.859375f, (float16_t)3.17578125f, + (float16_t)1.68261719f, (float16_t)-4.86328125f, (float16_t)1.51953125f, (float16_t)-0.136230469f, + (float16_t)-2.76367188f, (float16_t)-5.83203125f, (float16_t)-0.702148438f, (float16_t)4.0703125f, + (float16_t)-4.12890625f, (float16_t)-1.70605469f, (float16_t)-0.166503906f, (float16_t)5.9296875f, + (float16_t)-2.234375f, (float16_t)-3.13085938f, (float16_t)-0.377685547f, (float16_t)1.11132812f, + (float16_t)4.1171875f, (float16_t)-3.95507812f, (float16_t)-1.87695312f, (float16_t)3.18359375f, + (float16_t)-3.91210938f, (float16_t)-2.40820312f, (float16_t)2.83203125f, (float16_t)4.5546875f, + (float16_t)-4.734375f, (float16_t)5.39453125f, (float16_t)-1.30566406f, (float16_t)-5.0f, + (float16_t)-2.79101562f, (float16_t)3.94140625f, (float16_t)3.45117188f, (float16_t)1.48632812f, + (float16_t)-5.60546875f, (float16_t)-4.70703125f, (float16_t)4.1875f, (float16_t)2.15429688f, + (float16_t)1.73242188f, (float16_t)-4.08203125f, (float16_t)5.86328125f, (float16_t)1.13964844f, + (float16_t)3.47265625f, (float16_t)-2.0f, (float16_t)-5.48828125f, (float16_t)-0.0395507812f, + (float16_t)-4.1640625f, (float16_t)-5.82421875f, (float16_t)-5.2890625f, (float16_t)-5.140625f, + (float16_t)5.6171875f, (float16_t)-3.76757812f, (float16_t)4.828125f, (float16_t)2.546875f, + (float16_t)4.04296875f, (float16_t)-2.7578125f, (float16_t)1.359375f, (float16_t)-4.8984375f, + (float16_t)-5.99609375f, (float16_t)5.00390625f, (float16_t)-0.931152344f, (float16_t)-1.8046875f, + (float16_t)5.56640625f, (float16_t)-5.58984375f, (float16_t)-4.88671875f, (float16_t)0.520019531f, + (float16_t)1.83886719f, (float16_t)-1.97558594f, (float16_t)-1.08105469f, (float16_t)-4.15234375f, + (float16_t)3.87695312f, (float16_t)-0.961914062f, (float16_t)-1.03515625f, (float16_t)2.70703125f, + (float16_t)3.88671875f, (float16_t)4.66796875f, (float16_t)1.57519531f, (float16_t)3.3046875f, + (float16_t)-4.5078125f, (float16_t)-4.5234375f, (float16_t)3.72265625f, (float16_t)-3.99804688f, + (float16_t)4.16015625f, (float16_t)0.78125f, (float16_t)5.42578125f, (float16_t)-4.24609375f, + (float16_t)-3.72265625f, (float16_t)-4.6875f, (float16_t)4.33984375f, (float16_t)-0.772949219f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_2_data.h new file mode 100644 index 00000000..42cca772 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/input_tensor_2_data.h @@ -0,0 +1,34 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_batch_f16_input_tensor_2[126] = { + (float16_t)-5.4375f, (float16_t)-0.240356445f, (float16_t)1.8671875f, (float16_t)1.17871094f, + (float16_t)-5.35546875f, (float16_t)-3.02929688f, (float16_t)-3.35546875f, (float16_t)-2.14648438f, + (float16_t)-1.78320312f, (float16_t)3.0390625f, (float16_t)2.15429688f, (float16_t)0.682128906f, + (float16_t)-5.234375f, (float16_t)-5.94921875f, (float16_t)1.51367188f, (float16_t)5.4453125f, + (float16_t)-0.614746094f, (float16_t)2.43359375f, (float16_t)2.44140625f, (float16_t)2.13867188f, + (float16_t)-2.54101562f, (float16_t)-2.3359375f, (float16_t)4.24609375f, (float16_t)-3.74414062f, + (float16_t)-0.103942871f, (float16_t)-5.296875f, (float16_t)-3.1875f, (float16_t)-4.9765625f, + (float16_t)-1.69726562f, (float16_t)-4.03125f, (float16_t)-1.88867188f, (float16_t)1.87011719f, + (float16_t)-4.4296875f, (float16_t)-5.98828125f, (float16_t)0.672363281f, (float16_t)-4.22265625f, + (float16_t)1.84667969f, (float16_t)0.328369141f, (float16_t)0.711425781f, (float16_t)5.87109375f, + (float16_t)5.83203125f, (float16_t)5.7421875f, (float16_t)-5.6015625f, (float16_t)-4.1484375f, + (float16_t)1.45898438f, (float16_t)-4.984375f, (float16_t)4.8125f, (float16_t)-1.31835938f, + (float16_t)-1.1796875f, (float16_t)-5.1015625f, (float16_t)-4.1953125f, (float16_t)3.29101562f, + (float16_t)-0.598632812f, (float16_t)1.29589844f, (float16_t)0.530761719f, (float16_t)3.2109375f, + (float16_t)4.38671875f, (float16_t)-5.75390625f, (float16_t)4.70703125f, (float16_t)-0.090637207f, + (float16_t)0.280517578f, (float16_t)-0.338623047f, (float16_t)-3.39257812f, (float16_t)-0.982910156f, + (float16_t)-5.6640625f, (float16_t)-3.84179688f, (float16_t)5.12109375f, (float16_t)-2.56054688f, + (float16_t)-4.3828125f, (float16_t)5.15234375f, (float16_t)-1.16210938f, (float16_t)-5.5703125f, + (float16_t)-1.47558594f, (float16_t)3.25f, (float16_t)-1.6875f, (float16_t)2.70117188f, + (float16_t)3.796875f, (float16_t)-2.00585938f, (float16_t)4.296875f, (float16_t)5.35546875f, + (float16_t)-0.935058594f, (float16_t)-5.92578125f, (float16_t)-1.35546875f, (float16_t)-5.1640625f, + (float16_t)5.67578125f, (float16_t)0.986328125f, (float16_t)-0.736816406f, (float16_t)4.703125f, + (float16_t)-1.74023438f, (float16_t)5.8828125f, (float16_t)-1.6328125f, (float16_t)5.7734375f, + (float16_t)-5.58984375f, (float16_t)5.78515625f, (float16_t)1.06542969f, (float16_t)-4.421875f, + (float16_t)-3.93164062f, (float16_t)-1.09765625f, (float16_t)-4.11328125f, (float16_t)-5.91015625f, + (float16_t)5.71875f, (float16_t)-2.39648438f, (float16_t)1.15039062f, (float16_t)-4.046875f, + (float16_t)-4.26171875f, (float16_t)-4.4453125f, (float16_t)1.953125f, (float16_t)-2.375f, + (float16_t)4.75f, (float16_t)3.06640625f, (float16_t)-1.63769531f, (float16_t)-0.0238800049f, + (float16_t)2.21289062f, (float16_t)5.4296875f, (float16_t)5.79296875f, (float16_t)3.67578125f, + (float16_t)-2.02734375f, (float16_t)0.71484375f, (float16_t)0.21496582f, (float16_t)4.2734375f, + (float16_t)-3.38476562f, (float16_t)-2.0859375f, (float16_t)4.40234375f, (float16_t)4.31640625f, + (float16_t)3.828125f, (float16_t)4.19140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/output_ref_data.h new file mode 100644 index 00000000..54f1ec1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/output_ref_data.h @@ -0,0 +1,65 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_batch_f16_output_ref[252] = { + (float16_t)-5.4375f, (float16_t)-1.234375f, (float16_t)-0.603027344f, (float16_t)0.0302581787f, + (float16_t)-5.35546875f, (float16_t)-5.7890625f, (float16_t)-3.35546875f, (float16_t)-2.14648438f, + (float16_t)-3.4765625f, (float16_t)-5.22265625f, (float16_t)-2.25585938f, (float16_t)0.682128906f, + (float16_t)-5.234375f, (float16_t)-5.94921875f, (float16_t)1.51367188f, (float16_t)-2.3515625f, + (float16_t)-4.3125f, (float16_t)1.46679688f, (float16_t)2.44140625f, (float16_t)-4.74609375f, + (float16_t)-2.54101562f, (float16_t)-2.3359375f, (float16_t)-2.08984375f, (float16_t)-3.74414062f, + (float16_t)-0.103942871f, (float16_t)-5.296875f, (float16_t)-3.1875f, (float16_t)-4.9765625f, + (float16_t)-1.69726562f, (float16_t)-5.83203125f, (float16_t)-1.88867188f, (float16_t)-5.07421875f, + (float16_t)-4.4296875f, (float16_t)-5.98828125f, (float16_t)0.672363281f, (float16_t)-4.22265625f, + (float16_t)-5.14453125f, (float16_t)0.328369141f, (float16_t)0.126953125f, (float16_t)3.08007812f, + (float16_t)5.65625f, (float16_t)3.00195312f, (float16_t)-5.6015625f, (float16_t)-4.1484375f, + (float16_t)1.45898438f, (float16_t)-4.984375f, (float16_t)-3.47460938f, (float16_t)-1.31835938f, + (float16_t)-1.1796875f, (float16_t)-5.1015625f, (float16_t)-4.1953125f, (float16_t)-2.8515625f, + (float16_t)-0.598632812f, (float16_t)1.29589844f, (float16_t)-2.6953125f, (float16_t)3.2109375f, + (float16_t)-5.12109375f, (float16_t)-5.75390625f, (float16_t)3.40234375f, (float16_t)-1.32128906f, + (float16_t)0.280517578f, (float16_t)-0.338623047f, (float16_t)-3.39257812f, (float16_t)-0.982910156f, + (float16_t)-5.6640625f, (float16_t)-3.84179688f, (float16_t)0.244262695f, (float16_t)-2.56054688f, + (float16_t)-4.3828125f, (float16_t)-2.98046875f, (float16_t)-1.16210938f, (float16_t)-5.5703125f, + (float16_t)-1.47558594f, (float16_t)1.27148438f, (float16_t)-3.29101562f, (float16_t)-4.8203125f, + (float16_t)3.04296875f, (float16_t)-2.00585938f, (float16_t)-3.625f, (float16_t)4.4765625f, + (float16_t)-0.935058594f, (float16_t)-5.92578125f, (float16_t)-1.35546875f, (float16_t)-5.68359375f, + (float16_t)1.92675781f, (float16_t)0.986328125f, (float16_t)-1.22949219f, (float16_t)-2.42773438f, + (float16_t)-1.74023438f, (float16_t)-3.86328125f, (float16_t)-1.6328125f, (float16_t)-1.06445312f, + (float16_t)-5.58984375f, (float16_t)-5.96484375f, (float16_t)1.06542969f, (float16_t)-4.421875f, + (float16_t)-3.93164062f, (float16_t)-1.09765625f, (float16_t)-4.11328125f, (float16_t)-5.91015625f, + (float16_t)1.83984375f, (float16_t)-2.44140625f, (float16_t)1.15039062f, (float16_t)-4.046875f, + (float16_t)-4.26171875f, (float16_t)-4.4453125f, (float16_t)1.953125f, (float16_t)-4.77734375f, + (float16_t)-2.2578125f, (float16_t)3.06640625f, (float16_t)-1.63769531f, (float16_t)-0.0238800049f, + (float16_t)-0.0523071289f, (float16_t)-5.55859375f, (float16_t)4.47265625f, (float16_t)-5.2109375f, + (float16_t)-2.02734375f, (float16_t)0.71484375f, (float16_t)0.21496582f, (float16_t)-2.51367188f, + (float16_t)-3.38476562f, (float16_t)-2.0859375f, (float16_t)1.38183594f, (float16_t)-3.59765625f, + (float16_t)3.41992188f, (float16_t)-3.8671875f, (float16_t)-5.4375f, (float16_t)-1.01269531f, + (float16_t)1.73144531f, (float16_t)-0.9765625f, (float16_t)-5.35546875f, (float16_t)-3.02929688f, + (float16_t)-3.35546875f, (float16_t)-2.14648438f, (float16_t)-1.78320312f, (float16_t)3.0390625f, + (float16_t)-3.59179688f, (float16_t)-5.12109375f, (float16_t)-5.234375f, (float16_t)-5.94921875f, + (float16_t)0.820800781f, (float16_t)1.33105469f, (float16_t)-0.614746094f, (float16_t)-1.01074219f, + (float16_t)-2.52539062f, (float16_t)2.13867188f, (float16_t)-2.54101562f, (float16_t)-2.3359375f, + (float16_t)1.21386719f, (float16_t)-3.74414062f, (float16_t)-0.103942871f, (float16_t)-5.296875f, + (float16_t)-3.1875f, (float16_t)-4.9765625f, (float16_t)-5.4609375f, (float16_t)-4.03125f, + (float16_t)-1.88867188f, (float16_t)-2.1328125f, (float16_t)-4.4296875f, (float16_t)-5.98828125f, + (float16_t)-3.71679688f, (float16_t)-4.22265625f, (float16_t)-0.859375f, (float16_t)0.328369141f, + (float16_t)0.711425781f, (float16_t)-4.86328125f, (float16_t)1.51953125f, (float16_t)-0.136230469f, + (float16_t)-5.6015625f, (float16_t)-5.83203125f, (float16_t)-0.702148438f, (float16_t)-4.984375f, + (float16_t)-4.12890625f, (float16_t)-1.70605469f, (float16_t)-1.1796875f, (float16_t)-5.1015625f, + (float16_t)-4.1953125f, (float16_t)-3.13085938f, (float16_t)-0.598632812f, (float16_t)1.11132812f, + (float16_t)0.530761719f, (float16_t)-3.95507812f, (float16_t)-1.87695312f, (float16_t)-5.75390625f, + (float16_t)-3.91210938f, (float16_t)-2.40820312f, (float16_t)0.280517578f, (float16_t)-0.338623047f, + (float16_t)-4.734375f, (float16_t)-0.982910156f, (float16_t)-5.6640625f, (float16_t)-5.0f, + (float16_t)-2.79101562f, (float16_t)-2.56054688f, (float16_t)-4.3828125f, (float16_t)1.48632812f, + (float16_t)-5.60546875f, (float16_t)-5.5703125f, (float16_t)-1.47558594f, (float16_t)2.15429688f, + (float16_t)-1.6875f, (float16_t)-4.08203125f, (float16_t)3.796875f, (float16_t)-2.00585938f, + (float16_t)3.47265625f, (float16_t)-2.0f, (float16_t)-5.48828125f, (float16_t)-5.92578125f, + (float16_t)-4.1640625f, (float16_t)-5.82421875f, (float16_t)-5.2890625f, (float16_t)-5.140625f, + (float16_t)-0.736816406f, (float16_t)-3.76757812f, (float16_t)-1.74023438f, (float16_t)2.546875f, + (float16_t)-1.6328125f, (float16_t)-2.7578125f, (float16_t)-5.58984375f, (float16_t)-4.8984375f, + (float16_t)-5.99609375f, (float16_t)-4.421875f, (float16_t)-3.93164062f, (float16_t)-1.8046875f, + (float16_t)-4.11328125f, (float16_t)-5.91015625f, (float16_t)-4.88671875f, (float16_t)-2.39648438f, + (float16_t)1.15039062f, (float16_t)-4.046875f, (float16_t)-4.26171875f, (float16_t)-4.4453125f, + (float16_t)1.953125f, (float16_t)-2.375f, (float16_t)-1.03515625f, (float16_t)2.70703125f, + (float16_t)-1.63769531f, (float16_t)-0.0238800049f, (float16_t)1.57519531f, (float16_t)3.3046875f, + (float16_t)-4.5078125f, (float16_t)-4.5234375f, (float16_t)-2.02734375f, (float16_t)-3.99804688f, + (float16_t)0.21496582f, (float16_t)0.78125f, (float16_t)-3.38476562f, (float16_t)-4.24609375f, + (float16_t)-3.72265625f, (float16_t)-4.6875f, (float16_t)3.828125f, (float16_t)-0.772949219f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/config_data.h new file mode 100644 index 00000000..ff5c50e8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_BATCH_F32_BATCH_1 2 +#define MINIMUM_BROADCAST_BATCH_F32_HEIGHT_1 1 +#define MINIMUM_BROADCAST_BATCH_F32_WIDTH_1 6 +#define MINIMUM_BROADCAST_BATCH_F32_CHANNEL_1 21 +#define MINIMUM_BROADCAST_BATCH_F32_BATCH_2 1 +#define MINIMUM_BROADCAST_BATCH_F32_HEIGHT_2 1 +#define MINIMUM_BROADCAST_BATCH_F32_WIDTH_2 6 +#define MINIMUM_BROADCAST_BATCH_F32_CHANNEL_2 21 +#define MINIMUM_BROADCAST_BATCH_F32_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_BATCH_F32_OUTPUT_HEIGHT 1 +#define MINIMUM_BROADCAST_BATCH_F32_OUTPUT_WIDTH 6 +#define MINIMUM_BROADCAST_BATCH_F32_OUTPUT_CHANNEL 21 +#define MINIMUM_BROADCAST_BATCH_F32_DST_SIZE 252 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_1_data.h new file mode 100644 index 00000000..e3315a13 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_1_data.h @@ -0,0 +1,38 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_batch_f32_input_tensor_1[252] = { + -3.89020491f, -2.34340882f, 0.235927597f, -0.932157278f, -0.896355271f, 0.93340075f, -0.992572308f, + -0.925303817f, -0.543866515f, -5.4918766f, -3.14202261f, -4.77924919f, -1.34899497f, -1.03973699f, + 2.8744061f, -4.60905933f, -4.23203468f, 5.34513712f, 2.3958509f, 2.83377051f, 0.752108991f, + -4.10280943f, -4.38537979f, -4.48815393f, -1.23369622f, -1.06672084f, 1.73737645f, -3.26018333f, + -3.53009319f, 2.91890621f, -3.48479915f, -3.69669533f, -0.596043408f, 1.45522606f, 3.14343977f, + 0.943932772f, -2.71256375f, 1.25922167f, -0.756281734f, 5.3507514f, 1.31585228f, -5.10634661f, + -0.780170977f, 0.379496574f, -2.49842262f, 2.70329785f, 0.362587184f, 3.3068285f, -4.12018061f, + -3.43921733f, -3.01427722f, 0.813560367f, -1.76846707f, -3.87114429f, -3.67675662f, -4.67871189f, + -4.55503082f, -3.82054663f, -4.69966412f, 5.86293602f, 3.21333122f, 1.47398806f, -5.35755634f, + -2.22697067f, 5.15910387f, -1.13975847f, -4.24815416f, 3.94829535f, 3.26573706f, -4.73497725f, + 5.24929619f, -1.04320741f, 0.724264383f, -3.53937197f, 2.57190299f, 2.62385869f, -2.2189374f, + -4.02175236f, 1.65617752f, 3.73808718f, 0.796897888f, -3.57343173f, -5.08137131f, -0.44814989f, + 1.28084397f, -4.8410058f, -2.39694595f, 2.33501673f, -4.46222639f, 4.37415791f, 3.06122088f, + 4.21424627f, -5.73658657f, -5.32744646f, 2.00865746f, 2.62278199f, -5.04051399f, -2.99914217f, + -5.67134142f, 4.68149996f, -0.361203164f, 3.81403971f, -4.76054573f, -2.10023689f, -4.11576605f, + 5.84980345f, 5.87938118f, 0.0508162566f, -3.3453598f, 5.09210968f, 3.36546659f, -3.12634492f, + 0.336866438f, -4.1682477f, -5.36685085f, -4.72642231f, -3.84537101f, -1.64190054f, -4.21291876f, + -5.15124512f, -2.12995124f, 5.84704399f, 2.97440696f, -2.24303603f, -3.7217958f, -4.6558218f, + -5.52887917f, 0.977583408f, -5.4900527f, 0.132094711f, 4.39649487f, 4.14352512f, 2.48436022f, + -5.44278145f, 0.800169408f, -3.3678813f, 2.45196486f, -0.578519404f, -2.81112814f, 0.321295857f, + 3.55285001f, -0.542277455f, -4.5852952f, -2.84996724f, 2.15834236f, 4.40226936f, -2.59640551f, + -0.856803596f, 5.78809643f, 5.98048878f, -4.32741213f, -1.54485524f, 0.841110885f, -5.21666574f, + -0.39862892f, 0.408298612f, 0.300475359f, 0.887066782f, 5.88000965f, -3.44856119f, 1.62395012f, + 1.87353671f, -2.16198874f, -3.77239442f, 4.79820871f, -0.637339771f, -4.65500212f, -1.59509385f, + -2.91450477f, -2.78559065f, -2.83921146f, 3.0232079f, 0.376998216f, -2.06751561f, 4.08777952f, + 2.3528204f, 3.23181581f, 3.90872478f, -0.713514805f, -3.83183861f, -2.29222298f, 3.6493113f, + -1.43509889f, 4.76715565f, -3.02399945f, 2.29225397f, -5.48311663f, -5.77992296f, 4.92589045f, + -1.67181778f, -2.15448356f, -0.309278786f, -4.88169193f, 4.55816269f, 1.33139873f, -1.90110219f, + -2.64001942f, 1.91902804f, -4.026896f, -2.8991015f, 1.89324057f, 3.83865285f, -1.1165545f, + -0.359923244f, -1.59768033f, -3.83392668f, -5.15672159f, -3.16808462f, 2.64043689f, 4.92339706f, + 3.93019176f, 3.85062575f, -5.98793221f, -1.31706369f, -2.76034641f, -2.01376772f, -3.22300625f, + 2.113307f, -0.488925487f, -0.14490287f, 5.53942013f, -5.61371994f, 0.43211025f, 3.80504727f, + 4.19385719f, 2.47384453f, -1.96652639f, 3.14264607f, 4.61331081f, 2.39256167f, -4.26337957f, + -3.2865746f, 0.855261624f, 4.24240732f, 2.23182297f, 5.81700087f, 1.03320932f, -5.14296246f, + -0.749368966f, 2.75336766f, -4.53027105f, 2.89115429f, -3.33746195f, -1.76271975f, -3.1968677f, + -4.61339188f, 0.0210243687f, 0.298938602f, -5.06834698f, 0.637515664f, 5.4099021f, -1.15406406f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_2_data.h new file mode 100644 index 00000000..56f836ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/input_tensor_2_data.h @@ -0,0 +1,18 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_batch_f32_input_tensor_2[126] = { + 1.87550342f, 0.68271631f, 5.52706766f, -4.275033f, -2.05451155f, 5.37083864f, 4.28143263f, 4.48661518f, + 0.862548113f, 4.51229286f, 1.96613419f, 2.34727383f, 5.07087421f, 3.5214653f, -4.93070984f, 2.58662844f, + -3.96151137f, -4.79027128f, -1.88365829f, 3.52483416f, 5.94709921f, -4.47171926f, 4.74157333f, 1.31877291f, + -2.72319198f, 2.41001201f, 4.16614294f, -1.43777537f, -3.71816063f, 5.64602852f, 5.03518534f, 3.94848871f, + -2.87214017f, 1.06977379f, 1.65868449f, -3.30371666f, 1.07361925f, -2.61527491f, 2.64231181f, 2.03001404f, + -5.86262035f, 4.07391262f, -0.516260922f, 0.74434936f, 1.70365226f, 5.04304409f, -5.60890484f, -3.65526104f, + 2.70941496f, 1.27847171f, 4.54282999f, 4.91200876f, -1.94123828f, 0.116904333f, 3.26405382f, -5.04636621f, + 1.35589409f, 2.03741193f, 1.74992228f, -3.1139009f, -3.30946136f, 4.75391436f, -4.81672335f, -5.82831049f, + -2.21025181f, 1.97516453f, -3.82486987f, 2.29107213f, -4.88095522f, 3.10901213f, 1.59208655f, -4.29528093f, + -0.134034276f, -5.89308834f, 1.48895156f, 1.22243834f, -3.99888778f, 4.1981163f, -2.65067458f, -4.40800142f, + -1.85520077f, -3.43339825f, 2.0822401f, 1.32402527f, 0.879095435f, -3.97485018f, -1.05328012f, -4.48295975f, + 2.86206937f, 1.60542619f, -2.63001633f, 4.58636713f, -4.49965811f, -1.29900825f, 2.33631754f, 4.66109514f, + 5.65495443f, 1.15594995f, 3.38300323f, 4.43909788f, -0.773263752f, 3.25528884f, -3.4962194f, 5.20889187f, + 3.80801964f, 0.747231185f, 1.94890475f, -1.08402753f, 4.54263639f, -5.65433073f, -2.06671524f, -5.95984268f, + -0.0842600763f, -0.150296226f, -2.46189094f, 2.25747037f, 0.170792952f, -2.43174243f, -5.2219348f, -3.38525152f, + -1.08149636f, 5.66920471f, -3.54900289f, -4.9859972f, 3.30293107f, -5.79396343f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/output_ref_data.h new file mode 100644 index 00000000..75f2df3c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/output_ref_data.h @@ -0,0 +1,38 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_batch_f32_output_ref[252] = { + -3.89020491f, -2.34340882f, 0.235927597f, -4.275033f, -2.05451155f, 0.93340075f, -0.992572308f, + -0.925303817f, -0.543866515f, -5.4918766f, -3.14202261f, -4.77924919f, -1.34899497f, -1.03973699f, + -4.93070984f, -4.60905933f, -4.23203468f, -4.79027128f, -1.88365829f, 2.83377051f, 0.752108991f, + -4.47171926f, -4.38537979f, -4.48815393f, -2.72319198f, -1.06672084f, 1.73737645f, -3.26018333f, + -3.71816063f, 2.91890621f, -3.48479915f, -3.69669533f, -2.87214017f, 1.06977379f, 1.65868449f, + -3.30371666f, -2.71256375f, -2.61527491f, -0.756281734f, 2.03001404f, -5.86262035f, -5.10634661f, + -0.780170977f, 0.379496574f, -2.49842262f, 2.70329785f, -5.60890484f, -3.65526104f, -4.12018061f, + -3.43921733f, -3.01427722f, 0.813560367f, -1.94123828f, -3.87114429f, -3.67675662f, -5.04636621f, + -4.55503082f, -3.82054663f, -4.69966412f, -3.1139009f, -3.30946136f, 1.47398806f, -5.35755634f, + -5.82831049f, -2.21025181f, -1.13975847f, -4.24815416f, 2.29107213f, -4.88095522f, -4.73497725f, + 1.59208655f, -4.29528093f, -0.134034276f, -5.89308834f, 1.48895156f, 1.22243834f, -3.99888778f, + -4.02175236f, -2.65067458f, -4.40800142f, -1.85520077f, -3.57343173f, -5.08137131f, -0.44814989f, + 0.879095435f, -4.8410058f, -2.39694595f, -4.48295975f, -4.46222639f, 1.60542619f, -2.63001633f, + 4.21424627f, -5.73658657f, -5.32744646f, 2.00865746f, 2.62278199f, -5.04051399f, -2.99914217f, + -5.67134142f, 4.43909788f, -0.773263752f, 3.25528884f, -4.76054573f, -2.10023689f, -4.11576605f, + 0.747231185f, 1.94890475f, -1.08402753f, -3.3453598f, -5.65433073f, -2.06671524f, -5.95984268f, + -0.0842600763f, -4.1682477f, -5.36685085f, -4.72642231f, -3.84537101f, -2.43174243f, -5.2219348f, + -5.15124512f, -2.12995124f, 5.66920471f, -3.54900289f, -4.9859972f, -3.7217958f, -5.79396343f, + -5.52887917f, 0.68271631f, -5.4900527f, -4.275033f, -2.05451155f, 4.14352512f, 2.48436022f, + -5.44278145f, 0.800169408f, -3.3678813f, 1.96613419f, -0.578519404f, -2.81112814f, 0.321295857f, + -4.93070984f, -0.542277455f, -4.5852952f, -4.79027128f, -1.88365829f, 3.52483416f, -2.59640551f, + -4.47171926f, 4.74157333f, 1.31877291f, -4.32741213f, -1.54485524f, 0.841110885f, -5.21666574f, + -3.71816063f, 0.408298612f, 0.300475359f, 0.887066782f, -2.87214017f, -3.44856119f, 1.62395012f, + -3.30371666f, -2.16198874f, -3.77239442f, 2.64231181f, -0.637339771f, -5.86262035f, -1.59509385f, + -2.91450477f, -2.78559065f, -2.83921146f, 3.0232079f, -5.60890484f, -3.65526104f, 2.70941496f, + 1.27847171f, 3.23181581f, 3.90872478f, -1.94123828f, -3.83183861f, -2.29222298f, -5.04636621f, + -1.43509889f, 2.03741193f, -3.02399945f, -3.1139009f, -5.48311663f, -5.77992296f, -4.81672335f, + -5.82831049f, -2.21025181f, -0.309278786f, -4.88169193f, 2.29107213f, -4.88095522f, -1.90110219f, + -2.64001942f, -4.29528093f, -4.026896f, -5.89308834f, 1.48895156f, 1.22243834f, -3.99888778f, + -0.359923244f, -2.65067458f, -4.40800142f, -5.15672159f, -3.43339825f, 2.0822401f, 1.32402527f, + 0.879095435f, -3.97485018f, -5.98793221f, -4.48295975f, -2.76034641f, -2.01376772f, -3.22300625f, + 2.113307f, -4.49965811f, -1.29900825f, 2.33631754f, -5.61371994f, 0.43211025f, 1.15594995f, + 3.38300323f, 2.47384453f, -1.96652639f, 3.14264607f, -3.4962194f, 2.39256167f, -4.26337957f, + -3.2865746f, 0.855261624f, -1.08402753f, 2.23182297f, -5.65433073f, -2.06671524f, -5.95984268f, + -0.749368966f, -0.150296226f, -4.53027105f, 2.25747037f, -3.33746195f, -2.43174243f, -5.2219348f, + -4.61339188f, -1.08149636f, 0.298938602f, -5.06834698f, -4.9859972f, 3.30293107f, -5.79396343f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_batch_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/config_data.h new file mode 100644 index 00000000..9b21921c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_CH_F16_BATCH_1 2 +#define MINIMUM_BROADCAST_CH_F16_HEIGHT_1 2 +#define MINIMUM_BROADCAST_CH_F16_WIDTH_1 4 +#define MINIMUM_BROADCAST_CH_F16_CHANNEL_1 1 +#define MINIMUM_BROADCAST_CH_F16_BATCH_2 2 +#define MINIMUM_BROADCAST_CH_F16_HEIGHT_2 2 +#define MINIMUM_BROADCAST_CH_F16_WIDTH_2 4 +#define MINIMUM_BROADCAST_CH_F16_CHANNEL_2 24 +#define MINIMUM_BROADCAST_CH_F16_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_CH_F16_OUTPUT_HEIGHT 2 +#define MINIMUM_BROADCAST_CH_F16_OUTPUT_WIDTH 4 +#define MINIMUM_BROADCAST_CH_F16_OUTPUT_CHANNEL 24 +#define MINIMUM_BROADCAST_CH_F16_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_1_data.h new file mode 100644 index 00000000..f2d1e5c2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_1_data.h @@ -0,0 +1,17 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_ch_f16_input_tensor_1[16] = {(float16_t)-4.328125f, + (float16_t)-2.0078125f, + (float16_t)1.23730469f, + (float16_t)-4.9375f, + (float16_t)-1.40917969f, + (float16_t)-4.20703125f, + (float16_t)1.43847656f, + (float16_t)-1.71972656f, + (float16_t)1.046875f, + (float16_t)3.40429688f, + (float16_t)-5.4609375f, + (float16_t)2.78320312f, + (float16_t)2.9609375f, + (float16_t)-3.88867188f, + (float16_t)-0.138793945f, + (float16_t)2.96875f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_2_data.h new file mode 100644 index 00000000..03eae22d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/input_tensor_2_data.h @@ -0,0 +1,98 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_ch_f16_input_tensor_2[384] = { + (float16_t)5.41796875f, (float16_t)0.685058594f, (float16_t)5.9609375f, (float16_t)-5.40625f, + (float16_t)4.22265625f, (float16_t)0.128662109f, (float16_t)4.6015625f, (float16_t)3.60546875f, + (float16_t)-0.0541687012f, (float16_t)5.04296875f, (float16_t)-5.76953125f, (float16_t)-1.69238281f, + (float16_t)-2.48242188f, (float16_t)3.94726562f, (float16_t)-0.28515625f, (float16_t)2.94140625f, + (float16_t)3.47070312f, (float16_t)-4.94140625f, (float16_t)3.328125f, (float16_t)5.0625f, + (float16_t)-0.974121094f, (float16_t)4.07421875f, (float16_t)1.72070312f, (float16_t)-4.125f, + (float16_t)-4.90625f, (float16_t)5.08984375f, (float16_t)-0.601074219f, (float16_t)-5.88671875f, + (float16_t)1.04003906f, (float16_t)-5.77734375f, (float16_t)3.71289062f, (float16_t)-5.76953125f, + (float16_t)3.94726562f, (float16_t)2.35351562f, (float16_t)-2.6484375f, (float16_t)5.87890625f, + (float16_t)-5.84765625f, (float16_t)5.33203125f, (float16_t)4.8125f, (float16_t)-4.66015625f, + (float16_t)-2.74414062f, (float16_t)1.984375f, (float16_t)0.471435547f, (float16_t)-5.98828125f, + (float16_t)1.11621094f, (float16_t)2.2734375f, (float16_t)4.3359375f, (float16_t)-5.47265625f, + (float16_t)2.17578125f, (float16_t)-5.84375f, (float16_t)-3.75195312f, (float16_t)-3.70703125f, + (float16_t)5.7109375f, (float16_t)-0.865234375f, (float16_t)-1.42285156f, (float16_t)-1.98535156f, + (float16_t)1.14453125f, (float16_t)-0.494873047f, (float16_t)1.00976562f, (float16_t)-1.07128906f, + (float16_t)-0.403320312f, (float16_t)0.644042969f, (float16_t)-5.73828125f, (float16_t)0.231933594f, + (float16_t)5.7578125f, (float16_t)4.22265625f, (float16_t)-1.69140625f, (float16_t)0.601074219f, + (float16_t)-2.94140625f, (float16_t)-5.1796875f, (float16_t)-1.83007812f, (float16_t)5.38671875f, + (float16_t)-1.35058594f, (float16_t)0.304443359f, (float16_t)-1.02539062f, (float16_t)0.770996094f, + (float16_t)-0.320556641f, (float16_t)2.796875f, (float16_t)-5.2890625f, (float16_t)-4.44921875f, + (float16_t)-3.5859375f, (float16_t)4.03515625f, (float16_t)-0.665039062f, (float16_t)5.62890625f, + (float16_t)-5.91015625f, (float16_t)-5.8984375f, (float16_t)-5.7109375f, (float16_t)1.30664062f, + (float16_t)0.872558594f, (float16_t)2.76953125f, (float16_t)1.95214844f, (float16_t)-0.392822266f, + (float16_t)-4.16015625f, (float16_t)4.6640625f, (float16_t)-4.69921875f, (float16_t)0.211425781f, + (float16_t)3.32226562f, (float16_t)-0.768554688f, (float16_t)-5.63671875f, (float16_t)-4.1171875f, + (float16_t)4.30859375f, (float16_t)4.41015625f, (float16_t)-5.84765625f, (float16_t)-5.49609375f, + (float16_t)-3.25585938f, (float16_t)-3.28320312f, (float16_t)-4.6875f, (float16_t)3.66601562f, + (float16_t)-3.97851562f, (float16_t)-4.06640625f, (float16_t)3.83398438f, (float16_t)-3.34179688f, + (float16_t)0.194702148f, (float16_t)5.4296875f, (float16_t)3.86328125f, (float16_t)2.62304688f, + (float16_t)1.16113281f, (float16_t)-4.76171875f, (float16_t)1.40722656f, (float16_t)1.75683594f, + (float16_t)0.884277344f, (float16_t)0.766601562f, (float16_t)4.3046875f, (float16_t)-3.48046875f, + (float16_t)-5.54296875f, (float16_t)1.66699219f, (float16_t)-0.546386719f, (float16_t)-4.7109375f, + (float16_t)-4.0703125f, (float16_t)3.4375f, (float16_t)-2.15429688f, (float16_t)-4.44140625f, + (float16_t)-2.58203125f, (float16_t)5.93359375f, (float16_t)-5.40625f, (float16_t)-1.97558594f, + (float16_t)-3.21289062f, (float16_t)4.73828125f, (float16_t)3.84960938f, (float16_t)-5.390625f, + (float16_t)2.47460938f, (float16_t)-1.97167969f, (float16_t)3.96679688f, (float16_t)-3.08984375f, + (float16_t)1.29882812f, (float16_t)-0.493164062f, (float16_t)-2.68359375f, (float16_t)1.24609375f, + (float16_t)3.29101562f, (float16_t)2.04101562f, (float16_t)-2.11914062f, (float16_t)-1.96777344f, + (float16_t)-0.577148438f, (float16_t)-4.26953125f, (float16_t)5.7265625f, (float16_t)-4.2890625f, + (float16_t)0.00396728516f, (float16_t)-4.8984375f, (float16_t)-5.8046875f, (float16_t)-5.265625f, + (float16_t)-5.2578125f, (float16_t)1.74902344f, (float16_t)-2.80078125f, (float16_t)-3.00976562f, + (float16_t)1.17578125f, (float16_t)1.125f, (float16_t)-0.779785156f, (float16_t)-2.54296875f, + (float16_t)5.75f, (float16_t)-1.51757812f, (float16_t)-3.62109375f, (float16_t)-0.694335938f, + (float16_t)0.206298828f, (float16_t)-1.9296875f, (float16_t)-4.0546875f, (float16_t)-4.19140625f, + (float16_t)-0.980957031f, (float16_t)-3.99804688f, (float16_t)-4.4765625f, (float16_t)2.64453125f, + (float16_t)1.27636719f, (float16_t)-0.794433594f, (float16_t)-4.60546875f, (float16_t)-4.7109375f, + (float16_t)-2.29882812f, (float16_t)-2.04492188f, (float16_t)-4.5078125f, (float16_t)3.15429688f, + (float16_t)-2.91015625f, (float16_t)0.0889282227f, (float16_t)-3.6875f, (float16_t)3.32617188f, + (float16_t)-4.4296875f, (float16_t)-0.15612793f, (float16_t)-1.99121094f, (float16_t)-0.592285156f, + (float16_t)-1.17480469f, (float16_t)0.810058594f, (float16_t)2.1171875f, (float16_t)1.12207031f, + (float16_t)-0.249755859f, (float16_t)-3.67578125f, (float16_t)-2.52148438f, (float16_t)-2.47265625f, + (float16_t)1.30859375f, (float16_t)-5.984375f, (float16_t)-3.3671875f, (float16_t)4.49609375f, + (float16_t)-0.242553711f, (float16_t)-2.90625f, (float16_t)-3.62890625f, (float16_t)4.3671875f, + (float16_t)5.67578125f, (float16_t)1.40625f, (float16_t)2.19921875f, (float16_t)0.180786133f, + (float16_t)2.52734375f, (float16_t)0.866210938f, (float16_t)-3.61914062f, (float16_t)0.814453125f, + (float16_t)-5.58203125f, (float16_t)4.4375f, (float16_t)1.25292969f, (float16_t)3.78515625f, + (float16_t)-3.38476562f, (float16_t)-2.84570312f, (float16_t)-0.0251617432f, (float16_t)3.82421875f, + (float16_t)-5.75f, (float16_t)-4.1015625f, (float16_t)-2.79101562f, (float16_t)3.75f, + (float16_t)5.21875f, (float16_t)-1.15820312f, (float16_t)3.43359375f, (float16_t)0.628417969f, + (float16_t)4.05859375f, (float16_t)-0.662109375f, (float16_t)4.26171875f, (float16_t)-3.07226562f, + (float16_t)3.40234375f, (float16_t)-3.9296875f, (float16_t)-0.553710938f, (float16_t)0.687988281f, + (float16_t)-0.0600891113f, (float16_t)0.669433594f, (float16_t)4.39453125f, (float16_t)-0.291259766f, + (float16_t)5.140625f, (float16_t)1.57910156f, (float16_t)1.6015625f, (float16_t)-1.49609375f, + (float16_t)-5.54296875f, (float16_t)-1.5703125f, (float16_t)5.01953125f, (float16_t)5.45703125f, + (float16_t)4.7421875f, (float16_t)-1.91699219f, (float16_t)-5.52734375f, (float16_t)0.431640625f, + (float16_t)-5.734375f, (float16_t)5.453125f, (float16_t)0.153320312f, (float16_t)-5.57421875f, + (float16_t)4.328125f, (float16_t)0.537109375f, (float16_t)4.453125f, (float16_t)-1.12011719f, + (float16_t)-2.90039062f, (float16_t)4.3828125f, (float16_t)1.45800781f, (float16_t)-2.80273438f, + (float16_t)4.65625f, (float16_t)1.33007812f, (float16_t)-2.56835938f, (float16_t)-0.208007812f, + (float16_t)-2.74609375f, (float16_t)-3.99414062f, (float16_t)4.453125f, (float16_t)-5.2890625f, + (float16_t)1.96679688f, (float16_t)-3.85351562f, (float16_t)-4.33203125f, (float16_t)2.88476562f, + (float16_t)-5.82421875f, (float16_t)-5.14453125f, (float16_t)2.24609375f, (float16_t)-4.1484375f, + (float16_t)3.48046875f, (float16_t)4.17578125f, (float16_t)-1.55078125f, (float16_t)2.97460938f, + (float16_t)3.12109375f, (float16_t)-0.227539062f, (float16_t)-3.8046875f, (float16_t)5.57421875f, + (float16_t)3.19335938f, (float16_t)-4.40625f, (float16_t)-2.22265625f, (float16_t)-3.5703125f, + (float16_t)1.35742188f, (float16_t)-4.86328125f, (float16_t)2.19335938f, (float16_t)3.12890625f, + (float16_t)-4.828125f, (float16_t)-0.214233398f, (float16_t)0.193237305f, (float16_t)-5.59375f, + (float16_t)-1.93945312f, (float16_t)1.54101562f, (float16_t)4.2265625f, (float16_t)5.9765625f, + (float16_t)4.8671875f, (float16_t)-3.0390625f, (float16_t)-3.31054688f, (float16_t)5.30859375f, + (float16_t)-2.98046875f, (float16_t)0.770996094f, (float16_t)-1.57714844f, (float16_t)-5.1328125f, + (float16_t)4.70703125f, (float16_t)-3.7890625f, (float16_t)-1.1015625f, (float16_t)-1.31152344f, + (float16_t)-5.09375f, (float16_t)-4.58203125f, (float16_t)1.578125f, (float16_t)5.1328125f, + (float16_t)0.634277344f, (float16_t)4.640625f, (float16_t)4.77734375f, (float16_t)4.3203125f, + (float16_t)5.15625f, (float16_t)-3.31640625f, (float16_t)-0.414794922f, (float16_t)-1.9375f, + (float16_t)1.57519531f, (float16_t)1.68457031f, (float16_t)-5.42578125f, (float16_t)4.84375f, + (float16_t)-0.383544922f, (float16_t)2.92578125f, (float16_t)-3.08007812f, (float16_t)-0.984375f, + (float16_t)-3.70507812f, (float16_t)1.35546875f, (float16_t)-2.75390625f, (float16_t)-3.81640625f, + (float16_t)4.640625f, (float16_t)2.5546875f, (float16_t)3.9296875f, (float16_t)4.60546875f, + (float16_t)0.490966797f, (float16_t)1.50585938f, (float16_t)-2.57421875f, (float16_t)0.608398438f, + (float16_t)-3.04492188f, (float16_t)-3.18359375f, (float16_t)-1.55175781f, (float16_t)-0.790039062f, + (float16_t)5.015625f, (float16_t)5.6015625f, (float16_t)-4.59375f, (float16_t)4.35546875f, + (float16_t)-0.541015625f, (float16_t)-1.39355469f, (float16_t)-0.415771484f, (float16_t)-3.87304688f, + (float16_t)4.74609375f, (float16_t)1.09765625f, (float16_t)-2.6640625f, (float16_t)-4.2734375f, + (float16_t)-1.37597656f, (float16_t)-1.4296875f, (float16_t)0.21472168f, (float16_t)-3.52929688f, + (float16_t)-5.640625f, (float16_t)-3.80664062f, (float16_t)2.67382812f, (float16_t)4.0703125f, + (float16_t)-2.875f, (float16_t)5.171875f, (float16_t)5.10546875f, (float16_t)-1.984375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/output_ref_data.h new file mode 100644 index 00000000..37676710 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/output_ref_data.h @@ -0,0 +1,98 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_ch_f16_output_ref[384] = { + (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-5.40625f, + (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, + (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-5.76953125f, (float16_t)-4.328125f, + (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, + (float16_t)-4.328125f, (float16_t)-4.94140625f, (float16_t)-4.328125f, (float16_t)-4.328125f, + (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, (float16_t)-4.328125f, + (float16_t)-4.90625f, (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-5.88671875f, + (float16_t)-2.0078125f, (float16_t)-5.77734375f, (float16_t)-2.0078125f, (float16_t)-5.76953125f, + (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-2.6484375f, (float16_t)-2.0078125f, + (float16_t)-5.84765625f, (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-4.66015625f, + (float16_t)-2.74414062f, (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-5.98828125f, + (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-2.0078125f, (float16_t)-5.47265625f, + (float16_t)1.23730469f, (float16_t)-5.84375f, (float16_t)-3.75195312f, (float16_t)-3.70703125f, + (float16_t)1.23730469f, (float16_t)-0.865234375f, (float16_t)-1.42285156f, (float16_t)-1.98535156f, + (float16_t)1.14453125f, (float16_t)-0.494873047f, (float16_t)1.00976562f, (float16_t)-1.07128906f, + (float16_t)-0.403320312f, (float16_t)0.644042969f, (float16_t)-5.73828125f, (float16_t)0.231933594f, + (float16_t)1.23730469f, (float16_t)1.23730469f, (float16_t)-1.69140625f, (float16_t)0.601074219f, + (float16_t)-2.94140625f, (float16_t)-5.1796875f, (float16_t)-1.83007812f, (float16_t)1.23730469f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-5.2890625f, (float16_t)-4.9375f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, + (float16_t)-5.91015625f, (float16_t)-5.8984375f, (float16_t)-5.7109375f, (float16_t)-4.9375f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, + (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, (float16_t)-4.9375f, + (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)-5.63671875f, (float16_t)-4.1171875f, + (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)-5.84765625f, (float16_t)-5.49609375f, + (float16_t)-3.25585938f, (float16_t)-3.28320312f, (float16_t)-4.6875f, (float16_t)-1.40917969f, + (float16_t)-3.97851562f, (float16_t)-4.06640625f, (float16_t)-1.40917969f, (float16_t)-3.34179688f, + (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, + (float16_t)-1.40917969f, (float16_t)-4.76171875f, (float16_t)-1.40917969f, (float16_t)-1.40917969f, + (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, + (float16_t)-5.54296875f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.7109375f, + (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.44140625f, + (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-5.40625f, (float16_t)-4.20703125f, + (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-5.390625f, + (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, (float16_t)-4.20703125f, + (float16_t)1.29882812f, (float16_t)-0.493164062f, (float16_t)-2.68359375f, (float16_t)1.24609375f, + (float16_t)1.43847656f, (float16_t)1.43847656f, (float16_t)-2.11914062f, (float16_t)-1.96777344f, + (float16_t)-0.577148438f, (float16_t)-4.26953125f, (float16_t)1.43847656f, (float16_t)-4.2890625f, + (float16_t)0.00396728516f, (float16_t)-4.8984375f, (float16_t)-5.8046875f, (float16_t)-5.265625f, + (float16_t)-5.2578125f, (float16_t)1.43847656f, (float16_t)-2.80078125f, (float16_t)-3.00976562f, + (float16_t)1.17578125f, (float16_t)1.125f, (float16_t)-0.779785156f, (float16_t)-2.54296875f, + (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)-3.62109375f, (float16_t)-1.71972656f, + (float16_t)-1.71972656f, (float16_t)-1.9296875f, (float16_t)-4.0546875f, (float16_t)-4.19140625f, + (float16_t)-1.71972656f, (float16_t)-3.99804688f, (float16_t)-4.4765625f, (float16_t)-1.71972656f, + (float16_t)-1.71972656f, (float16_t)-1.71972656f, (float16_t)-4.60546875f, (float16_t)-4.7109375f, + (float16_t)-2.29882812f, (float16_t)-2.04492188f, (float16_t)-4.5078125f, (float16_t)-1.71972656f, + (float16_t)-2.91015625f, (float16_t)-1.71972656f, (float16_t)-3.6875f, (float16_t)-1.71972656f, + (float16_t)-4.4296875f, (float16_t)-0.15612793f, (float16_t)-1.99121094f, (float16_t)-0.592285156f, + (float16_t)-1.17480469f, (float16_t)0.810058594f, (float16_t)1.046875f, (float16_t)1.046875f, + (float16_t)-0.249755859f, (float16_t)-3.67578125f, (float16_t)-2.52148438f, (float16_t)-2.47265625f, + (float16_t)1.046875f, (float16_t)-5.984375f, (float16_t)-3.3671875f, (float16_t)1.046875f, + (float16_t)-0.242553711f, (float16_t)-2.90625f, (float16_t)-3.62890625f, (float16_t)1.046875f, + (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)1.046875f, (float16_t)0.180786133f, + (float16_t)2.52734375f, (float16_t)0.866210938f, (float16_t)-3.61914062f, (float16_t)0.814453125f, + (float16_t)-5.58203125f, (float16_t)3.40429688f, (float16_t)1.25292969f, (float16_t)3.40429688f, + (float16_t)-3.38476562f, (float16_t)-2.84570312f, (float16_t)-0.0251617432f, (float16_t)3.40429688f, + (float16_t)-5.75f, (float16_t)-4.1015625f, (float16_t)-2.79101562f, (float16_t)3.40429688f, + (float16_t)3.40429688f, (float16_t)-1.15820312f, (float16_t)3.40429688f, (float16_t)0.628417969f, + (float16_t)3.40429688f, (float16_t)-0.662109375f, (float16_t)3.40429688f, (float16_t)-3.07226562f, + (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, + (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, + (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, + (float16_t)-5.54296875f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, + (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.52734375f, (float16_t)-5.4609375f, + (float16_t)-5.734375f, (float16_t)-5.4609375f, (float16_t)-5.4609375f, (float16_t)-5.57421875f, + (float16_t)2.78320312f, (float16_t)0.537109375f, (float16_t)2.78320312f, (float16_t)-1.12011719f, + (float16_t)-2.90039062f, (float16_t)2.78320312f, (float16_t)1.45800781f, (float16_t)-2.80273438f, + (float16_t)2.78320312f, (float16_t)1.33007812f, (float16_t)-2.56835938f, (float16_t)-0.208007812f, + (float16_t)-2.74609375f, (float16_t)-3.99414062f, (float16_t)2.78320312f, (float16_t)-5.2890625f, + (float16_t)1.96679688f, (float16_t)-3.85351562f, (float16_t)-4.33203125f, (float16_t)2.78320312f, + (float16_t)-5.82421875f, (float16_t)-5.14453125f, (float16_t)2.24609375f, (float16_t)-4.1484375f, + (float16_t)2.9609375f, (float16_t)2.9609375f, (float16_t)-1.55078125f, (float16_t)2.9609375f, + (float16_t)2.9609375f, (float16_t)-0.227539062f, (float16_t)-3.8046875f, (float16_t)2.9609375f, + (float16_t)2.9609375f, (float16_t)-4.40625f, (float16_t)-2.22265625f, (float16_t)-3.5703125f, + (float16_t)1.35742188f, (float16_t)-4.86328125f, (float16_t)2.19335938f, (float16_t)2.9609375f, + (float16_t)-4.828125f, (float16_t)-0.214233398f, (float16_t)0.193237305f, (float16_t)-5.59375f, + (float16_t)-1.93945312f, (float16_t)1.54101562f, (float16_t)2.9609375f, (float16_t)2.9609375f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-5.1328125f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, + (float16_t)-5.09375f, (float16_t)-4.58203125f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, + (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, (float16_t)-3.88867188f, + (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-5.42578125f, (float16_t)-0.138793945f, + (float16_t)-0.383544922f, (float16_t)-0.138793945f, (float16_t)-3.08007812f, (float16_t)-0.984375f, + (float16_t)-3.70507812f, (float16_t)-0.138793945f, (float16_t)-2.75390625f, (float16_t)-3.81640625f, + (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-0.138793945f, + (float16_t)-0.138793945f, (float16_t)-0.138793945f, (float16_t)-2.57421875f, (float16_t)-0.138793945f, + (float16_t)-3.04492188f, (float16_t)-3.18359375f, (float16_t)-1.55175781f, (float16_t)-0.790039062f, + (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)-4.59375f, (float16_t)2.96875f, + (float16_t)-0.541015625f, (float16_t)-1.39355469f, (float16_t)-0.415771484f, (float16_t)-3.87304688f, + (float16_t)2.96875f, (float16_t)1.09765625f, (float16_t)-2.6640625f, (float16_t)-4.2734375f, + (float16_t)-1.37597656f, (float16_t)-1.4296875f, (float16_t)0.21472168f, (float16_t)-3.52929688f, + (float16_t)-5.640625f, (float16_t)-3.80664062f, (float16_t)2.67382812f, (float16_t)2.96875f, + (float16_t)-2.875f, (float16_t)2.96875f, (float16_t)2.96875f, (float16_t)-1.984375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/config_data.h new file mode 100644 index 00000000..76ad6c87 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_CH_F32_BATCH_1 2 +#define MINIMUM_BROADCAST_CH_F32_HEIGHT_1 2 +#define MINIMUM_BROADCAST_CH_F32_WIDTH_1 4 +#define MINIMUM_BROADCAST_CH_F32_CHANNEL_1 1 +#define MINIMUM_BROADCAST_CH_F32_BATCH_2 2 +#define MINIMUM_BROADCAST_CH_F32_HEIGHT_2 2 +#define MINIMUM_BROADCAST_CH_F32_WIDTH_2 4 +#define MINIMUM_BROADCAST_CH_F32_CHANNEL_2 24 +#define MINIMUM_BROADCAST_CH_F32_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_CH_F32_OUTPUT_HEIGHT 2 +#define MINIMUM_BROADCAST_CH_F32_OUTPUT_WIDTH 4 +#define MINIMUM_BROADCAST_CH_F32_OUTPUT_CHANNEL 24 +#define MINIMUM_BROADCAST_CH_F32_DST_SIZE 384 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_1_data.h new file mode 100644 index 00000000..19f16b19 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_1_data.h @@ -0,0 +1,17 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_ch_f32_input_tensor_1[16] = {-2.79946804f, + 3.99777508f, + 4.7493329f, + -5.07016373f, + 0.0300274566f, + 2.40418911f, + 3.73551178f, + -5.0873518f, + 2.60367012f, + 0.385420024f, + 0.468389601f, + 5.47010231f, + 5.08810282f, + 4.48744392f, + -5.41141224f, + 0.519257128f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_2_data.h new file mode 100644 index 00000000..ca5af508 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/input_tensor_2_data.h @@ -0,0 +1,57 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_ch_f32_input_tensor_2[384] = { + -5.81857395f, 3.53431511f, -3.43794775f, -5.9171896f, -4.02916431f, 3.92857432f, 3.08190894f, + -5.28822565f, 5.22775507f, -3.0644803f, 0.530017316f, -4.46785212f, -1.01627815f, 5.09683037f, + 5.95009279f, -1.06094968f, 4.61344671f, 5.40496969f, -5.03422785f, 1.639323f, -2.96253419f, + -1.90082014f, 1.01374424f, -2.55639839f, 1.58507955f, 2.49705005f, 0.249409929f, -0.0348887406f, + -2.89857435f, -0.0334655084f, 1.3208797f, -5.96453905f, -4.07583237f, -4.73021078f, -4.28760862f, + -3.04700613f, -1.76338267f, 1.72121751f, -2.82083559f, -0.0825804323f, 1.52107918f, 0.313556969f, + 0.204173967f, 5.04898643f, 4.94273233f, -4.03286743f, 2.67828941f, -2.33016348f, 5.42924309f, + 0.428456903f, 5.1180191f, 1.75475061f, -2.00222397f, 1.98119354f, 4.511343f, 5.1502614f, + -1.71982896f, -1.97188973f, 4.44592905f, 0.13798216f, 4.70152807f, -0.993848681f, -5.47357607f, + 3.4820888f, 5.44882917f, -4.24970531f, -2.15164828f, -1.70661306f, -5.46865845f, -1.82973289f, + -1.85645282f, -4.57259369f, 3.57256079f, 4.78576183f, 5.99715662f, 0.904260993f, -5.46233654f, + 0.706884801f, 2.22314954f, -3.85930896f, -5.47886658f, 4.83492184f, 2.8507092f, 2.26734591f, + 1.48972201f, -4.01050806f, 1.03246188f, -2.83248115f, 0.504711092f, 4.53716326f, 5.901752f, + 1.29180527f, -4.08421421f, -4.28865576f, 3.54089308f, 5.02479076f, -3.86136222f, -2.2738049f, + -3.1324358f, 4.04458475f, 4.14922571f, 4.94081879f, 2.29100823f, 4.80190563f, -4.19971228f, + -4.42897606f, 0.842420995f, -2.82134199f, 3.61959147f, -3.58570981f, 4.07256699f, -2.56117225f, + 5.9185853f, 4.94481802f, 0.788578749f, -2.02079344f, 0.798386693f, -4.98145437f, 0.718806624f, + -3.1369555f, -1.33506536f, 0.86048466f, 5.46794987f, 1.7890321f, 5.5152216f, 0.739144087f, + -0.518388808f, 5.35301304f, -2.88631177f, -2.90389919f, -0.892657638f, -2.42383909f, -1.87559426f, + 5.49149323f, 0.516366422f, 5.91928959f, -4.62314701f, -4.76797247f, -4.60599518f, -1.95736444f, + -0.832552612f, 2.28296232f, -2.90118241f, -4.90500116f, 0.862293124f, -4.51998568f, 4.86690187f, + 4.29681969f, 2.49267292f, -3.96154785f, -2.29110456f, -5.60855103f, -1.13048565f, -0.010748635f, + 1.21851254f, -3.0851903f, 5.04861927f, -3.34025097f, 2.01621222f, -5.31638145f, -2.90726972f, + -5.82931423f, 2.83189225f, -1.99061084f, 4.46657372f, 1.88980019f, 5.52644444f, -2.03232408f, + 2.26003933f, 3.57091999f, -5.84511423f, 5.37788057f, 2.7429285f, 5.44246864f, -3.48870587f, + -3.5988121f, -0.0197519846f, 5.20903635f, 5.51451159f, -3.85487938f, 5.84751558f, 5.60741901f, + 1.10836673f, -3.78942752f, -0.333450317f, -3.03335524f, -3.94971299f, 0.520921171f, 3.1513195f, + 1.45204759f, 1.53168178f, 2.47778535f, -2.13027263f, 3.16780257f, -1.18628061f, 0.0473567806f, + -5.67637491f, 2.78433776f, -2.04120827f, -3.09532642f, 1.19804502f, 0.281121045f, 2.65411663f, + 1.70114625f, 0.488126844f, -2.23086333f, 2.00305915f, -0.159159377f, -4.32881355f, 2.92691135f, + -5.48137999f, 2.30685282f, 3.91632938f, -2.46610713f, 0.617998421f, -5.16215611f, 2.99449587f, + -3.0749104f, 3.37036562f, 1.17670083f, -3.78520393f, 0.547186732f, 1.02675366f, -3.06451941f, + 3.12815475f, 3.77494121f, 5.92309141f, -1.46927381f, 0.194556773f, -4.34782696f, -0.362680972f, + 0.625895202f, -1.36787188f, -3.45114899f, 0.534722567f, 0.544402659f, -1.43822634f, -5.75769281f, + -3.98651624f, -2.41333675f, 3.44800067f, -1.30595577f, 3.86567545f, -0.164896145f, 4.82536936f, + 0.808961511f, -1.3541007f, 4.03132868f, -1.16697598f, -1.33790064f, 4.99936295f, 2.08136392f, + 0.469598144f, -4.95266676f, 4.30390692f, 1.07291138f, -3.43018198f, 0.554416895f, -5.04606915f, + 3.81235099f, 0.426200092f, 1.12604046f, -2.23211622f, 3.41484809f, 4.46840286f, 2.4824574f, + -3.6813519f, -0.8883003f, 0.574476182f, 5.36865616f, -2.53047442f, -2.13165665f, 5.5733614f, + -0.576437354f, 3.77873683f, 0.765248656f, -4.6694231f, 5.93600082f, 3.48022342f, 4.88055944f, + -2.81772208f, -0.666741133f, 5.23013449f, 4.88224077f, -1.8256669f, 4.22875547f, 5.51502514f, + 2.66109276f, -4.75895882f, -3.533108f, 2.93441272f, 5.50977659f, -0.343114585f, 3.00083137f, + -5.61721611f, 0.488634169f, -3.40017986f, 3.70441246f, 5.17440939f, -1.61307359f, 1.49967337f, + -3.11282516f, 4.33862638f, -3.11676121f, 0.918532968f, 1.55540657f, -0.487199336f, 2.42873263f, + 3.23270988f, 2.01436639f, 0.143748552f, -5.83948708f, 0.548818171f, 3.85125375f, 4.84875393f, + -5.1284771f, -5.99992418f, 3.84876227f, 3.69624925f, 5.1339426f, -1.04546225f, 1.94597697f, + 1.07050776f, -1.86275923f, 1.96258783f, 3.57243371f, -4.99783897f, 0.111679569f, -5.0894227f, + 3.11141491f, 4.1813035f, 1.0789907f, -3.65423322f, 4.68895626f, 1.60659707f, -3.76159954f, + -1.35881317f, 3.06219959f, 1.66579783f, 5.5488224f, 5.08280373f, -1.5901885f, -5.05826521f, + 1.31841838f, 4.58983517f, 2.70450354f, 1.44673026f, 3.86599016f, 3.99965858f, -1.10586941f, + 0.511591136f, 0.359958321f, -5.49746513f, 1.49873412f, 5.32756376f, 1.75317168f, 4.4250946f, + -1.33294952f, -2.27021098f, -5.44089699f, -0.506264865f, 2.85474324f, 3.92870903f, 4.35894823f, + 3.83739567f, 1.90712202f, 2.56696916f, 5.16120529f, -5.1342721f, 4.22690821f, -1.71033311f, + 4.97142172f, 3.69499302f, -2.18889523f, -0.172188058f, -2.74337077f, -3.99706244f, -1.58633614f, + -4.98113585f, 5.40567493f, 3.65765643f, -5.29397917f, 4.55982637f, 4.98912573f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/output_ref_data.h new file mode 100644 index 00000000..6f9e9cac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/output_ref_data.h @@ -0,0 +1,57 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_ch_f32_output_ref[384] = { + -5.81857395f, -2.79946804f, -3.43794775f, -5.9171896f, -4.02916431f, -2.79946804f, -2.79946804f, + -5.28822565f, -2.79946804f, -3.0644803f, -2.79946804f, -4.46785212f, -2.79946804f, -2.79946804f, + -2.79946804f, -2.79946804f, -2.79946804f, -2.79946804f, -5.03422785f, -2.79946804f, -2.96253419f, + -2.79946804f, -2.79946804f, -2.79946804f, 1.58507955f, 2.49705005f, 0.249409929f, -0.0348887406f, + -2.89857435f, -0.0334655084f, 1.3208797f, -5.96453905f, -4.07583237f, -4.73021078f, -4.28760862f, + -3.04700613f, -1.76338267f, 1.72121751f, -2.82083559f, -0.0825804323f, 1.52107918f, 0.313556969f, + 0.204173967f, 3.99777508f, 3.99777508f, -4.03286743f, 2.67828941f, -2.33016348f, 4.7493329f, + 0.428456903f, 4.7493329f, 1.75475061f, -2.00222397f, 1.98119354f, 4.511343f, 4.7493329f, + -1.71982896f, -1.97188973f, 4.44592905f, 0.13798216f, 4.70152807f, -0.993848681f, -5.47357607f, + 3.4820888f, 4.7493329f, -4.24970531f, -2.15164828f, -1.70661306f, -5.46865845f, -1.82973289f, + -1.85645282f, -4.57259369f, -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -5.46233654f, + -5.07016373f, -5.07016373f, -5.07016373f, -5.47886658f, -5.07016373f, -5.07016373f, -5.07016373f, + -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, + -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -5.07016373f, -3.86136222f, -2.2738049f, + -3.1324358f, 0.0300274566f, 0.0300274566f, 0.0300274566f, 0.0300274566f, 0.0300274566f, -4.19971228f, + -4.42897606f, 0.0300274566f, -2.82134199f, 0.0300274566f, -3.58570981f, 0.0300274566f, -2.56117225f, + 0.0300274566f, 0.0300274566f, 0.0300274566f, -2.02079344f, 0.0300274566f, -4.98145437f, 0.0300274566f, + -3.1369555f, -1.33506536f, 0.86048466f, 2.40418911f, 1.7890321f, 2.40418911f, 0.739144087f, + -0.518388808f, 2.40418911f, -2.88631177f, -2.90389919f, -0.892657638f, -2.42383909f, -1.87559426f, + 2.40418911f, 0.516366422f, 2.40418911f, -4.62314701f, -4.76797247f, -4.60599518f, -1.95736444f, + -0.832552612f, 2.28296232f, -2.90118241f, -4.90500116f, 0.862293124f, -4.51998568f, 3.73551178f, + 3.73551178f, 2.49267292f, -3.96154785f, -2.29110456f, -5.60855103f, -1.13048565f, -0.010748635f, + 1.21851254f, -3.0851903f, 3.73551178f, -3.34025097f, 2.01621222f, -5.31638145f, -2.90726972f, + -5.82931423f, 2.83189225f, -1.99061084f, 3.73551178f, 1.88980019f, 3.73551178f, -2.03232408f, + -5.0873518f, -5.0873518f, -5.84511423f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, + -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, + -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, -5.0873518f, + -5.0873518f, -5.0873518f, -5.0873518f, -2.13027263f, 2.60367012f, -1.18628061f, 0.0473567806f, + -5.67637491f, 2.60367012f, -2.04120827f, -3.09532642f, 1.19804502f, 0.281121045f, 2.60367012f, + 1.70114625f, 0.488126844f, -2.23086333f, 2.00305915f, -0.159159377f, -4.32881355f, 2.60367012f, + -5.48137999f, 2.30685282f, 2.60367012f, -2.46610713f, 0.617998421f, -5.16215611f, 0.385420024f, + -3.0749104f, 0.385420024f, 0.385420024f, -3.78520393f, 0.385420024f, 0.385420024f, -3.06451941f, + 0.385420024f, 0.385420024f, 0.385420024f, -1.46927381f, 0.194556773f, -4.34782696f, -0.362680972f, + 0.385420024f, -1.36787188f, -3.45114899f, 0.385420024f, 0.385420024f, -1.43822634f, -5.75769281f, + -3.98651624f, -2.41333675f, 0.468389601f, -1.30595577f, 0.468389601f, -0.164896145f, 0.468389601f, + 0.468389601f, -1.3541007f, 0.468389601f, -1.16697598f, -1.33790064f, 0.468389601f, 0.468389601f, + 0.468389601f, -4.95266676f, 0.468389601f, 0.468389601f, -3.43018198f, 0.468389601f, -5.04606915f, + 0.468389601f, 0.426200092f, 0.468389601f, -2.23211622f, 0.468389601f, 4.46840286f, 2.4824574f, + -3.6813519f, -0.8883003f, 0.574476182f, 5.36865616f, -2.53047442f, -2.13165665f, 5.47010231f, + -0.576437354f, 3.77873683f, 0.765248656f, -4.6694231f, 5.47010231f, 3.48022342f, 4.88055944f, + -2.81772208f, -0.666741133f, 5.23013449f, 4.88224077f, -1.8256669f, 4.22875547f, 5.47010231f, + 2.66109276f, -4.75895882f, -3.533108f, 2.93441272f, 5.08810282f, -0.343114585f, 3.00083137f, + -5.61721611f, 0.488634169f, -3.40017986f, 3.70441246f, 5.08810282f, -1.61307359f, 1.49967337f, + -3.11282516f, 4.33862638f, -3.11676121f, 0.918532968f, 1.55540657f, -0.487199336f, 2.42873263f, + 3.23270988f, 2.01436639f, 0.143748552f, -5.83948708f, 0.548818171f, 3.85125375f, 4.48744392f, + -5.1284771f, -5.99992418f, 3.84876227f, 3.69624925f, 4.48744392f, -1.04546225f, 1.94597697f, + 1.07050776f, -1.86275923f, 1.96258783f, 3.57243371f, -4.99783897f, 0.111679569f, -5.0894227f, + 3.11141491f, 4.1813035f, 1.0789907f, -3.65423322f, 4.48744392f, 1.60659707f, -3.76159954f, + -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, + -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, + -5.41141224f, -5.41141224f, -5.49746513f, -5.41141224f, -5.41141224f, -5.41141224f, -5.41141224f, + -5.41141224f, -5.41141224f, -5.44089699f, -0.506264865f, 0.519257128f, 0.519257128f, 0.519257128f, + 0.519257128f, 0.519257128f, 0.519257128f, 0.519257128f, -5.1342721f, 0.519257128f, -1.71033311f, + 0.519257128f, 0.519257128f, -2.18889523f, -0.172188058f, -2.74337077f, -3.99706244f, -1.58633614f, + -4.98113585f, 0.519257128f, 0.519257128f, -5.29397917f, 0.519257128f, 0.519257128f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_ch_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/config_data.h new file mode 100644 index 00000000..d4fb324a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_HEIGHT_F16_BATCH_1 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_HEIGHT_1 1 +#define MINIMUM_BROADCAST_HEIGHT_F16_WIDTH_1 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_CHANNEL_1 17 +#define MINIMUM_BROADCAST_HEIGHT_F16_BATCH_2 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_HEIGHT_2 4 +#define MINIMUM_BROADCAST_HEIGHT_F16_WIDTH_2 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_CHANNEL_2 17 +#define MINIMUM_BROADCAST_HEIGHT_F16_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_OUTPUT_HEIGHT 4 +#define MINIMUM_BROADCAST_HEIGHT_F16_OUTPUT_WIDTH 2 +#define MINIMUM_BROADCAST_HEIGHT_F16_OUTPUT_CHANNEL 17 +#define MINIMUM_BROADCAST_HEIGHT_F16_DST_SIZE 272 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_1_data.h new file mode 100644 index 00000000..1848d4ba --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_1_data.h @@ -0,0 +1,19 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_height_f16_input_tensor_1[68] = { + (float16_t)1.87597656f, (float16_t)0.682617188f, (float16_t)5.52734375f, (float16_t)-4.2734375f, + (float16_t)-2.0546875f, (float16_t)5.37109375f, (float16_t)4.28125f, (float16_t)4.48828125f, + (float16_t)0.862304688f, (float16_t)4.51171875f, (float16_t)1.96582031f, (float16_t)2.34765625f, + (float16_t)5.0703125f, (float16_t)3.52148438f, (float16_t)-4.9296875f, (float16_t)2.5859375f, + (float16_t)-3.9609375f, (float16_t)-4.7890625f, (float16_t)-1.88378906f, (float16_t)3.52539062f, + (float16_t)5.9453125f, (float16_t)-4.47265625f, (float16_t)4.7421875f, (float16_t)1.31835938f, + (float16_t)-2.72265625f, (float16_t)2.41015625f, (float16_t)4.16796875f, (float16_t)-1.4375f, + (float16_t)-3.71875f, (float16_t)5.64453125f, (float16_t)5.03515625f, (float16_t)3.94921875f, + (float16_t)-2.87304688f, (float16_t)1.06933594f, (float16_t)1.65820312f, (float16_t)-3.3046875f, + (float16_t)1.07324219f, (float16_t)-2.61523438f, (float16_t)2.64257812f, (float16_t)2.02929688f, + (float16_t)-5.86328125f, (float16_t)4.07421875f, (float16_t)-0.516113281f, (float16_t)0.744140625f, + (float16_t)1.70410156f, (float16_t)5.04296875f, (float16_t)-5.609375f, (float16_t)-3.65429688f, + (float16_t)2.70898438f, (float16_t)1.27832031f, (float16_t)4.54296875f, (float16_t)4.91015625f, + (float16_t)-1.94140625f, (float16_t)0.116882324f, (float16_t)3.26367188f, (float16_t)-5.046875f, + (float16_t)1.35546875f, (float16_t)2.03710938f, (float16_t)1.75f, (float16_t)-3.11328125f, + (float16_t)-3.30859375f, (float16_t)4.75390625f, (float16_t)-4.81640625f, (float16_t)-5.828125f, + (float16_t)-2.2109375f, (float16_t)1.97558594f, (float16_t)-3.82421875f, (float16_t)2.29101562f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_2_data.h new file mode 100644 index 00000000..1d2c9d22 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/input_tensor_2_data.h @@ -0,0 +1,70 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_height_f16_input_tensor_2[272] = { + (float16_t)-4.33203125f, (float16_t)-1.83984375f, (float16_t)-2.89257812f, (float16_t)-4.6328125f, + (float16_t)-1.78613281f, (float16_t)3.84570312f, (float16_t)-5.52734375f, (float16_t)-3.4296875f, + (float16_t)4.38671875f, (float16_t)-3.57226562f, (float16_t)-0.512695312f, (float16_t)-0.875f, + (float16_t)-0.341064453f, (float16_t)3.37695312f, (float16_t)-1.16113281f, (float16_t)2.25585938f, + (float16_t)-2.3515625f, (float16_t)4.6640625f, (float16_t)2.67773438f, (float16_t)3.78710938f, + (float16_t)5.54296875f, (float16_t)-2.20898438f, (float16_t)0.431396484f, (float16_t)4.484375f, + (float16_t)-3.76367188f, (float16_t)-3.28320312f, (float16_t)-1.41796875f, (float16_t)1.66894531f, + (float16_t)-3.9609375f, (float16_t)-2.28320312f, (float16_t)4.67578125f, (float16_t)5.9921875f, + (float16_t)-1.95703125f, (float16_t)-2.39257812f, (float16_t)-4.375f, (float16_t)-5.5234375f, + (float16_t)-4.97265625f, (float16_t)-0.891601562f, (float16_t)-1.078125f, (float16_t)3.890625f, + (float16_t)-4.8984375f, (float16_t)-0.882324219f, (float16_t)0.413574219f, (float16_t)-0.607910156f, + (float16_t)-5.41796875f, (float16_t)-2.23242188f, (float16_t)-2.12109375f, (float16_t)0.956542969f, + (float16_t)-5.30078125f, (float16_t)0.806152344f, (float16_t)1.85351562f, (float16_t)1.58691406f, + (float16_t)4.07421875f, (float16_t)-2.86914062f, (float16_t)-1.59863281f, (float16_t)2.84179688f, + (float16_t)-1.71972656f, (float16_t)5.4375f, (float16_t)0.353515625f, (float16_t)-1.29003906f, + (float16_t)-5.30859375f, (float16_t)0.0581054688f, (float16_t)2.6328125f, (float16_t)0.427734375f, + (float16_t)0.166015625f, (float16_t)2.6875f, (float16_t)5.484375f, (float16_t)-1.53808594f, + (float16_t)-2.21289062f, (float16_t)-5.92578125f, (float16_t)3.85351562f, (float16_t)5.6875f, + (float16_t)5.984375f, (float16_t)-1.43847656f, (float16_t)2.4453125f, (float16_t)-0.134277344f, + (float16_t)3.796875f, (float16_t)-1.37109375f, (float16_t)0.709472656f, (float16_t)-0.339355469f, + (float16_t)-0.94140625f, (float16_t)-3.796875f, (float16_t)-2.20898438f, (float16_t)-2.34375f, + (float16_t)1.3203125f, (float16_t)-5.359375f, (float16_t)-3.18554688f, (float16_t)-2.95507812f, + (float16_t)2.10546875f, (float16_t)2.85546875f, (float16_t)-2.58203125f, (float16_t)0.882324219f, + (float16_t)-0.126953125f, (float16_t)2.76953125f, (float16_t)2.49609375f, (float16_t)-3.24414062f, + (float16_t)0.154785156f, (float16_t)3.17578125f, (float16_t)0.874023438f, (float16_t)-1.63085938f, + (float16_t)3.36523438f, (float16_t)-0.468261719f, (float16_t)-2.63671875f, (float16_t)1.0625f, + (float16_t)-2.41796875f, (float16_t)0.334716797f, (float16_t)-4.37109375f, (float16_t)2.61328125f, + (float16_t)1.22851562f, (float16_t)0.90625f, (float16_t)3.87304688f, (float16_t)5.87109375f, + (float16_t)-2.47265625f, (float16_t)-5.78515625f, (float16_t)-5.390625f, (float16_t)-3.59375f, + (float16_t)3.67382812f, (float16_t)-1.84375f, (float16_t)3.5546875f, (float16_t)-3.72851562f, + (float16_t)3.4453125f, (float16_t)1.08203125f, (float16_t)-0.387939453f, (float16_t)4.9140625f, + (float16_t)-5.92578125f, (float16_t)2.99804688f, (float16_t)-2.56445312f, (float16_t)5.12109375f, + (float16_t)2.09375f, (float16_t)0.447021484f, (float16_t)-1.35644531f, (float16_t)0.155273438f, + (float16_t)-1.87695312f, (float16_t)-1.83886719f, (float16_t)-0.466064453f, (float16_t)-0.555664062f, + (float16_t)-5.125f, (float16_t)-2.046875f, (float16_t)-4.703125f, (float16_t)-5.28515625f, + (float16_t)-0.368652344f, (float16_t)-2.953125f, (float16_t)3.91210938f, (float16_t)5.80078125f, + (float16_t)2.72851562f, (float16_t)-5.93359375f, (float16_t)-4.37890625f, (float16_t)4.6875f, + (float16_t)-0.555175781f, (float16_t)-1.6953125f, (float16_t)-1.84179688f, (float16_t)5.2109375f, + (float16_t)-2.57421875f, (float16_t)4.53125f, (float16_t)-2.36914062f, (float16_t)4.453125f, + (float16_t)5.25390625f, (float16_t)0.694824219f, (float16_t)-5.875f, (float16_t)-5.9453125f, + (float16_t)4.65234375f, (float16_t)0.567382812f, (float16_t)0.508300781f, (float16_t)-4.94140625f, + (float16_t)-1.23925781f, (float16_t)4.70703125f, (float16_t)2.56835938f, (float16_t)2.86328125f, + (float16_t)-0.384521484f, (float16_t)1.48144531f, (float16_t)0.891113281f, (float16_t)-1.95117188f, + (float16_t)1.98046875f, (float16_t)2.61328125f, (float16_t)-4.28515625f, (float16_t)4.10546875f, + (float16_t)0.333496094f, (float16_t)-3.8984375f, (float16_t)-1.42675781f, (float16_t)-5.23046875f, + (float16_t)5.53515625f, (float16_t)4.58203125f, (float16_t)2.3671875f, (float16_t)-3.61523438f, + (float16_t)-4.234375f, (float16_t)-1.63964844f, (float16_t)-0.430419922f, (float16_t)4.42578125f, + (float16_t)5.8203125f, (float16_t)-3.04882812f, (float16_t)-4.26953125f, (float16_t)-1.62011719f, + (float16_t)-4.8984375f, (float16_t)-2.77734375f, (float16_t)-3.69335938f, (float16_t)3.47070312f, + (float16_t)5.734375f, (float16_t)-1.66894531f, (float16_t)4.4375f, (float16_t)4.609375f, + (float16_t)-3.3359375f, (float16_t)2.9296875f, (float16_t)5.69921875f, (float16_t)-4.546875f, + (float16_t)0.8515625f, (float16_t)2.07226562f, (float16_t)-0.936523438f, (float16_t)0.34375f, + (float16_t)4.03515625f, (float16_t)4.97265625f, (float16_t)-3.22460938f, (float16_t)5.67578125f, + (float16_t)3.15039062f, (float16_t)4.2734375f, (float16_t)5.61328125f, (float16_t)-0.101135254f, + (float16_t)4.453125f, (float16_t)-0.356933594f, (float16_t)1.35058594f, (float16_t)1.29882812f, + (float16_t)2.88867188f, (float16_t)-0.223022461f, (float16_t)4.65234375f, (float16_t)-3.78125f, + (float16_t)3.58789062f, (float16_t)-1.98925781f, (float16_t)-5.76171875f, (float16_t)-3.671875f, + (float16_t)4.26953125f, (float16_t)-4.0234375f, (float16_t)3.1171875f, (float16_t)5.02734375f, + (float16_t)0.682128906f, (float16_t)1.51757812f, (float16_t)0.703613281f, (float16_t)2.6796875f, + (float16_t)-5.796875f, (float16_t)-3.36328125f, (float16_t)-4.578125f, (float16_t)3.61523438f, + (float16_t)3.30664062f, (float16_t)2.18359375f, (float16_t)-3.90039062f, (float16_t)4.02734375f, + (float16_t)4.796875f, (float16_t)4.69921875f, (float16_t)-5.546875f, (float16_t)5.8515625f, + (float16_t)0.545898438f, (float16_t)2.44140625f, (float16_t)1.46289062f, (float16_t)0.8359375f, + (float16_t)-0.307373047f, (float16_t)2.29296875f, (float16_t)3.390625f, (float16_t)0.451416016f, + (float16_t)-5.7265625f, (float16_t)2.61523438f, (float16_t)-2.7734375f, (float16_t)-2.921875f, + (float16_t)-1.88378906f, (float16_t)-5.33203125f, (float16_t)-3.1875f, (float16_t)-0.233886719f, + (float16_t)0.457275391f, (float16_t)-4.90625f, (float16_t)-3.71484375f, (float16_t)4.25f, + (float16_t)-5.16796875f, (float16_t)-4.70703125f, (float16_t)-3.98632812f, (float16_t)4.1015625f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/output_ref_data.h new file mode 100644 index 00000000..571650e9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/output_ref_data.h @@ -0,0 +1,70 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_height_f16_output_ref[272] = { + (float16_t)-4.33203125f, (float16_t)-1.83984375f, (float16_t)-2.89257812f, (float16_t)-4.6328125f, + (float16_t)-2.0546875f, (float16_t)3.84570312f, (float16_t)-5.52734375f, (float16_t)-3.4296875f, + (float16_t)0.862304688f, (float16_t)-3.57226562f, (float16_t)-0.512695312f, (float16_t)-0.875f, + (float16_t)-0.341064453f, (float16_t)3.37695312f, (float16_t)-4.9296875f, (float16_t)2.25585938f, + (float16_t)-3.9609375f, (float16_t)-4.7890625f, (float16_t)-1.88378906f, (float16_t)3.52539062f, + (float16_t)5.54296875f, (float16_t)-4.47265625f, (float16_t)0.431396484f, (float16_t)1.31835938f, + (float16_t)-3.76367188f, (float16_t)-3.28320312f, (float16_t)-1.41796875f, (float16_t)-1.4375f, + (float16_t)-3.9609375f, (float16_t)-2.28320312f, (float16_t)4.67578125f, (float16_t)3.94921875f, + (float16_t)-2.87304688f, (float16_t)-2.39257812f, (float16_t)-4.375f, (float16_t)-5.5234375f, + (float16_t)-4.97265625f, (float16_t)-4.2734375f, (float16_t)-2.0546875f, (float16_t)3.890625f, + (float16_t)-4.8984375f, (float16_t)-0.882324219f, (float16_t)0.413574219f, (float16_t)-0.607910156f, + (float16_t)-5.41796875f, (float16_t)-2.23242188f, (float16_t)-2.12109375f, (float16_t)0.956542969f, + (float16_t)-5.30078125f, (float16_t)0.806152344f, (float16_t)-3.9609375f, (float16_t)-4.7890625f, + (float16_t)-1.88378906f, (float16_t)-2.86914062f, (float16_t)-1.59863281f, (float16_t)-4.47265625f, + (float16_t)-1.71972656f, (float16_t)1.31835938f, (float16_t)-2.72265625f, (float16_t)-1.29003906f, + (float16_t)-5.30859375f, (float16_t)-1.4375f, (float16_t)-3.71875f, (float16_t)0.427734375f, + (float16_t)0.166015625f, (float16_t)2.6875f, (float16_t)-2.87304688f, (float16_t)-1.53808594f, + (float16_t)-2.21289062f, (float16_t)-5.92578125f, (float16_t)3.85351562f, (float16_t)-4.2734375f, + (float16_t)-2.0546875f, (float16_t)-1.43847656f, (float16_t)2.4453125f, (float16_t)-0.134277344f, + (float16_t)0.862304688f, (float16_t)-1.37109375f, (float16_t)0.709472656f, (float16_t)-0.339355469f, + (float16_t)-0.94140625f, (float16_t)-3.796875f, (float16_t)-4.9296875f, (float16_t)-2.34375f, + (float16_t)-3.9609375f, (float16_t)-5.359375f, (float16_t)-3.18554688f, (float16_t)-2.95507812f, + (float16_t)2.10546875f, (float16_t)-4.47265625f, (float16_t)-2.58203125f, (float16_t)0.882324219f, + (float16_t)-2.72265625f, (float16_t)2.41015625f, (float16_t)2.49609375f, (float16_t)-3.24414062f, + (float16_t)-3.71875f, (float16_t)3.17578125f, (float16_t)0.874023438f, (float16_t)-1.63085938f, + (float16_t)-2.87304688f, (float16_t)-0.468261719f, (float16_t)-2.63671875f, (float16_t)0.682617188f, + (float16_t)-2.41796875f, (float16_t)-4.2734375f, (float16_t)-4.37109375f, (float16_t)2.61328125f, + (float16_t)1.22851562f, (float16_t)0.90625f, (float16_t)0.862304688f, (float16_t)4.51171875f, + (float16_t)-2.47265625f, (float16_t)-5.78515625f, (float16_t)-5.390625f, (float16_t)-3.59375f, + (float16_t)-4.9296875f, (float16_t)-1.84375f, (float16_t)-3.9609375f, (float16_t)-4.7890625f, + (float16_t)-1.88378906f, (float16_t)1.08203125f, (float16_t)-0.387939453f, (float16_t)-4.47265625f, + (float16_t)-5.92578125f, (float16_t)1.31835938f, (float16_t)-2.72265625f, (float16_t)2.41015625f, + (float16_t)2.09375f, (float16_t)-1.4375f, (float16_t)-3.71875f, (float16_t)0.155273438f, + (float16_t)-1.87695312f, (float16_t)-1.83886719f, (float16_t)-2.87304688f, (float16_t)-0.555664062f, + (float16_t)-5.125f, (float16_t)-3.3046875f, (float16_t)-4.703125f, (float16_t)-5.28515625f, + (float16_t)-0.368652344f, (float16_t)-2.953125f, (float16_t)-5.86328125f, (float16_t)4.07421875f, + (float16_t)-0.516113281f, (float16_t)-5.93359375f, (float16_t)-4.37890625f, (float16_t)4.6875f, + (float16_t)-5.609375f, (float16_t)-3.65429688f, (float16_t)-1.84179688f, (float16_t)1.27832031f, + (float16_t)-2.57421875f, (float16_t)4.53125f, (float16_t)-2.36914062f, (float16_t)0.116882324f, + (float16_t)3.26367188f, (float16_t)-5.046875f, (float16_t)-5.875f, (float16_t)-5.9453125f, + (float16_t)1.75f, (float16_t)-3.11328125f, (float16_t)-3.30859375f, (float16_t)-4.94140625f, + (float16_t)-4.81640625f, (float16_t)-5.828125f, (float16_t)-2.2109375f, (float16_t)1.97558594f, + (float16_t)-3.82421875f, (float16_t)1.48144531f, (float16_t)0.891113281f, (float16_t)-3.3046875f, + (float16_t)1.07324219f, (float16_t)-2.61523438f, (float16_t)-4.28515625f, (float16_t)2.02929688f, + (float16_t)-5.86328125f, (float16_t)-3.8984375f, (float16_t)-1.42675781f, (float16_t)-5.23046875f, + (float16_t)1.70410156f, (float16_t)4.58203125f, (float16_t)-5.609375f, (float16_t)-3.65429688f, + (float16_t)-4.234375f, (float16_t)-1.63964844f, (float16_t)-0.430419922f, (float16_t)4.42578125f, + (float16_t)-1.94140625f, (float16_t)-3.04882812f, (float16_t)-4.26953125f, (float16_t)-5.046875f, + (float16_t)-4.8984375f, (float16_t)-2.77734375f, (float16_t)-3.69335938f, (float16_t)-3.11328125f, + (float16_t)-3.30859375f, (float16_t)-1.66894531f, (float16_t)-4.81640625f, (float16_t)-5.828125f, + (float16_t)-3.3359375f, (float16_t)1.97558594f, (float16_t)-3.82421875f, (float16_t)-4.546875f, + (float16_t)0.8515625f, (float16_t)-3.3046875f, (float16_t)-0.936523438f, (float16_t)-2.61523438f, + (float16_t)2.64257812f, (float16_t)2.02929688f, (float16_t)-5.86328125f, (float16_t)4.07421875f, + (float16_t)-0.516113281f, (float16_t)0.744140625f, (float16_t)1.70410156f, (float16_t)-0.101135254f, + (float16_t)-5.609375f, (float16_t)-3.65429688f, (float16_t)1.35058594f, (float16_t)1.27832031f, + (float16_t)2.88867188f, (float16_t)-0.223022461f, (float16_t)-1.94140625f, (float16_t)-3.78125f, + (float16_t)3.26367188f, (float16_t)-5.046875f, (float16_t)-5.76171875f, (float16_t)-3.671875f, + (float16_t)1.75f, (float16_t)-4.0234375f, (float16_t)-3.30859375f, (float16_t)4.75390625f, + (float16_t)-4.81640625f, (float16_t)-5.828125f, (float16_t)-2.2109375f, (float16_t)1.97558594f, + (float16_t)-5.796875f, (float16_t)-3.36328125f, (float16_t)-4.578125f, (float16_t)-3.3046875f, + (float16_t)1.07324219f, (float16_t)-2.61523438f, (float16_t)-3.90039062f, (float16_t)2.02929688f, + (float16_t)-5.86328125f, (float16_t)4.07421875f, (float16_t)-5.546875f, (float16_t)0.744140625f, + (float16_t)0.545898438f, (float16_t)2.44140625f, (float16_t)-5.609375f, (float16_t)-3.65429688f, + (float16_t)-0.307373047f, (float16_t)1.27832031f, (float16_t)3.390625f, (float16_t)0.451416016f, + (float16_t)-5.7265625f, (float16_t)0.116882324f, (float16_t)-2.7734375f, (float16_t)-5.046875f, + (float16_t)-1.88378906f, (float16_t)-5.33203125f, (float16_t)-3.1875f, (float16_t)-3.11328125f, + (float16_t)-3.30859375f, (float16_t)-4.90625f, (float16_t)-4.81640625f, (float16_t)-5.828125f, + (float16_t)-5.16796875f, (float16_t)-4.70703125f, (float16_t)-3.98632812f, (float16_t)2.29101562f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/config_data.h new file mode 100644 index 00000000..9f30d409 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_HEIGHT_F32_BATCH_1 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_HEIGHT_1 1 +#define MINIMUM_BROADCAST_HEIGHT_F32_WIDTH_1 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_CHANNEL_1 17 +#define MINIMUM_BROADCAST_HEIGHT_F32_BATCH_2 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_HEIGHT_2 4 +#define MINIMUM_BROADCAST_HEIGHT_F32_WIDTH_2 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_CHANNEL_2 17 +#define MINIMUM_BROADCAST_HEIGHT_F32_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_OUTPUT_HEIGHT 4 +#define MINIMUM_BROADCAST_HEIGHT_F32_OUTPUT_WIDTH 2 +#define MINIMUM_BROADCAST_HEIGHT_F32_OUTPUT_CHANNEL 17 +#define MINIMUM_BROADCAST_HEIGHT_F32_DST_SIZE 272 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_1_data.h new file mode 100644 index 00000000..21816567 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_1_data.h @@ -0,0 +1,11 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_height_f32_input_tensor_1[68] = { + 3.47351384f, 4.80259657f, 3.53741288f, 0.419778824f, 2.00192738f, 3.01079535f, 2.32117105f, 3.71429944f, + 1.57537222f, -5.33611202f, 0.462889761f, 2.82877207f, -2.47482491f, 5.28843069f, -4.27755737f, 3.09172058f, + -4.22368145f, 5.52119684f, 2.02633309f, 0.5782305f, 2.41745877f, -5.0151577f, 0.678871512f, -0.867281556f, + -3.36905575f, 3.61806726f, -4.31763172f, -1.76629686f, 1.52691019f, -2.3601613f, 4.90055847f, -2.8335371f, + -3.8926878f, 4.03069782f, 3.65231347f, -2.17047858f, -3.90910792f, -0.559841454f, -4.87714386f, -3.39814925f, + -3.77397299f, 0.577274084f, -4.96852636f, -0.731654048f, 0.0965373665f, 3.43956685f, 5.99938393f, 0.780567706f, + 0.402634799f, 4.12220669f, 0.076542452f, -3.4154036f, -1.7341789f, -5.34286928f, -4.65397263f, -1.24971056f, + -4.09150505f, -4.71824408f, 1.62730765f, 4.06203556f, -1.30110705f, -4.68837166f, 0.961123347f, -0.70858562f, + -5.10217285f, 1.09769106f, 1.02084768f, 0.38991034f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_2_data.h new file mode 100644 index 00000000..9498767c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/input_tensor_2_data.h @@ -0,0 +1,41 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_height_f32_input_tensor_2[272] = { + 5.99916744f, -0.558638692f, -0.410337716f, 2.61993837f, -2.56251502f, -3.74399614f, 4.75914621f, + 1.99581099f, 1.20465505f, -3.98378134f, 0.199769154f, -4.9906044f, 4.48663568f, 1.22842491f, + -1.4426986f, -3.78419948f, 0.0543402731f, 5.48222923f, 5.18688965f, 5.39868402f, 4.84459543f, + 1.95429671f, -3.37382174f, 2.26324415f, 0.731634021f, -3.03866005f, 1.77108753f, 2.17121673f, + 2.68789387f, 1.75871813f, 0.240108013f, 4.25185823f, -3.42614007f, 2.96636248f, 1.34845722f, + -3.3797605f, -5.23072338f, -0.484633297f, -1.37728834f, -5.7080102f, -2.66574907f, 3.1910212f, + -3.37098312f, 1.53011608f, -2.92441368f, -4.52515697f, -2.1343286f, 2.81578422f, -2.68048739f, + -2.57961845f, -4.38411236f, -4.91851711f, 0.303323597f, -2.03369212f, -1.79894042f, -3.55140948f, + 3.78897119f, -0.598503947f, 5.2398653f, 1.58089876f, 2.74547958f, 3.64880919f, 1.68920982f, + -2.52018738f, 1.52753139f, 3.81733704f, -4.56635809f, -4.16392422f, -0.743269503f, 1.1294539f, + 4.91911364f, -4.72998333f, -5.80859184f, 4.64957142f, 0.0315940566f, -3.83376241f, -3.51480269f, + 3.27261281f, 3.5119164f, -0.179977134f, -1.78023529f, 1.18950224f, -4.77444601f, 1.56217444f, + 2.91743898f, 4.31399775f, -3.80754733f, 5.04363918f, -4.70095015f, -0.684813917f, 5.45226192f, + 1.64039111f, -0.0894357637f, 5.74309015f, 5.00561619f, 0.898507893f, 3.14885139f, -4.32917881f, + 4.26522732f, 3.06201792f, -4.97807932f, -3.5765698f, -3.08296061f, 2.13073516f, 0.0994178727f, + -1.57907212f, 5.38049269f, 1.55963659f, 0.384851635f, 2.20956135f, 2.98811626f, 0.419100136f, + 5.04293871f, 0.978068411f, 0.855557382f, -0.0823808908f, -1.89334512f, 5.43488169f, -0.92661494f, + 1.31700671f, 4.041749f, -3.35992146f, -1.74092817f, -4.28433704f, 4.36335468f, 5.0748415f, + -0.124468021f, 3.19889665f, -3.12557721f, -5.36447048f, 2.854599f, -0.7912485f, -0.86292547f, + -2.74308848f, 1.26041508f, -3.42368269f, 2.78720593f, 2.42831135f, 1.92243016f, -0.714776635f, + -5.40177011f, -3.32516909f, -0.2303866f, 0.482332259f, -5.11730671f, -3.02454233f, -1.40968156f, + -2.2198894f, 2.85761738f, 5.97991753f, -0.810782015f, 1.10585105f, 2.02300978f, 5.37282467f, + -1.27639103f, 3.09191298f, -0.233792335f, -1.72103059f, -3.72919679f, 5.446383f, 2.82199621f, + -2.90703487f, -2.35722733f, 5.09082365f, -4.11147165f, 3.00180459f, 2.85362482f, -3.43482065f, + -1.03137052f, 3.42322588f, 0.0902925357f, 3.98842335f, 3.22511339f, 3.57067037f, -0.655524194f, + 5.23435354f, 4.38254023f, -3.76337361f, -2.44550586f, -4.77990055f, 3.4826088f, -5.0515542f, + 3.57134748f, -5.39843035f, 5.00375938f, -1.77877581f, 4.17503166f, -5.47433615f, -0.623244941f, + 4.82289839f, -2.71706605f, -4.03740788f, -5.36289692f, 0.544187903f, 0.278381377f, 0.0860661939f, + -3.89292955f, 4.48523045f, 3.69429231f, 2.20803857f, -3.71398258f, 3.26196098f, 3.11023927f, + -1.02396655f, -3.86045313f, -0.586388707f, 2.80014014f, -3.70953989f, -4.71591854f, -5.6087842f, + -2.19708824f, 3.72728443f, -5.91473722f, 4.59176064f, -3.57918906f, 2.29640508f, -0.228720054f, + 2.78964972f, 2.92447424f, 5.36446524f, -0.259355694f, 5.97965384f, -3.86203241f, -4.87940454f, + -5.9310174f, -3.90473557f, -3.2123847f, -3.85786414f, 5.50049925f, 1.33119535f, 2.32632399f, + -5.45771551f, -1.66731846f, 2.71455693f, -5.56299686f, -3.51601291f, 2.53673315f, 1.53367102f, + 5.47524786f, -5.56260395f, 2.91291213f, 0.463025481f, -1.55778229f, -3.94566512f, 3.81586313f, + -2.59917521f, 5.74641657f, -0.705747545f, 1.51510179f, 2.41129589f, -3.87306046f, -3.53183699f, + 1.15185785f, -4.67194605f, -2.8368125f, 5.48901939f, -0.591653764f, -4.25708294f, 5.98635864f, + -3.91140389f, -3.46124911f, -1.36470068f, -5.49520206f, -0.792065859f, 2.22560525f, 5.91025019f, + -0.292450786f, -2.7891252f, 5.73723078f, -0.54810822f, 0.756070435f, 0.920532942f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/output_ref_data.h new file mode 100644 index 00000000..5e7d20f7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/output_ref_data.h @@ -0,0 +1,41 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_height_f32_output_ref[272] = { + 3.47351384f, -0.558638692f, -0.410337716f, 0.419778824f, -2.56251502f, -3.74399614f, 2.32117105f, + 1.99581099f, 1.20465505f, -5.33611202f, 0.199769154f, -4.9906044f, -2.47482491f, 1.22842491f, + -4.27755737f, -3.78419948f, -4.22368145f, 5.48222923f, 2.02633309f, 0.5782305f, 2.41745877f, + -5.0151577f, -3.37382174f, -0.867281556f, -3.36905575f, -3.03866005f, -4.31763172f, -1.76629686f, + 1.52691019f, -2.3601613f, 0.240108013f, -2.8335371f, -3.8926878f, 2.96636248f, 1.34845722f, + -3.3797605f, -5.23072338f, -0.484633297f, -1.37728834f, -5.7080102f, -2.66574907f, 3.1910212f, + -3.37098312f, -5.33611202f, -2.92441368f, -4.52515697f, -2.47482491f, 2.81578422f, -4.27755737f, + -2.57961845f, -4.38411236f, -4.91851711f, 0.303323597f, -2.03369212f, -1.79894042f, -5.0151577f, + 0.678871512f, -0.867281556f, -3.36905575f, 1.58089876f, -4.31763172f, -1.76629686f, 1.52691019f, + -2.52018738f, 1.52753139f, -2.8335371f, -4.56635809f, -4.16392422f, -0.743269503f, 1.1294539f, + 3.53741288f, -4.72998333f, -5.80859184f, 3.01079535f, 0.0315940566f, -3.83376241f, -3.51480269f, + -5.33611202f, 0.462889761f, -0.179977134f, -2.47482491f, 1.18950224f, -4.77444601f, 1.56217444f, + -4.22368145f, 4.31399775f, -3.80754733f, 0.5782305f, -4.70095015f, -5.0151577f, 0.678871512f, + -0.867281556f, -3.36905575f, 3.61806726f, -4.31763172f, -1.76629686f, 1.52691019f, -4.32917881f, + 4.26522732f, -2.8335371f, -4.97807932f, -3.5765698f, -3.08296061f, 2.13073516f, 0.0994178727f, + -1.57907212f, 2.00192738f, 1.55963659f, 0.384851635f, 2.20956135f, 1.57537222f, -5.33611202f, + 0.462889761f, 0.978068411f, -2.47482491f, -0.0823808908f, -4.27755737f, 3.09172058f, -4.22368145f, + 1.31700671f, 2.02633309f, -3.35992146f, -1.74092817f, -5.0151577f, 0.678871512f, -0.867281556f, + -3.36905575f, 3.19889665f, -4.31763172f, -5.36447048f, 1.52691019f, -2.3601613f, -0.86292547f, + -2.8335371f, -3.8926878f, -3.42368269f, 2.78720593f, -2.17047858f, -3.90910792f, -0.714776635f, + -5.40177011f, -3.39814925f, -3.77397299f, 0.482332259f, -5.11730671f, -3.02454233f, -1.40968156f, + -2.2198894f, 2.85761738f, 0.780567706f, -0.810782015f, 1.10585105f, 0.076542452f, -3.4154036f, + -1.7341789f, -5.34286928f, -4.65397263f, -1.72103059f, -4.09150505f, -4.71824408f, 1.62730765f, + -2.90703487f, -2.35722733f, -4.68837166f, -4.11147165f, -0.70858562f, -5.10217285f, -3.43482065f, + -1.03137052f, 0.38991034f, 0.0902925357f, -2.17047858f, -3.90910792f, -0.559841454f, -4.87714386f, + -3.39814925f, -3.77397299f, -3.76337361f, -4.96852636f, -4.77990055f, 0.0965373665f, -5.0515542f, + 3.57134748f, -5.39843035f, 0.402634799f, -1.77877581f, 0.076542452f, -5.47433615f, -1.7341789f, + -5.34286928f, -4.65397263f, -4.03740788f, -5.36289692f, -4.71824408f, 0.278381377f, 0.0860661939f, + -3.89292955f, -4.68837166f, 0.961123347f, -0.70858562f, -5.10217285f, 1.09769106f, 1.02084768f, + -1.02396655f, -3.86045313f, -2.17047858f, -3.90910792f, -3.70953989f, -4.87714386f, -5.6087842f, + -3.77397299f, 0.577274084f, -5.91473722f, -0.731654048f, -3.57918906f, 2.29640508f, -0.228720054f, + 0.780567706f, 0.402634799f, 4.12220669f, -0.259355694f, -3.4154036f, -3.86203241f, -5.34286928f, + -5.9310174f, -3.90473557f, -4.09150505f, -4.71824408f, 1.62730765f, 1.33119535f, -1.30110705f, + -5.45771551f, -1.66731846f, -0.70858562f, -5.56299686f, -3.51601291f, 1.02084768f, 0.38991034f, + 3.65231347f, -5.56260395f, -3.90910792f, -0.559841454f, -4.87714386f, -3.94566512f, -3.77397299f, + -2.59917521f, -4.96852636f, -0.731654048f, 0.0965373665f, 2.41129589f, -3.87306046f, -3.53183699f, + 0.402634799f, -4.67194605f, -2.8368125f, -3.4154036f, -1.7341789f, -5.34286928f, -4.65397263f, + -3.91140389f, -4.09150505f, -4.71824408f, -5.49520206f, -0.792065859f, -1.30110705f, -4.68837166f, + -0.292450786f, -2.7891252f, -5.10217285f, -0.54810822f, 0.756070435f, 0.38991034f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_height_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/config_data.h new file mode 100644 index 00000000..3418832c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_WIDTH_F16_BATCH_1 2 +#define MINIMUM_BROADCAST_WIDTH_F16_HEIGHT_1 1 +#define MINIMUM_BROADCAST_WIDTH_F16_WIDTH_1 4 +#define MINIMUM_BROADCAST_WIDTH_F16_CHANNEL_1 19 +#define MINIMUM_BROADCAST_WIDTH_F16_BATCH_2 2 +#define MINIMUM_BROADCAST_WIDTH_F16_HEIGHT_2 1 +#define MINIMUM_BROADCAST_WIDTH_F16_WIDTH_2 1 +#define MINIMUM_BROADCAST_WIDTH_F16_CHANNEL_2 19 +#define MINIMUM_BROADCAST_WIDTH_F16_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_WIDTH_F16_OUTPUT_HEIGHT 1 +#define MINIMUM_BROADCAST_WIDTH_F16_OUTPUT_WIDTH 4 +#define MINIMUM_BROADCAST_WIDTH_F16_OUTPUT_CHANNEL 19 +#define MINIMUM_BROADCAST_WIDTH_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_1_data.h new file mode 100644 index 00000000..a314c1a5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_1_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_width_f16_input_tensor_1[152] = { + (float16_t)6.0f, (float16_t)-0.55859375f, (float16_t)-0.410400391f, (float16_t)2.61914062f, + (float16_t)-2.5625f, (float16_t)-3.74414062f, (float16_t)4.7578125f, (float16_t)1.99609375f, + (float16_t)1.20507812f, (float16_t)-3.984375f, (float16_t)0.199829102f, (float16_t)-4.9921875f, + (float16_t)4.48828125f, (float16_t)1.22851562f, (float16_t)-1.44238281f, (float16_t)-3.78515625f, + (float16_t)0.0543518066f, (float16_t)5.48046875f, (float16_t)5.1875f, (float16_t)5.3984375f, + (float16_t)4.84375f, (float16_t)1.95410156f, (float16_t)-3.37304688f, (float16_t)2.26367188f, + (float16_t)0.731445312f, (float16_t)-3.0390625f, (float16_t)1.77148438f, (float16_t)2.171875f, + (float16_t)2.6875f, (float16_t)1.75878906f, (float16_t)0.240112305f, (float16_t)4.25f, + (float16_t)-3.42578125f, (float16_t)2.96679688f, (float16_t)1.34863281f, (float16_t)-3.37890625f, + (float16_t)-5.23046875f, (float16_t)-0.484619141f, (float16_t)-1.37695312f, (float16_t)-5.70703125f, + (float16_t)-2.66601562f, (float16_t)3.19140625f, (float16_t)-3.37109375f, (float16_t)1.53027344f, + (float16_t)-2.92382812f, (float16_t)-4.5234375f, (float16_t)-2.13476562f, (float16_t)2.81640625f, + (float16_t)-2.6796875f, (float16_t)-2.58007812f, (float16_t)-4.3828125f, (float16_t)-4.91796875f, + (float16_t)0.303222656f, (float16_t)-2.03320312f, (float16_t)-1.79882812f, (float16_t)-3.55078125f, + (float16_t)3.7890625f, (float16_t)-0.598632812f, (float16_t)5.23828125f, (float16_t)1.58105469f, + (float16_t)2.74609375f, (float16_t)3.6484375f, (float16_t)1.68945312f, (float16_t)-2.51953125f, + (float16_t)1.52734375f, (float16_t)3.81640625f, (float16_t)-4.56640625f, (float16_t)-4.1640625f, + (float16_t)-0.743164062f, (float16_t)1.12988281f, (float16_t)4.91796875f, (float16_t)-4.73046875f, + (float16_t)-5.80859375f, (float16_t)4.6484375f, (float16_t)0.0315856934f, (float16_t)-3.83398438f, + (float16_t)-3.515625f, (float16_t)3.2734375f, (float16_t)3.51171875f, (float16_t)-0.179931641f, + (float16_t)-1.78027344f, (float16_t)1.18945312f, (float16_t)-4.7734375f, (float16_t)1.5625f, + (float16_t)2.91796875f, (float16_t)4.3125f, (float16_t)-3.80664062f, (float16_t)5.04296875f, + (float16_t)-4.69921875f, (float16_t)-0.684570312f, (float16_t)5.453125f, (float16_t)1.640625f, + (float16_t)-0.0894165039f, (float16_t)5.7421875f, (float16_t)5.00390625f, (float16_t)0.8984375f, + (float16_t)3.1484375f, (float16_t)-4.328125f, (float16_t)4.265625f, (float16_t)3.0625f, + (float16_t)-4.9765625f, (float16_t)-3.57617188f, (float16_t)-3.08203125f, (float16_t)2.13085938f, + (float16_t)0.0994262695f, (float16_t)-1.57910156f, (float16_t)5.37890625f, (float16_t)1.55957031f, + (float16_t)0.384765625f, (float16_t)2.20898438f, (float16_t)2.98828125f, (float16_t)0.419189453f, + (float16_t)5.04296875f, (float16_t)0.978027344f, (float16_t)0.85546875f, (float16_t)-0.0823974609f, + (float16_t)-1.89355469f, (float16_t)5.43359375f, (float16_t)-0.926757812f, (float16_t)1.31738281f, + (float16_t)4.04296875f, (float16_t)-3.359375f, (float16_t)-1.74121094f, (float16_t)-4.28515625f, + (float16_t)4.36328125f, (float16_t)5.07421875f, (float16_t)-0.124450684f, (float16_t)3.19921875f, + (float16_t)-3.125f, (float16_t)-5.36328125f, (float16_t)2.85546875f, (float16_t)-0.791015625f, + (float16_t)-0.862792969f, (float16_t)-2.7421875f, (float16_t)1.26074219f, (float16_t)-3.42382812f, + (float16_t)2.78710938f, (float16_t)2.42773438f, (float16_t)1.92285156f, (float16_t)-0.71484375f, + (float16_t)-5.40234375f, (float16_t)-3.32421875f, (float16_t)-0.23034668f, (float16_t)0.482421875f, + (float16_t)-5.1171875f, (float16_t)-3.02539062f, (float16_t)-1.41015625f, (float16_t)-2.22070312f, + (float16_t)2.85742188f, (float16_t)5.98046875f, (float16_t)-0.810546875f, (float16_t)1.10546875f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_2_data.h new file mode 100644 index 00000000..da080aad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/input_tensor_2_data.h @@ -0,0 +1,12 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_width_f16_input_tensor_2[38] = { + (float16_t)-4.578125f, (float16_t)-1.17089844f, (float16_t)1.56054688f, (float16_t)1.10058594f, + (float16_t)5.07421875f, (float16_t)-0.922851562f, (float16_t)-3.35742188f, (float16_t)3.51757812f, + (float16_t)5.91796875f, (float16_t)0.639160156f, (float16_t)-4.1171875f, (float16_t)-4.72265625f, + (float16_t)-4.59765625f, (float16_t)-3.87890625f, (float16_t)3.62890625f, (float16_t)2.4296875f, + (float16_t)3.02929688f, (float16_t)-0.222167969f, (float16_t)-2.21289062f, (float16_t)-2.80859375f, + (float16_t)1.13574219f, (float16_t)4.33203125f, (float16_t)-2.10742188f, (float16_t)4.34765625f, + (float16_t)-1.04882812f, (float16_t)2.89453125f, (float16_t)-1.02148438f, (float16_t)4.3828125f, + (float16_t)-1.35839844f, (float16_t)4.57421875f, (float16_t)0.728027344f, (float16_t)-4.6875f, + (float16_t)2.62109375f, (float16_t)-5.41015625f, (float16_t)5.078125f, (float16_t)-4.66015625f, + (float16_t)1.82226562f, (float16_t)-5.30859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/output_ref_data.h new file mode 100644 index 00000000..2856320a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_broadcast_width_f16_output_ref[152] = { + (float16_t)-4.578125f, (float16_t)-1.17089844f, (float16_t)-0.410400391f, (float16_t)1.10058594f, + (float16_t)-2.5625f, (float16_t)-3.74414062f, (float16_t)-3.35742188f, (float16_t)1.99609375f, + (float16_t)1.20507812f, (float16_t)-3.984375f, (float16_t)-4.1171875f, (float16_t)-4.9921875f, + (float16_t)-4.59765625f, (float16_t)-3.87890625f, (float16_t)-1.44238281f, (float16_t)-3.78515625f, + (float16_t)0.0543518066f, (float16_t)-0.222167969f, (float16_t)-2.21289062f, (float16_t)-4.578125f, + (float16_t)-1.17089844f, (float16_t)1.56054688f, (float16_t)-3.37304688f, (float16_t)2.26367188f, + (float16_t)-0.922851562f, (float16_t)-3.35742188f, (float16_t)1.77148438f, (float16_t)2.171875f, + (float16_t)0.639160156f, (float16_t)-4.1171875f, (float16_t)-4.72265625f, (float16_t)-4.59765625f, + (float16_t)-3.87890625f, (float16_t)2.96679688f, (float16_t)1.34863281f, (float16_t)-3.37890625f, + (float16_t)-5.23046875f, (float16_t)-2.21289062f, (float16_t)-4.578125f, (float16_t)-5.70703125f, + (float16_t)-2.66601562f, (float16_t)1.10058594f, (float16_t)-3.37109375f, (float16_t)-0.922851562f, + (float16_t)-3.35742188f, (float16_t)-4.5234375f, (float16_t)-2.13476562f, (float16_t)0.639160156f, + (float16_t)-4.1171875f, (float16_t)-4.72265625f, (float16_t)-4.59765625f, (float16_t)-4.91796875f, + (float16_t)0.303222656f, (float16_t)-2.03320312f, (float16_t)-1.79882812f, (float16_t)-3.55078125f, + (float16_t)-2.21289062f, (float16_t)-4.578125f, (float16_t)-1.17089844f, (float16_t)1.56054688f, + (float16_t)1.10058594f, (float16_t)3.6484375f, (float16_t)-0.922851562f, (float16_t)-3.35742188f, + (float16_t)1.52734375f, (float16_t)3.81640625f, (float16_t)-4.56640625f, (float16_t)-4.1640625f, + (float16_t)-4.72265625f, (float16_t)-4.59765625f, (float16_t)-3.87890625f, (float16_t)-4.73046875f, + (float16_t)-5.80859375f, (float16_t)3.02929688f, (float16_t)-0.222167969f, (float16_t)-3.83398438f, + (float16_t)-3.515625f, (float16_t)1.13574219f, (float16_t)3.51171875f, (float16_t)-2.10742188f, + (float16_t)-1.78027344f, (float16_t)-1.04882812f, (float16_t)-4.7734375f, (float16_t)-1.02148438f, + (float16_t)2.91796875f, (float16_t)-1.35839844f, (float16_t)-3.80664062f, (float16_t)0.728027344f, + (float16_t)-4.69921875f, (float16_t)-0.684570312f, (float16_t)-5.41015625f, (float16_t)1.640625f, + (float16_t)-4.66015625f, (float16_t)1.82226562f, (float16_t)-5.30859375f, (float16_t)-2.80859375f, + (float16_t)1.13574219f, (float16_t)-4.328125f, (float16_t)-2.10742188f, (float16_t)3.0625f, + (float16_t)-4.9765625f, (float16_t)-3.57617188f, (float16_t)-3.08203125f, (float16_t)2.13085938f, + (float16_t)-1.35839844f, (float16_t)-1.57910156f, (float16_t)0.728027344f, (float16_t)-4.6875f, + (float16_t)0.384765625f, (float16_t)-5.41015625f, (float16_t)2.98828125f, (float16_t)-4.66015625f, + (float16_t)1.82226562f, (float16_t)-5.30859375f, (float16_t)-2.80859375f, (float16_t)-0.0823974609f, + (float16_t)-1.89355469f, (float16_t)-2.10742188f, (float16_t)-0.926757812f, (float16_t)-1.04882812f, + (float16_t)2.89453125f, (float16_t)-3.359375f, (float16_t)-1.74121094f, (float16_t)-4.28515625f, + (float16_t)4.36328125f, (float16_t)0.728027344f, (float16_t)-4.6875f, (float16_t)2.62109375f, + (float16_t)-5.41015625f, (float16_t)-5.36328125f, (float16_t)-4.66015625f, (float16_t)-0.791015625f, + (float16_t)-5.30859375f, (float16_t)-2.80859375f, (float16_t)1.13574219f, (float16_t)-3.42382812f, + (float16_t)-2.10742188f, (float16_t)2.42773438f, (float16_t)-1.04882812f, (float16_t)-0.71484375f, + (float16_t)-5.40234375f, (float16_t)-3.32421875f, (float16_t)-1.35839844f, (float16_t)0.482421875f, + (float16_t)-5.1171875f, (float16_t)-4.6875f, (float16_t)-1.41015625f, (float16_t)-5.41015625f, + (float16_t)2.85742188f, (float16_t)-4.66015625f, (float16_t)-0.810546875f, (float16_t)-5.30859375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/config_data.h new file mode 100644 index 00000000..52ca6323 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_BROADCAST_WIDTH_F32_BATCH_1 2 +#define MINIMUM_BROADCAST_WIDTH_F32_HEIGHT_1 1 +#define MINIMUM_BROADCAST_WIDTH_F32_WIDTH_1 4 +#define MINIMUM_BROADCAST_WIDTH_F32_CHANNEL_1 19 +#define MINIMUM_BROADCAST_WIDTH_F32_BATCH_2 2 +#define MINIMUM_BROADCAST_WIDTH_F32_HEIGHT_2 1 +#define MINIMUM_BROADCAST_WIDTH_F32_WIDTH_2 1 +#define MINIMUM_BROADCAST_WIDTH_F32_CHANNEL_2 19 +#define MINIMUM_BROADCAST_WIDTH_F32_OUTPUT_BATCH 2 +#define MINIMUM_BROADCAST_WIDTH_F32_OUTPUT_HEIGHT 1 +#define MINIMUM_BROADCAST_WIDTH_F32_OUTPUT_WIDTH 4 +#define MINIMUM_BROADCAST_WIDTH_F32_OUTPUT_CHANNEL 19 +#define MINIMUM_BROADCAST_WIDTH_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_1_data.h new file mode 100644 index 00000000..5813ee67 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_1_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_width_f32_input_tensor_1[152] = { + -1.15488911f, -0.766639471f, -5.11683559f, 5.72913885f, 5.50783443f, -3.15459538f, -5.49169397f, + 4.19145584f, -1.63117409f, 0.361239105f, 0.29814294f, -0.550040424f, -2.85840917f, 2.59882402f, + 1.18606985f, 3.05807424f, 5.03918123f, 4.73934507f, -1.30687177f, -2.15744615f, -2.60060549f, + -2.08618379f, 4.9535656f, 4.29568243f, 5.10192347f, 3.84195375f, 1.97685158f, -4.42658472f, + -5.20641088f, -2.45479226f, -2.77429295f, -1.05544245f, -2.34083891f, -5.90689993f, 0.684295356f, + -3.70651913f, 3.72191119f, 0.0442053638f, 1.64866841f, -3.89817357f, 2.61895967f, -1.44277596f, + -2.69061756f, -3.59258628f, 1.41181839f, -4.36192894f, 4.76783562f, -5.87606478f, 2.33501506f, + 3.46706343f, -0.875079155f, -3.68199778f, -1.55246544f, -0.550904036f, -3.62081337f, -4.52544022f, + 1.35972655f, 1.27638257f, -5.91330004f, 0.957579494f, 0.599104643f, 2.25359416f, -1.03884423f, + -0.357206196f, -1.42529285f, -2.12161565f, -3.45474601f, 0.362913549f, -3.28513098f, -1.95913923f, + 0.798217475f, -0.0369582213f, -1.46015084f, 5.62090731f, -3.07798743f, -0.854619503f, 5.51013517f, + 2.93422651f, 0.906709194f, 5.07127619f, 5.24610424f, 2.98853421f, -4.49895048f, 0.365762025f, + 0.616637707f, 3.61022949f, -1.84079361f, 3.28096008f, -1.88569856f, 4.67051458f, 0.299018294f, + -3.35452938f, 2.92276883f, -0.624852777f, -0.398369282f, -2.19472885f, 3.58462119f, -5.83771467f, + 4.07468271f, -4.69792366f, -0.0790890008f, -1.98215866f, 0.917364299f, -5.679317f, 2.23546958f, + 2.28170681f, 2.74448228f, -1.70383191f, 3.86907864f, -4.65257359f, -0.354852974f, 2.137712f, + 5.93039083f, -2.57386494f, 5.46465206f, -2.94817638f, 2.67286444f, -1.50875568f, 0.214210868f, + 3.24048519f, -5.58417463f, 4.20520449f, 2.30899405f, -2.88141561f, 2.28554606f, -5.39753437f, + 4.21850157f, -5.27150393f, 2.26024246f, 0.955751061f, 3.28911328f, -2.70307565f, -0.102202892f, + 2.01300979f, -1.42466474f, -4.85392952f, -4.44143486f, 2.94270587f, 2.60656881f, -4.24597359f, + -0.901760638f, -1.58985269f, 0.328746766f, 0.160811976f, 1.60033703f, 4.35619974f, 2.01416254f, + 0.266076028f, 2.48327899f, -3.86477876f, -0.987839699f, 0.992649436f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_2_data.h new file mode 100644 index 00000000..217b32d7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/input_tensor_2_data.h @@ -0,0 +1,7 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_width_f32_input_tensor_2[38] = { + -4.32866287f, -2.00825858f, 1.23698986f, -4.93670082f, -1.40911126f, -4.2084794f, 1.43887436f, -1.71943676f, + 1.04715466f, 3.40381026f, -5.46089363f, 2.78261137f, 2.96077704f, -3.88828516f, -0.138854384f, 2.96879935f, + -2.91716194f, 1.23262358f, 5.22867346f, -0.694068015f, -3.00308847f, 5.78685188f, -5.07802534f, 5.12143421f, + -0.335914761f, -5.85000706f, -5.33463717f, 1.9125936f, -2.4347291f, 4.47531509f, -5.04852724f, 2.8412118f, + -4.90123987f, -1.08357072f, 5.21622944f, -5.00559521f, 2.84628773f, 2.30211282f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/output_ref_data.h new file mode 100644 index 00000000..0c050a45 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/output_ref_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_broadcast_width_f32_output_ref[152] = { + -4.32866287f, -2.00825858f, -5.11683559f, -4.93670082f, -1.40911126f, -4.2084794f, -5.49169397f, + -1.71943676f, -1.63117409f, 0.361239105f, -5.46089363f, -0.550040424f, -2.85840917f, -3.88828516f, + -0.138854384f, 2.96879935f, -2.91716194f, 1.23262358f, -1.30687177f, -4.32866287f, -2.60060549f, + -2.08618379f, -4.93670082f, -1.40911126f, -4.2084794f, 1.43887436f, -1.71943676f, -4.42658472f, + -5.20641088f, -5.46089363f, -2.77429295f, -1.05544245f, -3.88828516f, -5.90689993f, 0.684295356f, + -3.70651913f, 1.23262358f, 0.0442053638f, -4.32866287f, -3.89817357f, 1.23698986f, -4.93670082f, + -2.69061756f, -4.2084794f, 1.41181839f, -4.36192894f, 1.04715466f, -5.87606478f, -5.46089363f, + 2.78261137f, -0.875079155f, -3.88828516f, -1.55246544f, -0.550904036f, -3.62081337f, -4.52544022f, + 1.35972655f, -4.32866287f, -5.91330004f, 0.957579494f, -4.93670082f, -1.40911126f, -4.2084794f, + -0.357206196f, -1.71943676f, -2.12161565f, -3.45474601f, -5.46089363f, -3.28513098f, -1.95913923f, + -3.88828516f, -0.138854384f, -1.46015084f, -2.91716194f, -3.07798743f, -0.854619503f, -0.694068015f, + -3.00308847f, 0.906709194f, -5.07802534f, 5.12143421f, -0.335914761f, -5.85000706f, -5.33463717f, + 0.616637707f, -2.4347291f, -1.84079361f, -5.04852724f, -1.88569856f, -4.90123987f, -1.08357072f, + -3.35452938f, -5.00559521f, -0.624852777f, -0.398369282f, -2.19472885f, -3.00308847f, -5.83771467f, + -5.07802534f, -4.69792366f, -0.335914761f, -5.85000706f, -5.33463717f, -5.679317f, -2.4347291f, + 2.28170681f, -5.04852724f, -1.70383191f, -4.90123987f, -4.65257359f, -0.354852974f, -5.00559521f, + 2.84628773f, -2.57386494f, -0.694068015f, -3.00308847f, 2.67286444f, -5.07802534f, 0.214210868f, + -0.335914761f, -5.85000706f, -5.33463717f, 1.9125936f, -2.88141561f, 2.28554606f, -5.39753437f, + 2.8412118f, -5.27150393f, -1.08357072f, 0.955751061f, -5.00559521f, -2.70307565f, -0.102202892f, + -0.694068015f, -3.00308847f, -4.85392952f, -5.07802534f, 2.94270587f, -0.335914761f, -5.85000706f, + -5.33463717f, -1.58985269f, -2.4347291f, 0.160811976f, -5.04852724f, 2.8412118f, -4.90123987f, + -1.08357072f, 2.48327899f, -5.00559521f, -0.987839699f, 0.992649436f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_broadcast_width_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/config_data.h new file mode 100644 index 00000000..27b2c9d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_NO_BROADCAST_F16_BATCH_1 2 +#define MINIMUM_NO_BROADCAST_F16_HEIGHT_1 2 +#define MINIMUM_NO_BROADCAST_F16_WIDTH_1 3 +#define MINIMUM_NO_BROADCAST_F16_CHANNEL_1 18 +#define MINIMUM_NO_BROADCAST_F16_BATCH_2 2 +#define MINIMUM_NO_BROADCAST_F16_HEIGHT_2 2 +#define MINIMUM_NO_BROADCAST_F16_WIDTH_2 3 +#define MINIMUM_NO_BROADCAST_F16_CHANNEL_2 18 +#define MINIMUM_NO_BROADCAST_F16_OUTPUT_BATCH 2 +#define MINIMUM_NO_BROADCAST_F16_OUTPUT_HEIGHT 2 +#define MINIMUM_NO_BROADCAST_F16_OUTPUT_WIDTH 3 +#define MINIMUM_NO_BROADCAST_F16_OUTPUT_CHANNEL 18 +#define MINIMUM_NO_BROADCAST_F16_DST_SIZE 216 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_1_data.h new file mode 100644 index 00000000..d7fbeead --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_1_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_no_broadcast_f16_input_tensor_1[216] = { + (float16_t)-4.8671875f, (float16_t)1.69335938f, (float16_t)1.41992188f, (float16_t)-2.07421875f, + (float16_t)0.375f, (float16_t)5.2109375f, (float16_t)-5.63671875f, (float16_t)-3.63085938f, + (float16_t)0.715332031f, (float16_t)-4.73046875f, (float16_t)-0.0118026733f, (float16_t)-0.588867188f, + (float16_t)-0.860839844f, (float16_t)-0.9453125f, (float16_t)-2.18164062f, (float16_t)3.94335938f, + (float16_t)2.390625f, (float16_t)-4.421875f, (float16_t)0.791992188f, (float16_t)-4.875f, + (float16_t)-3.41796875f, (float16_t)4.28125f, (float16_t)-1.37011719f, (float16_t)-2.23828125f, + (float16_t)-3.00585938f, (float16_t)-1.33007812f, (float16_t)-4.765625f, (float16_t)-4.53515625f, + (float16_t)-1.59765625f, (float16_t)5.578125f, (float16_t)2.8984375f, (float16_t)5.4765625f, + (float16_t)-0.344726562f, (float16_t)-2.63085938f, (float16_t)-4.42578125f, (float16_t)2.1484375f, + (float16_t)-1.38574219f, (float16_t)-3.17578125f, (float16_t)0.227416992f, (float16_t)-0.0990600586f, + (float16_t)5.57421875f, (float16_t)0.173339844f, (float16_t)-3.65625f, (float16_t)-2.66210938f, + (float16_t)-0.603515625f, (float16_t)-1.42773438f, (float16_t)2.68359375f, (float16_t)-4.4296875f, + (float16_t)3.78515625f, (float16_t)-5.92578125f, (float16_t)-4.39453125f, (float16_t)5.12890625f, + (float16_t)3.703125f, (float16_t)1.734375f, (float16_t)-0.916503906f, (float16_t)-3.42382812f, + (float16_t)-1.56054688f, (float16_t)5.53125f, (float16_t)-5.890625f, (float16_t)-2.38671875f, + (float16_t)5.70703125f, (float16_t)5.3203125f, (float16_t)2.73828125f, (float16_t)-4.09375f, + (float16_t)-5.9453125f, (float16_t)-1.98828125f, (float16_t)1.9765625f, (float16_t)-3.8671875f, + (float16_t)0.563476562f, (float16_t)-1.96484375f, (float16_t)-3.56445312f, (float16_t)4.2421875f, + (float16_t)-3.79101562f, (float16_t)4.13671875f, (float16_t)-5.70703125f, (float16_t)-2.26367188f, + (float16_t)-0.0217132568f, (float16_t)4.0390625f, (float16_t)-3.83398438f, (float16_t)4.58984375f, + (float16_t)-2.41210938f, (float16_t)-1.53417969f, (float16_t)-0.106201172f, (float16_t)-1.58300781f, + (float16_t)-0.923339844f, (float16_t)2.8984375f, (float16_t)-3.38867188f, (float16_t)3.55859375f, + (float16_t)0.707519531f, (float16_t)-1.76855469f, (float16_t)-0.686035156f, (float16_t)0.21105957f, + (float16_t)-5.12890625f, (float16_t)4.80859375f, (float16_t)5.33203125f, (float16_t)5.4609375f, + (float16_t)3.4296875f, (float16_t)-5.21875f, (float16_t)1.20996094f, (float16_t)3.04492188f, + (float16_t)-5.14453125f, (float16_t)-2.67773438f, (float16_t)1.46582031f, (float16_t)5.82421875f, + (float16_t)2.36914062f, (float16_t)-0.153442383f, (float16_t)-2.421875f, (float16_t)0.160400391f, + (float16_t)-3.59375f, (float16_t)3.265625f, (float16_t)0.94921875f, (float16_t)0.440429688f, + (float16_t)2.7421875f, (float16_t)-5.89453125f, (float16_t)2.8046875f, (float16_t)-5.12109375f, + (float16_t)5.41015625f, (float16_t)-0.995117188f, (float16_t)1.87011719f, (float16_t)1.03417969f, + (float16_t)-5.61328125f, (float16_t)0.348876953f, (float16_t)2.17773438f, (float16_t)-5.43359375f, + (float16_t)-3.97851562f, (float16_t)1.33886719f, (float16_t)-2.7265625f, (float16_t)-3.43164062f, + (float16_t)2.7265625f, (float16_t)-3.2890625f, (float16_t)-5.74609375f, (float16_t)1.59472656f, + (float16_t)-5.04296875f, (float16_t)-1.94335938f, (float16_t)-1.19433594f, (float16_t)3.87695312f, + (float16_t)-2.32617188f, (float16_t)0.864257812f, (float16_t)-4.59765625f, (float16_t)1.07421875f, + (float16_t)-3.51171875f, (float16_t)-1.78417969f, (float16_t)-5.640625f, (float16_t)1.35644531f, + (float16_t)-2.42578125f, (float16_t)5.31640625f, (float16_t)-3.41601562f, (float16_t)5.72265625f, + (float16_t)1.53417969f, (float16_t)-0.0532226562f, (float16_t)-1.62207031f, (float16_t)-1.98535156f, + (float16_t)0.267578125f, (float16_t)4.90234375f, (float16_t)-0.143554688f, (float16_t)-5.6328125f, + (float16_t)-2.30273438f, (float16_t)-5.44140625f, (float16_t)1.12597656f, (float16_t)2.61328125f, + (float16_t)5.79296875f, (float16_t)1.98925781f, (float16_t)-4.37890625f, (float16_t)-1.73144531f, + (float16_t)-4.7734375f, (float16_t)1.18164062f, (float16_t)-0.19909668f, (float16_t)3.12695312f, + (float16_t)-5.9921875f, (float16_t)-2.09765625f, (float16_t)-0.934570312f, (float16_t)-3.203125f, + (float16_t)2.24414062f, (float16_t)6.0f, (float16_t)-3.75195312f, (float16_t)5.73046875f, + (float16_t)-3.50195312f, (float16_t)-5.2734375f, (float16_t)-0.321044922f, (float16_t)5.8359375f, + (float16_t)-5.421875f, (float16_t)0.546386719f, (float16_t)-0.924316406f, (float16_t)4.828125f, + (float16_t)-0.913085938f, (float16_t)1.61230469f, (float16_t)-2.50195312f, (float16_t)-2.02539062f, + (float16_t)-5.99609375f, (float16_t)0.180786133f, (float16_t)-0.848144531f, (float16_t)-3.421875f, + (float16_t)-0.510742188f, (float16_t)4.58203125f, (float16_t)-4.14453125f, (float16_t)0.440917969f, + (float16_t)-3.71875f, (float16_t)2.78320312f, (float16_t)-5.83203125f, (float16_t)-4.70703125f, + (float16_t)1.11914062f, (float16_t)-2.5703125f, (float16_t)2.61132812f, (float16_t)-2.86523438f, + (float16_t)2.25585938f, (float16_t)2.98242188f, (float16_t)1.34179688f, (float16_t)-4.43359375f, + (float16_t)1.75976562f, (float16_t)-3.8046875f, (float16_t)3.828125f, (float16_t)-3.8828125f, + (float16_t)0.516113281f, (float16_t)-0.728027344f, (float16_t)0.443847656f, (float16_t)-3.8359375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_2_data.h new file mode 100644 index 00000000..da2216ee --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/input_tensor_2_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_no_broadcast_f16_input_tensor_2[216] = { + (float16_t)4.66015625f, (float16_t)1.38476562f, (float16_t)-2.46875f, (float16_t)4.828125f, + (float16_t)-5.703125f, (float16_t)-0.319580078f, (float16_t)1.37011719f, (float16_t)5.6796875f, + (float16_t)-4.06640625f, (float16_t)-3.5625f, (float16_t)3.47070312f, (float16_t)4.8203125f, + (float16_t)1.72070312f, (float16_t)5.54296875f, (float16_t)5.390625f, (float16_t)2.91796875f, + (float16_t)4.203125f, (float16_t)-2.59375f, (float16_t)4.625f, (float16_t)2.84375f, + (float16_t)0.0674438477f, (float16_t)0.859375f, (float16_t)5.828125f, (float16_t)5.87109375f, + (float16_t)5.83984375f, (float16_t)2.01367188f, (float16_t)-1.01269531f, (float16_t)3.21875f, + (float16_t)1.984375f, (float16_t)-3.93554688f, (float16_t)-0.596191406f, (float16_t)-3.32421875f, + (float16_t)3.72851562f, (float16_t)-3.953125f, (float16_t)-0.0460510254f, (float16_t)-0.134277344f, + (float16_t)-4.35546875f, (float16_t)4.625f, (float16_t)-1.62304688f, (float16_t)-5.0625f, + (float16_t)3.82226562f, (float16_t)-5.48828125f, (float16_t)5.70703125f, (float16_t)-3.20703125f, + (float16_t)-3.375f, (float16_t)5.80859375f, (float16_t)1.01269531f, (float16_t)0.00531005859f, + (float16_t)1.15429688f, (float16_t)5.515625f, (float16_t)4.46484375f, (float16_t)5.1328125f, + (float16_t)-1.45605469f, (float16_t)-1.86132812f, (float16_t)-1.78417969f, (float16_t)-1.6328125f, + (float16_t)-1.390625f, (float16_t)5.25390625f, (float16_t)2.18359375f, (float16_t)4.66796875f, + (float16_t)-3.84179688f, (float16_t)-4.94921875f, (float16_t)5.640625f, (float16_t)-0.994140625f, + (float16_t)-0.755859375f, (float16_t)-4.65234375f, (float16_t)-5.64453125f, (float16_t)-2.30664062f, + (float16_t)2.63085938f, (float16_t)-5.9453125f, (float16_t)-1.77734375f, (float16_t)-5.390625f, + (float16_t)1.703125f, (float16_t)4.98046875f, (float16_t)-5.54296875f, (float16_t)-0.662597656f, + (float16_t)3.89257812f, (float16_t)2.36914062f, (float16_t)-3.5078125f, (float16_t)-2.03320312f, + (float16_t)-3.0234375f, (float16_t)-1.19238281f, (float16_t)0.553710938f, (float16_t)-0.381103516f, + (float16_t)-5.18359375f, (float16_t)-1.625f, (float16_t)-4.60546875f, (float16_t)-1.09667969f, + (float16_t)2.73242188f, (float16_t)0.293945312f, (float16_t)-5.87890625f, (float16_t)-4.80078125f, + (float16_t)2.99804688f, (float16_t)3.74023438f, (float16_t)1.65917969f, (float16_t)3.24023438f, + (float16_t)-4.01953125f, (float16_t)2.71484375f, (float16_t)3.17382812f, (float16_t)-2.26367188f, + (float16_t)-4.2109375f, (float16_t)-5.921875f, (float16_t)0.673828125f, (float16_t)-2.73632812f, + (float16_t)-1.10644531f, (float16_t)5.41796875f, (float16_t)-0.0107040405f, (float16_t)4.05078125f, + (float16_t)-3.63867188f, (float16_t)4.35546875f, (float16_t)1.31738281f, (float16_t)2.22070312f, + (float16_t)-0.67578125f, (float16_t)-1.83789062f, (float16_t)2.51367188f, (float16_t)-2.18945312f, + (float16_t)-5.58203125f, (float16_t)3.84765625f, (float16_t)2.50195312f, (float16_t)5.44921875f, + (float16_t)-0.456298828f, (float16_t)2.57617188f, (float16_t)3.83789062f, (float16_t)5.640625f, + (float16_t)-1.0f, (float16_t)0.0395202637f, (float16_t)-3.171875f, (float16_t)-1.046875f, + (float16_t)1.62988281f, (float16_t)-3.0703125f, (float16_t)0.54296875f, (float16_t)1.10839844f, + (float16_t)4.4609375f, (float16_t)-5.28515625f, (float16_t)1.49804688f, (float16_t)-5.40625f, + (float16_t)-0.23828125f, (float16_t)-5.265625f, (float16_t)4.07421875f, (float16_t)-2.34765625f, + (float16_t)-1.73339844f, (float16_t)4.35546875f, (float16_t)5.4921875f, (float16_t)-2.50976562f, + (float16_t)5.27734375f, (float16_t)-1.87207031f, (float16_t)-2.13476562f, (float16_t)5.8515625f, + (float16_t)-3.06445312f, (float16_t)-5.3203125f, (float16_t)1.23925781f, (float16_t)0.362304688f, + (float16_t)4.23828125f, (float16_t)2.9375f, (float16_t)-1.76464844f, (float16_t)-3.29492188f, + (float16_t)-5.453125f, (float16_t)1.02734375f, (float16_t)0.335693359f, (float16_t)-1.3984375f, + (float16_t)-1.57128906f, (float16_t)2.96875f, (float16_t)0.298095703f, (float16_t)4.23828125f, + (float16_t)-1.83984375f, (float16_t)-5.90625f, (float16_t)3.46875f, (float16_t)-0.815917969f, + (float16_t)0.499755859f, (float16_t)-5.3046875f, (float16_t)4.59375f, (float16_t)-1.49804688f, + (float16_t)-2.99023438f, (float16_t)1.125f, (float16_t)-4.5390625f, (float16_t)-2.03710938f, + (float16_t)0.146118164f, (float16_t)4.91015625f, (float16_t)-5.65234375f, (float16_t)-1.91113281f, + (float16_t)-0.872070312f, (float16_t)-1.85253906f, (float16_t)2.0f, (float16_t)-4.1484375f, + (float16_t)2.51953125f, (float16_t)3.890625f, (float16_t)-1.33984375f, (float16_t)3.00195312f, + (float16_t)2.71875f, (float16_t)-0.284179688f, (float16_t)3.8671875f, (float16_t)-1.8125f, + (float16_t)5.3125f, (float16_t)0.90234375f, (float16_t)5.0546875f, (float16_t)3.18359375f, + (float16_t)1.41210938f, (float16_t)-0.141357422f, (float16_t)-5.16015625f, (float16_t)3.96484375f, + (float16_t)-5.0703125f, (float16_t)-2.01171875f, (float16_t)0.973144531f, (float16_t)-5.55859375f, + (float16_t)5.1015625f, (float16_t)-2.03710938f, (float16_t)-3.41992188f, (float16_t)-2.47265625f, + (float16_t)-4.00390625f, (float16_t)3.83007812f, (float16_t)4.5703125f, (float16_t)-1.87011719f, + (float16_t)3.3671875f, (float16_t)-2.82226562f, (float16_t)-1.74511719f, (float16_t)-3.36523438f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/output_ref_data.h new file mode 100644 index 00000000..ea2eaed9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/output_ref_data.h @@ -0,0 +1,56 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_no_broadcast_f16_output_ref[216] = { + (float16_t)-4.8671875f, (float16_t)1.38476562f, (float16_t)-2.46875f, (float16_t)-2.07421875f, + (float16_t)-5.703125f, (float16_t)-0.319580078f, (float16_t)-5.63671875f, (float16_t)-3.63085938f, + (float16_t)-4.06640625f, (float16_t)-4.73046875f, (float16_t)-0.0118026733f, (float16_t)-0.588867188f, + (float16_t)-0.860839844f, (float16_t)-0.9453125f, (float16_t)-2.18164062f, (float16_t)2.91796875f, + (float16_t)2.390625f, (float16_t)-4.421875f, (float16_t)0.791992188f, (float16_t)-4.875f, + (float16_t)-3.41796875f, (float16_t)0.859375f, (float16_t)-1.37011719f, (float16_t)-2.23828125f, + (float16_t)-3.00585938f, (float16_t)-1.33007812f, (float16_t)-4.765625f, (float16_t)-4.53515625f, + (float16_t)-1.59765625f, (float16_t)-3.93554688f, (float16_t)-0.596191406f, (float16_t)-3.32421875f, + (float16_t)-0.344726562f, (float16_t)-3.953125f, (float16_t)-4.42578125f, (float16_t)-0.134277344f, + (float16_t)-4.35546875f, (float16_t)-3.17578125f, (float16_t)-1.62304688f, (float16_t)-5.0625f, + (float16_t)3.82226562f, (float16_t)-5.48828125f, (float16_t)-3.65625f, (float16_t)-3.20703125f, + (float16_t)-3.375f, (float16_t)-1.42773438f, (float16_t)1.01269531f, (float16_t)-4.4296875f, + (float16_t)1.15429688f, (float16_t)-5.92578125f, (float16_t)-4.39453125f, (float16_t)5.12890625f, + (float16_t)-1.45605469f, (float16_t)-1.86132812f, (float16_t)-1.78417969f, (float16_t)-3.42382812f, + (float16_t)-1.56054688f, (float16_t)5.25390625f, (float16_t)-5.890625f, (float16_t)-2.38671875f, + (float16_t)-3.84179688f, (float16_t)-4.94921875f, (float16_t)2.73828125f, (float16_t)-4.09375f, + (float16_t)-5.9453125f, (float16_t)-4.65234375f, (float16_t)-5.64453125f, (float16_t)-3.8671875f, + (float16_t)0.563476562f, (float16_t)-5.9453125f, (float16_t)-3.56445312f, (float16_t)-5.390625f, + (float16_t)-3.79101562f, (float16_t)4.13671875f, (float16_t)-5.70703125f, (float16_t)-2.26367188f, + (float16_t)-0.0217132568f, (float16_t)2.36914062f, (float16_t)-3.83398438f, (float16_t)-2.03320312f, + (float16_t)-3.0234375f, (float16_t)-1.53417969f, (float16_t)-0.106201172f, (float16_t)-1.58300781f, + (float16_t)-5.18359375f, (float16_t)-1.625f, (float16_t)-4.60546875f, (float16_t)-1.09667969f, + (float16_t)0.707519531f, (float16_t)-1.76855469f, (float16_t)-5.87890625f, (float16_t)-4.80078125f, + (float16_t)-5.12890625f, (float16_t)3.74023438f, (float16_t)1.65917969f, (float16_t)3.24023438f, + (float16_t)-4.01953125f, (float16_t)-5.21875f, (float16_t)1.20996094f, (float16_t)-2.26367188f, + (float16_t)-5.14453125f, (float16_t)-5.921875f, (float16_t)0.673828125f, (float16_t)-2.73632812f, + (float16_t)-1.10644531f, (float16_t)-0.153442383f, (float16_t)-2.421875f, (float16_t)0.160400391f, + (float16_t)-3.63867188f, (float16_t)3.265625f, (float16_t)0.94921875f, (float16_t)0.440429688f, + (float16_t)-0.67578125f, (float16_t)-5.89453125f, (float16_t)2.51367188f, (float16_t)-5.12109375f, + (float16_t)-5.58203125f, (float16_t)-0.995117188f, (float16_t)1.87011719f, (float16_t)1.03417969f, + (float16_t)-5.61328125f, (float16_t)0.348876953f, (float16_t)2.17773438f, (float16_t)-5.43359375f, + (float16_t)-3.97851562f, (float16_t)0.0395202637f, (float16_t)-3.171875f, (float16_t)-3.43164062f, + (float16_t)1.62988281f, (float16_t)-3.2890625f, (float16_t)-5.74609375f, (float16_t)1.10839844f, + (float16_t)-5.04296875f, (float16_t)-5.28515625f, (float16_t)-1.19433594f, (float16_t)-5.40625f, + (float16_t)-2.32617188f, (float16_t)-5.265625f, (float16_t)-4.59765625f, (float16_t)-2.34765625f, + (float16_t)-3.51171875f, (float16_t)-1.78417969f, (float16_t)-5.640625f, (float16_t)-2.50976562f, + (float16_t)-2.42578125f, (float16_t)-1.87207031f, (float16_t)-3.41601562f, (float16_t)5.72265625f, + (float16_t)-3.06445312f, (float16_t)-5.3203125f, (float16_t)-1.62207031f, (float16_t)-1.98535156f, + (float16_t)0.267578125f, (float16_t)2.9375f, (float16_t)-1.76464844f, (float16_t)-5.6328125f, + (float16_t)-5.453125f, (float16_t)-5.44140625f, (float16_t)0.335693359f, (float16_t)-1.3984375f, + (float16_t)-1.57128906f, (float16_t)1.98925781f, (float16_t)-4.37890625f, (float16_t)-1.73144531f, + (float16_t)-4.7734375f, (float16_t)-5.90625f, (float16_t)-0.19909668f, (float16_t)-0.815917969f, + (float16_t)-5.9921875f, (float16_t)-5.3046875f, (float16_t)-0.934570312f, (float16_t)-3.203125f, + (float16_t)-2.99023438f, (float16_t)1.125f, (float16_t)-4.5390625f, (float16_t)-2.03710938f, + (float16_t)-3.50195312f, (float16_t)-5.2734375f, (float16_t)-5.65234375f, (float16_t)-1.91113281f, + (float16_t)-5.421875f, (float16_t)-1.85253906f, (float16_t)-0.924316406f, (float16_t)-4.1484375f, + (float16_t)-0.913085938f, (float16_t)1.61230469f, (float16_t)-2.50195312f, (float16_t)-2.02539062f, + (float16_t)-5.99609375f, (float16_t)-0.284179688f, (float16_t)-0.848144531f, (float16_t)-3.421875f, + (float16_t)-0.510742188f, (float16_t)0.90234375f, (float16_t)-4.14453125f, (float16_t)0.440917969f, + (float16_t)-3.71875f, (float16_t)-0.141357422f, (float16_t)-5.83203125f, (float16_t)-4.70703125f, + (float16_t)-5.0703125f, (float16_t)-2.5703125f, (float16_t)0.973144531f, (float16_t)-5.55859375f, + (float16_t)2.25585938f, (float16_t)-2.03710938f, (float16_t)-3.41992188f, (float16_t)-4.43359375f, + (float16_t)-4.00390625f, (float16_t)-3.8046875f, (float16_t)3.828125f, (float16_t)-3.8828125f, + (float16_t)0.516113281f, (float16_t)-2.82226562f, (float16_t)-1.74511719f, (float16_t)-3.8359375f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/config_data.h new file mode 100644 index 00000000..34e7509b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_NO_BROADCAST_F32_BATCH_1 2 +#define MINIMUM_NO_BROADCAST_F32_HEIGHT_1 2 +#define MINIMUM_NO_BROADCAST_F32_WIDTH_1 3 +#define MINIMUM_NO_BROADCAST_F32_CHANNEL_1 18 +#define MINIMUM_NO_BROADCAST_F32_BATCH_2 2 +#define MINIMUM_NO_BROADCAST_F32_HEIGHT_2 2 +#define MINIMUM_NO_BROADCAST_F32_WIDTH_2 3 +#define MINIMUM_NO_BROADCAST_F32_CHANNEL_2 18 +#define MINIMUM_NO_BROADCAST_F32_OUTPUT_BATCH 2 +#define MINIMUM_NO_BROADCAST_F32_OUTPUT_HEIGHT 2 +#define MINIMUM_NO_BROADCAST_F32_OUTPUT_WIDTH 3 +#define MINIMUM_NO_BROADCAST_F32_OUTPUT_CHANNEL 18 +#define MINIMUM_NO_BROADCAST_F32_DST_SIZE 216 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_1_data.h new file mode 100644 index 00000000..2a2e0cc6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_1_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_no_broadcast_f32_input_tensor_1[216] = { + 3.54859233f, -5.28985357f, -2.51128197f, 2.70166564f, 4.94923925f, 4.82310581f, 2.16511559f, + 0.981965423f, -5.58695507f, -2.40365553f, 0.0524275973f, -2.19667792f, -1.87678695f, 5.40697145f, + -4.71175385f, -1.56301522f, -0.920281589f, -4.81922865f, 1.8713007f, 3.75665832f, -4.88382912f, + -1.23323369f, 1.71281111f, -3.50359178f, 5.6102972f, -4.28991318f, -4.90166187f, -3.30401325f, + -1.62029207f, -3.31031895f, 1.01608908f, -3.35113597f, 3.74018764f, 3.36774468f, 5.4252491f, + -0.785462618f, -4.77387333f, 5.8336544f, -1.23996305f, -2.40769744f, 2.89907932f, 4.21094894f, + 0.89725256f, -4.79522324f, 2.08457208f, 5.41486788f, 5.13064432f, 1.40777779f, 2.96150303f, + 3.59984541f, 3.21541214f, 1.40501428f, 5.09268093f, 0.440592855f, -5.05080128f, -0.192876115f, + 4.12421274f, 2.77585912f, 5.2063942f, 2.52588582f, 1.44121981f, -0.269237578f, -0.320019692f, + 3.43214774f, 1.5963949f, 5.5356679f, 1.52252316f, 5.1000762f, 3.03940749f, -2.547544f, + -0.114425145f, 0.553405643f, -3.4035027f, 0.878228724f, -2.21927595f, -2.85408902f, 3.35580373f, + 1.9274497f, -2.59424734f, -5.39505768f, -1.43656182f, 3.85419321f, 3.98124409f, 4.01516056f, + 2.07432699f, 0.820593715f, -4.56682825f, 4.86053276f, -4.54668808f, -2.75101614f, -5.31336975f, + 3.70922637f, -1.19157362f, 4.82865095f, -0.823588371f, -3.1468513f, 2.88950896f, 3.42099786f, + 0.826356709f, -5.37847281f, 0.321523786f, -0.899172246f, -2.99052095f, -3.49646044f, 0.593757451f, + 3.45233393f, -1.53338265f, -1.95324016f, 4.60194254f, 0.515294492f, 4.64196157f, 5.67987537f, + -0.65898639f, 3.33762002f, 2.32736015f, 5.93743849f, -1.22947121f, -0.447180688f, 3.96796775f, + -5.29747248f, 4.21306467f, 0.0926673338f, 2.77063251f, -3.87053275f, 2.05644011f, 0.449764907f, + 2.00947928f, 4.29181004f, -3.56461143f, -0.324009448f, -2.66336775f, -4.09587049f, -5.52904034f, + -2.9178803f, 2.6352818f, -3.94158435f, 0.888885081f, 3.96802044f, -3.09868932f, 5.10338402f, + 2.33758497f, -1.0298084f, 0.682799041f, 0.639222562f, 2.28235793f, 4.37771082f, 4.74231815f, + 1.13132274f, 0.557658553f, -1.43524301f, -1.5479573f, -2.47179675f, -1.52044046f, 4.72074175f, + 2.65523434f, -5.45556927f, 4.86872292f, 5.73550129f, 3.25457716f, 0.79691422f, -4.13919973f, + 5.57994461f, -5.59512568f, 5.30571699f, -2.47375441f, -0.241560891f, 3.13949013f, -3.91538501f, + 5.00667667f, 3.39120007f, -4.83491755f, 3.90737414f, -2.16226339f, -5.67984056f, 1.27654409f, + 0.763260663f, -5.33619356f, -2.22619557f, 5.02738714f, 3.00979495f, 0.614150584f, -4.82739639f, + 4.68154621f, 2.88412523f, 0.915246606f, -4.77843332f, -2.74277258f, -0.578148186f, -2.21171713f, + 0.774448097f, -0.861319959f, 2.33596659f, -1.70530677f, -3.41349864f, 2.19503593f, 2.75131822f, + 4.96433735f, 5.64155579f, -5.43999147f, -2.15311599f, -3.29872584f, 0.793684125f, -5.74038792f, + 0.899059772f, -4.11331129f, -5.86711645f, -2.85813212f, -1.16106951f, -1.99973321f, 0.0278032254f, + 2.29012489f, 0.470339894f, -3.77278948f, -3.94610071f, -2.41746235f, -2.84770894f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_2_data.h new file mode 100644 index 00000000..6621f982 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/input_tensor_2_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_no_broadcast_f32_input_tensor_2[216] = { + 1.64868045f, -1.23483014f, -0.603016376f, 0.0302653424f, 4.80920267f, -5.78733635f, 0.277170986f, + 4.98945665f, -3.47682691f, -5.22103977f, -2.25548577f, 5.55512428f, -1.47584951f, -3.52635789f, + 4.42706442f, -2.35115767f, -4.31297874f, 1.46667027f, 4.27084017f, -4.74502754f, 2.06833124f, + 5.04725122f, -2.09016705f, 5.93409204f, 1.30367827f, -3.83405805f, 1.65284073f, -1.53480768f, + 4.67023563f, -5.83335543f, 3.75960088f, -5.07280207f, 1.57655847f, 2.52710843f, 4.66214371f, + 2.62744451f, -5.14495611f, 4.27641058f, 0.126966253f, 3.08029842f, 5.65467215f, 3.00247359f, + -2.97293901f, -3.67507553f, 1.91392922f, -0.0344589278f, -3.4736495f, 1.63074458f, 2.458565f, + -1.47876894f, 5.81185484f, -2.85187244f, 1.92804575f, 3.58561015f, -2.69492435f, 4.29240799f, + -5.12135792f, 3.15814757f, 3.40263367f, -1.32095861f, 2.53715348f, 3.69428015f, 4.64482021f, + -0.472669095f, -1.11361098f, 4.68285847f, 0.244310915f, -1.16603792f, -4.07679367f, -2.9807775f, + 0.751075983f, -0.0377908796f, 4.96147776f, 1.27174842f, -3.29075718f, -4.82068491f, 3.04203486f, + 4.06237125f, -3.62405396f, 4.47682571f, 5.33655357f, 5.56847239f, 2.29663014f, -5.68255615f, + 1.92700255f, 4.06727886f, -1.22926569f, -2.42786264f, 3.76449275f, -3.86243963f, 1.88661289f, + -1.06397855f, -4.34354687f, -5.96328783f, 4.24912167f, 0.812966049f, -0.0683127344f, 3.95533967f, + 0.5952636f, 3.65764165f, 1.83937132f, -2.44050574f, 5.43586397f, -3.00853562f, -3.85244465f, + -1.56594837f, 4.14728451f, -4.77647161f, -2.25760794f, 5.79724264f, 4.00587702f, 4.7503953f, + -0.052314017f, -5.55797291f, 4.47104979f, -5.21061039f, 5.10644579f, 5.55317116f, 4.26860285f, + -2.51352048f, -2.72639322f, 2.3423965f, 1.38179469f, -3.59772587f, 3.42076635f, -3.86810708f, + 2.75421023f, -1.01249564f, 1.73188424f, -0.976615489f, -0.632246912f, 3.79739285f, 3.69075036f, + 4.72792482f, 1.93570542f, 5.61335325f, -3.59094572f, -5.12004185f, 0.264076889f, 4.95587063f, + 0.820922136f, 1.33108222f, 2.88518643f, -1.01031649f, -2.52455568f, 3.47558236f, 2.1862762f, + 2.02685189f, 1.2136935f, 1.86666501f, 2.18381214f, 1.07662475f, 3.27757907f, 0.755448759f, + -5.46132278f, 3.11508441f, 0.389392763f, -2.13262653f, -1.25827265f, -5.55324984f, -3.7158885f, + 3.15487814f, -0.859482169f, 3.17575741f, 1.68295002f, -4.86285591f, 1.51954186f, -0.136206329f, + -2.76408339f, -5.8322525f, -0.702260375f, 4.07164717f, -4.12932348f, -1.70647979f, -0.166517183f, + 5.93039036f, -2.23390603f, -3.13046122f, -0.37762329f, 1.11165845f, 4.11828852f, -3.95584273f, + -1.87651229f, 3.18288994f, -3.91119719f, -2.40843892f, 2.8318944f, 4.55374575f, -4.73549318f, + 5.39617491f, -1.30608118f, -4.99970055f, -2.79176402f, 3.94070745f, 3.45115185f, 1.48599923f, + -5.60478306f, -4.70619297f, 4.18609476f, 2.15375543f, 1.73208523f, -4.08324862f, 5.86208868f, + 1.14004147f, 3.47199082f, -1.9995327f, -5.48905373f, -0.039547462f, -4.16275978f, -5.82250071f, + -5.28897762f, -5.13984585f, 5.61706352f, -3.76725245f, 4.82698345f, 2.54777336f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/output_ref_data.h new file mode 100644 index 00000000..0395313c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/output_ref_data.h @@ -0,0 +1,33 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_no_broadcast_f32_output_ref[216] = { + 1.64868045f, -5.28985357f, -2.51128197f, 0.0302653424f, 4.80920267f, -5.78733635f, 0.277170986f, + 0.981965423f, -5.58695507f, -5.22103977f, -2.25548577f, -2.19667792f, -1.87678695f, -3.52635789f, + -4.71175385f, -2.35115767f, -4.31297874f, -4.81922865f, 1.8713007f, -4.74502754f, -4.88382912f, + -1.23323369f, -2.09016705f, -3.50359178f, 1.30367827f, -4.28991318f, -4.90166187f, -3.30401325f, + -1.62029207f, -5.83335543f, 1.01608908f, -5.07280207f, 1.57655847f, 2.52710843f, 4.66214371f, + -0.785462618f, -5.14495611f, 4.27641058f, -1.23996305f, -2.40769744f, 2.89907932f, 3.00247359f, + -2.97293901f, -4.79522324f, 1.91392922f, -0.0344589278f, -3.4736495f, 1.40777779f, 2.458565f, + -1.47876894f, 3.21541214f, -2.85187244f, 1.92804575f, 0.440592855f, -5.05080128f, -0.192876115f, + -5.12135792f, 2.77585912f, 3.40263367f, -1.32095861f, 1.44121981f, -0.269237578f, -0.320019692f, + -0.472669095f, -1.11361098f, 4.68285847f, 0.244310915f, -1.16603792f, -4.07679367f, -2.9807775f, + -0.114425145f, -0.0377908796f, -3.4035027f, 0.878228724f, -3.29075718f, -4.82068491f, 3.04203486f, + 1.9274497f, -3.62405396f, -5.39505768f, -1.43656182f, 3.85419321f, 2.29663014f, -5.68255615f, + 1.92700255f, 0.820593715f, -4.56682825f, -2.42786264f, -4.54668808f, -3.86243963f, -5.31336975f, + -1.06397855f, -4.34354687f, -5.96328783f, -0.823588371f, -3.1468513f, -0.0683127344f, 3.42099786f, + 0.5952636f, -5.37847281f, 0.321523786f, -2.44050574f, -2.99052095f, -3.49646044f, -3.85244465f, + -1.56594837f, -1.53338265f, -4.77647161f, -2.25760794f, 0.515294492f, 4.00587702f, 4.7503953f, + -0.65898639f, -5.55797291f, 2.32736015f, -5.21061039f, -1.22947121f, -0.447180688f, 3.96796775f, + -5.29747248f, -2.72639322f, 0.0926673338f, 1.38179469f, -3.87053275f, 2.05644011f, -3.86810708f, + 2.00947928f, -1.01249564f, -3.56461143f, -0.976615489f, -2.66336775f, -4.09587049f, -5.52904034f, + -2.9178803f, 1.93570542f, -3.94158435f, -3.59094572f, -5.12004185f, -3.09868932f, 4.95587063f, + 0.820922136f, -1.0298084f, 0.682799041f, -1.01031649f, -2.52455568f, 3.47558236f, 2.1862762f, + 1.13132274f, 0.557658553f, -1.43524301f, -1.5479573f, -2.47179675f, -1.52044046f, 0.755448759f, + -5.46132278f, -5.45556927f, 0.389392763f, -2.13262653f, -1.25827265f, -5.55324984f, -4.13919973f, + 3.15487814f, -5.59512568f, 3.17575741f, -2.47375441f, -4.86285591f, 1.51954186f, -3.91538501f, + -2.76408339f, -5.8322525f, -4.83491755f, 3.90737414f, -4.12932348f, -5.67984056f, -0.166517183f, + 0.763260663f, -5.33619356f, -3.13046122f, -0.37762329f, 1.11165845f, 0.614150584f, -4.82739639f, + -1.87651229f, 2.88412523f, -3.91119719f, -4.77843332f, -2.74277258f, -0.578148186f, -4.73549318f, + 0.774448097f, -1.30608118f, -4.99970055f, -2.79176402f, -3.41349864f, 2.19503593f, 1.48599923f, + -5.60478306f, -4.70619297f, -5.43999147f, -2.15311599f, -3.29872584f, -4.08324862f, -5.74038792f, + 0.899059772f, -4.11331129f, -5.86711645f, -5.48905373f, -1.16106951f, -4.16275978f, -5.82250071f, + -5.28897762f, -5.13984585f, -3.77278948f, -3.94610071f, -2.41746235f, -2.84770894f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_no_broadcast_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/config_data.h new file mode 100644 index 00000000..51886ec9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_SCALAR_1_F16_BATCH_1 1 +#define MINIMUM_SCALAR_1_F16_HEIGHT_1 1 +#define MINIMUM_SCALAR_1_F16_WIDTH_1 1 +#define MINIMUM_SCALAR_1_F16_CHANNEL_1 1 +#define MINIMUM_SCALAR_1_F16_BATCH_2 1 +#define MINIMUM_SCALAR_1_F16_HEIGHT_2 2 +#define MINIMUM_SCALAR_1_F16_WIDTH_2 4 +#define MINIMUM_SCALAR_1_F16_CHANNEL_2 19 +#define MINIMUM_SCALAR_1_F16_OUTPUT_BATCH 1 +#define MINIMUM_SCALAR_1_F16_OUTPUT_HEIGHT 2 +#define MINIMUM_SCALAR_1_F16_OUTPUT_WIDTH 4 +#define MINIMUM_SCALAR_1_F16_OUTPUT_CHANNEL 19 +#define MINIMUM_SCALAR_1_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_1_data.h new file mode 100644 index 00000000..c1f687cf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_1_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_1_f16_input_tensor_1[1] = {(float16_t)3.7890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_2_data.h new file mode 100644 index 00000000..be54b5c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/input_tensor_2_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_1_f16_input_tensor_2[152] = { + (float16_t)-3.81835938f, (float16_t)-5.078125f, (float16_t)-4.58203125f, (float16_t)-2.87109375f, + (float16_t)-5.08984375f, (float16_t)3.71679688f, (float16_t)-5.2421875f, (float16_t)-1.89746094f, + (float16_t)5.94140625f, (float16_t)4.1640625f, (float16_t)4.6875f, (float16_t)-1.52246094f, + (float16_t)-3.47460938f, (float16_t)-3.30273438f, (float16_t)-0.9609375f, (float16_t)3.4453125f, + (float16_t)-4.84375f, (float16_t)-3.91601562f, (float16_t)-5.046875f, (float16_t)4.921875f, + (float16_t)-3.72851562f, (float16_t)-0.461669922f, (float16_t)0.561035156f, (float16_t)4.88671875f, + (float16_t)3.2109375f, (float16_t)2.90625f, (float16_t)-2.31054688f, (float16_t)3.5546875f, + (float16_t)-2.40429688f, (float16_t)-2.0625f, (float16_t)3.31054688f, (float16_t)-3.79492188f, + (float16_t)-2.47070312f, (float16_t)-0.967773438f, (float16_t)-3.03515625f, (float16_t)0.397216797f, + (float16_t)5.1484375f, (float16_t)5.48828125f, (float16_t)2.16796875f, (float16_t)3.6796875f, + (float16_t)0.378662109f, (float16_t)5.390625f, (float16_t)4.734375f, (float16_t)-5.984375f, + (float16_t)-2.375f, (float16_t)3.03320312f, (float16_t)4.78515625f, (float16_t)5.74609375f, + (float16_t)-2.7109375f, (float16_t)-3.66796875f, (float16_t)-2.00195312f, (float16_t)-1.1640625f, + (float16_t)0.877441406f, (float16_t)-3.57617188f, (float16_t)-2.56640625f, (float16_t)-1.28613281f, + (float16_t)-1.07226562f, (float16_t)-1.078125f, (float16_t)-1.87792969f, (float16_t)4.94921875f, + (float16_t)-1.27539062f, (float16_t)1.05273438f, (float16_t)5.48828125f, (float16_t)4.4296875f, + (float16_t)-1.19042969f, (float16_t)4.27734375f, (float16_t)-1.41699219f, (float16_t)1.27929688f, + (float16_t)-5.26953125f, (float16_t)-4.68359375f, (float16_t)-5.60546875f, (float16_t)0.746582031f, + (float16_t)3.81835938f, (float16_t)-3.54296875f, (float16_t)2.80859375f, (float16_t)-2.6328125f, + (float16_t)0.470458984f, (float16_t)-2.36328125f, (float16_t)5.07421875f, (float16_t)5.0390625f, + (float16_t)-1.72558594f, (float16_t)3.59375f, (float16_t)-1.01464844f, (float16_t)-2.30859375f, + (float16_t)-5.3125f, (float16_t)-0.561035156f, (float16_t)-4.19140625f, (float16_t)-1.94042969f, + (float16_t)-5.6953125f, (float16_t)-1.86230469f, (float16_t)0.603027344f, (float16_t)5.17578125f, + (float16_t)-2.66796875f, (float16_t)4.515625f, (float16_t)5.90234375f, (float16_t)-0.298828125f, + (float16_t)4.8984375f, (float16_t)5.015625f, (float16_t)3.0859375f, (float16_t)-1.88964844f, + (float16_t)5.5859375f, (float16_t)3.9296875f, (float16_t)-5.66796875f, (float16_t)1.83691406f, + (float16_t)1.07324219f, (float16_t)-1.90136719f, (float16_t)-2.35351562f, (float16_t)4.35546875f, + (float16_t)1.375f, (float16_t)3.80859375f, (float16_t)-5.31640625f, (float16_t)0.383056641f, + (float16_t)-2.64453125f, (float16_t)0.34375f, (float16_t)-0.781738281f, (float16_t)0.635253906f, + (float16_t)3.19335938f, (float16_t)1.24316406f, (float16_t)3.5546875f, (float16_t)-2.68945312f, + (float16_t)-1.56542969f, (float16_t)2.26171875f, (float16_t)0.468261719f, (float16_t)-3.26171875f, + (float16_t)-5.15234375f, (float16_t)1.33105469f, (float16_t)-1.9375f, (float16_t)5.58984375f, + (float16_t)5.89453125f, (float16_t)-3.9453125f, (float16_t)-5.91015625f, (float16_t)-5.67578125f, + (float16_t)-1.74316406f, (float16_t)1.81738281f, (float16_t)2.3671875f, (float16_t)3.44140625f, + (float16_t)-4.9453125f, (float16_t)2.25976562f, (float16_t)5.32421875f, (float16_t)-2.58984375f, + (float16_t)-0.644042969f, (float16_t)-3.40039062f, (float16_t)4.77734375f, (float16_t)-5.71875f, + (float16_t)-0.463134766f, (float16_t)-5.21484375f, (float16_t)-0.178955078f, (float16_t)5.33203125f, + (float16_t)4.58984375f, (float16_t)-2.26953125f, (float16_t)-3.87695312f, (float16_t)1.89355469f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/output_ref_data.h new file mode 100644 index 00000000..83a585d0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_1_f16_output_ref[152] = { + (float16_t)-3.81835938f, (float16_t)-5.078125f, (float16_t)-4.58203125f, (float16_t)-2.87109375f, + (float16_t)-5.08984375f, (float16_t)3.71679688f, (float16_t)-5.2421875f, (float16_t)-1.89746094f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)-1.52246094f, + (float16_t)-3.47460938f, (float16_t)-3.30273438f, (float16_t)-0.9609375f, (float16_t)3.4453125f, + (float16_t)-4.84375f, (float16_t)-3.91601562f, (float16_t)-5.046875f, (float16_t)3.7890625f, + (float16_t)-3.72851562f, (float16_t)-0.461669922f, (float16_t)0.561035156f, (float16_t)3.7890625f, + (float16_t)3.2109375f, (float16_t)2.90625f, (float16_t)-2.31054688f, (float16_t)3.5546875f, + (float16_t)-2.40429688f, (float16_t)-2.0625f, (float16_t)3.31054688f, (float16_t)-3.79492188f, + (float16_t)-2.47070312f, (float16_t)-0.967773438f, (float16_t)-3.03515625f, (float16_t)0.397216797f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)2.16796875f, (float16_t)3.6796875f, + (float16_t)0.378662109f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)-5.984375f, + (float16_t)-2.375f, (float16_t)3.03320312f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)-2.7109375f, (float16_t)-3.66796875f, (float16_t)-2.00195312f, (float16_t)-1.1640625f, + (float16_t)0.877441406f, (float16_t)-3.57617188f, (float16_t)-2.56640625f, (float16_t)-1.28613281f, + (float16_t)-1.07226562f, (float16_t)-1.078125f, (float16_t)-1.87792969f, (float16_t)3.7890625f, + (float16_t)-1.27539062f, (float16_t)1.05273438f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)-1.19042969f, (float16_t)3.7890625f, (float16_t)-1.41699219f, (float16_t)1.27929688f, + (float16_t)-5.26953125f, (float16_t)-4.68359375f, (float16_t)-5.60546875f, (float16_t)0.746582031f, + (float16_t)3.7890625f, (float16_t)-3.54296875f, (float16_t)2.80859375f, (float16_t)-2.6328125f, + (float16_t)0.470458984f, (float16_t)-2.36328125f, (float16_t)3.7890625f, (float16_t)3.7890625f, + (float16_t)-1.72558594f, (float16_t)3.59375f, (float16_t)-1.01464844f, (float16_t)-2.30859375f, + (float16_t)-5.3125f, (float16_t)-0.561035156f, (float16_t)-4.19140625f, (float16_t)-1.94042969f, + (float16_t)-5.6953125f, (float16_t)-1.86230469f, (float16_t)0.603027344f, (float16_t)3.7890625f, + (float16_t)-2.66796875f, (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)-0.298828125f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)3.0859375f, (float16_t)-1.88964844f, + (float16_t)3.7890625f, (float16_t)3.7890625f, (float16_t)-5.66796875f, (float16_t)1.83691406f, + (float16_t)1.07324219f, (float16_t)-1.90136719f, (float16_t)-2.35351562f, (float16_t)3.7890625f, + (float16_t)1.375f, (float16_t)3.7890625f, (float16_t)-5.31640625f, (float16_t)0.383056641f, + (float16_t)-2.64453125f, (float16_t)0.34375f, (float16_t)-0.781738281f, (float16_t)0.635253906f, + (float16_t)3.19335938f, (float16_t)1.24316406f, (float16_t)3.5546875f, (float16_t)-2.68945312f, + (float16_t)-1.56542969f, (float16_t)2.26171875f, (float16_t)0.468261719f, (float16_t)-3.26171875f, + (float16_t)-5.15234375f, (float16_t)1.33105469f, (float16_t)-1.9375f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)-3.9453125f, (float16_t)-5.91015625f, (float16_t)-5.67578125f, + (float16_t)-1.74316406f, (float16_t)1.81738281f, (float16_t)2.3671875f, (float16_t)3.44140625f, + (float16_t)-4.9453125f, (float16_t)2.25976562f, (float16_t)3.7890625f, (float16_t)-2.58984375f, + (float16_t)-0.644042969f, (float16_t)-3.40039062f, (float16_t)3.7890625f, (float16_t)-5.71875f, + (float16_t)-0.463134766f, (float16_t)-5.21484375f, (float16_t)-0.178955078f, (float16_t)3.7890625f, + (float16_t)3.7890625f, (float16_t)-2.26953125f, (float16_t)-3.87695312f, (float16_t)1.89355469f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/config_data.h new file mode 100644 index 00000000..4ac98cfc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_SCALAR_1_F32_BATCH_1 1 +#define MINIMUM_SCALAR_1_F32_HEIGHT_1 1 +#define MINIMUM_SCALAR_1_F32_WIDTH_1 1 +#define MINIMUM_SCALAR_1_F32_CHANNEL_1 1 +#define MINIMUM_SCALAR_1_F32_BATCH_2 1 +#define MINIMUM_SCALAR_1_F32_HEIGHT_2 2 +#define MINIMUM_SCALAR_1_F32_WIDTH_2 4 +#define MINIMUM_SCALAR_1_F32_CHANNEL_2 19 +#define MINIMUM_SCALAR_1_F32_OUTPUT_BATCH 1 +#define MINIMUM_SCALAR_1_F32_OUTPUT_HEIGHT 2 +#define MINIMUM_SCALAR_1_F32_OUTPUT_WIDTH 4 +#define MINIMUM_SCALAR_1_F32_OUTPUT_CHANNEL 19 +#define MINIMUM_SCALAR_1_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_1_data.h new file mode 100644 index 00000000..38f25371 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_1_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_1_f32_input_tensor_1[1] = {-1.33102453f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_2_data.h new file mode 100644 index 00000000..2dbe7f8a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/input_tensor_2_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_1_f32_input_tensor_2[152] = { + -2.33778191f, 2.41181374f, 4.50241995f, 3.90808606f, 4.82162666f, -4.37790203f, -5.99091196f, + -5.76504517f, -4.10262251f, -0.232303366f, -3.18678761f, -2.26306176f, -4.18038368f, 3.54770184f, + -0.083846584f, 3.17593169f, -4.90483618f, 5.53488922f, -5.41985416f, 0.852559626f, -3.27907586f, + 0.104315661f, 3.18602991f, 0.374441832f, 5.01691723f, -5.00389528f, 2.2601037f, 3.68813944f, + 4.46874666f, -4.74341869f, -0.0778963342f, -4.89908075f, 3.95771408f, -5.82906437f, 1.60777771f, + 3.25474477f, 4.23021936f, -0.916315556f, -5.8493619f, 4.13712692f, 0.606094241f, 4.42138147f, + 1.52516508f, 4.64856339f, -1.80117357f, 2.71755433f, -2.25655293f, 5.14123201f, 2.61878705f, + -1.96027207f, 3.90796041f, 0.0553211309f, -1.96053982f, 0.657846272f, -0.888796091f, 0.276572526f, + -5.59448242f, 1.66659868f, -1.91583514f, -0.421893299f, 5.67003775f, -2.143852f, 4.01850843f, + -0.107471697f, -2.41714859f, -2.6935029f, 2.56869054f, -1.95466328f, 5.8743372f, 5.63287449f, + 4.9808445f, -1.55709898f, 4.25145149f, -4.20448923f, -3.54530525f, -3.21836424f, 5.94866133f, + -5.81591415f, 3.33156276f, -1.57384706f, -5.73466253f, 0.178171813f, -2.59080482f, 3.28741193f, + -4.63642693f, 2.49446678f, 1.00580609f, -0.525342703f, -3.21013904f, -2.91663074f, -3.64932847f, + -2.73275614f, 1.60295427f, 2.9646976f, -0.616162777f, -0.886478901f, -5.8759079f, -5.42675447f, + 1.45218182f, -0.7907148f, 0.586581469f, 4.96738482f, 0.894516647f, -5.79615068f, 3.20638323f, + -2.15334439f, -5.83860826f, -1.68949926f, -3.01636457f, -1.91108048f, -0.750714719f, -5.01797915f, + 5.87076712f, 2.70632625f, -0.258777946f, -2.81812167f, 0.336809576f, 0.902599394f, 4.61446619f, + -3.74116731f, -3.84269691f, 0.310189217f, -1.07934666f, 3.19508886f, 2.19408035f, -3.17076492f, + 3.87453151f, -1.13749635f, -0.96239084f, 4.42107582f, -3.72210145f, -4.72606087f, -0.0133705167f, + -5.26969194f, -3.79380083f, -0.327241808f, 4.83841848f, 5.45226192f, 5.99788666f, -0.853680253f, + 4.88839293f, -5.24139977f, 4.04919863f, -3.61354518f, -5.89492702f, -2.86836815f, -5.63141441f, + 4.53382397f, 5.6887455f, 0.557972372f, -4.69218493f, -0.825421333f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/output_ref_data.h new file mode 100644 index 00000000..68b85e8f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/output_ref_data.h @@ -0,0 +1,21 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_1_f32_output_ref[152] = { + -2.33778191f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -4.37790203f, -5.99091196f, -5.76504517f, + -4.10262251f, -1.33102453f, -3.18678761f, -2.26306176f, -4.18038368f, -1.33102453f, -1.33102453f, -1.33102453f, + -4.90483618f, -1.33102453f, -5.41985416f, -1.33102453f, -3.27907586f, -1.33102453f, -1.33102453f, -1.33102453f, + -1.33102453f, -5.00389528f, -1.33102453f, -1.33102453f, -1.33102453f, -4.74341869f, -1.33102453f, -4.89908075f, + -1.33102453f, -5.82906437f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -5.8493619f, -1.33102453f, + -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -1.80117357f, -1.33102453f, -2.25655293f, -1.33102453f, + -1.33102453f, -1.96027207f, -1.33102453f, -1.33102453f, -1.96053982f, -1.33102453f, -1.33102453f, -1.33102453f, + -5.59448242f, -1.33102453f, -1.91583514f, -1.33102453f, -1.33102453f, -2.143852f, -1.33102453f, -1.33102453f, + -2.41714859f, -2.6935029f, -1.33102453f, -1.95466328f, -1.33102453f, -1.33102453f, -1.33102453f, -1.55709898f, + -1.33102453f, -4.20448923f, -3.54530525f, -3.21836424f, -1.33102453f, -5.81591415f, -1.33102453f, -1.57384706f, + -5.73466253f, -1.33102453f, -2.59080482f, -1.33102453f, -4.63642693f, -1.33102453f, -1.33102453f, -1.33102453f, + -3.21013904f, -2.91663074f, -3.64932847f, -2.73275614f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, + -5.8759079f, -5.42675447f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -5.79615068f, + -1.33102453f, -2.15334439f, -5.83860826f, -1.68949926f, -3.01636457f, -1.91108048f, -1.33102453f, -5.01797915f, + -1.33102453f, -1.33102453f, -1.33102453f, -2.81812167f, -1.33102453f, -1.33102453f, -1.33102453f, -3.74116731f, + -3.84269691f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -3.17076492f, -1.33102453f, -1.33102453f, + -1.33102453f, -1.33102453f, -3.72210145f, -4.72606087f, -1.33102453f, -5.26969194f, -3.79380083f, -1.33102453f, + -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -1.33102453f, -5.24139977f, -1.33102453f, -3.61354518f, + -5.89492702f, -2.86836815f, -5.63141441f, -1.33102453f, -1.33102453f, -1.33102453f, -4.69218493f, -1.33102453f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_1_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/config_data.h new file mode 100644 index 00000000..29ceb2d9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_SCALAR_2_F16_BATCH_1 1 +#define MINIMUM_SCALAR_2_F16_HEIGHT_1 2 +#define MINIMUM_SCALAR_2_F16_WIDTH_1 4 +#define MINIMUM_SCALAR_2_F16_CHANNEL_1 19 +#define MINIMUM_SCALAR_2_F16_BATCH_2 1 +#define MINIMUM_SCALAR_2_F16_HEIGHT_2 1 +#define MINIMUM_SCALAR_2_F16_WIDTH_2 1 +#define MINIMUM_SCALAR_2_F16_CHANNEL_2 1 +#define MINIMUM_SCALAR_2_F16_OUTPUT_BATCH 1 +#define MINIMUM_SCALAR_2_F16_OUTPUT_HEIGHT 2 +#define MINIMUM_SCALAR_2_F16_OUTPUT_WIDTH 4 +#define MINIMUM_SCALAR_2_F16_OUTPUT_CHANNEL 19 +#define MINIMUM_SCALAR_2_F16_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_1_data.h new file mode 100644 index 00000000..7132d023 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_1_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_2_f16_input_tensor_1[152] = { + (float16_t)-2.33789062f, (float16_t)2.41210938f, (float16_t)4.50390625f, (float16_t)3.90820312f, + (float16_t)4.8203125f, (float16_t)-4.37890625f, (float16_t)-5.9921875f, (float16_t)-5.765625f, + (float16_t)-4.1015625f, (float16_t)-0.232299805f, (float16_t)-3.1875f, (float16_t)-2.26367188f, + (float16_t)-4.1796875f, (float16_t)3.546875f, (float16_t)-0.0838623047f, (float16_t)3.17578125f, + (float16_t)-4.90625f, (float16_t)5.53515625f, (float16_t)-5.41796875f, (float16_t)0.852539062f, + (float16_t)-3.27929688f, (float16_t)0.104309082f, (float16_t)3.18554688f, (float16_t)0.374511719f, + (float16_t)5.015625f, (float16_t)-5.00390625f, (float16_t)2.25976562f, (float16_t)3.6875f, + (float16_t)4.46875f, (float16_t)-4.7421875f, (float16_t)-0.0778808594f, (float16_t)-4.8984375f, + (float16_t)3.95703125f, (float16_t)-5.828125f, (float16_t)1.60742188f, (float16_t)3.25390625f, + (float16_t)4.23046875f, (float16_t)-0.916503906f, (float16_t)-5.84765625f, (float16_t)4.13671875f, + (float16_t)0.605957031f, (float16_t)4.421875f, (float16_t)1.52539062f, (float16_t)4.6484375f, + (float16_t)-1.80078125f, (float16_t)2.71679688f, (float16_t)-2.25585938f, (float16_t)5.140625f, + (float16_t)2.61914062f, (float16_t)-1.95996094f, (float16_t)3.90820312f, (float16_t)0.0553283691f, + (float16_t)-1.9609375f, (float16_t)0.657714844f, (float16_t)-0.888671875f, (float16_t)0.276611328f, + (float16_t)-5.59375f, (float16_t)1.66699219f, (float16_t)-1.91601562f, (float16_t)-0.421875f, + (float16_t)5.671875f, (float16_t)-2.14453125f, (float16_t)4.01953125f, (float16_t)-0.10748291f, + (float16_t)-2.41796875f, (float16_t)-2.69335938f, (float16_t)2.56835938f, (float16_t)-1.95507812f, + (float16_t)5.875f, (float16_t)5.6328125f, (float16_t)4.98046875f, (float16_t)-1.55664062f, + (float16_t)4.25f, (float16_t)-4.203125f, (float16_t)-3.54492188f, (float16_t)-3.21875f, + (float16_t)5.94921875f, (float16_t)-5.81640625f, (float16_t)3.33203125f, (float16_t)-1.57421875f, + (float16_t)-5.734375f, (float16_t)0.178222656f, (float16_t)-2.58984375f, (float16_t)3.28710938f, + (float16_t)-4.63671875f, (float16_t)2.49414062f, (float16_t)1.00585938f, (float16_t)-0.525390625f, + (float16_t)-3.2109375f, (float16_t)-2.91601562f, (float16_t)-3.6484375f, (float16_t)-2.73242188f, + (float16_t)1.60253906f, (float16_t)2.96484375f, (float16_t)-0.616210938f, (float16_t)-0.88671875f, + (float16_t)-5.875f, (float16_t)-5.42578125f, (float16_t)1.45214844f, (float16_t)-0.790527344f, + (float16_t)0.586425781f, (float16_t)4.96875f, (float16_t)0.89453125f, (float16_t)-5.796875f, + (float16_t)3.20703125f, (float16_t)-2.15429688f, (float16_t)-5.83984375f, (float16_t)-1.68945312f, + (float16_t)-3.015625f, (float16_t)-1.91113281f, (float16_t)-0.750488281f, (float16_t)-5.01953125f, + (float16_t)5.87109375f, (float16_t)2.70703125f, (float16_t)-0.258789062f, (float16_t)-2.81835938f, + (float16_t)0.336914062f, (float16_t)0.902832031f, (float16_t)4.61328125f, (float16_t)-3.74023438f, + (float16_t)-3.84179688f, (float16_t)0.310302734f, (float16_t)-1.07910156f, (float16_t)3.1953125f, + (float16_t)2.19335938f, (float16_t)-3.16992188f, (float16_t)3.875f, (float16_t)-1.13769531f, + (float16_t)-0.962402344f, (float16_t)4.421875f, (float16_t)-3.72265625f, (float16_t)-4.7265625f, + (float16_t)-0.0133743286f, (float16_t)-5.26953125f, (float16_t)-3.79296875f, (float16_t)-0.327148438f, + (float16_t)4.83984375f, (float16_t)5.453125f, (float16_t)5.99609375f, (float16_t)-0.853515625f, + (float16_t)4.88671875f, (float16_t)-5.2421875f, (float16_t)4.05078125f, (float16_t)-3.61328125f, + (float16_t)-5.89453125f, (float16_t)-2.86914062f, (float16_t)-5.6328125f, (float16_t)4.53515625f, + (float16_t)5.6875f, (float16_t)0.558105469f, (float16_t)-4.69140625f, (float16_t)-0.825195312f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_2_data.h new file mode 100644 index 00000000..d822f54b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/input_tensor_2_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_2_f16_input_tensor_2[1] = {(float16_t)-3.98242188f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/output_ref_data.h new file mode 100644 index 00000000..7ec84ff0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/output_ref_data.h @@ -0,0 +1,40 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t minimum_scalar_2_f16_output_ref[152] = { + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-4.37890625f, (float16_t)-5.9921875f, (float16_t)-5.765625f, + (float16_t)-4.1015625f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-4.1796875f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-4.90625f, (float16_t)-3.98242188f, (float16_t)-5.41796875f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-5.00390625f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-4.7421875f, (float16_t)-3.98242188f, (float16_t)-4.8984375f, + (float16_t)-3.98242188f, (float16_t)-5.828125f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-5.84765625f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-5.59375f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-4.203125f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-5.81640625f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-5.734375f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-4.63671875f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-5.875f, (float16_t)-5.42578125f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-5.796875f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-5.83984375f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-5.01953125f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-4.7265625f, + (float16_t)-3.98242188f, (float16_t)-5.26953125f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-5.2421875f, (float16_t)-3.98242188f, (float16_t)-3.98242188f, + (float16_t)-5.89453125f, (float16_t)-3.98242188f, (float16_t)-5.6328125f, (float16_t)-3.98242188f, + (float16_t)-3.98242188f, (float16_t)-3.98242188f, (float16_t)-4.69140625f, (float16_t)-3.98242188f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/config_data.h new file mode 100644 index 00000000..5088cd97 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/config_data.h @@ -0,0 +1,14 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MINIMUM_SCALAR_2_F32_BATCH_1 1 +#define MINIMUM_SCALAR_2_F32_HEIGHT_1 2 +#define MINIMUM_SCALAR_2_F32_WIDTH_1 4 +#define MINIMUM_SCALAR_2_F32_CHANNEL_1 19 +#define MINIMUM_SCALAR_2_F32_BATCH_2 1 +#define MINIMUM_SCALAR_2_F32_HEIGHT_2 1 +#define MINIMUM_SCALAR_2_F32_WIDTH_2 1 +#define MINIMUM_SCALAR_2_F32_CHANNEL_2 1 +#define MINIMUM_SCALAR_2_F32_OUTPUT_BATCH 1 +#define MINIMUM_SCALAR_2_F32_OUTPUT_HEIGHT 2 +#define MINIMUM_SCALAR_2_F32_OUTPUT_WIDTH 4 +#define MINIMUM_SCALAR_2_F32_OUTPUT_CHANNEL 19 +#define MINIMUM_SCALAR_2_F32_DST_SIZE 152 diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_1_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_1_data.h new file mode 100644 index 00000000..8db4eabe --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_1_data.h @@ -0,0 +1,24 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_2_f32_input_tensor_1[152] = { + 4.21651506f, -2.44645262f, -2.58304834f, -1.00068307f, 1.71813881f, 0.229273468f, -1.32676303f, + -5.77569151f, -2.8990159f, -0.519005716f, -3.57659769f, 3.76164603f, -3.56612134f, -1.95359755f, + -2.09227586f, 1.60295153f, -5.26404715f, 0.855953693f, 3.37189221f, -4.4846859f, 1.06080365f, + 1.22572792f, 1.83382714f, 4.36309814f, 4.2877326f, -4.29909468f, -1.5225569f, 1.66976035f, + -2.90561366f, 5.75992727f, 0.269500077f, -0.296420217f, -5.91729784f, -5.40399265f, 5.14160442f, + -1.67769504f, -3.3278234f, 2.47849607f, -1.12402809f, -0.848127484f, -5.17140722f, -4.32199478f, + 4.58439922f, 0.692196608f, 1.94302189f, 1.94695878f, 5.45770121f, 5.34246206f, -5.76576233f, + -1.64440763f, -4.21322203f, 5.94327784f, -2.95788717f, -4.55879545f, 0.640772879f, -2.38353634f, + 1.83990824f, -5.49134874f, 3.56541514f, -1.19899225f, -4.9643693f, -3.67932487f, -1.2503525f, + 4.635993f, -2.05336976f, 2.46690202f, -2.13392019f, 3.44667435f, 4.79914713f, -5.53693724f, + 1.81337965f, 0.54110378f, -5.34813309f, 4.34579659f, 4.94211006f, 1.13815951f, 0.444551766f, + -0.516891301f, -1.43879521f, -1.8839252f, -5.48385143f, -1.4392699f, -4.77348852f, 3.65590119f, + 1.22632003f, 3.10053086f, 5.39067936f, 5.59765053f, 0.0950766951f, 3.0594244f, -3.6377964f, + -1.81328011f, 2.27910781f, -1.79949868f, -3.71277642f, 3.98877907f, 2.53513765f, -4.95961475f, + -5.23175526f, -4.81570244f, 3.48046589f, 2.47036457f, 5.9871664f, -4.1907835f, 4.46137905f, + 2.32326365f, -2.44084239f, 3.61053324f, 1.84585333f, 1.0879482f, -5.75145864f, -0.545061409f, + 2.0519383f, 0.912212133f, -5.49078751f, 4.44412899f, -1.25164509f, -2.78041625f, -3.41571951f, + 0.710473895f, -4.77362061f, -5.39968777f, -5.24263191f, 3.81943154f, 3.05806017f, 4.42284298f, + -0.992328644f, 0.672959805f, 3.50049853f, -3.66966677f, -0.235395417f, 5.1538763f, -1.54643023f, + 5.34219217f, -0.0929627866f, 1.08004832f, 4.36181021f, -5.76473093f, -0.570274293f, 2.73397088f, + 2.5359509f, 1.74922264f, -4.56324244f, 1.32867968f, 4.02708912f, 4.80426931f, -1.80577505f, + -2.12380052f, 5.92663383f, -0.64609313f, -1.7343936f, -1.41045737f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_2_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_2_data.h new file mode 100644 index 00000000..00ac8b62 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/input_tensor_2_data.h @@ -0,0 +1,2 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_2_f32_input_tensor_2[1] = {-4.86829948f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/output_ref_data.h new file mode 100644 index 00000000..1cb5fbe9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/output_ref_data.h @@ -0,0 +1,21 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t minimum_scalar_2_f32_output_ref[152] = { + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -5.77569151f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -5.26404715f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -5.91729784f, -5.40399265f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -5.17140722f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -5.76576233f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -5.49134874f, -4.86829948f, -4.86829948f, -4.9643693f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -5.53693724f, -4.86829948f, -4.86829948f, + -5.34813309f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -5.48385143f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.95961475f, -5.23175526f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -5.75145864f, -4.86829948f, + -4.86829948f, -4.86829948f, -5.49078751f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -5.39968777f, -5.24263191f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -5.76473093f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, + -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f, -4.86829948f}; diff --git a/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/test_data.h new file mode 100644 index 00000000..ba16b576 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/minimum_scalar_2_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by minmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_1_data.h" +#include "input_tensor_2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16/config_data.h new file mode 100644 index 00000000..fd71af31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16/config_data.h @@ -0,0 +1,9 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MUL_F16_DST_SIZE 160 +#define MUL_F16_OUT_ACTIVATION_MIN (float16_t) - 10.0f +#define MUL_F16_OUT_ACTIVATION_MAX (float16_t)10.0f +#define MUL_F16_INPUT1_OFFSET 0 +#define MUL_F16_INPUT2_OFFSET 0 +#define MUL_F16_OUTPUT_MULT 0 +#define MUL_F16_OUTPUT_SHIFT 0 +#define MUL_F16_OUTPUT_OFFSET 0 diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16/input1_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16/input1_data.h new file mode 100644 index 00000000..b99e6326 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16/input1_data.h @@ -0,0 +1,42 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_input1[160] = { + (float16_t)1.19042969f, (float16_t)1.71484375f, (float16_t)-0.336181641f, (float16_t)2.15820312f, + (float16_t)-0.786621094f, (float16_t)2.00976562f, (float16_t)1.89160156f, (float16_t)2.171875f, + (float16_t)0.199584961f, (float16_t)-2.25585938f, (float16_t)-0.971191406f, (float16_t)2.015625f, + (float16_t)-0.424072266f, (float16_t)0.371826172f, (float16_t)-0.981445312f, (float16_t)-2.33398438f, + (float16_t)0.717773438f, (float16_t)-0.294677734f, (float16_t)-2.02539062f, (float16_t)-0.966796875f, + (float16_t)-2.49414062f, (float16_t)0.2421875f, (float16_t)-2.42382812f, (float16_t)2.36328125f, + (float16_t)1.02539062f, (float16_t)-2.11132812f, (float16_t)1.06640625f, (float16_t)-2.08984375f, + (float16_t)1.26171875f, (float16_t)1.78613281f, (float16_t)-1.828125f, (float16_t)-1.63964844f, + (float16_t)0.234375f, (float16_t)-1.31738281f, (float16_t)1.00390625f, (float16_t)0.610351562f, + (float16_t)1.67578125f, (float16_t)1.328125f, (float16_t)-1.25585938f, (float16_t)2.15234375f, + (float16_t)-0.0316467285f, (float16_t)-2.296875f, (float16_t)-2.39648438f, (float16_t)0.112609863f, + (float16_t)-2.06640625f, (float16_t)2.14257812f, (float16_t)-1.91503906f, (float16_t)0.911132812f, + (float16_t)0.415039062f, (float16_t)-1.75488281f, (float16_t)1.16796875f, (float16_t)-2.40820312f, + (float16_t)1.39746094f, (float16_t)0.194702148f, (float16_t)-1.97167969f, (float16_t)-0.435058594f, + (float16_t)-1.21484375f, (float16_t)-0.405029297f, (float16_t)-2.36132812f, (float16_t)-1.54980469f, + (float16_t)-1.546875f, (float16_t)0.0570068359f, (float16_t)1.97363281f, (float16_t)0.0707397461f, + (float16_t)1.91894531f, (float16_t)-1.015625f, (float16_t)1.07324219f, (float16_t)2.40625f, + (float16_t)0.916015625f, (float16_t)-1.09765625f, (float16_t)-0.279541016f, (float16_t)1.03125f, + (float16_t)-1.30664062f, (float16_t)-2.05859375f, (float16_t)0.848632812f, (float16_t)0.155883789f, + (float16_t)0.713378906f, (float16_t)2.4765625f, (float16_t)-0.0186309814f, (float16_t)2.359375f, + (float16_t)1.56542969f, (float16_t)-0.220947266f, (float16_t)0.223754883f, (float16_t)-1.3515625f, + (float16_t)-1.35253906f, (float16_t)-0.931152344f, (float16_t)1.51269531f, (float16_t)0.282470703f, + (float16_t)1.57910156f, (float16_t)-1.63378906f, (float16_t)-1.56445312f, (float16_t)1.55859375f, + (float16_t)-1.99121094f, (float16_t)-1.18164062f, (float16_t)0.751464844f, (float16_t)-2.3984375f, + (float16_t)0.518066406f, (float16_t)-0.144287109f, (float16_t)-0.330322266f, (float16_t)-0.131958008f, + (float16_t)-0.306396484f, (float16_t)-1.13867188f, (float16_t)-1.14355469f, (float16_t)1.19140625f, + (float16_t)1.41210938f, (float16_t)-0.599121094f, (float16_t)0.784179688f, (float16_t)-0.0933227539f, + (float16_t)-0.823242188f, (float16_t)-0.446044922f, (float16_t)1.26367188f, (float16_t)0.125732422f, + (float16_t)2.1953125f, (float16_t)-0.0971069336f, (float16_t)-2.03515625f, (float16_t)-2.16601562f, + (float16_t)0.098815918f, (float16_t)-1.65917969f, (float16_t)-0.0561828613f, (float16_t)-1.06835938f, + (float16_t)-1.11914062f, (float16_t)-0.350097656f, (float16_t)0.578613281f, (float16_t)0.180175781f, + (float16_t)1.63574219f, (float16_t)0.0715332031f, (float16_t)1.58496094f, (float16_t)-0.877929688f, + (float16_t)-2.08789062f, (float16_t)0.549316406f, (float16_t)1.59863281f, (float16_t)0.0859985352f, + (float16_t)-0.170043945f, (float16_t)-1.69824219f, (float16_t)-1.97265625f, (float16_t)1.90917969f, + (float16_t)-0.13269043f, (float16_t)-1.53320312f, (float16_t)-0.0401000977f, (float16_t)-1.92773438f, + (float16_t)1.05175781f, (float16_t)-0.654785156f, (float16_t)-0.963378906f, (float16_t)1.22460938f, + (float16_t)1.72851562f, (float16_t)1.11523438f, (float16_t)-1.92089844f, (float16_t)-1.26464844f, + (float16_t)2.16210938f, (float16_t)1.06054688f, (float16_t)1.0234375f, (float16_t)2.10546875f, + (float16_t)2.19726562f, (float16_t)-1.46582031f, (float16_t)1.38574219f, (float16_t)-2.24414062f, + (float16_t)-1.45410156f, (float16_t)-2.06835938f, (float16_t)0.380126953f, (float16_t)-1.85351562f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16/input2_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16/input2_data.h new file mode 100644 index 00000000..f618cdda --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16/input2_data.h @@ -0,0 +1,42 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_input2[160] = { + (float16_t)2.34375f, (float16_t)0.471435547f, (float16_t)-2.15820312f, (float16_t)0.00358963013f, + (float16_t)-1.26953125f, (float16_t)-0.400146484f, (float16_t)1.72851562f, (float16_t)-1.56542969f, + (float16_t)-0.385498047f, (float16_t)1.06347656f, (float16_t)-1.02441406f, (float16_t)-1.93457031f, + (float16_t)2.39648438f, (float16_t)-0.897460938f, (float16_t)0.229980469f, (float16_t)0.177734375f, + (float16_t)-1.28320312f, (float16_t)0.10369873f, (float16_t)2.03320312f, (float16_t)0.713867188f, + (float16_t)0.514648438f, (float16_t)2.37695312f, (float16_t)1.68359375f, (float16_t)-1.1171875f, + (float16_t)2.10546875f, (float16_t)-0.44140625f, (float16_t)2.05664062f, (float16_t)-1.59570312f, + (float16_t)0.831054688f, (float16_t)-1.30371094f, (float16_t)1.99707031f, (float16_t)-1.94238281f, + (float16_t)0.333007812f, (float16_t)1.41015625f, (float16_t)-1.66601562f, (float16_t)-0.348388672f, + (float16_t)0.341308594f, (float16_t)0.612792969f, (float16_t)1.81054688f, (float16_t)-0.811523438f, + (float16_t)-1.10546875f, (float16_t)2.140625f, (float16_t)2.078125f, (float16_t)0.744628906f, + (float16_t)-0.466064453f, (float16_t)-2.36914062f, (float16_t)-2.02539062f, (float16_t)-0.924316406f, + (float16_t)-2.21875f, (float16_t)-0.677734375f, (float16_t)-1.20605469f, (float16_t)1.71582031f, + (float16_t)2.02539062f, (float16_t)1.29589844f, (float16_t)-2.46875f, (float16_t)-0.0827636719f, + (float16_t)1.9140625f, (float16_t)-0.78515625f, (float16_t)1.953125f, (float16_t)-0.907226562f, + (float16_t)-1.09472656f, (float16_t)-1.55078125f, (float16_t)1.22460938f, (float16_t)1.15136719f, + (float16_t)1.98632812f, (float16_t)0.893066406f, (float16_t)-1.86328125f, (float16_t)0.0350952148f, + (float16_t)1.62988281f, (float16_t)2.07617188f, (float16_t)-1.37597656f, (float16_t)-1.68945312f, + (float16_t)0.105651855f, (float16_t)-2.13476562f, (float16_t)2.32617188f, (float16_t)-1.203125f, + (float16_t)-2.46484375f, (float16_t)-0.837890625f, (float16_t)1.70019531f, (float16_t)-0.184692383f, + (float16_t)-0.206054688f, (float16_t)1.88671875f, (float16_t)-1.46777344f, (float16_t)-0.200561523f, + (float16_t)1.00878906f, (float16_t)2.30664062f, (float16_t)2.01367188f, (float16_t)-0.809570312f, + (float16_t)0.290283203f, (float16_t)1.11425781f, (float16_t)-1.19335938f, (float16_t)1.19042969f, + (float16_t)-1.41113281f, (float16_t)-0.045135498f, (float16_t)0.678710938f, (float16_t)-1.34570312f, + (float16_t)1.71484375f, (float16_t)-1.42773438f, (float16_t)1.74609375f, (float16_t)0.955566406f, + (float16_t)1.02636719f, (float16_t)-2.33007812f, (float16_t)1.85058594f, (float16_t)0.078125f, + (float16_t)0.497070312f, (float16_t)1.09765625f, (float16_t)1.12890625f, (float16_t)-2.2734375f, + (float16_t)-1.31445312f, (float16_t)-1.89941406f, (float16_t)-0.0348205566f, (float16_t)-1.05175781f, + (float16_t)-1.58789062f, (float16_t)2.07617188f, (float16_t)2.31640625f, (float16_t)0.319335938f, + (float16_t)1.94824219f, (float16_t)2.25390625f, (float16_t)-0.494873047f, (float16_t)-1.13378906f, + (float16_t)-2.47460938f, (float16_t)-1.30371094f, (float16_t)0.185424805f, (float16_t)1.19238281f, + (float16_t)-1.75683594f, (float16_t)1.5f, (float16_t)-1.37792969f, (float16_t)-1.19824219f, + (float16_t)0.663085938f, (float16_t)-2.35351562f, (float16_t)-0.0686645508f, (float16_t)-2.21289062f, + (float16_t)-0.658203125f, (float16_t)-1.35742188f, (float16_t)-0.772460938f, (float16_t)-1.93066406f, + (float16_t)-0.124267578f, (float16_t)-1.77832031f, (float16_t)0.193725586f, (float16_t)1.63476562f, + (float16_t)0.572265625f, (float16_t)-0.911132812f, (float16_t)-0.24597168f, (float16_t)-2.37890625f, + (float16_t)1.3984375f, (float16_t)1.02832031f, (float16_t)1.33007812f, (float16_t)1.4296875f, + (float16_t)-1.97167969f, (float16_t)2.44140625f, (float16_t)2.45703125f, (float16_t)2.09960938f, + (float16_t)2.41210938f, (float16_t)-2.12890625f, (float16_t)-1.79394531f, (float16_t)-0.950195312f, + (float16_t)0.608398438f, (float16_t)1.7265625f, (float16_t)0.854980469f, (float16_t)-0.955078125f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16/output_ref_data.h new file mode 100644 index 00000000..408b606d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16/output_ref_data.h @@ -0,0 +1,42 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_output_ref[160] = { + (float16_t)2.7890625f, (float16_t)0.80859375f, (float16_t)0.725585938f, (float16_t)0.00774765015f, + (float16_t)0.998535156f, (float16_t)-0.8046875f, (float16_t)3.26953125f, (float16_t)-3.40039062f, + (float16_t)-0.076965332f, (float16_t)-2.40039062f, (float16_t)0.994628906f, (float16_t)-3.8984375f, + (float16_t)-1.01660156f, (float16_t)-0.333740234f, (float16_t)-0.225708008f, (float16_t)-0.415039062f, + (float16_t)-0.921386719f, (float16_t)-0.0305633545f, (float16_t)-4.12109375f, (float16_t)-0.690429688f, + (float16_t)-1.28320312f, (float16_t)0.575683594f, (float16_t)-4.078125f, (float16_t)-2.63867188f, + (float16_t)2.15820312f, (float16_t)0.931640625f, (float16_t)2.19140625f, (float16_t)3.33398438f, + (float16_t)1.04785156f, (float16_t)-2.328125f, (float16_t)-3.65039062f, (float16_t)3.18359375f, + (float16_t)0.0780639648f, (float16_t)-1.85742188f, (float16_t)-1.671875f, (float16_t)-0.212524414f, + (float16_t)0.571777344f, (float16_t)0.813964844f, (float16_t)-2.2734375f, (float16_t)-1.74707031f, + (float16_t)0.0349731445f, (float16_t)-4.9140625f, (float16_t)-4.98046875f, (float16_t)0.0838623047f, + (float16_t)0.962890625f, (float16_t)-5.07421875f, (float16_t)3.87890625f, (float16_t)-0.842285156f, + (float16_t)-0.920898438f, (float16_t)1.18847656f, (float16_t)-1.40722656f, (float16_t)-4.1328125f, + (float16_t)2.83007812f, (float16_t)0.252197266f, (float16_t)4.8671875f, (float16_t)0.0360107422f, + (float16_t)-2.32421875f, (float16_t)0.317871094f, (float16_t)-4.609375f, (float16_t)1.40625f, + (float16_t)1.69335938f, (float16_t)-0.0884399414f, (float16_t)2.41601562f, (float16_t)0.0814208984f, + (float16_t)3.8125f, (float16_t)-0.907226562f, (float16_t)-2.0f, (float16_t)0.0844726562f, + (float16_t)1.49316406f, (float16_t)-2.27734375f, (float16_t)0.384765625f, (float16_t)-1.74316406f, + (float16_t)-0.138061523f, (float16_t)4.39453125f, (float16_t)1.97460938f, (float16_t)-0.1875f, + (float16_t)-1.7578125f, (float16_t)-2.07421875f, (float16_t)-0.0316772461f, (float16_t)-0.435546875f, + (float16_t)-0.322753906f, (float16_t)-0.416748047f, (float16_t)-0.328369141f, (float16_t)0.270996094f, + (float16_t)-1.36425781f, (float16_t)-2.1484375f, (float16_t)3.04492188f, (float16_t)-0.228515625f, + (float16_t)0.458251953f, (float16_t)-1.82128906f, (float16_t)1.8671875f, (float16_t)1.85546875f, + (float16_t)2.81054688f, (float16_t)0.053314209f, (float16_t)0.509765625f, (float16_t)3.2265625f, + (float16_t)0.888671875f, (float16_t)0.206054688f, (float16_t)-0.576660156f, (float16_t)-0.126098633f, + (float16_t)-0.314208984f, (float16_t)2.65234375f, (float16_t)-2.1171875f, (float16_t)0.0930786133f, + (float16_t)0.701660156f, (float16_t)-0.657714844f, (float16_t)0.884765625f, (float16_t)0.212158203f, + (float16_t)1.08203125f, (float16_t)0.847167969f, (float16_t)-0.0440063477f, (float16_t)-0.132202148f, + (float16_t)-3.48632812f, (float16_t)-0.201660156f, (float16_t)-4.7109375f, (float16_t)-0.691894531f, + (float16_t)0.192504883f, (float16_t)-3.7421875f, (float16_t)0.0278015137f, (float16_t)1.2109375f, + (float16_t)2.76953125f, (float16_t)0.456542969f, (float16_t)0.10736084f, (float16_t)0.21472168f, + (float16_t)-2.87304688f, (float16_t)0.107299805f, (float16_t)-2.18359375f, (float16_t)1.05273438f, + (float16_t)-1.38476562f, (float16_t)-1.29296875f, (float16_t)-0.109741211f, (float16_t)-0.190307617f, + (float16_t)0.111999512f, (float16_t)2.3046875f, (float16_t)1.5234375f, (float16_t)-3.68554688f, + (float16_t)0.0164794922f, (float16_t)2.7265625f, (float16_t)-0.00776672363f, (float16_t)-3.15039062f, + (float16_t)0.6015625f, (float16_t)0.596679688f, (float16_t)0.236938477f, (float16_t)-2.91210938f, + (float16_t)2.41796875f, (float16_t)1.14746094f, (float16_t)-2.5546875f, (float16_t)-1.80664062f, + (float16_t)-4.26171875f, (float16_t)2.58984375f, (float16_t)2.51367188f, (float16_t)4.421875f, + (float16_t)5.296875f, (float16_t)3.12109375f, (float16_t)-2.484375f, (float16_t)2.1328125f, + (float16_t)-0.884765625f, (float16_t)-3.5703125f, (float16_t)0.325195312f, (float16_t)1.76953125f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/config_data.h new file mode 100644 index 00000000..68763600 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/config_data.h @@ -0,0 +1,9 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MUL_F16_SPILL_DST_SIZE 245 +#define MUL_F16_SPILL_OUT_ACTIVATION_MIN (float16_t) - 1.5f +#define MUL_F16_SPILL_OUT_ACTIVATION_MAX (float16_t)1.0f +#define MUL_F16_SPILL_INPUT1_OFFSET 0 +#define MUL_F16_SPILL_INPUT2_OFFSET 0 +#define MUL_F16_SPILL_OUTPUT_MULT 0 +#define MUL_F16_SPILL_OUTPUT_SHIFT 0 +#define MUL_F16_SPILL_OUTPUT_OFFSET 0 diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input1_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input1_data.h new file mode 100644 index 00000000..129e4466 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input1_data.h @@ -0,0 +1,64 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_spill_input1[245] = { + (float16_t)1.52539062f, (float16_t)-0.848144531f, (float16_t)-0.52734375f, (float16_t)-1.30566406f, + (float16_t)0.450195312f, (float16_t)-0.23449707f, (float16_t)1.99023438f, (float16_t)0.643554688f, + (float16_t)-2.40429688f, (float16_t)1.55078125f, (float16_t)0.700195312f, (float16_t)-1.40234375f, + (float16_t)-0.616210938f, (float16_t)0.759765625f, (float16_t)0.946777344f, (float16_t)-1.39550781f, + (float16_t)-2.03125f, (float16_t)-0.956054688f, (float16_t)2.1171875f, (float16_t)-0.309326172f, + (float16_t)1.67480469f, (float16_t)1.21777344f, (float16_t)-1.89355469f, (float16_t)-1.97753906f, + (float16_t)1.75097656f, (float16_t)-0.169677734f, (float16_t)-0.716796875f, (float16_t)0.00659942627f, + (float16_t)1.06542969f, (float16_t)-1.11425781f, (float16_t)-0.546875f, (float16_t)-1.53125f, + (float16_t)-0.818847656f, (float16_t)-1.50390625f, (float16_t)-1.30664062f, (float16_t)-1.04003906f, + (float16_t)0.872558594f, (float16_t)2.05078125f, (float16_t)2.41796875f, (float16_t)1.15136719f, + (float16_t)-0.506835938f, (float16_t)-1.06445312f, (float16_t)-1.31054688f, (float16_t)0.190551758f, + (float16_t)1.29101562f, (float16_t)-1.35351562f, (float16_t)1.18066406f, (float16_t)-1.79296875f, + (float16_t)1.94921875f, (float16_t)2.13476562f, (float16_t)0.444335938f, (float16_t)1.171875f, + (float16_t)0.689453125f, (float16_t)0.384277344f, (float16_t)0.178588867f, (float16_t)-1.76464844f, + (float16_t)-0.083984375f, (float16_t)1.04980469f, (float16_t)0.146240234f, (float16_t)2.421875f, + (float16_t)-1.04589844f, (float16_t)-1.90039062f, (float16_t)0.911132812f, (float16_t)-1.86816406f, + (float16_t)-1.15625f, (float16_t)0.0044708252f, (float16_t)-1.95507812f, (float16_t)1.30371094f, + (float16_t)-1.84179688f, (float16_t)2.046875f, (float16_t)1.40722656f, (float16_t)-0.0715332031f, + (float16_t)-1.23730469f, (float16_t)-1.98730469f, (float16_t)-1.0390625f, (float16_t)-1.07128906f, + (float16_t)-0.94140625f, (float16_t)0.235961914f, (float16_t)-0.91015625f, (float16_t)2.0078125f, + (float16_t)0.889648438f, (float16_t)0.331787109f, (float16_t)0.794921875f, (float16_t)-1.52050781f, + (float16_t)0.504394531f, (float16_t)1.97070312f, (float16_t)-2.05078125f, (float16_t)-1.08007812f, + (float16_t)1.29785156f, (float16_t)1.18847656f, (float16_t)2.43359375f, (float16_t)-0.0947265625f, + (float16_t)0.0225830078f, (float16_t)1.08007812f, (float16_t)-2.29296875f, (float16_t)0.997558594f, + (float16_t)0.28515625f, (float16_t)1.94824219f, (float16_t)-0.158081055f, (float16_t)1.00292969f, + (float16_t)-2.26367188f, (float16_t)1.890625f, (float16_t)-1.51367188f, (float16_t)2.203125f, + (float16_t)0.615722656f, (float16_t)1.59960938f, (float16_t)0.327148438f, (float16_t)0.0824584961f, + (float16_t)-1.16210938f, (float16_t)1.54589844f, (float16_t)-1.60644531f, (float16_t)0.433837891f, + (float16_t)1.04980469f, (float16_t)-2.11523438f, (float16_t)0.573730469f, (float16_t)0.791015625f, + (float16_t)0.744140625f, (float16_t)-1.37304688f, (float16_t)-1.25683594f, (float16_t)1.83886719f, + (float16_t)-0.459716797f, (float16_t)1.74023438f, (float16_t)-0.799316406f, (float16_t)0.713867188f, + (float16_t)2.04101562f, (float16_t)-1.04980469f, (float16_t)-0.136108398f, (float16_t)-2.04296875f, + (float16_t)1.80664062f, (float16_t)0.395996094f, (float16_t)-2.09179688f, (float16_t)-1.08300781f, + (float16_t)-1.84960938f, (float16_t)-0.931152344f, (float16_t)1.22851562f, (float16_t)1.77734375f, + (float16_t)-1.42578125f, (float16_t)2.21679688f, (float16_t)-0.0484619141f, (float16_t)-1.55273438f, + (float16_t)0.0969238281f, (float16_t)-0.812011719f, (float16_t)-2.234375f, (float16_t)1.31054688f, + (float16_t)-0.482666016f, (float16_t)-1.11230469f, (float16_t)-2.0078125f, (float16_t)2.10742188f, + (float16_t)0.0985107422f, (float16_t)-0.91015625f, (float16_t)-2.16992188f, (float16_t)2.15625f, + (float16_t)-1.79394531f, (float16_t)-1.6015625f, (float16_t)2.359375f, (float16_t)-2.18359375f, + (float16_t)1.00585938f, (float16_t)-1.10351562f, (float16_t)-2.15625f, (float16_t)-2.23828125f, + (float16_t)1.81445312f, (float16_t)-2.36523438f, (float16_t)-0.191894531f, (float16_t)0.218261719f, + (float16_t)1.10742188f, (float16_t)2.21679688f, (float16_t)-1.40429688f, (float16_t)2.23046875f, + (float16_t)-2.33789062f, (float16_t)-1.12402344f, (float16_t)0.778320312f, (float16_t)-0.782226562f, + (float16_t)-0.137207031f, (float16_t)-0.969726562f, (float16_t)1.91894531f, (float16_t)-1.41015625f, + (float16_t)0.929199219f, (float16_t)-0.921386719f, (float16_t)0.756347656f, (float16_t)-1.12988281f, + (float16_t)-1.65820312f, (float16_t)-1.23632812f, (float16_t)1.90625f, (float16_t)-1.0078125f, + (float16_t)-0.409667969f, (float16_t)1.89941406f, (float16_t)0.554199219f, (float16_t)1.54492188f, + (float16_t)0.97265625f, (float16_t)2.45507812f, (float16_t)-2.08007812f, (float16_t)0.881347656f, + (float16_t)1.1015625f, (float16_t)-1.6640625f, (float16_t)0.145629883f, (float16_t)1.21679688f, + (float16_t)-0.797851562f, (float16_t)0.631835938f, (float16_t)1.38769531f, (float16_t)-0.595214844f, + (float16_t)-0.308837891f, (float16_t)-0.322021484f, (float16_t)-2.2265625f, (float16_t)-2.42773438f, + (float16_t)1.62304688f, (float16_t)-2.28710938f, (float16_t)0.334716797f, (float16_t)0.61328125f, + (float16_t)1.16699219f, (float16_t)-0.75390625f, (float16_t)0.166870117f, (float16_t)-0.535644531f, + (float16_t)0.654296875f, (float16_t)-2.47851562f, (float16_t)-0.831542969f, (float16_t)-2.484375f, + (float16_t)0.522949219f, (float16_t)-0.797851562f, (float16_t)-1.18945312f, (float16_t)-0.912109375f, + (float16_t)-0.182617188f, (float16_t)1.87109375f, (float16_t)-1.87109375f, (float16_t)2.41601562f, + (float16_t)-0.203979492f, (float16_t)-1.72753906f, (float16_t)0.462158203f, (float16_t)-1.14746094f, + (float16_t)-2.125f, (float16_t)-1.56347656f, (float16_t)2.17773438f, (float16_t)-1.36914062f, + (float16_t)-1.39941406f, (float16_t)0.0885620117f, (float16_t)0.478271484f, (float16_t)1.97070312f, + (float16_t)1.50878906f, (float16_t)-0.247314453f, (float16_t)1.18554688f, (float16_t)2.3359375f, + (float16_t)0.541015625f, (float16_t)1.78027344f, (float16_t)-0.193237305f, (float16_t)1.62207031f, + (float16_t)-1.96777344f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input2_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input2_data.h new file mode 100644 index 00000000..7c3d35c5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/input2_data.h @@ -0,0 +1,64 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_spill_input2[245] = { + (float16_t)2.25195312f, (float16_t)0.172851562f, (float16_t)1.22558594f, (float16_t)2.2734375f, + (float16_t)0.330078125f, (float16_t)-1.45898438f, (float16_t)2.03320312f, (float16_t)1.70996094f, + (float16_t)1.09765625f, (float16_t)-2.484375f, (float16_t)-1.95117188f, (float16_t)-2.45898438f, + (float16_t)-0.900390625f, (float16_t)1.078125f, (float16_t)0.575683594f, (float16_t)-2.19921875f, + (float16_t)2.32421875f, (float16_t)-2.35546875f, (float16_t)0.0512695312f, (float16_t)1.88574219f, + (float16_t)-2.453125f, (float16_t)0.677246094f, (float16_t)1.04785156f, (float16_t)-1.34277344f, + (float16_t)1.81738281f, (float16_t)2.19726562f, (float16_t)1.84863281f, (float16_t)0.752441406f, + (float16_t)1.7109375f, (float16_t)1.77050781f, (float16_t)-0.969726562f, (float16_t)0.504882812f, + (float16_t)0.288330078f, (float16_t)-2.28710938f, (float16_t)0.228881836f, (float16_t)-0.767089844f, + (float16_t)-1.21191406f, (float16_t)-0.904296875f, (float16_t)-0.379394531f, (float16_t)2.45898438f, + (float16_t)-2.37695312f, (float16_t)-0.352783203f, (float16_t)-0.543457031f, (float16_t)-0.768066406f, + (float16_t)-0.974121094f, (float16_t)1.31835938f, (float16_t)1.8984375f, (float16_t)-0.302978516f, + (float16_t)2.2265625f, (float16_t)-2.31445312f, (float16_t)0.248779297f, (float16_t)-1.53808594f, + (float16_t)2.1875f, (float16_t)1.40429688f, (float16_t)-1.69824219f, (float16_t)-0.450439453f, + (float16_t)0.584960938f, (float16_t)1.74511719f, (float16_t)-0.908691406f, (float16_t)2.18945312f, + (float16_t)0.959472656f, (float16_t)-1.265625f, (float16_t)-1.31445312f, (float16_t)-1.09667969f, + (float16_t)-2.4453125f, (float16_t)1.84277344f, (float16_t)0.493164062f, (float16_t)-1.88671875f, + (float16_t)1.74316406f, (float16_t)-2.48828125f, (float16_t)-1.0f, (float16_t)-1.88085938f, + (float16_t)-1.48730469f, (float16_t)-0.334228516f, (float16_t)1.70410156f, (float16_t)2.16796875f, + (float16_t)0.112060547f, (float16_t)1.96484375f, (float16_t)-0.130737305f, (float16_t)0.368408203f, + (float16_t)-0.427246094f, (float16_t)2.49804688f, (float16_t)-1.00878906f, (float16_t)-2.4609375f, + (float16_t)-0.963378906f, (float16_t)-0.666503906f, (float16_t)2.375f, (float16_t)1.96972656f, + (float16_t)-1.95605469f, (float16_t)-1.0390625f, (float16_t)-0.0637817383f, (float16_t)-0.725585938f, + (float16_t)0.708984375f, (float16_t)2.18945312f, (float16_t)1.15625f, (float16_t)0.732421875f, + (float16_t)0.635253906f, (float16_t)-2.27929688f, (float16_t)-1.66894531f, (float16_t)-1.453125f, + (float16_t)-2.2578125f, (float16_t)-0.262939453f, (float16_t)0.811523438f, (float16_t)0.105834961f, + (float16_t)0.94921875f, (float16_t)-1.53808594f, (float16_t)1.23632812f, (float16_t)0.0842285156f, + (float16_t)-0.346435547f, (float16_t)1.02148438f, (float16_t)-1.97558594f, (float16_t)0.993164062f, + (float16_t)-0.217163086f, (float16_t)-0.520996094f, (float16_t)1.06347656f, (float16_t)1.23242188f, + (float16_t)0.863769531f, (float16_t)1.28417969f, (float16_t)1.94140625f, (float16_t)0.645507812f, + (float16_t)-1.11035156f, (float16_t)0.963867188f, (float16_t)-1.4453125f, (float16_t)-1.60644531f, + (float16_t)-1.34960938f, (float16_t)-1.19824219f, (float16_t)-2.22070312f, (float16_t)0.796875f, + (float16_t)2.0234375f, (float16_t)2.39453125f, (float16_t)-1.99023438f, (float16_t)-1.80566406f, + (float16_t)0.692871094f, (float16_t)-0.641601562f, (float16_t)-1.33007812f, (float16_t)-1.71777344f, + (float16_t)2.05664062f, (float16_t)-2.11132812f, (float16_t)1.27734375f, (float16_t)-1.62011719f, + (float16_t)-2.17578125f, (float16_t)0.414550781f, (float16_t)1.06347656f, (float16_t)1.22070312f, + (float16_t)0.656738281f, (float16_t)-1.6484375f, (float16_t)1.42382812f, (float16_t)-0.471923828f, + (float16_t)-1.83007812f, (float16_t)1.37988281f, (float16_t)1.04980469f, (float16_t)2.01367188f, + (float16_t)-2.45117188f, (float16_t)-1.52050781f, (float16_t)-0.321289062f, (float16_t)-0.279785156f, + (float16_t)1.77929688f, (float16_t)-2.16601562f, (float16_t)-0.810058594f, (float16_t)2.04882812f, + (float16_t)1.55761719f, (float16_t)1.30273438f, (float16_t)-0.723144531f, (float16_t)0.281494141f, + (float16_t)-2.11523438f, (float16_t)2.34179688f, (float16_t)0.691894531f, (float16_t)-1.21289062f, + (float16_t)-0.316162109f, (float16_t)0.912597656f, (float16_t)-0.328125f, (float16_t)-1.38183594f, + (float16_t)-0.135742188f, (float16_t)0.596679688f, (float16_t)-2.22851562f, (float16_t)0.779296875f, + (float16_t)0.400146484f, (float16_t)0.659179688f, (float16_t)0.249145508f, (float16_t)1.53613281f, + (float16_t)0.784667969f, (float16_t)1.30957031f, (float16_t)1.85449219f, (float16_t)-2.40820312f, + (float16_t)1.38964844f, (float16_t)-0.264892578f, (float16_t)0.711425781f, (float16_t)-0.884277344f, + (float16_t)-2.109375f, (float16_t)1.6328125f, (float16_t)-0.931640625f, (float16_t)1.57226562f, + (float16_t)2.40039062f, (float16_t)1.4375f, (float16_t)1.41601562f, (float16_t)0.713378906f, + (float16_t)-1.625f, (float16_t)0.924316406f, (float16_t)0.645507812f, (float16_t)0.938476562f, + (float16_t)1.26953125f, (float16_t)0.14855957f, (float16_t)0.475341797f, (float16_t)-0.814453125f, + (float16_t)0.909667969f, (float16_t)0.314941406f, (float16_t)-2.2265625f, (float16_t)-0.654296875f, + (float16_t)-1.4296875f, (float16_t)-2.36914062f, (float16_t)-0.106872559f, (float16_t)0.473144531f, + (float16_t)1.43066406f, (float16_t)-2.06445312f, (float16_t)-2.234375f, (float16_t)1.39550781f, + (float16_t)-0.965820312f, (float16_t)-2.12695312f, (float16_t)0.197875977f, (float16_t)0.346923828f, + (float16_t)0.321044922f, (float16_t)-0.292480469f, (float16_t)0.555664062f, (float16_t)-1.02929688f, + (float16_t)-2.1171875f, (float16_t)-2.2109375f, (float16_t)-1.98046875f, (float16_t)-0.075012207f, + (float16_t)2.39453125f, (float16_t)0.983398438f, (float16_t)-1.32128906f, (float16_t)1.01757812f, + (float16_t)0.480712891f, (float16_t)-1.61230469f, (float16_t)-2.30273438f, (float16_t)-2.0078125f, + (float16_t)2.49609375f, (float16_t)2.375f, (float16_t)-2.22265625f, (float16_t)-2.30273438f, + (float16_t)-0.23425293f, (float16_t)-0.999511719f, (float16_t)-2.04101562f, (float16_t)-1.75683594f, + (float16_t)-1.18164062f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/output_ref_data.h new file mode 100644 index 00000000..c4e7d19b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/output_ref_data.h @@ -0,0 +1,246 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t mul_f16_spill_output_ref[245] = {(float16_t)1.0f, + (float16_t)-0.146606445f, + (float16_t)-0.646484375f, + (float16_t)-1.5f, + (float16_t)0.14855957f, + (float16_t)0.342041016f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.3671875f, + (float16_t)1.0f, + (float16_t)0.555175781f, + (float16_t)0.818847656f, + (float16_t)0.545410156f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.108459473f, + (float16_t)-0.583496094f, + (float16_t)-1.5f, + (float16_t)0.824707031f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-0.372558594f, + (float16_t)-1.32519531f, + (float16_t)0.00496292114f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)0.530273438f, + (float16_t)-0.772949219f, + (float16_t)-0.236206055f, + (float16_t)1.0f, + (float16_t)-0.299072266f, + (float16_t)0.797851562f, + (float16_t)-1.05664062f, + (float16_t)-1.5f, + (float16_t)-0.91796875f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)0.375732422f, + (float16_t)0.712402344f, + (float16_t)-0.146362305f, + (float16_t)-1.2578125f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.54296875f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)0.110595703f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.540039062f, + (float16_t)-0.303466797f, + (float16_t)0.794921875f, + (float16_t)-0.0491027832f, + (float16_t)1.0f, + (float16_t)-0.13293457f, + (float16_t)1.0f, + (float16_t)-1.00292969f, + (float16_t)1.0f, + (float16_t)-1.19726562f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)0.00823974609f, + (float16_t)-0.964355469f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.40722656f, + (float16_t)0.134521484f, + (float16_t)1.0f, + (float16_t)0.6640625f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-0.10546875f, + (float16_t)0.463623047f, + (float16_t)0.119018555f, + (float16_t)0.739746094f, + (float16_t)-0.380126953f, + (float16_t)0.829101562f, + (float16_t)-0.802246094f, + (float16_t)1.0f, + (float16_t)-0.486083984f, + (float16_t)-1.31347656f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.234375f, + (float16_t)-0.155273438f, + (float16_t)0.0687255859f, + (float16_t)0.0160064697f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)0.73046875f, + (float16_t)0.181152344f, + (float16_t)-1.5f, + (float16_t)0.263671875f, + (float16_t)-1.45703125f, + (float16_t)1.0f, + (float16_t)-0.497070312f, + (float16_t)-1.22851562f, + (float16_t)0.233276367f, + (float16_t)0.584472656f, + (float16_t)-1.5f, + (float16_t)0.404296875f, + (float16_t)0.00694274902f, + (float16_t)0.402832031f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)0.430908203f, + (float16_t)-0.228149414f, + (float16_t)1.0f, + (float16_t)0.610351562f, + (float16_t)0.975097656f, + (float16_t)0.643066406f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.510742188f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.14648438f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.302246094f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)0.947753906f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.28222656f, + (float16_t)0.597167969f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-0.061920166f, + (float16_t)1.0f, + (float16_t)-0.210693359f, + (float16_t)-0.336669922f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)-0.316894531f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)-0.994628906f, + (float16_t)-0.180297852f, + (float16_t)-1.25585938f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-0.7578125f, + (float16_t)0.610839844f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)0.138793945f, + (float16_t)0.0614318848f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)-0.971191406f, + (float16_t)-1.5f, + (float16_t)0.739257812f, + (float16_t)-1.02539062f, + (float16_t)-0.255371094f, + (float16_t)1.0f, + (float16_t)0.0186157227f, + (float16_t)-0.578613281f, + (float16_t)-1.5f, + (float16_t)-1.09863281f, + (float16_t)0.371826172f, + (float16_t)-0.606933594f, + (float16_t)0.188476562f, + (float16_t)-1.5f, + (float16_t)-1.30175781f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-0.569335938f, + (float16_t)-0.502929688f, + (float16_t)0.394287109f, + (float16_t)-1.36621094f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)0.206298828f, + (float16_t)0.868164062f, + (float16_t)1.0f, + (float16_t)0.583984375f, + (float16_t)0.895507812f, + (float16_t)-0.55859375f, + (float16_t)-0.391845703f, + (float16_t)-0.0478210449f, + (float16_t)-1.05859375f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-0.720703125f, + (float16_t)-0.745605469f, + (float16_t)-0.401367188f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)-0.0178375244f, + (float16_t)-0.253417969f, + (float16_t)0.936035156f, + (float16_t)1.0f, + (float16_t)1.0f, + (float16_t)-1.5f, + (float16_t)-0.504882812f, + (float16_t)1.0f, + (float16_t)-0.235229492f, + (float16_t)-0.316162109f, + (float16_t)-0.0586242676f, + (float16_t)-0.547363281f, + (float16_t)-1.04003906f, + (float16_t)-1.5f, + (float16_t)0.431884766f, + (float16_t)1.0f, + (float16_t)-0.915527344f, + (float16_t)0.0861206055f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-1.39355469f, + (float16_t)-0.672851562f, + (float16_t)-0.142822266f, + (float16_t)-1.1015625f, + (float16_t)-1.5f, + (float16_t)1.0f, + (float16_t)-0.587402344f, + (float16_t)-1.5f, + (float16_t)-1.5f, + (float16_t)-0.126708984f, + (float16_t)-1.5f, + (float16_t)0.394287109f, + (float16_t)-1.5f, + (float16_t)1.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f16_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f16_spill/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32/config_data.h new file mode 100644 index 00000000..1a2dbdc8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32/config_data.h @@ -0,0 +1,9 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MUL_F32_DST_SIZE 160 +#define MUL_F32_OUT_ACTIVATION_MIN -10.0f +#define MUL_F32_OUT_ACTIVATION_MAX 10.0f +#define MUL_F32_INPUT1_OFFSET 0 +#define MUL_F32_INPUT2_OFFSET 0 +#define MUL_F32_OUTPUT_MULT 0 +#define MUL_F32_OUTPUT_SHIFT 0 +#define MUL_F32_OUTPUT_OFFSET 0 diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32/input1_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32/input1_data.h new file mode 100644 index 00000000..c977761d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32/input1_data.h @@ -0,0 +1,25 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_input1[160] = { + 1.17061973f, -1.79022408f, 1.10234463f, 0.990000606f, 1.00186765f, 1.9831394f, -2.08723569f, + 1.58095264f, 1.94596183f, -0.0735564306f, -0.847839415f, 1.51840353f, -0.872432828f, -1.9377476f, + -0.92310065f, 2.48566794f, 0.240134925f, 0.49775061f, -1.21675658f, -1.07558954f, -0.37856248f, + 0.046520751f, -0.904950202f, 0.721140563f, -1.98645532f, -0.563110054f, -1.02778065f, -0.937972605f, + -0.331687689f, -1.02905476f, 1.84191799f, 1.45497f, -0.385220259f, -1.43599653f, 0.480193019f, + -0.565792918f, -0.722258508f, -1.04405093f, -1.55287027f, 0.703362107f, -0.423761547f, 0.836534142f, + 0.0986322165f, -1.7536931f, -1.99348092f, -1.34431636f, -0.185362145f, 1.27896965f, -2.31273079f, + -1.89086366f, 0.740489602f, -1.78651583f, 1.86875641f, 1.12364054f, -1.64005506f, 2.27217221f, + 1.27332056f, -1.96695721f, -2.44662619f, -1.51575911f, 1.74085832f, 2.15644765f, -0.492193192f, + 0.870865524f, 2.23688674f, 1.92202234f, 1.96244884f, 1.9034934f, 0.250568539f, 2.29801965f, + -2.47269917f, -0.756700814f, 0.47259593f, 0.621327758f, -1.61661828f, -0.568143129f, 1.65478218f, + 2.1099565f, 0.920528352f, -1.63898647f, 1.99718571f, -1.64170492f, 1.9563607f, 0.613744915f, + -1.18706763f, 0.79094708f, 1.920874f, 0.4166767f, 1.38948035f, 1.28205907f, 2.45868206f, + -0.548889339f, 0.922328651f, 0.531748533f, -2.011904f, 0.369832367f, 2.2988565f, -0.460701585f, + 0.696087599f, -1.00626838f, 2.17867064f, -1.24286306f, 0.275911152f, -2.34086895f, 0.517533422f, + 2.03416944f, -2.02842808f, -1.72010624f, 1.6279093f, 0.146353006f, 2.29303598f, 0.382932037f, + 1.43592262f, 1.79833961f, 0.659858525f, 2.02122164f, 1.52090704f, -0.278657705f, -0.0535798892f, + -0.516342223f, -1.36343753f, -0.805944681f, -0.776224315f, -1.92204511f, -2.27118754f, 1.66328263f, + 0.593030274f, -0.539339423f, 0.831442654f, 0.0884996355f, 2.07010961f, -1.65427327f, 0.615912139f, + 0.395730376f, 1.65379393f, -1.57625079f, 1.89656806f, -1.62308371f, 2.41377306f, -0.623967409f, + -2.09247375f, 1.19082367f, 1.40051937f, -0.300352186f, -0.0836451724f, 0.925207257f, 0.0504673198f, + 0.665778935f, 0.0180574972f, 1.17863798f, 2.49315524f, -1.33866012f, 0.00996230729f, -0.168817729f, + 0.0536938794f, 2.2659688f, 2.15622211f, -1.99863529f, -1.23613751f, -0.985259473f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32/input2_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32/input2_data.h new file mode 100644 index 00000000..2d429749 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32/input2_data.h @@ -0,0 +1,25 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_input2[160] = { + 0.637798369f, -1.36263585f, -1.01030552f, -0.310956031f, -2.48165846f, 1.9238584f, -0.0579318069f, + -0.507070601f, 1.85682428f, -2.16024995f, 0.300379723f, 0.396400452f, 0.245212421f, -1.89206886f, + -0.504995286f, -1.02116096f, 2.03260493f, 0.0198908336f, 1.61508131f, 0.318058968f, -2.18602633f, + -1.34412396f, -1.2155745f, -1.29092932f, -1.62498486f, 0.456473261f, -0.803424239f, 0.0481424704f, + -0.703568816f, 0.163294375f, -2.37008524f, 0.58835578f, 2.14485455f, -1.7140286f, 1.78785551f, + 0.66509676f, 1.89981747f, -0.718596399f, 2.46434116f, -1.60527098f, 1.18187523f, -1.84241295f, + -1.01728952f, -0.853554368f, 1.33530068f, -1.01214719f, 1.88210094f, -1.76988935f, -1.884655f, + -0.752321601f, -1.82260215f, 1.58846605f, -1.43856096f, 0.991502583f, -1.6973207f, -0.152311578f, + 1.71059811f, -0.135600954f, -1.77589798f, -1.62569106f, 1.38409901f, 0.449784517f, 2.09200692f, + 0.43326959f, -0.777993917f, -2.44728565f, 0.637454152f, -1.28932166f, 1.33440316f, 0.684843242f, + -0.910336196f, -2.36476374f, -2.44548869f, -1.15343964f, 0.689161956f, -1.21146905f, 2.36716318f, + -2.01821136f, 1.54092455f, 1.76783168f, 0.727753997f, 0.0908702388f, 1.42836952f, 0.675040185f, + 2.16822624f, -1.87115824f, 1.61980021f, 0.556556344f, 1.39997733f, -0.70470506f, 0.309011817f, + -0.250735432f, -0.291246951f, -0.321275383f, 2.05510449f, -1.27575922f, 1.94473863f, 1.9337517f, + 1.49765396f, -0.823674083f, 0.235191107f, -0.0698421672f, -0.112604931f, 0.766218007f, -1.03376079f, + 0.665063322f, 0.448070675f, -0.596905291f, -0.659082413f, 2.39155149f, -1.11266351f, -1.16014206f, + 2.42247772f, -2.11343145f, 2.01249266f, 0.542610586f, 1.42970026f, -1.04176509f, 1.25968277f, + 1.91630042f, -1.50425446f, -1.37650621f, 1.41245472f, 2.46958447f, -0.160706654f, 2.26838923f, + -2.43078876f, 0.500284255f, -0.201567948f, 1.3984673f, -0.374124885f, -0.790505111f, -0.0256832317f, + 2.0094347f, -1.48326492f, -0.657837033f, -1.29173684f, -2.31717968f, 1.39082503f, -0.16154623f, + -0.55628562f, -0.700595737f, -0.713245392f, 2.24659824f, -0.037778262f, 1.55764866f, 0.0300845839f, + 2.02029872f, -1.88931179f, -1.73026538f, -1.21449256f, -1.45024979f, -1.88058734f, 0.328126401f, + 1.4855572f, 1.27048635f, -0.654995322f, 0.280548662f, 0.288764328f, -1.61307955f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32/output_ref_data.h new file mode 100644 index 00000000..629f3d76 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32/output_ref_data.h @@ -0,0 +1,25 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_output_ref[160] = { + 0.746619344f, 2.43942356f, -1.11370492f, -0.307846665f, -2.48629332f, 3.81527948f, 0.120917335f, + -0.801654637f, 3.61330914f, 0.158900276f, -0.254673779f, 0.601895869f, -0.213931367f, 3.6663518f, + 0.466161489f, -2.53826714f, 0.488099426f, 0.00990067422f, -1.96516085f, -0.342100888f, 0.82754755f, + -0.0625296533f, 1.10003436f, -0.930941522f, 3.22795987f, -0.257044673f, 0.825743914f, -0.0451563187f, + 0.233365119f, -0.16803886f, -4.36550283f, 0.856040001f, -0.826241434f, 2.46133924f, 0.858515739f, + -0.37630704f, -1.37215936f, 0.750251234f, -3.82680225f, -1.12908673f, -0.500833273f, -1.54124129f, + -0.10033752f, 1.49687243f, -2.66189647f, 1.36064601f, -0.348870277f, -2.26363468f, 4.3586998f, + 1.42253757f, -1.34961796f, -2.83781981f, -2.68831992f, 1.11409247f, 2.78369951f, -0.346078128f, + 2.17813969f, 0.266721278f, 4.34495831f, 2.46415591f, 2.40952015f, 0.969936788f, -1.02967155f, + 0.377319545f, -1.74028432f, -4.70373774f, 1.2509712f, -2.45421529f, 0.334359437f, 1.57378328f, + 2.25098753f, 1.7894187f, -1.15572798f, -0.716664076f, -1.11411178f, 0.688287795f, 3.91713953f, + -4.25833797f, 1.41846478f, -2.89745212f, 1.45345986f, -0.149182111f, 2.79440594f, 0.414302468f, + -2.57383108f, -1.47998714f, 3.11143208f, 0.23190406f, 1.94524097f, -0.903473496f, 0.75976181f, + 0.137626007f, -0.268625408f, -0.170837715f, -4.13467312f, -0.471817046f, 4.47067499f, -0.890882492f, + 1.04249835f, 0.828837216f, 0.512403965f, 0.0868042484f, -0.0310689565f, -1.79361594f, -0.535005748f, + 1.35285151f, -0.908879161f, 1.02674055f, -1.0729264f, 0.350010753f, -2.55137753f, -0.444255561f, + 3.47849059f, -3.80066752f, 1.32796049f, 1.09673631f, 2.1744411f, 0.290295869f, -0.0674936622f, + -0.989466786f, 2.05095696f, 1.10938787f, -1.09638166f, -4.7466526f, 0.364994943f, 3.77297235f, + -1.4415313f, -0.269823015f, -0.167592183f, 0.123763844f, -0.774479508f, 1.30771148f, -0.0158186145f, + 0.795194328f, -2.45301461f, 1.03691614f, -2.44986677f, 3.76097655f, 3.35713601f, 0.100799583f, + 1.16401303f, -0.834285975f, -0.998914003f, -0.674770713f, 0.00315996935f, 1.4411478f, 0.00151828828f, + 1.34507239f, -0.0341162421f, -2.03935647f, -3.02791858f, 1.94139159f, -0.0187349897f, -0.0553935543f, + 0.0797653273f, 2.87888241f, -1.41231537f, -0.560714483f, -0.356952429f, 1.58930194f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/config_data.h new file mode 100644 index 00000000..d04fef2f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/config_data.h @@ -0,0 +1,9 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#define MUL_F32_SPILL_DST_SIZE 245 +#define MUL_F32_SPILL_OUT_ACTIVATION_MIN -1.5f +#define MUL_F32_SPILL_OUT_ACTIVATION_MAX 1.0f +#define MUL_F32_SPILL_INPUT1_OFFSET 0 +#define MUL_F32_SPILL_INPUT2_OFFSET 0 +#define MUL_F32_SPILL_OUTPUT_MULT 0 +#define MUL_F32_SPILL_OUTPUT_SHIFT 0 +#define MUL_F32_SPILL_OUTPUT_OFFSET 0 diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input1_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input1_data.h new file mode 100644 index 00000000..01ebd044 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input1_data.h @@ -0,0 +1,37 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_spill_input1[245] = { + 1.03750503f, 1.21068883f, -1.11422062f, -1.56176996f, 0.259232014f, 1.89764845f, -1.54006624f, + -1.78451788f, -0.79556036f, 0.798718452f, 0.10807725f, -1.35917175f, 2.17827511f, -1.97529209f, + -1.75666153f, 1.66806674f, 2.35733938f, 0.594319642f, 0.836750805f, -1.08989227f, 1.18662119f, + 0.681982756f, -0.121250972f, -0.864821315f, -1.87630153f, -0.501392305f, 1.12252939f, -1.55128586f, + -0.601947308f, 0.299833f, -1.68042493f, -1.83702242f, 1.91896391f, 1.18242478f, 1.73170221f, + -0.97632426f, -1.49509323f, -0.179876611f, 0.660464883f, 0.331544071f, -2.04634142f, -1.08085728f, + 0.965219676f, -2.27075005f, 1.94882834f, 2.35488439f, 0.461859107f, 1.48478115f, 1.36373413f, + 1.80742443f, 1.81965387f, -0.586421967f, -0.284066856f, -1.68561435f, 0.528397739f, 1.93431008f, + 1.83670795f, -0.301861346f, -2.4945631f, -1.23363197f, 2.02106619f, -2.0789659f, -2.02481675f, + -2.09008384f, -1.25956261f, 1.92714393f, -0.0147897564f, -2.38297009f, -1.61307919f, -0.699844718f, + -0.390825838f, 1.01854873f, -1.05537021f, 1.65787923f, 1.22305536f, 0.884709656f, -1.39824402f, + 0.823454022f, 1.19484389f, 1.50300419f, -1.31508398f, -1.3163538f, 1.33214331f, 0.811068535f, + -0.4218072f, -1.33247805f, -1.1738013f, -1.3275156f, 1.17738926f, -1.87640357f, -2.38964057f, + 1.31192696f, 1.26255369f, 0.973092675f, 1.3289181f, -1.13030291f, 0.59038806f, -2.35476303f, + -2.04206228f, 0.246769771f, -1.3939023f, -1.78300714f, 1.73347616f, 2.29483652f, -1.29103875f, + 1.63252556f, 0.416992217f, -0.715330243f, 0.624467909f, -0.218272731f, -0.645130396f, 1.68245065f, + -0.0486152545f, 0.84913218f, 1.3496455f, -0.401276946f, 0.191242546f, -1.49053431f, 0.152723834f, + 1.94188178f, 0.769763052f, 2.37212968f, -0.19226031f, 1.07876921f, -0.348193467f, 0.197050884f, + -1.64790952f, 1.43467546f, 2.4029541f, 0.176903635f, -0.880969107f, -1.60072684f, -2.27209878f, + -0.947716892f, -0.059713617f, 0.368846357f, 0.516945124f, -1.33302951f, -0.311419368f, -1.44336498f, + -0.750836253f, 1.79356301f, 0.708148837f, 2.22246766f, 0.493629426f, -1.8789562f, -1.37144232f, + -2.06234908f, -2.41583061f, -2.31739593f, 1.22730398f, 1.00830758f, 0.821060896f, -0.281397343f, + 2.42759681f, 1.20866644f, 2.4673903f, 2.48788524f, -0.424538732f, 2.40018511f, 0.210401118f, + -0.0791139677f, 2.17901802f, 1.3120749f, 1.13267803f, -1.70694757f, 1.77596545f, 1.06315827f, + 0.520660639f, 1.85103571f, 1.94080794f, -0.741487205f, 1.58489525f, 1.00404692f, 1.61007249f, + -1.91186213f, -0.91174984f, -0.0381464027f, 0.38193056f, 1.22230303f, 0.606832445f, -1.28138804f, + -0.408222944f, -1.88656402f, 0.633836865f, 0.638206184f, -0.130993843f, -2.07240796f, 1.94622695f, + -0.72816205f, -0.786292493f, -0.614833236f, 0.219617546f, -1.6572119f, -0.266010165f, 0.165832743f, + 0.631445169f, -1.99812889f, -1.60990632f, -2.01891446f, 0.48707059f, -0.00782717858f, -1.06404102f, + 1.01980042f, -0.417084992f, -2.19283605f, -0.691740096f, -1.55777752f, -0.768963039f, 2.14341807f, + -0.191038296f, -1.24557269f, -1.01733732f, 2.40801525f, 1.19577503f, 2.47455621f, 1.78955281f, + 1.84307897f, -2.40683746f, -0.804225743f, 1.04616725f, 1.65647173f, 0.799463511f, 2.19483709f, + -0.0873835012f, -1.07342267f, 1.26210737f, -1.13555312f, 2.20347309f, 2.16600728f, 1.18198848f, + 0.731324613f, -0.408260107f, -0.401499271f, -2.08223629f, -1.40528703f, 1.15685689f, 1.14238441f, + 0.870385885f, -2.00240612f, -0.307433695f, 0.306935906f, -1.64275599f, -0.758856177f, 0.399145335f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input2_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input2_data.h new file mode 100644 index 00000000..035c5e48 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/input2_data.h @@ -0,0 +1,37 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_spill_input2[245] = { + -0.627950728f, 2.17490387f, -1.43029213f, 2.49269152f, 0.251474589f, 1.60205567f, 2.33353066f, + 2.06078887f, -1.31474125f, 0.207662389f, -1.79761112f, -2.30646992f, 0.271213651f, -2.06202388f, + -1.46977973f, -1.60490525f, -2.16897297f, 1.52666032f, 0.170480445f, 2.3092227f, -1.41681182f, + -0.510832131f, 1.75651622f, 0.315524668f, -0.16642113f, -0.0552224852f, 1.9290663f, 1.71649075f, + 1.18228495f, 0.445128411f, -1.21222472f, -0.11267437f, -0.512841761f, -2.22532153f, 0.575084686f, + 2.24975872f, -1.68735254f, -2.14756465f, -1.55931652f, 1.88444507f, 1.6597054f, 0.975591838f, + -1.0684098f, 0.801979542f, -2.13947511f, 1.30454862f, 2.41841745f, -0.173585325f, -0.177624866f, + 0.490313023f, 2.14727664f, 2.39236212f, 2.11180329f, -1.10516965f, -2.11832333f, -0.566694856f, + -1.82458329f, -1.87779677f, -1.88707292f, -0.85222131f, -0.939799905f, 1.11092913f, 1.07340074f, + 1.94313157f, 0.027908396f, -2.49607205f, -1.91209412f, 1.9184227f, -0.737534642f, 0.507950246f, + -1.34580755f, 1.88195884f, 2.12668324f, 1.66509688f, -0.21861738f, -2.0830214f, 0.511030793f, + 1.47466099f, 0.0793641284f, -1.37974918f, 2.09487891f, 1.46974933f, -1.76980793f, 0.069376342f, + -2.34822774f, 2.25124145f, -2.14921832f, 1.99572313f, -0.241592869f, -1.03964257f, -1.95696592f, + -2.01918721f, 0.745001733f, -1.39057934f, -1.57854843f, -0.916009307f, 0.521648765f, -1.24253356f, + 1.31467521f, 0.907418311f, 0.271652728f, -2.26496792f, -1.91159654f, -0.550923884f, 0.765812099f, + -1.65939236f, 0.960094571f, 1.58648169f, -0.515286565f, 2.43855715f, 0.283644795f, -0.858800769f, + -0.4243204f, 0.42730242f, -2.0128777f, 1.699512f, 1.47511184f, -0.0354134105f, 0.435284615f, + -1.14579511f, 0.803178132f, -1.02605462f, 2.36373639f, -0.658143163f, 0.233100981f, 1.19560587f, + 0.158312857f, -0.279051483f, 2.1157608f, -1.74450827f, -2.43528175f, -1.14346683f, 0.0441439115f, + 0.117192186f, 2.05272722f, 0.540838778f, -2.48083162f, 0.649063408f, -0.50795716f, 0.848903835f, + -2.05312037f, -1.99280524f, -2.20039558f, -1.05670977f, -0.995393753f, 1.06864834f, 1.08837342f, + 2.22439146f, -0.416492999f, -0.18142128f, 0.000602132233f, -1.976017f, 0.620210588f, -1.69669437f, + 0.518520236f, 2.36103702f, 2.4931457f, 1.2691896f, -2.37063789f, -0.248394579f, -1.22155631f, + 1.37071598f, 0.299236536f, -1.06753659f, -1.78819191f, 1.33164346f, -1.54733491f, 2.13509107f, + -0.0352128223f, -0.244717225f, -0.434200108f, 1.38355017f, -0.974799275f, -0.575077653f, 1.67034793f, + 1.63375843f, -1.92014432f, 0.258586377f, 2.09438396f, -2.02084255f, -0.967933416f, 0.849691808f, + 2.28796506f, -0.0827779472f, -0.525283396f, 1.23228729f, 0.620341778f, -2.06170726f, -1.09660971f, + -1.29477549f, 0.315302014f, 2.27310228f, 2.13616323f, 0.771310568f, -0.419487596f, 1.03450871f, + -0.853021979f, -1.78792584f, 0.181842715f, 0.257341921f, -0.990792871f, 0.821400642f, 1.17591858f, + -2.49695468f, 1.76511312f, -1.08988512f, 1.5126965f, -1.7704227f, -1.46046221f, 1.38418007f, + -2.27509236f, 1.3498987f, -1.85518181f, 0.064697668f, -0.126326263f, 2.16842651f, 1.9688096f, + 0.31962961f, 0.84003216f, 1.72918046f, -0.104988694f, 2.30942345f, 2.2799387f, -2.06298113f, + 1.38346326f, -1.42994285f, -0.787729502f, -1.51218355f, -0.302825153f, -1.88248861f, 0.973706484f, + -2.27827477f, -1.51450276f, 1.66505325f, 0.943411946f, 0.691970706f, 1.35448909f, 0.131753922f, + -2.4298327f, -1.67154288f, 0.607437372f, 1.70446312f, -0.163765579f, 2.3224566f, -0.893867552f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/output_ref_data.h new file mode 100644 index 00000000..2ea172ea --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/output_ref_data.h @@ -0,0 +1,246 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t mul_f32_spill_output_ref[245] = {-0.651502013f, + 1.0f, + 1.0f, + -1.5f, + 0.0651902631f, + 1.0f, + -1.5f, + -1.5f, + 1.0f, + 0.165863782f, + -0.194280863f, + 1.0f, + 0.590777934f, + 1.0f, + 1.0f, + -1.5f, + -1.5f, + 0.907324195f, + 0.142649651f, + -1.5f, + -1.5f, + -0.348378718f, + -0.212979302f, + -0.272872448f, + 0.312256217f, + 0.0276881289f, + 1.0f, + -1.5f, + -0.71167326f, + 0.133464187f, + 1.0f, + 0.20698534f, + -0.984124839f, + -1.5f, + 0.995875418f, + -1.5f, + 1.0f, + 0.38629666f, + -1.02987385f, + 0.624776602f, + -1.5f, + -1.05447555f, + -1.03125012f, + -1.5f, + -1.5f, + 1.0f, + 1.0f, + -0.257736206f, + -0.242233098f, + 0.886203706f, + 1.0f, + -1.40293372f, + -0.599893332f, + 1.0f, + -1.11931729f, + -1.09616351f, + -1.5f, + 0.566834271f, + 1.0f, + 1.0f, + -1.5f, + -1.5f, + -1.5f, + -1.5f, + -0.035152372f, + -1.5f, + 0.0282794069f, + -1.5f, + 1.0f, + -0.355486304f, + 0.52597636f, + 1.0f, + -1.5f, + 1.0f, + -0.267381161f, + -1.5f, + -0.714545727f, + 1.0f, + 0.0948277414f, + -1.5f, + -1.5f, + -1.5f, + -1.5f, + 0.0562689677f, + 0.990499377f, + -1.5f, + 1.0f, + -1.5f, + -0.284448862f, + 1.0f, + 1.0f, + -1.5f, + 0.940604687f, + -1.35316253f, + -1.5f, + 1.0f, + 0.307975203f, + 1.0f, + -1.5f, + 0.223923415f, + -0.378657371f, + 1.0f, + -1.5f, + -1.2642802f, + -0.988693118f, + -1.5f, + 0.400351971f, + -1.13485837f, + -0.321779937f, + -0.532270551f, + -0.182987884f, + -1.4448899f, + 0.0206284449f, + 0.362836242f, + -1.5f, + -0.681975007f, + 0.282104135f, + 0.0527849048f, + 0.0664783344f, + -1.5f, + 0.618256867f, + -1.5f, + -0.454452693f, + -0.709984601f, + -0.0811642408f, + 0.235595196f, + -0.260885268f, + -0.400348306f, + 1.0f, + -0.308609843f, + 1.0f, + 1.0f, + -0.100299329f, + -0.111065015f, + -0.122575767f, + 0.19948642f, + -1.28245378f, + -0.865220666f, + 0.158187702f, + -1.22527802f, + 1.0f, + -1.5f, + -1.5f, + -1.5f, + -0.491355658f, + -1.5f, + -1.49264133f, + -1.5f, + 1.0f, + 0.420424938f, + 0.000738999282f, + -1.5f, + 0.509230673f, + 0.477445275f, + 1.0f, + 1.0f, + 1.0f, + 1.0f, + 1.0f, + -0.596192956f, + -0.257016808f, + -0.108442776f, + 0.652041793f, + -1.40068793f, + -1.5f, + -1.5f, + -1.5f, + 1.0f, + -0.0183339305f, + -0.45298031f, + -0.842698991f, + -1.02588475f, + -1.5f, + -0.577404916f, + 1.0f, + -1.5f, + 1.0f, + -0.0098641403f, + 0.799909234f, + -1.5f, + -0.587373376f, + -1.08878493f, + -0.933999836f, + 0.156165898f, + -0.332943976f, + 0.786453366f, + -0.0812609568f, + 1.0f, + -1.5f, + 0.942806363f, + -0.247919604f, + -1.39757884f, + 0.46913892f, + -1.27822506f, + 0.111587964f, + 0.171555415f, + -0.538636625f, + 1.0f, + -0.292749733f, + -0.519551337f, + -0.482586056f, + -0.00642924942f, + -1.25122559f, + -1.5f, + -0.73620218f, + 1.0f, + -1.0463928f, + 1.0f, + 1.0f, + 1.0f, + 0.434629768f, + -1.5f, + 1.0f, + 0.155792966f, + -0.151057795f, + 1.0f, + 1.0f, + 0.589102626f, + -1.5f, + -1.39065146f, + -0.109835736f, + 1.0f, + 1.0f, + -1.5f, + -0.120891862f, + 1.0f, + -0.994199216f, + 1.0f, + -0.667267084f, + -1.5f, + 1.0f, + -1.5f, + 0.618311048f, + -0.668517649f, + -1.5f, + -0.972417474f, + 1.0f, + 0.150513619f, + -1.5f, + 1.0f, + -0.186746716f, + 0.523160934f, + 0.269026875f, + -1.5f, + -0.356783062f}; diff --git a/Tests/UnitTest/TestCases/TestData/mul_f32_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/test_data.h new file mode 100644 index 00000000..8fda93ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/mul_f32_spill/test_data.h @@ -0,0 +1,5 @@ +// Generated by add_mul_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input1_data.h" +#include "input2_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/config_data.h new file mode 100644 index 00000000..8ce37050 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_BASIC_F16_INPUT_N 1 +#define PAD_BASIC_F16_INPUT_H 7 +#define PAD_BASIC_F16_INPUT_W 9 +#define PAD_BASIC_F16_INPUT_C 5 +#define PAD_BASIC_F16_PRE_PAD_N 0 +#define PAD_BASIC_F16_PRE_PAD_H 2 +#define PAD_BASIC_F16_PRE_PAD_W 1 +#define PAD_BASIC_F16_PRE_PAD_C 0 +#define PAD_BASIC_F16_POST_PAD_N 0 +#define PAD_BASIC_F16_POST_PAD_H 1 +#define PAD_BASIC_F16_POST_PAD_W 2 +#define PAD_BASIC_F16_POST_PAD_C 0 +#define PAD_BASIC_F16_OUTPUT_N 1 +#define PAD_BASIC_F16_OUTPUT_H 10 +#define PAD_BASIC_F16_OUTPUT_W 12 +#define PAD_BASIC_F16_OUTPUT_C 5 +#define PAD_BASIC_F16_OUTPUT_SIZE 600 +#define PAD_BASIC_F16_PAD_VALUE (float16_t)0.125f diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/input_tensor_data.h new file mode 100644 index 00000000..c15645e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/input_tensor_data.h @@ -0,0 +1,81 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_basic_f16_input_tensor[315] = { + (float16_t)-0.615234375f, (float16_t)2.75585938f, (float16_t)-1.41601562f, (float16_t)-3.14257812f, + (float16_t)1.86132812f, (float16_t)1.76171875f, (float16_t)2.54101562f, (float16_t)1.46484375f, + (float16_t)-2.14648438f, (float16_t)-2.21875f, (float16_t)-2.18164062f, (float16_t)-0.518554688f, + (float16_t)-2.16992188f, (float16_t)-0.671875f, (float16_t)0.402099609f, (float16_t)2.0625f, + (float16_t)-3.42578125f, (float16_t)0.774414062f, (float16_t)-2.58203125f, (float16_t)0.311523438f, + (float16_t)-2.19140625f, (float16_t)0.45703125f, (float16_t)-1.14746094f, (float16_t)3.7890625f, + (float16_t)-3.23242188f, (float16_t)3.42578125f, (float16_t)2.38476562f, (float16_t)3.81054688f, + (float16_t)-2.46679688f, (float16_t)3.64648438f, (float16_t)2.08984375f, (float16_t)0.845214844f, + (float16_t)3.890625f, (float16_t)2.21875f, (float16_t)-2.1171875f, (float16_t)-3.99804688f, + (float16_t)-2.98242188f, (float16_t)1.9765625f, (float16_t)-3.609375f, (float16_t)-3.17773438f, + (float16_t)-0.849609375f, (float16_t)-3.27929688f, (float16_t)-1.56152344f, (float16_t)-1.58789062f, + (float16_t)-0.0107116699f, (float16_t)-2.59375f, (float16_t)-0.591308594f, (float16_t)0.471923828f, + (float16_t)3.6328125f, (float16_t)-1.22070312f, (float16_t)0.171386719f, (float16_t)2.91210938f, + (float16_t)3.04882812f, (float16_t)-1.91015625f, (float16_t)-0.883300781f, (float16_t)2.12890625f, + (float16_t)-1.24707031f, (float16_t)-2.84570312f, (float16_t)1.02441406f, (float16_t)1.06054688f, + (float16_t)3.203125f, (float16_t)1.96777344f, (float16_t)3.1796875f, (float16_t)-0.787597656f, + (float16_t)3.84960938f, (float16_t)2.85742188f, (float16_t)-1.6953125f, (float16_t)-2.53320312f, + (float16_t)3.77148438f, (float16_t)3.94335938f, (float16_t)-1.86523438f, (float16_t)3.43164062f, + (float16_t)-3.01953125f, (float16_t)-0.490234375f, (float16_t)-3.34765625f, (float16_t)0.124084473f, + (float16_t)0.78515625f, (float16_t)-1.81933594f, (float16_t)-0.342773438f, (float16_t)-2.97265625f, + (float16_t)-2.85546875f, (float16_t)3.66601562f, (float16_t)0.652832031f, (float16_t)1.22265625f, + (float16_t)0.366699219f, (float16_t)1.77734375f, (float16_t)0.998535156f, (float16_t)-1.56835938f, + (float16_t)-0.467529297f, (float16_t)-2.38476562f, (float16_t)1.13574219f, (float16_t)-2.66210938f, + (float16_t)2.61914062f, (float16_t)0.470947266f, (float16_t)-1.78027344f, (float16_t)-2.62109375f, + (float16_t)1.08691406f, (float16_t)-2.3671875f, (float16_t)-3.55859375f, (float16_t)-1.28222656f, + (float16_t)2.1328125f, (float16_t)-2.76367188f, (float16_t)1.11425781f, (float16_t)-0.244262695f, + (float16_t)-1.70898438f, (float16_t)2.42578125f, (float16_t)-2.9296875f, (float16_t)-0.19934082f, + (float16_t)2.23632812f, (float16_t)2.1015625f, (float16_t)-1.74316406f, (float16_t)2.27539062f, + (float16_t)1.45214844f, (float16_t)-2.17382812f, (float16_t)-1.01367188f, (float16_t)-0.473876953f, + (float16_t)-3.71875f, (float16_t)3.921875f, (float16_t)-2.51367188f, (float16_t)0.642578125f, + (float16_t)2.40625f, (float16_t)3.2109375f, (float16_t)-3.71289062f, (float16_t)-0.581542969f, + (float16_t)-3.46289062f, (float16_t)-1.19433594f, (float16_t)-2.20117188f, (float16_t)3.47265625f, + (float16_t)-3.06835938f, (float16_t)1.109375f, (float16_t)-0.482421875f, (float16_t)1.34375f, + (float16_t)0.106872559f, (float16_t)-3.52734375f, (float16_t)-0.385009766f, (float16_t)1.24804688f, + (float16_t)-3.16796875f, (float16_t)-1.27441406f, (float16_t)-0.249755859f, (float16_t)1.50585938f, + (float16_t)3.95703125f, (float16_t)-0.561035156f, (float16_t)3.59570312f, (float16_t)0.231933594f, + (float16_t)3.94726562f, (float16_t)-0.62890625f, (float16_t)1.44042969f, (float16_t)-1.92480469f, + (float16_t)-0.238647461f, (float16_t)2.890625f, (float16_t)-0.751464844f, (float16_t)-1.9921875f, + (float16_t)0.641601562f, (float16_t)-0.469970703f, (float16_t)-1.42773438f, (float16_t)3.36132812f, + (float16_t)-3.640625f, (float16_t)-3.35546875f, (float16_t)0.821289062f, (float16_t)0.971191406f, + (float16_t)-3.27929688f, (float16_t)-1.56054688f, (float16_t)-2.234375f, (float16_t)-1.27441406f, + (float16_t)-0.958496094f, (float16_t)0.105651855f, (float16_t)-1.68066406f, (float16_t)1.27539062f, + (float16_t)-1.9140625f, (float16_t)-3.45703125f, (float16_t)1.19726562f, (float16_t)-3.76171875f, + (float16_t)0.795898438f, (float16_t)-1.61328125f, (float16_t)-3.54492188f, (float16_t)-0.0809326172f, + (float16_t)0.624023438f, (float16_t)-3.93554688f, (float16_t)1.47363281f, (float16_t)0.757324219f, + (float16_t)1.01660156f, (float16_t)-3.43359375f, (float16_t)0.727050781f, (float16_t)-0.243896484f, + (float16_t)-2.28710938f, (float16_t)3.92578125f, (float16_t)-0.93359375f, (float16_t)-3.75195312f, + (float16_t)3.11523438f, (float16_t)-1.25390625f, (float16_t)-2.83984375f, (float16_t)-2.83789062f, + (float16_t)2.25390625f, (float16_t)-3.25585938f, (float16_t)3.20507812f, (float16_t)2.97851562f, + (float16_t)3.45703125f, (float16_t)-3.17382812f, (float16_t)-3.5546875f, (float16_t)-2.41601562f, + (float16_t)1.77050781f, (float16_t)2.33398438f, (float16_t)-2.78710938f, (float16_t)-0.426269531f, + (float16_t)-2.97070312f, (float16_t)-0.920410156f, (float16_t)3.55664062f, (float16_t)3.4296875f, + (float16_t)-3.75585938f, (float16_t)0.941894531f, (float16_t)-0.639648438f, (float16_t)-2.86328125f, + (float16_t)0.798828125f, (float16_t)3.8515625f, (float16_t)0.732910156f, (float16_t)1.87011719f, + (float16_t)-0.863769531f, (float16_t)2.62890625f, (float16_t)-1.21191406f, (float16_t)-1.40039062f, + (float16_t)-3.9609375f, (float16_t)-2.52539062f, (float16_t)3.35351562f, (float16_t)2.25390625f, + (float16_t)-1.45507812f, (float16_t)2.65234375f, (float16_t)3.75f, (float16_t)2.0546875f, + (float16_t)1.50976562f, (float16_t)1.87988281f, (float16_t)-0.803710938f, (float16_t)0.173339844f, + (float16_t)0.665039062f, (float16_t)2.99023438f, (float16_t)-2.72460938f, (float16_t)-3.16015625f, + (float16_t)-2.52539062f, (float16_t)-3.58398438f, (float16_t)-3.71875f, (float16_t)1.67089844f, + (float16_t)2.59960938f, (float16_t)0.186645508f, (float16_t)3.67382812f, (float16_t)-0.858398438f, + (float16_t)1.30175781f, (float16_t)-2.20507812f, (float16_t)2.28710938f, (float16_t)-0.188720703f, + (float16_t)-0.335693359f, (float16_t)3.69921875f, (float16_t)-3.7421875f, (float16_t)-0.383544922f, + (float16_t)0.832519531f, (float16_t)-1.37792969f, (float16_t)2.24804688f, (float16_t)1.18066406f, + (float16_t)-1.046875f, (float16_t)-0.536132812f, (float16_t)-0.884765625f, (float16_t)-1.40820312f, + (float16_t)1.18359375f, (float16_t)-3.72265625f, (float16_t)0.962402344f, (float16_t)-1.79492188f, + (float16_t)2.58398438f, (float16_t)-3.95898438f, (float16_t)2.61132812f, (float16_t)3.66601562f, + (float16_t)-1.16015625f, (float16_t)3.67578125f, (float16_t)3.65039062f, (float16_t)-0.791503906f, + (float16_t)2.359375f, (float16_t)2.92382812f, (float16_t)3.47460938f, (float16_t)-3.05078125f, + (float16_t)0.399902344f, (float16_t)-1.57421875f, (float16_t)-0.216552734f, (float16_t)-1.04199219f, + (float16_t)3.50585938f, (float16_t)-1.0078125f, (float16_t)-2.5625f, (float16_t)-1.42578125f, + (float16_t)-2.3671875f, (float16_t)-3.87695312f, (float16_t)1.08398438f, (float16_t)1.04101562f, + (float16_t)-1.70410156f, (float16_t)0.594238281f, (float16_t)2.6015625f, (float16_t)3.734375f, + (float16_t)3.26953125f, (float16_t)2.52929688f, (float16_t)2.375f, (float16_t)2.1640625f, + (float16_t)-3.79296875f, (float16_t)2.82226562f, (float16_t)-1.13769531f, (float16_t)3.23632812f, + (float16_t)-3.79296875f, (float16_t)0.220092773f, (float16_t)-0.0275421143f, (float16_t)-3.74023438f, + (float16_t)1.61816406f, (float16_t)2.91601562f, (float16_t)3.44921875f, (float16_t)2.42578125f, + (float16_t)1.97949219f, (float16_t)-1.77246094f, (float16_t)-3.63085938f, (float16_t)-0.540039062f, + (float16_t)-0.190795898f, (float16_t)-0.682128906f, (float16_t)-1.97753906f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/output_ref_data.h new file mode 100644 index 00000000..5773777a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/output_ref_data.h @@ -0,0 +1,152 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_basic_f16_output_ref[600] = { + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)-0.615234375f, (float16_t)2.75585938f, (float16_t)-1.41601562f, + (float16_t)-3.14257812f, (float16_t)1.86132812f, (float16_t)1.76171875f, (float16_t)2.54101562f, + (float16_t)1.46484375f, (float16_t)-2.14648438f, (float16_t)-2.21875f, (float16_t)-2.18164062f, + (float16_t)-0.518554688f, (float16_t)-2.16992188f, (float16_t)-0.671875f, (float16_t)0.402099609f, + (float16_t)2.0625f, (float16_t)-3.42578125f, (float16_t)0.774414062f, (float16_t)-2.58203125f, + (float16_t)0.311523438f, (float16_t)-2.19140625f, (float16_t)0.45703125f, (float16_t)-1.14746094f, + (float16_t)3.7890625f, (float16_t)-3.23242188f, (float16_t)3.42578125f, (float16_t)2.38476562f, + (float16_t)3.81054688f, (float16_t)-2.46679688f, (float16_t)3.64648438f, (float16_t)2.08984375f, + (float16_t)0.845214844f, (float16_t)3.890625f, (float16_t)2.21875f, (float16_t)-2.1171875f, + (float16_t)-3.99804688f, (float16_t)-2.98242188f, (float16_t)1.9765625f, (float16_t)-3.609375f, + (float16_t)-3.17773438f, (float16_t)-0.849609375f, (float16_t)-3.27929688f, (float16_t)-1.56152344f, + (float16_t)-1.58789062f, (float16_t)-0.0107116699f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)-2.59375f, (float16_t)-0.591308594f, (float16_t)0.471923828f, + (float16_t)3.6328125f, (float16_t)-1.22070312f, (float16_t)0.171386719f, (float16_t)2.91210938f, + (float16_t)3.04882812f, (float16_t)-1.91015625f, (float16_t)-0.883300781f, (float16_t)2.12890625f, + (float16_t)-1.24707031f, (float16_t)-2.84570312f, (float16_t)1.02441406f, (float16_t)1.06054688f, + (float16_t)3.203125f, (float16_t)1.96777344f, (float16_t)3.1796875f, (float16_t)-0.787597656f, + (float16_t)3.84960938f, (float16_t)2.85742188f, (float16_t)-1.6953125f, (float16_t)-2.53320312f, + (float16_t)3.77148438f, (float16_t)3.94335938f, (float16_t)-1.86523438f, (float16_t)3.43164062f, + (float16_t)-3.01953125f, (float16_t)-0.490234375f, (float16_t)-3.34765625f, (float16_t)0.124084473f, + (float16_t)0.78515625f, (float16_t)-1.81933594f, (float16_t)-0.342773438f, (float16_t)-2.97265625f, + (float16_t)-2.85546875f, (float16_t)3.66601562f, (float16_t)0.652832031f, (float16_t)1.22265625f, + 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(float16_t)-3.74023438f, (float16_t)1.61816406f, + (float16_t)2.91601562f, (float16_t)3.44921875f, (float16_t)2.42578125f, (float16_t)1.97949219f, + (float16_t)-1.77246094f, (float16_t)-3.63085938f, (float16_t)-0.540039062f, (float16_t)-0.190795898f, + (float16_t)-0.682128906f, (float16_t)-1.97753906f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, + (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f, (float16_t)0.125f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/config_data.h new file mode 100644 index 00000000..4475150f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_BASIC_F32_INPUT_N 1 +#define PAD_BASIC_F32_INPUT_H 7 +#define PAD_BASIC_F32_INPUT_W 9 +#define PAD_BASIC_F32_INPUT_C 5 +#define PAD_BASIC_F32_PRE_PAD_N 0 +#define PAD_BASIC_F32_PRE_PAD_H 2 +#define PAD_BASIC_F32_PRE_PAD_W 1 +#define PAD_BASIC_F32_PRE_PAD_C 0 +#define PAD_BASIC_F32_POST_PAD_N 0 +#define PAD_BASIC_F32_POST_PAD_H 1 +#define PAD_BASIC_F32_POST_PAD_W 2 +#define PAD_BASIC_F32_POST_PAD_C 0 +#define PAD_BASIC_F32_OUTPUT_N 1 +#define PAD_BASIC_F32_OUTPUT_H 10 +#define PAD_BASIC_F32_OUTPUT_W 12 +#define PAD_BASIC_F32_OUTPUT_C 5 +#define PAD_BASIC_F32_OUTPUT_SIZE 600 +#define PAD_BASIC_F32_PAD_VALUE 0.125f diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/input_tensor_data.h new file mode 100644 index 00000000..00ed31ac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/input_tensor_data.h @@ -0,0 +1,47 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_basic_f32_input_tensor[315] = { + -0.851262569f, -2.96413827f, 0.0305343065f, -1.68388832f, -0.63215059f, 1.12496245f, 1.71773756f, + 1.15241909f, 2.1442399f, 0.854739666f, 1.45663989f, 0.0244127195f, -3.05115891f, -1.15579247f, + 0.970802724f, -0.967401862f, -0.864141941f, -2.90561914f, -2.02391386f, -3.66048002f, 2.93582559f, + -1.96839595f, -1.01667523f, -3.89268804f, 0.0896789581f, -1.86319506f, 0.0768589675f, 1.86204123f, + -0.751610935f, -3.0129981f, 2.44571567f, -0.969920754f, -2.25018358f, 1.32216787f, -1.93554914f, + 1.81179321f, -0.727910161f, 3.34558463f, -1.00105321f, -1.90789974f, -0.795648456f, -0.295596838f, + 1.84380054f, 1.6343987f, -1.70636845f, 1.78779817f, -2.82002068f, 1.36047447f, 0.867956996f, + 3.60750699f, -1.51914918f, 3.51146221f, 1.2093544f, -3.63042164f, 1.79315555f, -2.02431393f, + 0.624501765f, 1.93735468f, -2.24615479f, 1.14643657f, 2.08312964f, -3.27628303f, 3.65507889f, + -2.42534089f, -0.256407738f, 1.6062206f, 3.70445132f, 0.896572471f, -3.62409568f, -2.02926612f, + 2.46628785f, -0.301025689f, -0.136838257f, 1.05255735f, 0.0382424928f, 0.982686758f, -3.70555401f, + -3.69886804f, 1.70381987f, -1.81729424f, -3.59189939f, -1.79182112f, 3.10883117f, 3.76760101f, + 3.37596273f, 1.62467563f, -0.498761296f, 1.56601954f, 2.09831643f, -1.73353541f, -1.60601294f, + 1.3029362f, 1.93253839f, -2.33799338f, -1.30981576f, -1.68211031f, 1.16438186f, -1.49199247f, + -0.177964836f, -2.07698655f, 1.46125138f, -1.97311771f, 3.5902915f, -1.70906997f, -2.7017324f, + 1.80825925f, -1.34513593f, 0.986857414f, -2.06932402f, 2.89227128f, -3.04048347f, 1.6145035f, + -3.9423337f, -3.54079771f, -3.2805829f, -1.79433954f, -1.44512987f, -2.59295154f, 1.50398004f, + -3.64275527f, -1.73407698f, 3.487046f, 0.423988461f, 3.30892539f, -1.97320569f, 0.268707752f, + 1.55700195f, -3.74031997f, 2.01825452f, -1.57656145f, 1.76554489f, 0.326667875f, -2.47481537f, + 3.65779972f, 3.15902925f, 2.63131499f, -3.14859176f, -0.997390032f, -1.20467031f, 2.21032619f, + -1.76924586f, 0.642202377f, 0.278176546f, 0.145728454f, -1.11196029f, -2.01806045f, 0.322141588f, + -1.12646985f, -1.07507145f, 1.72732878f, 1.88165164f, 2.72870588f, -3.76698375f, 2.13202572f, + -0.840832233f, -3.62208152f, -2.9742167f, -1.08599961f, -1.23291886f, -2.5547092f, 2.94218683f, + 0.70580709f, 0.999319375f, 2.62544608f, -3.15573478f, 3.81955838f, 1.44674516f, 2.5526154f, + 0.245138556f, 0.101800121f, 0.451636553f, 3.3426919f, -0.600549698f, 1.32474697f, 2.32352614f, + 2.46365571f, 1.0765959f, 2.99040437f, 3.64721894f, -2.80435967f, -0.400220424f, -1.17947686f, + -3.84920168f, 2.008389f, -2.63169193f, -2.78069663f, -3.6790185f, 1.93286836f, -1.96083272f, + -1.94803286f, 1.14150977f, -0.269870669f, -3.91356516f, 0.897772193f, 2.29915667f, -3.18600082f, + 0.799750268f, 2.04519463f, -1.2170037f, 3.75417686f, -0.508928001f, 3.26570511f, 0.428518236f, + 0.838441312f, 3.8804903f, 1.86222613f, 2.59939218f, 1.29434931f, -2.90471315f, -1.77256536f, + 0.607833624f, 3.99430609f, -2.83389068f, -3.20123625f, -0.641655266f, -0.443926543f, -1.52683103f, + 0.505921781f, -2.73763323f, -1.09038174f, 3.90661383f, 3.66068435f, -2.05198216f, -0.0194622651f, + 0.911627591f, -2.34721613f, -1.55258942f, -1.00661838f, 1.03735602f, 2.85131097f, -1.87352419f, + 2.9444685f, -3.65782356f, -2.77959847f, 3.19181871f, 0.880091488f, 2.73344994f, 0.67603159f, + -1.33721411f, -2.37102532f, -1.14067161f, 2.36507058f, -1.34516692f, -2.75297141f, -1.88507771f, + -1.00749636f, 3.36465812f, -3.33842492f, -3.99275351f, -2.52483487f, 2.02326965f, 0.905177295f, + -2.79976463f, -1.31361568f, 1.04678166f, -2.38264513f, -0.367629588f, 1.57011831f, 3.75710869f, + 0.602375746f, -3.23761892f, -2.5053463f, -1.51726139f, -0.397426307f, 3.10831881f, -0.254475594f, + -0.331198037f, -1.57487321f, -0.211769491f, -2.18954015f, 2.05184269f, 0.294230938f, 2.55604529f, + 1.99079132f, 0.76087749f, 3.20271158f, -1.17838728f, 2.81995273f, 3.87430906f, 1.3856467f, + 0.353117377f, 1.83593941f, -2.77444625f, -2.92582035f, -3.43438363f, -2.35635495f, 2.1534493f, + -2.33303428f, -0.23942031f, -3.59250402f, -3.87034678f, 3.57504129f, -2.60450625f, -1.20090461f, + 2.6526885f, 0.211482972f, 1.6133486f, 2.84030962f, -2.72710228f, -3.61878109f, 1.40186799f, + 1.15577841f, 3.10570574f, 2.96033835f, -1.09952748f, -1.3936708f, -3.29456806f, -2.8926599f, + 3.24352813f, 3.15888071f, 3.75745654f, 0.890945077f, 3.43585443f, -1.12156415f, -1.35743499f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/output_ref_data.h new file mode 100644 index 00000000..91ebcb45 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/output_ref_data.h @@ -0,0 +1,88 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_basic_f32_output_ref[600] = { + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, -0.851262569f, + -2.96413827f, 0.0305343065f, -1.68388832f, -0.63215059f, 1.12496245f, 1.71773756f, 1.15241909f, + 2.1442399f, 0.854739666f, 1.45663989f, 0.0244127195f, -3.05115891f, -1.15579247f, 0.970802724f, + -0.967401862f, -0.864141941f, -2.90561914f, -2.02391386f, -3.66048002f, 2.93582559f, -1.96839595f, + -1.01667523f, -3.89268804f, 0.0896789581f, -1.86319506f, 0.0768589675f, 1.86204123f, -0.751610935f, + -3.0129981f, 2.44571567f, -0.969920754f, -2.25018358f, 1.32216787f, -1.93554914f, 1.81179321f, + -0.727910161f, 3.34558463f, -1.00105321f, -1.90789974f, -0.795648456f, -0.295596838f, 1.84380054f, + 1.6343987f, -1.70636845f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 1.78779817f, -2.82002068f, 1.36047447f, 0.867956996f, + 3.60750699f, -1.51914918f, 3.51146221f, 1.2093544f, -3.63042164f, 1.79315555f, -2.02431393f, + 0.624501765f, 1.93735468f, -2.24615479f, 1.14643657f, 2.08312964f, -3.27628303f, 3.65507889f, + -2.42534089f, -0.256407738f, 1.6062206f, 3.70445132f, 0.896572471f, -3.62409568f, -2.02926612f, + 2.46628785f, -0.301025689f, -0.136838257f, 1.05255735f, 0.0382424928f, 0.982686758f, -3.70555401f, + -3.69886804f, 1.70381987f, -1.81729424f, -3.59189939f, -1.79182112f, 3.10883117f, 3.76760101f, + 3.37596273f, 1.62467563f, -0.498761296f, 1.56601954f, 2.09831643f, -1.73353541f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + -1.60601294f, 1.3029362f, 1.93253839f, -2.33799338f, -1.30981576f, -1.68211031f, 1.16438186f, + -1.49199247f, -0.177964836f, -2.07698655f, 1.46125138f, -1.97311771f, 3.5902915f, -1.70906997f, + -2.7017324f, 1.80825925f, -1.34513593f, 0.986857414f, -2.06932402f, 2.89227128f, -3.04048347f, + 1.6145035f, -3.9423337f, -3.54079771f, -3.2805829f, -1.79433954f, -1.44512987f, -2.59295154f, + 1.50398004f, -3.64275527f, -1.73407698f, 3.487046f, 0.423988461f, 3.30892539f, -1.97320569f, + 0.268707752f, 1.55700195f, -3.74031997f, 2.01825452f, -1.57656145f, 1.76554489f, 0.326667875f, + -2.47481537f, 3.65779972f, 3.15902925f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 2.63131499f, -3.14859176f, -0.997390032f, + -1.20467031f, 2.21032619f, -1.76924586f, 0.642202377f, 0.278176546f, 0.145728454f, -1.11196029f, + -2.01806045f, 0.322141588f, -1.12646985f, -1.07507145f, 1.72732878f, 1.88165164f, 2.72870588f, + -3.76698375f, 2.13202572f, -0.840832233f, -3.62208152f, -2.9742167f, -1.08599961f, -1.23291886f, + -2.5547092f, 2.94218683f, 0.70580709f, 0.999319375f, 2.62544608f, -3.15573478f, 3.81955838f, + 1.44674516f, 2.5526154f, 0.245138556f, 0.101800121f, 0.451636553f, 3.3426919f, -0.600549698f, + 1.32474697f, 2.32352614f, 2.46365571f, 1.0765959f, 2.99040437f, 3.64721894f, -2.80435967f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, -0.400220424f, -1.17947686f, -3.84920168f, 2.008389f, -2.63169193f, -2.78069663f, + -3.6790185f, 1.93286836f, -1.96083272f, -1.94803286f, 1.14150977f, -0.269870669f, -3.91356516f, + 0.897772193f, 2.29915667f, -3.18600082f, 0.799750268f, 2.04519463f, -1.2170037f, 3.75417686f, + -0.508928001f, 3.26570511f, 0.428518236f, 0.838441312f, 3.8804903f, 1.86222613f, 2.59939218f, + 1.29434931f, -2.90471315f, -1.77256536f, 0.607833624f, 3.99430609f, -2.83389068f, -3.20123625f, + -0.641655266f, -0.443926543f, -1.52683103f, 0.505921781f, -2.73763323f, -1.09038174f, 3.90661383f, + 3.66068435f, -2.05198216f, -0.0194622651f, 0.911627591f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, -2.34721613f, -1.55258942f, + -1.00661838f, 1.03735602f, 2.85131097f, -1.87352419f, 2.9444685f, -3.65782356f, -2.77959847f, + 3.19181871f, 0.880091488f, 2.73344994f, 0.67603159f, -1.33721411f, -2.37102532f, -1.14067161f, + 2.36507058f, -1.34516692f, -2.75297141f, -1.88507771f, -1.00749636f, 3.36465812f, -3.33842492f, + -3.99275351f, -2.52483487f, 2.02326965f, 0.905177295f, -2.79976463f, -1.31361568f, 1.04678166f, + -2.38264513f, -0.367629588f, 1.57011831f, 3.75710869f, 0.602375746f, -3.23761892f, -2.5053463f, + -1.51726139f, -0.397426307f, 3.10831881f, -0.254475594f, -0.331198037f, -1.57487321f, -0.211769491f, + -2.18954015f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 2.05184269f, 0.294230938f, 2.55604529f, 1.99079132f, 0.76087749f, + 3.20271158f, -1.17838728f, 2.81995273f, 3.87430906f, 1.3856467f, 0.353117377f, 1.83593941f, + -2.77444625f, -2.92582035f, -3.43438363f, -2.35635495f, 2.1534493f, -2.33303428f, -0.23942031f, + -3.59250402f, -3.87034678f, 3.57504129f, -2.60450625f, -1.20090461f, 2.6526885f, 0.211482972f, + 1.6133486f, 2.84030962f, -2.72710228f, -3.61878109f, 1.40186799f, 1.15577841f, 3.10570574f, + 2.96033835f, -1.09952748f, -1.3936708f, -3.29456806f, -2.8926599f, 3.24352813f, 3.15888071f, + 3.75745654f, 0.890945077f, 3.43585443f, -1.12156415f, -1.35743499f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, 0.125f, + 0.125f, 0.125f, 0.125f, 0.125f, 0.125f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_basic_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/config_data.h new file mode 100644 index 00000000..057cabb1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_INT8_1_F16_INPUT_N 1 +#define PAD_INT8_1_F16_INPUT_H 2 +#define PAD_INT8_1_F16_INPUT_W 2 +#define PAD_INT8_1_F16_INPUT_C 2 +#define PAD_INT8_1_F16_PRE_PAD_N 0 +#define PAD_INT8_1_F16_PRE_PAD_H 0 +#define PAD_INT8_1_F16_PRE_PAD_W 1 +#define PAD_INT8_1_F16_PRE_PAD_C 1 +#define PAD_INT8_1_F16_POST_PAD_N 0 +#define PAD_INT8_1_F16_POST_PAD_H 0 +#define PAD_INT8_1_F16_POST_PAD_W 2 +#define PAD_INT8_1_F16_POST_PAD_C 2 +#define PAD_INT8_1_F16_OUTPUT_N 1 +#define PAD_INT8_1_F16_OUTPUT_H 2 +#define PAD_INT8_1_F16_OUTPUT_W 5 +#define PAD_INT8_1_F16_OUTPUT_C 5 +#define PAD_INT8_1_F16_OUTPUT_SIZE 50 +#define PAD_INT8_1_F16_PAD_VALUE (float16_t) - 3.0f diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/input_tensor_data.h new file mode 100644 index 00000000..a7ea1e3f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/input_tensor_data.h @@ -0,0 +1,9 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_int8_1_f16_input_tensor[8] = {(float16_t)-1.875f, + (float16_t)3.82226562f, + (float16_t)0.394042969f, + (float16_t)2.51171875f, + (float16_t)-0.326660156f, + (float16_t)0.556640625f, + (float16_t)3.71484375f, + (float16_t)-0.600585938f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/output_ref_data.h new file mode 100644 index 00000000..4bef7a99 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/output_ref_data.h @@ -0,0 +1,12 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_int8_1_f16_output_ref[50] = { + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-1.875f, (float16_t)3.82226562f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)0.394042969f, (float16_t)2.51171875f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-0.326660156f, (float16_t)0.556640625f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)3.71484375f, (float16_t)-0.600585938f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, + (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f, (float16_t)-3.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/config_data.h new file mode 100644 index 00000000..d571923a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_INT8_1_F32_INPUT_N 1 +#define PAD_INT8_1_F32_INPUT_H 2 +#define PAD_INT8_1_F32_INPUT_W 2 +#define PAD_INT8_1_F32_INPUT_C 2 +#define PAD_INT8_1_F32_PRE_PAD_N 0 +#define PAD_INT8_1_F32_PRE_PAD_H 0 +#define PAD_INT8_1_F32_PRE_PAD_W 1 +#define PAD_INT8_1_F32_PRE_PAD_C 1 +#define PAD_INT8_1_F32_POST_PAD_N 0 +#define PAD_INT8_1_F32_POST_PAD_H 0 +#define PAD_INT8_1_F32_POST_PAD_W 2 +#define PAD_INT8_1_F32_POST_PAD_C 2 +#define PAD_INT8_1_F32_OUTPUT_N 1 +#define PAD_INT8_1_F32_OUTPUT_H 2 +#define PAD_INT8_1_F32_OUTPUT_W 5 +#define PAD_INT8_1_F32_OUTPUT_C 5 +#define PAD_INT8_1_F32_OUTPUT_SIZE 50 +#define PAD_INT8_1_F32_PAD_VALUE -3.0f diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/input_tensor_data.h new file mode 100644 index 00000000..e707bd13 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/input_tensor_data.h @@ -0,0 +1,3 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_int8_1_f32_input_tensor[8] = + {-2.14788055f, 2.17099571f, -3.4749589f, 0.665352285f, 2.15102077f, 0.742655993f, 2.1601727f, -1.57520151f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/output_ref_data.h new file mode 100644 index 00000000..d6a9680d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_int8_1_f32_output_ref[50] = { + -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -2.14788055f, 2.17099571f, -3.0f, -3.0f, + -3.0f, -3.4749589f, 0.665352285f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, + -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, + -3.0f, 2.15102077f, 0.742655993f, -3.0f, -3.0f, -3.0f, 2.1601727f, -1.57520151f, -3.0f, -3.0f, + -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f, -3.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_1_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/config_data.h new file mode 100644 index 00000000..041c4899 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_INT8_2_F16_INPUT_N 1 +#define PAD_INT8_2_F16_INPUT_H 2 +#define PAD_INT8_2_F16_INPUT_W 2 +#define PAD_INT8_2_F16_INPUT_C 2 +#define PAD_INT8_2_F16_PRE_PAD_N 0 +#define PAD_INT8_2_F16_PRE_PAD_H 2 +#define PAD_INT8_2_F16_PRE_PAD_W 2 +#define PAD_INT8_2_F16_PRE_PAD_C 0 +#define PAD_INT8_2_F16_POST_PAD_N 0 +#define PAD_INT8_2_F16_POST_PAD_H 1 +#define PAD_INT8_2_F16_POST_PAD_W 1 +#define PAD_INT8_2_F16_POST_PAD_C 0 +#define PAD_INT8_2_F16_OUTPUT_N 1 +#define PAD_INT8_2_F16_OUTPUT_H 5 +#define PAD_INT8_2_F16_OUTPUT_W 5 +#define PAD_INT8_2_F16_OUTPUT_C 2 +#define PAD_INT8_2_F16_OUTPUT_SIZE 50 +#define PAD_INT8_2_F16_PAD_VALUE (float16_t) - 2.5f diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/input_tensor_data.h new file mode 100644 index 00000000..34a6a483 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/input_tensor_data.h @@ -0,0 +1,9 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_int8_2_f16_input_tensor[8] = {(float16_t)3.9609375f, + (float16_t)-3.25195312f, + (float16_t)3.578125f, + (float16_t)-3.20507812f, + (float16_t)-1.89550781f, + (float16_t)-1.41601562f, + (float16_t)1.59863281f, + (float16_t)3.94140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/output_ref_data.h new file mode 100644 index 00000000..90f9573b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/output_ref_data.h @@ -0,0 +1,12 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t pad_int8_2_f16_output_ref[50] = { + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)3.9609375f, + (float16_t)-3.25195312f, (float16_t)3.578125f, (float16_t)-3.20507812f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-1.89550781f, + (float16_t)-1.41601562f, (float16_t)1.59863281f, (float16_t)3.94140625f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, + (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f, (float16_t)-2.5f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/config_data.h new file mode 100644 index 00000000..c3e911b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/config_data.h @@ -0,0 +1,19 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#define PAD_INT8_2_F32_INPUT_N 1 +#define PAD_INT8_2_F32_INPUT_H 2 +#define PAD_INT8_2_F32_INPUT_W 2 +#define PAD_INT8_2_F32_INPUT_C 2 +#define PAD_INT8_2_F32_PRE_PAD_N 0 +#define PAD_INT8_2_F32_PRE_PAD_H 2 +#define PAD_INT8_2_F32_PRE_PAD_W 2 +#define PAD_INT8_2_F32_PRE_PAD_C 0 +#define PAD_INT8_2_F32_POST_PAD_N 0 +#define PAD_INT8_2_F32_POST_PAD_H 1 +#define PAD_INT8_2_F32_POST_PAD_W 1 +#define PAD_INT8_2_F32_POST_PAD_C 0 +#define PAD_INT8_2_F32_OUTPUT_N 1 +#define PAD_INT8_2_F32_OUTPUT_H 5 +#define PAD_INT8_2_F32_OUTPUT_W 5 +#define PAD_INT8_2_F32_OUTPUT_C 2 +#define PAD_INT8_2_F32_OUTPUT_SIZE 50 +#define PAD_INT8_2_F32_PAD_VALUE -2.5f diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/input_tensor_data.h new file mode 100644 index 00000000..13873aa9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/input_tensor_data.h @@ -0,0 +1,3 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_int8_2_f32_input_tensor[8] = + {-0.387408972f, 2.13197255f, -1.916363f, -2.67770052f, -1.62773919f, -1.76577926f, -1.05694222f, -1.04419398f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/output_ref_data.h new file mode 100644 index 00000000..db7a4af6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t pad_int8_2_f32_output_ref[50] = { + -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, + -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, + -2.5f, -2.5f, -2.5f, -2.5f, -0.387408972f, 2.13197255f, -1.916363f, -2.67770052f, -2.5f, -2.5f, + -2.5f, -2.5f, -2.5f, -2.5f, -1.62773919f, -1.76577926f, -1.05694222f, -1.04419398f, -2.5f, -2.5f, + -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f, -2.5f}; diff --git a/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/test_data.h new file mode 100644 index 00000000..2a5d24ad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/pad_int8_2_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by pad_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f16/config_data.h new file mode 100644 index 00000000..c0e7ab7b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f16/config_data.h @@ -0,0 +1,4 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SOFTMAX_F16_NUM_ROWS 2 +#define SOFTMAX_F16_ROW_SIZE 5 +#define SOFTMAX_F16_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f16/input_data.h new file mode 100644 index 00000000..a4a3d162 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f16/input_data.h @@ -0,0 +1,11 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t softmax_f16_input[10] = {(float16_t)1.0f, + (float16_t)0.25f, + (float16_t)-0.5f, + (float16_t)2.0f, + (float16_t)-1.25f, + (float16_t)-2.0f, + (float16_t)-0.125f, + (float16_t)0.75f, + (float16_t)3.0f, + (float16_t)1.5f}; diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f16/output_ref_data.h new file mode 100644 index 00000000..60f7dfcb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f16/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t softmax_f16_output_ref[10] = {(float16_t)0.221313477f, + (float16_t)0.104553223f, + (float16_t)0.0493774414f, + (float16_t)0.6015625f, + (float16_t)0.0233154297f, + (float16_t)0.0048866272f, + (float16_t)0.0318603516f, + (float16_t)0.0764160156f, + (float16_t)0.725097656f, + (float16_t)0.161743164f}; diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f16/test_data.h new file mode 100644 index 00000000..f51cff64 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f32/config_data.h new file mode 100644 index 00000000..6125768b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f32/config_data.h @@ -0,0 +1,4 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SOFTMAX_F32_NUM_ROWS 2 +#define SOFTMAX_F32_ROW_SIZE 5 +#define SOFTMAX_F32_DST_SIZE 10 diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f32/input_data.h new file mode 100644 index 00000000..fe9829dc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f32/input_data.h @@ -0,0 +1,2 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t softmax_f32_input[10] = {1.0f, 0.25f, -0.5f, 2.0f, -1.25f, -2.0f, -0.125f, 0.75f, 3.0f, 1.5f}; diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f32/output_ref_data.h new file mode 100644 index 00000000..543de662 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f32/output_ref_data.h @@ -0,0 +1,11 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t softmax_f32_output_ref[10] = {0.221279189f, + 0.104524888f, + 0.0493740626f, + 0.6014992f, + 0.0233226549f, + 0.00488538714f, + 0.0318567269f, + 0.0764203146f, + 0.725055754f, + 0.161781818f}; diff --git a/Tests/UnitTest/TestCases/TestData/softmax_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/softmax_f32/test_data.h new file mode 100644 index 00000000..f51cff64 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/softmax_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by softmax_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/bias_data.h new file mode 100644 index 00000000..a6a08304 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/bias_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_bias[8] = {(float16_t)-0.0536499023f, + (float16_t)0.138305664f, + (float16_t)-0.24621582f, + (float16_t)0.00514602661f, + (float16_t)0.207397461f, + (float16_t)0.104675293f, + (float16_t)0.0415039062f, + (float16_t)0.0168457031f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/config_data.h new file mode 100644 index 00000000..5b2c52f5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_BATCH2_F16_INPUT_BATCHES 2 +#define SVDF_BATCH2_F16_INPUT_SIZE 20 +#define SVDF_BATCH2_F16_UNIT_COUNT 8 +#define SVDF_BATCH2_F16_RANK 2 +#define SVDF_BATCH2_F16_FEATURE_BATCHES 16 +#define SVDF_BATCH2_F16_TIME_BATCHES 3 +#define SVDF_BATCH2_F16_SEQUENCE_STEPS 4 +#define SVDF_BATCH2_F16_DST_SIZE 16 +#define SVDF_BATCH2_F16_INPUT_ACTIVATION_MIN (float16_t) - 1.0f +#define SVDF_BATCH2_F16_INPUT_ACTIVATION_MAX (float16_t)1.0f +#define SVDF_BATCH2_F16_OUTPUT_ACTIVATION_MIN (float16_t) - 100.0f +#define SVDF_BATCH2_F16_OUTPUT_ACTIVATION_MAX (float16_t)100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/initial_state_data.h new file mode 100644 index 00000000..2f3888de --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/initial_state_data.h @@ -0,0 +1,18 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_initial_state[96] = { + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/input_sequence_data.h new file mode 100644 index 00000000..932f5208 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/input_sequence_data.h @@ -0,0 +1,42 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_input_sequence[160] = { + (float16_t)-1.00488281f, (float16_t)0.555664062f, (float16_t)0.761230469f, (float16_t)0.969238281f, + (float16_t)0.763183594f, (float16_t)-0.20690918f, (float16_t)0.312255859f, (float16_t)-0.0492858887f, + (float16_t)-0.917480469f, (float16_t)-1.45703125f, (float16_t)0.998535156f, (float16_t)1.0390625f, + (float16_t)-0.219116211f, (float16_t)-0.375f, (float16_t)0.762207031f, (float16_t)0.789550781f, + (float16_t)-0.554199219f, (float16_t)0.627929688f, (float16_t)-0.330078125f, (float16_t)1.0234375f, + (float16_t)-0.580078125f, (float16_t)1.41503906f, (float16_t)0.335449219f, (float16_t)1.19238281f, + (float16_t)-0.760253906f, (float16_t)-0.0176239014f, (float16_t)-0.564941406f, (float16_t)1.40234375f, + (float16_t)0.0483703613f, (float16_t)1.08203125f, (float16_t)1.18554688f, (float16_t)-0.240234375f, + (float16_t)0.553710938f, (float16_t)-0.250244141f, (float16_t)-0.944335938f, (float16_t)0.993652344f, + (float16_t)0.635742188f, (float16_t)-0.710449219f, (float16_t)0.92578125f, (float16_t)-1.36523438f, + (float16_t)-1.11132812f, (float16_t)-0.625f, (float16_t)-0.232788086f, (float16_t)-0.177978516f, + (float16_t)1.42773438f, (float16_t)1.20019531f, (float16_t)1.04394531f, (float16_t)0.433105469f, + (float16_t)-0.837890625f, (float16_t)0.622558594f, (float16_t)-0.314453125f, (float16_t)-0.359130859f, + (float16_t)-0.419921875f, (float16_t)-0.286865234f, (float16_t)0.686035156f, (float16_t)0.28125f, + (float16_t)-0.731933594f, (float16_t)-0.782226562f, (float16_t)0.201782227f, (float16_t)-0.84765625f, + (float16_t)-0.862304688f, (float16_t)-0.336669922f, (float16_t)-1.23828125f, (float16_t)-0.11505127f, + (float16_t)0.629882812f, (float16_t)-0.186035156f, (float16_t)1.39648438f, (float16_t)0.247070312f, + (float16_t)-0.643066406f, (float16_t)1.375f, (float16_t)0.187988281f, (float16_t)0.750976562f, + (float16_t)-0.758789062f, (float16_t)0.596191406f, (float16_t)1.18359375f, (float16_t)0.543457031f, + (float16_t)-0.135864258f, (float16_t)0.272460938f, (float16_t)0.509277344f, (float16_t)-0.299072266f, + (float16_t)0.413574219f, (float16_t)-1.4140625f, (float16_t)-0.6953125f, (float16_t)-1.41699219f, + (float16_t)-1.16601562f, (float16_t)0.824707031f, (float16_t)1.34472656f, (float16_t)0.535644531f, + (float16_t)1.19824219f, (float16_t)-1.43164062f, (float16_t)-0.946777344f, (float16_t)-0.536132812f, + (float16_t)-1.09179688f, (float16_t)-1.24609375f, (float16_t)-0.522949219f, (float16_t)0.634277344f, + (float16_t)-1.125f, (float16_t)0.672851562f, (float16_t)0.576660156f, (float16_t)-0.873046875f, + (float16_t)-1.18066406f, (float16_t)0.845214844f, (float16_t)-1.42089844f, (float16_t)-1.30371094f, + (float16_t)0.720214844f, (float16_t)0.687988281f, (float16_t)0.618652344f, (float16_t)0.744628906f, + (float16_t)-0.210083008f, (float16_t)1.39648438f, (float16_t)0.0913696289f, (float16_t)1.0625f, + (float16_t)0.431152344f, (float16_t)-0.380371094f, (float16_t)0.325195312f, (float16_t)0.270263672f, + (float16_t)0.879882812f, (float16_t)0.390380859f, (float16_t)-1.34765625f, (float16_t)-1.41699219f, + (float16_t)-0.779785156f, (float16_t)1.09082031f, (float16_t)-0.17578125f, (float16_t)0.940917969f, + (float16_t)1.23144531f, (float16_t)-0.627441406f, (float16_t)-1.42285156f, (float16_t)0.701171875f, + (float16_t)-0.151733398f, (float16_t)0.639648438f, (float16_t)-1.42871094f, (float16_t)-0.517089844f, + (float16_t)0.0253601074f, (float16_t)0.7890625f, (float16_t)-1.27832031f, (float16_t)0.686035156f, + (float16_t)1.08691406f, (float16_t)0.282958984f, (float16_t)0.990234375f, (float16_t)-0.270751953f, + (float16_t)0.600097656f, (float16_t)1.26660156f, (float16_t)-0.571289062f, (float16_t)1.12011719f, + (float16_t)1.35253906f, (float16_t)-0.614746094f, (float16_t)1.05859375f, (float16_t)-0.0590515137f, + (float16_t)0.127807617f, (float16_t)-0.901855469f, (float16_t)0.760742188f, (float16_t)0.642578125f, + (float16_t)0.0974731445f, (float16_t)-0.642578125f, (float16_t)0.170166016f, (float16_t)-0.420166016f, + (float16_t)0.994628906f, (float16_t)-0.467285156f, (float16_t)-1.29785156f, (float16_t)-0.749023438f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/output_ref_data.h new file mode 100644 index 00000000..d504eb9f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_output_ref[16] = {(float16_t)0.00849151611f, + (float16_t)-0.138305664f, + (float16_t)0.0397644043f, + (float16_t)0.330322266f, + (float16_t)0.96875f, + (float16_t)0.0833740234f, + (float16_t)0.584472656f, + (float16_t)-0.11730957f, + (float16_t)0.235595703f, + (float16_t)0.552246094f, + (float16_t)-0.463134766f, + (float16_t)-0.782714844f, + (float16_t)-0.186767578f, + (float16_t)0.365478516f, + (float16_t)0.340332031f, + (float16_t)-0.0932006836f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_feature_data.h new file mode 100644 index 00000000..9391cf56 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_feature_data.h @@ -0,0 +1,82 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_weights_feature[320] = { + (float16_t)-0.0336608887f, (float16_t)-0.199462891f, (float16_t)-0.0627441406f, (float16_t)-0.134765625f, + (float16_t)-0.147460938f, (float16_t)0.333740234f, (float16_t)-0.272460938f, (float16_t)0.282470703f, + (float16_t)-0.282958984f, (float16_t)-0.4296875f, (float16_t)0.0570678711f, (float16_t)-0.402832031f, + (float16_t)-0.4765625f, (float16_t)-0.237670898f, (float16_t)0.141113281f, (float16_t)0.0135116577f, + (float16_t)0.0944213867f, (float16_t)-0.483642578f, (float16_t)0.475341797f, (float16_t)0.403564453f, + (float16_t)-0.0437316895f, (float16_t)0.112182617f, (float16_t)-0.112243652f, (float16_t)0.266357422f, + (float16_t)-0.0589599609f, (float16_t)-0.376708984f, (float16_t)0.300537109f, (float16_t)0.394775391f, + (float16_t)-0.468505859f, (float16_t)0.229492188f, (float16_t)-0.124023438f, (float16_t)0.0800170898f, + (float16_t)-0.492431641f, (float16_t)-0.138305664f, (float16_t)0.063659668f, (float16_t)0.0040397644f, + (float16_t)-0.433105469f, (float16_t)0.334228516f, (float16_t)-0.285888672f, (float16_t)-0.13671875f, + (float16_t)-0.0398254395f, (float16_t)0.412597656f, (float16_t)-0.0714111328f, (float16_t)-0.385253906f, + (float16_t)-0.0645141602f, (float16_t)0.352294922f, (float16_t)0.0980224609f, (float16_t)-0.327148438f, + (float16_t)-0.260009766f, (float16_t)0.299804688f, (float16_t)-0.114440918f, (float16_t)0.223754883f, + (float16_t)-0.137207031f, (float16_t)0.465576172f, (float16_t)-0.328857422f, (float16_t)0.348876953f, + (float16_t)-0.490234375f, (float16_t)0.145263672f, (float16_t)0.298339844f, (float16_t)-0.265625f, + (float16_t)0.0141372681f, (float16_t)0.282958984f, (float16_t)-0.229736328f, (float16_t)-0.395996094f, + (float16_t)0.0451660156f, (float16_t)0.221679688f, (float16_t)-0.196289062f, (float16_t)0.119995117f, + (float16_t)0.341796875f, (float16_t)-0.170288086f, (float16_t)0.457519531f, (float16_t)0.327880859f, + (float16_t)-0.102233887f, (float16_t)-0.0994873047f, (float16_t)0.231201172f, (float16_t)-0.315429688f, + (float16_t)-0.0904541016f, (float16_t)-0.280273438f, (float16_t)-0.173461914f, (float16_t)0.271240234f, + (float16_t)-0.096862793f, (float16_t)-0.0106201172f, (float16_t)-0.349609375f, (float16_t)-0.420166016f, + (float16_t)0.404541016f, (float16_t)0.0224151611f, (float16_t)0.0144348145f, (float16_t)-0.443359375f, + (float16_t)0.351318359f, (float16_t)-0.314453125f, (float16_t)0.00368499756f, (float16_t)0.284179688f, + (float16_t)-0.0903320312f, (float16_t)0.0324707031f, (float16_t)-0.226318359f, (float16_t)0.0476379395f, + (float16_t)0.467773438f, (float16_t)-0.453613281f, (float16_t)-0.450683594f, (float16_t)-0.130126953f, + (float16_t)-0.3203125f, (float16_t)0.0224151611f, (float16_t)-0.336669922f, (float16_t)-0.0799560547f, + (float16_t)0.486328125f, (float16_t)-0.211425781f, (float16_t)0.242919922f, (float16_t)0.446289062f, + (float16_t)-0.393310547f, (float16_t)0.329833984f, (float16_t)-0.424560547f, (float16_t)-0.382324219f, + (float16_t)-0.0321655273f, (float16_t)-0.0701293945f, (float16_t)0.404541016f, (float16_t)-0.0966186523f, + (float16_t)-0.250976562f, (float16_t)0.235107422f, (float16_t)0.240722656f, (float16_t)0.056640625f, + (float16_t)0.333251953f, (float16_t)-0.135620117f, (float16_t)-0.369628906f, (float16_t)-0.385009766f, + (float16_t)0.414550781f, (float16_t)-0.470214844f, (float16_t)-0.260498047f, (float16_t)0.232421875f, + (float16_t)0.00181388855f, (float16_t)0.0878295898f, (float16_t)0.0140151978f, (float16_t)-0.174316406f, + (float16_t)0.476074219f, (float16_t)0.143310547f, (float16_t)0.426025391f, (float16_t)0.123413086f, + (float16_t)-0.1484375f, (float16_t)0.262451172f, (float16_t)-0.376708984f, (float16_t)-0.293945312f, + (float16_t)0.293457031f, (float16_t)-0.391845703f, (float16_t)-0.29296875f, (float16_t)0.218261719f, + (float16_t)-0.165649414f, (float16_t)-0.243041992f, (float16_t)-0.0597839355f, (float16_t)0.169677734f, + (float16_t)-0.299316406f, (float16_t)-0.151000977f, (float16_t)0.460205078f, (float16_t)-0.253662109f, + (float16_t)0.136962891f, (float16_t)0.344726562f, (float16_t)-0.0964355469f, (float16_t)0.161865234f, + (float16_t)0.223876953f, (float16_t)-0.409912109f, (float16_t)-0.37890625f, (float16_t)0.0459594727f, + (float16_t)-0.171508789f, (float16_t)0.472900391f, (float16_t)-0.0541381836f, (float16_t)-0.00677871704f, + (float16_t)0.423583984f, (float16_t)0.322753906f, (float16_t)-0.255126953f, (float16_t)0.0780029297f, + (float16_t)-0.0195465088f, (float16_t)-0.312255859f, (float16_t)-0.499267578f, (float16_t)0.162719727f, + (float16_t)-0.339355469f, (float16_t)0.249633789f, (float16_t)-0.381591797f, (float16_t)-0.264892578f, + (float16_t)-0.4453125f, (float16_t)-0.0844116211f, (float16_t)0.183959961f, (float16_t)0.154174805f, + (float16_t)0.0556945801f, (float16_t)-0.0825805664f, (float16_t)-0.466308594f, (float16_t)0.0864868164f, + (float16_t)-0.316894531f, (float16_t)-0.275878906f, (float16_t)-0.288085938f, (float16_t)-0.135375977f, + (float16_t)-0.406494141f, (float16_t)-0.0493774414f, (float16_t)0.229248047f, (float16_t)0.275390625f, + (float16_t)-0.0649414062f, (float16_t)0.399902344f, (float16_t)-0.00048661232f, (float16_t)0.404541016f, + (float16_t)0.471679688f, (float16_t)0.412353516f, (float16_t)0.23449707f, (float16_t)-0.344238281f, + (float16_t)-0.0169219971f, (float16_t)-0.344970703f, (float16_t)-0.421386719f, (float16_t)-0.170654297f, + (float16_t)0.131347656f, (float16_t)-0.0405273438f, (float16_t)-0.0897216797f, (float16_t)-0.0206298828f, + (float16_t)-0.00500106812f, (float16_t)-0.085144043f, (float16_t)0.0539855957f, (float16_t)0.130371094f, + (float16_t)0.0225830078f, (float16_t)0.144287109f, (float16_t)0.390136719f, (float16_t)0.343261719f, + (float16_t)-0.496582031f, (float16_t)-0.204711914f, (float16_t)-0.454833984f, (float16_t)-0.346679688f, + (float16_t)-0.472900391f, (float16_t)0.182128906f, (float16_t)0.306884766f, (float16_t)-0.157714844f, + (float16_t)-0.303466797f, (float16_t)-0.187744141f, (float16_t)-0.310302734f, (float16_t)-0.45703125f, + (float16_t)0.0506591797f, (float16_t)0.192993164f, (float16_t)-0.320068359f, (float16_t)-0.290527344f, + (float16_t)0.484375f, (float16_t)-0.444580078f, (float16_t)0.173339844f, (float16_t)-0.459472656f, + (float16_t)-0.0415344238f, (float16_t)-0.318847656f, (float16_t)0.260253906f, (float16_t)-0.284667969f, + (float16_t)-0.172363281f, (float16_t)0.209838867f, (float16_t)0.432861328f, (float16_t)0.284912109f, + (float16_t)-0.373046875f, (float16_t)0.112670898f, (float16_t)0.471435547f, (float16_t)0.0625610352f, + (float16_t)0.3515625f, (float16_t)0.352294922f, (float16_t)-0.361083984f, (float16_t)0.190307617f, + (float16_t)0.130615234f, (float16_t)0.305908203f, (float16_t)0.169311523f, (float16_t)0.387695312f, + (float16_t)0.129882812f, (float16_t)-0.462158203f, (float16_t)-0.299560547f, (float16_t)0.333007812f, + (float16_t)-0.0587463379f, (float16_t)0.233398438f, (float16_t)-0.245117188f, (float16_t)0.360839844f, + (float16_t)-0.370849609f, (float16_t)0.246337891f, (float16_t)0.373535156f, (float16_t)0.439208984f, + (float16_t)0.237792969f, (float16_t)-0.0667724609f, (float16_t)-0.227905273f, (float16_t)-0.366455078f, + (float16_t)-0.468994141f, (float16_t)0.163818359f, (float16_t)0.413330078f, (float16_t)0.263916016f, + (float16_t)-0.397705078f, (float16_t)0.359863281f, (float16_t)-0.448730469f, (float16_t)-0.208618164f, + (float16_t)0.194091797f, (float16_t)-0.0124816895f, (float16_t)-0.119262695f, (float16_t)-0.327636719f, + (float16_t)0.0707397461f, (float16_t)-0.327148438f, (float16_t)-0.419189453f, (float16_t)0.316650391f, + (float16_t)-0.329345703f, (float16_t)-0.457275391f, (float16_t)-0.344970703f, (float16_t)-0.0318603516f, + (float16_t)-0.493164062f, (float16_t)-0.306884766f, (float16_t)0.447021484f, (float16_t)0.0361633301f, + (float16_t)0.306884766f, (float16_t)-0.294433594f, (float16_t)-0.089050293f, (float16_t)0.00150871277f, + (float16_t)-0.388183594f, (float16_t)0.161987305f, (float16_t)-0.268066406f, (float16_t)0.278564453f, + (float16_t)0.388183594f, (float16_t)0.398681641f, (float16_t)-0.499023438f, (float16_t)-0.289306641f, + (float16_t)-0.182617188f, (float16_t)0.207275391f, (float16_t)0.486328125f, (float16_t)-0.225341797f, + (float16_t)0.39453125f, (float16_t)0.467041016f, (float16_t)-0.447509766f, (float16_t)0.0258026123f, + (float16_t)-0.12298584f, (float16_t)0.497070312f, (float16_t)0.384277344f, (float16_t)0.409667969f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_time_data.h new file mode 100644 index 00000000..9141fc69 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f16/weights_time_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_batch2_f16_weights_time[48] = { + (float16_t)0.382324219f, (float16_t)-0.210571289f, (float16_t)-0.23815918f, (float16_t)-0.162109375f, + (float16_t)0.153076172f, (float16_t)0.196411133f, (float16_t)0.401367188f, (float16_t)-0.290527344f, + (float16_t)0.245849609f, (float16_t)0.472167969f, (float16_t)0.428466797f, (float16_t)0.336425781f, + (float16_t)-0.114746094f, (float16_t)-0.29296875f, (float16_t)-0.0184936523f, (float16_t)-0.0629272461f, + (float16_t)0.315429688f, (float16_t)0.356933594f, (float16_t)-0.37890625f, (float16_t)-0.433837891f, + (float16_t)-0.0223388672f, (float16_t)0.191650391f, (float16_t)-0.126708984f, (float16_t)-0.193969727f, + (float16_t)0.355224609f, (float16_t)-0.368896484f, (float16_t)0.475097656f, (float16_t)-0.306640625f, + (float16_t)0.0493469238f, (float16_t)-0.169677734f, (float16_t)-0.229858398f, (float16_t)0.429443359f, + (float16_t)0.46875f, (float16_t)0.388671875f, (float16_t)-0.438720703f, (float16_t)-0.237182617f, + (float16_t)0.0935058594f, (float16_t)-0.135009766f, (float16_t)-0.143188477f, (float16_t)0.0646362305f, + (float16_t)0.305419922f, (float16_t)-0.0527038574f, (float16_t)0.0687866211f, (float16_t)-0.256835938f, + (float16_t)-0.138183594f, (float16_t)0.410888672f, (float16_t)-0.0739135742f, (float16_t)0.0841064453f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/bias_data.h new file mode 100644 index 00000000..5e02b72c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/bias_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_bias[8] = {-0.0801542401f, + 0.102831051f, + -0.209339321f, + 0.1675722f, + -0.175365955f, + -0.244661346f, + -0.212981522f, + -0.0593195707f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/config_data.h new file mode 100644 index 00000000..24ad5a8a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_BATCH2_F32_INPUT_BATCHES 2 +#define SVDF_BATCH2_F32_INPUT_SIZE 20 +#define SVDF_BATCH2_F32_UNIT_COUNT 8 +#define SVDF_BATCH2_F32_RANK 2 +#define SVDF_BATCH2_F32_FEATURE_BATCHES 16 +#define SVDF_BATCH2_F32_TIME_BATCHES 3 +#define SVDF_BATCH2_F32_SEQUENCE_STEPS 4 +#define SVDF_BATCH2_F32_DST_SIZE 16 +#define SVDF_BATCH2_F32_INPUT_ACTIVATION_MIN -1.0f +#define SVDF_BATCH2_F32_INPUT_ACTIVATION_MAX 1.0f +#define SVDF_BATCH2_F32_OUTPUT_ACTIVATION_MIN -100.0f +#define SVDF_BATCH2_F32_OUTPUT_ACTIVATION_MAX 100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/initial_state_data.h new file mode 100644 index 00000000..be7d95eb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/initial_state_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_initial_state[96] = { + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/input_sequence_data.h new file mode 100644 index 00000000..481eef62 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/input_sequence_data.h @@ -0,0 +1,25 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_input_sequence[160] = { + 0.0398993976f, -1.03094721f, 0.401649266f, -1.46929836f, 1.40551984f, 0.543528736f, -0.696874738f, + 1.0963999f, -1.32203496f, -0.195150077f, -0.152053133f, -0.233601525f, -1.03202105f, 0.783150613f, + 1.03563941f, -1.28857982f, -1.32909763f, -0.576945007f, 1.08007658f, 1.39181054f, 0.00593258766f, + 0.776709914f, 0.557678044f, -0.982363939f, -1.08014834f, 0.614889145f, -0.170838058f, 0.0150263906f, + 0.976379216f, -0.532130599f, -0.0403371453f, -0.16352585f, -1.31435776f, -0.264847308f, -0.247652128f, + 0.839127958f, -0.264639646f, -1.16923845f, 0.909551501f, 0.967211723f, -0.445891172f, 0.476010144f, + -0.890104234f, -0.526766956f, 0.56376642f, 0.139543504f, -1.45238733f, -1.25522304f, -0.186055511f, + -0.439229071f, 1.28000224f, 0.129476979f, -0.888638794f, 0.523143649f, 1.23985755f, -0.922137141f, + -0.791694999f, -1.38360715f, -0.0361174755f, -1.32343221f, -1.06406653f, 0.932226419f, -0.163031623f, + -1.14401805f, 1.21541297f, 1.06715357f, 0.51766175f, 0.0817594826f, -1.20299888f, 1.15490794f, + 1.36726725f, -1.17126274f, 0.655327499f, 0.926757812f, 0.323617101f, -0.86718899f, -0.124908373f, + 0.512289762f, 1.11540091f, -1.04928493f, 1.02273393f, 1.35202503f, 0.978316784f, -1.48734522f, + 1.32598281f, 0.743242085f, -0.548249066f, -0.180900529f, 0.696686268f, 0.0284845177f, -1.2206924f, + -0.713575304f, 0.0718324259f, -0.707469404f, -1.00984812f, -1.02775526f, -0.737852991f, 1.45545137f, + 1.02975845f, -0.675141692f, 0.374092311f, 0.50281024f, -0.943475068f, -0.609338403f, 0.427048773f, + -1.42365491f, -0.242221281f, 1.43663514f, -0.983923733f, -0.470163405f, 1.23239517f, 0.782592535f, + -0.909925818f, 0.784198284f, -0.609891117f, 0.637844801f, 1.29236102f, -0.444467247f, 1.35627472f, + -0.87879771f, 0.231509671f, -0.0767577812f, 0.21134752f, 1.26023376f, -1.06653512f, -0.944906771f, + -1.49447417f, 1.32067609f, -1.34562922f, 1.07799768f, -1.01948118f, 1.38904154f, 0.894554973f, + -1.47849488f, -1.11226988f, 0.369016826f, -0.334437996f, 0.0665308237f, -0.100092642f, -1.27148545f, + -1.14657092f, 1.12036014f, -0.753083229f, -0.915366232f, -1.49056637f, 0.241709918f, -0.826218128f, + 1.00805259f, 0.701423287f, -0.492348582f, -1.23562956f, -0.378545165f, 1.29642487f, -0.0854010582f, + 1.09267974f, -0.859332919f, 1.44936955f, 1.42711878f, -0.516342521f, -1.34819746f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/output_ref_data.h new file mode 100644 index 00000000..4ad6e326 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_output_ref[16] = {0.310285032f, + 0.597107887f, + -0.435895592f, + 1.11575747f, + -0.0585297942f, + -0.00509424508f, + -0.409945041f, + 0.630609274f, + 0.489323318f, + -0.129344448f, + -1.67699575f, + 0.370855927f, + -0.348684132f, + 0.689604938f, + 0.0879325271f, + -0.507556498f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_feature_data.h new file mode 100644 index 00000000..4beabe2e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_feature_data.h @@ -0,0 +1,48 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_weights_feature[320] = { + 0.204333156f, -0.161776021f, 0.430329353f, -0.29468894f, -0.0885180831f, 0.444438517f, -0.172249749f, + 0.16021505f, -0.288652301f, -0.482291847f, -0.470496297f, 0.312101841f, 0.176438719f, -0.210031137f, + -0.230565399f, 0.258222461f, 0.191136688f, -0.262809575f, -0.2323737f, -0.238924995f, -0.365558833f, + 0.363715768f, 0.213851109f, -0.271289349f, -0.0976540148f, -0.113894448f, -0.403976232f, -0.158269927f, + 0.447548032f, 0.276771694f, -0.303178549f, -0.392418176f, -0.361742198f, 0.0399243459f, 0.319178909f, + 0.390386134f, 0.256272435f, -0.314131588f, -0.396318376f, 0.421748966f, -0.073383905f, 0.414376229f, + 0.200505555f, -0.32892105f, -0.321312696f, 0.325044632f, 0.291015834f, 0.21888271f, 0.164824918f, + -0.217726037f, 0.184049025f, 0.239248812f, 0.461880088f, -0.457734942f, -0.453202605f, 0.486728311f, + -0.482676089f, 0.0732177645f, -0.083936289f, 0.308527082f, -0.278853536f, -0.228977561f, 0.0494600236f, + 0.135368153f, 0.155415371f, 0.0054962663f, 0.327994704f, 0.326935291f, 0.302002251f, -0.371772975f, + -0.0907149538f, -0.4751679f, 0.16540353f, 0.331766307f, 0.37537083f, -0.0588313863f, -0.220018148f, + 0.392255962f, -0.476005107f, -0.393474847f, -0.388273567f, 0.0464557968f, -0.249468237f, -0.0425808653f, + 0.196154341f, -0.133602023f, -0.483574152f, -0.486381143f, 0.342903167f, 0.180367276f, 0.128956556f, + 0.239848256f, 0.0624869652f, 0.180432558f, -0.399410516f, -0.10006772f, 0.271457702f, 0.249307424f, + 0.481896609f, -0.110753745f, 0.259333163f, 0.411091715f, -0.249539256f, -0.129033953f, -0.272933185f, + -0.23667033f, -0.137063324f, 0.196593106f, -0.294974834f, 0.342500925f, 0.201595172f, -0.480253011f, + 0.027342869f, 0.310134083f, -0.28203702f, -0.052095551f, 0.344904184f, 0.481709957f, -0.250298351f, + 0.143801257f, -0.0470074564f, -0.0947680324f, 0.067528218f, -0.187291682f, 0.254751116f, 0.184426516f, + -0.0992986932f, 0.333474874f, 0.302506566f, -0.321374625f, -0.233379498f, 0.0554395169f, 0.174476489f, + -0.192631915f, 0.296703309f, -0.33130452f, -0.385848194f, -0.299253941f, -0.339854121f, -0.0729631633f, + -0.310018897f, -0.477483541f, 0.381487697f, -0.00462635932f, -0.139409721f, -0.436854869f, 0.263349712f, + -0.436580211f, 0.282314718f, -0.184417382f, 0.356952518f, -0.418576211f, -0.0673886687f, -0.273792982f, + 0.350100338f, -0.00177401165f, -0.288369715f, -0.380708337f, -0.192165762f, 0.209525451f, 0.399175018f, + 0.326538354f, -0.411733478f, -0.254585534f, 0.0922030732f, -0.156989247f, -0.148134708f, -0.0669745207f, + 0.408380806f, -0.457941651f, 0.424045295f, -0.355915874f, -0.178747073f, 0.319992006f, 0.000537972141f, + 0.00724621909f, -0.392201066f, -0.193142042f, -0.134660736f, -0.191700116f, -0.392734587f, -0.40468204f, + 0.235851377f, -0.38091889f, 0.239417732f, 0.412221879f, -0.198194519f, 0.00175342453f, 0.328161269f, + 0.0988857821f, -0.483513713f, 0.224682331f, -0.229514629f, -0.419996291f, -0.037303403f, -0.1163138f, + 0.204938367f, 0.301101148f, -0.302030653f, -0.0481414571f, -0.17012693f, -0.252449989f, 0.361995876f, + 0.128261328f, 0.231404543f, -0.0600326397f, 0.435008854f, -0.398154736f, 0.0277363975f, 0.20761165f, + -0.146004751f, -0.154444307f, -0.00126661698f, 0.0546923466f, -0.35497871f, 0.179566637f, -0.0119010182f, + 0.0441000164f, 0.247315466f, 0.194597602f, -0.371650696f, -0.322541416f, 0.486396343f, -0.196145937f, + 0.26807487f, -0.490291268f, 0.078917563f, -0.0524128005f, 0.284061909f, 0.369813144f, -0.296933889f, + -0.1984182f, -0.498621076f, -0.0647505894f, 0.0226924866f, 0.0250814483f, -0.0117705446f, 0.282932609f, + -0.40610975f, -0.314759552f, -0.0106972679f, -0.255416423f, 0.37584877f, -0.0184815619f, 0.246340364f, + -0.305507749f, -0.264649659f, 0.27821514f, 0.306046724f, -0.391408443f, 0.0376197733f, 0.234401643f, + -0.0295102745f, 0.304924846f, -0.22720103f, -0.328486651f, 0.0813630819f, 0.446757346f, -0.012714589f, + -0.351891994f, 0.0189547371f, 0.323647618f, -0.380752891f, -0.154229254f, -0.416927993f, -0.119104534f, + 0.3386724f, 0.0048266123f, -0.273408324f, 0.342649966f, -0.193450376f, -0.449726045f, 0.319363654f, + -0.160846382f, 0.131364465f, 0.41424939f, -0.0209121183f, 0.296185017f, 0.315547585f, 0.482676387f, + -0.295335889f, -0.331712276f, -0.065505825f, -0.205411881f, -0.345518261f, 0.279779673f, 0.469121575f, + -0.400342941f, 0.344830483f, 0.106109343f, -0.479208797f, 0.204625204f, 0.369564712f, -0.0676522925f, + -0.0746005625f, -0.241753653f, 0.100698069f, 0.398415446f, 0.468986779f, -0.229571432f, -0.467748225f, + 0.020078579f, 0.102154702f, -0.119619891f, -0.115834549f, 0.260670364f, -0.378958493f, -0.0701540858f, + 0.480267614f, 0.445907295f, -0.0342513621f, -0.115120247f, -0.391292542f, 0.149279505f, 0.0737104267f, + 0.0233072657f, -0.0399828292f, 0.127301499f, -0.3776851f, 0.00542048598f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_time_data.h new file mode 100644 index 00000000..8722657a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_batch2_f32/weights_time_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_batch2_f32_weights_time[48] = { + -0.316255689f, 0.389232993f, -0.0190205667f, 0.140301034f, -0.182851344f, 0.307877153f, -0.298229188f, + 0.322633237f, -0.0237764195f, -0.241250128f, 0.0740285963f, 0.153307796f, -0.05358449f, -0.412489742f, + -0.0963249654f, -0.415361613f, -0.230799243f, -0.302381456f, -0.00127009663f, 0.0627608746f, 0.228648961f, + 0.488478631f, -0.319045156f, -0.222521767f, -0.444152385f, -0.144963339f, -0.242460504f, -0.0865147337f, + 0.332321584f, 0.271542281f, -0.410054177f, -0.461751133f, -0.436456531f, 0.360277802f, -0.309714258f, + -0.219776288f, -0.135557622f, -0.222848609f, 0.0959258303f, 0.0640698597f, -0.318847626f, 0.233991653f, + -0.170324743f, -0.101142682f, -0.468589246f, 0.211507395f, -0.420218468f, -0.44716236f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/bias_data.h new file mode 100644 index 00000000..874dbae8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/bias_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_bias[12] = {(float16_t)-0.0167388916f, + (float16_t)-0.224609375f, + (float16_t)0.173095703f, + (float16_t)0.0245666504f, + (float16_t)-0.197875977f, + (float16_t)-0.187744141f, + (float16_t)0.175048828f, + (float16_t)-0.168212891f, + (float16_t)-0.237548828f, + (float16_t)0.0484313965f, + (float16_t)-0.185546875f, + (float16_t)0.188720703f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/config_data.h new file mode 100644 index 00000000..cb9fe669 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_MATCH_1_F16_INPUT_BATCHES 1 +#define SVDF_MATCH_1_F16_INPUT_SIZE 20 +#define SVDF_MATCH_1_F16_UNIT_COUNT 12 +#define SVDF_MATCH_1_F16_RANK 1 +#define SVDF_MATCH_1_F16_FEATURE_BATCHES 12 +#define SVDF_MATCH_1_F16_TIME_BATCHES 2 +#define SVDF_MATCH_1_F16_SEQUENCE_STEPS 3 +#define SVDF_MATCH_1_F16_DST_SIZE 12 +#define SVDF_MATCH_1_F16_INPUT_ACTIVATION_MIN (float16_t) - 1.0f +#define SVDF_MATCH_1_F16_INPUT_ACTIVATION_MAX (float16_t)1.0f +#define SVDF_MATCH_1_F16_OUTPUT_ACTIVATION_MIN (float16_t) - 100.0f +#define SVDF_MATCH_1_F16_OUTPUT_ACTIVATION_MAX (float16_t)100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/initial_state_data.h new file mode 100644 index 00000000..63cc7dc7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/initial_state_data.h @@ -0,0 +1,6 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_initial_state[24] = { + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/input_sequence_data.h new file mode 100644 index 00000000..797c9283 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/input_sequence_data.h @@ -0,0 +1,17 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_input_sequence[60] = { + (float16_t)-0.101013184f, (float16_t)-0.598144531f, (float16_t)-0.188354492f, (float16_t)-0.404296875f, + (float16_t)-0.442382812f, (float16_t)1.00097656f, (float16_t)-0.816894531f, (float16_t)0.84765625f, + (float16_t)-0.849121094f, (float16_t)-1.2890625f, (float16_t)0.171264648f, (float16_t)-1.20800781f, + (float16_t)-1.4296875f, (float16_t)-0.712890625f, (float16_t)0.423339844f, (float16_t)0.0405273438f, + (float16_t)0.283203125f, (float16_t)-1.45117188f, (float16_t)1.42675781f, (float16_t)1.2109375f, + (float16_t)-0.131225586f, (float16_t)0.336669922f, (float16_t)-0.336669922f, (float16_t)0.799316406f, + (float16_t)-0.176879883f, (float16_t)-1.12988281f, (float16_t)0.901855469f, (float16_t)1.18457031f, + (float16_t)-1.40527344f, (float16_t)0.688476562f, (float16_t)-0.372070312f, (float16_t)0.240112305f, + (float16_t)-1.47753906f, (float16_t)-0.414794922f, (float16_t)0.190917969f, (float16_t)0.0121231079f, + (float16_t)-1.29980469f, (float16_t)1.00292969f, (float16_t)-0.857421875f, (float16_t)-0.41015625f, + (float16_t)-0.119506836f, (float16_t)1.23730469f, (float16_t)-0.214233398f, (float16_t)-1.15527344f, + (float16_t)-0.193603516f, (float16_t)1.05761719f, (float16_t)0.293945312f, (float16_t)-0.981933594f, + (float16_t)-0.779785156f, (float16_t)0.898925781f, (float16_t)-0.343505859f, (float16_t)0.671386719f, + (float16_t)-0.411865234f, (float16_t)1.39746094f, (float16_t)-0.986328125f, (float16_t)1.046875f, + (float16_t)-1.47070312f, (float16_t)0.435791016f, (float16_t)0.895019531f, (float16_t)-0.797363281f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/output_ref_data.h new file mode 100644 index 00000000..bb07155d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_output_ref[12] = {(float16_t)-0.0107421875f, + (float16_t)0.140380859f, + (float16_t)-0.0532836914f, + (float16_t)-0.0414428711f, + (float16_t)-0.0414733887f, + (float16_t)-0.191162109f, + (float16_t)0.768066406f, + (float16_t)-0.392822266f, + (float16_t)-0.62890625f, + (float16_t)0.0573425293f, + (float16_t)0.0954589844f, + (float16_t)0.124633789f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_feature_data.h new file mode 100644 index 00000000..e3384357 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_feature_data.h @@ -0,0 +1,62 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_weights_feature[240] = { + (float16_t)0.382324219f, (float16_t)-0.210571289f, (float16_t)-0.23815918f, (float16_t)-0.162109375f, + (float16_t)0.153076172f, (float16_t)0.196411133f, (float16_t)0.401367188f, (float16_t)-0.290527344f, + (float16_t)0.245849609f, (float16_t)0.472167969f, (float16_t)0.428466797f, (float16_t)0.336425781f, + (float16_t)-0.114746094f, (float16_t)-0.29296875f, (float16_t)-0.0184936523f, (float16_t)-0.0629272461f, + (float16_t)0.315429688f, (float16_t)0.356933594f, (float16_t)-0.37890625f, (float16_t)-0.433837891f, + (float16_t)-0.0223388672f, (float16_t)0.191650391f, (float16_t)-0.126708984f, (float16_t)-0.193969727f, + (float16_t)0.355224609f, (float16_t)-0.368896484f, (float16_t)0.475097656f, (float16_t)-0.306640625f, + (float16_t)0.0493469238f, (float16_t)-0.169677734f, (float16_t)-0.229858398f, (float16_t)0.429443359f, + (float16_t)0.46875f, (float16_t)0.388671875f, (float16_t)-0.438720703f, (float16_t)-0.237182617f, + (float16_t)0.0935058594f, (float16_t)-0.135009766f, (float16_t)-0.143188477f, (float16_t)0.0646362305f, + (float16_t)0.305419922f, (float16_t)-0.0527038574f, (float16_t)0.0687866211f, (float16_t)-0.256835938f, + (float16_t)-0.138183594f, (float16_t)0.410888672f, (float16_t)-0.0739135742f, (float16_t)0.0841064453f, + (float16_t)-0.223632812f, (float16_t)-0.12878418f, (float16_t)0.394042969f, (float16_t)0.0412902832f, + (float16_t)-0.263671875f, (float16_t)0.0860595703f, (float16_t)-0.127807617f, (float16_t)0.241455078f, + (float16_t)-0.0209960938f, (float16_t)-0.460205078f, (float16_t)0.215698242f, (float16_t)0.274414062f, + (float16_t)0.218383789f, (float16_t)-0.435546875f, (float16_t)0.315673828f, (float16_t)-0.488037109f, + (float16_t)-0.351074219f, (float16_t)-0.413574219f, (float16_t)-0.188354492f, (float16_t)0.171875f, + (float16_t)0.176757812f, (float16_t)-0.102966309f, (float16_t)-0.229736328f, (float16_t)0.3203125f, + (float16_t)0.131835938f, (float16_t)-0.198730469f, (float16_t)0.151611328f, (float16_t)-0.440429688f, + (float16_t)0.239501953f, (float16_t)-0.135253906f, (float16_t)-0.464599609f, (float16_t)-0.032043457f, + (float16_t)0.278320312f, (float16_t)-0.0465393066f, (float16_t)0.38671875f, (float16_t)-0.309326172f, + (float16_t)0.214599609f, (float16_t)-0.0270385742f, (float16_t)-0.0928344727f, (float16_t)0.223022461f, + (float16_t)-0.337646484f, (float16_t)0.378173828f, (float16_t)0.235595703f, (float16_t)-0.316650391f, + (float16_t)-0.205200195f, (float16_t)-0.080078125f, (float16_t)-0.121826172f, (float16_t)-0.156982422f, + (float16_t)0.22277832f, (float16_t)-0.382324219f, (float16_t)-0.233764648f, (float16_t)0.330078125f, + (float16_t)0.383056641f, (float16_t)-0.384765625f, (float16_t)0.172119141f, (float16_t)-0.0757446289f, + (float16_t)0.348632812f, (float16_t)0.356933594f, (float16_t)0.132446289f, (float16_t)0.145263672f, + (float16_t)-0.412353516f, (float16_t)0.287841797f, (float16_t)0.4453125f, (float16_t)-0.212036133f, + (float16_t)-0.314941406f, (float16_t)0.472167969f, (float16_t)0.453613281f, (float16_t)-0.339355469f, + (float16_t)-0.4765625f, (float16_t)-0.0461730957f, (float16_t)-0.145751953f, (float16_t)0.125366211f, + (float16_t)0.226318359f, (float16_t)-0.490478516f, (float16_t)0.322998047f, (float16_t)0.112426758f, + (float16_t)-0.0327758789f, (float16_t)-0.215942383f, (float16_t)0.0673828125f, (float16_t)0.394042969f, + (float16_t)0.442382812f, (float16_t)-0.163085938f, (float16_t)0.0842895508f, (float16_t)-0.391601562f, + (float16_t)0.490966797f, (float16_t)0.46484375f, (float16_t)0.450683594f, (float16_t)0.396240234f, + (float16_t)0.416259766f, (float16_t)0.174804688f, (float16_t)-0.196289062f, (float16_t)0.360839844f, + (float16_t)-0.147338867f, (float16_t)-0.305175781f, (float16_t)0.125854492f, (float16_t)0.0761108398f, + (float16_t)0.278808594f, (float16_t)-0.213867188f, (float16_t)0.355957031f, (float16_t)-0.412841797f, + (float16_t)-0.132446289f, (float16_t)0.340576172f, (float16_t)-0.053527832f, (float16_t)0.0687255859f, + (float16_t)0.0829467773f, (float16_t)0.254394531f, (float16_t)-0.148071289f, (float16_t)-0.346435547f, + (float16_t)0.384277344f, (float16_t)0.19921875f, (float16_t)0.134033203f, (float16_t)0.161865234f, + (float16_t)0.493652344f, (float16_t)0.223022461f, (float16_t)-0.213500977f, (float16_t)-0.284667969f, + (float16_t)0.368652344f, (float16_t)-0.138061523f, (float16_t)-0.0219116211f, (float16_t)-0.214599609f, + (float16_t)0.432373047f, (float16_t)-0.362792969f, (float16_t)-0.0450744629f, (float16_t)-0.0756835938f, + (float16_t)0.113769531f, (float16_t)0.0440979004f, (float16_t)-0.126586914f, (float16_t)0.271972656f, + (float16_t)0.422363281f, (float16_t)-0.109375f, (float16_t)-0.109680176f, (float16_t)-0.0546875f, + (float16_t)-0.163085938f, (float16_t)-0.388427734f, (float16_t)-0.295166016f, (float16_t)0.137817383f, + (float16_t)-0.00241661072f, (float16_t)-0.442626953f, (float16_t)0.294677734f, (float16_t)0.0258636475f, + (float16_t)-0.467529297f, (float16_t)0.23034668f, (float16_t)-0.372558594f, (float16_t)-0.48828125f, + (float16_t)0.15612793f, (float16_t)-0.0416564941f, (float16_t)-0.479003906f, (float16_t)-0.0325012207f, + (float16_t)0.209960938f, (float16_t)0.4765625f, (float16_t)-0.439453125f, (float16_t)-0.386474609f, + (float16_t)-0.447998047f, (float16_t)-0.244628906f, (float16_t)0.159667969f, (float16_t)0.491943359f, + (float16_t)-0.246459961f, (float16_t)0.329833984f, (float16_t)0.161621094f, (float16_t)0.411132812f, + (float16_t)-0.326416016f, (float16_t)0.28125f, (float16_t)-0.276855469f, (float16_t)-0.154052734f, + (float16_t)-0.353271484f, (float16_t)0.113098145f, (float16_t)0.336181641f, (float16_t)-0.291015625f, + (float16_t)0.361572266f, (float16_t)0.265380859f, (float16_t)0.409667969f, (float16_t)-0.0185089111f, + (float16_t)0.487548828f, (float16_t)0.205200195f, (float16_t)0.102722168f, (float16_t)-0.387207031f, + (float16_t)0.0311279297f, (float16_t)-0.357910156f, (float16_t)-0.428466797f, (float16_t)-0.490478516f, + (float16_t)-0.274414062f, (float16_t)-0.292480469f, (float16_t)0.333251953f, (float16_t)0.0844116211f, + (float16_t)0.178955078f, (float16_t)0.195068359f, (float16_t)-0.137451172f, (float16_t)0.189575195f, + (float16_t)-0.108459473f, (float16_t)0.253173828f, (float16_t)0.130249023f, (float16_t)0.337890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_time_data.h new file mode 100644 index 00000000..61ca846d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f16/weights_time_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_1_f16_weights_time[24] = { + (float16_t)-0.107299805f, (float16_t)0.276611328f, (float16_t)-0.492431641f, (float16_t)0.0102920532f, + (float16_t)0.414794922f, (float16_t)0.209350586f, (float16_t)0.0830078125f, (float16_t)0.0336914062f, + (float16_t)0.487548828f, (float16_t)-0.171386719f, (float16_t)0.10949707f, (float16_t)-0.247192383f, + (float16_t)-0.280273438f, (float16_t)-0.3125f, (float16_t)0.481445312f, (float16_t)0.442626953f, + (float16_t)0.344726562f, (float16_t)0.111206055f, (float16_t)-0.0256500244f, (float16_t)-0.0748291016f, + (float16_t)0.234619141f, (float16_t)-0.0682373047f, (float16_t)-0.0597839355f, (float16_t)-0.123840332f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/bias_data.h new file mode 100644 index 00000000..734b261e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/bias_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_bias[12] = {-0.192471355f, + 0.158799261f, + 0.0522863083f, + 0.178179339f, + -0.102457002f, + 0.102572344f, + 0.0237188339f, + -0.0561561435f, + 0.0885431245f, + -0.209797069f, + 0.155939877f, + 0.196953952f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/config_data.h new file mode 100644 index 00000000..f4b30c3c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_MATCH_1_F32_INPUT_BATCHES 1 +#define SVDF_MATCH_1_F32_INPUT_SIZE 20 +#define SVDF_MATCH_1_F32_UNIT_COUNT 12 +#define SVDF_MATCH_1_F32_RANK 1 +#define SVDF_MATCH_1_F32_FEATURE_BATCHES 12 +#define SVDF_MATCH_1_F32_TIME_BATCHES 2 +#define SVDF_MATCH_1_F32_SEQUENCE_STEPS 3 +#define SVDF_MATCH_1_F32_DST_SIZE 12 +#define SVDF_MATCH_1_F32_INPUT_ACTIVATION_MIN -1.0f +#define SVDF_MATCH_1_F32_INPUT_ACTIVATION_MAX 1.0f +#define SVDF_MATCH_1_F32_OUTPUT_ACTIVATION_MIN -100.0f +#define SVDF_MATCH_1_F32_OUTPUT_ACTIVATION_MAX 100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/initial_state_data.h new file mode 100644 index 00000000..f9ec1ce1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/initial_state_data.h @@ -0,0 +1,4 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_initial_state[24] = {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/input_sequence_data.h new file mode 100644 index 00000000..0e9159be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/input_sequence_data.h @@ -0,0 +1,11 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_input_sequence[60] = { + 0.612999499f, -0.485328048f, 1.29098809f, -0.88406682f, -0.265554249f, 1.33331561f, -0.516749203f, + 0.48064515f, -0.865956903f, -1.44687545f, -1.41148889f, 0.936305523f, 0.529316187f, -0.630093396f, + -0.691696227f, 0.774667323f, 0.573410034f, -0.788428724f, -0.697121084f, -0.71677494f, -1.09667647f, + 1.0911473f, 0.641553342f, -0.813868046f, -0.292962044f, -0.341683328f, -1.21192873f, -0.474809766f, + 1.3426441f, 0.830315053f, -0.909535587f, -1.17725456f, -1.08522666f, 0.119773038f, 0.957536757f, + 1.17115843f, 0.768817306f, -0.942394733f, -1.18895519f, 1.26524687f, -0.220151722f, 1.24312866f, + 0.601516664f, -0.986763179f, -0.963938117f, 0.975133836f, 0.873047531f, 0.656648099f, 0.494474769f, + -0.653178096f, 0.55214709f, 0.717746437f, 1.38564026f, -1.37320483f, -1.35960782f, 1.46018493f, + -1.44802833f, 0.219653293f, -0.251808882f, 0.925581276f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/output_ref_data.h new file mode 100644 index 00000000..ba9064bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/output_ref_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_output_ref[12] = {0.0227753669f, + -0.522919655f, + -0.152971879f, + 0.239851058f, + 0.353781283f, + -0.0310471728f, + 0.0693816245f, + 0.15106152f, + 0.218208849f, + 0.124543831f, + 0.555289388f, + -0.437801898f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_feature_data.h new file mode 100644 index 00000000..3f4b1789 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_feature_data.h @@ -0,0 +1,37 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_weights_feature[240] = { + -0.316255689f, 0.389232993f, -0.0190205667f, 0.140301034f, -0.182851344f, 0.307877153f, -0.298229188f, + 0.322633237f, -0.0237764195f, -0.241250128f, 0.0740285963f, 0.153307796f, -0.05358449f, -0.412489742f, + -0.0963249654f, -0.415361613f, -0.230799243f, -0.302381456f, -0.00127009663f, 0.0627608746f, 0.228648961f, + 0.488478631f, -0.319045156f, -0.222521767f, -0.444152385f, -0.144963339f, -0.242460504f, -0.0865147337f, + 0.332321584f, 0.271542281f, -0.410054177f, -0.461751133f, -0.436456531f, 0.360277802f, -0.309714258f, + -0.219776288f, -0.135557622f, -0.222848609f, 0.0959258303f, 0.0640698597f, -0.318847626f, 0.233991653f, + -0.170324743f, -0.101142682f, -0.468589246f, 0.211507395f, -0.420218468f, -0.44716236f, 0.00878787786f, + 0.413436413f, -0.108457081f, 0.326860726f, -0.468978167f, -0.0481856577f, -0.421548754f, 0.254873782f, + 0.498070806f, -0.490242153f, 0.251532286f, -0.0341143198f, -0.120055668f, -0.330040544f, 0.457448751f, + 0.406885177f, 0.299104154f, 0.458132267f, -0.0526044518f, 0.190221801f, 0.188474149f, 0.162973151f, + -0.103403226f, 0.162797943f, -0.0850746632f, 0.0321331099f, -0.125996307f, 0.126432195f, -0.255264759f, + -0.487921298f, -0.247111931f, -0.434045196f, -0.329202682f, -0.0572943054f, 8.32677106e-05f, -0.357098252f, + 0.382947266f, 0.13360624f, 0.413241118f, 0.471298277f, -0.450381607f, 0.0244802292f, -0.131228492f, + 0.0699879006f, 0.0529684424f, -0.408151925f, -0.491357863f, -0.0920882374f, -0.0625867024f, 0.0273517519f, + 0.18490012f, -0.268387109f, -0.30465278f, -0.230944186f, 0.493361682f, -0.132623419f, 0.227385134f, + -0.468703657f, 0.147996947f, -0.150828257f, -0.17899482f, -0.454842895f, 0.453857332f, -0.377019107f, + 0.323611975f, -0.459301114f, 0.44006443f, -0.0549289882f, 0.367510736f, -0.222961515f, -0.147745147f, + 0.22058636f, -0.427425474f, -0.0495542549f, 0.401579529f, -0.180284157f, -0.455501348f, 0.418076009f, + -0.40575859f, -0.253202796f, -0.448913634f, 0.221191704f, 0.38029179f, -0.267169684f, 0.129376099f, + -0.398450464f, -0.15173243f, -0.0458992422f, 0.481960446f, 0.145524591f, -0.0990576744f, -0.0951941609f, + 0.242234454f, 0.49874714f, -0.0199313685f, 0.00398764946f, 0.171212941f, -0.105077215f, 0.0804955512f, + -0.437902004f, -0.388476282f, 0.0664337277f, -0.0895905644f, 0.33063513f, -0.118412636f, 0.247872278f, + 0.398201883f, 0.489658803f, 0.0705161616f, 0.153444931f, -0.293962538f, -0.446902722f, 0.259573489f, + 0.260275126f, 0.349468052f, 0.194252133f, 0.317418456f, 0.00464549707f, 0.361841977f, 0.053654626f, + 0.135070369f, -0.0287831202f, 0.330563337f, -0.392202854f, -0.0681287721f, 0.120873027f, 0.123072788f, + -0.118089192f, -0.271438122f, -0.158057183f, -0.0121432077f, -0.20598194f, 0.300415188f, 0.117470302f, + 0.258015454f, -0.386861444f, 0.078210853f, -0.0482487865f, 0.199034959f, 0.128148407f, 0.235954538f, + -0.381090969f, -0.15254648f, -0.477977335f, -0.0219406504f, 0.360960275f, 0.456749171f, -0.233119652f, + 0.438762307f, 0.466529012f, 0.339822054f, -0.0887408555f, -0.176755369f, -0.229365617f, -0.290354192f, + 0.459172159f, 0.399916559f, 0.138092235f, -0.133805037f, -0.170880422f, -0.451296329f, 0.29198575f, + 0.432803631f, -0.356382489f, -0.168620571f, -0.169613704f, -0.444962829f, 0.0252255462f, 0.242743388f, + -0.482858598f, -0.197062388f, 0.138240561f, -0.164471313f, 0.387879431f, 0.204839662f, 0.492966473f, + -0.169664636f, -0.372623295f, -0.439400852f, -0.426674783f, -0.117728449f, 0.241007298f, -0.392883986f, + 0.0371534936f, 0.155555904f, 0.30236572f, 0.151066512f, 0.00391924661f, 0.0236915611f, 0.437673539f, + -0.447930008f, 0.462776124f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_time_data.h new file mode 100644 index 00000000..2a20b4e4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_1_f32/weights_time_data.h @@ -0,0 +1,6 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_1_f32_weights_time[24] = { + -0.16030848f, 0.205662102f, -0.418678641f, 0.335144401f, -0.350731909f, -0.489322692f, + -0.425963044f, -0.118639141f, -0.306386948f, 0.149851307f, -0.103002913f, 0.268922985f, + 0.222208053f, -0.238944992f, 0.356472015f, -0.0941074565f, -0.189903751f, -0.474392354f, + -0.00248838449f, -0.335050523f, -0.329703867f, -0.380220592f, -0.391437858f, 0.267174065f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/bias_data.h new file mode 100644 index 00000000..813451bd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/bias_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_bias[13] = {(float16_t)-0.0391235352f, + (float16_t)0.0301513672f, + (float16_t)-0.0329589844f, + (float16_t)-0.0195770264f, + (float16_t)0.013168335f, + (float16_t)0.0825195312f, + (float16_t)0.160522461f, + (float16_t)0.130615234f, + (float16_t)-0.0397644043f, + (float16_t)-0.186157227f, + (float16_t)-0.121582031f, + (float16_t)0.163330078f, + (float16_t)0.206787109f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/config_data.h new file mode 100644 index 00000000..4da25d7a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_MATCH_2_F16_INPUT_BATCHES 2 +#define SVDF_MATCH_2_F16_INPUT_SIZE 40 +#define SVDF_MATCH_2_F16_UNIT_COUNT 13 +#define SVDF_MATCH_2_F16_RANK 2 +#define SVDF_MATCH_2_F16_FEATURE_BATCHES 26 +#define SVDF_MATCH_2_F16_TIME_BATCHES 3 +#define SVDF_MATCH_2_F16_SEQUENCE_STEPS 4 +#define SVDF_MATCH_2_F16_DST_SIZE 26 +#define SVDF_MATCH_2_F16_INPUT_ACTIVATION_MIN (float16_t) - 1.0f +#define SVDF_MATCH_2_F16_INPUT_ACTIVATION_MAX (float16_t)1.0f +#define SVDF_MATCH_2_F16_OUTPUT_ACTIVATION_MIN (float16_t) - 100.0f +#define SVDF_MATCH_2_F16_OUTPUT_ACTIVATION_MAX (float16_t)100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/initial_state_data.h new file mode 100644 index 00000000..0b7f750e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/initial_state_data.h @@ -0,0 +1,28 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_initial_state[156] = { + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/input_sequence_data.h new file mode 100644 index 00000000..e4a3295d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/input_sequence_data.h @@ -0,0 +1,82 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_input_sequence[320] = { + (float16_t)1.14746094f, (float16_t)-0.631835938f, (float16_t)-0.714355469f, (float16_t)-0.486083984f, + (float16_t)0.459472656f, (float16_t)0.589355469f, (float16_t)1.20410156f, (float16_t)-0.871582031f, + (float16_t)0.737792969f, (float16_t)1.41699219f, (float16_t)1.28515625f, (float16_t)1.00878906f, + (float16_t)-0.344238281f, (float16_t)-0.878417969f, (float16_t)-0.055480957f, (float16_t)-0.188720703f, + (float16_t)0.946289062f, (float16_t)1.07128906f, (float16_t)-1.13671875f, (float16_t)-1.30175781f, + (float16_t)-0.0670166016f, (float16_t)0.574707031f, (float16_t)-0.379882812f, (float16_t)-0.58203125f, + (float16_t)1.06542969f, (float16_t)-1.10644531f, (float16_t)1.42578125f, (float16_t)-0.919921875f, + (float16_t)0.147949219f, (float16_t)-0.508789062f, (float16_t)-0.689453125f, (float16_t)1.28808594f, + (float16_t)1.40625f, (float16_t)1.16601562f, (float16_t)-1.31640625f, (float16_t)-0.711425781f, + (float16_t)0.280517578f, (float16_t)-0.405273438f, (float16_t)-0.4296875f, (float16_t)0.193847656f, + (float16_t)0.916015625f, (float16_t)-0.158081055f, (float16_t)0.206420898f, (float16_t)-0.770507812f, + (float16_t)-0.414550781f, (float16_t)1.23242188f, (float16_t)-0.221679688f, (float16_t)0.252197266f, + (float16_t)-0.670898438f, (float16_t)-0.386474609f, (float16_t)1.18164062f, (float16_t)0.123840332f, + (float16_t)-0.790527344f, (float16_t)0.258300781f, (float16_t)-0.383300781f, (float16_t)0.724609375f, + (float16_t)-0.0629882812f, (float16_t)-1.38085938f, (float16_t)0.646972656f, (float16_t)0.823730469f, + (float16_t)0.655273438f, (float16_t)-1.30664062f, (float16_t)0.946777344f, (float16_t)-1.46386719f, + (float16_t)-1.05371094f, (float16_t)-1.24121094f, (float16_t)-0.564941406f, (float16_t)0.515625f, + (float16_t)0.530273438f, (float16_t)-0.309082031f, (float16_t)-0.689453125f, (float16_t)0.9609375f, + (float16_t)0.395263672f, (float16_t)-0.596191406f, (float16_t)0.454833984f, (float16_t)-1.32128906f, + (float16_t)0.718261719f, (float16_t)-0.405761719f, (float16_t)-1.39355469f, (float16_t)-0.0961303711f, + (float16_t)0.834960938f, (float16_t)-0.139648438f, (float16_t)1.16015625f, (float16_t)-0.927734375f, + (float16_t)0.644042969f, (float16_t)-0.0811157227f, (float16_t)-0.278320312f, (float16_t)0.669433594f, + (float16_t)-1.01367188f, (float16_t)1.13476562f, (float16_t)0.706542969f, (float16_t)-0.950195312f, + (float16_t)-0.615722656f, (float16_t)-0.240234375f, (float16_t)-0.365478516f, (float16_t)-0.470947266f, + (float16_t)0.668457031f, (float16_t)-1.14746094f, (float16_t)-0.701660156f, (float16_t)0.990234375f, + (float16_t)1.14941406f, (float16_t)-1.15429688f, (float16_t)0.516601562f, (float16_t)-0.227294922f, + (float16_t)1.04589844f, (float16_t)1.0703125f, (float16_t)0.397216797f, (float16_t)0.436035156f, + (float16_t)-1.23730469f, (float16_t)0.863769531f, (float16_t)1.3359375f, (float16_t)-0.636230469f, + (float16_t)-0.944824219f, (float16_t)1.41601562f, (float16_t)1.36132812f, (float16_t)-1.01757812f, + (float16_t)-1.4296875f, (float16_t)-0.138549805f, (float16_t)-0.437255859f, (float16_t)0.375976562f, + (float16_t)0.678710938f, (float16_t)-1.47167969f, (float16_t)0.969238281f, (float16_t)0.337158203f, + (float16_t)-0.0983276367f, (float16_t)-0.647949219f, (float16_t)0.202148438f, (float16_t)1.18164062f, + (float16_t)1.32714844f, (float16_t)-0.489257812f, (float16_t)0.252929688f, (float16_t)-1.17480469f, + (float16_t)1.47265625f, (float16_t)1.39453125f, (float16_t)1.35253906f, (float16_t)1.18847656f, + (float16_t)1.24804688f, (float16_t)0.524414062f, (float16_t)-0.588867188f, (float16_t)1.08203125f, + (float16_t)-0.441894531f, (float16_t)-0.915527344f, (float16_t)0.377685547f, (float16_t)0.228393555f, + (float16_t)0.836425781f, (float16_t)-0.641601562f, (float16_t)1.06835938f, (float16_t)-1.23828125f, + (float16_t)-0.397216797f, (float16_t)1.02246094f, (float16_t)-0.160644531f, (float16_t)0.206176758f, + (float16_t)0.248779297f, (float16_t)0.763671875f, (float16_t)-0.444335938f, (float16_t)-1.0390625f, + (float16_t)1.15332031f, (float16_t)0.59765625f, (float16_t)0.402099609f, (float16_t)0.485351562f, + (float16_t)1.48144531f, (float16_t)0.668945312f, (float16_t)-0.640625f, (float16_t)-0.854003906f, + (float16_t)1.10644531f, (float16_t)-0.4140625f, (float16_t)-0.0657348633f, (float16_t)-0.644042969f, + (float16_t)1.296875f, (float16_t)-1.08789062f, (float16_t)-0.135253906f, (float16_t)-0.227050781f, + (float16_t)0.341308594f, (float16_t)0.132202148f, (float16_t)-0.379882812f, (float16_t)0.81640625f, + (float16_t)1.26757812f, (float16_t)-0.328125f, (float16_t)-0.328857422f, (float16_t)-0.1640625f, + (float16_t)-0.489257812f, (float16_t)-1.16601562f, (float16_t)-0.885253906f, (float16_t)0.413574219f, + (float16_t)-0.0072517395f, (float16_t)-1.328125f, (float16_t)0.883789062f, (float16_t)0.0775756836f, + (float16_t)-1.40234375f, (float16_t)0.69140625f, (float16_t)-1.1171875f, (float16_t)-1.46484375f, + (float16_t)0.468261719f, (float16_t)-0.124938965f, (float16_t)-1.4375f, (float16_t)-0.0974731445f, + (float16_t)0.629882812f, (float16_t)1.4296875f, (float16_t)-1.31835938f, (float16_t)-1.15917969f, + (float16_t)-1.34375f, (float16_t)-0.733886719f, (float16_t)0.479003906f, (float16_t)1.47558594f, + (float16_t)-0.739257812f, (float16_t)0.989257812f, (float16_t)0.484619141f, (float16_t)1.23339844f, + (float16_t)-0.979492188f, (float16_t)0.84375f, (float16_t)-0.830566406f, (float16_t)-0.462158203f, + (float16_t)-1.05957031f, (float16_t)0.339355469f, (float16_t)1.00878906f, (float16_t)-0.873046875f, + (float16_t)1.08398438f, (float16_t)0.795898438f, (float16_t)1.22851562f, (float16_t)-0.0555114746f, + (float16_t)1.46289062f, (float16_t)0.615722656f, (float16_t)0.308105469f, (float16_t)-1.16210938f, + (float16_t)0.0933837891f, (float16_t)-1.07324219f, (float16_t)-1.28515625f, (float16_t)-1.47167969f, + (float16_t)-0.823730469f, (float16_t)-0.876953125f, (float16_t)0.999511719f, (float16_t)0.253173828f, + (float16_t)0.536621094f, (float16_t)0.584960938f, (float16_t)-0.412353516f, (float16_t)0.568359375f, + (float16_t)-0.325439453f, (float16_t)0.759277344f, (float16_t)0.390869141f, (float16_t)1.01367188f, + (float16_t)0.974609375f, (float16_t)1.38769531f, (float16_t)-0.719238281f, (float16_t)1.15429688f, + (float16_t)0.495605469f, (float16_t)-1.08105469f, (float16_t)0.408447266f, (float16_t)-0.239135742f, + (float16_t)0.619628906f, (float16_t)0.0380554199f, (float16_t)0.394287109f, (float16_t)-1.1328125f, + (float16_t)-0.490478516f, (float16_t)0.257324219f, (float16_t)-1.08398438f, (float16_t)-0.965820312f, + (float16_t)0.401367188f, (float16_t)-0.942871094f, (float16_t)-0.5546875f, (float16_t)-0.862304688f, + (float16_t)0.0597229004f, (float16_t)0.622070312f, (float16_t)-0.823242188f, (float16_t)-0.834960938f, + (float16_t)-0.857910156f, (float16_t)-0.338134766f, (float16_t)1.36523438f, (float16_t)0.441894531f, + (float16_t)-0.704589844f, (float16_t)-1.4921875f, (float16_t)-1.359375f, (float16_t)0.917480469f, + (float16_t)0.0388183594f, (float16_t)-0.337402344f, (float16_t)0.450683594f, (float16_t)-0.0491027832f, + (float16_t)-0.309814453f, (float16_t)-0.872558594f, (float16_t)-0.7109375f, (float16_t)-0.491943359f, + (float16_t)-1.02832031f, (float16_t)1.47460938f, (float16_t)-0.280029297f, (float16_t)0.731933594f, + (float16_t)-1.23828125f, (float16_t)0.408935547f, (float16_t)-0.224487305f, (float16_t)0.188232422f, + (float16_t)-0.569335938f, (float16_t)0.481933594f, (float16_t)1.03613281f, (float16_t)1.18457031f, + (float16_t)0.391845703f, (float16_t)0.655761719f, (float16_t)0.114135742f, (float16_t)1.49707031f, + (float16_t)1.18164062f, (float16_t)1.18359375f, (float16_t)-0.782226562f, (float16_t)0.796875f, + (float16_t)-0.26953125f, (float16_t)-0.238525391f, (float16_t)0.952148438f, (float16_t)0.372070312f, + (float16_t)-0.538574219f, (float16_t)0.840820312f, (float16_t)1.39160156f, (float16_t)0.151855469f, + (float16_t)-0.629882812f, (float16_t)-1.4375f, (float16_t)-1.00195312f, (float16_t)0.157836914f, + (float16_t)0.473388672f, (float16_t)0.927246094f, (float16_t)-1.43359375f, (float16_t)-0.280761719f, + (float16_t)0.691894531f, (float16_t)0.00661468506f, (float16_t)0.372558594f, (float16_t)0.2890625f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/output_ref_data.h new file mode 100644 index 00000000..7a2e6b4d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/output_ref_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_output_ref[26] = { + (float16_t)0.593261719f, (float16_t)-0.118713379f, (float16_t)-0.487548828f, (float16_t)-0.756347656f, + (float16_t)-0.493652344f, (float16_t)-0.152709961f, (float16_t)0.489990234f, (float16_t)0.795898438f, + (float16_t)-0.560546875f, (float16_t)-0.833496094f, (float16_t)-0.571777344f, (float16_t)0.859863281f, + (float16_t)0.708007812f, (float16_t)0.478271484f, (float16_t)0.380615234f, (float16_t)-0.487792969f, + (float16_t)-0.799804688f, (float16_t)-0.769042969f, (float16_t)0.0900878906f, (float16_t)0.0455932617f, + (float16_t)0.0462036133f, (float16_t)-0.541992188f, (float16_t)-0.927246094f, (float16_t)-0.165161133f, + (float16_t)-0.0802001953f, (float16_t)-0.187133789f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_feature_data.h new file mode 100644 index 00000000..1cf7a514 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_feature_data.h @@ -0,0 +1,349 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_weights_feature[1040] = { + (float16_t)-0.107299805f, (float16_t)0.276611328f, (float16_t)-0.492431641f, + (float16_t)0.0102920532f, (float16_t)0.414794922f, (float16_t)0.209350586f, + (float16_t)0.0830078125f, (float16_t)0.0336914062f, (float16_t)0.487548828f, + (float16_t)-0.171386719f, (float16_t)0.10949707f, (float16_t)-0.247192383f, + (float16_t)-0.280273438f, (float16_t)-0.3125f, (float16_t)0.481445312f, + (float16_t)0.442626953f, (float16_t)0.344726562f, (float16_t)0.111206055f, + (float16_t)-0.0256500244f, (float16_t)-0.0748291016f, (float16_t)0.234619141f, + (float16_t)-0.0682373047f, (float16_t)-0.0597839355f, (float16_t)-0.123840332f, + (float16_t)0.463134766f, (float16_t)0.243286133f, (float16_t)0.172729492f, + (float16_t)0.300537109f, (float16_t)0.00532913208f, (float16_t)-0.039642334f, + (float16_t)0.12878418f, (float16_t)-0.0349121094f, (float16_t)0.211425781f, + (float16_t)-0.482177734f, (float16_t)-0.267578125f, (float16_t)0.0336914062f, + (float16_t)0.0706176758f, (float16_t)-0.421142578f, (float16_t)-0.172119141f, + (float16_t)0.348144531f, (float16_t)0.113830566f, (float16_t)-0.281005859f, + (float16_t)0.450439453f, (float16_t)0.181518555f, (float16_t)-0.190551758f, + (float16_t)0.308837891f, (float16_t)-0.0598754883f, (float16_t)-0.440917969f, + (float16_t)-0.240844727f, (float16_t)0.3515625f, (float16_t)0.241821289f, + (float16_t)-0.138916016f, (float16_t)-0.413330078f, (float16_t)-0.292480469f, + (float16_t)-0.44140625f, (float16_t)-0.181030273f, (float16_t)-0.00941467285f, + (float16_t)0.439941406f, (float16_t)-0.350097656f, (float16_t)0.432861328f, + (float16_t)0.299804688f, (float16_t)0.0319519043f, (float16_t)0.354736328f, + (float16_t)0.466064453f, (float16_t)0.266845703f, (float16_t)0.230834961f, + (float16_t)-0.330810547f, (float16_t)0.499511719f, (float16_t)0.299804688f, + (float16_t)0.325195312f, (float16_t)0.120422363f, (float16_t)-0.0967407227f, + (float16_t)0.190185547f, (float16_t)-0.0895996094f, (float16_t)0.19519043f, + (float16_t)0.100585938f, (float16_t)0.128662109f, (float16_t)-0.203979492f, + (float16_t)0.419433594f, (float16_t)0.121520996f, (float16_t)-0.392822266f, + (float16_t)0.0385437012f, (float16_t)0.416015625f, (float16_t)-0.0596923828f, + (float16_t)0.426025391f, (float16_t)0.223510742f, (float16_t)-0.366699219f, + (float16_t)-0.165161133f, (float16_t)0.294189453f, (float16_t)-0.314941406f, + (float16_t)0.358886719f, (float16_t)0.174438477f, (float16_t)0.108886719f, + (float16_t)-0.236450195f, (float16_t)-0.0979003906f, (float16_t)-0.396728516f, + (float16_t)-0.123474121f, (float16_t)-0.13671875f, (float16_t)-0.0856323242f, + (float16_t)0.330322266f, (float16_t)0.497558594f, (float16_t)0.458740234f, + (float16_t)0.438720703f, (float16_t)-0.00352859497f, (float16_t)0.0184173584f, + (float16_t)-0.374267578f, (float16_t)-0.114318848f, (float16_t)0.196533203f, + (float16_t)-0.110717773f, (float16_t)-0.403320312f, (float16_t)-0.256835938f, + (float16_t)-0.323242188f, (float16_t)-0.3984375f, (float16_t)-0.096496582f, + (float16_t)-0.0672607422f, (float16_t)-0.493164062f, (float16_t)0.158447266f, + (float16_t)-0.418212891f, (float16_t)-0.424072266f, (float16_t)0.454101562f, + (float16_t)0.428466797f, (float16_t)0.114501953f, (float16_t)-0.413818359f, + (float16_t)-0.345214844f, (float16_t)0.136474609f, (float16_t)-0.105041504f, + (float16_t)0.193969727f, (float16_t)-0.454833984f, (float16_t)-0.443115234f, + (float16_t)0.367431641f, (float16_t)-0.138671875f, (float16_t)0.485107422f, + (float16_t)-0.0354919434f, (float16_t)0.306152344f, (float16_t)-0.280273438f, + (float16_t)0.064453125f, (float16_t)-0.430419922f, (float16_t)0.498535156f, + (float16_t)-0.0714111328f, (float16_t)0.441650391f, (float16_t)0.34375f, + (float16_t)-0.248046875f, (float16_t)0.456054688f, (float16_t)-0.477050781f, + (float16_t)0.451660156f, (float16_t)0.358886719f, (float16_t)-0.318847656f, + (float16_t)0.357910156f, (float16_t)-0.1640625f, (float16_t)0.381835938f, + (float16_t)-0.251953125f, (float16_t)-0.279541016f, (float16_t)0.183837891f, + (float16_t)0.286376953f, (float16_t)-0.343505859f, (float16_t)-0.12890625f, + (float16_t)0.459472656f, (float16_t)-0.0609741211f, (float16_t)0.0392456055f, + (float16_t)0.369873047f, (float16_t)0.0803222656f, (float16_t)0.223388672f, + (float16_t)-0.0694580078f, (float16_t)0.388916016f, (float16_t)-0.0869140625f, + (float16_t)-0.130981445f, (float16_t)0.0705566406f, (float16_t)-0.433105469f, + (float16_t)-0.190673828f, (float16_t)0.200561523f, (float16_t)-0.2265625f, + (float16_t)-0.0875854492f, (float16_t)0.338623047f, (float16_t)0.333007812f, + (float16_t)-0.0390319824f, (float16_t)-0.266845703f, (float16_t)-0.224487305f, + (float16_t)0.35546875f, (float16_t)-0.269042969f, (float16_t)-0.461181641f, + (float16_t)-0.117736816f, (float16_t)0.297363281f, (float16_t)0.396484375f, + (float16_t)-0.483642578f, (float16_t)0.0807495117f, (float16_t)-0.432617188f, + (float16_t)0.165161133f, (float16_t)0.437744141f, (float16_t)0.351806641f, + (float16_t)0.0881958008f, (float16_t)0.107055664f, (float16_t)0.376220703f, + (float16_t)-0.337890625f, (float16_t)-0.0982055664f, (float16_t)-0.306396484f, + (float16_t)-0.445556641f, (float16_t)0.365478516f, (float16_t)-0.349365234f, + (float16_t)-0.237182617f, (float16_t)-0.169311523f, (float16_t)-0.214355469f, + (float16_t)0.212768555f, (float16_t)-0.277832031f, (float16_t)0.440185547f, + (float16_t)0.382080078f, (float16_t)0.193969727f, (float16_t)0.0258178711f, + (float16_t)-0.196044922f, (float16_t)-0.167236328f, (float16_t)-0.316894531f, + (float16_t)0.380126953f, (float16_t)0.173828125f, (float16_t)-0.359863281f, + (float16_t)0.458251953f, (float16_t)0.454833984f, (float16_t)-0.293212891f, + (float16_t)0.109680176f, (float16_t)0.132080078f, (float16_t)-0.303222656f, + (float16_t)-0.277587891f, (float16_t)-0.453613281f, (float16_t)0.476318359f, + (float16_t)0.0229034424f, (float16_t)0.059753418f, (float16_t)-0.463134766f, + (float16_t)-0.218994141f, (float16_t)0.478027344f, (float16_t)0.0734863281f, + (float16_t)-0.200195312f, (float16_t)0.0797119141f, (float16_t)-0.465332031f, + (float16_t)0.106506348f, 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(float16_t)0.350830078f, (float16_t)-0.149169922f, (float16_t)0.104248047f, + (float16_t)0.333984375f, (float16_t)0.226196289f, (float16_t)-0.400390625f, + (float16_t)-0.0745239258f, (float16_t)0.415771484f, (float16_t)0.406982422f, + (float16_t)0.441162109f, (float16_t)0.0665283203f, (float16_t)0.153320312f, + (float16_t)0.219360352f, (float16_t)-0.275390625f, (float16_t)-0.413574219f, + (float16_t)0.150024414f, (float16_t)-0.260498047f, (float16_t)0.377197266f, + (float16_t)-0.246337891f, (float16_t)-0.106750488f, (float16_t)-0.119506836f, + (float16_t)0.155029297f, (float16_t)0.310058594f, (float16_t)0.372558594f, + (float16_t)0.049041748f, (float16_t)0.280761719f, (float16_t)0.0923461914f, + (float16_t)-0.306884766f, (float16_t)-0.345458984f, (float16_t)-0.365722656f, + (float16_t)-0.213134766f, (float16_t)0.348876953f, (float16_t)-0.280761719f, + (float16_t)-0.337158203f, (float16_t)0.302490234f, (float16_t)-0.310546875f, + (float16_t)-0.0855712891f, (float16_t)-0.334228516f, (float16_t)0.151367188f, + (float16_t)-0.343261719f, (float16_t)-0.122924805f, (float16_t)-0.165649414f, + (float16_t)0.08984375f, (float16_t)0.426025391f, (float16_t)-0.260742188f, + (float16_t)0.170532227f, (float16_t)0.327392578f, (float16_t)-0.0816040039f, + (float16_t)0.365478516f, (float16_t)0.392578125f, (float16_t)0.0252685547f, + (float16_t)-0.283447266f, (float16_t)0.158081055f, (float16_t)0.235107422f, + (float16_t)0.00465774536f, (float16_t)0.232543945f, (float16_t)0.376953125f, + (float16_t)0.323730469f, (float16_t)-0.284423828f, (float16_t)0.120483398f, + (float16_t)-0.19921875f, (float16_t)0.203613281f, (float16_t)0.350097656f, + (float16_t)0.319335938f, (float16_t)0.361328125f, (float16_t)0.00522613525f, + (float16_t)0.409912109f, (float16_t)-0.322021484f, (float16_t)0.429931641f, + (float16_t)-0.441894531f, (float16_t)-0.230224609f, (float16_t)0.107788086f, + (float16_t)0.447509766f, (float16_t)-0.0654296875f, (float16_t)0.0765991211f, + (float16_t)0.281738281f, (float16_t)-0.170410156f, (float16_t)0.207885742f, + (float16_t)-0.260253906f, (float16_t)0.297119141f, (float16_t)-0.266845703f, + (float16_t)-0.282226562f, (float16_t)-0.435546875f, (float16_t)0.266113281f, + (float16_t)0.409423828f, (float16_t)0.323730469f, (float16_t)0.299560547f, + (float16_t)-0.451171875f, (float16_t)0.220336914f, (float16_t)-0.264160156f, + (float16_t)0.280273438f, (float16_t)0.470214844f, (float16_t)0.0961914062f, + (float16_t)0.185791016f, (float16_t)0.439697266f, (float16_t)0.395996094f, + (float16_t)-0.363525391f, (float16_t)-0.108581543f, (float16_t)0.137939453f, + (float16_t)0.267089844f, (float16_t)0.313720703f, (float16_t)-0.241455078f, + (float16_t)0.16796875f, (float16_t)0.0810546875f, (float16_t)0.485839844f, + (float16_t)-0.364013672f, (float16_t)-0.276855469f, (float16_t)0.0922851562f, + (float16_t)-0.143920898f, (float16_t)0.256103516f, (float16_t)-0.223144531f, + (float16_t)0.432128906f, (float16_t)-0.0367736816f, (float16_t)0.365722656f, + (float16_t)0.125f, (float16_t)-0.403564453f, (float16_t)-0.118347168f, + (float16_t)0.065246582f, (float16_t)0.349365234f, (float16_t)-0.361328125f, + (float16_t)-0.16418457f, (float16_t)0.379638672f, (float16_t)0.292480469f, + (float16_t)0.154663086f, (float16_t)0.320800781f, (float16_t)0.354492188f, + (float16_t)0.298339844f, (float16_t)-0.0111083984f, (float16_t)-0.458740234f, + (float16_t)-0.197021484f, (float16_t)-0.382080078f, (float16_t)-0.0292358398f, + (float16_t)0.175292969f, (float16_t)-0.357666016f, (float16_t)0.22253418f, + (float16_t)0.288818359f, (float16_t)0.0622558594f, (float16_t)-0.222900391f, + (float16_t)0.338623047f, (float16_t)0.337890625f, (float16_t)-0.364746094f, + (float16_t)-0.0376586914f, (float16_t)-0.176025391f, (float16_t)0.0623168945f, + (float16_t)0.180175781f, (float16_t)0.128051758f, (float16_t)-0.0176849365f, + (float16_t)-0.0287475586f, (float16_t)-0.227539062f, (float16_t)-0.483642578f, + (float16_t)0.271484375f, (float16_t)-0.434570312f, (float16_t)-0.473144531f, + (float16_t)0.327392578f, (float16_t)-0.238647461f, (float16_t)-0.189453125f, + (float16_t)0.38671875f, (float16_t)0.439453125f, (float16_t)-0.449462891f, + (float16_t)0.491455078f, (float16_t)0.369140625f, (float16_t)0.300292969f, + (float16_t)0.209106445f, (float16_t)-0.149047852f, (float16_t)-0.238769531f, + (float16_t)0.105224609f, (float16_t)-0.186889648f, (float16_t)-0.0254211426f, + (float16_t)-0.287597656f, (float16_t)0.0903930664f, (float16_t)-0.239746094f, + (float16_t)-0.0657348633f, (float16_t)-0.0814208984f, (float16_t)-0.171875f, + (float16_t)-0.174682617f, (float16_t)-0.106079102f, (float16_t)-0.358154297f, + (float16_t)-0.049987793f, (float16_t)-0.33984375f, (float16_t)-0.0662231445f, + (float16_t)-0.0390014648f, (float16_t)-0.470458984f, (float16_t)-0.00494003296f, + (float16_t)0.202758789f, (float16_t)-0.338867188f, (float16_t)-0.463623047f, + (float16_t)0.185913086f, (float16_t)0.350830078f, (float16_t)0.343994141f, + (float16_t)-0.0199737549f, (float16_t)0.343994141f, (float16_t)-0.485595703f, + (float16_t)-0.0822753906f, (float16_t)0.16809082f, (float16_t)-0.391357422f, + (float16_t)-0.411132812f, (float16_t)0.0825805664f, (float16_t)0.0776977539f, + (float16_t)0.0843505859f, (float16_t)0.329345703f, (float16_t)0.213012695f, + (float16_t)0.403808594f, (float16_t)0.384033203f, (float16_t)0.115478516f, + (float16_t)-0.137207031f, (float16_t)-0.0200958252f, (float16_t)-0.0993652344f, + (float16_t)-0.2890625f, (float16_t)0.0357971191f, (float16_t)0.0647583008f, + (float16_t)-0.205566406f, (float16_t)0.214111328f, (float16_t)-0.403808594f, + (float16_t)0.474365234f, (float16_t)-0.189941406f, (float16_t)0.196289062f, + (float16_t)0.205566406f, (float16_t)0.119812012f, (float16_t)-0.112426758f, + (float16_t)-0.101318359f, (float16_t)0.280517578f, (float16_t)0.0802001953f, + (float16_t)-0.00344276428f, (float16_t)0.353759766f, (float16_t)-0.122680664f, + (float16_t)0.341796875f, (float16_t)0.465087891f, (float16_t)-0.318359375f, + (float16_t)-0.391601562f, (float16_t)0.308105469f, (float16_t)-0.0964355469f, + (float16_t)-0.125f, (float16_t)0.187744141f, (float16_t)-0.0252838135f, + (float16_t)-0.264404297f, (float16_t)-0.284912109f, (float16_t)0.133300781f, + (float16_t)0.340576172f, (float16_t)0.163452148f, (float16_t)-0.397705078f, + (float16_t)-0.194213867f, (float16_t)-0.338378906f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_time_data.h new file mode 100644 index 00000000..4dc6ad09 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f16/weights_time_data.h @@ -0,0 +1,22 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_match_2_f16_weights_time[78] = { + (float16_t)-0.0334777832f, (float16_t)-0.44921875f, (float16_t)0.346191406f, (float16_t)0.0491333008f, + (float16_t)-0.395751953f, (float16_t)-0.375488281f, (float16_t)0.350097656f, (float16_t)-0.336425781f, + (float16_t)-0.475097656f, (float16_t)0.096862793f, (float16_t)-0.37109375f, (float16_t)0.377441406f, + (float16_t)-0.0620117188f, (float16_t)0.117004395f, (float16_t)-0.12878418f, (float16_t)-0.212280273f, + (float16_t)0.0159759521f, (float16_t)-0.495849609f, (float16_t)0.0788574219f, (float16_t)0.491210938f, + (float16_t)0.0382080078f, (float16_t)-0.286621094f, (float16_t)0.00320625305f, (float16_t)0.140869141f, + (float16_t)0.486572266f, (float16_t)0.284912109f, (float16_t)-0.469238281f, (float16_t)-0.385986328f, + (float16_t)-0.324707031f, (float16_t)-0.198974609f, (float16_t)-0.185424805f, (float16_t)-0.387451172f, + (float16_t)-0.165283203f, (float16_t)0.0400085449f, (float16_t)-0.451660156f, (float16_t)0.134399414f, + (float16_t)-0.0863647461f, (float16_t)0.471191406f, (float16_t)-0.0209960938f, (float16_t)-0.216796875f, + (float16_t)0.238891602f, (float16_t)0.228271484f, (float16_t)0.0506286621f, (float16_t)0.474853516f, + (float16_t)-0.208251953f, (float16_t)-0.270996094f, (float16_t)0.352783203f, (float16_t)0.087097168f, + (float16_t)-0.190429688f, (float16_t)-0.252441406f, (float16_t)-0.456054688f, (float16_t)0.160522461f, + (float16_t)-0.10333252f, (float16_t)-0.125488281f, (float16_t)0.0919189453f, (float16_t)-0.317626953f, + (float16_t)0.159423828f, (float16_t)0.487548828f, (float16_t)-0.0774536133f, (float16_t)-0.222900391f, + (float16_t)-0.440917969f, (float16_t)0.446777344f, (float16_t)0.0109176636f, (float16_t)0.0315551758f, + (float16_t)-0.421630859f, (float16_t)0.427246094f, (float16_t)0.0166931152f, (float16_t)-0.264404297f, + (float16_t)0.343261719f, (float16_t)-0.495361328f, (float16_t)-0.0534973145f, (float16_t)-0.118652344f, + (float16_t)0.0184631348f, (float16_t)0.0570373535f, (float16_t)0.490234375f, (float16_t)-0.0875244141f, + (float16_t)0.0126647949f, (float16_t)-0.0295410156f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/bias_data.h new file mode 100644 index 00000000..87d7c697 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/bias_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_bias[13] = {0.188273147f, + 0.22707507f, + -0.00121727295f, + -0.0686713308f, + 0.162811309f, + -0.0556286871f, + 0.0875384659f, + 0.161679357f, + -0.0673902184f, + 0.0608956404f, + -0.241361111f, + 0.161413401f, + 0.113105059f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/config_data.h new file mode 100644 index 00000000..675aae31 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_MATCH_2_F32_INPUT_BATCHES 2 +#define SVDF_MATCH_2_F32_INPUT_SIZE 40 +#define SVDF_MATCH_2_F32_UNIT_COUNT 13 +#define SVDF_MATCH_2_F32_RANK 2 +#define SVDF_MATCH_2_F32_FEATURE_BATCHES 26 +#define SVDF_MATCH_2_F32_TIME_BATCHES 3 +#define SVDF_MATCH_2_F32_SEQUENCE_STEPS 4 +#define SVDF_MATCH_2_F32_DST_SIZE 26 +#define SVDF_MATCH_2_F32_INPUT_ACTIVATION_MIN -1.0f +#define SVDF_MATCH_2_F32_INPUT_ACTIVATION_MAX 1.0f +#define SVDF_MATCH_2_F32_OUTPUT_ACTIVATION_MIN -100.0f +#define SVDF_MATCH_2_F32_OUTPUT_ACTIVATION_MAX 100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/initial_state_data.h new file mode 100644 index 00000000..3356c8e0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/initial_state_data.h @@ -0,0 +1,11 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_initial_state[156] = { + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/input_sequence_data.h new file mode 100644 index 00000000..5c450af5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/input_sequence_data.h @@ -0,0 +1,48 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_input_sequence[320] = { + -0.948767066f, 1.16769898f, -0.0570616983f, 0.420903116f, -0.548554003f, 0.92363143f, -0.894687593f, + 0.96789974f, -0.0713292584f, -0.723750353f, 0.222085789f, 0.459923416f, -0.160753474f, -1.2374692f, + -0.288974881f, -1.24608481f, -0.692397773f, -0.907144368f, -0.00381029001f, 0.188282624f, 0.685946882f, + 1.46543586f, -0.957135439f, -0.667565286f, -1.33245718f, -0.434890032f, -0.727381527f, -0.259544194f, + 0.996964753f, 0.814626873f, -1.2301625f, -1.38525331f, -1.30936968f, 1.08083344f, -0.929142773f, + -0.659328878f, -0.406672865f, -0.668545842f, 0.287777483f, 0.192209572f, -0.95654285f, 0.701974928f, + -0.510974228f, -0.303428054f, -1.40576768f, 0.6345222f, -1.2606554f, -1.34148705f, 0.0263636336f, + 1.24030924f, -0.325371236f, 0.980582178f, -1.4069345f, -0.144556969f, -1.26464617f, 0.764621317f, + 1.49421239f, -1.47072649f, 0.754596829f, -0.102342963f, -0.360166997f, -0.990121663f, 1.37234616f, + 1.22065556f, 0.897312462f, 1.3743968f, -0.157813355f, 0.570665419f, 0.565422475f, 0.488919437f, + -0.310209692f, 0.488393843f, -0.255223989f, 0.0963993222f, -0.377988935f, 0.379296601f, -0.765794218f, + -1.46376383f, -0.741335809f, -1.30213559f, -0.987608016f, -0.171882913f, 0.000249803124f, -1.07129478f, + 1.14884174f, 0.400818706f, 1.23972332f, 1.41389489f, -1.35114491f, 0.0734406859f, -0.39368549f, + 0.209963709f, 0.158905327f, -1.22445583f, -1.47407365f, -0.276264697f, -0.1877601f, 0.0820552558f, + 0.554700375f, -0.805161297f, -0.913958371f, -0.692832589f, 1.48008502f, -0.397870272f, 0.682155371f, + -1.406111f, 0.443990856f, -0.452484757f, -0.536984444f, -1.36452866f, 1.36157203f, -1.13105738f, + 0.970835865f, -1.37790334f, 1.32019329f, -0.164786965f, 1.10253227f, -0.668884516f, -0.443235457f, + 0.661759079f, -1.28227639f, -0.148662761f, 1.20473862f, -0.540852427f, -1.36650407f, 1.254228f, + -1.21727574f, -0.759608328f, -1.34674084f, 0.663575113f, 1.14087534f, -0.801509023f, 0.388128281f, + -1.19535136f, -0.455197275f, -0.137697726f, 1.44588137f, 0.436573774f, -0.297173023f, -0.285582483f, + 0.726703346f, 1.49624133f, -0.0597941056f, 0.0119629484f, 0.513638794f, -0.315231651f, 0.241486654f, + -1.31370604f, -1.16542888f, 0.199301183f, -0.268771708f, 0.991905391f, -0.355237931f, 0.743616819f, + 1.19460559f, 1.46897638f, 0.211548492f, 0.460334778f, -0.881887615f, -1.34070814f, 0.778720498f, + 0.780825377f, 1.0484041f, 0.5827564f, 0.952255368f, 0.0139364908f, 1.08552599f, 0.160963878f, + 0.405211121f, -0.0863493606f, 0.99168998f, -1.17660856f, -0.204386324f, 0.362619072f, 0.369218349f, + -0.354267567f, -0.814314425f, -0.474171549f, -0.0364296213f, -0.61794585f, 0.901245594f, 0.352410913f, + 0.774046361f, -1.16058433f, 0.234632552f, -0.144746348f, 0.597104907f, 0.38444522f, 0.707863629f, + -1.14327288f, -0.457639456f, -1.43393195f, -0.0658219531f, 1.08288085f, 1.37024748f, -0.69935894f, + 1.31628692f, 1.39958704f, 1.01946616f, -0.266222566f, -0.530266106f, -0.688096881f, -0.871062577f, + 1.37751639f, 1.19974971f, 0.414276719f, -0.40141511f, -0.512641251f, -1.35388899f, 0.875957251f, + 1.29841089f, -1.06914747f, -0.5058617f, -0.508841097f, -1.33488846f, 0.0756766349f, 0.728230178f, + -1.44857585f, -0.591187179f, 0.414721668f, -0.493413925f, 1.16363823f, 0.614519f, 1.47889948f, + -0.508993924f, -1.11786985f, -1.31820261f, -1.28002429f, -0.353185356f, 0.723021924f, -1.17865193f, + 0.111460477f, 0.466667712f, 0.907097161f, 0.453199506f, 0.0117577398f, 0.0710746869f, 1.31302059f, + -1.34379005f, 1.38832843f, 0.920178294f, -0.746888578f, -1.37165451f, 0.00198715273f, 1.28986168f, + 0.0483703464f, 1.0543052f, -0.238262162f, -0.421211183f, -1.4239223f, 0.981604755f, -1.24629641f, + 0.416523397f, 0.162694439f, -1.14561582f, 0.108874455f, 0.915214658f, 0.345379561f, -0.265134037f, + 0.0475867018f, 0.374624699f, 0.882078171f, 0.0226635505f, -1.32899451f, -1.10171032f, 0.827636361f, + 0.677270591f, -0.571971834f, -1.02777219f, 0.387384325f, 0.884885132f, 0.795030653f, 1.18409514f, + 1.09855855f, -0.929246724f, -0.811124802f, -0.63475877f, 0.903395593f, 0.610611737f, 0.344812781f, + -1.40311062f, 0.646935761f, 1.42384374f, -0.894215226f, -1.4051125f, 0.162969127f, 0.31967777f, + 0.149949864f, -0.364690691f, -1.48716056f, -1.48525679f, -0.604530513f, 1.21390116f, -1.19798148f, + 1.00370753f, -0.826111615f, -0.554248691f, -0.693268299f, 0.181257397f, -1.14581072f, 0.269219458f, + 1.27000618f, 0.541867971f, 0.0805026069f, -0.0786588043f, -0.381310999f, 0.015151063f, -1.38352418f, + -0.879419446f, 0.385235965f, -1.37942946f, 0.177582264f, -1.42025566f, -0.0580686256f, 0.0141159315f, + -0.795122623f, -1.16098607f, -0.28121236f, -1.33782494f, -0.304401577f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/output_ref_data.h new file mode 100644 index 00000000..9489fdf8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/output_ref_data.h @@ -0,0 +1,6 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_output_ref[26] = { + -0.54839915f, -0.899059772f, -0.597526312f, -1.60796916f, 0.546160996f, -0.572720408f, 0.473444402f, + -0.484875709f, -0.0593730956f, 0.257692635f, -0.302548975f, -0.856168628f, -0.503371537f, 0.48843497f, + -0.564275265f, -0.267670482f, 0.395874441f, 0.440957218f, -0.256307185f, 0.881885648f, -0.499886721f, + 0.139185831f, -0.774673998f, -0.432635337f, 1.06685209f, -0.41673696f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_feature_data.h new file mode 100644 index 00000000..99142b5f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_feature_data.h @@ -0,0 +1,151 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_weights_feature[1040] = { + -0.16030848f, 0.205662102f, -0.418678641f, 0.335144401f, -0.350731909f, -0.489322692f, -0.425963044f, + -0.118639141f, -0.306386948f, 0.149851307f, -0.103002913f, 0.268922985f, 0.222208053f, -0.238944992f, + 0.356472015f, -0.0941074565f, -0.189903751f, -0.474392354f, -0.00248838449f, -0.335050523f, -0.329703867f, + -0.380220592f, -0.391437858f, 0.267174065f, 0.499592721f, -0.423061341f, 0.412445724f, 0.309476703f, + 0.00448159175f, -0.0760189369f, -0.217666686f, -0.302360028f, -0.0265648235f, 0.0594772138f, 0.455541849f, + 0.313797951f, -0.13429372f, -0.47112748f, -0.330025584f, -0.319020689f, -0.379791111f, -0.279872745f, + 0.0835890695f, -0.486745775f, 0.00911268778f, -0.283374399f, 0.31515041f, -0.254874289f, 0.400320619f, + 0.464673877f, -0.427346438f, 0.257057279f, 0.194784954f, -0.00250186748f, -0.0162702296f, -0.0685399175f, + 0.457664251f, -0.110176273f, -0.0801604465f, -0.369948894f, 0.0312537253f, 0.253295958f, -0.118291125f, + -0.463346094f, 0.259367377f, -0.366426498f, -0.309449375f, -0.15543364f, 0.192026481f, -0.0932659209f, + -0.422476053f, 0.393929631f, 0.213537827f, 0.176049054f, -0.215321288f, 0.336459607f, 0.337677687f, + 0.0174879469f, 0.421437085f, -0.0164625328f, 0.279272854f, -0.0369372629f, -0.342424244f, -0.0655709356f, + 0.0938652977f, 0.225999162f, -0.154726535f, -0.0304907486f, -0.0939483643f, -0.154660463f, -0.254212052f, + -0.167380631f, -0.202891216f, -0.328697801f, 0.371889442f, -0.0061466964f, 0.14470382f, 0.222215965f, + -0.0253074877f, -0.258564204f, -0.310790211f, 0.0513772517f, 0.198124379f, 0.0432037897f, 0.178227812f, + 0.209424689f, 0.122009821f, 0.389486134f, 0.197676614f, 0.147726506f, 0.37631005f, -0.363545477f, + -0.291839004f, 0.179882169f, 0.0454668701f, 0.0778112262f, 0.192888126f, 0.292368591f, -0.444771469f, + 0.475398809f, -0.221184134f, -0.221999377f, -0.244622007f, -0.365461677f, -0.291882932f, -0.371237814f, + -0.086922735f, 0.313414276f, -0.477109164f, 0.345453829f, 0.210402682f, 0.409712911f, 0.060747683f, + -0.393739611f, -0.399288177f, -0.0247608554f, 0.110369146f, 0.364473164f, 0.44566673f, -0.138341874f, + -0.176066801f, -0.245877072f, 0.0597114116f, -0.388440579f, 0.374871314f, -0.352293849f, -0.454515934f, + -0.0522263013f, 0.467047364f, -0.281636894f, 0.154743567f, 0.0711878538f, 0.142210439f, -0.0607475527f, + -0.292630315f, -0.193585023f, -0.0355512351f, -0.354350269f, -0.392894745f, -0.152469561f, -0.147016272f, + 0.338172227f, -0.280524284f, 0.0790587813f, -0.325853437f, -0.315745711f, 0.324864239f, -0.0223373268f, + 0.175104111f, -0.126004085f, 0.290805191f, -0.0631526858f, -0.266172379f, -0.0758632272f, -0.435903639f, + -0.434967577f, -0.269393057f, 0.121782191f, -0.238290951f, 0.290364355f, -0.209213451f, -0.117777415f, + 0.364066154f, 0.182146013f, -0.392305195f, 0.0146534722f, 0.292246848f, 0.062245369f, 0.087009415f, + -0.0722243413f, 0.488564074f, -0.420123488f, -0.0951684192f, 0.364950627f, 0.441603005f, 0.228452891f, + 0.0184981059f, -0.176077083f, -0.192948073f, 0.266556531f, 0.168753877f, 0.131614506f, -0.373622686f, + 0.272130311f, 0.466961682f, -0.397646844f, -0.175274655f, 0.0712917298f, -0.4903467f, 0.24159427f, + 0.37807098f, -0.0450564921f, 0.489239186f, -0.0280874781f, -0.079555586f, -0.0339144506f, -0.0468422435f, + 0.43010062f, -0.0140788713f, -0.0442853831f, -0.266692996f, -0.367335886f, -0.475129575f, 0.342286915f, + 0.273952186f, 0.122780629f, 0.083583191f, -0.36176911f, -0.154193461f, -0.231403813f, 0.0181055013f, + -0.318429559f, 0.314511359f, -0.256397456f, 0.429460704f, 0.19147101f, -0.0249585267f, 0.141956702f, + 0.261139333f, 0.118203253f, -0.0260272641f, 0.0166928377f, -0.0849031731f, 0.31723091f, 0.175764531f, + -0.367682368f, 0.128787205f, -0.0804706141f, 0.293841243f, 0.127431691f, 0.0977014676f, -0.153861493f, + 0.210872069f, -0.0959228352f, 0.30902788f, 0.0905296355f, 0.0476500839f, -0.293314278f, 0.030576475f, + 0.386174291f, -0.472136378f, -0.33841601f, 0.243396237f, 0.417664349f, 0.00800535921f, -0.0104374159f, + 0.304240674f, -0.281262189f, -0.165613428f, 0.070562236f, 0.320250571f, 0.174344733f, -0.226311728f, + 0.26752913f, 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0.184952512f, -0.258273512f, 0.449345112f, -0.329764098f, + -0.495748937f, -0.015752947f, 0.192505941f, 0.404160917f, -0.0859981477f, 0.363898367f, 0.231502205f, + -0.193105236f, 0.0498738512f, 0.118851729f, 0.232886016f, 0.367508769f, 0.0326819569f, -0.123003155f, + 0.11254748f, -0.269022226f, -0.124110565f, 0.37968415f, -0.436024725f, -0.358833283f, -0.340765476f, + 0.415250748f, 0.188313678f, 0.0945832804f, 0.325036496f, -0.411836654f, 0.439204931f, 0.443838507f, + 0.0819402859f, 0.0993777215f, 0.0932687744f, 0.301267684f, 0.22967121f, -0.383063585f, -0.0879548788f, + 0.383573651f, -0.338567227f, 0.322819948f, -0.323442817f, -0.0928066298f, 0.110474408f, 0.113604523f, + -0.447438031f, 0.459658921f, 0.237564221f, -0.429322809f, 0.158432931f, -0.443460673f, 0.214933977f, + -0.156835914f, 0.0874282196f, 0.303395838f, 0.410519481f, -0.251015186f, -0.299283832f, 0.339093566f, + -0.280447155f, 0.017720677f, -0.362982273f, 0.0141018284f, -0.0669345781f, 0.367385179f, 0.37005794f, + 0.210143775f, 0.152328655f, 0.0101734456f, 0.260875463f, 0.409880906f, -0.420392424f, -0.0511664525f, + -0.481775671f, -0.103259012f, 0.400012404f, -0.49858731f, 0.0339822359f, 0.460028797f, 0.0989061296f, + -0.297054291f, -0.210195482f, 0.261248916f, 0.454066128f, -0.3639808f, 0.0957617164f, -0.0176411085f, + 0.399710834f, -0.103607491f, 0.379834592f, 0.0928960666f, 0.325612277f, 0.248209059f, -0.396142751f, + 0.00639978796f, -0.12309707f, -0.162701026f, 0.418240756f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_time_data.h new file mode 100644 index 00000000..c6ac27d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_match_2_f32/weights_time_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_match_2_f32_weights_time[78] = { + -0.38494271f, 0.317598522f, 0.104572617f, 0.356358677f, -0.204914004f, 0.205144688f, 0.0474376678f, + -0.112312287f, 0.177086249f, -0.419594139f, 0.311879754f, 0.393907905f, -0.482231259f, -0.287710011f, + -0.161186531f, 0.185661539f, -0.439231426f, 0.211460918f, 0.261774927f, 0.496105909f, 0.421393126f, + -0.0493611209f, -0.452484787f, 0.226016417f, -0.152519166f, 0.317906111f, 0.210324436f, -0.315164626f, + 0.194294885f, 0.199403033f, -0.0441358946f, -0.251036733f, 0.323650688f, -0.45792672f, -0.0729413629f, + 0.0808740035f, 0.295033723f, -0.483268142f, 0.000979864504f, -0.452571124f, -0.469072104f, 0.382940561f, + 0.197270691f, 0.383423358f, 0.0991279855f, -0.491114527f, -0.483953178f, 0.0751822144f, 0.287043244f, + -0.182583854f, 0.124068491f, -0.261558861f, -0.167727813f, 0.0890038237f, 0.0290378444f, -0.422700018f, + 0.177388385f, 0.488476425f, -0.0644929707f, -0.00635612849f, -0.0592980273f, 0.0824852958f, -0.450838536f, + 0.137797579f, -0.144154891f, -0.189846829f, 0.390874982f, -0.112324297f, 0.442031682f, -0.0282248743f, + -0.355136275f, 0.389953017f, -0.274947256f, -0.355128497f, 0.0956587195f, 0.458354712f, 0.439362794f, + -0.251641244f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/bias_data.h new file mode 100644 index 00000000..2efee8ba --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/bias_data.h @@ -0,0 +1,7 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_bias[6] = {(float16_t)0.191162109f, + (float16_t)-0.105285645f, + (float16_t)-0.11907959f, + (float16_t)-0.0810546875f, + (float16_t)0.0765380859f, + (float16_t)0.0982055664f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/config_data.h new file mode 100644 index 00000000..b5d83171 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_SMALL_F16_INPUT_BATCHES 1 +#define SVDF_SMALL_F16_INPUT_SIZE 16 +#define SVDF_SMALL_F16_UNIT_COUNT 6 +#define SVDF_SMALL_F16_RANK 2 +#define SVDF_SMALL_F16_FEATURE_BATCHES 12 +#define SVDF_SMALL_F16_TIME_BATCHES 4 +#define SVDF_SMALL_F16_SEQUENCE_STEPS 3 +#define SVDF_SMALL_F16_DST_SIZE 6 +#define SVDF_SMALL_F16_INPUT_ACTIVATION_MIN (float16_t) - 1.0f +#define SVDF_SMALL_F16_INPUT_ACTIVATION_MAX (float16_t)1.0f +#define SVDF_SMALL_F16_OUTPUT_ACTIVATION_MIN (float16_t) - 100.0f +#define SVDF_SMALL_F16_OUTPUT_ACTIVATION_MAX (float16_t)100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/initial_state_data.h new file mode 100644 index 00000000..e28aa9a4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/initial_state_data.h @@ -0,0 +1,10 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_initial_state[48] = { + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, + (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f, (float16_t)0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/input_sequence_data.h new file mode 100644 index 00000000..63f41a55 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/input_sequence_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_input_sequence[48] = { + (float16_t)-0.711914062f, (float16_t)-0.440185547f, (float16_t)-0.81640625f, (float16_t)0.00646209717f, + (float16_t)0.633300781f, (float16_t)-1.08105469f, (float16_t)-1.03710938f, (float16_t)0.987792969f, + (float16_t)-0.8359375f, (float16_t)-0.806640625f, (float16_t)-1.37988281f, (float16_t)0.551757812f, + (float16_t)-0.941894531f, (float16_t)1.40234375f, (float16_t)-0.0493774414f, (float16_t)-0.546875f, + (float16_t)1.42773438f, (float16_t)0.0186309814f, (float16_t)1.34570312f, (float16_t)0.853027344f, + (float16_t)-0.862304688f, (float16_t)-1.02539062f, (float16_t)-0.891601562f, (float16_t)-0.149047852f, + (float16_t)1.11425781f, (float16_t)1.16992188f, (float16_t)0.402587891f, (float16_t)0.442871094f, + (float16_t)-1.17480469f, (float16_t)-1.0390625f, (float16_t)-0.823242188f, (float16_t)-0.423583984f, + (float16_t)-0.0833129883f, (float16_t)-0.375732422f, (float16_t)-1.23925781f, (float16_t)-0.632324219f, + (float16_t)1.26855469f, (float16_t)-0.344726562f, (float16_t)0.184204102f, (float16_t)0.153930664f, + (float16_t)0.0788574219f, (float16_t)-0.5625f, (float16_t)-0.602050781f, (float16_t)-0.323730469f, + (float16_t)-0.078125f, (float16_t)0.986328125f, (float16_t)1.1796875f, (float16_t)-0.305175781f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/output_ref_data.h new file mode 100644 index 00000000..4c27bf65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/output_ref_data.h @@ -0,0 +1,7 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_output_ref[6] = {(float16_t)0.925292969f, + (float16_t)-0.883300781f, + (float16_t)0.302490234f, + (float16_t)-0.490234375f, + (float16_t)-0.383300781f, + (float16_t)-0.0218811035f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_feature_data.h new file mode 100644 index 00000000..27957331 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_feature_data.h @@ -0,0 +1,50 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_weights_feature[192] = { + (float16_t)-0.334960938f, (float16_t)0.185302734f, (float16_t)0.253662109f, (float16_t)0.322998047f, + (float16_t)0.254394531f, (float16_t)-0.0689697266f, (float16_t)0.104064941f, (float16_t)-0.0164337158f, + (float16_t)-0.305908203f, (float16_t)-0.485595703f, (float16_t)0.332763672f, (float16_t)0.346435547f, + (float16_t)-0.073059082f, (float16_t)-0.125f, (float16_t)0.254150391f, (float16_t)0.263183594f, + (float16_t)-0.184692383f, (float16_t)0.209350586f, (float16_t)-0.110046387f, (float16_t)0.341064453f, + (float16_t)-0.193481445f, (float16_t)0.471679688f, (float16_t)0.111877441f, (float16_t)0.397705078f, + (float16_t)-0.253417969f, (float16_t)-0.00587463379f, (float16_t)-0.188354492f, (float16_t)0.467285156f, + (float16_t)0.01612854f, (float16_t)0.360839844f, (float16_t)0.395019531f, (float16_t)-0.080078125f, + (float16_t)0.184570312f, (float16_t)-0.0833740234f, (float16_t)-0.314697266f, (float16_t)0.331298828f, + (float16_t)0.211914062f, (float16_t)-0.236694336f, (float16_t)0.30859375f, (float16_t)-0.455078125f, + (float16_t)-0.370361328f, (float16_t)-0.208374023f, (float16_t)-0.0775756836f, (float16_t)-0.0593261719f, + (float16_t)0.475830078f, (float16_t)0.400146484f, (float16_t)0.347900391f, (float16_t)0.144287109f, + (float16_t)-0.279296875f, (float16_t)0.207519531f, (float16_t)-0.104797363f, (float16_t)-0.119750977f, + (float16_t)-0.139892578f, (float16_t)-0.0956420898f, (float16_t)0.228637695f, (float16_t)0.09375f, + (float16_t)-0.243896484f, (float16_t)-0.260742188f, (float16_t)0.0672607422f, (float16_t)-0.282470703f, + (float16_t)-0.287597656f, (float16_t)-0.112243652f, (float16_t)-0.412841797f, (float16_t)-0.0383605957f, + (float16_t)0.209960938f, (float16_t)-0.0619812012f, (float16_t)0.465332031f, (float16_t)0.0823364258f, + (float16_t)-0.214355469f, (float16_t)0.458251953f, (float16_t)0.0626831055f, (float16_t)0.250244141f, + (float16_t)-0.252929688f, (float16_t)0.198730469f, (float16_t)0.394775391f, (float16_t)0.181152344f, + (float16_t)-0.0453186035f, (float16_t)0.0908203125f, (float16_t)0.169799805f, (float16_t)-0.0997314453f, + (float16_t)0.137817383f, (float16_t)-0.471435547f, (float16_t)-0.231811523f, (float16_t)-0.472167969f, + (float16_t)-0.388671875f, (float16_t)0.274902344f, (float16_t)0.448242188f, (float16_t)0.178588867f, + (float16_t)0.399414062f, (float16_t)-0.477050781f, (float16_t)-0.315673828f, (float16_t)-0.178833008f, + (float16_t)-0.364013672f, (float16_t)-0.415283203f, (float16_t)-0.174316406f, (float16_t)0.211425781f, + (float16_t)-0.375f, (float16_t)0.224243164f, (float16_t)0.192260742f, (float16_t)-0.291015625f, + (float16_t)-0.393310547f, (float16_t)0.281738281f, (float16_t)-0.473632812f, (float16_t)-0.434570312f, + (float16_t)0.240112305f, (float16_t)0.229248047f, (float16_t)0.206176758f, (float16_t)0.248291016f, + (float16_t)-0.0700683594f, (float16_t)0.465576172f, (float16_t)0.0304412842f, (float16_t)0.354248047f, + (float16_t)0.143676758f, (float16_t)-0.126831055f, (float16_t)0.108398438f, (float16_t)0.0900268555f, + (float16_t)0.293457031f, (float16_t)0.130126953f, (float16_t)-0.44921875f, (float16_t)-0.472167969f, + (float16_t)-0.260009766f, (float16_t)0.363525391f, (float16_t)-0.05859375f, (float16_t)0.313476562f, + (float16_t)0.410400391f, (float16_t)-0.209106445f, (float16_t)-0.474365234f, (float16_t)0.233764648f, + (float16_t)-0.0505981445f, (float16_t)0.213256836f, (float16_t)-0.476074219f, (float16_t)-0.172363281f, + (float16_t)0.00845336914f, (float16_t)0.262939453f, (float16_t)-0.426025391f, (float16_t)0.228759766f, + (float16_t)0.362304688f, (float16_t)0.0942993164f, (float16_t)0.330078125f, (float16_t)-0.0902709961f, + (float16_t)0.200073242f, (float16_t)0.422119141f, (float16_t)-0.190429688f, (float16_t)0.373291016f, + (float16_t)0.450683594f, (float16_t)-0.204956055f, (float16_t)0.353027344f, (float16_t)-0.0196838379f, + (float16_t)0.0425720215f, (float16_t)-0.30078125f, (float16_t)0.253662109f, (float16_t)0.214233398f, + (float16_t)0.0324707031f, (float16_t)-0.214111328f, (float16_t)0.0567321777f, (float16_t)-0.140014648f, + (float16_t)0.331542969f, (float16_t)-0.155761719f, (float16_t)-0.432617188f, (float16_t)-0.249633789f, + (float16_t)-0.10748291f, (float16_t)-0.0149536133f, (float16_t)0.00556564331f, (float16_t)0.245117188f, + (float16_t)0.175170898f, (float16_t)-0.0910644531f, (float16_t)-0.291992188f, (float16_t)0.268066406f, + (float16_t)-0.0635375977f, (float16_t)-0.471191406f, (float16_t)0.303222656f, (float16_t)-0.461669922f, + (float16_t)0.405273438f, (float16_t)0.237182617f, (float16_t)0.354492188f, (float16_t)0.0843505859f, + (float16_t)0.235351562f, (float16_t)-0.304443359f, (float16_t)-0.187866211f, (float16_t)0.280029297f, + (float16_t)0.0701904297f, (float16_t)-0.336669922f, (float16_t)-0.0964355469f, (float16_t)0.389892578f, + (float16_t)0.181030273f, (float16_t)-0.448974609f, (float16_t)-0.374755859f, (float16_t)0.189453125f, + (float16_t)0.235717773f, (float16_t)0.217407227f, (float16_t)0.480224609f, (float16_t)0.18347168f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_time_data.h new file mode 100644 index 00000000..95dff1ed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f16/weights_time_data.h @@ -0,0 +1,14 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t svdf_small_f16_weights_time[48] = { + (float16_t)-0.0336608887f, (float16_t)-0.199462891f, (float16_t)-0.0627441406f, (float16_t)-0.134765625f, + (float16_t)-0.147460938f, (float16_t)0.333740234f, (float16_t)-0.272460938f, (float16_t)0.282470703f, + (float16_t)-0.282958984f, (float16_t)-0.4296875f, (float16_t)0.0570678711f, (float16_t)-0.402832031f, + (float16_t)-0.4765625f, (float16_t)-0.237670898f, (float16_t)0.141113281f, (float16_t)0.0135116577f, + (float16_t)0.0944213867f, (float16_t)-0.483642578f, (float16_t)0.475341797f, (float16_t)0.403564453f, + (float16_t)-0.0437316895f, (float16_t)0.112182617f, (float16_t)-0.112243652f, (float16_t)0.266357422f, + (float16_t)-0.0589599609f, (float16_t)-0.376708984f, (float16_t)0.300537109f, (float16_t)0.394775391f, + (float16_t)-0.468505859f, (float16_t)0.229492188f, (float16_t)-0.124023438f, (float16_t)0.0800170898f, + (float16_t)-0.492431641f, (float16_t)-0.138305664f, (float16_t)0.063659668f, (float16_t)0.0040397644f, + (float16_t)-0.433105469f, (float16_t)0.334228516f, (float16_t)-0.285888672f, (float16_t)-0.13671875f, + (float16_t)-0.0398254395f, (float16_t)0.412597656f, (float16_t)-0.0714111328f, (float16_t)-0.385253906f, + (float16_t)-0.0645141602f, (float16_t)0.352294922f, (float16_t)0.0980224609f, (float16_t)-0.327148438f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/bias_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/bias_data.h new file mode 100644 index 00000000..70609bdb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/bias_data.h @@ -0,0 +1,3 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_bias[6] = + {-0.158127844f, 0.194616497f, -0.00951028336f, 0.0701505169f, -0.0914256722f, 0.153938577f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/config_data.h new file mode 100644 index 00000000..9d56f74f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/config_data.h @@ -0,0 +1,13 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#define SVDF_SMALL_F32_INPUT_BATCHES 1 +#define SVDF_SMALL_F32_INPUT_SIZE 16 +#define SVDF_SMALL_F32_UNIT_COUNT 6 +#define SVDF_SMALL_F32_RANK 2 +#define SVDF_SMALL_F32_FEATURE_BATCHES 12 +#define SVDF_SMALL_F32_TIME_BATCHES 4 +#define SVDF_SMALL_F32_SEQUENCE_STEPS 3 +#define SVDF_SMALL_F32_DST_SIZE 6 +#define SVDF_SMALL_F32_INPUT_ACTIVATION_MIN -1.0f +#define SVDF_SMALL_F32_INPUT_ACTIVATION_MAX 1.0f +#define SVDF_SMALL_F32_OUTPUT_ACTIVATION_MIN -100.0f +#define SVDF_SMALL_F32_OUTPUT_ACTIVATION_MAX 100.0f diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/initial_state_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/initial_state_data.h new file mode 100644 index 00000000..17ffb6ef --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/initial_state_data.h @@ -0,0 +1,5 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_initial_state[48] = { + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/input_sequence_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/input_sequence_data.h new file mode 100644 index 00000000..4f9cf6d2 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/input_sequence_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_input_sequence[48] = { + 0.0545968004f, 0.661644936f, -1.07618558f, 0.650533199f, -1.27745199f, -0.242788985f, 0.132571504f, + 0.357916266f, 1.43518162f, 1.09792852f, -0.410364121f, -0.701004088f, 0.506687462f, -1.14866114f, + -0.789623499f, -1.4659996f, 0.646616459f, -0.670450509f, -0.73466903f, -0.126845613f, 0.553114891f, + -0.268389672f, -0.823736906f, -0.24892588f, -1.0670737f, 0.0629318655f, -1.15218425f, 0.162472248f, + 0.318885028f, 1.48616397f, 0.195078656f, -0.0855162144f, 1.47125924f, -0.835796833f, 1.41317439f, + -1.33146894f, -1.3012563f, -0.35446465f, -0.153026924f, -0.104667716f, -1.24886191f, -1.16564465f, + 0.26589179f, 0.168305963f, 0.140778303f, -0.527798533f, -0.764337838f, 1.25889659f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/output_ref_data.h new file mode 100644 index 00000000..23fa97a5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/output_ref_data.h @@ -0,0 +1,3 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_output_ref[6] = + {0.306442022f, -0.180808574f, 0.246582955f, 0.879285574f, -1.05671418f, -0.104684159f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/test_data.h new file mode 100644 index 00000000..ae84fb53 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/test_data.h @@ -0,0 +1,8 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "bias_data.h" +#include "config_data.h" +#include "initial_state_data.h" +#include "input_sequence_data.h" +#include "output_ref_data.h" +#include "weights_feature_data.h" +#include "weights_time_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_feature_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_feature_data.h new file mode 100644 index 00000000..7ab33ff5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_feature_data.h @@ -0,0 +1,30 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_weights_feature[192] = { + 0.0132997986f, -0.343649089f, 0.133883089f, -0.489766121f, 0.468506604f, 0.181176245f, -0.232291579f, + 0.365466654f, -0.440678328f, -0.0650500283f, -0.0506843776f, -0.0778671727f, -0.344007015f, 0.261050195f, + 0.345213115f, -0.429526627f, -0.443032563f, -0.192315012f, 0.360025555f, 0.463936836f, 0.00197752914f, + 0.258903295f, 0.185892686f, -0.327454656f, -0.360049427f, 0.204963043f, -0.0569460168f, 0.0050087967f, + 0.325459719f, -0.177376866f, -0.0134457154f, -0.0545086153f, -0.438119262f, -0.0882824361f, -0.0825507119f, + 0.279709309f, -0.0882132128f, -0.389746159f, 0.303183824f, 0.322403908f, -0.148630396f, 0.158670053f, + -0.296701431f, -0.17558898f, 0.187922135f, 0.0465145037f, -0.484129131f, -0.418407679f, -0.0620185062f, + -0.14640969f, 0.426667422f, 0.0431589931f, -0.296212941f, 0.174381211f, 0.413285851f, -0.307379037f, + -0.263898343f, -0.461202413f, -0.0120391585f, -0.441144079f, -0.354688853f, 0.31074214f, -0.0543438755f, + -0.381339341f, 0.405137688f, 0.355717838f, 0.172553912f, 0.0272531603f, -0.400999606f, 0.384969294f, + 0.45575577f, -0.390420914f, 0.2184425f, 0.308919251f, 0.107872367f, -0.289063007f, -0.0416361243f, + 0.170763254f, 0.371800333f, -0.349761665f, 0.340911329f, 0.450675011f, 0.326105595f, -0.495781749f, + 0.44199428f, 0.247747362f, -0.182749689f, -0.0603001751f, 0.232228771f, 0.00949483924f, -0.406897485f, + -0.23785843f, 0.0239441413f, -0.235823125f, -0.336616039f, -0.342585087f, -0.245950997f, 0.485150456f, + 0.343252808f, -0.225047231f, 0.124697439f, 0.167603418f, -0.314491689f, -0.203112796f, 0.142349586f, + -0.474551618f, -0.080740422f, 0.478878379f, -0.327974588f, -0.15672113f, 0.410798371f, 0.260864168f, + -0.303308606f, 0.261399448f, -0.203297034f, 0.212614939f, 0.430786997f, -0.148155749f, 0.452091575f, + -0.29293257f, 0.0771698952f, -0.0255859252f, 0.0704491735f, 0.42007795f, -0.355511725f, -0.314968914f, + -0.498158038f, 0.440225333f, -0.448543042f, 0.359332561f, -0.339827061f, 0.463013858f, 0.298184991f, + -0.492831647f, -0.370756626f, 0.123005606f, -0.111479335f, 0.0221769419f, -0.033364214f, -0.423828483f, + -0.382190317f, 0.373453349f, -0.251027733f, -0.305122077f, -0.496855438f, 0.0805699676f, -0.275406033f, + 0.336017519f, 0.233807757f, -0.164116189f, -0.411876529f, -0.126181722f, 0.432141632f, -0.0284670182f, + 0.36422658f, -0.286444306f, 0.483123183f, 0.475706249f, -0.172114179f, -0.449399143f, -0.30985269f, + 0.098803252f, 0.232805043f, -0.33725813f, 0.188169122f, 0.365782082f, 0.264469743f, -0.422832668f, + 0.0775145739f, -0.492385119f, -0.39316988f, -0.158016145f, 0.343401045f, -0.113613546f, 0.427163601f, + -0.13734296f, 0.130594999f, 0.232123449f, -0.204656467f, -0.0908655673f, 0.224160939f, -0.153879344f, + 0.124112561f, -0.220148414f, -0.212289304f, -0.462795258f, 0.497526884f, -0.348704338f, 0.43279621f, + 0.283516049f, -0.29925555f, -0.259909838f}; diff --git a/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_time_data.h b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_time_data.h new file mode 100644 index 00000000..71c95aa8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/svdf_small_f32/weights_time_data.h @@ -0,0 +1,9 @@ +// Generated by svdf_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t svdf_small_f32_weights_time[48] = { + 0.204333156f, -0.161776021f, 0.430329353f, -0.29468894f, -0.0885180831f, 0.444438517f, -0.172249749f, + 0.16021505f, -0.288652301f, -0.482291847f, -0.470496297f, 0.312101841f, 0.176438719f, -0.210031137f, + -0.230565399f, 0.258222461f, 0.191136688f, -0.262809575f, -0.2323737f, -0.238924995f, -0.365558833f, + 0.363715768f, 0.213851109f, -0.271289349f, -0.0976540148f, -0.113894448f, -0.403976232f, -0.158269927f, + 0.447548032f, 0.276771694f, -0.303178549f, -0.392418176f, -0.361742198f, 0.0399243459f, 0.319178909f, + 0.390386134f, 0.256272435f, -0.314131588f, -0.396318376f, 0.421748966f, -0.073383905f, 0.414376229f, + 0.200505555f, -0.32892105f, -0.321312696f, 0.325044632f, 0.291015834f, 0.21888271f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/config_data.h new file mode 100644 index 00000000..90beec65 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_3DIM2_F16_NUM_DIMS 3 +#define TRANSPOSE_3DIM2_F16_INPUT_N 5 +#define TRANSPOSE_3DIM2_F16_INPUT_H 4 +#define TRANSPOSE_3DIM2_F16_INPUT_W 20 +#define TRANSPOSE_3DIM2_F16_INPUT_C 1 +#define TRANSPOSE_3DIM2_F16_OUTPUT_N 4 +#define TRANSPOSE_3DIM2_F16_OUTPUT_H 20 +#define TRANSPOSE_3DIM2_F16_OUTPUT_W 5 +#define TRANSPOSE_3DIM2_F16_OUTPUT_C 1 +#define TRANSPOSE_3DIM2_F16_PERM_0 1 +#define TRANSPOSE_3DIM2_F16_PERM_1 2 +#define TRANSPOSE_3DIM2_F16_PERM_2 0 +#define TRANSPOSE_3DIM2_F16_PERM_3 3 +#define TRANSPOSE_3DIM2_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_3DIM2_F16_SIZE 400 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/input_tensor_data.h new file mode 100644 index 00000000..700e316a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/input_tensor_data.h @@ -0,0 +1,102 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_3dim2_f16_input_tensor[400] = { + (float16_t)2.20507812f, (float16_t)-1.36621094f, (float16_t)-2.48046875f, (float16_t)-3.15820312f, + (float16_t)-2.29101562f, (float16_t)-3.12695312f, (float16_t)3.00585938f, (float16_t)3.1640625f, + (float16_t)-2.25976562f, (float16_t)-1.92480469f, (float16_t)0.10559082f, (float16_t)1.65429688f, + (float16_t)-2.04296875f, (float16_t)0.572265625f, (float16_t)0.533203125f, (float16_t)-1.81054688f, + (float16_t)-2.6796875f, (float16_t)-0.690429688f, (float16_t)1.57714844f, (float16_t)0.872558594f, + (float16_t)3.16796875f, (float16_t)0.553222656f, (float16_t)1.00195312f, (float16_t)-2.70703125f, + (float16_t)1.296875f, (float16_t)1.30566406f, (float16_t)-3.74609375f, (float16_t)2.94140625f, + (float16_t)-2.70507812f, (float16_t)3.86328125f, (float16_t)-2.00976562f, (float16_t)2.15625f, + (float16_t)3.93945312f, (float16_t)1.69433594f, (float16_t)0.773925781f, (float16_t)-1.50195312f, + (float16_t)-1.99414062f, (float16_t)-0.00271987915f, (float16_t)2.33984375f, (float16_t)3.75585938f, + (float16_t)1.89941406f, (float16_t)-0.343017578f, (float16_t)1.34960938f, (float16_t)-1.79882812f, + (float16_t)-2.30859375f, (float16_t)1.52636719f, (float16_t)2.22460938f, (float16_t)-0.541992188f, + (float16_t)2.93945312f, (float16_t)-3.23242188f, (float16_t)-2.52148438f, (float16_t)-1.0390625f, + (float16_t)-0.586425781f, (float16_t)-3.72070312f, (float16_t)-2.3984375f, (float16_t)-0.254882812f, + (float16_t)3.2421875f, (float16_t)0.565429688f, (float16_t)3.6953125f, (float16_t)1.31738281f, + (float16_t)-3.12890625f, (float16_t)-1.88183594f, (float16_t)-2.99804688f, (float16_t)0.241943359f, + (float16_t)3.59375f, (float16_t)-3.13476562f, (float16_t)-1.03417969f, (float16_t)3.34765625f, + (float16_t)0.759277344f, (float16_t)1.80859375f, (float16_t)3.32617188f, (float16_t)1.43554688f, + (float16_t)2.18164062f, (float16_t)3.95117188f, (float16_t)-3.484375f, (float16_t)1.1953125f, + (float16_t)-2.64257812f, (float16_t)1.42382812f, (float16_t)2.60742188f, (float16_t)3.86132812f, + (float16_t)-1.57421875f, (float16_t)-0.976074219f, (float16_t)-3.59570312f, (float16_t)1.85351562f, + (float16_t)-0.218383789f, (float16_t)1.1328125f, (float16_t)2.36328125f, (float16_t)0.594238281f, + (float16_t)1.51953125f, (float16_t)3.33007812f, (float16_t)-3.84179688f, (float16_t)-2.85742188f, + (float16_t)3.98242188f, (float16_t)-0.0861206055f, (float16_t)2.19335938f, (float16_t)1.35058594f, + (float16_t)-3.16601562f, (float16_t)2.1953125f, (float16_t)-1.19238281f, (float16_t)-0.622070312f, + (float16_t)-0.248168945f, (float16_t)3.19921875f, (float16_t)3.265625f, (float16_t)2.64648438f, + (float16_t)3.07617188f, (float16_t)1.203125f, (float16_t)2.60546875f, (float16_t)0.243530273f, + (float16_t)0.866210938f, (float16_t)-2.8203125f, (float16_t)3.02734375f, (float16_t)-3.9453125f, + (float16_t)-1.65039062f, (float16_t)0.928222656f, (float16_t)-3.32226562f, (float16_t)3.92773438f, + (float16_t)3.421875f, (float16_t)2.21875f, (float16_t)-0.364501953f, (float16_t)-3.375f, + (float16_t)-0.89453125f, (float16_t)-1.21777344f, (float16_t)-2.08398438f, (float16_t)-2.203125f, + (float16_t)1.51855469f, (float16_t)3.84375f, (float16_t)-2.12890625f, (float16_t)3.27148438f, + (float16_t)-1.51464844f, (float16_t)0.941894531f, (float16_t)2.56640625f, (float16_t)-1.39160156f, + (float16_t)-3.52734375f, (float16_t)2.36523438f, (float16_t)2.06835938f, (float16_t)-3.8046875f, + (float16_t)-0.324707031f, (float16_t)0.344482422f, (float16_t)1.64746094f, (float16_t)0.877929688f, + (float16_t)0.281982422f, (float16_t)3.16015625f, (float16_t)-1.37890625f, (float16_t)-0.321533203f, + (float16_t)-3.6015625f, (float16_t)3.58203125f, (float16_t)2.03320312f, (float16_t)-3.96289062f, + (float16_t)2.6875f, (float16_t)3.95117188f, (float16_t)3.078125f, (float16_t)3.08984375f, + (float16_t)-1.69433594f, (float16_t)0.266357422f, (float16_t)-0.184936523f, (float16_t)2.953125f, + (float16_t)1.71875f, (float16_t)-3.81054688f, (float16_t)-3.234375f, (float16_t)2.10742188f, + (float16_t)1.30664062f, (float16_t)1.8359375f, (float16_t)-2.56640625f, (float16_t)0.922363281f, + (float16_t)3.35351562f, (float16_t)3.11523438f, (float16_t)1.04394531f, (float16_t)1.70800781f, + (float16_t)2.70117188f, (float16_t)1.28222656f, (float16_t)-1.10644531f, (float16_t)-0.763671875f, + (float16_t)-3.45117188f, (float16_t)-3.38085938f, (float16_t)3.91796875f, (float16_t)2.51757812f, + (float16_t)-3.21679688f, (float16_t)3.89648438f, (float16_t)2.96679688f, (float16_t)3.10351562f, + (float16_t)-2.984375f, (float16_t)-0.686035156f, (float16_t)3.91015625f, (float16_t)-3.5625f, + (float16_t)1.88671875f, (float16_t)-0.120666504f, (float16_t)2.3984375f, (float16_t)-0.0330200195f, + (float16_t)-3.82226562f, (float16_t)2.68164062f, (float16_t)-1.10742188f, (float16_t)1.07421875f, + (float16_t)0.912109375f, (float16_t)-3.40039062f, (float16_t)-2.703125f, (float16_t)-0.105651855f, + (float16_t)0.212646484f, (float16_t)-0.526367188f, (float16_t)0.0916748047f, (float16_t)-1.81738281f, + (float16_t)-3.53515625f, (float16_t)0.897949219f, (float16_t)-1.59277344f, (float16_t)-0.423583984f, + (float16_t)-2.41992188f, (float16_t)-1.33203125f, (float16_t)1.10253906f, (float16_t)2.28125f, + (float16_t)1.77636719f, (float16_t)-2.15234375f, (float16_t)1.92871094f, (float16_t)-0.724121094f, + (float16_t)2.40234375f, (float16_t)-2.08984375f, (float16_t)-2.40234375f, (float16_t)-0.857421875f, + (float16_t)0.533691406f, (float16_t)-2.4765625f, (float16_t)-1.234375f, (float16_t)0.442382812f, + (float16_t)3.50585938f, (float16_t)-2.421875f, (float16_t)-2.0234375f, (float16_t)3.67773438f, + (float16_t)0.30078125f, (float16_t)3.00585938f, (float16_t)1.84667969f, (float16_t)-2.1953125f, + (float16_t)-1.20800781f, (float16_t)-2.77929688f, (float16_t)-3.05273438f, (float16_t)-0.396972656f, + (float16_t)-1.94238281f, (float16_t)3.64648438f, (float16_t)-2.48242188f, (float16_t)-3.74023438f, + (float16_t)-0.776367188f, (float16_t)3.421875f, (float16_t)2.19726562f, (float16_t)1.66210938f, + (float16_t)-3.52539062f, (float16_t)-1.74902344f, (float16_t)0.0440979004f, (float16_t)1.22363281f, + (float16_t)-0.942382812f, (float16_t)-0.571777344f, (float16_t)-2.875f, (float16_t)-0.0883789062f, + (float16_t)-3.3515625f, (float16_t)1.42675781f, (float16_t)-0.721191406f, (float16_t)-1.02539062f, + (float16_t)3.51953125f, (float16_t)-0.836425781f, (float16_t)-3.5703125f, (float16_t)1.06933594f, + (float16_t)-3.29882812f, (float16_t)-3.890625f, (float16_t)-3.765625f, (float16_t)0.973632812f, + (float16_t)-2.78125f, (float16_t)-1.8515625f, (float16_t)-1.04882812f, (float16_t)-1.14257812f, + (float16_t)1.47070312f, (float16_t)1.53417969f, (float16_t)1.07714844f, (float16_t)-3.171875f, + (float16_t)-0.442626953f, (float16_t)1.01367188f, (float16_t)1.21777344f, (float16_t)1.54980469f, + (float16_t)3.77734375f, (float16_t)-2.06640625f, (float16_t)3.77148438f, (float16_t)-3.94726562f, + (float16_t)-2.05664062f, (float16_t)3.50390625f, (float16_t)-3.59375f, (float16_t)3.18945312f, + (float16_t)1.05175781f, (float16_t)0.186157227f, (float16_t)1.32421875f, (float16_t)2.02929688f, + (float16_t)1.15332031f, (float16_t)3.30273438f, (float16_t)2.34179688f, (float16_t)1.91894531f, + (float16_t)-2.35742188f, (float16_t)-2.5078125f, (float16_t)-2.03515625f, (float16_t)1.89160156f, + (float16_t)2.96875f, (float16_t)-2.84765625f, (float16_t)0.596191406f, (float16_t)-3.68359375f, + (float16_t)-0.840332031f, (float16_t)-1.82226562f, (float16_t)-3.14648438f, (float16_t)0.346923828f, + (float16_t)3.45703125f, (float16_t)2.98046875f, (float16_t)-0.353027344f, (float16_t)1.01269531f, + (float16_t)-3.9140625f, (float16_t)-2.14648438f, (float16_t)-0.260253906f, (float16_t)3.89648438f, + (float16_t)-3.19921875f, (float16_t)2.75390625f, (float16_t)1.90625f, (float16_t)1.13476562f, + (float16_t)-0.142700195f, (float16_t)-2.47851562f, (float16_t)-1.13867188f, (float16_t)-0.716308594f, + (float16_t)0.932128906f, (float16_t)0.434082031f, (float16_t)3.48242188f, (float16_t)-0.0787353516f, + (float16_t)1.78808594f, (float16_t)-1.25097656f, (float16_t)-3.10351562f, (float16_t)-3.34960938f, + (float16_t)2.71289062f, (float16_t)-1.11523438f, (float16_t)-0.395263672f, (float16_t)-0.231933594f, + (float16_t)0.298095703f, (float16_t)3.50976562f, (float16_t)-3.22851562f, (float16_t)-2.421875f, + (float16_t)-3.8359375f, (float16_t)3.3515625f, (float16_t)-3.09960938f, (float16_t)-1.42871094f, + (float16_t)1.96484375f, (float16_t)-2.16210938f, (float16_t)-1.72460938f, (float16_t)1.62011719f, + (float16_t)-1.046875f, (float16_t)2.11523438f, (float16_t)1.52929688f, (float16_t)-3.0625f, + (float16_t)-0.415283203f, (float16_t)2.58398438f, (float16_t)-0.870117188f, (float16_t)-1.87792969f, + (float16_t)-1.42089844f, (float16_t)2.5078125f, (float16_t)3.9921875f, (float16_t)-0.00368309021f, + (float16_t)-3.98242188f, (float16_t)-3.14257812f, (float16_t)3.34765625f, (float16_t)0.837402344f, + (float16_t)3.82226562f, (float16_t)-0.291748047f, (float16_t)0.210571289f, (float16_t)2.56445312f, + (float16_t)3.21679688f, (float16_t)-2.51171875f, (float16_t)-3.171875f, (float16_t)-3.7578125f, + (float16_t)3.2265625f, (float16_t)-2.40234375f, (float16_t)3.39648438f, (float16_t)0.6015625f, + (float16_t)1.96875f, (float16_t)-2.48242188f, (float16_t)-0.632324219f, (float16_t)3.53710938f, + (float16_t)0.713378906f, (float16_t)2.49804688f, (float16_t)-3.04101562f, (float16_t)2.50195312f, + (float16_t)3.39453125f, (float16_t)-0.58984375f, (float16_t)-3.37890625f, (float16_t)-2.58203125f, + (float16_t)3.79492188f, (float16_t)1.49902344f, (float16_t)0.254638672f, (float16_t)-0.998046875f, + (float16_t)-0.51171875f, (float16_t)0.449462891f, (float16_t)-0.272949219f, (float16_t)1.59667969f, + (float16_t)-0.580078125f, (float16_t)3.39453125f, (float16_t)1.81640625f, (float16_t)1.40234375f, + (float16_t)1.20214844f, (float16_t)-3.03515625f, (float16_t)-3.05859375f, (float16_t)2.32617188f, + (float16_t)-2.19726562f, (float16_t)-1.45507812f, (float16_t)2.58984375f, (float16_t)-0.138427734f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/output_ref_data.h new file mode 100644 index 00000000..1ac11a70 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/output_ref_data.h @@ -0,0 +1,102 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_3dim2_f16_output_ref[400] = { + (float16_t)2.20507812f, (float16_t)-1.57421875f, (float16_t)1.30664062f, (float16_t)-3.52539062f, + (float16_t)1.78808594f, (float16_t)-1.36621094f, (float16_t)-0.976074219f, (float16_t)1.8359375f, + (float16_t)-1.74902344f, (float16_t)-1.25097656f, (float16_t)-2.48046875f, (float16_t)-3.59570312f, + (float16_t)-2.56640625f, (float16_t)0.0440979004f, (float16_t)-3.10351562f, (float16_t)-3.15820312f, + (float16_t)1.85351562f, (float16_t)0.922363281f, (float16_t)1.22363281f, (float16_t)-3.34960938f, + (float16_t)-2.29101562f, (float16_t)-0.218383789f, (float16_t)3.35351562f, (float16_t)-0.942382812f, + (float16_t)2.71289062f, (float16_t)-3.12695312f, (float16_t)1.1328125f, (float16_t)3.11523438f, + (float16_t)-0.571777344f, (float16_t)-1.11523438f, (float16_t)3.00585938f, (float16_t)2.36328125f, + (float16_t)1.04394531f, (float16_t)-2.875f, (float16_t)-0.395263672f, (float16_t)3.1640625f, + (float16_t)0.594238281f, (float16_t)1.70800781f, (float16_t)-0.0883789062f, (float16_t)-0.231933594f, + (float16_t)-2.25976562f, (float16_t)1.51953125f, (float16_t)2.70117188f, (float16_t)-3.3515625f, + (float16_t)0.298095703f, (float16_t)-1.92480469f, (float16_t)3.33007812f, (float16_t)1.28222656f, + (float16_t)1.42675781f, (float16_t)3.50976562f, (float16_t)0.10559082f, (float16_t)-3.84179688f, + (float16_t)-1.10644531f, (float16_t)-0.721191406f, (float16_t)-3.22851562f, (float16_t)1.65429688f, + (float16_t)-2.85742188f, (float16_t)-0.763671875f, (float16_t)-1.02539062f, (float16_t)-2.421875f, + (float16_t)-2.04296875f, (float16_t)3.98242188f, (float16_t)-3.45117188f, (float16_t)3.51953125f, + (float16_t)-3.8359375f, (float16_t)0.572265625f, (float16_t)-0.0861206055f, (float16_t)-3.38085938f, + (float16_t)-0.836425781f, (float16_t)3.3515625f, (float16_t)0.533203125f, (float16_t)2.19335938f, + (float16_t)3.91796875f, (float16_t)-3.5703125f, (float16_t)-3.09960938f, (float16_t)-1.81054688f, + (float16_t)1.35058594f, (float16_t)2.51757812f, (float16_t)1.06933594f, (float16_t)-1.42871094f, + (float16_t)-2.6796875f, (float16_t)-3.16601562f, (float16_t)-3.21679688f, (float16_t)-3.29882812f, + (float16_t)1.96484375f, (float16_t)-0.690429688f, (float16_t)2.1953125f, (float16_t)3.89648438f, + (float16_t)-3.890625f, (float16_t)-2.16210938f, (float16_t)1.57714844f, (float16_t)-1.19238281f, + (float16_t)2.96679688f, (float16_t)-3.765625f, (float16_t)-1.72460938f, (float16_t)0.872558594f, + (float16_t)-0.622070312f, (float16_t)3.10351562f, (float16_t)0.973632812f, (float16_t)1.62011719f, + (float16_t)3.16796875f, (float16_t)-0.248168945f, (float16_t)-2.984375f, (float16_t)-2.78125f, + (float16_t)-1.046875f, (float16_t)0.553222656f, (float16_t)3.19921875f, (float16_t)-0.686035156f, + (float16_t)-1.8515625f, (float16_t)2.11523438f, (float16_t)1.00195312f, (float16_t)3.265625f, + (float16_t)3.91015625f, (float16_t)-1.04882812f, (float16_t)1.52929688f, (float16_t)-2.70703125f, + (float16_t)2.64648438f, (float16_t)-3.5625f, (float16_t)-1.14257812f, (float16_t)-3.0625f, + (float16_t)1.296875f, (float16_t)3.07617188f, (float16_t)1.88671875f, (float16_t)1.47070312f, + (float16_t)-0.415283203f, (float16_t)1.30566406f, (float16_t)1.203125f, (float16_t)-0.120666504f, + (float16_t)1.53417969f, (float16_t)2.58398438f, (float16_t)-3.74609375f, (float16_t)2.60546875f, + (float16_t)2.3984375f, (float16_t)1.07714844f, (float16_t)-0.870117188f, (float16_t)2.94140625f, + (float16_t)0.243530273f, (float16_t)-0.0330200195f, (float16_t)-3.171875f, (float16_t)-1.87792969f, + (float16_t)-2.70507812f, (float16_t)0.866210938f, (float16_t)-3.82226562f, (float16_t)-0.442626953f, + (float16_t)-1.42089844f, (float16_t)3.86328125f, (float16_t)-2.8203125f, (float16_t)2.68164062f, + (float16_t)1.01367188f, (float16_t)2.5078125f, (float16_t)-2.00976562f, (float16_t)3.02734375f, + (float16_t)-1.10742188f, (float16_t)1.21777344f, (float16_t)3.9921875f, (float16_t)2.15625f, + (float16_t)-3.9453125f, (float16_t)1.07421875f, (float16_t)1.54980469f, (float16_t)-0.00368309021f, + (float16_t)3.93945312f, (float16_t)-1.65039062f, (float16_t)0.912109375f, (float16_t)3.77734375f, + (float16_t)-3.98242188f, (float16_t)1.69433594f, (float16_t)0.928222656f, (float16_t)-3.40039062f, + (float16_t)-2.06640625f, (float16_t)-3.14257812f, (float16_t)0.773925781f, (float16_t)-3.32226562f, + (float16_t)-2.703125f, (float16_t)3.77148438f, (float16_t)3.34765625f, (float16_t)-1.50195312f, + (float16_t)3.92773438f, (float16_t)-0.105651855f, (float16_t)-3.94726562f, (float16_t)0.837402344f, + (float16_t)-1.99414062f, (float16_t)3.421875f, (float16_t)0.212646484f, (float16_t)-2.05664062f, + (float16_t)3.82226562f, (float16_t)-0.00271987915f, (float16_t)2.21875f, (float16_t)-0.526367188f, + (float16_t)3.50390625f, (float16_t)-0.291748047f, (float16_t)2.33984375f, (float16_t)-0.364501953f, + (float16_t)0.0916748047f, (float16_t)-3.59375f, (float16_t)0.210571289f, (float16_t)3.75585938f, + (float16_t)-3.375f, (float16_t)-1.81738281f, (float16_t)3.18945312f, (float16_t)2.56445312f, + (float16_t)1.89941406f, (float16_t)-0.89453125f, (float16_t)-3.53515625f, (float16_t)1.05175781f, + (float16_t)3.21679688f, (float16_t)-0.343017578f, (float16_t)-1.21777344f, (float16_t)0.897949219f, + (float16_t)0.186157227f, (float16_t)-2.51171875f, (float16_t)1.34960938f, (float16_t)-2.08398438f, + (float16_t)-1.59277344f, (float16_t)1.32421875f, (float16_t)-3.171875f, (float16_t)-1.79882812f, + (float16_t)-2.203125f, (float16_t)-0.423583984f, (float16_t)2.02929688f, (float16_t)-3.7578125f, + (float16_t)-2.30859375f, (float16_t)1.51855469f, (float16_t)-2.41992188f, (float16_t)1.15332031f, + (float16_t)3.2265625f, (float16_t)1.52636719f, (float16_t)3.84375f, (float16_t)-1.33203125f, + (float16_t)3.30273438f, (float16_t)-2.40234375f, (float16_t)2.22460938f, (float16_t)-2.12890625f, + (float16_t)1.10253906f, (float16_t)2.34179688f, (float16_t)3.39648438f, (float16_t)-0.541992188f, + (float16_t)3.27148438f, (float16_t)2.28125f, (float16_t)1.91894531f, (float16_t)0.6015625f, + (float16_t)2.93945312f, (float16_t)-1.51464844f, (float16_t)1.77636719f, (float16_t)-2.35742188f, + (float16_t)1.96875f, (float16_t)-3.23242188f, (float16_t)0.941894531f, (float16_t)-2.15234375f, + (float16_t)-2.5078125f, (float16_t)-2.48242188f, (float16_t)-2.52148438f, (float16_t)2.56640625f, + (float16_t)1.92871094f, (float16_t)-2.03515625f, (float16_t)-0.632324219f, (float16_t)-1.0390625f, + (float16_t)-1.39160156f, (float16_t)-0.724121094f, (float16_t)1.89160156f, (float16_t)3.53710938f, + (float16_t)-0.586425781f, (float16_t)-3.52734375f, (float16_t)2.40234375f, (float16_t)2.96875f, + (float16_t)0.713378906f, (float16_t)-3.72070312f, (float16_t)2.36523438f, (float16_t)-2.08984375f, + (float16_t)-2.84765625f, (float16_t)2.49804688f, (float16_t)-2.3984375f, (float16_t)2.06835938f, + (float16_t)-2.40234375f, (float16_t)0.596191406f, (float16_t)-3.04101562f, (float16_t)-0.254882812f, + (float16_t)-3.8046875f, (float16_t)-0.857421875f, (float16_t)-3.68359375f, (float16_t)2.50195312f, + (float16_t)3.2421875f, (float16_t)-0.324707031f, (float16_t)0.533691406f, (float16_t)-0.840332031f, + (float16_t)3.39453125f, (float16_t)0.565429688f, (float16_t)0.344482422f, (float16_t)-2.4765625f, + (float16_t)-1.82226562f, (float16_t)-0.58984375f, (float16_t)3.6953125f, (float16_t)1.64746094f, + (float16_t)-1.234375f, (float16_t)-3.14648438f, (float16_t)-3.37890625f, (float16_t)1.31738281f, + (float16_t)0.877929688f, (float16_t)0.442382812f, (float16_t)0.346923828f, (float16_t)-2.58203125f, + (float16_t)-3.12890625f, (float16_t)0.281982422f, (float16_t)3.50585938f, (float16_t)3.45703125f, + (float16_t)3.79492188f, (float16_t)-1.88183594f, (float16_t)3.16015625f, (float16_t)-2.421875f, + (float16_t)2.98046875f, (float16_t)1.49902344f, (float16_t)-2.99804688f, (float16_t)-1.37890625f, + (float16_t)-2.0234375f, (float16_t)-0.353027344f, (float16_t)0.254638672f, (float16_t)0.241943359f, + (float16_t)-0.321533203f, (float16_t)3.67773438f, (float16_t)1.01269531f, (float16_t)-0.998046875f, + (float16_t)3.59375f, (float16_t)-3.6015625f, (float16_t)0.30078125f, (float16_t)-3.9140625f, + (float16_t)-0.51171875f, (float16_t)-3.13476562f, (float16_t)3.58203125f, (float16_t)3.00585938f, + (float16_t)-2.14648438f, (float16_t)0.449462891f, (float16_t)-1.03417969f, (float16_t)2.03320312f, + (float16_t)1.84667969f, (float16_t)-0.260253906f, (float16_t)-0.272949219f, (float16_t)3.34765625f, + (float16_t)-3.96289062f, (float16_t)-2.1953125f, (float16_t)3.89648438f, (float16_t)1.59667969f, + (float16_t)0.759277344f, (float16_t)2.6875f, (float16_t)-1.20800781f, (float16_t)-3.19921875f, + (float16_t)-0.580078125f, (float16_t)1.80859375f, (float16_t)3.95117188f, (float16_t)-2.77929688f, + (float16_t)2.75390625f, (float16_t)3.39453125f, (float16_t)3.32617188f, (float16_t)3.078125f, + (float16_t)-3.05273438f, (float16_t)1.90625f, (float16_t)1.81640625f, (float16_t)1.43554688f, + (float16_t)3.08984375f, (float16_t)-0.396972656f, (float16_t)1.13476562f, (float16_t)1.40234375f, + (float16_t)2.18164062f, (float16_t)-1.69433594f, (float16_t)-1.94238281f, (float16_t)-0.142700195f, + (float16_t)1.20214844f, (float16_t)3.95117188f, (float16_t)0.266357422f, (float16_t)3.64648438f, + (float16_t)-2.47851562f, (float16_t)-3.03515625f, (float16_t)-3.484375f, (float16_t)-0.184936523f, + (float16_t)-2.48242188f, (float16_t)-1.13867188f, (float16_t)-3.05859375f, (float16_t)1.1953125f, + (float16_t)2.953125f, (float16_t)-3.74023438f, (float16_t)-0.716308594f, (float16_t)2.32617188f, + (float16_t)-2.64257812f, (float16_t)1.71875f, (float16_t)-0.776367188f, (float16_t)0.932128906f, + (float16_t)-2.19726562f, (float16_t)1.42382812f, (float16_t)-3.81054688f, (float16_t)3.421875f, + (float16_t)0.434082031f, (float16_t)-1.45507812f, (float16_t)2.60742188f, (float16_t)-3.234375f, + (float16_t)2.19726562f, (float16_t)3.48242188f, (float16_t)2.58984375f, (float16_t)3.86132812f, + (float16_t)2.10742188f, (float16_t)1.66210938f, (float16_t)-0.0787353516f, (float16_t)-0.138427734f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/config_data.h new file mode 100644 index 00000000..6187433b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_3DIM2_F32_NUM_DIMS 3 +#define TRANSPOSE_3DIM2_F32_INPUT_N 5 +#define TRANSPOSE_3DIM2_F32_INPUT_H 4 +#define TRANSPOSE_3DIM2_F32_INPUT_W 20 +#define TRANSPOSE_3DIM2_F32_INPUT_C 1 +#define TRANSPOSE_3DIM2_F32_OUTPUT_N 4 +#define TRANSPOSE_3DIM2_F32_OUTPUT_H 20 +#define TRANSPOSE_3DIM2_F32_OUTPUT_W 5 +#define TRANSPOSE_3DIM2_F32_OUTPUT_C 1 +#define TRANSPOSE_3DIM2_F32_PERM_0 1 +#define TRANSPOSE_3DIM2_F32_PERM_1 2 +#define TRANSPOSE_3DIM2_F32_PERM_2 0 +#define TRANSPOSE_3DIM2_F32_PERM_3 3 +#define TRANSPOSE_3DIM2_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_3DIM2_F32_SIZE 400 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/input_tensor_data.h new file mode 100644 index 00000000..50d8bcdf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/input_tensor_data.h @@ -0,0 +1,60 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_3dim2_f32_input_tensor[400] = { + -2.36721349f, -2.06731868f, -0.962865114f, -1.03554618f, -3.12117839f, -3.57183146f, 0.0662412196f, + -2.48399282f, -1.1302079f, 2.85221362f, 0.693226099f, -2.19009805f, -1.8229866f, -2.66106224f, + 1.34327805f, -0.567150712f, -0.950093985f, 1.01293004f, -3.09053731f, -1.53105974f, -1.98219442f, + -0.0455532856f, -2.0441308f, -2.23871732f, -3.18360162f, 3.88304567f, 1.71959436f, 2.66489601f, + 3.79179287f, 3.52211928f, -0.752259612f, 0.969504237f, 1.28875494f, 0.87826246f, -1.35828662f, + 3.39414525f, 1.66849375f, 2.4477787f, 0.921465456f, -1.27533495f, 0.7222175f, 2.60827065f, + -2.99610615f, 0.880812407f, -2.57179499f, 3.17854762f, 1.32231557f, 0.0615116842f, -2.16920495f, + -0.90941304f, -0.690868437f, 3.13686562f, -1.12952685f, 2.22102976f, -0.255130619f, 2.12162852f, + 2.73849392f, -0.298376232f, -3.40580368f, -2.37680149f, 2.72341752f, 0.624380231f, 3.03121853f, + -0.817884982f, 1.47399545f, -2.46961975f, -0.610474408f, -3.73674035f, -0.227221146f, -0.0898260102f, + 0.555719078f, -1.41745758f, 1.33890879f, 0.973159492f, 3.72771478f, -0.216285795f, 1.31443763f, + 0.888924897f, -1.38094866f, 1.17300165f, -1.59791958f, -2.02219319f, 0.983003139f, -0.624149203f, + 3.61103892f, 3.99186802f, -1.28401697f, 3.56520414f, 1.81015646f, 1.93266678f, 3.61069822f, + 3.51127481f, -0.199827924f, 2.19029307f, -0.703149319f, -0.0331225246f, -1.02979696f, 0.96579951f, + 0.129036322f, 2.34121943f, 3.43808055f, 0.837438881f, 1.74579382f, 1.14424849f, -0.852301359f, + -0.601938665f, -1.23167968f, 3.80491042f, 1.49640155f, -0.54840076f, 3.90506101f, 0.380133897f, + 1.80413067f, 0.113637291f, -0.358752191f, -1.38470399f, -0.528936327f, -1.82863808f, 1.91577244f, + 1.83754623f, 1.86975753f, 1.92931247f, -2.76005793f, 2.48004365f, 3.95942903f, 3.32421994f, + 3.44184184f, -0.726811409f, 2.654778f, -2.15425467f, -0.724731207f, 2.63676333f, -3.27350807f, + 2.51231146f, 0.642629981f, -2.76652455f, 0.267314106f, 3.61058807f, -1.39547992f, 0.598253787f, + 2.63248634f, -2.45157027f, -3.6273911f, 3.75035524f, -2.97705412f, -3.58974147f, -1.97448301f, + 3.10546851f, 1.81610608f, 1.30714488f, -3.08842897f, -0.704986811f, 0.929741979f, 0.507600069f, + 2.55975223f, -3.23066235f, 2.4545207f, 3.25976801f, -2.43764973f, 0.937467396f, -2.27391958f, + 1.65145516f, -2.51604223f, -0.404243827f, -2.44378781f, -1.88185239f, -0.622983456f, 1.20446754f, + -0.294576377f, -0.0792458281f, 2.90380931f, -1.28315711f, -3.83038735f, -1.47584426f, -1.57605195f, + -0.725776374f, -2.88716459f, 1.44723916f, 2.06899047f, -0.4930363f, 3.16805458f, -3.9367137f, + 1.60523987f, -0.395775914f, -0.170226529f, 3.37279058f, -1.41407418f, -2.21906424f, -0.0681484565f, + 0.684212983f, -2.32590699f, 2.91453004f, -1.10218298f, 2.8265605f, -0.18171069f, -0.792463779f, + 2.88953567f, 1.60097897f, -0.950953305f, 0.500855684f, -1.68206799f, 3.46111989f, 3.95834351f, + -3.96304178f, 1.18222451f, -0.202634633f, 1.01693845f, -3.02750587f, 0.5042454f, -0.969670832f, + 3.3761003f, 2.47470999f, -3.60854387f, -3.20260072f, -3.92749977f, 3.8275547f, -0.785562754f, + -2.56302142f, 1.31550169f, 2.89787602f, -0.432656825f, 3.92785954f, 2.88036442f, -2.51500273f, + -3.69577146f, -0.393397391f, 0.798625231f, 0.106771149f, -1.60109758f, 3.37084866f, -2.51955414f, + 2.68004704f, -3.30688572f, 1.47569358f, -0.790301979f, 0.23342894f, 0.872805834f, 2.37339091f, + -2.46851873f, 3.35720992f, -3.33364534f, 2.71016765f, -2.20966959f, -2.58680153f, -1.31614232f, + -2.45648932f, 0.852795184f, 3.28836107f, -1.80856085f, -1.17945266f, -3.27398801f, -2.49994802f, + 3.52614474f, -1.69637752f, 1.7444222f, -2.18600678f, 1.80205643f, -2.0553658f, -2.00870681f, + -1.59297168f, -3.61818027f, 0.645714104f, -0.593883038f, 1.94583011f, -2.54734063f, 1.69149232f, + -3.56075573f, 1.43539786f, 3.30141234f, -0.843286276f, 2.67421126f, 0.377195477f, 2.10417461f, + -0.239783943f, -0.0507148989f, 0.361675739f, -1.10321939f, 1.12641692f, 2.08575702f, 0.618734717f, + -3.43859005f, -0.635878682f, 2.42248321f, 3.60704446f, 3.42926121f, -0.766977012f, -0.845973313f, + -1.53280389f, 2.23810339f, -1.2069757f, 1.08533299f, -2.32578397f, 1.95894396f, 1.19390273f, + 0.399990499f, -2.53076839f, 3.52402329f, 0.545060754f, 1.15115356f, 1.87868643f, 3.03228998f, + -3.99366379f, 1.40229321f, -3.63477302f, -2.16951418f, -3.22590137f, 0.379687816f, 3.18330407f, + -0.462050229f, 0.0531331226f, 3.66199899f, 1.98098803f, 3.02598596f, -2.40909505f, 2.58498907f, + 2.1412828f, 3.36024094f, 2.14692545f, 1.29822862f, -3.54707837f, 2.11212015f, -2.09063578f, + 3.31787443f, 2.44088578f, 1.46000004f, 2.20451379f, -0.220140055f, -1.44301069f, 1.9174794f, + 0.470627338f, -1.10341203f, -3.27953577f, -0.213962555f, -3.06174541f, -2.48091149f, -2.95279145f, + -0.630485058f, 2.19614458f, -3.54455423f, -0.0528769903f, 0.592184901f, 0.594545484f, -3.57782459f, + -2.4279058f, 0.65996027f, -1.61327541f, -2.56732941f, 0.0385650657f, 2.71136236f, -0.093044281f, + 3.55579257f, 0.722985685f, 2.44067979f, 3.61256027f, -0.159272864f, -2.56844759f, 3.41449547f, + -1.65032589f, 3.63121414f, -1.87954462f, -3.75095201f, 0.597339809f, 3.91187263f, -0.379242331f, + -0.746565461f, 0.0245444067f, 1.75989962f, -1.29680276f, 3.23021817f, 0.469348371f, 3.10420609f, + 3.78621078f, 3.16676569f, 1.08406985f, -2.02146459f, -1.60986447f, 2.21708393f, 3.99728584f, + 0.905933619f, 1.24281609f, 0.704904497f, 0.338121682f, -1.96472895f, 1.80775285f, 3.19294f, + -2.76322055f, 0.0878947675f, 1.87742209f, 3.10222459f, 3.37639713f, -2.50856662f, -2.88344479f, + -3.14327216f, 0.522866905f, -0.55140835f, -3.51049423f, 2.90262723f, -0.388978302f, -0.0806924105f, + 3.76387835f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/output_ref_data.h new file mode 100644 index 00000000..20c6d73a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/output_ref_data.h @@ -0,0 +1,60 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_3dim2_f32_output_ref[400] = { + -2.36721349f, -1.59791958f, -2.27391958f, -3.33364534f, 2.11212015f, -2.06731868f, -2.02219319f, + 1.65145516f, 2.71016765f, -2.09063578f, -0.962865114f, 0.983003139f, -2.51604223f, -2.20966959f, + 3.31787443f, -1.03554618f, -0.624149203f, -0.404243827f, -2.58680153f, 2.44088578f, -3.12117839f, + 3.61103892f, -2.44378781f, -1.31614232f, 1.46000004f, -3.57183146f, 3.99186802f, -1.88185239f, + -2.45648932f, 2.20451379f, 0.0662412196f, -1.28401697f, -0.622983456f, 0.852795184f, -0.220140055f, + -2.48399282f, 3.56520414f, 1.20446754f, 3.28836107f, -1.44301069f, -1.1302079f, 1.81015646f, + -0.294576377f, -1.80856085f, 1.9174794f, 2.85221362f, 1.93266678f, -0.0792458281f, -1.17945266f, + 0.470627338f, 0.693226099f, 3.61069822f, 2.90380931f, -3.27398801f, -1.10341203f, -2.19009805f, + 3.51127481f, -1.28315711f, -2.49994802f, -3.27953577f, -1.8229866f, -0.199827924f, -3.83038735f, + 3.52614474f, -0.213962555f, -2.66106224f, 2.19029307f, -1.47584426f, -1.69637752f, -3.06174541f, + 1.34327805f, -0.703149319f, -1.57605195f, 1.7444222f, -2.48091149f, -0.567150712f, -0.0331225246f, + -0.725776374f, -2.18600678f, -2.95279145f, -0.950093985f, -1.02979696f, -2.88716459f, 1.80205643f, + -0.630485058f, 1.01293004f, 0.96579951f, 1.44723916f, -2.0553658f, 2.19614458f, -3.09053731f, + 0.129036322f, 2.06899047f, -2.00870681f, -3.54455423f, -1.53105974f, 2.34121943f, -0.4930363f, + -1.59297168f, -0.0528769903f, -1.98219442f, 3.43808055f, 3.16805458f, -3.61818027f, 0.592184901f, + -0.0455532856f, 0.837438881f, -3.9367137f, 0.645714104f, 0.594545484f, -2.0441308f, 1.74579382f, + 1.60523987f, -0.593883038f, -3.57782459f, -2.23871732f, 1.14424849f, -0.395775914f, 1.94583011f, + -2.4279058f, -3.18360162f, -0.852301359f, -0.170226529f, -2.54734063f, 0.65996027f, 3.88304567f, + -0.601938665f, 3.37279058f, 1.69149232f, -1.61327541f, 1.71959436f, -1.23167968f, -1.41407418f, + -3.56075573f, -2.56732941f, 2.66489601f, 3.80491042f, -2.21906424f, 1.43539786f, 0.0385650657f, + 3.79179287f, 1.49640155f, -0.0681484565f, 3.30141234f, 2.71136236f, 3.52211928f, -0.54840076f, + 0.684212983f, -0.843286276f, -0.093044281f, -0.752259612f, 3.90506101f, -2.32590699f, 2.67421126f, + 3.55579257f, 0.969504237f, 0.380133897f, 2.91453004f, 0.377195477f, 0.722985685f, 1.28875494f, + 1.80413067f, -1.10218298f, 2.10417461f, 2.44067979f, 0.87826246f, 0.113637291f, 2.8265605f, + -0.239783943f, 3.61256027f, -1.35828662f, -0.358752191f, -0.18171069f, -0.0507148989f, -0.159272864f, + 3.39414525f, -1.38470399f, -0.792463779f, 0.361675739f, -2.56844759f, 1.66849375f, -0.528936327f, + 2.88953567f, -1.10321939f, 3.41449547f, 2.4477787f, -1.82863808f, 1.60097897f, 1.12641692f, + -1.65032589f, 0.921465456f, 1.91577244f, -0.950953305f, 2.08575702f, 3.63121414f, -1.27533495f, + 1.83754623f, 0.500855684f, 0.618734717f, -1.87954462f, 0.7222175f, 1.86975753f, -1.68206799f, + -3.43859005f, -3.75095201f, 2.60827065f, 1.92931247f, 3.46111989f, -0.635878682f, 0.597339809f, + -2.99610615f, -2.76005793f, 3.95834351f, 2.42248321f, 3.91187263f, 0.880812407f, 2.48004365f, + -3.96304178f, 3.60704446f, -0.379242331f, -2.57179499f, 3.95942903f, 1.18222451f, 3.42926121f, + -0.746565461f, 3.17854762f, 3.32421994f, -0.202634633f, -0.766977012f, 0.0245444067f, 1.32231557f, + 3.44184184f, 1.01693845f, -0.845973313f, 1.75989962f, 0.0615116842f, -0.726811409f, -3.02750587f, + -1.53280389f, -1.29680276f, -2.16920495f, 2.654778f, 0.5042454f, 2.23810339f, 3.23021817f, + -0.90941304f, -2.15425467f, -0.969670832f, -1.2069757f, 0.469348371f, -0.690868437f, -0.724731207f, + 3.3761003f, 1.08533299f, 3.10420609f, 3.13686562f, 2.63676333f, 2.47470999f, -2.32578397f, + 3.78621078f, -1.12952685f, -3.27350807f, -3.60854387f, 1.95894396f, 3.16676569f, 2.22102976f, + 2.51231146f, -3.20260072f, 1.19390273f, 1.08406985f, -0.255130619f, 0.642629981f, -3.92749977f, + 0.399990499f, -2.02146459f, 2.12162852f, -2.76652455f, 3.8275547f, -2.53076839f, -1.60986447f, + 2.73849392f, 0.267314106f, -0.785562754f, 3.52402329f, 2.21708393f, -0.298376232f, 3.61058807f, + -2.56302142f, 0.545060754f, 3.99728584f, -3.40580368f, -1.39547992f, 1.31550169f, 1.15115356f, + 0.905933619f, -2.37680149f, 0.598253787f, 2.89787602f, 1.87868643f, 1.24281609f, 2.72341752f, + 2.63248634f, -0.432656825f, 3.03228998f, 0.704904497f, 0.624380231f, -2.45157027f, 3.92785954f, + -3.99366379f, 0.338121682f, 3.03121853f, -3.6273911f, 2.88036442f, 1.40229321f, -1.96472895f, + -0.817884982f, 3.75035524f, -2.51500273f, -3.63477302f, 1.80775285f, 1.47399545f, -2.97705412f, + -3.69577146f, -2.16951418f, 3.19294f, -2.46961975f, -3.58974147f, -0.393397391f, -3.22590137f, + -2.76322055f, -0.610474408f, -1.97448301f, 0.798625231f, 0.379687816f, 0.0878947675f, -3.73674035f, + 3.10546851f, 0.106771149f, 3.18330407f, 1.87742209f, -0.227221146f, 1.81610608f, -1.60109758f, + -0.462050229f, 3.10222459f, -0.0898260102f, 1.30714488f, 3.37084866f, 0.0531331226f, 3.37639713f, + 0.555719078f, -3.08842897f, -2.51955414f, 3.66199899f, -2.50856662f, -1.41745758f, -0.704986811f, + 2.68004704f, 1.98098803f, -2.88344479f, 1.33890879f, 0.929741979f, -3.30688572f, 3.02598596f, + -3.14327216f, 0.973159492f, 0.507600069f, 1.47569358f, -2.40909505f, 0.522866905f, 3.72771478f, + 2.55975223f, -0.790301979f, 2.58498907f, -0.55140835f, -0.216285795f, -3.23066235f, 0.23342894f, + 2.1412828f, -3.51049423f, 1.31443763f, 2.4545207f, 0.872805834f, 3.36024094f, 2.90262723f, + 0.888924897f, 3.25976801f, 2.37339091f, 2.14692545f, -0.388978302f, -1.38094866f, -2.43764973f, + -2.46851873f, 1.29822862f, -0.0806924105f, 1.17300165f, 0.937467396f, 3.35720992f, -3.54707837f, + 3.76387835f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim2_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/config_data.h new file mode 100644 index 00000000..08794b35 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_3DIM_F16_NUM_DIMS 3 +#define TRANSPOSE_3DIM_F16_INPUT_N 5 +#define TRANSPOSE_3DIM_F16_INPUT_H 4 +#define TRANSPOSE_3DIM_F16_INPUT_W 20 +#define TRANSPOSE_3DIM_F16_INPUT_C 1 +#define TRANSPOSE_3DIM_F16_OUTPUT_N 5 +#define TRANSPOSE_3DIM_F16_OUTPUT_H 20 +#define TRANSPOSE_3DIM_F16_OUTPUT_W 4 +#define TRANSPOSE_3DIM_F16_OUTPUT_C 1 +#define TRANSPOSE_3DIM_F16_PERM_0 0 +#define TRANSPOSE_3DIM_F16_PERM_1 2 +#define TRANSPOSE_3DIM_F16_PERM_2 1 +#define TRANSPOSE_3DIM_F16_PERM_3 3 +#define TRANSPOSE_3DIM_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_3DIM_F16_SIZE 400 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/input_tensor_data.h new file mode 100644 index 00000000..a1c4a459 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/input_tensor_data.h @@ -0,0 +1,102 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_3dim_f16_input_tensor[400] = { + (float16_t)1.12011719f, (float16_t)2.86328125f, (float16_t)0.375976562f, (float16_t)3.37304688f, + (float16_t)-1.32714844f, (float16_t)-1.00488281f, (float16_t)-1.89257812f, (float16_t)1.94824219f, + (float16_t)3.58398438f, (float16_t)0.9375f, (float16_t)-1.33691406f, (float16_t)-2.15820312f, + (float16_t)0.254882812f, (float16_t)2.34179688f, (float16_t)3.31835938f, (float16_t)2.953125f, + (float16_t)-2.17578125f, (float16_t)3.375f, (float16_t)-0.0257415771f, (float16_t)2.59179688f, + (float16_t)-1.52050781f, (float16_t)-2.38476562f, (float16_t)3.2109375f, (float16_t)0.768554688f, + (float16_t)-1.95800781f, (float16_t)1.24023438f, (float16_t)-1.18652344f, (float16_t)0.65625f, + (float16_t)3.76757812f, (float16_t)1.2265625f, (float16_t)3.33007812f, (float16_t)-2.8984375f, + (float16_t)1.11621094f, (float16_t)3.08007812f, (float16_t)-3.01953125f, (float16_t)-0.62109375f, + (float16_t)-3.82617188f, (float16_t)-1.08496094f, (float16_t)2.19726562f, (float16_t)1.96875f, + (float16_t)2.04882812f, (float16_t)3.82421875f, (float16_t)1.32128906f, (float16_t)-3.72460938f, + (float16_t)-2.29101562f, (float16_t)2.31054688f, (float16_t)-2.30078125f, (float16_t)3.62304688f, + (float16_t)-1.79296875f, (float16_t)2.609375f, (float16_t)2.37109375f, (float16_t)3.34570312f, + (float16_t)1.72753906f, (float16_t)1.82910156f, (float16_t)3.65820312f, (float16_t)-3.7578125f, + (float16_t)3.11328125f, (float16_t)-0.484619141f, (float16_t)0.501953125f, (float16_t)0.049041748f, + (float16_t)3.390625f, (float16_t)0.450927734f, (float16_t)2.61914062f, (float16_t)-2.17382812f, + (float16_t)-3.83984375f, (float16_t)3.73828125f, (float16_t)2.03515625f, (float16_t)1.09082031f, + (float16_t)1.31445312f, (float16_t)1.90136719f, (float16_t)3.52929688f, (float16_t)0.81640625f, + (float16_t)1.17382812f, (float16_t)-0.498046875f, (float16_t)0.244628906f, (float16_t)-3.04492188f, + (float16_t)1.72949219f, (float16_t)-0.245849609f, (float16_t)1.51367188f, (float16_t)-3.43164062f, + (float16_t)0.24230957f, (float16_t)-0.951171875f, (float16_t)-3.45117188f, (float16_t)-2.79882812f, + (float16_t)-0.872070312f, (float16_t)1.93261719f, (float16_t)2.43554688f, (float16_t)-0.567871094f, + (float16_t)-1.40332031f, (float16_t)-2.765625f, (float16_t)3.57617188f, (float16_t)3.52539062f, + (float16_t)-2.2734375f, (float16_t)-0.9765625f, (float16_t)-0.5078125f, (float16_t)3.08789062f, + (float16_t)-1.41699219f, (float16_t)3.59765625f, (float16_t)2.12109375f, (float16_t)2.77929688f, + (float16_t)-3.81445312f, (float16_t)3.31054688f, (float16_t)-3.80859375f, (float16_t)-2.578125f, + (float16_t)-3.375f, (float16_t)-3.30273438f, (float16_t)3.34179688f, (float16_t)2.8828125f, + (float16_t)-0.810546875f, (float16_t)-0.655761719f, (float16_t)-0.992675781f, (float16_t)3.46484375f, + (float16_t)3.359375f, (float16_t)3.94921875f, (float16_t)-2.88476562f, (float16_t)0.672363281f, + (float16_t)-3.62109375f, (float16_t)3.55273438f, (float16_t)3.1796875f, (float16_t)-1.53417969f, + (float16_t)-2.85546875f, (float16_t)3.55859375f, (float16_t)-2.24414062f, (float16_t)-0.670410156f, + (float16_t)-1.26171875f, (float16_t)-0.148071289f, (float16_t)-1.7890625f, (float16_t)-1.2265625f, + (float16_t)2.203125f, (float16_t)3.09375f, (float16_t)0.330322266f, (float16_t)-3.67578125f, + (float16_t)-0.770507812f, (float16_t)1.203125f, (float16_t)3.2890625f, (float16_t)1.23242188f, + (float16_t)0.976074219f, (float16_t)-1.72167969f, (float16_t)0.938476562f, (float16_t)-0.842773438f, + (float16_t)1.80078125f, (float16_t)-3.39648438f, (float16_t)-0.120727539f, (float16_t)-1.55664062f, + (float16_t)-3.47070312f, (float16_t)3.24023438f, (float16_t)-3.47460938f, (float16_t)-1.43359375f, + (float16_t)3.00195312f, (float16_t)-0.668945312f, (float16_t)-3.3125f, (float16_t)-2.28515625f, + (float16_t)-0.60546875f, (float16_t)-3.13085938f, (float16_t)-3.86328125f, (float16_t)0.292724609f, + (float16_t)0.570800781f, (float16_t)2.0078125f, (float16_t)-0.627441406f, (float16_t)0.532226562f, + (float16_t)-0.934082031f, (float16_t)1.02539062f, (float16_t)-3.421875f, (float16_t)-1.66992188f, + (float16_t)3.87304688f, (float16_t)-0.666015625f, (float16_t)3.26367188f, (float16_t)2.96875f, + (float16_t)-3.10546875f, (float16_t)-3.16210938f, (float16_t)0.996582031f, (float16_t)1.11035156f, + (float16_t)-2.40039062f, (float16_t)2.79882812f, (float16_t)-2.84179688f, (float16_t)-2.09179688f, + (float16_t)3.53515625f, (float16_t)-3.13476562f, (float16_t)3.77734375f, (float16_t)1.22558594f, + (float16_t)-0.809570312f, (float16_t)-0.75f, (float16_t)-0.927246094f, (float16_t)-1.35253906f, + (float16_t)0.837402344f, (float16_t)-0.573730469f, (float16_t)1.21972656f, (float16_t)-2.74023438f, + (float16_t)1.90527344f, (float16_t)0.143066406f, (float16_t)-2.4296875f, (float16_t)0.192504883f, + (float16_t)2.796875f, (float16_t)-1.56640625f, (float16_t)-0.210205078f, (float16_t)-3.703125f, + (float16_t)-3.99414062f, (float16_t)0.457275391f, (float16_t)2.77148438f, (float16_t)-3.37695312f, + (float16_t)-1.85839844f, (float16_t)1.38867188f, (float16_t)0.393554688f, (float16_t)-2.15234375f, + (float16_t)-2.1171875f, (float16_t)3.65820312f, (float16_t)-0.536132812f, (float16_t)2.75390625f, + (float16_t)-0.348632812f, (float16_t)-3.77929688f, (float16_t)0.286865234f, (float16_t)-1.09179688f, + (float16_t)1.13867188f, (float16_t)-1.29003906f, (float16_t)2.22265625f, (float16_t)1.56054688f, + (float16_t)3.20507812f, (float16_t)0.683105469f, (float16_t)-3.03125f, (float16_t)-0.9765625f, + (float16_t)3.10742188f, (float16_t)3.50585938f, (float16_t)2.70117188f, (float16_t)0.740234375f, + (float16_t)-1.58496094f, (float16_t)-2.80859375f, (float16_t)-2.95703125f, (float16_t)-2.98632812f, + (float16_t)-1.49609375f, (float16_t)-0.515136719f, (float16_t)-2.36132812f, (float16_t)0.166992188f, + (float16_t)-3.83789062f, (float16_t)-3.875f, (float16_t)0.321533203f, (float16_t)2.67773438f, + (float16_t)1.91894531f, (float16_t)3.23632812f, (float16_t)-3.3828125f, (float16_t)-0.328613281f, + (float16_t)1.375f, (float16_t)-2.49804688f, (float16_t)3.36328125f, (float16_t)-2.8359375f, + (float16_t)-0.966796875f, (float16_t)2.79101562f, (float16_t)2.234375f, (float16_t)2.72460938f, + (float16_t)-0.0880737305f, (float16_t)-2.99804688f, (float16_t)3.23046875f, (float16_t)3.59375f, + (float16_t)3.5859375f, (float16_t)0.208007812f, (float16_t)-0.841308594f, (float16_t)-2.91601562f, + (float16_t)-2.51953125f, (float16_t)-2.75f, (float16_t)-1.74121094f, (float16_t)-1.54980469f, + (float16_t)0.575195312f, (float16_t)-3.80273438f, (float16_t)-0.473388672f, (float16_t)0.717773438f, + (float16_t)3.921875f, (float16_t)0.331054688f, (float16_t)0.844238281f, (float16_t)-0.536621094f, + (float16_t)0.834472656f, (float16_t)2.08984375f, (float16_t)-1.59863281f, (float16_t)-1.81152344f, + (float16_t)-0.981933594f, (float16_t)-2.84179688f, (float16_t)-3.33789062f, (float16_t)-2.10351562f, + (float16_t)-0.801757812f, (float16_t)-0.326904297f, (float16_t)-2.9375f, (float16_t)-0.877929688f, + (float16_t)1.40820312f, (float16_t)-2.24609375f, (float16_t)-2.98828125f, (float16_t)-2.59179688f, + (float16_t)-0.900390625f, (float16_t)-1.77050781f, (float16_t)1.30371094f, (float16_t)-2.49609375f, + (float16_t)-3.765625f, (float16_t)-3.6796875f, (float16_t)-0.643554688f, (float16_t)2.67773438f, + (float16_t)3.05078125f, (float16_t)-0.16796875f, (float16_t)-0.983886719f, (float16_t)-2.578125f, + (float16_t)-2.50195312f, (float16_t)-3.30078125f, (float16_t)-2.16992188f, (float16_t)1.38085938f, + (float16_t)2.14648438f, (float16_t)3.70507812f, (float16_t)2.28320312f, (float16_t)-0.261962891f, + (float16_t)-1.54101562f, (float16_t)-3.17382812f, (float16_t)2.01757812f, (float16_t)-2.51367188f, + (float16_t)-0.767089844f, (float16_t)-2.29101562f, (float16_t)-1.49804688f, (float16_t)1.05957031f, + (float16_t)1.91113281f, (float16_t)3.74414062f, (float16_t)-2.40429688f, (float16_t)-0.965332031f, + (float16_t)-0.927246094f, (float16_t)-0.609375f, (float16_t)-1.05371094f, (float16_t)-1.50195312f, + (float16_t)0.825195312f, (float16_t)-0.659179688f, (float16_t)-3.7734375f, (float16_t)2.9453125f, + (float16_t)-2.05664062f, (float16_t)0.901855469f, (float16_t)2.6875f, (float16_t)-0.890136719f, + (float16_t)1.71679688f, (float16_t)-1.17675781f, (float16_t)-0.8515625f, (float16_t)1.18945312f, + (float16_t)-2.98828125f, (float16_t)1.03808594f, (float16_t)-1.53710938f, (float16_t)-2.74804688f, + (float16_t)2.58203125f, (float16_t)-1.65527344f, (float16_t)2.23828125f, (float16_t)3.88476562f, + (float16_t)-1.46777344f, (float16_t)2.37695312f, (float16_t)-0.510742188f, (float16_t)1.13671875f, + (float16_t)1.46484375f, (float16_t)1.16113281f, (float16_t)3.26757812f, (float16_t)-0.910644531f, + (float16_t)-0.712402344f, (float16_t)-1.23632812f, (float16_t)-2.34960938f, (float16_t)-1.62304688f, + (float16_t)-2.90429688f, (float16_t)-2.23632812f, (float16_t)-1.77734375f, (float16_t)-2.15820312f, + (float16_t)-0.814941406f, (float16_t)0.161010742f, (float16_t)0.383544922f, (float16_t)-3.10546875f, + (float16_t)3.5859375f, (float16_t)2.49023438f, (float16_t)1.65917969f, (float16_t)-1.20996094f, + (float16_t)-1.35449219f, (float16_t)1.84570312f, (float16_t)-2.89453125f, (float16_t)1.68945312f, + (float16_t)-0.602539062f, (float16_t)3.04101562f, (float16_t)1.85546875f, (float16_t)2.61328125f, + (float16_t)-1.11523438f, (float16_t)-0.236450195f, (float16_t)2.24414062f, (float16_t)0.166625977f, + (float16_t)-1.48730469f, (float16_t)-2.96289062f, (float16_t)-2.03710938f, (float16_t)3.56445312f, + (float16_t)2.77148438f, (float16_t)-2.46679688f, (float16_t)1.91699219f, (float16_t)3.08789062f, + (float16_t)0.94921875f, (float16_t)3.87304688f, (float16_t)-3.88085938f, (float16_t)-2.30859375f, + (float16_t)-3.63671875f, (float16_t)-1.6484375f, (float16_t)-2.82617188f, (float16_t)-2.04296875f, + (float16_t)3.81640625f, (float16_t)-1.29785156f, (float16_t)0.331787109f, (float16_t)1.42871094f, + (float16_t)-0.0761108398f, (float16_t)2.6640625f, (float16_t)-1.54394531f, (float16_t)3.54101562f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/output_ref_data.h new file mode 100644 index 00000000..2b44272c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/output_ref_data.h @@ -0,0 +1,102 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_3dim_f16_output_ref[400] = { + (float16_t)1.12011719f, (float16_t)-1.52050781f, (float16_t)2.04882812f, (float16_t)3.390625f, + (float16_t)2.86328125f, (float16_t)-2.38476562f, (float16_t)3.82421875f, (float16_t)0.450927734f, + (float16_t)0.375976562f, (float16_t)3.2109375f, (float16_t)1.32128906f, (float16_t)2.61914062f, + (float16_t)3.37304688f, (float16_t)0.768554688f, (float16_t)-3.72460938f, (float16_t)-2.17382812f, + (float16_t)-1.32714844f, (float16_t)-1.95800781f, (float16_t)-2.29101562f, (float16_t)-3.83984375f, + (float16_t)-1.00488281f, (float16_t)1.24023438f, (float16_t)2.31054688f, (float16_t)3.73828125f, + (float16_t)-1.89257812f, (float16_t)-1.18652344f, (float16_t)-2.30078125f, (float16_t)2.03515625f, + (float16_t)1.94824219f, (float16_t)0.65625f, (float16_t)3.62304688f, (float16_t)1.09082031f, + (float16_t)3.58398438f, (float16_t)3.76757812f, (float16_t)-1.79296875f, (float16_t)1.31445312f, + (float16_t)0.9375f, (float16_t)1.2265625f, (float16_t)2.609375f, (float16_t)1.90136719f, + (float16_t)-1.33691406f, (float16_t)3.33007812f, (float16_t)2.37109375f, (float16_t)3.52929688f, + (float16_t)-2.15820312f, (float16_t)-2.8984375f, (float16_t)3.34570312f, (float16_t)0.81640625f, + (float16_t)0.254882812f, (float16_t)1.11621094f, (float16_t)1.72753906f, (float16_t)1.17382812f, + (float16_t)2.34179688f, (float16_t)3.08007812f, (float16_t)1.82910156f, (float16_t)-0.498046875f, + (float16_t)3.31835938f, (float16_t)-3.01953125f, (float16_t)3.65820312f, (float16_t)0.244628906f, + (float16_t)2.953125f, (float16_t)-0.62109375f, (float16_t)-3.7578125f, (float16_t)-3.04492188f, + (float16_t)-2.17578125f, (float16_t)-3.82617188f, (float16_t)3.11328125f, (float16_t)1.72949219f, + (float16_t)3.375f, (float16_t)-1.08496094f, (float16_t)-0.484619141f, (float16_t)-0.245849609f, + (float16_t)-0.0257415771f, (float16_t)2.19726562f, (float16_t)0.501953125f, (float16_t)1.51367188f, + (float16_t)2.59179688f, (float16_t)1.96875f, (float16_t)0.049041748f, (float16_t)-3.43164062f, + (float16_t)0.24230957f, (float16_t)-3.81445312f, (float16_t)-2.85546875f, (float16_t)1.80078125f, + (float16_t)-0.951171875f, (float16_t)3.31054688f, (float16_t)3.55859375f, (float16_t)-3.39648438f, + (float16_t)-3.45117188f, (float16_t)-3.80859375f, (float16_t)-2.24414062f, (float16_t)-0.120727539f, + (float16_t)-2.79882812f, (float16_t)-2.578125f, (float16_t)-0.670410156f, (float16_t)-1.55664062f, + (float16_t)-0.872070312f, (float16_t)-3.375f, (float16_t)-1.26171875f, (float16_t)-3.47070312f, + (float16_t)1.93261719f, (float16_t)-3.30273438f, (float16_t)-0.148071289f, (float16_t)3.24023438f, + (float16_t)2.43554688f, (float16_t)3.34179688f, (float16_t)-1.7890625f, (float16_t)-3.47460938f, + (float16_t)-0.567871094f, (float16_t)2.8828125f, (float16_t)-1.2265625f, (float16_t)-1.43359375f, + (float16_t)-1.40332031f, (float16_t)-0.810546875f, (float16_t)2.203125f, (float16_t)3.00195312f, + (float16_t)-2.765625f, (float16_t)-0.655761719f, (float16_t)3.09375f, (float16_t)-0.668945312f, + (float16_t)3.57617188f, (float16_t)-0.992675781f, (float16_t)0.330322266f, (float16_t)-3.3125f, + (float16_t)3.52539062f, (float16_t)3.46484375f, (float16_t)-3.67578125f, (float16_t)-2.28515625f, + (float16_t)-2.2734375f, (float16_t)3.359375f, (float16_t)-0.770507812f, (float16_t)-0.60546875f, + (float16_t)-0.9765625f, (float16_t)3.94921875f, (float16_t)1.203125f, (float16_t)-3.13085938f, + (float16_t)-0.5078125f, (float16_t)-2.88476562f, (float16_t)3.2890625f, (float16_t)-3.86328125f, + (float16_t)3.08789062f, (float16_t)0.672363281f, (float16_t)1.23242188f, (float16_t)0.292724609f, + (float16_t)-1.41699219f, (float16_t)-3.62109375f, (float16_t)0.976074219f, (float16_t)0.570800781f, + (float16_t)3.59765625f, (float16_t)3.55273438f, (float16_t)-1.72167969f, (float16_t)2.0078125f, + (float16_t)2.12109375f, (float16_t)3.1796875f, (float16_t)0.938476562f, (float16_t)-0.627441406f, + (float16_t)2.77929688f, (float16_t)-1.53417969f, (float16_t)-0.842773438f, (float16_t)0.532226562f, + (float16_t)-0.934082031f, (float16_t)-0.809570312f, (float16_t)-1.85839844f, (float16_t)3.10742188f, + (float16_t)1.02539062f, (float16_t)-0.75f, (float16_t)1.38867188f, (float16_t)3.50585938f, + (float16_t)-3.421875f, (float16_t)-0.927246094f, (float16_t)0.393554688f, (float16_t)2.70117188f, + (float16_t)-1.66992188f, (float16_t)-1.35253906f, (float16_t)-2.15234375f, (float16_t)0.740234375f, + (float16_t)3.87304688f, (float16_t)0.837402344f, (float16_t)-2.1171875f, (float16_t)-1.58496094f, + (float16_t)-0.666015625f, (float16_t)-0.573730469f, (float16_t)3.65820312f, (float16_t)-2.80859375f, + (float16_t)3.26367188f, (float16_t)1.21972656f, (float16_t)-0.536132812f, (float16_t)-2.95703125f, + (float16_t)2.96875f, (float16_t)-2.74023438f, (float16_t)2.75390625f, (float16_t)-2.98632812f, + (float16_t)-3.10546875f, (float16_t)1.90527344f, (float16_t)-0.348632812f, (float16_t)-1.49609375f, + (float16_t)-3.16210938f, (float16_t)0.143066406f, (float16_t)-3.77929688f, (float16_t)-0.515136719f, + (float16_t)0.996582031f, (float16_t)-2.4296875f, (float16_t)0.286865234f, (float16_t)-2.36132812f, + (float16_t)1.11035156f, (float16_t)0.192504883f, (float16_t)-1.09179688f, (float16_t)0.166992188f, + (float16_t)-2.40039062f, (float16_t)2.796875f, (float16_t)1.13867188f, (float16_t)-3.83789062f, + (float16_t)2.79882812f, (float16_t)-1.56640625f, (float16_t)-1.29003906f, (float16_t)-3.875f, + (float16_t)-2.84179688f, (float16_t)-0.210205078f, (float16_t)2.22265625f, (float16_t)0.321533203f, + (float16_t)-2.09179688f, (float16_t)-3.703125f, (float16_t)1.56054688f, (float16_t)2.67773438f, + (float16_t)3.53515625f, (float16_t)-3.99414062f, (float16_t)3.20507812f, (float16_t)1.91894531f, + (float16_t)-3.13476562f, (float16_t)0.457275391f, (float16_t)0.683105469f, (float16_t)3.23632812f, + (float16_t)3.77734375f, (float16_t)2.77148438f, (float16_t)-3.03125f, (float16_t)-3.3828125f, + (float16_t)1.22558594f, (float16_t)-3.37695312f, (float16_t)-0.9765625f, (float16_t)-0.328613281f, + (float16_t)1.375f, (float16_t)0.575195312f, (float16_t)1.40820312f, (float16_t)2.14648438f, + (float16_t)-2.49804688f, (float16_t)-3.80273438f, (float16_t)-2.24609375f, (float16_t)3.70507812f, + (float16_t)3.36328125f, (float16_t)-0.473388672f, (float16_t)-2.98828125f, (float16_t)2.28320312f, + (float16_t)-2.8359375f, (float16_t)0.717773438f, (float16_t)-2.59179688f, (float16_t)-0.261962891f, + (float16_t)-0.966796875f, (float16_t)3.921875f, (float16_t)-0.900390625f, (float16_t)-1.54101562f, + (float16_t)2.79101562f, (float16_t)0.331054688f, (float16_t)-1.77050781f, (float16_t)-3.17382812f, + (float16_t)2.234375f, (float16_t)0.844238281f, (float16_t)1.30371094f, (float16_t)2.01757812f, + (float16_t)2.72460938f, (float16_t)-0.536621094f, (float16_t)-2.49609375f, (float16_t)-2.51367188f, + (float16_t)-0.0880737305f, (float16_t)0.834472656f, (float16_t)-3.765625f, (float16_t)-0.767089844f, + (float16_t)-2.99804688f, (float16_t)2.08984375f, (float16_t)-3.6796875f, (float16_t)-2.29101562f, + (float16_t)3.23046875f, (float16_t)-1.59863281f, (float16_t)-0.643554688f, (float16_t)-1.49804688f, + (float16_t)3.59375f, (float16_t)-1.81152344f, (float16_t)2.67773438f, (float16_t)1.05957031f, + (float16_t)3.5859375f, (float16_t)-0.981933594f, (float16_t)3.05078125f, (float16_t)1.91113281f, + (float16_t)0.208007812f, (float16_t)-2.84179688f, (float16_t)-0.16796875f, (float16_t)3.74414062f, + (float16_t)-0.841308594f, (float16_t)-3.33789062f, (float16_t)-0.983886719f, (float16_t)-2.40429688f, + (float16_t)-2.91601562f, (float16_t)-2.10351562f, (float16_t)-2.578125f, (float16_t)-0.965332031f, + (float16_t)-2.51953125f, (float16_t)-0.801757812f, (float16_t)-2.50195312f, (float16_t)-0.927246094f, + (float16_t)-2.75f, (float16_t)-0.326904297f, (float16_t)-3.30078125f, (float16_t)-0.609375f, + (float16_t)-1.74121094f, (float16_t)-2.9375f, (float16_t)-2.16992188f, (float16_t)-1.05371094f, + (float16_t)-1.54980469f, (float16_t)-0.877929688f, (float16_t)1.38085938f, (float16_t)-1.50195312f, + (float16_t)0.825195312f, (float16_t)-1.46777344f, (float16_t)3.5859375f, (float16_t)2.77148438f, + (float16_t)-0.659179688f, (float16_t)2.37695312f, (float16_t)2.49023438f, (float16_t)-2.46679688f, + (float16_t)-3.7734375f, (float16_t)-0.510742188f, (float16_t)1.65917969f, (float16_t)1.91699219f, + (float16_t)2.9453125f, (float16_t)1.13671875f, (float16_t)-1.20996094f, (float16_t)3.08789062f, + (float16_t)-2.05664062f, (float16_t)1.46484375f, (float16_t)-1.35449219f, (float16_t)0.94921875f, + (float16_t)0.901855469f, (float16_t)1.16113281f, (float16_t)1.84570312f, (float16_t)3.87304688f, + (float16_t)2.6875f, (float16_t)3.26757812f, (float16_t)-2.89453125f, (float16_t)-3.88085938f, + (float16_t)-0.890136719f, (float16_t)-0.910644531f, (float16_t)1.68945312f, (float16_t)-2.30859375f, + (float16_t)1.71679688f, (float16_t)-0.712402344f, (float16_t)-0.602539062f, (float16_t)-3.63671875f, + (float16_t)-1.17675781f, (float16_t)-1.23632812f, (float16_t)3.04101562f, (float16_t)-1.6484375f, + (float16_t)-0.8515625f, (float16_t)-2.34960938f, (float16_t)1.85546875f, (float16_t)-2.82617188f, + (float16_t)1.18945312f, (float16_t)-1.62304688f, (float16_t)2.61328125f, (float16_t)-2.04296875f, + (float16_t)-2.98828125f, (float16_t)-2.90429688f, (float16_t)-1.11523438f, (float16_t)3.81640625f, + (float16_t)1.03808594f, (float16_t)-2.23632812f, (float16_t)-0.236450195f, (float16_t)-1.29785156f, + (float16_t)-1.53710938f, (float16_t)-1.77734375f, (float16_t)2.24414062f, (float16_t)0.331787109f, + (float16_t)-2.74804688f, (float16_t)-2.15820312f, (float16_t)0.166625977f, (float16_t)1.42871094f, + (float16_t)2.58203125f, (float16_t)-0.814941406f, (float16_t)-1.48730469f, (float16_t)-0.0761108398f, + (float16_t)-1.65527344f, (float16_t)0.161010742f, (float16_t)-2.96289062f, (float16_t)2.6640625f, + (float16_t)2.23828125f, (float16_t)0.383544922f, (float16_t)-2.03710938f, (float16_t)-1.54394531f, + (float16_t)3.88476562f, (float16_t)-3.10546875f, (float16_t)3.56445312f, (float16_t)3.54101562f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/config_data.h new file mode 100644 index 00000000..3b511aed --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_3DIM_F32_NUM_DIMS 3 +#define TRANSPOSE_3DIM_F32_INPUT_N 5 +#define TRANSPOSE_3DIM_F32_INPUT_H 4 +#define TRANSPOSE_3DIM_F32_INPUT_W 20 +#define TRANSPOSE_3DIM_F32_INPUT_C 1 +#define TRANSPOSE_3DIM_F32_OUTPUT_N 5 +#define TRANSPOSE_3DIM_F32_OUTPUT_H 20 +#define TRANSPOSE_3DIM_F32_OUTPUT_W 4 +#define TRANSPOSE_3DIM_F32_OUTPUT_C 1 +#define TRANSPOSE_3DIM_F32_PERM_0 0 +#define TRANSPOSE_3DIM_F32_PERM_1 2 +#define TRANSPOSE_3DIM_F32_PERM_2 1 +#define TRANSPOSE_3DIM_F32_PERM_3 3 +#define TRANSPOSE_3DIM_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_3DIM_F32_SIZE 400 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/input_tensor_data.h new file mode 100644 index 00000000..a4a2e107 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/input_tensor_data.h @@ -0,0 +1,60 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_3dim_f32_input_tensor[400] = { + 1.22466362f, -0.707320929f, -1.55641401f, 0.758707285f, 2.52341843f, 2.65069008f, -1.52889156f, + 2.50975919f, 3.52185655f, -0.250298858f, -0.405010968f, -2.1753571f, 3.14269137f, 0.22654283f, + 0.787199736f, -2.6870544f, 2.09091592f, 2.55656743f, -3.07132435f, 2.9913249f, -0.396778882f, + 1.819242f, 1.90086353f, -0.488020986f, -1.88081706f, 2.84914136f, -3.72633767f, -2.79726553f, + 1.03112054f, -0.982185125f, -3.55050945f, 1.12651694f, 0.52148658f, -0.201117098f, -2.39741087f, + -3.79440236f, -1.56663013f, -0.216764778f, 0.874847472f, -2.8406527f, -2.20510817f, 0.94376421f, + -1.84565306f, -0.290237188f, -0.0259658583f, 2.07793713f, -3.0485096f, -0.0569472387f, 1.32804179f, + 1.81008101f, 0.885113776f, 0.0280812066f, 2.09819841f, 2.42624021f, 3.98022199f, -3.54584622f, + 1.01888037f, 1.60779953f, -3.27088976f, -3.23606205f, -1.22420037f, -0.114240788f, -1.31110334f, + 0.594539821f, 0.580082655f, -3.38571835f, 2.63072753f, 3.24228263f, 1.47730565f, 2.68251109f, + 1.80291736f, 3.44022584f, 2.13155508f, 0.187460661f, -2.8262825f, 3.76408052f, -0.289131403f, + 1.86742163f, -2.59754181f, 2.5136373f, 3.16901755f, 2.82593513f, -0.706178248f, -1.14200115f, + 1.8670727f, 3.27977824f, 2.88492417f, 2.58031726f, 2.48660326f, 3.95683098f, 3.92565584f, + 3.43024588f, 2.08881164f, 0.556327283f, 2.17064357f, 2.96018505f, -0.155414835f, -0.133942634f, + -1.68690896f, -0.457958758f, 2.28065395f, -3.00944662f, -3.57164907f, -3.91191483f, -2.93384409f, + 1.57459044f, -3.53003478f, 3.60204816f, 1.88747072f, -0.729956925f, 1.26174128f, 0.0685403645f, + -0.0570218824f, 2.64416075f, -2.39602709f, -0.901881754f, -3.69585752f, 2.39761162f, -3.89033341f, + -0.817369223f, 3.45626259f, 3.08145237f, -2.59272337f, -0.360361993f, -0.0518450029f, 0.571596801f, + -2.68133116f, -0.317718834f, -0.0814501569f, -3.29481125f, -2.41711044f, -1.20025229f, 1.26533723f, + -2.2974329f, 0.291992337f, 3.72383547f, -2.17430544f, 1.66409898f, -2.2937429f, -0.956764519f, + -0.485517621f, 3.27816725f, 0.406821191f, 3.27543163f, -0.755063474f, 2.1220243f, -2.30029106f, + 3.06377554f, -0.617016494f, 1.58477998f, -3.07143068f, 1.32607186f, 3.10447812f, -3.95985341f, + 0.59108144f, -2.787117f, -2.68683577f, -1.89092112f, 3.54498196f, 2.01602411f, 3.15114903f, + -1.33552063f, -3.14562821f, -1.06109488f, 0.307646483f, -1.69556606f, 1.58109379f, 3.11095119f, + 1.32746494f, -0.707198501f, -2.79761291f, -3.612396f, 0.994799614f, -3.78815246f, 2.98140264f, + -2.02370453f, 1.25259018f, -1.0392828f, -2.9291501f, 2.34388638f, 0.141456008f, -3.24126482f, + 2.56758523f, 1.77250409f, 1.00306404f, 1.96547496f, 0.362234771f, -0.40154776f, 0.794305265f, + -0.0215728134f, -0.572619855f, -3.46645522f, -2.75314045f, 1.44944477f, 3.6383388f, 3.483742f, + 3.46032453f, 1.71399117f, -1.21370697f, -2.52970862f, 0.645556211f, -0.475131482f, 0.189826801f, + 1.17759931f, 1.0793258f, -2.98420238f, -3.99324536f, 0.78988564f, -1.2546916f, -0.424735636f, + 1.87442172f, -0.95692867f, -3.32566452f, 0.264055997f, 2.26245308f, 2.0171237f, -0.545272708f, + 3.84565592f, 2.9491415f, 2.15675378f, 1.60246146f, -2.24794555f, 2.30572057f, 0.120730117f, + 0.686562598f, 3.46068168f, 2.01351738f, -3.09395933f, 3.769279f, -1.80690873f, -3.31049871f, + -1.61751902f, -1.46446884f, -3.88705015f, 3.50611734f, 0.423909009f, -1.50972795f, 2.12838316f, + -0.727065742f, 0.459073633f, -2.87099934f, -1.99888909f, -3.14510655f, 3.38719153f, -2.51384711f, + 0.623678803f, -2.9636929f, -2.68113995f, 1.45181072f, -2.71573782f, -0.231671989f, -3.47214055f, + 2.02778363f, -0.23239249f, -0.719110966f, 2.0566287f, 2.96909189f, -1.51481009f, -3.23919702f, + -2.15946817f, 0.643730223f, -1.18405056f, -0.689144731f, -3.77029634f, 0.0597862713f, 1.48473454f, + 3.97495747f, -1.60527551f, 1.00271249f, 1.61292028f, -3.73296118f, -2.92922068f, 1.72467172f, + -1.97479868f, -3.76779103f, -3.96722603f, 2.97159767f, 3.92108536f, -3.66853809f, -3.27602577f, + 1.09912741f, 0.170946732f, -2.73018742f, -3.97049618f, -0.0899690688f, -0.891321957f, -3.83436394f, + 2.75470877f, 2.51812983f, -2.94556141f, 0.896928012f, -0.690783381f, 1.12545431f, -1.64713836f, + -1.35185373f, -2.72450137f, 3.45251584f, -2.2969327f, 3.3803401f, 2.54860377f, -2.06047344f, + -2.56545138f, -1.55980277f, 0.271474272f, -1.55073988f, -2.13182545f, -2.38300514f, 0.794500828f, + 1.41758549f, -2.46509981f, -1.77820766f, 3.31464338f, 0.467807502f, 3.86390638f, -1.39281714f, + -3.01441813f, 3.45864296f, 1.25395191f, 0.598968029f, 0.878599882f, -3.71174026f, 3.29032135f, + -2.9722805f, -2.64838767f, 3.14470887f, 2.8112154f, 0.101041965f, 0.419182509f, 1.54604053f, + 1.38805974f, -2.66913319f, -1.80166125f, 1.99545801f, -0.540147901f, -0.808188021f, 2.58347416f, + -2.18719649f, 0.257128865f, -2.53945565f, 0.838760674f, 0.183586612f, 0.33375293f, -1.77311265f, + -2.11140633f, -1.79718733f, -2.37245584f, 1.6237092f, -2.60933971f, -0.952655852f, 3.57220435f, + 3.96190643f, -2.09056568f, -0.597329259f, 3.8139081f, 0.148193121f, 2.1195848f, 2.84642339f, + -0.85827589f, 0.585864604f, -1.17737567f, -1.02712142f, -0.467371881f, 1.23414326f, 3.04510379f, + -1.71407378f, -2.36389613f, -1.97081423f, -3.71935701f, 3.76013064f, 1.52620947f, -2.55970454f, + 0.398174286f, 0.451946497f, -0.0938877985f, 0.0239147414f, 3.88576722f, 0.755754769f, 3.35177183f, + -1.36460257f, 3.59638906f, -0.96563977f, 1.71004474f, -0.498609781f, -1.81664586f, -2.91959953f, + 3.08417416f, -2.48656297f, -2.56841063f, 0.859717607f, 1.25329721f, -2.37997389f, -2.13200927f, + 2.43015218f, 0.805444956f, 0.290940017f, -2.77782536f, 1.61508906f, 0.528297663f, 2.07764649f, + -0.504594088f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/output_ref_data.h new file mode 100644 index 00000000..b5237ea6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/output_ref_data.h @@ -0,0 +1,60 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_3dim_f32_output_ref[400] = { + 1.22466362f, -0.396778882f, -2.20510817f, -1.22420037f, -0.707320929f, 1.819242f, 0.94376421f, + -0.114240788f, -1.55641401f, 1.90086353f, -1.84565306f, -1.31110334f, 0.758707285f, -0.488020986f, + -0.290237188f, 0.594539821f, 2.52341843f, -1.88081706f, -0.0259658583f, 0.580082655f, 2.65069008f, + 2.84914136f, 2.07793713f, -3.38571835f, -1.52889156f, -3.72633767f, -3.0485096f, 2.63072753f, + 2.50975919f, -2.79726553f, -0.0569472387f, 3.24228263f, 3.52185655f, 1.03112054f, 1.32804179f, + 1.47730565f, -0.250298858f, -0.982185125f, 1.81008101f, 2.68251109f, -0.405010968f, -3.55050945f, + 0.885113776f, 1.80291736f, -2.1753571f, 1.12651694f, 0.0280812066f, 3.44022584f, 3.14269137f, + 0.52148658f, 2.09819841f, 2.13155508f, 0.22654283f, -0.201117098f, 2.42624021f, 0.187460661f, + 0.787199736f, -2.39741087f, 3.98022199f, -2.8262825f, -2.6870544f, -3.79440236f, -3.54584622f, + 3.76408052f, 2.09091592f, -1.56663013f, 1.01888037f, -0.289131403f, 2.55656743f, -0.216764778f, + 1.60779953f, 1.86742163f, -3.07132435f, 0.874847472f, -3.27088976f, -2.59754181f, 2.9913249f, + -2.8406527f, -3.23606205f, 2.5136373f, 3.16901755f, 2.28065395f, 3.45626259f, -0.485517621f, + 2.82593513f, -3.00944662f, 3.08145237f, 3.27816725f, -0.706178248f, -3.57164907f, -2.59272337f, + 0.406821191f, -1.14200115f, -3.91191483f, -0.360361993f, 3.27543163f, 1.8670727f, -2.93384409f, + -0.0518450029f, -0.755063474f, 3.27977824f, 1.57459044f, 0.571596801f, 2.1220243f, 2.88492417f, + -3.53003478f, -2.68133116f, -2.30029106f, 2.58031726f, 3.60204816f, -0.317718834f, 3.06377554f, + 2.48660326f, 1.88747072f, -0.0814501569f, -0.617016494f, 3.95683098f, -0.729956925f, -3.29481125f, + 1.58477998f, 3.92565584f, 1.26174128f, -2.41711044f, -3.07143068f, 3.43024588f, 0.0685403645f, + -1.20025229f, 1.32607186f, 2.08881164f, -0.0570218824f, 1.26533723f, 3.10447812f, 0.556327283f, + 2.64416075f, -2.2974329f, -3.95985341f, 2.17064357f, -2.39602709f, 0.291992337f, 0.59108144f, + 2.96018505f, -0.901881754f, 3.72383547f, -2.787117f, -0.155414835f, -3.69585752f, -2.17430544f, + -2.68683577f, -0.133942634f, 2.39761162f, 1.66409898f, -1.89092112f, -1.68690896f, -3.89033341f, + -2.2937429f, 3.54498196f, -0.457958758f, -0.817369223f, -0.956764519f, 2.01602411f, 3.15114903f, + 0.141456008f, 0.645556211f, 1.60246146f, -1.33552063f, -3.24126482f, -0.475131482f, -2.24794555f, + -3.14562821f, 2.56758523f, 0.189826801f, 2.30572057f, -1.06109488f, 1.77250409f, 1.17759931f, + 0.120730117f, 0.307646483f, 1.00306404f, 1.0793258f, 0.686562598f, -1.69556606f, 1.96547496f, + -2.98420238f, 3.46068168f, 1.58109379f, 0.362234771f, -3.99324536f, 2.01351738f, 3.11095119f, + -0.40154776f, 0.78988564f, -3.09395933f, 1.32746494f, 0.794305265f, -1.2546916f, 3.769279f, + -0.707198501f, -0.0215728134f, -0.424735636f, -1.80690873f, -2.79761291f, -0.572619855f, 1.87442172f, + -3.31049871f, -3.612396f, -3.46645522f, -0.95692867f, -1.61751902f, 0.994799614f, -2.75314045f, + -3.32566452f, -1.46446884f, -3.78815246f, 1.44944477f, 0.264055997f, -3.88705015f, 2.98140264f, + 3.6383388f, 2.26245308f, 3.50611734f, -2.02370453f, 3.483742f, 2.0171237f, 0.423909009f, + 1.25259018f, 3.46032453f, -0.545272708f, -1.50972795f, -1.0392828f, 1.71399117f, 3.84565592f, + 2.12838316f, -2.9291501f, -1.21370697f, 2.9491415f, -0.727065742f, 2.34388638f, -2.52970862f, + 2.15675378f, 0.459073633f, -2.87099934f, 0.643730223f, 1.09912741f, -2.06047344f, -1.99888909f, + -1.18405056f, 0.170946732f, -2.56545138f, -3.14510655f, -0.689144731f, -2.73018742f, -1.55980277f, + 3.38719153f, -3.77029634f, -3.97049618f, 0.271474272f, -2.51384711f, 0.0597862713f, -0.0899690688f, + -1.55073988f, 0.623678803f, 1.48473454f, -0.891321957f, -2.13182545f, -2.9636929f, 3.97495747f, + -3.83436394f, -2.38300514f, -2.68113995f, -1.60527551f, 2.75470877f, 0.794500828f, 1.45181072f, + 1.00271249f, 2.51812983f, 1.41758549f, -2.71573782f, 1.61292028f, -2.94556141f, -2.46509981f, + -0.231671989f, -3.73296118f, 0.896928012f, -1.77820766f, -3.47214055f, -2.92922068f, -0.690783381f, + 3.31464338f, 2.02778363f, 1.72467172f, 1.12545431f, 0.467807502f, -0.23239249f, -1.97479868f, + -1.64713836f, 3.86390638f, -0.719110966f, -3.76779103f, -1.35185373f, -1.39281714f, 2.0566287f, + -3.96722603f, -2.72450137f, -3.01441813f, 2.96909189f, 2.97159767f, 3.45251584f, 3.45864296f, + -1.51481009f, 3.92108536f, -2.2969327f, 1.25395191f, -3.23919702f, -3.66853809f, 3.3803401f, + 0.598968029f, -2.15946817f, -3.27602577f, 2.54860377f, 0.878599882f, -3.71174026f, 0.183586612f, + -1.02712142f, -0.96563977f, 3.29032135f, 0.33375293f, -0.467371881f, 1.71004474f, -2.9722805f, + -1.77311265f, 1.23414326f, -0.498609781f, -2.64838767f, -2.11140633f, 3.04510379f, -1.81664586f, + 3.14470887f, -1.79718733f, -1.71407378f, -2.91959953f, 2.8112154f, -2.37245584f, -2.36389613f, + 3.08417416f, 0.101041965f, 1.6237092f, -1.97081423f, -2.48656297f, 0.419182509f, -2.60933971f, + -3.71935701f, -2.56841063f, 1.54604053f, -0.952655852f, 3.76013064f, 0.859717607f, 1.38805974f, + 3.57220435f, 1.52620947f, 1.25329721f, -2.66913319f, 3.96190643f, -2.55970454f, -2.37997389f, + -1.80166125f, -2.09056568f, 0.398174286f, -2.13200927f, 1.99545801f, -0.597329259f, 0.451946497f, + 2.43015218f, -0.540147901f, 3.8139081f, -0.0938877985f, 0.805444956f, -0.808188021f, 0.148193121f, + 0.0239147414f, 0.290940017f, 2.58347416f, 2.1195848f, 3.88576722f, -2.77782536f, -2.18719649f, + 2.84642339f, 0.755754769f, 1.61508906f, 0.257128865f, -0.85827589f, 3.35177183f, 0.528297663f, + -2.53945565f, 0.585864604f, -1.36460257f, 2.07764649f, 0.838760674f, -1.17737567f, 3.59638906f, + -0.504594088f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_3dim_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/config_data.h new file mode 100644 index 00000000..af7b520c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CHWN_F16_NUM_DIMS 4 +#define TRANSPOSE_CHWN_F16_INPUT_N 5 +#define TRANSPOSE_CHWN_F16_INPUT_H 4 +#define TRANSPOSE_CHWN_F16_INPUT_W 12 +#define TRANSPOSE_CHWN_F16_INPUT_C 17 +#define TRANSPOSE_CHWN_F16_OUTPUT_N 17 +#define TRANSPOSE_CHWN_F16_OUTPUT_H 4 +#define TRANSPOSE_CHWN_F16_OUTPUT_W 12 +#define TRANSPOSE_CHWN_F16_OUTPUT_C 5 +#define TRANSPOSE_CHWN_F16_PERM_0 3 +#define TRANSPOSE_CHWN_F16_PERM_1 1 +#define TRANSPOSE_CHWN_F16_PERM_2 2 +#define TRANSPOSE_CHWN_F16_PERM_3 0 +#define TRANSPOSE_CHWN_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CHWN_F16_SIZE 4080 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/input_tensor_data.h new file mode 100644 index 00000000..fda73a2b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/input_tensor_data.h @@ -0,0 +1,4081 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_chwn_f16_input_tensor[4080] = {(float16_t)-3.66210938f, + (float16_t)-0.385986328f, + (float16_t)0.459960938f, + (float16_t)-0.297119141f, + (float16_t)1.09179688f, + (float16_t)3.57226562f, + (float16_t)-1.51367188f, + (float16_t)-0.234008789f, + (float16_t)-2.4140625f, + (float16_t)2.68945312f, + (float16_t)1.97265625f, + (float16_t)0.901855469f, + (float16_t)-2.7109375f, + (float16_t)0.824707031f, + (float16_t)-0.3515625f, + (float16_t)3.51757812f, + (float16_t)3.00195312f, + (float16_t)-2.00976562f, + (float16_t)-1.25097656f, + (float16_t)1.15917969f, + (float16_t)1.64746094f, + (float16_t)3.16601562f, + (float16_t)-2.26757812f, + (float16_t)0.544921875f, + (float16_t)3.28125f, + (float16_t)3.42773438f, + (float16_t)1.06445312f, + (float16_t)-3.17578125f, + (float16_t)2.41210938f, + (float16_t)0.891601562f, + (float16_t)0.95703125f, + (float16_t)1.31738281f, + (float16_t)0.390625f, + (float16_t)-3.90625f, + (float16_t)1.08300781f, + (float16_t)-3.41601562f, + (float16_t)0.609375f, + (float16_t)1.54101562f, + (float16_t)3.0859375f, + (float16_t)-3.85546875f, + (float16_t)1.984375f, + (float16_t)-2.2421875f, + (float16_t)-0.869140625f, + (float16_t)1.83886719f, + (float16_t)-3.09570312f, + (float16_t)-2.59765625f, + (float16_t)3.49023438f, + (float16_t)1.38574219f, + (float16_t)2.99414062f, + (float16_t)-0.319580078f, + (float16_t)-3.1875f, + (float16_t)1.90527344f, + (float16_t)1.85351562f, + (float16_t)2.34765625f, + (float16_t)3.203125f, + (float16_t)0.0919799805f, + (float16_t)1.41894531f, + (float16_t)2.82226562f, + (float16_t)-2.52148438f, + (float16_t)3.32226562f, + (float16_t)2.70507812f, + (float16_t)-3.30078125f, + (float16_t)-1.16992188f, + (float16_t)0.611328125f, + (float16_t)3.93945312f, + (float16_t)-2.0546875f, + (float16_t)2.88085938f, + (float16_t)2.48046875f, + (float16_t)0.84375f, + (float16_t)-2.80664062f, + (float16_t)0.252929688f, + (float16_t)-1.375f, + (float16_t)0.0565795898f, + (float16_t)1.09277344f, + (float16_t)3.46875f, + (float16_t)-1.74511719f, + (float16_t)2.67578125f, + (float16_t)-2.27734375f, + (float16_t)0.100463867f, + (float16_t)-2.02148438f, + (float16_t)-3.23046875f, + (float16_t)1.90917969f, + (float16_t)2.22851562f, + (float16_t)-1.34277344f, + (float16_t)-2.11132812f, + (float16_t)0.480957031f, + (float16_t)3.9296875f, + (float16_t)-1.61523438f, + (float16_t)-1.57519531f, + (float16_t)2.48632812f, + (float16_t)2.6953125f, + (float16_t)-2.8203125f, + (float16_t)-0.655273438f, + (float16_t)3.8515625f, + (float16_t)3.390625f, + (float16_t)0.421142578f, + (float16_t)-1.19335938f, + (float16_t)2.33203125f, + (float16_t)3.43164062f, + (float16_t)-0.922851562f, + (float16_t)-1.96386719f, + (float16_t)0.46484375f, + (float16_t)-0.300537109f, + (float16_t)3.06445312f, + (float16_t)-2.16796875f, + (float16_t)-2.26367188f, + (float16_t)-3.74414062f, + (float16_t)3.79101562f, + (float16_t)1.07714844f, + (float16_t)-0.289550781f, + (float16_t)-1.43652344f, + (float16_t)-2.8515625f, + (float16_t)2.71289062f, + (float16_t)0.770996094f, + (float16_t)3.3515625f, + (float16_t)-2.44335938f, + (float16_t)-1.69140625f, + (float16_t)1.39550781f, + (float16_t)2.02734375f, + (float16_t)2.95507812f, + (float16_t)-0.507324219f, + (float16_t)3.60742188f, + (float16_t)-0.203979492f, + (float16_t)-2.50390625f, + (float16_t)-0.853027344f, + (float16_t)1.70410156f, + (float16_t)3.46289062f, + (float16_t)2.1015625f, + (float16_t)-2.46289062f, + (float16_t)-2.171875f, + (float16_t)-0.356201172f, + (float16_t)-1.44921875f, + (float16_t)-3.20507812f, + (float16_t)1.66601562f, + (float16_t)0.7109375f, + (float16_t)2.99804688f, + (float16_t)-0.111755371f, + (float16_t)-0.98828125f, + (float16_t)-1.9296875f, + (float16_t)1.01660156f, + (float16_t)3.48632812f, + (float16_t)-1.07226562f, + (float16_t)2.30273438f, + (float16_t)3.49023438f, + (float16_t)-0.457275391f, + (float16_t)0.316894531f, + (float16_t)-2.7890625f, + (float16_t)0.627441406f, + (float16_t)3.1015625f, + (float16_t)2.5859375f, + (float16_t)-0.825195312f, + (float16_t)1.25585938f, + (float16_t)-3.57617188f, + (float16_t)-0.953125f, + (float16_t)1.53222656f, + (float16_t)-2.08398438f, + (float16_t)1.01074219f, + (float16_t)1.96484375f, + (float16_t)-0.853027344f, + (float16_t)2.15039062f, + (float16_t)-0.378417969f, + (float16_t)-3.5234375f, + (float16_t)-0.529785156f, + (float16_t)0.695800781f, + (float16_t)-2.08984375f, + (float16_t)0.798828125f, + (float16_t)-1.03027344f, + (float16_t)-3.5390625f, + (float16_t)-3.2578125f, + (float16_t)1.11425781f, + (float16_t)-3.96484375f, + (float16_t)0.874023438f, + (float16_t)3.73046875f, + (float16_t)2.70117188f, + (float16_t)0.645019531f, + (float16_t)0.65234375f, + (float16_t)-3.56835938f, + (float16_t)-1.71484375f, + (float16_t)-1.88085938f, + (float16_t)3.1953125f, + (float16_t)-1.97558594f, + (float16_t)3.96679688f, + (float16_t)2.13476562f, + (float16_t)3.02148438f, + (float16_t)-1.47753906f, + (float16_t)0.650390625f, + (float16_t)2.25976562f, + (float16_t)-1.87988281f, + (float16_t)-0.239746094f, + (float16_t)1.20507812f, + (float16_t)-1.07128906f, + (float16_t)2.17578125f, + (float16_t)2.86523438f, + (float16_t)-2.234375f, + (float16_t)2.546875f, + (float16_t)3.34960938f, + (float16_t)1.90429688f, + (float16_t)-3.20117188f, + (float16_t)-3.74804688f, + (float16_t)-1.31933594f, + (float16_t)0.406005859f, + (float16_t)1.27832031f, + (float16_t)-1.81445312f, + (float16_t)1.67285156f, + (float16_t)1.17871094f, + (float16_t)2.64257812f, + (float16_t)-2.4453125f, + (float16_t)-1.59472656f, + (float16_t)1.22070312f, + (float16_t)1.24902344f, + (float16_t)-1.43945312f, + (float16_t)-3.078125f, + (float16_t)-1.55175781f, + (float16_t)0.510742188f, + (float16_t)2.22070312f, + (float16_t)2.578125f, + (float16_t)2.63867188f, + (float16_t)1.20117188f, + (float16_t)-3.98046875f, + (float16_t)-3.234375f, + (float16_t)-2.21484375f, + (float16_t)-1.21875f, + (float16_t)-1.15722656f, + (float16_t)0.819335938f, + (float16_t)-0.302490234f, + (float16_t)3.84765625f, + (float16_t)2.02539062f, + (float16_t)1.58984375f, + (float16_t)-2.73828125f, + (float16_t)-0.660644531f, + (float16_t)0.270507812f, + (float16_t)-2.05859375f, + (float16_t)-2.828125f, + (float16_t)-2.44335938f, + (float16_t)3.62695312f, + (float16_t)-1.79101562f, + (float16_t)2.91601562f, + (float16_t)1.46875f, + (float16_t)1.98925781f, + (float16_t)-2.22265625f, + (float16_t)1.64453125f, + (float16_t)-1.88183594f, + (float16_t)1.63476562f, + (float16_t)-1.53125f, + (float16_t)-3.1953125f, + (float16_t)1.62011719f, + (float16_t)-1.21386719f, + (float16_t)2.453125f, + (float16_t)3.7421875f, + (float16_t)0.783203125f, + (float16_t)-3.74023438f, + (float16_t)1.57910156f, + (float16_t)-2.95507812f, + (float16_t)-0.711425781f, + (float16_t)-3.67382812f, + (float16_t)1.875f, + (float16_t)-3.68945312f, + (float16_t)-0.95703125f, + (float16_t)3.81835938f, + (float16_t)1.34765625f, + (float16_t)3.3828125f, + (float16_t)0.571777344f, + (float16_t)3.83398438f, + (float16_t)-2.50976562f, + (float16_t)1.8046875f, + (float16_t)3.65820312f, + (float16_t)2.35351562f, + (float16_t)0.52734375f, + (float16_t)1.23046875f, + (float16_t)-3.70703125f, + (float16_t)0.856445312f, + (float16_t)0.0383605957f, + (float16_t)-3.19140625f, + (float16_t)3.97460938f, + (float16_t)3.328125f, + (float16_t)0.926757812f, + (float16_t)-3.9609375f, + (float16_t)-0.484619141f, + (float16_t)0.343994141f, + (float16_t)0.109375f, + (float16_t)2.73242188f, + (float16_t)2.48046875f, + (float16_t)3.29296875f, + (float16_t)3.30859375f, + (float16_t)0.178100586f, + (float16_t)-2.76171875f, + (float16_t)-0.143188477f, + (float16_t)3.95703125f, + (float16_t)3.52734375f, + (float16_t)2.8515625f, + (float16_t)1.77539062f, + (float16_t)2.33984375f, + (float16_t)-3.67382812f, + (float16_t)0.721191406f, + (float16_t)-2.59960938f, + (float16_t)-3.28515625f, + (float16_t)0.0971069336f, + (float16_t)-3.4765625f, + (float16_t)3.58984375f, + (float16_t)-2.10742188f, + (float16_t)2.9296875f, + (float16_t)1.140625f, + (float16_t)-0.686523438f, + (float16_t)-3.10546875f, + (float16_t)-2.27148438f, + (float16_t)-3.41601562f, + (float16_t)-1.77929688f, + (float16_t)-2.82226562f, + (float16_t)0.539550781f, + (float16_t)-2.44335938f, + (float16_t)1.68847656f, + (float16_t)-3.54492188f, + (float16_t)-2.02734375f, + (float16_t)-3.84960938f, + (float16_t)3.91601562f, + (float16_t)-0.426757812f, + (float16_t)2.99609375f, + (float16_t)3.734375f, + 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(float16_t)-1.54101562f, + (float16_t)-3.87304688f, + (float16_t)0.734375f, + (float16_t)-1.38769531f, + (float16_t)-0.838378906f, + (float16_t)-3.94921875f, + (float16_t)3.79101562f, + (float16_t)-0.316162109f, + (float16_t)1.66015625f, + (float16_t)1.13183594f, + (float16_t)-2.98632812f, + (float16_t)2.09960938f, + (float16_t)-0.76953125f, + (float16_t)0.10369873f, + (float16_t)-1.22949219f, + (float16_t)-0.561523438f, + (float16_t)1.22949219f, + (float16_t)3.62695312f, + (float16_t)1.03710938f, + (float16_t)0.440185547f, + (float16_t)-3.72851562f, + (float16_t)2.76757812f, + (float16_t)-2.00585938f, + (float16_t)2.75585938f, + (float16_t)-2.80078125f, + (float16_t)-0.670898438f, + (float16_t)3.3984375f, + (float16_t)-3.34570312f, + (float16_t)-1.49121094f, + (float16_t)-3.5390625f, + (float16_t)1.27441406f, + (float16_t)3.6875f, + (float16_t)1.48339844f, + (float16_t)2.56835938f, + (float16_t)2.03125f, + (float16_t)-0.387695312f, + (float16_t)-3.23046875f, + (float16_t)0.474365234f, 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(float16_t)-2.57226562f, + (float16_t)1.86523438f, + (float16_t)-1.00683594f, + (float16_t)-1.37011719f, + (float16_t)-1.66601562f, + (float16_t)1.00683594f, + (float16_t)-0.169311523f, + (float16_t)-0.729492188f, + (float16_t)1.34277344f, + (float16_t)3.8984375f, + (float16_t)-2.63671875f, + (float16_t)0.166870117f, + (float16_t)0.517089844f, + (float16_t)-2.82421875f, + (float16_t)-3.5546875f, + (float16_t)2.74414062f, + (float16_t)0.471923828f, + (float16_t)0.511230469f, + (float16_t)0.715332031f, + (float16_t)-3.18945312f, + (float16_t)1.78613281f, + (float16_t)-0.583007812f, + (float16_t)-0.139160156f, + (float16_t)2.65820312f, + (float16_t)-2.7734375f, + (float16_t)-1.1015625f, + (float16_t)-0.324462891f, + (float16_t)3.23828125f, + (float16_t)3.04882812f, + (float16_t)-2.79492188f, + (float16_t)1.37011719f, + (float16_t)2.73046875f, + (float16_t)1.35449219f, + (float16_t)-0.5390625f, + (float16_t)3.66601562f, + (float16_t)0.584960938f, + (float16_t)-3.00585938f, + (float16_t)-1.16503906f, + (float16_t)3.29101562f, + (float16_t)3.61328125f, + (float16_t)2.81445312f, + (float16_t)0.155761719f, + (float16_t)2.92578125f, + (float16_t)-1.03027344f, + (float16_t)-0.519042969f, + (float16_t)-1.97167969f, + (float16_t)-2.234375f, + (float16_t)3.97265625f, + (float16_t)-3.4375f, + (float16_t)-1.63183594f, + (float16_t)-0.644042969f, + (float16_t)-2.52734375f, + (float16_t)1.4140625f, + (float16_t)-2.06445312f, + (float16_t)2.01367188f, + (float16_t)-0.770996094f, + (float16_t)1.69824219f, + (float16_t)-2.38085938f, + (float16_t)2.05273438f, + (float16_t)2.00976562f, + (float16_t)-2.43554688f, + (float16_t)2.84179688f, + (float16_t)-2.01953125f, + (float16_t)-0.786621094f, + (float16_t)3.4375f, + (float16_t)-1.29003906f, + (float16_t)-2.6953125f, + (float16_t)3.33007812f, + (float16_t)-3.13671875f, + (float16_t)0.743164062f, + (float16_t)1.35449219f, + (float16_t)-0.347167969f, + (float16_t)0.44921875f, + (float16_t)3.2734375f, + (float16_t)1.92578125f, + (float16_t)1.68066406f, + (float16_t)0.401611328f, + (float16_t)-0.555664062f, + (float16_t)1.02929688f, + (float16_t)-2.55273438f, + (float16_t)-0.876953125f, + (float16_t)0.164794922f, + (float16_t)-1.90722656f, + (float16_t)3.78125f, + (float16_t)2.55664062f, + (float16_t)0.298095703f, + (float16_t)3.44921875f, + (float16_t)1.19140625f, + (float16_t)-1.86816406f, + (float16_t)3.41015625f, + (float16_t)-3.29296875f, + (float16_t)-2.42578125f, + (float16_t)2.04296875f, + (float16_t)-1.35742188f, + (float16_t)-2.50195312f, + (float16_t)3.21484375f, + (float16_t)-0.0687866211f, + (float16_t)-0.823242188f, + (float16_t)0.520507812f, + (float16_t)1.88769531f, + (float16_t)-1.890625f, + (float16_t)-2.46875f, + (float16_t)-1.79101562f, + (float16_t)3.31835938f, + (float16_t)-2.45898438f, + (float16_t)-2.5859375f, + (float16_t)-2.625f, + (float16_t)-2.26367188f, + (float16_t)2.39453125f, + (float16_t)3.73046875f, + (float16_t)2.45898438f, + (float16_t)3.97265625f, + (float16_t)-2.63867188f, + (float16_t)0.962890625f, + (float16_t)0.966308594f, + (float16_t)-1.98535156f, + (float16_t)-1.48828125f, + (float16_t)-3.53320312f, + (float16_t)0.279052734f, + (float16_t)0.195678711f, + (float16_t)-1.47949219f, + (float16_t)-1.00976562f, + (float16_t)0.236206055f, + (float16_t)-0.704589844f, + (float16_t)2.2734375f, + (float16_t)2.57226562f, + (float16_t)-2.7265625f, + (float16_t)1.70605469f, + (float16_t)0.853027344f, + (float16_t)1.2578125f, + (float16_t)0.298583984f, + (float16_t)-2.37890625f, + (float16_t)-3.98828125f, + (float16_t)2.30664062f, + (float16_t)3.98828125f, + (float16_t)-1.75097656f, + (float16_t)1.09863281f, + (float16_t)-2.32421875f, + (float16_t)-3.61132812f, + (float16_t)2.64648438f, + (float16_t)-3.140625f, + (float16_t)-3.3984375f, + (float16_t)3.1953125f, + (float16_t)-0.449462891f, + (float16_t)2.63476562f, + (float16_t)3.70117188f, + (float16_t)-0.9921875f, + (float16_t)2.2734375f, + (float16_t)0.362792969f, + (float16_t)1.89941406f, + (float16_t)-3.55273438f, + (float16_t)3.07226562f, + (float16_t)-1.36328125f, + (float16_t)2.046875f, + (float16_t)-0.014175415f, + (float16_t)2.11523438f, + (float16_t)-3.85351562f, + (float16_t)0.631347656f, + (float16_t)-3.54296875f, + (float16_t)-1.97558594f, + (float16_t)3.84765625f, + (float16_t)-2.56445312f, + (float16_t)-1.84765625f, + (float16_t)1.16796875f, + (float16_t)2.36523438f, + (float16_t)-3.33203125f, + (float16_t)3.49023438f, + (float16_t)3.64648438f, + (float16_t)1.58398438f, + (float16_t)0.481201172f, + (float16_t)2.61914062f, + (float16_t)1.33105469f, + (float16_t)2.92382812f, + (float16_t)-1.82324219f, + (float16_t)-2.0f, + (float16_t)-3.14257812f, + (float16_t)-1.31445312f, + (float16_t)-1.98046875f, + (float16_t)-0.562988281f, + (float16_t)1.56640625f, + (float16_t)-1.26953125f, + (float16_t)-3.45117188f, + (float16_t)-3.734375f, + (float16_t)-3.43554688f, + (float16_t)-0.361816406f, + (float16_t)-3.20898438f, + (float16_t)2.62890625f, + (float16_t)-2.45117188f, + (float16_t)-1.078125f, + (float16_t)-3.68945312f, + (float16_t)0.78125f, + (float16_t)1.21875f, + (float16_t)-3.85351562f, + (float16_t)3.0390625f, + (float16_t)2.04492188f, + (float16_t)-0.788574219f, + (float16_t)2.86523438f, + (float16_t)2.140625f, + (float16_t)-3.8203125f, + (float16_t)3.9921875f, + (float16_t)1.78027344f, + (float16_t)2.17773438f, + (float16_t)3.58007812f, + (float16_t)2.140625f, + (float16_t)0.758789062f, + (float16_t)-0.977050781f, + (float16_t)2.33789062f, + (float16_t)-3.14648438f, + (float16_t)1.52246094f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/output_ref_data.h new file mode 100644 index 00000000..3ca03c45 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/output_ref_data.h @@ -0,0 +1,4081 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_chwn_f16_output_ref[4080] = {(float16_t)-3.66210938f, + (float16_t)-3.72460938f, + (float16_t)0.348144531f, + (float16_t)-3.86328125f, + (float16_t)-0.853027344f, + (float16_t)-2.00976562f, + (float16_t)-1.95410156f, + (float16_t)1.79003906f, + (float16_t)3.94921875f, + (float16_t)2.41210938f, + (float16_t)1.08300781f, + (float16_t)3.40429688f, + (float16_t)3.5390625f, + (float16_t)2.48242188f, + (float16_t)2.0390625f, + (float16_t)1.90527344f, + (float16_t)1.34082031f, + (float16_t)0.80859375f, + (float16_t)-0.665039062f, + (float16_t)-0.492431641f, + (float16_t)0.84375f, + (float16_t)-0.530273438f, + (float16_t)0.80859375f, + (float16_t)0.517578125f, + (float16_t)-1.14941406f, + (float16_t)0.480957031f, + (float16_t)-1.98828125f, + (float16_t)2.27539062f, + (float16_t)-3.58789062f, + (float16_t)0.155761719f, + (float16_t)-0.300537109f, + (float16_t)2.22070312f, + (float16_t)1.74316406f, + (float16_t)0.595703125f, + (float16_t)0.578613281f, + (float16_t)2.95507812f, + (float16_t)-1.03808594f, + (float16_t)2.90234375f, + (float16_t)2.34179688f, + (float16_t)-2.38085938f, + (float16_t)-0.111755371f, + (float16_t)0.128662109f, + (float16_t)0.948242188f, + (float16_t)2.69726562f, + (float16_t)-2.09179688f, + (float16_t)-0.953125f, + (float16_t)-2.1640625f, + (float16_t)-1.85449219f, + (float16_t)-2.59375f, + (float16_t)-1.44140625f, + (float16_t)-3.96484375f, + (float16_t)-2.20898438f, + (float16_t)-3.80859375f, + (float16_t)-3.01367188f, + (float16_t)-0.355957031f, + (float16_t)-1.87988281f, + (float16_t)-3.33007812f, + (float16_t)0.622558594f, + (float16_t)2.359375f, + (float16_t)-3.23242188f, + (float16_t)1.17871094f, + (float16_t)-3.37695312f, + (float16_t)1.27929688f, + (float16_t)-3.58007812f, + (float16_t)2.09179688f, + (float16_t)-1.21875f, + (float16_t)1.38867188f, + (float16_t)-1.14746094f, + (float16_t)-3.98632812f, + (float16_t)-3.6171875f, + (float16_t)1.98925781f, + (float16_t)1.91210938f, + (float16_t)-2.69140625f, + (float16_t)-2.47460938f, + (float16_t)-0.512695312f, + (float16_t)1.875f, + (float16_t)2.76171875f, + (float16_t)-1.12792969f, + (float16_t)3.921875f, + (float16_t)0.963378906f, + (float16_t)-3.19140625f, + (float16_t)-1.7734375f, + (float16_t)-3.92382812f, + (float16_t)2.75976562f, + (float16_t)3.81640625f, + (float16_t)2.8515625f, + (float16_t)-3.64648438f, + (float16_t)-1.15039062f, + (float16_t)-3.31835938f, + (float16_t)1.93164062f, + (float16_t)-1.77929688f, + (float16_t)-1.02050781f, + (float16_t)-0.165039062f, + (float16_t)2.91796875f, + (float16_t)3.34375f, + (float16_t)2.828125f, + (float16_t)-1.54589844f, + (float16_t)2.45898438f, + (float16_t)0.400390625f, + (float16_t)-0.370361328f, + (float16_t)-2.0546875f, + (float16_t)1.40917969f, + (float16_t)0.641113281f, + (float16_t)1.26464844f, + (float16_t)-3.921875f, + (float16_t)0.607910156f, + (float16_t)1.07324219f, + (float16_t)0.0686035156f, + (float16_t)-2.91015625f, + (float16_t)3.92578125f, + (float16_t)0.121948242f, + 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(float16_t)1.52246094f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/config_data.h new file mode 100644 index 00000000..04aa6308 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CHWN_F32_NUM_DIMS 4 +#define TRANSPOSE_CHWN_F32_INPUT_N 5 +#define TRANSPOSE_CHWN_F32_INPUT_H 4 +#define TRANSPOSE_CHWN_F32_INPUT_W 12 +#define TRANSPOSE_CHWN_F32_INPUT_C 17 +#define TRANSPOSE_CHWN_F32_OUTPUT_N 17 +#define TRANSPOSE_CHWN_F32_OUTPUT_H 4 +#define TRANSPOSE_CHWN_F32_OUTPUT_W 12 +#define TRANSPOSE_CHWN_F32_OUTPUT_C 5 +#define TRANSPOSE_CHWN_F32_PERM_0 3 +#define TRANSPOSE_CHWN_F32_PERM_1 1 +#define TRANSPOSE_CHWN_F32_PERM_2 2 +#define TRANSPOSE_CHWN_F32_PERM_3 0 +#define TRANSPOSE_CHWN_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CHWN_F32_SIZE 4080 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/input_tensor_data.h new file mode 100644 index 00000000..75aed3a9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/input_tensor_data.h @@ -0,0 +1,585 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_chwn_f32_input_tensor[4080] = { + 1.45370829f, -3.27457976f, 2.30639529f, -0.18065992f, 2.71873236f, -1.07401955f, -0.941300988f, + 2.72939372f, -3.00796437f, -1.57312751f, -1.40298402f, 3.31061339f, -0.342969567f, 1.4202714f, + -3.60591769f, 3.42963099f, 3.21324492f, 2.44805861f, 1.82336533f, 2.51757646f, 2.0738306f, + -1.84941554f, -3.93222237f, 3.23210239f, -0.0277040191f, -1.77012265f, -3.69059849f, -3.53577232f, + -3.01335025f, -3.7471981f, -3.02641368f, 1.92026055f, -2.63252044f, -2.00217938f, 1.66109729f, + 2.77899504f, 1.24007189f, -1.52360415f, 0.357252508f, -2.94917488f, 1.67588317f, 3.14842439f, + 1.82813299f, -2.76205349f, -1.02953851f, -1.8173728f, -0.642132044f, 2.98109794f, -3.84561825f, + -2.52008414f, -1.25521696f, 3.88945079f, -3.10614014f, -2.97478032f, 2.36030078f, 3.35626459f, + 0.707462668f, 0.827058017f, -2.80628657f, -1.61890078f, -1.09196138f, -3.56415534f, 3.80668664f, + 2.67204714f, -1.92495966f, -1.36120844f, -3.92449117f, 2.89852142f, -2.86877275f, -0.154486865f, + 3.63167834f, 1.92918622f, -2.00973439f, 3.23181129f, -3.48302412f, -0.507562339f, -1.64167368f, + -3.77570915f, -3.37214708f, -2.62858558f, -2.88563824f, -1.59512091f, -1.05006075f, 0.514812768f, + -1.07474136f, 3.70822239f, -0.745091915f, -3.72859192f, 3.75170445f, 3.60635281f, -2.76547313f, + 0.0547499694f, -3.1079123f, -0.880925119f, -3.84976673f, 1.81781995f, -1.69903386f, 2.1254499f, + 2.18733811f, -3.89938378f, -2.52330518f, -0.519602776f, 0.399813384f, 3.97163057f, 2.86375928f, + 2.31687808f, -0.411295861f, -3.08351874f, 2.68429661f, 3.96984792f, -0.0418583974f, -1.33415818f, + 2.72509885f, -2.1697762f, 2.59618688f, -3.93576217f, -1.82113552f, 2.10474801f, 2.98269677f, + 0.300799429f, 3.66460133f, -2.60067439f, 2.33322048f, 1.19269061f, -0.56709218f, 0.648165762f, + 2.26167107f, 1.65283144f, 0.740756154f, 0.0197342392f, -2.51480269f, -2.48807311f, 3.21821308f, + 0.0035556776f, 0.393649817f, 0.781893134f, 2.53584933f, -3.48604727f, 2.78900194f, 0.48889783f, + 1.87996852f, -2.01413941f, 2.17652082f, -1.68192792f, 1.83601105f, 0.229161218f, -3.76827407f, + 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-1.80706954f, 0.354724973f, 1.64195263f, 1.5165472f, -1.85250223f, + -1.93484211f, 1.88627183f, -3.82752061f, -1.85234666f, 1.38723278f, 2.43294477f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/output_ref_data.h new file mode 100644 index 00000000..91757c7e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/output_ref_data.h @@ -0,0 +1,585 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_chwn_f32_output_ref[4080] = { + 1.45370829f, -0.287117928f, 1.37188756f, -1.30364847f, 2.54864883f, 2.44805861f, 2.00202942f, + 0.248314217f, -0.659145772f, -1.77043951f, 1.66109729f, -0.715539098f, 3.06870699f, 1.33873916f, + 2.6386497f, 3.88945079f, 3.86587048f, -2.3481288f, -3.80422282f, 3.75195336f, -2.86877275f, + -3.34973311f, -0.0444280095f, 3.54932165f, 0.987258434f, 3.70822239f, 2.94961667f, -0.468229681f, + 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b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_chwn_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/biases_data.h new file mode 100644 index 00000000..98e8a90d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/biases_data.h @@ -0,0 +1,7 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_f16_biases[6] = {(float16_t)0.0219116211f, + (float16_t)-0.37890625f, + (float16_t)-0.328857422f, + (float16_t)0.124145508f, + (float16_t)0.415771484f, + (float16_t)0.317871094f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/config_data.h new file mode 100644 index 00000000..e4253c42 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CONV_BASIC_F16_INPUT_BATCHES 1 +#define TRANSPOSE_CONV_BASIC_F16_INPUT_H 5 +#define TRANSPOSE_CONV_BASIC_F16_INPUT_W 6 +#define TRANSPOSE_CONV_BASIC_F16_IN_CH 4 +#define TRANSPOSE_CONV_BASIC_F16_FILTER_H 3 +#define TRANSPOSE_CONV_BASIC_F16_FILTER_W 3 +#define TRANSPOSE_CONV_BASIC_F16_OUT_CH 6 +#define TRANSPOSE_CONV_BASIC_F16_STRIDE_H 2 +#define TRANSPOSE_CONV_BASIC_F16_STRIDE_W 2 +#define TRANSPOSE_CONV_BASIC_F16_PADDING_H 1 +#define TRANSPOSE_CONV_BASIC_F16_PADDING_W 1 +#define TRANSPOSE_CONV_BASIC_F16_PADDING_OFFSET_H 0 +#define TRANSPOSE_CONV_BASIC_F16_PADDING_OFFSET_W 0 +#define TRANSPOSE_CONV_BASIC_F16_DILATION_H 1 +#define TRANSPOSE_CONV_BASIC_F16_DILATION_W 1 +#define TRANSPOSE_CONV_BASIC_F16_OUTPUT_H 10 +#define TRANSPOSE_CONV_BASIC_F16_OUTPUT_W 12 +#define TRANSPOSE_CONV_BASIC_F16_OUTPUT_C 6 +#define TRANSPOSE_CONV_BASIC_F16_DST_SIZE 720 +#define TRANSPOSE_CONV_BASIC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CONV_BASIC_F16_USE_WRAPPER 0 +#define TRANSPOSE_CONV_BASIC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define TRANSPOSE_CONV_BASIC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/input_data.h new file mode 100644 index 00000000..c0a3526b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/input_data.h @@ -0,0 +1,32 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_f16_input[120] = { + (float16_t)-0.319091797f, (float16_t)-0.626464844f, (float16_t)-0.602050781f, (float16_t)0.998535156f, + (float16_t)-0.831542969f, (float16_t)0.319580078f, (float16_t)0.275878906f, (float16_t)-0.614746094f, + (float16_t)-0.973632812f, (float16_t)0.978027344f, (float16_t)0.272949219f, (float16_t)0.981445312f, + (float16_t)-0.189941406f, (float16_t)-0.252685547f, (float16_t)0.401367188f, (float16_t)0.401611328f, + (float16_t)-0.73046875f, (float16_t)-0.948242188f, (float16_t)-0.376464844f, (float16_t)0.321533203f, + (float16_t)0.196533203f, (float16_t)0.645019531f, (float16_t)0.102478027f, (float16_t)0.907714844f, + (float16_t)-0.393310547f, (float16_t)0.315673828f, (float16_t)-0.484863281f, (float16_t)0.119384766f, + (float16_t)0.144897461f, (float16_t)0.863769531f, (float16_t)0.264892578f, (float16_t)-0.602539062f, + (float16_t)-0.629394531f, (float16_t)0.622070312f, (float16_t)0.52734375f, (float16_t)-0.19152832f, + (float16_t)-0.515625f, (float16_t)-0.436035156f, (float16_t)0.460693359f, (float16_t)-0.776855469f, + (float16_t)0.465820312f, (float16_t)-0.600097656f, (float16_t)0.77734375f, (float16_t)-0.594726562f, + (float16_t)0.697265625f, (float16_t)0.565429688f, (float16_t)0.354736328f, (float16_t)-0.368896484f, + (float16_t)-0.768066406f, (float16_t)-0.434570312f, (float16_t)-0.0344543457f, (float16_t)0.506347656f, + (float16_t)0.556640625f, (float16_t)-0.476318359f, (float16_t)-0.308837891f, (float16_t)0.489257812f, + (float16_t)0.405273438f, (float16_t)-0.783691406f, (float16_t)0.218139648f, (float16_t)-0.0670776367f, + (float16_t)0.956542969f, (float16_t)0.413574219f, (float16_t)-0.70703125f, (float16_t)0.157836914f, + (float16_t)-0.709960938f, (float16_t)-0.0533752441f, (float16_t)-0.71484375f, (float16_t)-0.211181641f, + (float16_t)0.0244293213f, (float16_t)-0.603027344f, (float16_t)-0.400878906f, (float16_t)-0.568847656f, + (float16_t)0.731933594f, (float16_t)-0.413330078f, (float16_t)-0.287353516f, (float16_t)-0.00640869141f, + (float16_t)0.6796875f, (float16_t)-0.618164062f, (float16_t)-0.263916016f, (float16_t)-0.607910156f, + (float16_t)0.405761719f, (float16_t)0.448974609f, (float16_t)-0.0328369141f, (float16_t)-0.978515625f, + (float16_t)-0.374755859f, (float16_t)-0.984375f, (float16_t)-0.145629883f, (float16_t)-0.0556945801f, + (float16_t)-0.275146484f, (float16_t)0.825195312f, (float16_t)0.541015625f, (float16_t)0.583984375f, + (float16_t)-0.613769531f, (float16_t)0.976074219f, (float16_t)-0.755371094f, (float16_t)-0.0153579712f, + (float16_t)0.831542969f, (float16_t)0.793945312f, (float16_t)-0.967773438f, (float16_t)0.491455078f, + (float16_t)0.254638672f, (float16_t)0.821777344f, (float16_t)0.357177734f, (float16_t)-0.562011719f, + (float16_t)0.108642578f, (float16_t)0.0426025391f, (float16_t)0.2734375f, (float16_t)-0.745117188f, + (float16_t)-0.689453125f, (float16_t)-0.168579102f, (float16_t)-0.349609375f, (float16_t)-0.477539062f, + (float16_t)-0.294677734f, (float16_t)0.783203125f, (float16_t)0.282470703f, (float16_t)-0.720703125f, + (float16_t)0.122924805f, (float16_t)0.684082031f, (float16_t)-0.00866699219f, (float16_t)0.955566406f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/output_ref_data.h new file mode 100644 index 00000000..ab6a3ec4 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/output_ref_data.h @@ -0,0 +1,182 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_f16_output_ref[720] = { + (float16_t)0.635253906f, (float16_t)-1.19628906f, (float16_t)-0.610351562f, (float16_t)-0.682617188f, + (float16_t)0.105712891f, (float16_t)1.46972656f, (float16_t)0.208374023f, (float16_t)0.0158081055f, + (float16_t)-1.38476562f, (float16_t)0.362792969f, (float16_t)-0.0296783447f, (float16_t)0.758789062f, + (float16_t)0.439697266f, (float16_t)0.765625f, (float16_t)-0.379150391f, (float16_t)1.20214844f, + (float16_t)0.456787109f, (float16_t)0.396728516f, (float16_t)0.142700195f, (float16_t)-0.169067383f, + (float16_t)-1.19628906f, (float16_t)-0.426513672f, (float16_t)0.313720703f, (float16_t)-0.582519531f, + (float16_t)0.490478516f, (float16_t)0.457763672f, (float16_t)-1.61035156f, (float16_t)0.265136719f, + (float16_t)1.04980469f, (float16_t)0.926269531f, (float16_t)-0.227172852f, (float16_t)-0.776367188f, + (float16_t)-0.153442383f, (float16_t)-0.30078125f, (float16_t)0.840820312f, (float16_t)-0.391113281f, + (float16_t)-0.0526428223f, (float16_t)-0.524414062f, (float16_t)-0.4765625f, (float16_t)-0.0477294922f, + (float16_t)0.544921875f, (float16_t)0.562988281f, (float16_t)-0.230102539f, (float16_t)-0.967285156f, + (float16_t)-0.813964844f, (float16_t)0.749511719f, (float16_t)1.38769531f, (float16_t)0.275634766f, + (float16_t)0.827148438f, (float16_t)-0.719726562f, (float16_t)-0.192016602f, (float16_t)0.0271911621f, + (float16_t)-0.111816406f, (float16_t)1.50292969f, (float16_t)0.183227539f, (float16_t)-0.0913696289f, + (float16_t)-1.35644531f, (float16_t)1.08105469f, (float16_t)0.693847656f, (float16_t)0.383300781f, + (float16_t)-0.226806641f, (float16_t)-0.551269531f, (float16_t)-1.09960938f, (float16_t)-0.490478516f, + (float16_t)0.927246094f, (float16_t)0.273681641f, (float16_t)-0.235839844f, (float16_t)-0.460693359f, + (float16_t)-0.366455078f, (float16_t)0.133422852f, (float16_t)0.655273438f, (float16_t)0.249145508f, + (float16_t)0.8359375f, (float16_t)0.000999450684f, (float16_t)-0.63671875f, (float16_t)-0.079284668f, + (float16_t)-0.298583984f, (float16_t)0.123291016f, (float16_t)-0.225952148f, (float16_t)0.796386719f, + (float16_t)-0.404785156f, (float16_t)0.595214844f, (float16_t)0.239746094f, (float16_t)0.502441406f, + (float16_t)-0.200561523f, (float16_t)0.696777344f, (float16_t)-0.512207031f, (float16_t)-0.190429688f, + (float16_t)0.969726562f, (float16_t)-0.581054688f, (float16_t)-1.26855469f, (float16_t)-0.497070312f, + (float16_t)-0.715332031f, (float16_t)0.384765625f, (float16_t)0.0868530273f, (float16_t)0.725585938f, + (float16_t)-0.323242188f, (float16_t)-0.2890625f, (float16_t)0.645019531f, (float16_t)-0.0183258057f, + (float16_t)-0.361816406f, (float16_t)-1.7109375f, (float16_t)-0.456542969f, (float16_t)0.0287322998f, + (float16_t)-0.729003906f, (float16_t)1.38476562f, (float16_t)-0.361328125f, (float16_t)1.86035156f, + (float16_t)0.615234375f, (float16_t)-0.0668334961f, (float16_t)0.388671875f, (float16_t)-0.406494141f, + (float16_t)-0.642089844f, (float16_t)-0.705078125f, (float16_t)-0.193359375f, (float16_t)-0.420410156f, + (float16_t)0.772949219f, (float16_t)0.235107422f, (float16_t)0.932617188f, (float16_t)1.40136719f, + (float16_t)0.0596923828f, (float16_t)-0.351806641f, (float16_t)0.208618164f, (float16_t)-0.267089844f, + (float16_t)0.135864258f, (float16_t)-0.193237305f, (float16_t)-0.180541992f, (float16_t)-0.961425781f, + (float16_t)1.71679688f, (float16_t)0.335449219f, (float16_t)0.165039062f, (float16_t)-0.0795288086f, + (float16_t)-0.465087891f, (float16_t)-0.425537109f, (float16_t)0.422607422f, (float16_t)0.520019531f, + (float16_t)0.640136719f, (float16_t)-0.684570312f, (float16_t)0.353759766f, (float16_t)-0.725585938f, + (float16_t)0.117492676f, (float16_t)0.649902344f, (float16_t)-1.69824219f, (float16_t)0.705078125f, + (float16_t)0.555664062f, (float16_t)-0.0798950195f, (float16_t)-0.639160156f, (float16_t)0.259521484f, + (float16_t)0.341064453f, (float16_t)0.717773438f, (float16_t)0.59765625f, (float16_t)-0.0493469238f, + (float16_t)-0.337890625f, (float16_t)-0.588867188f, (float16_t)-0.924804688f, (float16_t)-0.00526046753f, + (float16_t)-0.293457031f, (float16_t)0.429443359f, (float16_t)-0.415771484f, (float16_t)0.738769531f, + (float16_t)0.755859375f, (float16_t)-0.497558594f, (float16_t)-0.0780639648f, (float16_t)0.048614502f, + (float16_t)0.00652313232f, (float16_t)-1.14160156f, (float16_t)-0.0275878906f, (float16_t)-0.0130615234f, + (float16_t)0.128662109f, (float16_t)0.655761719f, (float16_t)-0.717773438f, (float16_t)0.88671875f, + (float16_t)0.787109375f, (float16_t)0.214355469f, (float16_t)0.385986328f, (float16_t)-0.513183594f, + (float16_t)0.70703125f, (float16_t)-1.01757812f, (float16_t)0.136108398f, (float16_t)-0.194946289f, + (float16_t)0.16394043f, (float16_t)0.200073242f, (float16_t)0.167724609f, (float16_t)0.984863281f, + (float16_t)0.217041016f, (float16_t)0.330566406f, (float16_t)0.359375f, (float16_t)-0.109069824f, + (float16_t)1.42480469f, (float16_t)-0.403320312f, (float16_t)0.703125f, (float16_t)0.555175781f, + (float16_t)-0.708007812f, (float16_t)-0.598144531f, (float16_t)0.392089844f, (float16_t)0.28125f, + (float16_t)0.398193359f, (float16_t)-0.344482422f, (float16_t)0.735351562f, (float16_t)0.5390625f, + (float16_t)0.900390625f, (float16_t)0.311279297f, (float16_t)0.791015625f, (float16_t)0.728515625f, + (float16_t)-0.719238281f, (float16_t)-0.235229492f, (float16_t)-0.252685547f, (float16_t)0.180175781f, + (float16_t)0.766601562f, (float16_t)-0.702148438f, (float16_t)-0.294189453f, (float16_t)-0.298828125f, + (float16_t)0.140991211f, (float16_t)-0.259765625f, (float16_t)0.628417969f, (float16_t)0.352294922f, + (float16_t)-0.13269043f, (float16_t)-1.14257812f, (float16_t)-0.217651367f, (float16_t)0.303466797f, + (float16_t)-0.00852203369f, (float16_t)0.440673828f, (float16_t)-0.489746094f, (float16_t)0.075378418f, + (float16_t)-0.0522766113f, (float16_t)1.39941406f, (float16_t)1.3203125f, (float16_t)-0.220092773f, + (float16_t)-0.975097656f, (float16_t)-1.03027344f, (float16_t)0.0943603516f, (float16_t)0.667480469f, + (float16_t)0.584960938f, (float16_t)0.622070312f, (float16_t)-0.243164062f, (float16_t)-0.876953125f, + (float16_t)0.604003906f, (float16_t)0.745605469f, (float16_t)0.8203125f, (float16_t)0.274414062f, + (float16_t)-0.836914062f, (float16_t)-0.727539062f, (float16_t)0.895019531f, (float16_t)0.141967773f, + (float16_t)-0.335205078f, (float16_t)-0.619140625f, (float16_t)-0.460693359f, (float16_t)0.505371094f, + (float16_t)-1.39941406f, (float16_t)-0.595214844f, (float16_t)0.520507812f, (float16_t)0.389160156f, + (float16_t)-0.126342773f, (float16_t)0.749511719f, (float16_t)-0.844238281f, (float16_t)-0.24206543f, + (float16_t)0.621582031f, (float16_t)0.437011719f, (float16_t)0.509765625f, (float16_t)0.347412109f, + (float16_t)-2.33789062f, (float16_t)-1.67089844f, (float16_t)-0.0340270996f, (float16_t)1.30859375f, + (float16_t)1.24707031f, (float16_t)0.65625f, (float16_t)-0.988769531f, (float16_t)-0.580566406f, + (float16_t)-0.318359375f, (float16_t)0.690917969f, (float16_t)0.148681641f, (float16_t)-0.281494141f, + (float16_t)-0.951171875f, (float16_t)0.1328125f, (float16_t)0.963378906f, (float16_t)-0.611328125f, + (float16_t)0.192871094f, (float16_t)-0.326416016f, (float16_t)0.139648438f, (float16_t)0.405517578f, + (float16_t)-0.0302276611f, (float16_t)0.272705078f, (float16_t)0.0329589844f, (float16_t)-0.825683594f, + (float16_t)-0.0717163086f, (float16_t)-0.0991821289f, (float16_t)0.322753906f, (float16_t)-0.455322266f, + (float16_t)0.622070312f, (float16_t)-0.391845703f, (float16_t)-0.573242188f, (float16_t)0.0982055664f, + (float16_t)0.270263672f, (float16_t)1.26464844f, (float16_t)0.239013672f, (float16_t)-0.90625f, + (float16_t)-0.471679688f, (float16_t)0.9609375f, (float16_t)0.835449219f, (float16_t)0.0780639648f, + (float16_t)-0.168334961f, (float16_t)-1.37695312f, (float16_t)-0.199707031f, (float16_t)-0.740234375f, + (float16_t)0.248657227f, (float16_t)0.454345703f, (float16_t)-0.767578125f, (float16_t)-0.337646484f, + (float16_t)-0.0762939453f, (float16_t)0.730957031f, (float16_t)1.25488281f, (float16_t)1.34765625f, + (float16_t)-0.335205078f, (float16_t)-1.06445312f, (float16_t)0.202636719f, (float16_t)-0.217773438f, + (float16_t)0.205810547f, (float16_t)0.227661133f, (float16_t)0.780273438f, (float16_t)-0.246337891f, + (float16_t)-0.369384766f, (float16_t)1.28710938f, (float16_t)0.500488281f, (float16_t)0.427490234f, + (float16_t)-0.292724609f, (float16_t)-1.01171875f, (float16_t)-0.361816406f, (float16_t)-0.592285156f, + (float16_t)0.411132812f, (float16_t)-0.163574219f, (float16_t)-0.5546875f, (float16_t)-0.765625f, + (float16_t)-1.59277344f, (float16_t)0.0559997559f, (float16_t)-0.290771484f, (float16_t)0.486816406f, + (float16_t)0.94140625f, (float16_t)0.0244750977f, (float16_t)-0.345947266f, (float16_t)0.562988281f, + (float16_t)0.00243759155f, (float16_t)1.03808594f, (float16_t)0.374511719f, (float16_t)-1.15136719f, + (float16_t)-0.496826172f, (float16_t)-0.237670898f, (float16_t)-0.782226562f, (float16_t)-0.0886230469f, + (float16_t)0.277587891f, (float16_t)-0.5703125f, (float16_t)0.302978516f, (float16_t)0.312744141f, + (float16_t)-0.108764648f, (float16_t)0.458251953f, (float16_t)-0.0163421631f, (float16_t)-0.283203125f, + (float16_t)-0.581542969f, 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(float16_t)-0.421630859f, (float16_t)0.327392578f, (float16_t)-0.391113281f, (float16_t)0.649414062f, + (float16_t)0.793945312f, (float16_t)-0.578125f, (float16_t)0.238037109f, (float16_t)-0.254638672f, + (float16_t)0.868164062f, (float16_t)-1.02441406f, (float16_t)-0.149169922f, (float16_t)0.0529174805f, + (float16_t)-0.212036133f, (float16_t)0.0148010254f, (float16_t)0.0700683594f, (float16_t)0.669433594f, + (float16_t)0.393798828f, (float16_t)-0.209472656f, (float16_t)0.416748047f, (float16_t)-0.58203125f, + (float16_t)-0.337646484f, (float16_t)-0.328857422f, (float16_t)-0.0767211914f, (float16_t)0.00318717957f, + (float16_t)0.728027344f, (float16_t)0.154785156f, (float16_t)-0.149414062f, (float16_t)0.785644531f, + (float16_t)0.0479736328f, (float16_t)0.805664062f, (float16_t)0.912597656f, (float16_t)-0.00814819336f, + (float16_t)-0.400146484f, (float16_t)-0.667480469f, (float16_t)-1.01953125f, (float16_t)-0.06640625f, + (float16_t)0.0176849365f, (float16_t)0.744140625f, (float16_t)-0.422607422f, (float16_t)1.07226562f, + (float16_t)0.668945312f, (float16_t)-0.21472168f, (float16_t)-0.017578125f, (float16_t)-0.451660156f, + (float16_t)-0.355224609f, (float16_t)-0.412353516f, (float16_t)0.303710938f, (float16_t)-0.553222656f, + (float16_t)-0.113891602f, (float16_t)-0.525390625f, (float16_t)-1.16113281f, (float16_t)-0.492431641f, + (float16_t)0.90234375f, (float16_t)0.375976562f, (float16_t)-0.260498047f, (float16_t)-0.509765625f, + (float16_t)-0.443359375f, (float16_t)0.0919799805f, (float16_t)0.547851562f, (float16_t)0.215576172f, + (float16_t)-0.266113281f, (float16_t)-1.390625f, (float16_t)-0.272949219f, (float16_t)1.18652344f, + (float16_t)1.13867188f, (float16_t)0.865722656f, (float16_t)1.10839844f, (float16_t)-0.5078125f, + (float16_t)0.698242188f, (float16_t)1.29394531f, (float16_t)-0.906738281f, (float16_t)-0.535644531f, + (float16_t)-0.459472656f, (float16_t)-0.571777344f, (float16_t)-0.170654297f, (float16_t)0.416015625f, + (float16_t)0.747070312f, (float16_t)0.323730469f, (float16_t)0.333007812f, (float16_t)-0.182250977f, + (float16_t)-1.43066406f, (float16_t)-0.0137634277f, (float16_t)-0.149780273f, (float16_t)0.28515625f, + (float16_t)-0.0681762695f, (float16_t)-0.228515625f, (float16_t)-0.642578125f, (float16_t)0.0433044434f, + (float16_t)0.817382812f, (float16_t)0.698242188f, (float16_t)-0.0476989746f, (float16_t)-0.0506896973f, + (float16_t)-1.24023438f, (float16_t)-0.340087891f, (float16_t)1.08300781f, (float16_t)0.375244141f, + (float16_t)-0.112792969f, (float16_t)-0.166381836f, (float16_t)-0.620605469f, (float16_t)0.00981140137f, + (float16_t)0.639648438f, (float16_t)0.395019531f, (float16_t)-0.214477539f, (float16_t)0.271240234f, + (float16_t)-0.731933594f, (float16_t)0.403808594f, (float16_t)0.620117188f, (float16_t)0.466064453f, + (float16_t)-0.576660156f, (float16_t)-0.389892578f, (float16_t)-0.155761719f, (float16_t)0.277099609f, + (float16_t)0.642089844f, (float16_t)0.0828857422f, (float16_t)-0.699707031f, (float16_t)-1.21289062f, + (float16_t)-0.401123047f, (float16_t)0.793945312f, (float16_t)1.09082031f, (float16_t)-0.391601562f, + (float16_t)-0.21484375f, (float16_t)-0.812988281f, (float16_t)0.492675781f, (float16_t)0.493164062f, + (float16_t)-0.155029297f, (float16_t)-0.477539062f, (float16_t)-0.123901367f, (float16_t)-0.479980469f, + (float16_t)0.0682983398f, (float16_t)0.895019531f, (float16_t)-0.427978516f, (float16_t)0.306640625f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/test_data.h new file mode 100644 index 00000000..b1ead1be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/weights_data.h new file mode 100644 index 00000000..817397fc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f16/weights_data.h @@ -0,0 +1,56 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_f16_weights[216] = { + (float16_t)0.0735473633f, (float16_t)-0.149291992f, (float16_t)-0.109680176f, (float16_t)-0.602539062f, + (float16_t)-0.538085938f, (float16_t)0.117675781f, (float16_t)-0.463378906f, (float16_t)-0.499267578f, + (float16_t)0.293457031f, (float16_t)0.241699219f, (float16_t)0.377197266f, (float16_t)-0.0591430664f, + (float16_t)0.178955078f, (float16_t)0.503417969f, (float16_t)-0.586914062f, (float16_t)-0.606445312f, + (float16_t)-0.716796875f, (float16_t)-0.0728149414f, (float16_t)-0.567871094f, (float16_t)-0.00295829773f, + (float16_t)-0.551269531f, (float16_t)-0.222412109f, (float16_t)0.482910156f, (float16_t)-0.0611572266f, + (float16_t)0.404052734f, (float16_t)-0.368164062f, (float16_t)0.123962402f, (float16_t)-0.299560547f, + (float16_t)0.203979492f, (float16_t)-0.581542969f, (float16_t)0.0686645508f, (float16_t)0.142578125f, + (float16_t)0.631347656f, (float16_t)-0.0172729492f, (float16_t)-0.696289062f, (float16_t)-0.227783203f, + (float16_t)-0.194213867f, (float16_t)0.225097656f, (float16_t)-0.40234375f, (float16_t)-0.0505371094f, + (float16_t)-0.0285491943f, (float16_t)0.55859375f, (float16_t)-0.602050781f, (float16_t)-0.716308594f, + (float16_t)-0.415039062f, (float16_t)0.0759277344f, (float16_t)0.0933227539f, (float16_t)0.0182495117f, + (float16_t)0.000513076782f, (float16_t)0.457763672f, (float16_t)0.593261719f, (float16_t)-0.09375f, + (float16_t)-0.684570312f, (float16_t)0.626464844f, (float16_t)0.220214844f, (float16_t)-0.51171875f, + (float16_t)0.285644531f, (float16_t)-0.290527344f, (float16_t)0.164550781f, (float16_t)0.0358886719f, + (float16_t)-0.348144531f, (float16_t)-0.234741211f, (float16_t)0.346191406f, (float16_t)-0.555175781f, + (float16_t)-0.318115234f, (float16_t)-0.391845703f, (float16_t)0.384765625f, (float16_t)-0.129394531f, + (float16_t)-0.296142578f, (float16_t)-0.164306641f, (float16_t)-0.0719604492f, (float16_t)0.0491638184f, + (float16_t)0.350585938f, (float16_t)-0.725097656f, (float16_t)0.62109375f, (float16_t)0.186889648f, + (float16_t)0.240112305f, (float16_t)-0.639160156f, (float16_t)0.175537109f, (float16_t)-0.227539062f, + (float16_t)0.458007812f, (float16_t)-0.64453125f, (float16_t)0.115966797f, (float16_t)-0.252197266f, + (float16_t)0.69921875f, (float16_t)-0.347900391f, (float16_t)0.639160156f, (float16_t)-0.486083984f, + (float16_t)0.252685547f, (float16_t)-0.509765625f, (float16_t)-0.029876709f, (float16_t)-0.538574219f, + (float16_t)-0.0188598633f, (float16_t)0.247680664f, (float16_t)0.666503906f, (float16_t)-0.288818359f, + (float16_t)-0.192993164f, (float16_t)0.425048828f, (float16_t)0.288574219f, (float16_t)0.684570312f, + (float16_t)-0.272949219f, (float16_t)0.485839844f, (float16_t)0.385986328f, (float16_t)0.55078125f, + (float16_t)0.520996094f, (float16_t)-0.329833984f, (float16_t)-0.570800781f, (float16_t)0.579589844f, + (float16_t)0.347167969f, (float16_t)-0.452636719f, (float16_t)0.129516602f, (float16_t)-0.338867188f, + (float16_t)0.230224609f, (float16_t)-0.0756835938f, (float16_t)0.00375366211f, (float16_t)0.11517334f, + (float16_t)-0.267578125f, (float16_t)0.177124023f, (float16_t)-0.105773926f, (float16_t)0.0882568359f, + (float16_t)0.159912109f, (float16_t)-0.0428771973f, (float16_t)-0.426025391f, (float16_t)0.402587891f, + (float16_t)-0.59765625f, (float16_t)0.240600586f, (float16_t)0.180175781f, (float16_t)-0.739257812f, + (float16_t)0.195068359f, (float16_t)-0.732910156f, (float16_t)0.189086914f, (float16_t)0.467529297f, + (float16_t)0.0599060059f, (float16_t)0.732910156f, (float16_t)-0.467285156f, (float16_t)0.101501465f, + (float16_t)0.281005859f, (float16_t)0.474365234f, (float16_t)-0.464355469f, (float16_t)0.00624084473f, + (float16_t)-0.185180664f, (float16_t)0.528808594f, (float16_t)-0.299560547f, (float16_t)0.449462891f, + (float16_t)0.166870117f, (float16_t)-0.227905273f, (float16_t)-0.393798828f, (float16_t)-0.146362305f, + (float16_t)0.377929688f, (float16_t)0.710449219f, (float16_t)0.664550781f, (float16_t)0.272216797f, + (float16_t)-0.749023438f, (float16_t)-0.589355469f, (float16_t)-0.444335938f, (float16_t)0.668945312f, + (float16_t)-0.0135345459f, (float16_t)-0.522460938f, (float16_t)0.557128906f, (float16_t)0.749023438f, + (float16_t)0.0827636719f, (float16_t)0.404785156f, (float16_t)0.403076172f, (float16_t)0.212646484f, + (float16_t)0.426269531f, (float16_t)-0.364501953f, (float16_t)0.707519531f, (float16_t)0.350830078f, + (float16_t)-0.451660156f, (float16_t)-0.144897461f, (float16_t)-0.525878906f, (float16_t)0.501464844f, + (float16_t)0.478515625f, (float16_t)0.0551452637f, (float16_t)-0.6484375f, (float16_t)-0.704101562f, + (float16_t)-0.377197266f, (float16_t)-0.236206055f, (float16_t)-0.217651367f, (float16_t)-0.667480469f, + (float16_t)-0.209838867f, (float16_t)-0.421142578f, (float16_t)0.645507812f, (float16_t)0.22265625f, + (float16_t)-0.00166893005f, (float16_t)-0.00982666016f, (float16_t)3.57627869e-07f, (float16_t)0.636230469f, + (float16_t)0.194091797f, (float16_t)0.428466797f, (float16_t)0.135253906f, (float16_t)-0.036895752f, + (float16_t)0.0244293213f, (float16_t)-0.219360352f, (float16_t)0.569335938f, (float16_t)0.0190124512f, + (float16_t)-0.703125f, (float16_t)-0.399169922f, (float16_t)-0.407226562f, (float16_t)0.433105469f, + (float16_t)0.633300781f, (float16_t)-0.482177734f, (float16_t)-0.21887207f, (float16_t)0.154541016f, + (float16_t)-0.194458008f, (float16_t)-0.693359375f, (float16_t)0.444091797f, (float16_t)-0.235961914f, + (float16_t)0.733886719f, (float16_t)-0.402832031f, (float16_t)-0.592773438f, (float16_t)-0.644042969f, + (float16_t)-0.576171875f, (float16_t)-0.0906982422f, (float16_t)0.0385437012f, (float16_t)0.127685547f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/biases_data.h new file mode 100644 index 00000000..d4fa021e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_f32_biases[6] = + {0.337419599f, 0.0628587902f, -0.319512516f, -0.222603902f, 0.274047256f, -0.482694238f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/config_data.h new file mode 100644 index 00000000..84a83f77 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CONV_BASIC_F32_INPUT_BATCHES 1 +#define TRANSPOSE_CONV_BASIC_F32_INPUT_H 5 +#define TRANSPOSE_CONV_BASIC_F32_INPUT_W 6 +#define TRANSPOSE_CONV_BASIC_F32_IN_CH 4 +#define TRANSPOSE_CONV_BASIC_F32_FILTER_H 3 +#define TRANSPOSE_CONV_BASIC_F32_FILTER_W 3 +#define TRANSPOSE_CONV_BASIC_F32_OUT_CH 6 +#define TRANSPOSE_CONV_BASIC_F32_STRIDE_H 2 +#define TRANSPOSE_CONV_BASIC_F32_STRIDE_W 2 +#define TRANSPOSE_CONV_BASIC_F32_PADDING_H 1 +#define TRANSPOSE_CONV_BASIC_F32_PADDING_W 1 +#define TRANSPOSE_CONV_BASIC_F32_PADDING_OFFSET_H 0 +#define TRANSPOSE_CONV_BASIC_F32_PADDING_OFFSET_W 0 +#define TRANSPOSE_CONV_BASIC_F32_DILATION_H 1 +#define TRANSPOSE_CONV_BASIC_F32_DILATION_W 1 +#define TRANSPOSE_CONV_BASIC_F32_OUTPUT_H 10 +#define TRANSPOSE_CONV_BASIC_F32_OUTPUT_W 12 +#define TRANSPOSE_CONV_BASIC_F32_OUTPUT_C 6 +#define TRANSPOSE_CONV_BASIC_F32_DST_SIZE 720 +#define TRANSPOSE_CONV_BASIC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CONV_BASIC_F32_USE_WRAPPER 0 +#define TRANSPOSE_CONV_BASIC_F32_OUT_ACTIVATION_MIN -1000.0f +#define TRANSPOSE_CONV_BASIC_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/input_data.h new file mode 100644 index 00000000..de5b8d8e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/input_data.h @@ -0,0 +1,20 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_f32_input[120] = { + -0.35070762f, -0.207154453f, -0.277935028f, 0.801493824f, 0.155012518f, -0.0563985333f, -0.778644562f, + -0.450311691f, 0.657995462f, 0.185143173f, 0.0817323476f, -0.115284339f, -0.988836825f, -0.890666962f, + -0.922576964f, 0.377558559f, 0.154547274f, 0.235119209f, 0.964856207f, -0.163764268f, -0.710109949f, + -0.402574509f, -0.227891147f, -0.994462252f, -0.640382826f, 0.124195941f, 0.725602269f, -0.0445622541f, + 0.966770649f, -0.513400078f, -0.566292286f, 0.660759866f, 0.663071334f, 0.471565902f, -0.394868225f, + -0.269031137f, 0.615817308f, 0.0840967521f, 0.20694004f, -0.791161358f, 0.0675662756f, -0.31934154f, + 0.621960044f, 0.468323559f, -0.429328322f, -0.592330873f, -0.958367944f, 0.306992799f, -0.735897481f, + -0.228257671f, -0.0867858753f, 0.951446474f, 0.526713192f, -0.106127441f, -0.0592682324f, -0.481301993f, + -0.110692494f, -0.173626885f, -0.270601928f, 0.0471571423f, 0.200240925f, -0.870713711f, -0.616791427f, + -0.600603342f, -0.771683693f, -0.478065997f, 0.452766299f, -0.549100459f, -0.884781599f, 0.246856987f, + 0.418027639f, -0.229249984f, -0.951996922f, -0.519650102f, 0.667104483f, 0.994377494f, -0.306729823f, + -0.489083737f, -0.0218586046f, 0.182900056f, 0.340663761f, 0.368174464f, 0.603652358f, -0.182723433f, + 0.851479888f, -0.0453063883f, 0.596320748f, -0.295771033f, -0.84247458f, -0.191050559f, -0.590841651f, + -0.407930106f, 0.405258745f, -0.131665975f, 0.836074889f, 0.00783542357f, 0.336077362f, -0.744916737f, + 0.00406632014f, 0.260516107f, -0.874367416f, -0.799745977f, -0.74711287f, 0.431540936f, -0.87002492f, + -0.83451426f, -0.310687035f, -0.0590392426f, 0.161230847f, 0.432952344f, -0.96260041f, 0.573886693f, + 0.90822047f, 0.75434798f, 0.25308764f, -0.524912596f, 0.508668184f, -0.0176399499f, -0.0197860282f, + -0.251639128f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/output_ref_data.h new file mode 100644 index 00000000..5c12fd62 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/output_ref_data.h @@ -0,0 +1,105 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_f32_output_ref[720] = { + 0.44880116f, -0.211401701f, -0.892720819f, -0.516390443f, -0.277041912f, -0.337151945f, -0.261779755f, + -0.974535465f, -0.600547552f, 0.176092103f, 0.476953745f, -0.449579298f, 0.961724162f, 0.888537645f, + -0.523856282f, 0.06261383f, 0.37254405f, -0.421712756f, 0.362656623f, -0.379971087f, 0.478650063f, + -0.342725992f, 1.06910038f, -0.972905219f, -0.164926857f, 0.00180387869f, 0.200983316f, 0.0325862914f, + 0.827245593f, -0.08348611f, -0.979658961f, 1.43153882f, 0.228458196f, -0.643613696f, -0.292922258f, + -0.146420509f, 1.7407409f, 0.820649385f, -1.55284655f, -0.535559595f, -1.07037652f, -0.917390168f, + 0.710401297f, -0.744299889f, 0.0225662887f, -0.425135255f, -0.452261448f, 0.713844419f, -0.389920622f, + -0.545703292f, 0.337032646f, -0.26438573f, 0.63374567f, -0.619245589f, -0.502467275f, 0.586700916f, + -1.60546315f, 0.612460911f, -0.105708241f, -0.899876237f, 1.57118142f, 1.08486521f, -0.683545709f, + -0.183201179f, -0.216453522f, -1.4069277f, 0.796341658f, -0.235755265f, 0.813931346f, -1.09299099f, + 0.584817529f, 0.00196409225f, -0.171662182f, 0.270394415f, -0.786319375f, -0.987921834f, 1.35615039f, + -0.988718271f, 0.216558516f, -1.6482048f, -1.13870502f, -0.384532303f, 0.185864747f, 1.0313791f, + 0.0470600128f, 0.201196343f, -0.0319875479f, 1.2187289f, 1.00569212f, -0.0212135911f, -0.426224023f, + 0.693014383f, -0.642510474f, -1.34371424f, -0.147392571f, -0.328730911f, 1.12606728f, 0.159151763f, + -0.28407222f, -0.241573378f, -0.335290551f, 0.00483122468f, 0.618631363f, 0.322017968f, -1.05136931f, + -0.575166583f, 0.0183457136f, -0.93526566f, -0.588243961f, 0.194431275f, -0.746339321f, 0.531614721f, + 2.31641102f, 0.331226856f, 1.08510792f, -1.32752931f, -1.45689404f, 1.04436553f, 0.836935699f, + -0.395043612f, 0.56600225f, 0.673943281f, -0.0920446664f, -0.73230052f, -0.353218198f, -1.28848588f, + 1.29128587f, -0.0466753542f, 2.1569922f, -0.359853655f, 0.911521077f, -1.53939271f, -0.479121834f, + -1.11470056f, 0.076877147f, 0.897781253f, 0.0445468426f, -0.00938278437f, -0.943281293f, -0.176001579f, + -1.88717592f, -0.465685487f, -0.421990633f, -0.2202501f, 0.271675646f, -0.426297903f, -0.368439317f, + -0.549440324f, 0.0340639353f, -1.06357169f, 1.48296821f, -0.513990045f, -0.0260733366f, 0.288438022f, + 0.680100024f, -0.414063185f, -0.0503825843f, 0.224994794f, -0.0836740732f, 0.0466596931f, 0.326843798f, + 0.494041175f, 0.0502895415f, -0.991741419f, -0.33864817f, 0.250787973f, 1.03238201f, -1.32038212f, + 0.0946803242f, 0.263242781f, -0.0307132602f, 0.137222961f, 1.01870847f, -0.00362178683f, -0.266604513f, + -0.00616285205f, -0.711848676f, 0.684089541f, 1.05510366f, -1.83538771f, 0.139511243f, 0.437148213f, + 0.403329819f, 0.177291289f, 0.896214247f, -0.450600177f, 0.711134672f, 1.17902851f, 0.146745712f, + -0.229330555f, 0.0312362909f, -0.60632956f, -0.196760505f, -0.488703221f, -0.0311541855f, -0.402784377f, + 0.0449993312f, -0.411430746f, 0.0261795223f, 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1.17884028f, -1.40755749f, 1.11273491f, + -1.65179908f, 0.978711843f, 0.354800731f, 0.404293925f, 0.0574238487f, -0.883659363f, -0.184220985f, + 0.456633151f, 0.14606908f, 0.133448333f, -0.961305141f, -0.745064259f, 0.289711416f, 1.26573038f, + -1.53941143f, -0.466787368f, -0.106852204f, 0.53696382f, 0.196601227f, 1.46149492f, -0.0461683571f, + 0.164867789f, 0.515108228f, -0.655665815f, 0.471814394f, 1.12090135f, -1.94129097f, 0.140169412f, + 0.259226978f, 0.0839863718f, 0.0315125734f, 0.625284195f, -0.252523899f, 0.208487332f, 0.326568633f, + -0.160845309f, -0.231787682f, 0.433368027f, -0.809565008f, 0.111829117f, -0.034471035f, -0.264291555f, + -1.00742185f, 0.766147256f, -0.751072466f, 0.549479604f, -0.45037213f, -1.23890507f, 0.370438635f, + 0.891286671f, -1.30700946f, -0.778222084f, -0.0673745126f, -0.86232686f, 0.146855935f, 2.19781446f, + -0.263751149f, 0.92137897f, -0.842618823f, -2.33743787f, 0.546891689f, 1.11834049f, -0.880826831f, + -0.625742912f, -0.22889173f, -0.649559498f, -0.0216292292f, 1.54345059f, -0.174363405f, -0.418622285f, + -1.82701957f, -1.89331841f, 1.0442965f, 0.403117508f, 0.142432243f, 0.335094541f, 0.282856524f, + -0.508747578f, 0.306524873f, 1.00656068f, -0.374118656f, 0.0814552605f, 0.626840711f, -0.158036932f, + -0.97115469f, -0.515611291f, 0.830949426f, 1.33677185f, 0.0549443513f, 0.277042598f, -0.43963176f, + -1.34320986f, -0.408033133f, -0.184901923f, 0.887511969f, 0.923968434f, -0.789868355f, -0.7945683f, + -0.527580261f, 0.629095793f, -0.0724821836f, 0.00802651048f, -0.585124016f, -0.183212459f, -0.411402762f, + 0.266661972f, 0.35120374f, 0.0362539291f, -0.342909038f, -0.06619367f, -0.846580327f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/test_data.h new file mode 100644 index 00000000..b1ead1be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/weights_data.h new file mode 100644 index 00000000..1699cccc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_f32/weights_data.h @@ -0,0 +1,33 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_f32_weights[216] = { + -0.0434978679f, 0.373166144f, 0.171320766f, -0.295683295f, 0.127451271f, 0.278450698f, -0.0207553469f, + -0.300122321f, 0.289636731f, 0.519953847f, 0.609640181f, 0.0930318013f, 0.570069671f, 0.590160131f, + 0.272918761f, 0.631259084f, -0.657772601f, -0.387790352f, -0.633539736f, -0.468774229f, -0.350553066f, + -0.317198277f, 0.606226683f, -0.221676156f, -0.68945998f, 0.11512804f, 0.465715349f, -0.676644564f, + 0.54097569f, 0.506087303f, 0.248750001f, -0.120601945f, 0.0299085118f, -0.322958112f, 0.2617082f, + 0.343706191f, -0.189933777f, 0.713993788f, 0.158854574f, 0.000160639f, 0.607796669f, -0.148781002f, + 0.485059977f, 0.15651162f, -0.0756933689f, 0.121007957f, -0.538320005f, 0.694474995f, -0.692813873f, + -0.679007232f, 0.493340015f, 0.347975373f, 0.00501684099f, -0.498169363f, -0.626575053f, -0.686026752f, + 0.430185676f, 0.514927566f, -0.005813553f, -0.21402216f, -0.48387742f, 0.151438892f, 0.630559385f, + -0.164585784f, -0.0239435397f, 0.280492097f, 0.165897772f, 0.456730783f, 0.676903844f, 0.734355509f, + -0.117135853f, 0.180171415f, -0.70541054f, -0.630616188f, -0.702505291f, 0.380706996f, -0.0770589784f, + -0.637695968f, 0.051871784f, -0.292154402f, 0.692812562f, 0.424208224f, 0.440680534f, 0.456796408f, + 0.0420231745f, -0.0928533748f, -0.383928925f, 0.702720582f, 0.696116745f, -0.0479905978f, 0.540682971f, + -0.235487327f, -0.0964919031f, -0.410193056f, -0.71726197f, -0.740433395f, -0.0653036311f, 0.437497497f, + 0.482869238f, -0.0310883373f, 0.433021009f, -0.0272699818f, 0.00366279949f, -0.424681395f, 0.682008505f, + 0.60679704f, 0.571432233f, -0.12263862f, -0.689014614f, 0.370346218f, 0.60991472f, 0.348531187f, + 0.628641427f, -0.691663742f, -0.62296325f, -0.109672457f, 0.258040577f, 0.388027161f, 0.656121492f, + -0.329552591f, 0.680447817f, -0.729113281f, 0.32890439f, -0.706216693f, 0.364558071f, -0.016955968f, + -0.141793892f, -0.260582477f, 0.267835975f, 0.123666875f, 0.429702699f, 0.53544867f, 0.193327978f, + -0.162692711f, -0.466460168f, -0.0818940103f, -0.74198848f, 0.690176547f, -0.408550531f, -0.0754926652f, + -0.402061254f, -0.713073432f, 0.187973127f, -0.299441844f, -0.158537924f, 0.400617272f, -0.14185831f, + 0.134734929f, 0.419558138f, 0.0958875045f, 0.317975044f, 0.54777801f, 0.397976875f, -0.278408021f, + 0.435088128f, 0.246247888f, 0.618849814f, 0.720274687f, -0.643093765f, -0.1566021f, 0.605198383f, + 0.663477242f, 0.0760072991f, -0.224922642f, 0.0912753865f, 0.25994581f, -0.106683373f, -0.458460063f, + -0.469173729f, -0.0892573968f, 0.326807708f, 0.339716434f, -0.593586743f, -0.679683208f, -0.735241592f, + 0.722694039f, -0.356857628f, -0.256021857f, 0.401394427f, 0.617125928f, 0.703346312f, 0.0208721496f, + 0.724535048f, 0.0887735412f, 0.252415031f, 0.509853423f, -0.15638116f, -0.369119525f, -0.362675101f, + -0.0633100569f, -0.617394805f, 0.0262733698f, -0.543192506f, -0.347481012f, 0.209541976f, 0.515765011f, + 0.667104363f, 0.152215973f, -0.210838482f, 0.439721018f, -0.580293715f, -0.529367447f, -0.14450103f, + 0.174420223f, 0.499249339f, 0.594327509f, -0.658226907f, -0.671798587f, -0.187267452f, 0.0538829453f, + -0.49362433f, -0.626818597f, -0.371354371f, 0.219406635f, 0.198004827f, 0.664451122f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/biases_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/biases_data.h new file mode 100644 index 00000000..ab5e599d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/biases_data.h @@ -0,0 +1,7 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_nhwc_f16_biases[6] = {(float16_t)0.220458984f, + (float16_t)-0.387939453f, + (float16_t)-0.273681641f, + (float16_t)0.242919922f, + (float16_t)-0.248657227f, + (float16_t)-0.158081055f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/config_data.h new file mode 100644 index 00000000..7f71b3b9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/config_data.h @@ -0,0 +1,24 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CONV_BASIC_NHWC_F16_INPUT_BATCHES 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_INPUT_H 5 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_INPUT_W 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_IN_CH 4 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_FILTER_H 3 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_FILTER_W 3 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUT_CH 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_STRIDE_H 2 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_STRIDE_W 2 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_PADDING_H 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_PADDING_W 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_PADDING_OFFSET_H 0 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_PADDING_OFFSET_W 0 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_DILATION_H 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_DILATION_W 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUTPUT_H 10 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUTPUT_W 12 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUTPUT_C 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_DST_SIZE 720 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CONV_BASIC_NHWC_F16_USE_WRAPPER 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUT_ACTIVATION_MIN (float16_t) - 1000.0f +#define TRANSPOSE_CONV_BASIC_NHWC_F16_OUT_ACTIVATION_MAX (float16_t)1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/input_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/input_data.h new file mode 100644 index 00000000..82533642 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/input_data.h @@ -0,0 +1,32 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_nhwc_f16_input[120] = { + (float16_t)0.376708984f, (float16_t)0.714355469f, (float16_t)-0.805175781f, (float16_t)-0.109436035f, + (float16_t)0.782714844f, (float16_t)0.181640625f, (float16_t)0.832519531f, (float16_t)0.551269531f, + (float16_t)0.699707031f, (float16_t)0.548828125f, (float16_t)0.0797119141f, (float16_t)0.0342712402f, + (float16_t)-0.10748291f, (float16_t)0.207885742f, (float16_t)-0.325439453f, (float16_t)0.0356140137f, + (float16_t)-0.380126953f, (float16_t)-0.0809936523f, (float16_t)-0.244628906f, (float16_t)-0.769042969f, + (float16_t)0.175292969f, (float16_t)-0.581054688f, (float16_t)-0.678710938f, (float16_t)-0.210571289f, + (float16_t)-0.713378906f, (float16_t)-0.747070312f, (float16_t)0.433105469f, (float16_t)0.951660156f, + (float16_t)-0.714355469f, (float16_t)0.493896484f, (float16_t)-0.655273438f, (float16_t)0.657714844f, + (float16_t)-0.503417969f, (float16_t)0.32421875f, (float16_t)-0.0592956543f, (float16_t)0.717773438f, + (float16_t)0.685546875f, (float16_t)0.100219727f, (float16_t)0.0935058594f, (float16_t)-0.161132812f, + (float16_t)-0.202636719f, (float16_t)0.333007812f, (float16_t)0.537109375f, (float16_t)-0.921875f, + (float16_t)0.719726562f, (float16_t)0.266601562f, (float16_t)-0.0718994141f, (float16_t)-0.796875f, + (float16_t)-0.11529541f, (float16_t)0.604003906f, (float16_t)-0.742675781f, (float16_t)0.151733398f, + (float16_t)-0.552246094f, (float16_t)0.406005859f, (float16_t)-0.453613281f, (float16_t)0.212768555f, + (float16_t)-0.574707031f, (float16_t)0.50390625f, (float16_t)0.690429688f, (float16_t)0.721679688f, + (float16_t)0.360107422f, (float16_t)0.372558594f, (float16_t)0.84375f, (float16_t)-0.735839844f, + (float16_t)0.548339844f, (float16_t)-0.736816406f, (float16_t)-0.426757812f, (float16_t)0.434326172f, + (float16_t)-0.691894531f, (float16_t)-0.158203125f, (float16_t)-0.124633789f, (float16_t)0.331054688f, + (float16_t)-0.579101562f, (float16_t)-0.085144043f, (float16_t)-0.14831543f, (float16_t)-0.778320312f, + (float16_t)-0.115661621f, (float16_t)0.414306641f, (float16_t)0.105529785f, (float16_t)0.382568359f, + (float16_t)-0.903808594f, (float16_t)0.223999023f, (float16_t)-0.303466797f, (float16_t)-0.924316406f, + (float16_t)0.590332031f, (float16_t)-0.745117188f, (float16_t)-0.84765625f, (float16_t)-0.096862793f, + (float16_t)-0.624511719f, (float16_t)-0.358886719f, (float16_t)0.489257812f, (float16_t)-0.314941406f, + (float16_t)-0.377685547f, (float16_t)0.126831055f, (float16_t)0.683105469f, (float16_t)-0.188110352f, + (float16_t)-0.266845703f, (float16_t)-0.312011719f, (float16_t)-0.0558166504f, (float16_t)0.603027344f, + (float16_t)-0.436523438f, (float16_t)0.775390625f, (float16_t)0.291015625f, (float16_t)0.891113281f, + (float16_t)-0.143432617f, (float16_t)-0.390136719f, (float16_t)0.786132812f, (float16_t)0.678222656f, + (float16_t)0.340332031f, (float16_t)-0.279052734f, (float16_t)0.463867188f, (float16_t)-0.0217285156f, + (float16_t)-0.782714844f, (float16_t)-0.439453125f, (float16_t)0.599609375f, (float16_t)-0.932617188f, + (float16_t)0.513671875f, (float16_t)-0.577148438f, (float16_t)0.959960938f, (float16_t)0.364746094f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/output_ref_data.h new file mode 100644 index 00000000..1a88015f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/output_ref_data.h @@ -0,0 +1,182 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_nhwc_f16_output_ref[720] = { + (float16_t)0.166137695f, (float16_t)-0.983398438f, (float16_t)-0.658203125f, (float16_t)0.402587891f, + (float16_t)-0.476318359f, (float16_t)0.596191406f, (float16_t)0.334228516f, (float16_t)-0.231323242f, + (float16_t)0.62890625f, (float16_t)-0.0285949707f, (float16_t)-0.230957031f, (float16_t)0.290039062f, + (float16_t)0.298828125f, (float16_t)0.191894531f, (float16_t)-0.458740234f, (float16_t)-0.19909668f, + (float16_t)-0.201660156f, (float16_t)0.0349121094f, (float16_t)0.441650391f, (float16_t)-0.653808594f, + (float16_t)-0.0178222656f, (float16_t)-0.71875f, (float16_t)0.523925781f, (float16_t)0.270263672f, + (float16_t)0.406494141f, (float16_t)-0.195556641f, (float16_t)-0.412841797f, (float16_t)0.202758789f, + (float16_t)-0.15637207f, (float16_t)0.372802734f, (float16_t)-0.197387695f, (float16_t)-0.275390625f, + (float16_t)0.361816406f, (float16_t)0.00745391846f, (float16_t)-0.448974609f, (float16_t)-0.259521484f, + (float16_t)0.125976562f, (float16_t)-0.813964844f, (float16_t)-0.395751953f, (float16_t)0.309082031f, + (float16_t)-0.409912109f, (float16_t)-0.0314331055f, (float16_t)-0.419921875f, (float16_t)-0.337158203f, + (float16_t)-0.569824219f, (float16_t)0.419433594f, (float16_t)0.0135803223f, (float16_t)-0.18371582f, + (float16_t)0.55859375f, (float16_t)-0.212646484f, (float16_t)0.268554688f, (float16_t)0.735351562f, + (float16_t)0.172485352f, (float16_t)-0.352783203f, (float16_t)0.008934021f, (float16_t)-0.564453125f, + (float16_t)-0.359619141f, (float16_t)0.436523438f, (float16_t)-0.743652344f, (float16_t)-0.0609436035f, + (float16_t)-0.13659668f, (float16_t)-0.250244141f, (float16_t)-0.7421875f, (float16_t)0.126464844f, + (float16_t)-0.519042969f, (float16_t)0.123779297f, (float16_t)0.0715332031f, (float16_t)-0.127319336f, + (float16_t)-0.23425293f, (float16_t)0.952636719f, (float16_t)-0.174072266f, (float16_t)-0.271972656f, + (float16_t)1.02832031f, (float16_t)2.203125f, (float16_t)-0.634277344f, (float16_t)-0.889160156f, + (float16_t)0.740722656f, (float16_t)-0.530273438f, (float16_t)0.404296875f, (float16_t)-0.998046875f, + (float16_t)-1.57226562f, (float16_t)-1.20996094f, (float16_t)0.453125f, (float16_t)-0.205078125f, + (float16_t)0.345703125f, (float16_t)0.165039062f, (float16_t)-0.426513672f, (float16_t)0.1796875f, + (float16_t)-0.676757812f, (float16_t)-0.181640625f, (float16_t)-0.152832031f, (float16_t)-1.10058594f, + (float16_t)-0.0972900391f, (float16_t)0.436035156f, (float16_t)0.440673828f, (float16_t)-1.9375f, + (float16_t)0.592773438f, (float16_t)1.11621094f, (float16_t)-0.713378906f, (float16_t)-0.469726562f, + (float16_t)0.0138320923f, (float16_t)-0.360107422f, (float16_t)0.332519531f, (float16_t)-0.184814453f, + (float16_t)0.470458984f, (float16_t)0.935546875f, (float16_t)-0.858886719f, (float16_t)-2.00976562f, + (float16_t)0.266357422f, (float16_t)-0.587402344f, (float16_t)-0.640136719f, (float16_t)-0.240356445f, + (float16_t)0.0810546875f, (float16_t)-0.0508422852f, (float16_t)0.5390625f, (float16_t)-0.414306641f, + (float16_t)0.515136719f, (float16_t)-0.0283203125f, (float16_t)-0.931640625f, (float16_t)0.322509766f, + (float16_t)-0.595703125f, (float16_t)-0.799804688f, (float16_t)-0.526367188f, (float16_t)0.839355469f, + (float16_t)-0.439941406f, (float16_t)-0.823730469f, (float16_t)1.10058594f, (float16_t)-0.0233459473f, + (float16_t)-0.362304688f, (float16_t)0.279296875f, (float16_t)-0.418457031f, (float16_t)0.260253906f, + (float16_t)0.495361328f, (float16_t)-1.28222656f, (float16_t)-0.921875f, (float16_t)0.383789062f, + (float16_t)-0.043762207f, (float16_t)-0.8828125f, (float16_t)0.303466797f, (float16_t)0.0472106934f, + (float16_t)0.347167969f, (float16_t)0.655761719f, (float16_t)-0.454833984f, (float16_t)1.37011719f, + (float16_t)-0.406738281f, (float16_t)-0.841308594f, (float16_t)-0.57421875f, (float16_t)-0.302001953f, + (float16_t)-0.823242188f, (float16_t)-0.914550781f, (float16_t)0.043182373f, (float16_t)0.434326172f, + (float16_t)-1.2421875f, (float16_t)-0.140869141f, (float16_t)-0.180175781f, (float16_t)-0.991210938f, + (float16_t)-0.297363281f, (float16_t)-2.03320312f, (float16_t)-0.707519531f, (float16_t)0.211669922f, + (float16_t)-0.987792969f, (float16_t)-0.169067383f, (float16_t)-0.3515625f, (float16_t)0.622558594f, + (float16_t)-0.482421875f, (float16_t)-0.228149414f, (float16_t)-0.5078125f, (float16_t)-0.71484375f, + (float16_t)-0.132080078f, (float16_t)-1.44921875f, (float16_t)-0.52734375f, (float16_t)0.0816650391f, + (float16_t)-0.75390625f, (float16_t)-0.338623047f, (float16_t)0.0405578613f, (float16_t)-0.200317383f, + (float16_t)-0.938964844f, (float16_t)-0.830566406f, (float16_t)0.727539062f, (float16_t)0.242431641f, + (float16_t)0.385742188f, (float16_t)0.189453125f, (float16_t)-0.369384766f, (float16_t)0.179321289f, + (float16_t)-0.0701293945f, (float16_t)0.248657227f, (float16_t)0.167602539f, (float16_t)-0.974609375f, + (float16_t)0.0530090332f, (float16_t)0.722167969f, (float16_t)0.231323242f, (float16_t)0.230224609f, + (float16_t)1.12402344f, (float16_t)0.302246094f, (float16_t)0.866699219f, (float16_t)0.907714844f, + (float16_t)0.734863281f, (float16_t)-0.514160156f, (float16_t)0.327880859f, (float16_t)-1.58789062f, + (float16_t)-0.0702514648f, (float16_t)-0.362548828f, (float16_t)-0.0997924805f, (float16_t)0.990722656f, + (float16_t)0.754394531f, (float16_t)0.462402344f, (float16_t)0.0105819702f, (float16_t)0.563964844f, + (float16_t)0.329589844f, (float16_t)0.343505859f, (float16_t)0.240478516f, (float16_t)-0.765136719f, + (float16_t)0.727539062f, (float16_t)0.698730469f, (float16_t)-0.905273438f, (float16_t)-0.00788879395f, + (float16_t)-0.0727539062f, (float16_t)-2.18945312f, (float16_t)0.303466797f, (float16_t)0.827636719f, + (float16_t)-1.6875f, (float16_t)0.0750732422f, (float16_t)0.261474609f, (float16_t)-0.0332641602f, + (float16_t)-0.212280273f, (float16_t)1.58691406f, (float16_t)0.675292969f, (float16_t)-0.964355469f, + (float16_t)-0.0422058105f, (float16_t)-0.280029297f, (float16_t)-1.10253906f, (float16_t)-0.258789062f, + (float16_t)-0.897460938f, (float16_t)-0.562011719f, (float16_t)0.523925781f, (float16_t)0.887695312f, + (float16_t)-1.12304688f, (float16_t)1.96289062f, (float16_t)-0.177734375f, (float16_t)-0.763671875f, + (float16_t)-0.383789062f, (float16_t)0.35546875f, (float16_t)-0.26171875f, (float16_t)-0.743652344f, + (float16_t)-0.520996094f, (float16_t)-0.744140625f, (float16_t)0.556640625f, (float16_t)0.172607422f, + (float16_t)-0.6484375f, (float16_t)0.66015625f, (float16_t)1.36816406f, (float16_t)-0.821777344f, + (float16_t)-0.417480469f, (float16_t)-0.181884766f, (float16_t)-1.10839844f, (float16_t)-0.155761719f, + (float16_t)0.53125f, (float16_t)-1.32324219f, (float16_t)-0.93359375f, (float16_t)-2.48632812f, + (float16_t)0.782714844f, (float16_t)-0.298095703f, (float16_t)-0.433837891f, (float16_t)0.321044922f, + (float16_t)0.894042969f, (float16_t)-0.382080078f, (float16_t)1.0546875f, (float16_t)0.826171875f, + (float16_t)-0.138427734f, (float16_t)2.2109375f, (float16_t)-1.42773438f, (float16_t)-1.1171875f, + (float16_t)-0.682617188f, (float16_t)-1.45800781f, (float16_t)-0.869140625f, (float16_t)-0.257324219f, + (float16_t)0.852050781f, (float16_t)1.078125f, (float16_t)-0.372802734f, (float16_t)0.392822266f, + (float16_t)0.0672607422f, (float16_t)-0.182617188f, (float16_t)0.6796875f, (float16_t)-0.633789062f, + (float16_t)-0.476074219f, (float16_t)-0.0848388672f, (float16_t)-1.33886719f, (float16_t)-1.39746094f, + (float16_t)0.00298118591f, (float16_t)-1.39453125f, (float16_t)-0.625488281f, (float16_t)0.359375f, + (float16_t)-0.649414062f, (float16_t)0.245849609f, (float16_t)-0.775878906f, (float16_t)0.357177734f, + (float16_t)1.23381615e-05f, (float16_t)0.0532531738f, (float16_t)-0.618652344f, (float16_t)-0.551757812f, + (float16_t)0.0308532715f, (float16_t)-1.42871094f, (float16_t)-0.361328125f, (float16_t)0.388183594f, + (float16_t)-0.576171875f, (float16_t)-0.20300293f, (float16_t)0.259277344f, (float16_t)0.0575866699f, + (float16_t)-0.0467834473f, (float16_t)0.237304688f, (float16_t)-0.7578125f, (float16_t)-0.443603516f, + (float16_t)0.250244141f, (float16_t)-1.10351562f, (float16_t)-0.0114364624f, (float16_t)0.164306641f, + (float16_t)-0.353515625f, (float16_t)-0.686035156f, (float16_t)0.539550781f, (float16_t)-0.988769531f, + (float16_t)-1.015625f, (float16_t)-0.803222656f, (float16_t)0.944824219f, (float16_t)0.601074219f, + (float16_t)1.15234375f, (float16_t)0.725585938f, (float16_t)0.687988281f, (float16_t)0.672851562f, + (float16_t)0.761230469f, (float16_t)-0.275390625f, (float16_t)0.854003906f, (float16_t)-1.00292969f, + (float16_t)0.395019531f, (float16_t)-0.0631103516f, (float16_t)-1.19238281f, (float16_t)-0.00176334381f, + (float16_t)-0.473144531f, (float16_t)-0.207641602f, (float16_t)-1.24707031f, (float16_t)-0.3671875f, + (float16_t)-0.879882812f, (float16_t)0.251953125f, (float16_t)0.0798950195f, (float16_t)0.395263672f, + (float16_t)-0.332275391f, (float16_t)1.26757812f, (float16_t)-0.334960938f, (float16_t)-1.08007812f, + (float16_t)-0.0803833008f, (float16_t)-1.078125f, (float16_t)-0.329101562f, (float16_t)0.197631836f, + (float16_t)-0.573730469f, (float16_t)-0.556640625f, (float16_t)0.161254883f, (float16_t)-0.16418457f, + (float16_t)-0.934082031f, (float16_t)-0.0103530884f, (float16_t)0.0536193848f, (float16_t)-0.190673828f, + (float16_t)-0.363037109f, (float16_t)-0.0373840332f, (float16_t)-1.8515625f, (float16_t)0.123596191f, + (float16_t)-0.126464844f, (float16_t)-1.36328125f, (float16_t)1.36816406f, 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(float16_t)0.610839844f, (float16_t)-0.880859375f, (float16_t)0.399902344f, + (float16_t)0.223632812f, (float16_t)0.645996094f, (float16_t)-0.321777344f, (float16_t)-0.25f, + (float16_t)-0.107666016f, (float16_t)-0.370117188f, (float16_t)0.788574219f, (float16_t)-0.656738281f, + (float16_t)-0.459960938f, (float16_t)0.291748047f, (float16_t)0.0712890625f, (float16_t)-0.291748047f, + (float16_t)0.0531311035f, (float16_t)-0.905273438f, (float16_t)-0.237426758f, (float16_t)0.173339844f, + (float16_t)-0.485351562f, (float16_t)-0.489257812f, (float16_t)0.342285156f, (float16_t)-0.289550781f, + (float16_t)-1.07128906f, (float16_t)1.10058594f, (float16_t)0.373046875f, (float16_t)-0.17199707f, + (float16_t)-0.656738281f, (float16_t)-0.505859375f, (float16_t)-0.775390625f, (float16_t)-0.352294922f, + (float16_t)-0.322998047f, (float16_t)-0.0700683594f, (float16_t)-0.111938477f, (float16_t)-0.419433594f, + (float16_t)-0.951660156f, (float16_t)0.521972656f, (float16_t)0.724121094f, (float16_t)0.0909423828f, + (float16_t)-0.303466797f, (float16_t)-1.3046875f, (float16_t)0.308837891f, (float16_t)0.5703125f, + (float16_t)-0.676757812f, (float16_t)-0.0395507812f, (float16_t)0.0693359375f, (float16_t)-0.672851562f, + (float16_t)-0.336914062f, (float16_t)-0.147460938f, (float16_t)1.23339844f, (float16_t)0.477539062f, + (float16_t)0.240722656f, (float16_t)-0.557617188f, (float16_t)0.0572814941f, (float16_t)0.539550781f, + (float16_t)-0.261962891f, (float16_t)-0.0987548828f, (float16_t)-0.0906982422f, (float16_t)-1.43457031f, + (float16_t)0.723632812f, (float16_t)-0.694335938f, (float16_t)-0.289306641f, (float16_t)0.341552734f, + (float16_t)0.0444946289f, (float16_t)-1.37304688f, (float16_t)1.38574219f, (float16_t)1.50976562f, + (float16_t)-1.09667969f, (float16_t)1.27050781f, (float16_t)-1.07714844f, (float16_t)-0.199707031f, + (float16_t)-1.52929688f, (float16_t)-0.669921875f, (float16_t)1.50292969f, (float16_t)0.69140625f, + (float16_t)0.0523376465f, (float16_t)-0.968261719f, (float16_t)0.323242188f, (float16_t)0.712402344f, + (float16_t)-0.383544922f, (float16_t)-0.178222656f, (float16_t)0.6875f, (float16_t)-1.01367188f, + (float16_t)0.140625f, (float16_t)-0.177978516f, (float16_t)0.341308594f, (float16_t)-0.432373047f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/test_data.h new file mode 100644 index 00000000..b1ead1be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/test_data.h @@ -0,0 +1,6 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/weights_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/weights_data.h new file mode 100644 index 00000000..201b8994 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f16/weights_data.h @@ -0,0 +1,56 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_conv_basic_nhwc_f16_weights[216] = { + (float16_t)-0.397216797f, (float16_t)0.344238281f, (float16_t)0.474121094f, (float16_t)-0.451660156f, + (float16_t)0.289306641f, (float16_t)-0.247314453f, (float16_t)-0.449951172f, (float16_t)0.720214844f, + (float16_t)-0.71484375f, (float16_t)-0.640625f, (float16_t)0.470458984f, (float16_t)-0.495117188f, + (float16_t)0.134033203f, (float16_t)-0.305908203f, (float16_t)0.614257812f, (float16_t)0.302734375f, + (float16_t)-0.00872802734f, (float16_t)0.324951172f, (float16_t)0.434326172f, (float16_t)-0.607910156f, + (float16_t)-0.0394897461f, (float16_t)-0.286621094f, (float16_t)0.517578125f, (float16_t)-0.203491211f, + (float16_t)-0.167724609f, (float16_t)-0.430908203f, (float16_t)-0.708496094f, (float16_t)0.442382812f, + (float16_t)0.421386719f, (float16_t)-0.347167969f, (float16_t)-0.489257812f, (float16_t)-0.316162109f, + (float16_t)0.694335938f, (float16_t)-0.348144531f, (float16_t)-0.255859375f, (float16_t)0.426269531f, + (float16_t)0.62890625f, (float16_t)0.0901489258f, (float16_t)0.740234375f, (float16_t)0.158569336f, + (float16_t)-0.481689453f, (float16_t)-0.216430664f, (float16_t)0.489257812f, (float16_t)0.705078125f, + (float16_t)0.156005859f, (float16_t)-0.0795898438f, (float16_t)-0.690917969f, (float16_t)0.354492188f, + (float16_t)-0.172973633f, (float16_t)-0.0447387695f, (float16_t)-0.306884766f, (float16_t)0.356933594f, + (float16_t)0.665527344f, (float16_t)-0.549804688f, (float16_t)0.66015625f, (float16_t)-0.708984375f, + (float16_t)0.0384216309f, (float16_t)-0.0240783691f, (float16_t)-0.5234375f, (float16_t)0.547363281f, + (float16_t)0.236572266f, (float16_t)-0.552246094f, (float16_t)-0.428466797f, (float16_t)0.534667969f, + (float16_t)0.740234375f, (float16_t)0.698730469f, (float16_t)-0.498779297f, (float16_t)-0.215087891f, + (float16_t)-0.365722656f, (float16_t)-0.33203125f, (float16_t)-0.680175781f, (float16_t)0.0642700195f, + (float16_t)0.347412109f, (float16_t)0.0689697266f, (float16_t)-0.182495117f, (float16_t)0.17578125f, + (float16_t)0.14831543f, (float16_t)-0.347412109f, (float16_t)0.11328125f, (float16_t)0.709472656f, + (float16_t)0.677734375f, (float16_t)0.612304688f, (float16_t)-0.12109375f, (float16_t)0.390380859f, + (float16_t)-0.0690307617f, (float16_t)0.202880859f, (float16_t)0.112121582f, (float16_t)0.382324219f, + (float16_t)-0.422851562f, (float16_t)0.244018555f, (float16_t)0.592773438f, (float16_t)-0.7109375f, + (float16_t)0.598144531f, (float16_t)0.391113281f, (float16_t)-0.0587463379f, (float16_t)-0.579589844f, + (float16_t)-0.289794922f, (float16_t)-0.450439453f, (float16_t)-0.331054688f, (float16_t)-0.606933594f, + (float16_t)-0.59765625f, (float16_t)-0.686035156f, (float16_t)0.716796875f, (float16_t)-0.492919922f, + (float16_t)0.685546875f, (float16_t)-0.184082031f, (float16_t)-0.158325195f, (float16_t)0.296875f, + (float16_t)0.519042969f, (float16_t)-0.472167969f, (float16_t)0.409667969f, (float16_t)0.087097168f, + (float16_t)-0.329833984f, (float16_t)-0.20715332f, (float16_t)-0.471435547f, (float16_t)-0.462646484f, + (float16_t)-0.188354492f, (float16_t)0.741210938f, (float16_t)-0.693847656f, (float16_t)-0.120483398f, + (float16_t)-0.637695312f, (float16_t)-0.398681641f, (float16_t)0.358886719f, (float16_t)-0.0699462891f, + (float16_t)-0.266845703f, (float16_t)0.298828125f, (float16_t)0.0160064697f, (float16_t)-0.545410156f, + (float16_t)0.36328125f, (float16_t)-0.574707031f, (float16_t)-0.334228516f, (float16_t)-0.40625f, + (float16_t)-0.149047852f, (float16_t)0.21862793f, (float16_t)-0.356445312f, (float16_t)0.480224609f, + (float16_t)-0.31640625f, (float16_t)-0.562988281f, (float16_t)0.510253906f, (float16_t)-0.499755859f, + (float16_t)-0.161010742f, (float16_t)0.0246276855f, (float16_t)-0.486816406f, (float16_t)0.392822266f, + (float16_t)0.0839233398f, (float16_t)0.634277344f, (float16_t)-0.254150391f, (float16_t)0.16003418f, + (float16_t)0.340820312f, (float16_t)-0.438720703f, (float16_t)0.456542969f, (float16_t)-0.000514984131f, + (float16_t)0.131591797f, (float16_t)0.579101562f, (float16_t)0.635253906f, (float16_t)0.335693359f, + (float16_t)0.556152344f, (float16_t)0.231933594f, (float16_t)0.108459473f, (float16_t)-0.666015625f, + (float16_t)0.00127029419f, (float16_t)0.138549805f, (float16_t)0.504394531f, (float16_t)-0.723632812f, + (float16_t)0.001080513f, (float16_t)-0.268066406f, (float16_t)-0.11151123f, (float16_t)0.745117188f, + (float16_t)0.578125f, (float16_t)-0.130859375f, (float16_t)0.706054688f, (float16_t)0.235229492f, + (float16_t)0.633300781f, (float16_t)0.332519531f, (float16_t)-0.291992188f, (float16_t)0.0330505371f, + (float16_t)-0.602539062f, (float16_t)-0.586914062f, (float16_t)0.432128906f, (float16_t)0.399658203f, + (float16_t)0.361328125f, (float16_t)0.520507812f, (float16_t)-0.0550842285f, (float16_t)0.223388672f, + (float16_t)0.653320312f, (float16_t)-0.364013672f, (float16_t)-0.604492188f, (float16_t)0.69140625f, + (float16_t)0.645507812f, (float16_t)-0.721679688f, (float16_t)-0.0952148438f, (float16_t)-0.743652344f, + (float16_t)0.574707031f, (float16_t)0.301513672f, (float16_t)0.193237305f, (float16_t)-0.352783203f, + (float16_t)0.637695312f, (float16_t)0.216552734f, (float16_t)-0.454589844f, (float16_t)0.0599365234f, + (float16_t)-0.135253906f, (float16_t)0.124023438f, (float16_t)0.113586426f, (float16_t)-0.279296875f, + (float16_t)0.303955078f, (float16_t)-0.411621094f, (float16_t)0.749511719f, (float16_t)-0.367919922f, + (float16_t)-0.641601562f, (float16_t)0.231445312f, (float16_t)0.70703125f, (float16_t)-0.647949219f, + (float16_t)-0.363769531f, (float16_t)-0.648925781f, (float16_t)-0.615722656f, (float16_t)0.354248047f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/biases_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/biases_data.h new file mode 100644 index 00000000..d2fbbdc3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/biases_data.h @@ -0,0 +1,3 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_nhwc_f32_biases[6] = + {-0.274051696f, 0.4390634f, -0.261632621f, 0.173772573f, 0.0213841889f, -0.331626803f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/config_data.h new file mode 100644 index 00000000..db0f6dd9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/config_data.h @@ -0,0 +1,24 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_CONV_BASIC_NHWC_F32_INPUT_BATCHES 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_INPUT_H 5 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_INPUT_W 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_IN_CH 4 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_FILTER_H 3 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_FILTER_W 3 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUT_CH 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_STRIDE_H 2 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_STRIDE_W 2 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_PADDING_H 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_PADDING_W 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_PADDING_OFFSET_H 0 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_PADDING_OFFSET_W 0 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_DILATION_H 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_DILATION_W 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUTPUT_H 10 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUTPUT_W 12 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUTPUT_C 6 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_DST_SIZE 720 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_CONV_BASIC_NHWC_F32_USE_WRAPPER 1 +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUT_ACTIVATION_MIN -1000.0f +#define TRANSPOSE_CONV_BASIC_NHWC_F32_OUT_ACTIVATION_MAX 1000.0f diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/input_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/input_data.h new file mode 100644 index 00000000..b1d7a4f3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/input_data.h @@ -0,0 +1,20 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_nhwc_f32_input[120] = { + 0.411830217f, -0.879075587f, 0.415222079f, 0.197922885f, 0.358075023f, 0.0382010192f, 0.443575799f, + 0.779227436f, -0.401128531f, 0.289966643f, -0.42730543f, 0.533165514f, 0.789016366f, -0.71878922f, + 0.100702368f, -0.465318829f, 0.153851107f, 0.396190673f, 0.275625288f, -0.230716765f, 0.25586462f, + 0.806457579f, -0.249122187f, 0.377248496f, 0.436451703f, 0.703771889f, 0.0302626286f, -0.160859182f, + 0.556254148f, -0.782167375f, -0.735551476f, 0.0194223896f, 0.654465973f, -0.311418116f, -0.424011737f, + -0.383611113f, 0.188602999f, -0.484826833f, 0.924942136f, 0.971089423f, 0.791942179f, -0.938555658f, + 0.531836033f, -0.135144651f, -0.84866035f, 0.138966486f, -0.870333016f, 0.678278983f, 0.101992324f, + -0.239434153f, -0.118654765f, 0.79592669f, 0.843792558f, 0.524112463f, 0.906962693f, -0.347698092f, + -0.697086394f, -0.465984702f, 0.238230214f, -0.461968452f, -0.809960842f, 0.00707509182f, -0.909636319f, + -0.844991148f, 0.983231246f, -0.804768503f, 0.120412648f, -0.429673642f, 0.45308584f, -0.507642388f, + -0.849303544f, 0.191883236f, 0.223755211f, 0.0282370448f, -0.0551588833f, -0.297938347f, 0.525944412f, + 0.0814181939f, 0.885401428f, 0.714711428f, 0.524181366f, -0.881572127f, -0.232790202f, 0.498965234f, + 0.979245186f, -0.0620893389f, -0.0272237007f, 0.48468551f, 0.30653286f, -0.263557076f, 0.885005295f, + -0.255054981f, -0.737187743f, 0.146300986f, -0.247763127f, -0.464321017f, -0.689310014f, -0.200540632f, + -0.145633981f, -0.972878993f, 0.887595594f, -0.305159509f, 0.0166773144f, 0.615140676f, -0.423134983f, + 0.472264498f, 0.739825428f, 0.157051519f, -0.770257831f, -0.797947764f, -0.438421905f, 0.51275754f, + -0.291881084f, -0.59286803f, -0.0421294942f, -0.0185563453f, 0.528469563f, 0.397847623f, 0.879700303f, + -0.156454757f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/output_ref_data.h new file mode 100644 index 00000000..20c0bfcc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/output_ref_data.h @@ -0,0 +1,105 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_nhwc_f32_output_ref[720] = { + 0.05535537f, 0.631168723f, -0.316015631f, 0.0718919709f, 0.0284584016f, -0.561290562f, 0.420758814f, + -0.033138603f, -0.576485276f, -1.43218052f, -0.626738966f, -0.44272235f, -0.0793417245f, 0.715038836f, + 0.501701891f, -0.439349234f, -0.182107717f, 0.035998404f, -0.255354583f, 0.74768281f, 0.32379365f, + 0.0692292154f, -0.60522604f, 0.0612924993f, -0.0744979978f, 0.425231963f, 0.0792802572f, -0.0448118895f, + 0.280107379f, -0.431975186f, -0.840969324f, 0.222254589f, 0.0445392132f, -0.105286032f, -0.126985148f, + -0.018134445f, -0.313363403f, 0.175821692f, -0.593865633f, 0.614120901f, 0.000472862273f, -0.566634476f, + 0.0155851245f, 0.210231513f, -0.775540173f, -0.683941066f, 0.219609082f, -0.0338736475f, -0.576414585f, + 0.465087622f, -0.182462543f, 0.138651699f, -0.288778752f, 0.0745607018f, -0.675052166f, 0.682315588f, + -0.661122799f, 0.355813593f, 0.0704559758f, -0.684599638f, -0.476099402f, 0.22387071f, 0.472918332f, + -0.0414942652f, -0.0901539326f, 0.11931017f, -0.430698514f, 0.105637938f, 0.906598151f, 0.3912763f, + 0.0602255464f, -0.239095107f, -0.939335227f, 1.28996718f, -1.54085231f, 0.388497949f, 0.557240069f, + -0.389631331f, -0.21477066f, 0.494659305f, -0.664284587f, 1.84524488f, 0.886771977f, -0.464193702f, + -1.08635426f, 1.32186341f, -0.889604151f, -0.341007531f, 0.0450831503f, -0.17990306f, -0.771848202f, + 0.669772744f, -1.90864086f, 1.11095834f, 1.72513676f, -1.10902846f, -1.11997962f, 1.13747883f, + -0.844507873f, 0.0151010603f, 0.725826681f, -0.631346226f, 0.671344161f, 0.351579338f, 1.3184104f, + -0.906457901f, 0.649725497f, 0.550116539f, 0.562670112f, -0.538158417f, 0.263109982f, 1.5021286f, + -0.683262885f, -0.135757327f, 0.199275523f, -0.452468544f, 0.144120634f, 1.30182743f, 0.597688615f, + -2.28890514f, 0.111019075f, 0.414654195f, -0.142243952f, 0.630740464f, 0.0370169878f, -0.856106162f, + -0.643174469f, 0.0610874295f, -0.555430114f, 0.613967776f, 1.07745171f, -1.00541103f, -0.276998848f, + 0.109196395f, 0.970897496f, 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-0.265285969f, 0.425021619f, 0.625442982f, -0.572267234f, 0.110889599f, + 0.667987168f, -1.19485927f, -0.174882203f, 0.42434299f, -0.439645231f, -0.130005896f, -0.648892939f, + -0.28998372f, 0.0140806437f, 0.558506966f, -0.661440492f, 0.268117934f, 0.265053451f, -0.786694288f, + -0.525827169f, 0.00922170281f, -0.994469881f, -0.594603419f, 0.342606455f, -1.03644562f, -0.663092613f, + 0.687901378f, 0.0537244678f, -0.126344055f, -0.648329496f, 0.468052536f, -0.0871622413f, 0.844952881f, + -0.17974472f, 0.552548766f, 0.527218223f, -0.0322059989f, 0.21421957f, -0.249209255f, -0.109668463f, + 0.927670956f, -0.543847919f, -0.575865567f, -0.790537f, 0.687341332f, 1.2146821f, 0.25998491f, + 0.170329332f, 0.12597844f, -0.549920797f, 1.07182562f, -0.379250288f, -0.335077167f, 0.468468308f, + -0.0708157718f, 0.180663615f, 0.660819888f, -1.33191657f, 1.14223385f, 0.362996042f, -1.13766968f, + -0.413942754f, 0.394450575f, -0.623639107f, 0.0639854521f, -0.358018428f, -0.498663157f, -1.21883535f, + 0.355599046f, -0.484121859f, -1.19322968f, -0.480503559f, -0.436336875f, -0.98565495f, 0.367641211f, + -1.25862503f, 0.781001568f, -0.382083058f, -0.889029741f, -0.626465559f, 1.25265217f, 0.61907953f, + 0.333791494f, 0.150704309f, -0.61906606f, -0.448026597f, 0.355797768f, -0.772753f, 0.585965574f, + -0.365553409f, -0.604233861f, 0.144394666f, 0.797081113f, 0.150517493f, 1.18024278f, -0.369116187f, + -0.783274412f, 0.0370494723f, 0.65017575f, -0.0813273042f, -0.292725831f, -0.0343536437f, -0.0651676655f, + -0.103795111f, -0.359404832f, -0.946073592f, -0.230285496f, -0.418424875f, -0.394406945f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/test_data.h new file mode 100644 index 00000000..b1ead1be --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/test_data.h @@ -0,0 +1,6 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "biases_data.h" +#include "config_data.h" +#include "input_data.h" +#include "output_ref_data.h" +#include "weights_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/weights_data.h b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/weights_data.h new file mode 100644 index 00000000..574069ae --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_conv_basic_nhwc_f32/weights_data.h @@ -0,0 +1,33 @@ +// Generated by transpose_conv_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_conv_basic_nhwc_f32_weights[216] = { + 0.00304916431f, -0.280530185f, 0.191889301f, -0.18307656f, -0.534706473f, -0.330619186f, 0.414815873f, + -0.00225321762f, -0.422641486f, -0.079586558f, 0.442483842f, -0.545233548f, -0.455800802f, -0.600866318f, + 0.382079333f, 0.595059395f, -0.17514509f, -0.406335533f, -0.0825997442f, 0.397299588f, 0.43285054f, + -0.174589336f, -0.0320754908f, -0.356766909f, 0.737128079f, -0.0277478062f, 0.219731733f, -0.486773163f, + 0.253955662f, 0.191902414f, -0.0136382803f, -0.719332516f, 0.440604627f, 0.15522112f, -0.039992325f, + 0.569894135f, 0.0367615782f, 0.543777823f, -0.172832072f, -0.442735672f, 0.475078702f, 0.256157309f, + -0.422077239f, -0.743039131f, 0.367431492f, 0.0923330337f, -0.0970253199f, 0.309267551f, -0.0626493841f, + 0.141388565f, -0.720678508f, -0.411436856f, -0.354747772f, -0.0731751919f, 0.564535499f, 0.199406281f, + -0.379768878f, -0.0593405366f, 0.711066306f, -0.0298424941f, 0.0184712075f, -0.158017159f, 0.422607422f, + 0.426086277f, 0.38774085f, -0.0721703246f, 0.116204739f, 0.430216581f, -0.362049937f, 0.0304635074f, + -0.627110183f, 0.431988418f, -0.381174326f, -0.369680524f, 0.690627456f, 0.0733752474f, -0.677414775f, + -0.338902265f, 0.0758967251f, 0.288700849f, -0.314784527f, -0.251338243f, 0.668673694f, -0.415207088f, + -0.567941308f, -0.399608254f, 0.450945079f, 0.21384649f, 0.416240036f, 0.443303734f, 0.0402137302f, + 0.743706942f, 0.704675078f, 0.745814919f, -0.549930513f, 0.661265194f, 0.727447391f, -0.497366816f, + -0.174731061f, 0.140435651f, 0.505962014f, 0.666827738f, -0.503151715f, -0.576822937f, -0.524365723f, + -0.393428206f, -0.305316538f, -0.113654785f, 0.441895127f, 0.285758376f, -0.676912308f, -0.552655101f, + 0.576087832f, -0.0203928519f, 0.518225789f, 0.69539535f, 0.116252407f, -0.169392392f, 0.328440338f, + 0.280869544f, -0.680091083f, 0.124685727f, -0.312904149f, -0.548610151f, 0.117926084f, -0.16771163f, + -0.439745635f, -0.582475543f, -0.627961636f, 0.743308723f, 0.416218281f, -0.311680168f, 0.691066027f, + 0.423283845f, 0.307513654f, 0.314413041f, -0.500061989f, -0.432665616f, -0.0907391533f, -0.317838967f, + 0.306053966f, -0.572092831f, -0.285383165f, 0.556971371f, 0.534862697f, 0.0278717391f, -0.226100907f, + 0.354033023f, 0.739813805f, 0.696733534f, -0.0543400869f, -0.421279818f, 0.743540585f, -0.596415699f, + -0.360410988f, -0.32418561f, -0.19592531f, 0.165737092f, 0.534232378f, -0.649287701f, -0.163902313f, + -0.297944695f, -0.507191718f, 0.117497399f, -0.105403922f, 0.396502912f, 0.384434611f, -0.419302076f, + -0.0930686593f, 0.0353051834f, -0.459745884f, 0.490950853f, 0.519300759f, 0.428446949f, -0.532723665f, + 0.204480246f, 0.595594406f, -0.45151639f, -0.68432188f, -0.173023522f, 0.000282270892f, 0.140558779f, + -0.0348301753f, -0.0657655597f, -0.104548015f, 0.274458438f, -0.500791609f, 0.608580589f, -0.16014798f, + 0.716882825f, -0.199053854f, -0.738633394f, 0.0712485611f, -0.403939486f, -0.661239862f, -0.485600919f, + 0.230274439f, 0.617036104f, 0.500663757f, 0.0507116877f, 0.642050505f, -0.205116272f, 0.103775583f, + 0.316831082f, 0.631721973f, -0.287928909f, -0.40395394f, -0.0534296706f, 0.454454035f, 0.246562079f, + -0.100597315f, -0.106710397f, 0.0437015593f, 0.546668828f, -0.333840221f, 0.0619133525f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/config_data.h new file mode 100644 index 00000000..e2e4baff --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_DEFAULT_F16_NUM_DIMS 4 +#define TRANSPOSE_DEFAULT_F16_INPUT_N 4 +#define TRANSPOSE_DEFAULT_F16_INPUT_H 3 +#define TRANSPOSE_DEFAULT_F16_INPUT_W 3 +#define TRANSPOSE_DEFAULT_F16_INPUT_C 22 +#define TRANSPOSE_DEFAULT_F16_OUTPUT_N 22 +#define TRANSPOSE_DEFAULT_F16_OUTPUT_H 3 +#define TRANSPOSE_DEFAULT_F16_OUTPUT_W 3 +#define TRANSPOSE_DEFAULT_F16_OUTPUT_C 4 +#define TRANSPOSE_DEFAULT_F16_PERM_0 3 +#define TRANSPOSE_DEFAULT_F16_PERM_1 2 +#define TRANSPOSE_DEFAULT_F16_PERM_2 1 +#define TRANSPOSE_DEFAULT_F16_PERM_3 0 +#define TRANSPOSE_DEFAULT_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_DEFAULT_F16_SIZE 792 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/input_tensor_data.h new file mode 100644 index 00000000..6635385e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/input_tensor_data.h @@ -0,0 +1,200 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_default_f16_input_tensor[792] = { + (float16_t)0.427734375f, (float16_t)-2.2109375f, (float16_t)-2.94140625f, (float16_t)-3.76367188f, + (float16_t)-2.3828125f, (float16_t)-1.24609375f, (float16_t)3.71875f, (float16_t)-0.618164062f, + (float16_t)0.716796875f, (float16_t)-1.87207031f, (float16_t)0.724121094f, (float16_t)0.724609375f, + (float16_t)-2.64453125f, (float16_t)0.85546875f, (float16_t)-2.54296875f, (float16_t)-2.80273438f, + (float16_t)-0.752929688f, (float16_t)3.30273438f, (float16_t)-2.64648438f, (float16_t)-1.89746094f, + (float16_t)1.56640625f, (float16_t)-2.4296875f, (float16_t)2.2265625f, (float16_t)1.17578125f, + (float16_t)-0.0747680664f, (float16_t)-3.7265625f, (float16_t)-3.16210938f, (float16_t)3.09179688f, + (float16_t)-0.823730469f, (float16_t)0.830078125f, (float16_t)-3.640625f, (float16_t)-3.4453125f, + (float16_t)-1.68457031f, (float16_t)0.260498047f, (float16_t)-0.965332031f, (float16_t)-0.459472656f, + (float16_t)1.81542969f, (float16_t)-1.28125f, (float16_t)3.54296875f, (float16_t)-0.374267578f, + (float16_t)-3.33398438f, (float16_t)2.41210938f, (float16_t)3.19726562f, (float16_t)-1.38183594f, + (float16_t)-0.246582031f, (float16_t)2.54492188f, (float16_t)-2.20507812f, (float16_t)-3.93945312f, + (float16_t)1.41113281f, (float16_t)-3.86328125f, (float16_t)-1.44824219f, (float16_t)3.48242188f, + (float16_t)-0.991699219f, (float16_t)1.17480469f, (float16_t)-0.592285156f, (float16_t)-2.09765625f, + (float16_t)1.74316406f, (float16_t)-3.3359375f, (float16_t)2.18945312f, (float16_t)-1.17089844f, + (float16_t)1.23339844f, (float16_t)3.9140625f, (float16_t)-3.14648438f, (float16_t)2.15039062f, + (float16_t)-3.9140625f, (float16_t)-3.421875f, (float16_t)3.53515625f, (float16_t)1.30761719f, + (float16_t)3.33398438f, (float16_t)-1.37597656f, (float16_t)3.00195312f, (float16_t)-2.640625f, + (float16_t)3.48828125f, (float16_t)0.743652344f, (float16_t)1.57714844f, (float16_t)-3.453125f, + (float16_t)-3.66015625f, (float16_t)3.56445312f, (float16_t)-1.02441406f, (float16_t)2.46484375f, + (float16_t)-0.919433594f, (float16_t)0.647949219f, (float16_t)3.80273438f, (float16_t)0.281494141f, + (float16_t)1.30273438f, (float16_t)0.172973633f, (float16_t)-2.54882812f, (float16_t)-0.770019531f, + (float16_t)1.60253906f, (float16_t)1.99902344f, (float16_t)2.11132812f, (float16_t)1.84375f, + (float16_t)0.926757812f, (float16_t)3.57617188f, (float16_t)2.6484375f, (float16_t)-3.41015625f, + (float16_t)2.015625f, (float16_t)-1.36816406f, (float16_t)-1.04199219f, (float16_t)-2.625f, + (float16_t)2.7734375f, (float16_t)1.41503906f, (float16_t)1.15332031f, (float16_t)0.300292969f, + (float16_t)3.58398438f, (float16_t)3.26367188f, (float16_t)0.2890625f, (float16_t)0.884765625f, + (float16_t)-2.32617188f, (float16_t)0.309814453f, (float16_t)3.546875f, (float16_t)3.25976562f, + (float16_t)3.70703125f, (float16_t)3.17382812f, (float16_t)1.73632812f, (float16_t)-3.8125f, + (float16_t)-0.0617675781f, (float16_t)-1.24707031f, (float16_t)0.0915527344f, (float16_t)0.749023438f, + (float16_t)2.1953125f, (float16_t)-0.0213928223f, (float16_t)-2.83007812f, (float16_t)1.56640625f, + (float16_t)2.6328125f, (float16_t)1.87597656f, (float16_t)2.05273438f, (float16_t)2.05859375f, + (float16_t)-0.6796875f, (float16_t)-0.0656738281f, (float16_t)-3.2109375f, (float16_t)3.27539062f, + (float16_t)-2.44140625f, (float16_t)0.530761719f, (float16_t)-0.289794922f, (float16_t)2.60351562f, + (float16_t)-3.41210938f, (float16_t)-1.54296875f, (float16_t)3.91601562f, (float16_t)3.2890625f, + (float16_t)-1.48144531f, (float16_t)0.370605469f, (float16_t)2.36914062f, (float16_t)3.84179688f, + (float16_t)1.20117188f, (float16_t)-0.599121094f, (float16_t)-0.368164062f, (float16_t)-3.31054688f, + (float16_t)3.19335938f, (float16_t)2.82617188f, (float16_t)-0.331542969f, (float16_t)3.93945312f, + (float16_t)-1.4375f, (float16_t)-2.94140625f, (float16_t)-3.9296875f, (float16_t)-1.41503906f, + (float16_t)-0.374267578f, (float16_t)-1.64648438f, (float16_t)3.38476562f, (float16_t)-2.06054688f, + (float16_t)2.58984375f, (float16_t)-3.16601562f, (float16_t)-3.05859375f, (float16_t)3.31835938f, + (float16_t)-3.01757812f, (float16_t)-1.44726562f, (float16_t)-1.56347656f, (float16_t)-0.583984375f, + (float16_t)3.06835938f, (float16_t)-1.80957031f, (float16_t)-3.6796875f, (float16_t)-3.62304688f, + (float16_t)1.60546875f, (float16_t)-0.249755859f, (float16_t)3.18359375f, (float16_t)-1.37011719f, + (float16_t)1.08496094f, (float16_t)3.42382812f, (float16_t)0.427734375f, (float16_t)-0.498535156f, + (float16_t)1.47070312f, (float16_t)2.66015625f, (float16_t)3.48828125f, (float16_t)1.31640625f, + (float16_t)1.04492188f, (float16_t)3.34960938f, (float16_t)-2.05859375f, (float16_t)-3.96875f, + (float16_t)2.93945312f, (float16_t)-0.0963745117f, (float16_t)0.247192383f, (float16_t)3.33007812f, + (float16_t)3.72460938f, (float16_t)0.836914062f, (float16_t)1.01269531f, (float16_t)3.15625f, + (float16_t)3.3125f, (float16_t)1.89160156f, (float16_t)-2.67382812f, (float16_t)-1.62890625f, + (float16_t)0.0493774414f, (float16_t)2.32226562f, (float16_t)-0.369140625f, (float16_t)-0.527832031f, + (float16_t)-2.9140625f, (float16_t)0.300048828f, (float16_t)3.16210938f, (float16_t)-3.68554688f, + (float16_t)0.309570312f, (float16_t)-3.5234375f, (float16_t)2.49609375f, (float16_t)-0.458007812f, + (float16_t)2.06640625f, (float16_t)1.71289062f, (float16_t)-2.828125f, (float16_t)0.393554688f, + (float16_t)-0.78125f, (float16_t)1.20507812f, (float16_t)-3.96679688f, (float16_t)-2.50585938f, + (float16_t)-0.368408203f, (float16_t)-0.607910156f, (float16_t)3.57617188f, (float16_t)2.38476562f, + (float16_t)1.00488281f, (float16_t)1.09863281f, (float16_t)-0.0182800293f, (float16_t)-1.37304688f, + (float16_t)-2.52539062f, (float16_t)1.60839844f, (float16_t)2.3984375f, (float16_t)-2.18554688f, + (float16_t)0.633300781f, (float16_t)3.24609375f, (float16_t)0.833007812f, (float16_t)3.89453125f, + (float16_t)-3.55664062f, (float16_t)-1.42578125f, (float16_t)2.11523438f, (float16_t)-1.19238281f, + (float16_t)-2.57421875f, (float16_t)-0.748046875f, (float16_t)2.42578125f, (float16_t)-3.69921875f, + (float16_t)-3.23828125f, (float16_t)-3.2734375f, (float16_t)0.999511719f, (float16_t)-2.04882812f, + (float16_t)0.877441406f, (float16_t)3.08789062f, (float16_t)2.74609375f, (float16_t)-0.155761719f, + (float16_t)3.515625f, (float16_t)-1.99804688f, (float16_t)1.93457031f, (float16_t)-0.705078125f, + (float16_t)1.90527344f, (float16_t)-3.55664062f, (float16_t)-2.95117188f, (float16_t)-0.422851562f, + (float16_t)2.21679688f, (float16_t)-2.71875f, (float16_t)0.670410156f, (float16_t)-1.45996094f, + (float16_t)-2.98828125f, (float16_t)0.298828125f, (float16_t)1.25292969f, (float16_t)-3.28710938f, + (float16_t)-3.41796875f, (float16_t)-1.96289062f, (float16_t)-1.765625f, (float16_t)1.54589844f, + (float16_t)3.77539062f, (float16_t)3.36523438f, (float16_t)0.700195312f, (float16_t)3.2109375f, + (float16_t)1.93652344f, (float16_t)-0.452880859f, (float16_t)-0.939941406f, (float16_t)-3.77148438f, + (float16_t)0.0848388672f, (float16_t)-3.08203125f, (float16_t)-1.015625f, (float16_t)2.94726562f, + (float16_t)-2.40039062f, (float16_t)-2.65625f, (float16_t)2.4765625f, (float16_t)0.15246582f, + (float16_t)2.12695312f, (float16_t)-2.05078125f, (float16_t)1.03515625f, (float16_t)1.20800781f, + (float16_t)0.186401367f, (float16_t)-0.0585021973f, (float16_t)1.49511719f, (float16_t)-3.015625f, + (float16_t)-1.64941406f, (float16_t)2.25976562f, (float16_t)-3.91796875f, (float16_t)2.87890625f, + (float16_t)2.55664062f, (float16_t)-2.80859375f, (float16_t)-3.37890625f, (float16_t)1.40234375f, + (float16_t)3.43945312f, (float16_t)-2.22460938f, (float16_t)2.1875f, (float16_t)-3.01171875f, + (float16_t)-0.827636719f, (float16_t)3.51367188f, (float16_t)-1.70800781f, (float16_t)-2.40820312f, + (float16_t)3.77734375f, (float16_t)3.93554688f, (float16_t)1.20898438f, (float16_t)-1.25195312f, + (float16_t)2.45507812f, (float16_t)-1.40527344f, (float16_t)-0.486572266f, (float16_t)3.9453125f, + (float16_t)-1.49121094f, (float16_t)1.79785156f, (float16_t)0.580078125f, (float16_t)3.03320312f, + (float16_t)3.59179688f, (float16_t)3.328125f, (float16_t)-2.14453125f, (float16_t)0.0405273438f, + (float16_t)3.90820312f, (float16_t)0.760742188f, (float16_t)-3.2421875f, (float16_t)-1.18945312f, + (float16_t)-0.348632812f, (float16_t)-3.3046875f, (float16_t)0.670410156f, (float16_t)0.604003906f, + (float16_t)3.27539062f, (float16_t)-3.99804688f, (float16_t)0.768066406f, (float16_t)1.0078125f, + (float16_t)3.47070312f, (float16_t)1.64648438f, (float16_t)-2.27539062f, (float16_t)-1.03417969f, + (float16_t)-2.12695312f, (float16_t)1.46289062f, (float16_t)1.7890625f, (float16_t)1.88574219f, + (float16_t)0.760253906f, (float16_t)3.25195312f, (float16_t)-3.04492188f, (float16_t)-2.00585938f, + (float16_t)-3.84375f, (float16_t)-0.216308594f, (float16_t)-2.0859375f, (float16_t)-2.11328125f, + (float16_t)1.90039062f, (float16_t)-0.99609375f, (float16_t)-1.27539062f, (float16_t)0.137451172f, + (float16_t)-1.8359375f, (float16_t)-0.463378906f, (float16_t)-2.12890625f, (float16_t)-2.78515625f, + (float16_t)-3.63671875f, (float16_t)2.17382812f, (float16_t)3.73242188f, (float16_t)2.54296875f, + (float16_t)0.80859375f, (float16_t)-3.62695312f, (float16_t)0.377197266f, (float16_t)-0.713378906f, + (float16_t)-2.984375f, (float16_t)3.58984375f, (float16_t)-3.48632812f, (float16_t)2.43359375f, + (float16_t)1.25390625f, (float16_t)-1.84863281f, (float16_t)-1.80175781f, (float16_t)2.22265625f, + (float16_t)2.41015625f, (float16_t)-3.16992188f, (float16_t)3.74609375f, (float16_t)-0.129516602f, + (float16_t)2.81445312f, (float16_t)1.71972656f, (float16_t)-1.44335938f, (float16_t)-1.0546875f, + (float16_t)2.14453125f, (float16_t)2.45703125f, (float16_t)3.51953125f, (float16_t)-0.23840332f, + (float16_t)-1.30566406f, (float16_t)1.34863281f, (float16_t)0.22277832f, (float16_t)0.429199219f, + (float16_t)2.6171875f, (float16_t)2.046875f, (float16_t)0.811523438f, (float16_t)0.247314453f, + (float16_t)2.72070312f, (float16_t)2.6640625f, (float16_t)2.25195312f, (float16_t)-2.8671875f, + (float16_t)-0.508789062f, (float16_t)0.475097656f, (float16_t)-0.370117188f, (float16_t)-3.015625f, + (float16_t)-2.91601562f, (float16_t)-2.88085938f, (float16_t)-1.84570312f, (float16_t)-2.41210938f, + (float16_t)2.86132812f, (float16_t)-3.25390625f, (float16_t)-2.64453125f, (float16_t)0.866210938f, + (float16_t)0.837890625f, (float16_t)0.336914062f, (float16_t)-2.47070312f, (float16_t)-0.833007812f, + (float16_t)-1.68945312f, (float16_t)3.02539062f, (float16_t)-1.32421875f, (float16_t)1.04980469f, + (float16_t)-0.680175781f, (float16_t)1.15625f, (float16_t)-2.69335938f, (float16_t)1.53710938f, + (float16_t)-3.19921875f, (float16_t)3.42773438f, (float16_t)-0.734863281f, (float16_t)-0.424804688f, + (float16_t)-3.88085938f, (float16_t)3.93164062f, (float16_t)-3.35742188f, (float16_t)-2.24804688f, + (float16_t)-3.05664062f, (float16_t)0.444335938f, (float16_t)0.454833984f, (float16_t)0.92578125f, + (float16_t)-0.390136719f, (float16_t)-2.34179688f, (float16_t)-3.3984375f, (float16_t)-0.080871582f, + (float16_t)2.8828125f, (float16_t)2.55078125f, (float16_t)-0.808105469f, (float16_t)2.2109375f, + (float16_t)0.0972900391f, (float16_t)-3.5625f, (float16_t)3.5703125f, (float16_t)-2.90625f, + (float16_t)-2.63671875f, (float16_t)-1.3203125f, (float16_t)-1.49023438f, (float16_t)-0.376953125f, + (float16_t)-1.51660156f, (float16_t)-3.1640625f, (float16_t)2.87695312f, (float16_t)2.01367188f, + (float16_t)-3.70703125f, (float16_t)1.32128906f, (float16_t)3.27148438f, (float16_t)0.613769531f, + (float16_t)-1.43359375f, (float16_t)2.99609375f, (float16_t)-0.718261719f, (float16_t)-0.497314453f, + (float16_t)-1.49121094f, (float16_t)-3.78710938f, (float16_t)-2.16992188f, (float16_t)3.49023438f, + (float16_t)-1.47265625f, (float16_t)2.24609375f, (float16_t)-1.484375f, (float16_t)3.78710938f, + (float16_t)-1.18554688f, (float16_t)-0.592773438f, (float16_t)2.8359375f, (float16_t)3.9921875f, + (float16_t)-0.922363281f, (float16_t)2.41210938f, (float16_t)0.622070312f, (float16_t)0.557128906f, + (float16_t)-3.98242188f, (float16_t)-3.671875f, (float16_t)-2.55078125f, (float16_t)-3.08789062f, + (float16_t)-2.61914062f, (float16_t)2.06640625f, (float16_t)-1.03125f, (float16_t)-3.39453125f, + (float16_t)-1.46777344f, (float16_t)3.4609375f, (float16_t)-3.625f, (float16_t)-2.51367188f, + (float16_t)-1.83203125f, (float16_t)1.59765625f, (float16_t)-0.421386719f, (float16_t)-0.506347656f, + (float16_t)1.00878906f, (float16_t)2.94140625f, (float16_t)0.384033203f, (float16_t)-0.390136719f, + (float16_t)0.598144531f, (float16_t)3.25390625f, (float16_t)3.04296875f, (float16_t)3.8046875f, + (float16_t)-1.25195312f, (float16_t)2.48046875f, (float16_t)0.57421875f, (float16_t)0.529296875f, + (float16_t)-3.92773438f, (float16_t)3.51171875f, (float16_t)-3.5546875f, (float16_t)2.32617188f, + (float16_t)-1.91015625f, (float16_t)-0.660644531f, (float16_t)-2.296875f, (float16_t)2.50585938f, + (float16_t)-1.06152344f, (float16_t)-1.33105469f, (float16_t)3.08398438f, (float16_t)-1.84179688f, + (float16_t)-1.35253906f, (float16_t)-0.519042969f, (float16_t)-0.0567932129f, (float16_t)2.75976562f, + (float16_t)-2.30859375f, (float16_t)3.92578125f, (float16_t)3.6640625f, (float16_t)1.74707031f, + (float16_t)-1.70996094f, (float16_t)-2.63476562f, (float16_t)1.33984375f, (float16_t)-1.984375f, + (float16_t)-3.38085938f, (float16_t)-0.225097656f, (float16_t)-3.8203125f, (float16_t)-2.25195312f, + (float16_t)-2.484375f, (float16_t)-2.02734375f, (float16_t)-3.93164062f, (float16_t)1.95410156f, + (float16_t)-1.33007812f, (float16_t)0.14074707f, (float16_t)3.75f, (float16_t)-3.45507812f, + (float16_t)-0.091003418f, (float16_t)-0.480957031f, (float16_t)-0.757324219f, (float16_t)-2.4375f, + (float16_t)-2.22265625f, (float16_t)-2.52734375f, (float16_t)2.53515625f, (float16_t)1.42480469f, + (float16_t)1.94335938f, (float16_t)2.88867188f, (float16_t)-3.1953125f, (float16_t)3.7265625f, + (float16_t)-0.0862426758f, (float16_t)2.6484375f, (float16_t)3.69335938f, (float16_t)-2.08398438f, + (float16_t)-2.83007812f, (float16_t)2.37890625f, (float16_t)-2.3359375f, (float16_t)3.89453125f, + (float16_t)0.831054688f, (float16_t)0.207641602f, (float16_t)0.233520508f, (float16_t)0.793945312f, + (float16_t)1.01074219f, (float16_t)3.95507812f, (float16_t)-1.49023438f, (float16_t)3.82421875f, + (float16_t)-0.000345945358f, (float16_t)-3.4140625f, (float16_t)-0.713378906f, (float16_t)0.780761719f, + (float16_t)-1.65332031f, (float16_t)0.263183594f, (float16_t)0.668945312f, (float16_t)-3.23046875f, + (float16_t)-2.796875f, (float16_t)-1.99414062f, (float16_t)-1.73339844f, (float16_t)-1.79492188f, + (float16_t)2.22265625f, (float16_t)-1.36425781f, (float16_t)-3.953125f, (float16_t)-1.54980469f, + (float16_t)-2.43164062f, (float16_t)1.75488281f, (float16_t)1.68261719f, (float16_t)-3.92578125f, + (float16_t)0.803222656f, (float16_t)3.9140625f, (float16_t)-0.0322265625f, (float16_t)-1.39453125f, + (float16_t)-1.67382812f, (float16_t)-2.90820312f, (float16_t)3.8515625f, (float16_t)0.544433594f, + (float16_t)2.84375f, (float16_t)-0.438720703f, (float16_t)0.301269531f, (float16_t)-0.0357971191f, + (float16_t)3.53515625f, (float16_t)-1.87988281f, (float16_t)3.85546875f, (float16_t)1.15234375f, + (float16_t)2.74804688f, (float16_t)-3.31640625f, (float16_t)-3.2578125f, (float16_t)-3.60351562f, + (float16_t)-0.481201172f, (float16_t)3.27148438f, (float16_t)-2.65625f, (float16_t)0.0896606445f, + (float16_t)2.83007812f, (float16_t)3.1875f, (float16_t)-1.85742188f, (float16_t)-3.88085938f, + (float16_t)-2.91796875f, (float16_t)0.688476562f, (float16_t)-1.20996094f, (float16_t)-1.83886719f, + (float16_t)-0.979980469f, (float16_t)-0.765625f, (float16_t)-1.2265625f, (float16_t)3.49804688f, + (float16_t)-0.735839844f, (float16_t)2.34375f, (float16_t)-3.62890625f, (float16_t)3.60546875f, + (float16_t)3.046875f, (float16_t)3.27734375f, (float16_t)-3.49414062f, (float16_t)-3.81640625f, + (float16_t)-1.55175781f, (float16_t)3.53515625f, (float16_t)-3.00585938f, (float16_t)2.703125f, + (float16_t)-3.00976562f, (float16_t)-2.14648438f, (float16_t)-2.6484375f, (float16_t)1.5390625f, + (float16_t)0.961914062f, (float16_t)0.53125f, (float16_t)-2.34375f, (float16_t)3.16015625f, + (float16_t)-1.59277344f, (float16_t)-3.1171875f, (float16_t)-2.28710938f, (float16_t)1.44140625f, + (float16_t)0.349609375f, (float16_t)0.43359375f, (float16_t)1.96484375f, (float16_t)3.71484375f, + (float16_t)1.30566406f, (float16_t)1.61425781f, (float16_t)3.74414062f, (float16_t)-2.68359375f, + (float16_t)1.08105469f, (float16_t)-0.741210938f, (float16_t)-3.46484375f, (float16_t)3.69921875f, + (float16_t)-3.8515625f, (float16_t)-0.326416016f, (float16_t)-3.140625f, (float16_t)1.36816406f, + (float16_t)0.291992188f, (float16_t)3.76757812f, (float16_t)-3.55078125f, (float16_t)2.57226562f, + (float16_t)-0.154418945f, (float16_t)0.776367188f, (float16_t)-1.31054688f, (float16_t)0.531738281f, + (float16_t)-3.63867188f, (float16_t)-2.02734375f, (float16_t)1.93554688f, (float16_t)-2.7109375f, + (float16_t)-0.432617188f, (float16_t)3.1328125f, (float16_t)-3.83007812f, (float16_t)-2.87109375f, + (float16_t)-2.00585938f, (float16_t)3.36523438f, (float16_t)0.174682617f, (float16_t)-2.4453125f, + (float16_t)-0.201904297f, (float16_t)3.56445312f, (float16_t)-1.82324219f, (float16_t)-3.12109375f, + (float16_t)1.81445312f, (float16_t)-2.79296875f, (float16_t)0.463134766f, (float16_t)-2.04296875f, + (float16_t)2.26367188f, (float16_t)2.9609375f, (float16_t)-0.921386719f, (float16_t)-2.69726562f, + (float16_t)1.49609375f, (float16_t)-2.72070312f, (float16_t)-3.484375f, (float16_t)1.76269531f, + (float16_t)3.4453125f, (float16_t)1.171875f, (float16_t)-1.19824219f, (float16_t)-0.747070312f, + (float16_t)3.29296875f, (float16_t)-3.81835938f, (float16_t)1.2578125f, (float16_t)-1.25488281f, + (float16_t)-1.72167969f, (float16_t)-1.046875f, (float16_t)2.83984375f, (float16_t)-1.89550781f, + (float16_t)2.00390625f, (float16_t)-3.4375f, (float16_t)0.478759766f, (float16_t)3.7734375f, + (float16_t)-3.73632812f, (float16_t)-3.06054688f, (float16_t)-3.95898438f, (float16_t)3.94140625f, + (float16_t)2.70507812f, (float16_t)0.526855469f, (float16_t)-3.24023438f, (float16_t)-2.34570312f, + (float16_t)2.00976562f, (float16_t)-2.609375f, (float16_t)-0.551757812f, (float16_t)-0.627929688f, + (float16_t)0.396728516f, (float16_t)-1.62597656f, (float16_t)2.76367188f, (float16_t)0.435302734f, + (float16_t)3.8515625f, (float16_t)-3.13671875f, (float16_t)3.22070312f, (float16_t)-1.79101562f, + (float16_t)3.13867188f, (float16_t)0.795410156f, (float16_t)-1.05566406f, (float16_t)-2.42773438f, + (float16_t)-0.315429688f, (float16_t)-0.033416748f, (float16_t)-0.0487670898f, (float16_t)-0.800292969f, + (float16_t)2.2890625f, (float16_t)-0.874023438f, (float16_t)-2.02148438f, (float16_t)3.87304688f, + (float16_t)-3.96484375f, (float16_t)-0.0590209961f, (float16_t)-2.265625f, (float16_t)0.282714844f, + (float16_t)-0.254882812f, (float16_t)-1.38769531f, (float16_t)0.209594727f, (float16_t)-3.4296875f, + (float16_t)-2.60546875f, (float16_t)-0.841796875f, (float16_t)2.48046875f, (float16_t)-2.0f, + (float16_t)-1.1015625f, (float16_t)2.54101562f, (float16_t)-0.78515625f, (float16_t)-0.799804688f, + (float16_t)-3.47851562f, (float16_t)-1.90625f, (float16_t)-2.08984375f, (float16_t)2.80078125f, + (float16_t)1.07617188f, (float16_t)-1.76269531f, (float16_t)3.43164062f, (float16_t)-3.84570312f, + (float16_t)-0.372802734f, (float16_t)-3.53320312f, (float16_t)0.615722656f, (float16_t)3.6640625f, + (float16_t)3.87695312f, (float16_t)-1.02050781f, (float16_t)-1.46191406f, (float16_t)3.56054688f, + (float16_t)-0.176635742f, (float16_t)-1.43359375f, (float16_t)-2.21679688f, (float16_t)2.28125f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/output_ref_data.h new file mode 100644 index 00000000..2ddad46d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/output_ref_data.h @@ -0,0 +1,793 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_default_f16_output_ref[792] = {(float16_t)0.427734375f, + (float16_t)-2.67382812f, + (float16_t)2.6171875f, + (float16_t)1.68261719f, + (float16_t)3.53515625f, + (float16_t)-2.98828125f, + (float16_t)3.27148438f, + (float16_t)1.30566406f, + (float16_t)-2.44140625f, + (float16_t)-3.2421875f, + (float16_t)-2.30859375f, + (float16_t)-3.95898438f, + (float16_t)2.2265625f, + (float16_t)-0.368408203f, + (float16_t)-2.47070312f, + (float16_t)-0.481201172f, + (float16_t)1.60253906f, + (float16_t)2.4765625f, + (float16_t)-3.98242188f, + (float16_t)1.93554688f, + (float16_t)-3.9296875f, + (float16_t)-3.84375f, + (float16_t)-0.757324219f, + (float16_t)-0.315429688f, + (float16_t)-0.246582031f, + (float16_t)2.42578125f, + (float16_t)-0.390136719f, + (float16_t)-3.49414062f, + (float16_t)3.546875f, + (float16_t)-0.827636719f, + (float16_t)3.04296875f, + (float16_t)1.49609375f, + (float16_t)1.08496094f, + (float16_t)-3.48632812f, + (float16_t)1.01074219f, + (float16_t)-0.78515625f, + (float16_t)-2.2109375f, + (float16_t)-1.62890625f, + (float16_t)2.046875f, + (float16_t)-3.92578125f, + (float16_t)1.30761719f, + (float16_t)0.298828125f, + (float16_t)0.613769531f, + (float16_t)1.61425781f, + (float16_t)0.530761719f, + (float16_t)-1.18945312f, + (float16_t)3.92578125f, + (float16_t)3.94140625f, + (float16_t)1.17578125f, + (float16_t)-0.607910156f, + (float16_t)-0.833007812f, + (float16_t)3.27148438f, + (float16_t)1.99902344f, + (float16_t)0.15246582f, + (float16_t)-3.671875f, + (float16_t)-2.7109375f, + (float16_t)-1.41503906f, + (float16_t)-0.216308594f, + (float16_t)-2.4375f, + (float16_t)-0.033416748f, + (float16_t)2.54492188f, + (float16_t)-3.69921875f, + (float16_t)-2.34179688f, + (float16_t)-3.81640625f, + (float16_t)3.25976562f, + (float16_t)3.51367188f, + (float16_t)3.8046875f, + (float16_t)-2.72070312f, + (float16_t)3.42382812f, + (float16_t)2.43359375f, + (float16_t)3.95507812f, + (float16_t)-0.799804688f, + (float16_t)-2.94140625f, + (float16_t)0.0493774414f, + (float16_t)0.811523438f, + (float16_t)0.803222656f, + (float16_t)3.33398438f, + (float16_t)1.25292969f, + (float16_t)-1.43359375f, + (float16_t)3.74414062f, + (float16_t)-0.289794922f, + (float16_t)-0.348632812f, + (float16_t)3.6640625f, + (float16_t)2.70507812f, + (float16_t)-0.0747680664f, + (float16_t)3.57617188f, + (float16_t)-1.68945312f, + (float16_t)-2.65625f, + (float16_t)2.11132812f, + (float16_t)2.12695312f, + (float16_t)-2.55078125f, + (float16_t)-0.432617188f, + (float16_t)-0.374267578f, + (float16_t)-2.0859375f, + (float16_t)-2.22265625f, + (float16_t)-0.0487670898f, + (float16_t)-2.20507812f, + (float16_t)-3.23828125f, + (float16_t)-3.3984375f, + (float16_t)-1.55175781f, + (float16_t)3.70703125f, + (float16_t)-1.70800781f, + (float16_t)-1.25195312f, + (float16_t)-3.484375f, + (float16_t)0.427734375f, + (float16_t)1.25390625f, + (float16_t)-1.49023438f, + (float16_t)-3.47851562f, + (float16_t)-3.76367188f, + (float16_t)2.32226562f, + (float16_t)0.247314453f, + (float16_t)3.9140625f, + (float16_t)-1.37597656f, + (float16_t)-3.28710938f, + (float16_t)2.99609375f, + (float16_t)-2.68359375f, + (float16_t)2.60351562f, + (float16_t)-3.3046875f, + (float16_t)1.74707031f, + (float16_t)0.526855469f, + (float16_t)-3.7265625f, + (float16_t)2.38476562f, + (float16_t)3.02539062f, + (float16_t)0.0896606445f, + (float16_t)1.84375f, + (float16_t)-2.05078125f, + (float16_t)-3.08789062f, + (float16_t)3.1328125f, + (float16_t)-1.64648438f, + (float16_t)-2.11328125f, + (float16_t)-2.52734375f, + (float16_t)-0.800292969f, + (float16_t)-3.93945312f, + (float16_t)-3.2734375f, + (float16_t)-0.080871582f, + (float16_t)3.53515625f, + (float16_t)3.17382812f, + (float16_t)-2.40820312f, + (float16_t)2.48046875f, + (float16_t)1.76269531f, + (float16_t)-0.498535156f, + (float16_t)-1.84863281f, + (float16_t)3.82421875f, + (float16_t)-1.90625f, + (float16_t)-2.3828125f, + (float16_t)-0.369140625f, + (float16_t)2.72070312f, + (float16_t)-0.0322265625f, + (float16_t)3.00195312f, + (float16_t)-3.41796875f, + (float16_t)-0.718261719f, + (float16_t)1.08105469f, + (float16_t)-3.41210938f, + (float16_t)0.670410156f, + (float16_t)-1.70996094f, + (float16_t)-3.24023438f, + (float16_t)-3.16210938f, + (float16_t)1.00488281f, + (float16_t)-1.32421875f, + (float16_t)2.83007812f, + (float16_t)0.926757812f, + (float16_t)1.03515625f, + (float16_t)-2.61914062f, + (float16_t)-3.83007812f, + (float16_t)3.38476562f, + (float16_t)1.90039062f, + (float16_t)2.53515625f, + (float16_t)2.2890625f, + (float16_t)1.41113281f, + (float16_t)0.999511719f, + (float16_t)2.8828125f, + (float16_t)-3.00585938f, + (float16_t)1.73632812f, + (float16_t)3.77734375f, + (float16_t)0.57421875f, + (float16_t)3.4453125f, + (float16_t)1.47070312f, + (float16_t)-1.80175781f, + (float16_t)-0.000345945358f, + (float16_t)-2.08984375f, + (float16_t)-1.24609375f, + (float16_t)-0.527832031f, + (float16_t)2.6640625f, + (float16_t)-1.39453125f, + (float16_t)-2.640625f, + (float16_t)-1.96289062f, + (float16_t)-0.497314453f, + 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(float16_t)-3.2109375f, + (float16_t)3.90820312f, + (float16_t)-0.0567932129f, + (float16_t)-3.73632812f, + (float16_t)3.3125f, + (float16_t)0.22277832f, + (float16_t)-2.43164062f, + (float16_t)-2.21679688f, + (float16_t)-2.4296875f, + (float16_t)-2.50585938f, + (float16_t)0.336914062f, + (float16_t)-3.60351562f, + (float16_t)-0.770019531f, + (float16_t)-2.65625f, + (float16_t)0.557128906f, + (float16_t)-2.02734375f, + (float16_t)-2.94140625f, + (float16_t)-2.00585938f, + (float16_t)-0.480957031f, + (float16_t)-2.42773438f, + (float16_t)-1.38183594f, + (float16_t)-0.748046875f, + (float16_t)0.92578125f, + (float16_t)3.27734375f, + (float16_t)0.309814453f, + (float16_t)-3.01171875f, + (float16_t)3.25390625f, + (float16_t)-2.69726562f, + (float16_t)-1.37011719f, + (float16_t)3.58984375f, + (float16_t)0.793945312f, + (float16_t)2.54101562f, + (float16_t)-3.421875f, + (float16_t)-1.45996094f, + (float16_t)1.32128906f, + (float16_t)3.71484375f, + (float16_t)3.27539062f, + (float16_t)0.760742188f, + (float16_t)2.75976562f, + (float16_t)-3.06054688f, + (float16_t)1.89160156f, + (float16_t)0.429199219f, + (float16_t)1.75488281f, + (float16_t)2.28125f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/config_data.h new file mode 100644 index 00000000..3081f0c6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_DEFAULT_F32_NUM_DIMS 4 +#define TRANSPOSE_DEFAULT_F32_INPUT_N 4 +#define TRANSPOSE_DEFAULT_F32_INPUT_H 3 +#define TRANSPOSE_DEFAULT_F32_INPUT_W 3 +#define TRANSPOSE_DEFAULT_F32_INPUT_C 22 +#define TRANSPOSE_DEFAULT_F32_OUTPUT_N 22 +#define TRANSPOSE_DEFAULT_F32_OUTPUT_H 3 +#define TRANSPOSE_DEFAULT_F32_OUTPUT_W 3 +#define TRANSPOSE_DEFAULT_F32_OUTPUT_C 4 +#define TRANSPOSE_DEFAULT_F32_PERM_0 3 +#define TRANSPOSE_DEFAULT_F32_PERM_1 2 +#define TRANSPOSE_DEFAULT_F32_PERM_2 1 +#define TRANSPOSE_DEFAULT_F32_PERM_3 0 +#define TRANSPOSE_DEFAULT_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_DEFAULT_F32_SIZE 792 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/input_tensor_data.h new file mode 100644 index 00000000..b227dc6a --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/input_tensor_data.h @@ -0,0 +1,116 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_default_f32_input_tensor[792] = { + -2.42567062f, 0.717977941f, 1.14263833f, 2.20276928f, 3.29296541f, -2.96116376f, -3.88980627f, + -1.08685696f, -2.09567213f, -2.50852585f, -2.12668681f, -2.33289099f, -2.7190733f, 3.81487656f, + -3.2879746f, 0.663243592f, 1.99835801f, -2.86450028f, -2.95841718f, -0.455534399f, 2.57405353f, + -3.11011457f, -3.27102661f, 2.60393596f, 1.21283185f, 1.78429472f, -0.107321955f, 3.78284717f, + -0.180199653f, -0.397378981f, -2.84912753f, -1.0455972f, 0.375558883f, -0.326120168f, -2.10035372f, + 3.49356246f, 2.76753259f, -1.02941585f, -0.907216907f, -3.90411901f, 0.35752064f, -1.07348728f, + -3.35234976f, 2.39493346f, 0.579717219f, -3.93644881f, 1.75555241f, -0.868292451f, 0.284788281f, + -0.196241289f, 3.6341958f, -1.06300783f, -3.59536886f, 2.63171911f, 2.99967051f, 3.31496763f, + -1.55823255f, -0.295614958f, -2.36664343f, -3.27180099f, -2.09203935f, 1.06732273f, 0.721406698f, + -2.73011374f, -2.47247362f, 0.96764338f, 1.55854559f, 1.05631459f, -2.47959018f, 2.24148846f, + -0.85702759f, 1.34986246f, 0.246959135f, -0.752715051f, 3.96443963f, -3.56383514f, 1.03058791f, + -0.190995172f, 1.36453533f, 3.05114055f, -3.13839555f, -0.379153699f, -0.178625092f, -0.152628496f, + -1.51085329f, -3.01031709f, -2.64530253f, 0.723366559f, 2.53046584f, 2.0263288f, 3.72889113f, + -0.656830668f, -3.99804044f, -0.0394540802f, 1.01059282f, 0.654166579f, -2.67225409f, 3.47205138f, + 2.78072405f, 0.269261956f, 2.73394918f, 1.39432669f, -2.09103274f, 0.862965286f, -2.64898872f, + 1.70573378f, 3.39287949f, 3.68031549f, -0.328680128f, 3.7624495f, -0.868014693f, -0.764206231f, + 2.7942853f, -3.01735878f, -1.6311866f, 3.75900793f, -3.79872179f, 1.76181304f, -3.37573075f, + 2.25410891f, -1.91075242f, -2.6929729f, -1.87817514f, 0.754914105f, 0.899063408f, 0.191622153f, + 1.37488556f, -3.38552952f, -3.01307416f, 3.66487741f, -3.01531935f, 1.79228568f, 1.91096771f, + 0.980469465f, 2.79743791f, -2.35450792f, -2.97728848f, 1.93662012f, 2.3953557f, 2.24645853f, + -2.63727617f, 0.270007074f, -1.25428414f, -3.9808166f, 2.44186592f, -1.63973165f, -2.15473962f, + -1.99543715f, -1.89381969f, 0.267458469f, 3.68196583f, -0.311422527f, -0.701008916f, -3.65116143f, + 3.76858616f, -2.29982638f, -1.81401992f, 2.05710363f, 2.10993218f, 3.60845065f, 0.547972977f, + 1.85376036f, 2.98739362f, -2.74819684f, -1.43194318f, -0.872543573f, 0.23551555f, -1.84515035f, + -1.61023998f, 2.56278515f, 0.0681160316f, 1.46115398f, 0.901849508f, 0.947857678f, 2.94287348f, + 1.34186208f, -1.4014045f, 1.40756094f, 1.06511438f, 2.33928895f, 1.39072669f, -1.49610543f, + 2.57793283f, 2.010144f, -1.05753338f, 2.95233226f, -2.05676055f, 3.74257016f, 2.75228715f, + -3.0870862f, -3.07643962f, 3.09660077f, -0.035573706f, -3.83201623f, 3.36805987f, -2.32721448f, + -3.98706961f, 3.78739834f, -0.0501429178f, 3.28890419f, 2.57677722f, 1.71475232f, 1.07662773f, + 3.94095635f, -1.99101043f, -2.64912295f, 0.228176221f, 2.9620595f, -3.32440853f, -2.82459354f, + -1.89728594f, 0.36565122f, 1.51495647f, -1.42127073f, 3.76410079f, 2.93308902f, -2.0510745f, + -3.25827599f, 0.870837927f, 1.07800019f, -3.35655951f, -3.62744784f, 2.21201587f, -3.00429726f, + 0.293973804f, -3.47370911f, 3.58087564f, 2.36890817f, 3.83561659f, -1.13606739f, -2.69446349f, + 0.650222838f, 2.81578493f, 0.855392575f, -0.907065392f, -2.1944294f, -1.08960676f, 1.19178188f, + 1.03588545f, 0.622981071f, -1.2198478f, -1.29000688f, -0.66865468f, 3.17527604f, 1.28442585f, + -0.043599356f, 0.883806348f, -1.8830725f, -1.27138579f, 3.15337276f, 3.16163087f, 2.31229186f, + 1.9729737f, -2.71718836f, -3.67638183f, 1.95105565f, -2.98977709f, -0.264483124f, -1.10155332f, + -2.74778748f, 0.195290595f, 1.92482924f, -0.402169853f, 0.0170953758f, -2.78179884f, -3.72088337f, + -2.65982246f, -3.65458441f, -0.868379056f, -2.43472838f, 3.03036475f, -0.756773412f, 0.299537063f, + -2.47048402f, 3.50486851f, 3.48434687f, -1.69397664f, -2.91440964f, -3.4000411f, -1.67709422f, + -3.47350097f, 2.15842891f, -2.37673903f, 3.505373f, 0.862156153f, 1.25737512f, -3.69056606f, + 0.503704488f, -0.890837252f, 1.69868946f, 3.14328837f, 1.52625263f, 1.84811795f, -3.06296372f, + -2.97534323f, 2.66268444f, -0.499832541f, -1.61339116f, -2.75664639f, 1.53680682f, -1.28649604f, + -2.48899579f, 2.72057939f, 1.99073052f, 3.50091052f, 3.23127031f, -1.58753884f, 0.546642303f, + -2.54462695f, 3.35287929f, -3.12155414f, 2.62187481f, -0.116575383f, -0.585002899f, -2.68935871f, + -1.85703218f, -0.062880002f, 1.7804811f, -0.542152762f, -2.36689019f, -0.229491457f, -3.65327024f, + 3.14369917f, 1.50405955f, 0.761211812f, -2.113518f, 2.5531559f, 0.949686229f, 0.488110304f, + 3.30247259f, 2.49567199f, -2.32758141f, -2.71987677f, 3.13759947f, 0.0526052341f, -2.93092513f, + 1.79427099f, 1.74417019f, 1.26612902f, 3.98303676f, 2.68667698f, -2.2101512f, -2.02735996f, + -0.28724739f, -3.72321224f, -0.512708604f, -0.0460286997f, -2.26301265f, 1.42742038f, -1.38658524f, + -0.842228115f, 0.612931311f, 3.71628141f, 3.80187273f, 3.58566618f, 3.51781631f, 2.96378493f, + -3.94009018f, -1.70781219f, 0.695963502f, 2.52244306f, -3.11812353f, 1.67709053f, -1.84012711f, + -0.482121676f, 3.14317441f, 1.60329306f, 3.13917232f, 1.2898345f, 0.873984993f, -2.49326158f, + 1.57823563f, 1.35847569f, 3.11472988f, -3.13591552f, -1.05658257f, -3.23873496f, 2.30656266f, + 0.247705579f, -1.23039114f, 1.59393167f, 2.62314558f, 2.2593646f, -1.01736796f, -3.4066751f, + -3.76655769f, -0.990807116f, 0.817389727f, 0.570640683f, 0.611730576f, 1.16457593f, -0.166733459f, + 2.09038854f, -1.53484309f, 3.66921782f, -2.0016284f, -2.5215013f, -2.13633752f, 0.887983978f, + 0.0414884612f, 1.49504888f, -1.37205553f, -3.91358399f, -1.06803179f, 3.51571465f, 1.63934171f, + 3.45168638f, 0.234272763f, 2.79411125f, -0.730487704f, 0.315647304f, -2.42148638f, -2.39347458f, + -0.11877837f, 1.60730112f, 2.81029344f, 3.08459663f, 1.72893429f, -2.18835855f, -0.4472242f, + -3.90422845f, -1.58321726f, -3.43489122f, -0.590021312f, -3.37958908f, -2.45027852f, 3.08758187f, + 0.345714062f, -0.382473081f, 2.57715726f, 3.77982092f, 2.559448f, 1.87118256f, 2.51101494f, + -0.0323000029f, 1.70008278f, -2.61789441f, -2.69721031f, 3.22778201f, 0.646772683f, 1.40676033f, + 1.95331597f, -2.64030361f, 0.781075716f, -0.861477137f, -2.03961873f, 2.48499274f, 2.68845224f, + 1.33805132f, 3.6579721f, 2.2389586f, 0.995749891f, 1.26111722f, -0.992454827f, 1.70809889f, + -2.90918493f, 3.51864982f, 3.6759057f, 2.54023576f, -3.161129f, -0.161649078f, 0.382012039f, + 1.14175189f, -1.06119668f, -3.49201941f, -2.81835008f, 2.20738626f, -3.52614427f, -1.04786634f, + -0.719927371f, 1.29703403f, -1.57873452f, -3.89641929f, 1.01794398f, -1.14923084f, 2.04442644f, + 2.73643684f, -1.10528362f, 1.31280017f, 3.90040779f, -3.31795692f, -1.11789966f, 1.18629348f, + -2.23370743f, 1.12204671f, -0.542429388f, 1.88857973f, -2.71618819f, 3.79502821f, -0.557375491f, + 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0.871237516f, + -1.43592322f, -3.98295927f, 1.25433338f, 2.27433276f, -1.96115494f, 0.179764643f, 0.381205261f, + -3.47258353f, -3.32646751f, -2.95218158f, 3.63015056f, 0.318302453f, -1.80925393f, -2.37872958f, + -1.92276478f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/output_ref_data.h new file mode 100644 index 00000000..fe45376e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/output_ref_data.h @@ -0,0 +1,116 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_default_f32_output_ref[792] = { + -2.42567062f, -0.0501429178f, -2.5215013f, -2.11444569f, 1.55854559f, -2.78179884f, 1.14175189f, + 3.79571819f, 1.91096771f, 2.49567199f, 1.84350193f, 3.43709922f, -3.27102661f, -3.35655951f, + -2.18835855f, -3.06637239f, 2.53046584f, -3.69056606f, 1.12204671f, -3.39319062f, 3.76858616f, + 3.71628141f, 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-1.61023998f, + 1.60329306f, -3.90831494f, -2.78497124f, -2.36664343f, -2.98977709f, 1.70809889f, -0.677888036f, + 0.899063408f, 3.14369917f, -2.73528361f, -2.89357495f, -3.07643962f, 0.570640683f, -2.70902872f, + -3.47258353f, 0.663243592f, -1.42127073f, -2.42148638f, 2.15734744f, -0.379153699f, -1.67709422f, + -1.10528362f, -0.000111837973f, -1.99543715f, -0.512708604f, -2.22069454f, -0.424630821f, -1.02941585f, + -2.1944294f, 2.51101494f, 1.65161586f, 0.862965286f, -2.48899579f, -2.06151676f, 3.00097251f, + 2.56278515f, 3.13917232f, -3.67375875f, -2.83512259f, -3.27180099f, -0.264483124f, -2.90918493f, + 1.0583837f, 0.191622153f, 1.50405955f, -3.77923346f, 2.81478596f, 3.09660077f, 0.611730576f, + 0.204162687f, -3.32646751f, 1.99835801f, 3.76410079f, -2.39347458f, 3.55319166f, -0.178625092f, + -3.47350097f, 1.31280017f, -0.777083158f, -1.89381969f, -0.0460286997f, -1.91771066f, -1.92165363f, + -0.907216907f, -1.08960676f, -0.0323000029f, -0.790615857f, -2.64898872f, 2.72057939f, 3.94002962f, + -0.0647356734f, 0.0681160316f, 1.2898345f, 0.0778195634f, 0.986479759f, -2.09203935f, -1.10155332f, + 3.51864982f, 1.88747036f, 1.37488556f, 0.761211812f, 0.722647011f, 2.62739229f, -0.035573706f, + 1.16457593f, 1.07972991f, -2.95218158f, -2.86450028f, 2.93308902f, -0.11877837f, 1.38283825f, + -0.152628496f, 2.15842891f, 3.90040779f, -0.00683664251f, 0.267458469f, -2.26301265f, -3.65521073f, + 3.34687877f, -3.90411901f, 1.19178188f, 1.70008278f, -2.95934534f, 1.70573378f, 1.99073052f, + -0.808926165f, 2.46370387f, 1.46115398f, 0.873984993f, -2.85861802f, -2.14158559f, 1.06732273f, + -2.74778748f, 3.6759057f, 0.236359313f, -3.38552952f, -2.113518f, 2.45175433f, -0.793219507f, + -3.83201623f, -0.166733459f, 1.60896146f, 3.63015056f, -2.95841718f, -2.0510745f, 1.60730112f, + 2.07078028f, -1.51085329f, -2.37673903f, -3.31795692f, -3.22018409f, 3.68196583f, 1.42742038f, + -1.13692367f, 3.22202158f, 0.35752064f, 1.03588545f, -2.61789441f, -0.757669985f, 3.39287949f, + 3.50091052f, -1.03537917f, 0.196600273f, 0.901849508f, -2.49326158f, -3.35788393f, 2.74513602f, + 0.721406698f, 0.195290595f, 2.54023576f, 2.19982409f, -3.01307416f, 2.5531559f, -1.34805977f, + 0.285811454f, 3.36805987f, 2.09038854f, 0.0473414771f, 0.318302453f, -0.455534399f, -3.25827599f, + 2.81029344f, -0.512866378f, -3.01031709f, 3.505373f, -1.11789966f, -3.24277925f, -0.311422527f, + -1.38658524f, -3.31004286f, 2.53795981f, -1.07348728f, 0.622981071f, -2.69721031f, -2.2999053f, + 3.68031549f, 3.23127031f, -3.611835f, -0.954537511f, 0.947857678f, 1.57823563f, -2.15693879f, + -0.494912058f, -2.73011374f, 1.92482924f, -3.161129f, 3.06730747f, 3.66487741f, 0.949686229f, + 3.67441177f, -3.56789017f, -2.32721448f, -1.53484309f, 0.660002708f, -1.80925393f, 2.57405353f, + 0.870837927f, 3.08459663f, -1.73829854f, -2.64530253f, 0.862156153f, 1.18629348f, 2.60145402f, + -0.701008916f, -0.842228115f, 0.65057379f, 2.63025618f, -3.35234976f, -1.2198478f, 3.22778201f, + 3.73359036f, -0.328680128f, -1.58753884f, -2.29478335f, -1.50744092f, 2.94287348f, 1.35847569f, + 1.62000573f, -1.05213618f, -2.47247362f, -0.402169853f, -0.161649078f, -0.268284351f, -3.01531935f, + 0.488110304f, 3.45297074f, -2.19147253f, -3.98706961f, 3.66921782f, 1.36413765f, -2.37872958f, + -3.11011457f, 1.07800019f, 1.72893429f, 0.0208080541f, 0.723366559f, 1.25737512f, -2.23370743f, + 2.81404257f, -3.65116143f, 0.612931311f, 2.80582595f, -0.529732525f, 2.39493346f, -1.29000688f, + 0.646772683f, 1.89462018f, 3.7624495f, 0.546642303f, 0.892090559f, 1.90897202f, 1.34186208f, + 3.11472988f, 0.210342064f, 0.504398823f, 0.96764338f, 0.0170953758f, 0.382012039f, -2.50623918f, + 1.79228568f, 3.30247259f, -2.4387095f, 1.99716353f, 3.78739834f, -2.0016284f, 2.3181293f, + -1.92276478f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_default_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_default_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/config_data.h new file mode 100644 index 00000000..6136cfd0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_MATRIX_F16_NUM_DIMS 2 +#define TRANSPOSE_MATRIX_F16_INPUT_N 5 +#define TRANSPOSE_MATRIX_F16_INPUT_H 20 +#define TRANSPOSE_MATRIX_F16_INPUT_W 1 +#define TRANSPOSE_MATRIX_F16_INPUT_C 1 +#define TRANSPOSE_MATRIX_F16_OUTPUT_N 20 +#define TRANSPOSE_MATRIX_F16_OUTPUT_H 5 +#define TRANSPOSE_MATRIX_F16_OUTPUT_W 1 +#define TRANSPOSE_MATRIX_F16_OUTPUT_C 1 +#define TRANSPOSE_MATRIX_F16_PERM_0 1 +#define TRANSPOSE_MATRIX_F16_PERM_1 0 +#define TRANSPOSE_MATRIX_F16_PERM_2 2 +#define TRANSPOSE_MATRIX_F16_PERM_3 3 +#define TRANSPOSE_MATRIX_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_MATRIX_F16_SIZE 100 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/input_tensor_data.h new file mode 100644 index 00000000..1631b9bd --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/input_tensor_data.h @@ -0,0 +1,27 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_matrix_f16_input_tensor[100] = { + (float16_t)2.70507812f, (float16_t)-1.83496094f, (float16_t)2.71679688f, (float16_t)2.25f, + (float16_t)0.858886719f, (float16_t)2.25976562f, (float16_t)2.16992188f, (float16_t)0.497070312f, + (float16_t)0.148071289f, (float16_t)2.31054688f, (float16_t)1.28222656f, (float16_t)1.65136719f, + (float16_t)3.37304688f, (float16_t)-1.30371094f, (float16_t)2.65429688f, (float16_t)2.71875f, + (float16_t)-1.80859375f, (float16_t)3.21484375f, (float16_t)-1.09472656f, (float16_t)3.0f, + (float16_t)3.22070312f, (float16_t)1.89550781f, (float16_t)3.765625f, (float16_t)3.0546875f, + (float16_t)2.69335938f, (float16_t)-0.878417969f, (float16_t)-2.3203125f, (float16_t)3.24023438f, + (float16_t)-2.01367188f, (float16_t)-0.418701172f, (float16_t)3.8515625f, (float16_t)3.4921875f, + (float16_t)3.6640625f, (float16_t)2.22265625f, (float16_t)0.5546875f, (float16_t)1.30859375f, + (float16_t)3.60546875f, (float16_t)1.40136719f, (float16_t)-1.81054688f, (float16_t)2.390625f, + (float16_t)-2.0234375f, (float16_t)-2.75976562f, (float16_t)-2.328125f, (float16_t)-3.16015625f, + (float16_t)2.01367188f, (float16_t)-2.97265625f, (float16_t)-1.74804688f, (float16_t)1.91992188f, + (float16_t)-1.36425781f, (float16_t)-3.24023438f, (float16_t)-2.2890625f, (float16_t)-2.14453125f, + (float16_t)3.4921875f, (float16_t)-3.84960938f, (float16_t)1.21777344f, (float16_t)-3.94726562f, + (float16_t)2.66210938f, (float16_t)0.950195312f, (float16_t)2.96484375f, (float16_t)-2.86914062f, + (float16_t)-3.76757812f, (float16_t)-2.10546875f, (float16_t)-3.6484375f, (float16_t)0.552246094f, + (float16_t)-3.25195312f, (float16_t)0.318115234f, (float16_t)-2.4453125f, (float16_t)3.28515625f, + (float16_t)3.76171875f, (float16_t)-0.692871094f, (float16_t)-2.44335938f, (float16_t)-2.24414062f, + (float16_t)-1.53125f, (float16_t)-1.63574219f, (float16_t)-3.44726562f, (float16_t)1.25292969f, + (float16_t)-2.27148438f, (float16_t)2.4765625f, (float16_t)2.86328125f, (float16_t)2.15820312f, + (float16_t)0.137573242f, (float16_t)0.445556641f, (float16_t)-0.403320312f, (float16_t)-3.05859375f, + (float16_t)0.907226562f, (float16_t)3.62109375f, (float16_t)-3.47460938f, (float16_t)0.552734375f, + (float16_t)-3.51171875f, (float16_t)-1.67871094f, (float16_t)-2.13867188f, (float16_t)2.11914062f, + (float16_t)0.61328125f, (float16_t)-1.82324219f, (float16_t)0.381347656f, (float16_t)0.143920898f, + (float16_t)3.4765625f, (float16_t)-3.3359375f, (float16_t)-0.690917969f, (float16_t)2.91210938f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/output_ref_data.h new file mode 100644 index 00000000..0b3d0880 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/output_ref_data.h @@ -0,0 +1,27 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_matrix_f16_output_ref[100] = { + (float16_t)2.70507812f, (float16_t)3.22070312f, (float16_t)-2.0234375f, (float16_t)-3.76757812f, + (float16_t)0.137573242f, (float16_t)-1.83496094f, (float16_t)1.89550781f, (float16_t)-2.75976562f, + (float16_t)-2.10546875f, (float16_t)0.445556641f, (float16_t)2.71679688f, (float16_t)3.765625f, + (float16_t)-2.328125f, (float16_t)-3.6484375f, (float16_t)-0.403320312f, (float16_t)2.25f, + (float16_t)3.0546875f, (float16_t)-3.16015625f, (float16_t)0.552246094f, (float16_t)-3.05859375f, + (float16_t)0.858886719f, (float16_t)2.69335938f, (float16_t)2.01367188f, (float16_t)-3.25195312f, + (float16_t)0.907226562f, (float16_t)2.25976562f, (float16_t)-0.878417969f, (float16_t)-2.97265625f, + (float16_t)0.318115234f, (float16_t)3.62109375f, (float16_t)2.16992188f, (float16_t)-2.3203125f, + (float16_t)-1.74804688f, (float16_t)-2.4453125f, (float16_t)-3.47460938f, (float16_t)0.497070312f, + (float16_t)3.24023438f, (float16_t)1.91992188f, (float16_t)3.28515625f, (float16_t)0.552734375f, + (float16_t)0.148071289f, (float16_t)-2.01367188f, (float16_t)-1.36425781f, (float16_t)3.76171875f, + (float16_t)-3.51171875f, (float16_t)2.31054688f, (float16_t)-0.418701172f, (float16_t)-3.24023438f, + (float16_t)-0.692871094f, (float16_t)-1.67871094f, (float16_t)1.28222656f, (float16_t)3.8515625f, + (float16_t)-2.2890625f, (float16_t)-2.44335938f, (float16_t)-2.13867188f, (float16_t)1.65136719f, + (float16_t)3.4921875f, (float16_t)-2.14453125f, (float16_t)-2.24414062f, (float16_t)2.11914062f, + (float16_t)3.37304688f, (float16_t)3.6640625f, (float16_t)3.4921875f, (float16_t)-1.53125f, + (float16_t)0.61328125f, (float16_t)-1.30371094f, (float16_t)2.22265625f, (float16_t)-3.84960938f, + (float16_t)-1.63574219f, (float16_t)-1.82324219f, (float16_t)2.65429688f, (float16_t)0.5546875f, + (float16_t)1.21777344f, (float16_t)-3.44726562f, (float16_t)0.381347656f, (float16_t)2.71875f, + (float16_t)1.30859375f, (float16_t)-3.94726562f, (float16_t)1.25292969f, (float16_t)0.143920898f, + (float16_t)-1.80859375f, (float16_t)3.60546875f, (float16_t)2.66210938f, (float16_t)-2.27148438f, + (float16_t)3.4765625f, (float16_t)3.21484375f, (float16_t)1.40136719f, (float16_t)0.950195312f, + (float16_t)2.4765625f, (float16_t)-3.3359375f, (float16_t)-1.09472656f, (float16_t)-1.81054688f, + (float16_t)2.96484375f, (float16_t)2.86328125f, (float16_t)-0.690917969f, (float16_t)3.0f, + (float16_t)2.390625f, (float16_t)-2.86914062f, (float16_t)2.15820312f, (float16_t)2.91210938f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/config_data.h new file mode 100644 index 00000000..c9c6e3d8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_MATRIX_F32_NUM_DIMS 2 +#define TRANSPOSE_MATRIX_F32_INPUT_N 5 +#define TRANSPOSE_MATRIX_F32_INPUT_H 20 +#define TRANSPOSE_MATRIX_F32_INPUT_W 1 +#define TRANSPOSE_MATRIX_F32_INPUT_C 1 +#define TRANSPOSE_MATRIX_F32_OUTPUT_N 20 +#define TRANSPOSE_MATRIX_F32_OUTPUT_H 5 +#define TRANSPOSE_MATRIX_F32_OUTPUT_W 1 +#define TRANSPOSE_MATRIX_F32_OUTPUT_C 1 +#define TRANSPOSE_MATRIX_F32_PERM_0 1 +#define TRANSPOSE_MATRIX_F32_PERM_1 0 +#define TRANSPOSE_MATRIX_F32_PERM_2 2 +#define TRANSPOSE_MATRIX_F32_PERM_3 3 +#define TRANSPOSE_MATRIX_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_MATRIX_F32_SIZE 100 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/input_tensor_data.h new file mode 100644 index 00000000..5035033c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/input_tensor_data.h @@ -0,0 +1,17 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_matrix_f32_input_tensor[100] = { + 2.68596768f, 2.17270565f, -0.0942590013f, -1.32333684f, 1.27162194f, -2.37203097f, -3.2495451f, + 2.24760723f, 3.03902817f, 1.19161606f, 2.78855109f, -2.98440075f, -3.21872163f, 2.64247966f, + 3.44781566f, -0.786358237f, -2.21996713f, -0.888754368f, -0.00652130786f, 1.54644489f, 1.62222135f, + 3.40906763f, 1.79172182f, -2.94639111f, -1.0440352f, 1.88678658f, -3.95518875f, -1.69602311f, + 3.36631012f, 3.33254862f, -1.88305724f, -2.3276701f, -0.479022861f, 1.37004256f, -3.07156587f, + -3.86061454f, -2.57654476f, 1.16355336f, -3.69478989f, -0.104268059f, 3.52736688f, -0.201742664f, + 1.56462288f, -3.35132265f, 3.92656446f, -0.208400294f, 3.32654548f, 1.19899678f, 1.32354641f, + -2.46509433f, 1.33541536f, 1.71968019f, -0.0308940317f, -1.48817086f, 3.41999674f, -0.650733888f, + -3.94477797f, -2.25331759f, 2.40238762f, 0.409342498f, -0.484987766f, 2.59484172f, -0.747654617f, + 2.69704103f, -2.76998186f, 0.330014616f, 0.486699373f, 1.48577631f, -2.9120636f, 3.52374578f, + 2.20883822f, -0.340292603f, 0.474363893f, 0.0336844847f, -2.93850541f, 1.88564003f, -2.09024882f, + 3.30503845f, 3.75420308f, -0.981823146f, 3.97409368f, -2.9113574f, 2.14799452f, 2.46523142f, + 3.75545406f, -1.76906729f, 2.35428238f, 3.55610037f, -3.66261697f, -0.0413394943f, 1.26847184f, + 1.43036771f, -1.85595345f, -0.364636123f, -0.667216778f, -3.45570254f, 0.572744071f, -0.311121553f, + -2.23211646f, 1.01610255f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/output_ref_data.h new file mode 100644 index 00000000..1d8209bf --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/output_ref_data.h @@ -0,0 +1,17 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_matrix_f32_output_ref[100] = { + 2.68596768f, 1.62222135f, 3.52736688f, -0.484987766f, 3.97409368f, 2.17270565f, 3.40906763f, + -0.201742664f, 2.59484172f, -2.9113574f, -0.0942590013f, 1.79172182f, 1.56462288f, -0.747654617f, + 2.14799452f, -1.32333684f, -2.94639111f, -3.35132265f, 2.69704103f, 2.46523142f, 1.27162194f, + -1.0440352f, 3.92656446f, -2.76998186f, 3.75545406f, -2.37203097f, 1.88678658f, -0.208400294f, + 0.330014616f, -1.76906729f, -3.2495451f, -3.95518875f, 3.32654548f, 0.486699373f, 2.35428238f, + 2.24760723f, -1.69602311f, 1.19899678f, 1.48577631f, 3.55610037f, 3.03902817f, 3.36631012f, + 1.32354641f, -2.9120636f, -3.66261697f, 1.19161606f, 3.33254862f, -2.46509433f, 3.52374578f, + -0.0413394943f, 2.78855109f, -1.88305724f, 1.33541536f, 2.20883822f, 1.26847184f, -2.98440075f, + -2.3276701f, 1.71968019f, -0.340292603f, 1.43036771f, -3.21872163f, -0.479022861f, -0.0308940317f, + 0.474363893f, -1.85595345f, 2.64247966f, 1.37004256f, -1.48817086f, 0.0336844847f, -0.364636123f, + 3.44781566f, -3.07156587f, 3.41999674f, -2.93850541f, -0.667216778f, -0.786358237f, -3.86061454f, + -0.650733888f, 1.88564003f, -3.45570254f, -2.21996713f, -2.57654476f, -3.94477797f, -2.09024882f, + 0.572744071f, -0.888754368f, 1.16355336f, -2.25331759f, 3.30503845f, -0.311121553f, -0.00652130786f, + -3.69478989f, 2.40238762f, 3.75420308f, -2.23211646f, 1.54644489f, -0.104268059f, 0.409342498f, + -0.981823146f, 1.01610255f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/config_data.h new file mode 100644 index 00000000..1c6d9a9c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_MATRIX_SMALL_F16_NUM_DIMS 2 +#define TRANSPOSE_MATRIX_SMALL_F16_INPUT_N 2 +#define TRANSPOSE_MATRIX_SMALL_F16_INPUT_H 4 +#define TRANSPOSE_MATRIX_SMALL_F16_INPUT_W 1 +#define TRANSPOSE_MATRIX_SMALL_F16_INPUT_C 1 +#define TRANSPOSE_MATRIX_SMALL_F16_OUTPUT_N 4 +#define TRANSPOSE_MATRIX_SMALL_F16_OUTPUT_H 2 +#define TRANSPOSE_MATRIX_SMALL_F16_OUTPUT_W 1 +#define TRANSPOSE_MATRIX_SMALL_F16_OUTPUT_C 1 +#define TRANSPOSE_MATRIX_SMALL_F16_PERM_0 1 +#define TRANSPOSE_MATRIX_SMALL_F16_PERM_1 0 +#define TRANSPOSE_MATRIX_SMALL_F16_PERM_2 2 +#define TRANSPOSE_MATRIX_SMALL_F16_PERM_3 3 +#define TRANSPOSE_MATRIX_SMALL_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_MATRIX_SMALL_F16_SIZE 8 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/input_tensor_data.h new file mode 100644 index 00000000..a4dd15f8 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/input_tensor_data.h @@ -0,0 +1,9 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_matrix_small_f16_input_tensor[8] = {(float16_t)-3.23046875f, + (float16_t)2.15039062f, + (float16_t)-0.00286102295f, + (float16_t)3.29296875f, + (float16_t)1.43164062f, + (float16_t)1.55761719f, + (float16_t)-3.94335938f, + (float16_t)2.74023438f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/output_ref_data.h new file mode 100644 index 00000000..03dd3dac --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/output_ref_data.h @@ -0,0 +1,9 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_matrix_small_f16_output_ref[8] = {(float16_t)-3.23046875f, + (float16_t)1.43164062f, + (float16_t)2.15039062f, + (float16_t)1.55761719f, + (float16_t)-0.00286102295f, + (float16_t)-3.94335938f, + (float16_t)3.29296875f, + (float16_t)2.74023438f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_matrix_small_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/config_data.h new file mode 100644 index 00000000..b6896987 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NCHW_F16_NUM_DIMS 4 +#define TRANSPOSE_NCHW_F16_INPUT_N 4 +#define TRANSPOSE_NCHW_F16_INPUT_H 9 +#define TRANSPOSE_NCHW_F16_INPUT_W 3 +#define TRANSPOSE_NCHW_F16_INPUT_C 19 +#define TRANSPOSE_NCHW_F16_OUTPUT_N 4 +#define TRANSPOSE_NCHW_F16_OUTPUT_H 19 +#define TRANSPOSE_NCHW_F16_OUTPUT_W 9 +#define TRANSPOSE_NCHW_F16_OUTPUT_C 3 +#define TRANSPOSE_NCHW_F16_PERM_0 0 +#define TRANSPOSE_NCHW_F16_PERM_1 3 +#define TRANSPOSE_NCHW_F16_PERM_2 1 +#define TRANSPOSE_NCHW_F16_PERM_3 2 +#define TRANSPOSE_NCHW_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NCHW_F16_SIZE 2052 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/input_tensor_data.h new file mode 100644 index 00000000..5b979be1 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/input_tensor_data.h @@ -0,0 +1,686 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nchw_f16_input_tensor[2052] = { + (float16_t)-1.34375f, (float16_t)0.218505859f, (float16_t)1.5546875f, + (float16_t)-3.74609375f, (float16_t)1.56835938f, (float16_t)-3.81054688f, + (float16_t)-2.69335938f, (float16_t)-3.77929688f, (float16_t)1.03417969f, + (float16_t)-3.28515625f, (float16_t)0.849121094f, (float16_t)-0.998535156f, + (float16_t)3.0078125f, (float16_t)0.932128906f, (float16_t)3.11328125f, + (float16_t)2.87109375f, (float16_t)3.05859375f, (float16_t)-3.953125f, + (float16_t)1.69628906f, (float16_t)0.771972656f, (float16_t)0.8984375f, + (float16_t)2.53320312f, (float16_t)-0.15625f, (float16_t)3.765625f, + (float16_t)1.89746094f, (float16_t)-2.56445312f, (float16_t)2.50195312f, + (float16_t)-1.19726562f, (float16_t)-0.275390625f, (float16_t)-2.8359375f, + (float16_t)-3.74609375f, (float16_t)2.07617188f, (float16_t)0.263916016f, + (float16_t)1.3046875f, (float16_t)-0.0798950195f, (float16_t)-0.0443725586f, + (float16_t)-2.0546875f, (float16_t)2.00195312f, (float16_t)0.96875f, + (float16_t)-3.078125f, (float16_t)2.82617188f, (float16_t)1.92089844f, + (float16_t)-3.4609375f, (float16_t)-3.4296875f, (float16_t)3.37695312f, + (float16_t)-0.303710938f, (float16_t)0.135986328f, (float16_t)-2.07421875f, + (float16_t)1.23242188f, (float16_t)1.99902344f, (float16_t)1.84960938f, + (float16_t)-1.88964844f, (float16_t)1.32226562f, (float16_t)-1.7109375f, + (float16_t)-1.56738281f, (float16_t)-2.91015625f, (float16_t)-0.463623047f, + (float16_t)-3.82617188f, (float16_t)-0.916015625f, (float16_t)-2.51171875f, + (float16_t)2.75585938f, (float16_t)0.0749511719f, (float16_t)2.0f, + (float16_t)3.83984375f, (float16_t)-2.65039062f, (float16_t)-1.70507812f, + (float16_t)-1.05566406f, (float16_t)1.12695312f, (float16_t)0.673828125f, + (float16_t)3.83984375f, (float16_t)-0.478271484f, (float16_t)-3.45703125f, + (float16_t)3.31445312f, (float16_t)-1.75097656f, (float16_t)3.65820312f, + (float16_t)3.9609375f, (float16_t)2.58007812f, (float16_t)3.41210938f, + (float16_t)2.10351562f, (float16_t)-3.26171875f, (float16_t)-3.54296875f, + (float16_t)-0.741210938f, (float16_t)0.770019531f, (float16_t)3.99023438f, + (float16_t)2.80664062f, (float16_t)-0.276367188f, (float16_t)3.734375f, + (float16_t)3.63671875f, (float16_t)-1.40527344f, (float16_t)-0.870117188f, + (float16_t)3.5859375f, (float16_t)-2.79101562f, (float16_t)-3.5625f, + (float16_t)2.93554688f, (float16_t)3.18945312f, (float16_t)-0.791992188f, + (float16_t)3.84960938f, (float16_t)-3.58007812f, (float16_t)1.1171875f, + (float16_t)-2.06445312f, (float16_t)1.54785156f, (float16_t)-2.20898438f, + (float16_t)-3.55078125f, (float16_t)-2.765625f, (float16_t)0.218017578f, + (float16_t)1.91699219f, (float16_t)-1.76953125f, (float16_t)-1.92382812f, + (float16_t)-1.43457031f, (float16_t)1.22070312f, (float16_t)-0.735351562f, + (float16_t)2.48632812f, (float16_t)-0.879882812f, (float16_t)-1.37402344f, + (float16_t)-3.77539062f, (float16_t)0.305175781f, (float16_t)1.01171875f, + (float16_t)2.31640625f, (float16_t)-3.33007812f, (float16_t)-2.19335938f, + (float16_t)2.640625f, (float16_t)-2.63476562f, (float16_t)3.6875f, + (float16_t)2.0546875f, (float16_t)-1.4140625f, (float16_t)0.916992188f, + (float16_t)-0.106628418f, (float16_t)2.29882812f, (float16_t)1.87890625f, + (float16_t)-1.19628906f, (float16_t)-0.65625f, (float16_t)-2.65820312f, + (float16_t)0.821289062f, (float16_t)-3.359375f, (float16_t)-1.29003906f, + (float16_t)-0.699707031f, (float16_t)-1.40234375f, (float16_t)-0.675292969f, + (float16_t)-3.68359375f, (float16_t)-2.41796875f, (float16_t)2.44726562f, + (float16_t)1.77832031f, (float16_t)2.79882812f, (float16_t)-3.81445312f, + (float16_t)0.346435547f, (float16_t)-2.60742188f, (float16_t)-2.46679688f, + (float16_t)2.81054688f, (float16_t)3.1484375f, (float16_t)-3.0390625f, + (float16_t)2.24023438f, (float16_t)3.27929688f, (float16_t)3.8828125f, + (float16_t)3.54296875f, (float16_t)2.54492188f, (float16_t)1.37011719f, + (float16_t)2.16796875f, (float16_t)-3.25f, (float16_t)-0.318115234f, + (float16_t)1.17089844f, (float16_t)-3.15234375f, (float16_t)-0.315673828f, + (float16_t)0.626464844f, (float16_t)-2.29882812f, (float16_t)0.680664062f, + (float16_t)3.21484375f, (float16_t)-0.0478210449f, (float16_t)-1.79296875f, + (float16_t)0.00138378143f, (float16_t)-0.0922851562f, (float16_t)-0.313476562f, + (float16_t)0.251464844f, (float16_t)-0.428222656f, (float16_t)3.50195312f, + (float16_t)-0.414550781f, (float16_t)-2.984375f, (float16_t)-2.93554688f, + (float16_t)-2.38671875f, (float16_t)-2.4296875f, (float16_t)-0.547851562f, + (float16_t)-1.07421875f, (float16_t)1.86035156f, (float16_t)2.6875f, + (float16_t)1.11523438f, (float16_t)3.81640625f, (float16_t)3.0625f, + (float16_t)1.54296875f, (float16_t)3.62695312f, (float16_t)-3.28710938f, + (float16_t)2.96875f, (float16_t)1.41015625f, (float16_t)-1.25390625f, + (float16_t)3.12304688f, (float16_t)-1.125f, (float16_t)-2.9296875f, + (float16_t)2.33007812f, (float16_t)1.37011719f, (float16_t)-3.07421875f, + (float16_t)-1.42871094f, (float16_t)-0.620605469f, (float16_t)-3.90820312f, + (float16_t)3.01367188f, (float16_t)-3.73046875f, (float16_t)0.129882812f, + (float16_t)-1.34960938f, (float16_t)-0.444091797f, (float16_t)2.3125f, + (float16_t)3.390625f, (float16_t)-3.6953125f, (float16_t)-0.573242188f, + (float16_t)-2.33007812f, (float16_t)-2.31640625f, (float16_t)1.15429688f, + (float16_t)-0.138916016f, (float16_t)-1.99414062f, (float16_t)-2.86523438f, + (float16_t)1.95117188f, (float16_t)2.88671875f, (float16_t)1.7265625f, + 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(float16_t)3.38867188f, + (float16_t)-2.9375f, (float16_t)-3.52148438f, (float16_t)3.71875f, + (float16_t)-1.53125f, (float16_t)2.51171875f, (float16_t)2.33398438f, + (float16_t)0.938964844f, (float16_t)-0.696289062f, (float16_t)2.97265625f, + (float16_t)-1.49121094f, (float16_t)-1.23632812f, (float16_t)-2.96679688f, + (float16_t)2.20898438f, (float16_t)-0.243041992f, (float16_t)1.41503906f, + (float16_t)-1.49609375f, (float16_t)3.47851562f, (float16_t)-1.27734375f, + (float16_t)2.56835938f, (float16_t)2.56835938f, (float16_t)-2.7265625f, + (float16_t)0.0964355469f, (float16_t)-1.09375f, (float16_t)3.0859375f, + (float16_t)2.546875f, (float16_t)3.55859375f, (float16_t)2.50195312f, + (float16_t)1.63769531f, (float16_t)-0.241943359f, (float16_t)2.24023438f, + (float16_t)1.38964844f, (float16_t)3.54296875f, (float16_t)0.440673828f, + (float16_t)-2.32617188f, (float16_t)1.27246094f, (float16_t)-2.52929688f, + (float16_t)-0.367431641f, (float16_t)-0.352294922f, (float16_t)0.145263672f, + (float16_t)-0.676757812f, (float16_t)-1.72851562f, (float16_t)-0.65234375f, + (float16_t)1.28320312f, (float16_t)-1.27734375f, (float16_t)3.91796875f, + (float16_t)-3.46289062f, (float16_t)0.218505859f, (float16_t)-1.625f, + (float16_t)2.52734375f, (float16_t)3.09960938f, (float16_t)-3.4453125f, + (float16_t)-2.79492188f, (float16_t)-2.64453125f, (float16_t)1.07714844f, + (float16_t)-0.986816406f, (float16_t)-0.476074219f, (float16_t)-2.5078125f, + (float16_t)-2.95507812f, (float16_t)1.83203125f, (float16_t)-0.766113281f, + (float16_t)-3.21679688f, (float16_t)-3.97265625f, (float16_t)-1.77148438f, + (float16_t)-1.50390625f, (float16_t)3.39257812f, (float16_t)-3.37109375f, + (float16_t)-2.01367188f, (float16_t)3.52539062f, (float16_t)1.91113281f, + (float16_t)3.8984375f, (float16_t)-0.177856445f, (float16_t)2.60351562f, + (float16_t)3.13671875f, (float16_t)3.48632812f, (float16_t)2.78515625f, + (float16_t)-1.32910156f, (float16_t)-0.510742188f, (float16_t)1.0234375f, + (float16_t)-1.5078125f, (float16_t)-3.59765625f, (float16_t)2.36132812f, + (float16_t)1.46386719f, (float16_t)1.83691406f, (float16_t)-2.5f, + (float16_t)2.7734375f, (float16_t)3.51367188f, (float16_t)-2.38476562f, + (float16_t)-1.56445312f, (float16_t)1.36621094f, (float16_t)-3.953125f, + (float16_t)-0.283691406f, (float16_t)-0.126098633f, (float16_t)1.62207031f, + (float16_t)2.4765625f, (float16_t)1.56640625f, (float16_t)-2.08789062f, + (float16_t)-1.38574219f, (float16_t)1.10449219f, (float16_t)0.856933594f, + (float16_t)-1.40625f, (float16_t)1.74414062f, (float16_t)2.97851562f, + (float16_t)-3.57226562f, (float16_t)2.83398438f, (float16_t)-3.48242188f, + (float16_t)-2.5390625f, (float16_t)3.9296875f, (float16_t)-1.38769531f, + (float16_t)-3.27539062f, (float16_t)2.171875f, (float16_t)2.66796875f, + (float16_t)-3.453125f, (float16_t)1.42578125f, (float16_t)-0.926757812f, + (float16_t)1.38378906f, (float16_t)-3.41210938f, (float16_t)-1.53808594f, + (float16_t)-3.73046875f, (float16_t)0.508300781f, (float16_t)-1.50585938f, + (float16_t)-3.22265625f, (float16_t)-3.79492188f, (float16_t)-0.862304688f, + (float16_t)1.5390625f, (float16_t)0.950683594f, (float16_t)0.887695312f, + (float16_t)-2.15625f, (float16_t)-0.218261719f, (float16_t)3.13671875f, + (float16_t)-2.64257812f, (float16_t)2.52148438f, (float16_t)3.1953125f, + (float16_t)3.84765625f, (float16_t)-0.958496094f, (float16_t)-1.20996094f, + (float16_t)2.73242188f, (float16_t)-3.06054688f, (float16_t)-0.693847656f, + (float16_t)-0.496826172f, (float16_t)3.6640625f, (float16_t)-2.09570312f, + (float16_t)0.0618896484f, (float16_t)2.66015625f, (float16_t)-3.43945312f, + (float16_t)-0.62109375f, (float16_t)-1.69628906f, (float16_t)-0.249755859f, + (float16_t)-2.87109375f, (float16_t)2.72070312f, (float16_t)-2.8046875f, + (float16_t)-2.3203125f, (float16_t)1.18066406f, (float16_t)3.27734375f, + (float16_t)-1.17480469f, (float16_t)-0.550292969f, (float16_t)-1.87695312f, + (float16_t)-2.46289062f, (float16_t)1.17285156f, (float16_t)3.46289062f, + (float16_t)-2.68945312f, (float16_t)-3.33203125f, (float16_t)0.406738281f, + (float16_t)0.0538330078f, (float16_t)-3.61523438f, (float16_t)2.90234375f, + (float16_t)1.453125f, (float16_t)1.43261719f, (float16_t)3.31445312f, + (float16_t)2.26171875f, (float16_t)-1.35449219f, (float16_t)2.546875f, + (float16_t)-1.09082031f, (float16_t)1.03125f, (float16_t)3.56445312f, + (float16_t)-2.18554688f, (float16_t)-0.0195770264f, (float16_t)2.08203125f, + (float16_t)-2.18945312f, (float16_t)3.2109375f, (float16_t)-2.64453125f, + (float16_t)-0.398681641f, (float16_t)0.0661010742f, (float16_t)-0.695800781f, + (float16_t)0.973632812f, (float16_t)-3.1484375f, (float16_t)-2.2734375f, + (float16_t)-3.49414062f, (float16_t)0.51171875f, (float16_t)-2.79492188f, + (float16_t)-0.627929688f, (float16_t)-1.24804688f, (float16_t)0.263183594f, + (float16_t)-3.78515625f, (float16_t)-0.592285156f, (float16_t)0.416015625f, + (float16_t)2.171875f, (float16_t)-2.7734375f, (float16_t)2.83789062f, + (float16_t)1.75390625f, (float16_t)0.0185089111f, (float16_t)1.3671875f, + (float16_t)-0.611816406f, (float16_t)1.44921875f, (float16_t)-1.69628906f, + (float16_t)-2.31445312f, (float16_t)2.40429688f, (float16_t)0.0866699219f, + (float16_t)1.7109375f, (float16_t)-0.173095703f, (float16_t)1.74316406f, + (float16_t)-3.23632812f, (float16_t)-0.919921875f, (float16_t)-2.04296875f, + (float16_t)-3.015625f, (float16_t)2.3125f, (float16_t)3.4765625f, + (float16_t)0.599121094f, (float16_t)-1.55664062f, (float16_t)2.63085938f, + (float16_t)3.97070312f, (float16_t)3.16015625f, (float16_t)2.4140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/output_ref_data.h new file mode 100644 index 00000000..a8f2bd1f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/output_ref_data.h @@ -0,0 +1,1027 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nchw_f16_output_ref[2052] = {(float16_t)-1.34375f, (float16_t)0.771972656f, + (float16_t)0.96875f, (float16_t)-3.82617188f, + (float16_t)2.58007812f, (float16_t)-0.791992188f, + (float16_t)-3.77539062f, (float16_t)-3.359375f, + (float16_t)3.8828125f, (float16_t)0.251464844f, + (float16_t)1.41015625f, (float16_t)-0.573242188f, + (float16_t)0.930175781f, (float16_t)-3.22851562f, + (float16_t)-2.46289062f, (float16_t)1.38671875f, + (float16_t)-0.934082031f, (float16_t)2.95507812f, + (float16_t)3.66015625f, (float16_t)-2.26171875f, + (float16_t)2.21484375f, (float16_t)3.64453125f, + (float16_t)-1.43066406f, (float16_t)-3.18359375f, + (float16_t)-1.10839844f, (float16_t)0.299072266f, + (float16_t)-0.577636719f, (float16_t)0.218505859f, + (float16_t)0.8984375f, (float16_t)-3.078125f, + (float16_t)-0.916015625f, (float16_t)3.41210938f, + (float16_t)3.84960938f, (float16_t)0.305175781f, + (float16_t)-1.29003906f, (float16_t)3.54296875f, + (float16_t)-0.428222656f, (float16_t)-1.25390625f, + (float16_t)-2.33007812f, (float16_t)-1.1796875f, + (float16_t)-0.487304688f, (float16_t)1.02246094f, + (float16_t)-0.411376953f, (float16_t)1.74804688f, + (float16_t)-0.829101562f, (float16_t)-0.508789062f, + (float16_t)2.75585938f, (float16_t)-2.03320312f, + (float16_t)2.59765625f, (float16_t)-1.46484375f, + (float16_t)1.52929688f, (float16_t)3.7265625f, + (float16_t)-1.6328125f, (float16_t)1.70898438f, + (float16_t)1.5546875f, (float16_t)2.53320312f, + (float16_t)2.82617188f, (float16_t)-2.51171875f, + (float16_t)2.10351562f, (float16_t)-3.58007812f, + (float16_t)1.01171875f, (float16_t)-0.699707031f, + (float16_t)2.54492188f, (float16_t)3.50195312f, + (float16_t)3.12304688f, (float16_t)-2.31640625f, + (float16_t)-0.639160156f, (float16_t)-0.596679688f, + (float16_t)-3.42578125f, (float16_t)0.165161133f, + (float16_t)-1.07714844f, (float16_t)-0.381347656f, + (float16_t)-0.436523438f, (float16_t)1.71386719f, + (float16_t)-1.11523438f, (float16_t)2.00585938f, + (float16_t)1.85253906f, (float16_t)-1.09667969f, + (float16_t)-3.578125f, (float16_t)2.4765625f, + (float16_t)3.30664062f, (float16_t)-3.74609375f, + (float16_t)-0.15625f, (float16_t)1.92089844f, + (float16_t)2.75585938f, (float16_t)-3.26171875f, + (float16_t)1.1171875f, (float16_t)2.31640625f, + (float16_t)-1.40234375f, (float16_t)1.37011719f, + (float16_t)-0.414550781f, (float16_t)-1.125f, + (float16_t)1.15429688f, (float16_t)0.432617188f, + (float16_t)0.650878906f, (float16_t)-0.899902344f, + (float16_t)1.89453125f, (float16_t)-1.26367188f, + (float16_t)1.32226562f, (float16_t)-2.74609375f, + (float16_t)-2.33398438f, (float16_t)0.514648438f, + (float16_t)-2.25f, (float16_t)3.12890625f, + (float16_t)3.1796875f, (float16_t)3.7578125f, + (float16_t)-1.33300781f, (float16_t)2.0625f, + (float16_t)1.56835938f, (float16_t)3.765625f, + (float16_t)-3.4609375f, (float16_t)0.0749511719f, + (float16_t)-3.54296875f, (float16_t)-2.06445312f, + (float16_t)-3.33007812f, (float16_t)-0.675292969f, + (float16_t)2.16796875f, (float16_t)-2.984375f, + (float16_t)-2.9296875f, (float16_t)-0.138916016f, + (float16_t)-0.867675781f, (float16_t)-3.60546875f, + (float16_t)2.19140625f, (float16_t)-0.0723266602f, + (float16_t)-1.78515625f, (float16_t)1.65917969f, + (float16_t)-1.84277344f, (float16_t)-1.32128906f, + (float16_t)1.32714844f, (float16_t)-2.76953125f, + (float16_t)-3.27539062f, (float16_t)3.21679688f, + (float16_t)-0.274658203f, (float16_t)1.54003906f, + (float16_t)1.56054688f, (float16_t)-3.81054688f, + (float16_t)1.89746094f, (float16_t)-3.4296875f, + (float16_t)2.0f, (float16_t)-0.741210938f, + (float16_t)1.54785156f, (float16_t)-2.19335938f, + (float16_t)-3.68359375f, (float16_t)-3.25f, + (float16_t)-2.93554688f, (float16_t)2.33007812f, + (float16_t)-1.99414062f, (float16_t)-2.82617188f, + (float16_t)-3.09765625f, (float16_t)0.318603516f, + (float16_t)-1.77050781f, (float16_t)1.00292969f, + (float16_t)-1.89550781f, (float16_t)1.45410156f, + (float16_t)0.795410156f, (float16_t)2.1328125f, + (float16_t)2.73242188f, (float16_t)-3.30273438f, + (float16_t)-1.86621094f, (float16_t)1.89648438f, + (float16_t)1.65527344f, (float16_t)0.776855469f, + (float16_t)-2.69335938f, (float16_t)-2.56445312f, + (float16_t)3.37695312f, (float16_t)3.83984375f, + (float16_t)0.770019531f, (float16_t)-2.20898438f, + (float16_t)2.640625f, (float16_t)-2.41796875f, + (float16_t)-0.318115234f, (float16_t)-2.38671875f, + (float16_t)1.37011719f, (float16_t)-2.86523438f, + (float16_t)0.993164062f, (float16_t)-0.215209961f, + (float16_t)2.28710938f, (float16_t)0.3046875f, + (float16_t)-0.427490234f, (float16_t)-3.26171875f, + (float16_t)2.27734375f, (float16_t)3.16992188f, + (float16_t)3.578125f, (float16_t)0.797851562f, + (float16_t)-3.18554688f, (float16_t)-0.725097656f, + (float16_t)-2.4296875f, (float16_t)-1.03710938f, + (float16_t)-2.54882812f, (float16_t)-3.77929688f, + 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(float16_t)0.5546875f, (float16_t)-2.00976562f, + (float16_t)-1.23632812f, (float16_t)2.24023438f, + (float16_t)2.52734375f, (float16_t)3.52539062f, + (float16_t)-2.38476562f, (float16_t)-2.5390625f, + (float16_t)0.950683594f, (float16_t)-3.43945312f, + (float16_t)0.0538330078f, (float16_t)0.0661010742f, + (float16_t)1.3671875f, (float16_t)3.97070312f, + (float16_t)2.9921875f, (float16_t)-1.64550781f, + (float16_t)1.96679688f, (float16_t)-3.95898438f, + (float16_t)1.61523438f, (float16_t)0.916503906f, + (float16_t)2.31640625f, (float16_t)-3.09375f, + (float16_t)3.42382812f, (float16_t)0.723632812f, + (float16_t)-0.698242188f, (float16_t)-3.8828125f, + (float16_t)-0.458251953f, (float16_t)-2.40234375f, + (float16_t)-3.546875f, (float16_t)-2.96679688f, + (float16_t)1.38964844f, (float16_t)3.09960938f, + (float16_t)1.91113281f, (float16_t)-1.56445312f, + (float16_t)3.9296875f, (float16_t)0.887695312f, + (float16_t)-0.62109375f, (float16_t)-3.61523438f, + (float16_t)-0.695800781f, (float16_t)-0.611816406f, + (float16_t)3.16015625f, (float16_t)2.89648438f, + (float16_t)-3.33398438f, (float16_t)0.19128418f, + (float16_t)0.625488281f, (float16_t)1.27246094f, + (float16_t)2.87304688f, (float16_t)3.828125f, + (float16_t)0.638671875f, (float16_t)-0.887695312f, + (float16_t)3.93554688f, (float16_t)0.193359375f, + (float16_t)3.23632812f, (float16_t)-2.93359375f, + (float16_t)-2.49414062f, (float16_t)-0.0312042236f, + (float16_t)2.20898438f, (float16_t)3.54296875f, + (float16_t)-3.4453125f, (float16_t)3.8984375f, + (float16_t)1.36621094f, (float16_t)-1.38769531f, + (float16_t)-2.15625f, (float16_t)-1.69628906f, + (float16_t)2.90234375f, (float16_t)0.973632812f, + (float16_t)1.44921875f, (float16_t)2.4140625f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/config_data.h new file mode 100644 index 00000000..14998f16 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NCHW_F32_NUM_DIMS 4 +#define TRANSPOSE_NCHW_F32_INPUT_N 4 +#define TRANSPOSE_NCHW_F32_INPUT_H 9 +#define TRANSPOSE_NCHW_F32_INPUT_W 3 +#define TRANSPOSE_NCHW_F32_INPUT_C 19 +#define TRANSPOSE_NCHW_F32_OUTPUT_N 4 +#define TRANSPOSE_NCHW_F32_OUTPUT_H 19 +#define TRANSPOSE_NCHW_F32_OUTPUT_W 9 +#define TRANSPOSE_NCHW_F32_OUTPUT_C 3 +#define TRANSPOSE_NCHW_F32_PERM_0 0 +#define TRANSPOSE_NCHW_F32_PERM_1 3 +#define TRANSPOSE_NCHW_F32_PERM_2 1 +#define TRANSPOSE_NCHW_F32_PERM_3 2 +#define TRANSPOSE_NCHW_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NCHW_F32_SIZE 2052 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/input_tensor_data.h new file mode 100644 index 00000000..6683687b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/input_tensor_data.h @@ -0,0 +1,296 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nchw_f32_input_tensor[2052] = { + 0.0492551662f, -2.78354955f, 0.994608879f, 1.21166408f, 0.155296996f, -2.76764107f, 3.48331571f, + 0.0468873344f, 2.21746206f, 3.36229801f, 1.53509521f, -0.817786694f, 0.398673475f, 1.77804482f, + -3.04093766f, 2.13477397f, -1.88806224f, -0.645114183f, -3.46027732f, 3.80824232f, 1.95781398f, + -2.21695209f, 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-2.28190923f, 0.213809535f, -3.10679698f, 3.5822506f, 3.65510345f, -2.20165372f, + 2.81398439f, 0.590205908f, -3.11473536f, -1.0967803f, -3.577564f, -1.74626279f, 2.5183177f, + 3.98846817f, 1.47988546f, -3.72535539f, -0.0860044062f, 1.51146626f, 1.82392061f, -0.547595859f, + 0.0299175512f, 0.189839646f, 1.57097685f, -1.13777196f, 2.84002995f, -2.56513309f, -0.631041467f, + 1.01970625f, -3.92631149f, -1.08390105f, -3.36465335f, -1.56456423f, 3.44449878f, 0.338413626f, + -3.47113156f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/output_ref_data.h new file mode 100644 index 00000000..505c4f9e --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/output_ref_data.h @@ -0,0 +1,296 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nchw_f32_output_ref[2052] = { + 0.0492551662f, 3.80824232f, -3.79778862f, 2.18134427f, 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-1.8881402f, -0.908955514f, + 3.34415865f, -3.77685499f, -0.294195324f, 1.30123711f, 0.258055508f, 1.25384641f, 3.4367528f, + 0.0273702107f, 3.84323549f, -2.05153394f, 3.00243258f, 0.0357743315f, 2.059865f, 0.423164159f, + 0.421031117f, 2.7890811f, 2.06054735f, 0.952040315f, 1.42092109f, -0.829170227f, -0.130185887f, + 1.47988546f, 0.338413626f, 3.51428127f, 2.37427926f, 0.622047365f, -2.5088954f, -3.02901101f, + -0.142691627f, 1.13857162f, -1.5506053f, -2.94830704f, -0.913293362f, -0.0597489439f, 3.69062519f, + -2.06029081f, -2.91882849f, -2.49570465f, -0.425132394f, 0.792950809f, -2.39164901f, -3.53222585f, + -0.839304984f, -1.12165022f, -0.65590328f, 1.29780543f, -0.246963859f, -1.18163979f, -3.72535539f, + -3.47113156f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nchw_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/config_data.h new file mode 100644 index 00000000..752df20c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NCWH_F16_NUM_DIMS 4 +#define TRANSPOSE_NCWH_F16_INPUT_N 5 +#define TRANSPOSE_NCWH_F16_INPUT_H 4 +#define TRANSPOSE_NCWH_F16_INPUT_W 3 +#define TRANSPOSE_NCWH_F16_INPUT_C 9 +#define TRANSPOSE_NCWH_F16_OUTPUT_N 5 +#define TRANSPOSE_NCWH_F16_OUTPUT_H 9 +#define TRANSPOSE_NCWH_F16_OUTPUT_W 3 +#define TRANSPOSE_NCWH_F16_OUTPUT_C 4 +#define TRANSPOSE_NCWH_F16_PERM_0 0 +#define TRANSPOSE_NCWH_F16_PERM_1 3 +#define TRANSPOSE_NCWH_F16_PERM_2 2 +#define TRANSPOSE_NCWH_F16_PERM_3 1 +#define TRANSPOSE_NCWH_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NCWH_F16_SIZE 540 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/input_tensor_data.h new file mode 100644 index 00000000..3f4c58fb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/input_tensor_data.h @@ -0,0 +1,137 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_ncwh_f16_input_tensor[540] = { + (float16_t)0.640625f, (float16_t)-2.75585938f, (float16_t)-2.13867188f, (float16_t)-1.86816406f, + (float16_t)2.23632812f, (float16_t)-2.22460938f, (float16_t)-0.515625f, (float16_t)3.609375f, + (float16_t)-2.44140625f, (float16_t)-0.715820312f, (float16_t)2.73828125f, (float16_t)1.20800781f, + (float16_t)-2.32421875f, (float16_t)1.125f, (float16_t)1.40722656f, (float16_t)-0.711425781f, + (float16_t)-0.480224609f, (float16_t)-0.16418457f, (float16_t)3.59765625f, (float16_t)0.484130859f, + (float16_t)3.00585938f, (float16_t)3.45703125f, (float16_t)1.32421875f, (float16_t)1.61230469f, + (float16_t)-3.74414062f, (float16_t)3.29492188f, (float16_t)-0.55859375f, (float16_t)3.44140625f, + (float16_t)-1.10644531f, (float16_t)-2.11914062f, (float16_t)-3.2890625f, (float16_t)2.3046875f, + (float16_t)-3.44335938f, (float16_t)-1.33789062f, (float16_t)-3.81445312f, (float16_t)-3.70898438f, + (float16_t)3.43945312f, (float16_t)2.5234375f, (float16_t)0.0920410156f, (float16_t)0.103210449f, + (float16_t)-1.27734375f, (float16_t)1.02734375f, (float16_t)-0.856445312f, (float16_t)-1.7265625f, + (float16_t)0.807128906f, (float16_t)-3.52929688f, (float16_t)-0.818847656f, (float16_t)0.778320312f, + (float16_t)-0.494628906f, (float16_t)-1.23535156f, (float16_t)-3.36914062f, (float16_t)1.11425781f, + (float16_t)-0.259033203f, (float16_t)1.15527344f, (float16_t)0.0606384277f, (float16_t)-0.642089844f, + (float16_t)1.07226562f, (float16_t)2.62695312f, (float16_t)3.3203125f, (float16_t)3.18359375f, + (float16_t)-3.70703125f, (float16_t)2.5703125f, (float16_t)-0.836914062f, (float16_t)-1.40039062f, + (float16_t)-1.81835938f, (float16_t)2.96289062f, (float16_t)-0.989746094f, (float16_t)0.406982422f, + (float16_t)-3.58789062f, (float16_t)1.64160156f, (float16_t)2.10351562f, (float16_t)-1.12304688f, + (float16_t)-0.756347656f, (float16_t)2.48632812f, (float16_t)-0.458251953f, (float16_t)1.29980469f, + (float16_t)-0.610839844f, (float16_t)-0.778320312f, (float16_t)-1.83984375f, (float16_t)-0.247558594f, + (float16_t)2.984375f, (float16_t)-2.02734375f, (float16_t)3.19335938f, (float16_t)2.19726562f, + (float16_t)-2.25195312f, (float16_t)3.87109375f, (float16_t)1.45898438f, (float16_t)-1.296875f, + (float16_t)-2.06835938f, (float16_t)-2.84179688f, (float16_t)2.23632812f, (float16_t)-1.01269531f, + (float16_t)-2.3046875f, (float16_t)0.102478027f, (float16_t)-2.26757812f, (float16_t)-2.48046875f, + (float16_t)-0.560546875f, (float16_t)-0.450195312f, (float16_t)-2.45507812f, (float16_t)1.58398438f, + (float16_t)-1.16015625f, (float16_t)3.04101562f, (float16_t)1.94824219f, (float16_t)0.832519531f, + (float16_t)-3.31445312f, (float16_t)1.14941406f, (float16_t)0.738769531f, (float16_t)3.74414062f, + (float16_t)-2.7421875f, (float16_t)1.99316406f, (float16_t)-2.04492188f, (float16_t)1.9375f, + (float16_t)-1.42675781f, (float16_t)-2.45117188f, (float16_t)3.71484375f, (float16_t)1.00488281f, + (float16_t)1.20703125f, (float16_t)0.470703125f, (float16_t)0.778320312f, (float16_t)0.959472656f, + (float16_t)-0.255371094f, (float16_t)-2.53320312f, (float16_t)-2.6484375f, (float16_t)2.56640625f, + (float16_t)-1.76757812f, (float16_t)-0.182861328f, (float16_t)-2.01171875f, (float16_t)2.24804688f, + (float16_t)0.611328125f, (float16_t)3.69140625f, (float16_t)-0.439941406f, (float16_t)-1.99023438f, + (float16_t)2.19726562f, (float16_t)3.73046875f, (float16_t)0.941894531f, (float16_t)2.625f, + (float16_t)0.791503906f, (float16_t)3.37695312f, (float16_t)2.07617188f, (float16_t)1.50585938f, + (float16_t)0.861816406f, (float16_t)1.76757812f, (float16_t)-3.60351562f, (float16_t)1.20410156f, + (float16_t)3.59570312f, (float16_t)-2.0390625f, (float16_t)-0.00074005127f, (float16_t)2.75195312f, + (float16_t)-1.94726562f, (float16_t)-3.49414062f, (float16_t)3.26757812f, (float16_t)-2.375f, + (float16_t)-3.34375f, (float16_t)3.62109375f, (float16_t)-2.42382812f, (float16_t)-2.19140625f, + (float16_t)2.13476562f, (float16_t)1.83886719f, (float16_t)-0.60546875f, (float16_t)1.52148438f, + (float16_t)-0.181396484f, (float16_t)3.54296875f, (float16_t)0.537109375f, (float16_t)3.75390625f, + (float16_t)2.76367188f, (float16_t)1.40625f, (float16_t)1.41699219f, (float16_t)-1.07324219f, + (float16_t)2.05859375f, (float16_t)0.143920898f, (float16_t)-0.15234375f, (float16_t)-2.5546875f, + (float16_t)0.966796875f, (float16_t)0.322998047f, (float16_t)-2.8984375f, (float16_t)3.546875f, + (float16_t)-1.65722656f, (float16_t)-1.03808594f, (float16_t)1.046875f, (float16_t)-1.40917969f, + (float16_t)-1.89453125f, (float16_t)0.854980469f, (float16_t)2.06054688f, (float16_t)3.98242188f, + (float16_t)2.9921875f, (float16_t)3.36523438f, (float16_t)-0.499511719f, (float16_t)-3.50585938f, + (float16_t)3.69921875f, (float16_t)-1.11621094f, (float16_t)-0.269042969f, (float16_t)3.66601562f, + (float16_t)-0.139526367f, (float16_t)-0.959472656f, (float16_t)1.62792969f, (float16_t)-1.86914062f, + (float16_t)3.14257812f, (float16_t)-0.15246582f, (float16_t)0.750976562f, (float16_t)1.09472656f, + (float16_t)-0.35546875f, (float16_t)0.631347656f, (float16_t)2.0703125f, (float16_t)3.35351562f, + (float16_t)3.68164062f, (float16_t)1.83984375f, (float16_t)2.22460938f, (float16_t)0.13269043f, + (float16_t)-3.80078125f, (float16_t)-0.924316406f, (float16_t)-0.211669922f, (float16_t)1.27148438f, + (float16_t)2.44140625f, (float16_t)-2.87109375f, (float16_t)-0.356933594f, (float16_t)0.8046875f, + (float16_t)2.24414062f, (float16_t)0.392089844f, (float16_t)-1.42089844f, (float16_t)-3.65820312f, + (float16_t)2.86523438f, (float16_t)-2.4921875f, (float16_t)2.35742188f, (float16_t)1.61132812f, + (float16_t)1.49707031f, (float16_t)3.84960938f, (float16_t)1.78417969f, (float16_t)1.19335938f, + (float16_t)2.14453125f, (float16_t)-0.456298828f, (float16_t)-0.685546875f, (float16_t)2.19140625f, + (float16_t)3.99023438f, (float16_t)-1.76660156f, (float16_t)-1.03125f, (float16_t)1.27148438f, + (float16_t)3.36328125f, (float16_t)-0.137573242f, (float16_t)1.02148438f, (float16_t)-1.1015625f, + (float16_t)2.89453125f, (float16_t)-0.888183594f, (float16_t)1.25390625f, (float16_t)3.84179688f, + (float16_t)2.96679688f, (float16_t)1.59082031f, (float16_t)2.04492188f, (float16_t)3.15625f, + (float16_t)1.51855469f, (float16_t)-1.53320312f, (float16_t)2.4140625f, (float16_t)3.25f, + (float16_t)3.59179688f, (float16_t)0.483886719f, (float16_t)2.7578125f, (float16_t)-1.49902344f, + (float16_t)1.25976562f, (float16_t)-0.324707031f, (float16_t)3.48828125f, (float16_t)-1.42871094f, + (float16_t)2.23046875f, (float16_t)-0.203857422f, (float16_t)-0.321777344f, (float16_t)-2.98632812f, + (float16_t)-2.23046875f, (float16_t)-1.31054688f, (float16_t)0.159912109f, (float16_t)2.16992188f, + (float16_t)2.77929688f, (float16_t)-0.0639648438f, (float16_t)1.13085938f, (float16_t)2.00390625f, + (float16_t)1.48144531f, (float16_t)-3.90625f, (float16_t)2.5703125f, (float16_t)0.73046875f, + (float16_t)2.9609375f, (float16_t)2.1875f, (float16_t)-3.62890625f, (float16_t)-3.00976562f, + (float16_t)3.18945312f, (float16_t)0.123596191f, (float16_t)-2.56640625f, (float16_t)-1.90039062f, + (float16_t)-0.121765137f, (float16_t)0.730957031f, (float16_t)0.345947266f, (float16_t)1.35253906f, + (float16_t)-0.837890625f, (float16_t)-2.0f, (float16_t)3.08007812f, (float16_t)-0.774902344f, + (float16_t)-3.56054688f, (float16_t)0.352294922f, (float16_t)-1.16699219f, (float16_t)-3.67382812f, + (float16_t)1.04785156f, (float16_t)-0.379150391f, (float16_t)2.984375f, (float16_t)-3.17773438f, + (float16_t)0.300537109f, (float16_t)0.262695312f, (float16_t)0.589355469f, (float16_t)0.154296875f, + (float16_t)-3.890625f, (float16_t)-0.324951172f, (float16_t)-2.85351562f, (float16_t)-0.504394531f, + (float16_t)-2.12890625f, (float16_t)2.66992188f, (float16_t)2.83203125f, (float16_t)1.80175781f, + (float16_t)-1.63085938f, (float16_t)2.73828125f, (float16_t)2.1953125f, (float16_t)-3.51171875f, + (float16_t)3.16601562f, (float16_t)0.314453125f, (float16_t)1.17480469f, (float16_t)-3.20898438f, + (float16_t)3.71484375f, (float16_t)-1.48632812f, (float16_t)-0.775390625f, (float16_t)-1.32714844f, + (float16_t)0.11340332f, (float16_t)1.81933594f, (float16_t)0.797851562f, (float16_t)-0.455322266f, + (float16_t)3.1484375f, (float16_t)-3.15429688f, (float16_t)2.97460938f, (float16_t)3.71679688f, + (float16_t)-2.44921875f, (float16_t)1.52832031f, (float16_t)-2.70507812f, (float16_t)1.31738281f, + (float16_t)-3.43359375f, (float16_t)-2.2265625f, (float16_t)-3.62890625f, (float16_t)2.92578125f, + (float16_t)-1.59960938f, (float16_t)-2.27929688f, (float16_t)0.0488891602f, (float16_t)-0.750976562f, + (float16_t)3.79101562f, (float16_t)-1.58398438f, (float16_t)-2.9375f, (float16_t)-0.145996094f, + (float16_t)3.16015625f, (float16_t)1.84570312f, (float16_t)-2.3203125f, (float16_t)0.447265625f, + (float16_t)1.78613281f, (float16_t)-2.18359375f, (float16_t)1.69921875f, (float16_t)-2.27929688f, + (float16_t)-2.83398438f, (float16_t)-2.56640625f, (float16_t)-3.84179688f, (float16_t)-2.7109375f, + (float16_t)0.399169922f, (float16_t)-2.09960938f, (float16_t)1.890625f, (float16_t)2.24609375f, + (float16_t)3.56054688f, (float16_t)-3.25585938f, (float16_t)1.32128906f, (float16_t)-1.00488281f, + (float16_t)1.32519531f, (float16_t)-3.13085938f, (float16_t)2.94140625f, (float16_t)2.171875f, + (float16_t)-0.898925781f, (float16_t)-2.51171875f, (float16_t)0.569335938f, (float16_t)2.53515625f, + (float16_t)3.73046875f, (float16_t)1.30273438f, (float16_t)-0.476074219f, (float16_t)2.80859375f, + (float16_t)3.56445312f, (float16_t)-1.10742188f, (float16_t)0.171020508f, (float16_t)1.14746094f, + (float16_t)-1.19824219f, (float16_t)2.13476562f, (float16_t)-3.66015625f, (float16_t)1.92871094f, + (float16_t)3.4375f, (float16_t)0.608398438f, (float16_t)-1.85839844f, (float16_t)-1.0234375f, + (float16_t)1.41699219f, (float16_t)-1.04199219f, (float16_t)-0.614257812f, (float16_t)-3.98046875f, + (float16_t)-1.37109375f, (float16_t)-3.23242188f, (float16_t)-3.15625f, (float16_t)2.9140625f, + (float16_t)-0.262939453f, (float16_t)2.18945312f, (float16_t)-0.108764648f, (float16_t)3.18164062f, + (float16_t)3.61914062f, (float16_t)-3.11914062f, (float16_t)2.2421875f, (float16_t)1.74707031f, + (float16_t)1.20117188f, (float16_t)-1.39257812f, (float16_t)-3.83007812f, (float16_t)3.12695312f, + (float16_t)3.3046875f, (float16_t)1.11230469f, (float16_t)-3.234375f, (float16_t)0.704101562f, + (float16_t)0.607421875f, (float16_t)2.16601562f, (float16_t)-0.920410156f, (float16_t)-1.13378906f, + (float16_t)3.7578125f, (float16_t)3.18164062f, (float16_t)3.70898438f, (float16_t)-3.77148438f, + (float16_t)0.26171875f, (float16_t)1.71972656f, (float16_t)2.2109375f, (float16_t)-3.25195312f, + (float16_t)3.25585938f, (float16_t)2.50976562f, (float16_t)3.50585938f, (float16_t)3.76171875f, + (float16_t)-3.15625f, (float16_t)2.8359375f, (float16_t)1.21289062f, (float16_t)0.778320312f, + (float16_t)1.62304688f, (float16_t)-1.70703125f, (float16_t)-2.27539062f, (float16_t)-2.12109375f, + (float16_t)-3.7109375f, (float16_t)-3.40234375f, (float16_t)-1.890625f, (float16_t)3.55273438f, + (float16_t)-2.73828125f, (float16_t)3.99804688f, (float16_t)-0.0470275879f, (float16_t)-2.87304688f, + (float16_t)2.22265625f, (float16_t)1.79101562f, (float16_t)-3.40820312f, (float16_t)3.109375f, + (float16_t)-2.3984375f, (float16_t)1.05664062f, (float16_t)0.349853516f, (float16_t)1.30078125f, + (float16_t)-3.09375f, (float16_t)0.684570312f, (float16_t)-2.14453125f, (float16_t)0.761230469f, + (float16_t)-1.87011719f, (float16_t)-1.62402344f, (float16_t)0.453857422f, (float16_t)-2.30078125f, + (float16_t)2.59570312f, (float16_t)0.3984375f, (float16_t)-0.131347656f, (float16_t)-2.60546875f, + (float16_t)-1.81738281f, (float16_t)3.59179688f, (float16_t)1.62011719f, (float16_t)0.688964844f, + (float16_t)0.864746094f, (float16_t)-1.97558594f, (float16_t)-3.1484375f, (float16_t)-2.23242188f, + (float16_t)-3.40429688f, (float16_t)2.38085938f, (float16_t)-1.1953125f, (float16_t)0.611328125f, + (float16_t)-1.14160156f, (float16_t)-1.60058594f, (float16_t)-2.34570312f, (float16_t)-2.3203125f, + (float16_t)-3.77929688f, (float16_t)0.240844727f, (float16_t)-3.88867188f, (float16_t)1.47949219f, + (float16_t)-3.22460938f, (float16_t)-1.8046875f, (float16_t)-1.49023438f, (float16_t)-0.325927734f, + (float16_t)0.520996094f, (float16_t)0.347900391f, (float16_t)2.9140625f, (float16_t)-1.19824219f, + (float16_t)1.51269531f, (float16_t)3.47851562f, (float16_t)1.38574219f, (float16_t)3.6015625f, + (float16_t)0.479248047f, (float16_t)3.734375f, (float16_t)2.51757812f, (float16_t)-2.05273438f, + (float16_t)0.360107422f, (float16_t)1.0625f, (float16_t)3.45703125f, (float16_t)-0.223266602f, + (float16_t)0.922851562f, (float16_t)1.59082031f, (float16_t)-2.22460938f, (float16_t)-1.44921875f, + (float16_t)2.24414062f, (float16_t)-3.19726562f, (float16_t)-1.88085938f, (float16_t)2.95703125f, + (float16_t)-1.65039062f, (float16_t)-1.96582031f, (float16_t)-3.49804688f, (float16_t)2.08203125f, + (float16_t)1.69921875f, (float16_t)3.45898438f, (float16_t)-1.98632812f, (float16_t)-2.375f, + (float16_t)1.73730469f, (float16_t)0.388916016f, (float16_t)2.45703125f, (float16_t)0.647949219f, + (float16_t)3.25976562f, (float16_t)3.41015625f, (float16_t)3.84960938f, (float16_t)-3.36132812f, + (float16_t)-0.352783203f, (float16_t)0.25f, (float16_t)3.88867188f, (float16_t)-3.6015625f, + (float16_t)-0.192504883f, (float16_t)-3.11523438f, (float16_t)3.8671875f, (float16_t)0.57421875f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/output_ref_data.h new file mode 100644 index 00000000..44042b94 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/output_ref_data.h @@ -0,0 +1,137 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_ncwh_f16_output_ref[540] = { + (float16_t)0.640625f, (float16_t)3.44140625f, (float16_t)0.0606384277f, (float16_t)-2.02734375f, + (float16_t)-0.715820312f, (float16_t)3.43945312f, (float16_t)-1.40039062f, (float16_t)2.23632812f, + (float16_t)3.59765625f, (float16_t)-3.52929688f, (float16_t)-0.756347656f, (float16_t)1.58398438f, + (float16_t)-2.75585938f, (float16_t)-1.10644531f, (float16_t)-0.642089844f, (float16_t)3.19335938f, + (float16_t)2.73828125f, (float16_t)2.5234375f, (float16_t)-1.81835938f, (float16_t)-1.01269531f, + (float16_t)0.484130859f, (float16_t)-0.818847656f, (float16_t)2.48632812f, (float16_t)-1.16015625f, + (float16_t)-2.13867188f, (float16_t)-2.11914062f, (float16_t)1.07226562f, (float16_t)2.19726562f, + (float16_t)1.20800781f, (float16_t)0.0920410156f, (float16_t)2.96289062f, (float16_t)-2.3046875f, + (float16_t)3.00585938f, (float16_t)0.778320312f, (float16_t)-0.458251953f, (float16_t)3.04101562f, + (float16_t)-1.86816406f, (float16_t)-3.2890625f, (float16_t)2.62695312f, (float16_t)-2.25195312f, + (float16_t)-2.32421875f, (float16_t)0.103210449f, (float16_t)-0.989746094f, (float16_t)0.102478027f, + (float16_t)3.45703125f, (float16_t)-0.494628906f, (float16_t)1.29980469f, (float16_t)1.94824219f, + (float16_t)2.23632812f, (float16_t)2.3046875f, (float16_t)3.3203125f, (float16_t)3.87109375f, + (float16_t)1.125f, (float16_t)-1.27734375f, (float16_t)0.406982422f, (float16_t)-2.26757812f, + (float16_t)1.32421875f, (float16_t)-1.23535156f, (float16_t)-0.610839844f, (float16_t)0.832519531f, + (float16_t)-2.22460938f, (float16_t)-3.44335938f, (float16_t)3.18359375f, (float16_t)1.45898438f, + (float16_t)1.40722656f, (float16_t)1.02734375f, (float16_t)-3.58789062f, (float16_t)-2.48046875f, + (float16_t)1.61230469f, (float16_t)-3.36914062f, (float16_t)-0.778320312f, (float16_t)-3.31445312f, + (float16_t)-0.515625f, (float16_t)-1.33789062f, (float16_t)-3.70703125f, (float16_t)-1.296875f, + (float16_t)-0.711425781f, (float16_t)-0.856445312f, (float16_t)1.64160156f, (float16_t)-0.560546875f, + (float16_t)-3.74414062f, (float16_t)1.11425781f, (float16_t)-1.83984375f, (float16_t)1.14941406f, + (float16_t)3.609375f, (float16_t)-3.81445312f, (float16_t)2.5703125f, (float16_t)-2.06835938f, + (float16_t)-0.480224609f, (float16_t)-1.7265625f, (float16_t)2.10351562f, (float16_t)-0.450195312f, + (float16_t)3.29492188f, (float16_t)-0.259033203f, (float16_t)-0.247558594f, (float16_t)0.738769531f, + (float16_t)-2.44140625f, (float16_t)-3.70898438f, (float16_t)-0.836914062f, (float16_t)-2.84179688f, + (float16_t)-0.16418457f, (float16_t)0.807128906f, (float16_t)-1.12304688f, (float16_t)-2.45507812f, + (float16_t)-0.55859375f, (float16_t)1.15527344f, (float16_t)2.984375f, (float16_t)3.74414062f, + (float16_t)-2.7421875f, (float16_t)2.625f, (float16_t)0.537109375f, (float16_t)-1.11621094f, + (float16_t)0.470703125f, (float16_t)3.59570312f, (float16_t)-2.5546875f, (float16_t)0.750976562f, + (float16_t)-2.01171875f, (float16_t)3.62109375f, (float16_t)-1.89453125f, (float16_t)0.13269043f, + (float16_t)1.99316406f, (float16_t)0.791503906f, (float16_t)3.75390625f, (float16_t)-0.269042969f, + (float16_t)0.778320312f, (float16_t)-2.0390625f, (float16_t)0.966796875f, (float16_t)1.09472656f, + (float16_t)2.24804688f, (float16_t)-2.42382812f, (float16_t)0.854980469f, (float16_t)-3.80078125f, + (float16_t)-2.04492188f, (float16_t)3.37695312f, (float16_t)2.76367188f, (float16_t)3.66601562f, + (float16_t)0.959472656f, (float16_t)-0.00074005127f, (float16_t)0.322998047f, (float16_t)-0.35546875f, + (float16_t)0.611328125f, (float16_t)-2.19140625f, (float16_t)2.06054688f, (float16_t)-0.924316406f, + (float16_t)1.9375f, (float16_t)2.07617188f, (float16_t)1.40625f, (float16_t)-0.139526367f, + (float16_t)-0.255371094f, (float16_t)2.75195312f, (float16_t)-2.8984375f, (float16_t)0.631347656f, + (float16_t)3.69140625f, (float16_t)2.13476562f, (float16_t)3.98242188f, (float16_t)-0.211669922f, + (float16_t)-1.42675781f, (float16_t)1.50585938f, (float16_t)1.41699219f, (float16_t)-0.959472656f, + (float16_t)-2.53320312f, (float16_t)-1.94726562f, (float16_t)3.546875f, (float16_t)2.0703125f, + (float16_t)-0.439941406f, (float16_t)1.83886719f, (float16_t)2.9921875f, (float16_t)1.27148438f, + (float16_t)-2.45117188f, (float16_t)0.861816406f, (float16_t)-1.07324219f, (float16_t)1.62792969f, + (float16_t)-2.6484375f, (float16_t)-3.49414062f, (float16_t)-1.65722656f, (float16_t)3.35351562f, + (float16_t)-1.99023438f, (float16_t)-0.60546875f, (float16_t)3.36523438f, (float16_t)2.44140625f, + (float16_t)3.71484375f, (float16_t)1.76757812f, (float16_t)2.05859375f, (float16_t)-1.86914062f, + (float16_t)2.56640625f, (float16_t)3.26757812f, (float16_t)-1.03808594f, (float16_t)3.68164062f, + (float16_t)2.19726562f, (float16_t)1.52148438f, (float16_t)-0.499511719f, (float16_t)-2.87109375f, + (float16_t)1.00488281f, (float16_t)-3.60351562f, (float16_t)0.143920898f, (float16_t)3.14257812f, + (float16_t)-1.76757812f, (float16_t)-2.375f, (float16_t)1.046875f, (float16_t)1.83984375f, + (float16_t)3.73046875f, (float16_t)-0.181396484f, (float16_t)-3.50585938f, (float16_t)-0.356933594f, + (float16_t)1.20703125f, (float16_t)1.20410156f, (float16_t)-0.15234375f, (float16_t)-0.15246582f, + (float16_t)-0.182861328f, (float16_t)-3.34375f, (float16_t)-1.40917969f, (float16_t)2.22460938f, + (float16_t)0.941894531f, (float16_t)3.54296875f, (float16_t)3.69921875f, (float16_t)0.8046875f, + (float16_t)2.24414062f, (float16_t)3.84179688f, (float16_t)1.13085938f, (float16_t)-0.379150391f, + (float16_t)3.84960938f, (float16_t)3.59179688f, (float16_t)-3.00976562f, (float16_t)-2.85351562f, + (float16_t)-1.03125f, (float16_t)-0.203857422f, (float16_t)-0.837890625f, (float16_t)-3.51171875f, + (float16_t)0.392089844f, (float16_t)2.96679688f, (float16_t)2.00390625f, (float16_t)2.984375f, + (float16_t)1.78417969f, (float16_t)0.483886719f, (float16_t)3.18945312f, (float16_t)-0.504394531f, + (float16_t)1.27148438f, (float16_t)-0.321777344f, (float16_t)-2.0f, (float16_t)3.16601562f, + (float16_t)-1.42089844f, (float16_t)1.59082031f, (float16_t)1.48144531f, (float16_t)-3.17773438f, + (float16_t)1.19335938f, (float16_t)2.7578125f, (float16_t)0.123596191f, (float16_t)-2.12890625f, + (float16_t)3.36328125f, (float16_t)-2.98632812f, (float16_t)3.08007812f, (float16_t)0.314453125f, + (float16_t)-3.65820312f, (float16_t)2.04492188f, (float16_t)-3.90625f, (float16_t)0.300537109f, + (float16_t)2.14453125f, (float16_t)-1.49902344f, (float16_t)-2.56640625f, (float16_t)2.66992188f, + (float16_t)-0.137573242f, (float16_t)-2.23046875f, (float16_t)-0.774902344f, (float16_t)1.17480469f, + (float16_t)2.86523438f, (float16_t)3.15625f, (float16_t)2.5703125f, (float16_t)0.262695312f, + (float16_t)-0.456298828f, (float16_t)1.25976562f, (float16_t)-1.90039062f, (float16_t)2.83203125f, + (float16_t)1.02148438f, (float16_t)-1.31054688f, (float16_t)-3.56054688f, (float16_t)-3.20898438f, + (float16_t)-2.4921875f, (float16_t)1.51855469f, (float16_t)0.73046875f, (float16_t)0.589355469f, + (float16_t)-0.685546875f, (float16_t)-0.324707031f, (float16_t)-0.121765137f, (float16_t)1.80175781f, + (float16_t)-1.1015625f, (float16_t)0.159912109f, (float16_t)0.352294922f, (float16_t)3.71484375f, + (float16_t)2.35742188f, (float16_t)-1.53320312f, (float16_t)2.9609375f, (float16_t)0.154296875f, + (float16_t)2.19140625f, (float16_t)3.48828125f, (float16_t)0.730957031f, (float16_t)-1.63085938f, + (float16_t)2.89453125f, (float16_t)2.16992188f, (float16_t)-1.16699219f, (float16_t)-1.48632812f, + (float16_t)1.61132812f, (float16_t)2.4140625f, (float16_t)2.1875f, (float16_t)-3.890625f, + (float16_t)3.99023438f, (float16_t)-1.42871094f, (float16_t)0.345947266f, (float16_t)2.73828125f, + (float16_t)-0.888183594f, (float16_t)2.77929688f, (float16_t)-3.67382812f, (float16_t)-0.775390625f, + (float16_t)1.49707031f, (float16_t)3.25f, (float16_t)-3.62890625f, (float16_t)-0.324951172f, + (float16_t)-1.76660156f, (float16_t)2.23046875f, (float16_t)1.35253906f, (float16_t)2.1953125f, + (float16_t)1.25390625f, (float16_t)-0.0639648438f, (float16_t)1.04785156f, (float16_t)-1.32714844f, + (float16_t)0.11340332f, (float16_t)0.447265625f, (float16_t)-0.476074219f, (float16_t)-3.11914062f, + (float16_t)1.52832031f, (float16_t)0.399169922f, (float16_t)1.92871094f, (float16_t)-3.234375f, + (float16_t)0.0488891602f, (float16_t)-3.13085938f, (float16_t)-1.37109375f, (float16_t)-3.77148438f, + (float16_t)1.81933594f, (float16_t)1.78613281f, (float16_t)2.80859375f, (float16_t)2.2421875f, + (float16_t)-2.70507812f, (float16_t)-2.09960938f, (float16_t)3.4375f, (float16_t)0.704101562f, + (float16_t)-0.750976562f, (float16_t)2.94140625f, (float16_t)-3.23242188f, (float16_t)0.26171875f, + (float16_t)0.797851562f, (float16_t)-2.18359375f, (float16_t)3.56445312f, (float16_t)1.74707031f, + (float16_t)1.31738281f, (float16_t)1.890625f, (float16_t)0.608398438f, (float16_t)0.607421875f, + (float16_t)3.79101562f, (float16_t)2.171875f, (float16_t)-3.15625f, (float16_t)1.71972656f, + (float16_t)-0.455322266f, (float16_t)1.69921875f, (float16_t)-1.10742188f, (float16_t)1.20117188f, + (float16_t)-3.43359375f, (float16_t)2.24609375f, (float16_t)-1.85839844f, (float16_t)2.16601562f, + (float16_t)-1.58398438f, (float16_t)-0.898925781f, (float16_t)2.9140625f, (float16_t)2.2109375f, + (float16_t)3.1484375f, (float16_t)-2.27929688f, (float16_t)0.171020508f, (float16_t)-1.39257812f, + (float16_t)-2.2265625f, (float16_t)3.56054688f, (float16_t)-1.0234375f, (float16_t)-0.920410156f, + (float16_t)-2.9375f, (float16_t)-2.51171875f, (float16_t)-0.262939453f, (float16_t)-3.25195312f, + (float16_t)-3.15429688f, (float16_t)-2.83398438f, (float16_t)1.14746094f, (float16_t)-3.83007812f, + (float16_t)-3.62890625f, (float16_t)-3.25585938f, (float16_t)1.41699219f, (float16_t)-1.13378906f, + (float16_t)-0.145996094f, (float16_t)0.569335938f, (float16_t)2.18945312f, (float16_t)3.25585938f, + (float16_t)2.97460938f, (float16_t)-2.56640625f, (float16_t)-1.19824219f, (float16_t)3.12695312f, + (float16_t)2.92578125f, (float16_t)1.32128906f, (float16_t)-1.04199219f, (float16_t)3.7578125f, + (float16_t)3.16015625f, (float16_t)2.53515625f, (float16_t)-0.108764648f, (float16_t)2.50976562f, + (float16_t)3.71679688f, (float16_t)-3.84179688f, (float16_t)2.13476562f, (float16_t)3.3046875f, + (float16_t)-1.59960938f, (float16_t)-1.00488281f, (float16_t)-0.614257812f, (float16_t)3.18164062f, + (float16_t)1.84570312f, (float16_t)3.73046875f, (float16_t)3.18164062f, (float16_t)3.50585938f, + (float16_t)-2.44921875f, (float16_t)-2.7109375f, (float16_t)-3.66015625f, (float16_t)1.11230469f, + (float16_t)-2.27929688f, (float16_t)1.32519531f, (float16_t)-3.98046875f, (float16_t)3.70898438f, + (float16_t)-2.3203125f, (float16_t)1.30273438f, (float16_t)3.61914062f, (float16_t)3.76171875f, + (float16_t)-3.15625f, (float16_t)0.761230469f, (float16_t)-3.88867188f, (float16_t)-3.19726562f, + (float16_t)-3.40234375f, (float16_t)-1.81738281f, (float16_t)-1.19824219f, (float16_t)-1.98632812f, + (float16_t)-3.40820312f, (float16_t)2.38085938f, (float16_t)0.360107422f, (float16_t)-3.36132812f, + (float16_t)2.8359375f, (float16_t)-1.87011719f, (float16_t)1.47949219f, (float16_t)-1.88085938f, + (float16_t)-1.890625f, (float16_t)3.59179688f, (float16_t)1.51269531f, (float16_t)-2.375f, + (float16_t)3.109375f, (float16_t)-1.1953125f, (float16_t)1.0625f, (float16_t)-0.352783203f, + (float16_t)1.21289062f, (float16_t)-1.62402344f, (float16_t)-3.22460938f, (float16_t)2.95703125f, + (float16_t)3.55273438f, (float16_t)1.62011719f, (float16_t)3.47851562f, (float16_t)1.73730469f, + (float16_t)-2.3984375f, (float16_t)0.611328125f, (float16_t)3.45703125f, (float16_t)0.25f, + (float16_t)0.778320312f, (float16_t)0.453857422f, (float16_t)-1.8046875f, (float16_t)-1.65039062f, + (float16_t)-2.73828125f, (float16_t)0.688964844f, (float16_t)1.38574219f, (float16_t)0.388916016f, + (float16_t)1.05664062f, (float16_t)-1.14160156f, (float16_t)-0.223266602f, (float16_t)3.88867188f, + (float16_t)1.62304688f, (float16_t)-2.30078125f, (float16_t)-1.49023438f, (float16_t)-1.96582031f, + (float16_t)3.99804688f, (float16_t)0.864746094f, (float16_t)3.6015625f, (float16_t)2.45703125f, + (float16_t)0.349853516f, (float16_t)-1.60058594f, (float16_t)0.922851562f, (float16_t)-3.6015625f, + (float16_t)-1.70703125f, (float16_t)2.59570312f, (float16_t)-0.325927734f, (float16_t)-3.49804688f, + (float16_t)-0.0470275879f, (float16_t)-1.97558594f, (float16_t)0.479248047f, (float16_t)0.647949219f, + (float16_t)1.30078125f, (float16_t)-2.34570312f, (float16_t)1.59082031f, (float16_t)-0.192504883f, + (float16_t)-2.27539062f, (float16_t)0.3984375f, (float16_t)0.520996094f, (float16_t)2.08203125f, + (float16_t)-2.87304688f, (float16_t)-3.1484375f, (float16_t)3.734375f, (float16_t)3.25976562f, + (float16_t)-3.09375f, (float16_t)-2.3203125f, (float16_t)-2.22460938f, (float16_t)-3.11523438f, + (float16_t)-2.12109375f, (float16_t)-0.131347656f, (float16_t)0.347900391f, (float16_t)1.69921875f, + (float16_t)2.22265625f, (float16_t)-2.23242188f, (float16_t)2.51757812f, (float16_t)3.41015625f, + (float16_t)0.684570312f, (float16_t)-3.77929688f, (float16_t)-1.44921875f, (float16_t)3.8671875f, + (float16_t)-3.7109375f, (float16_t)-2.60546875f, (float16_t)2.9140625f, (float16_t)3.45898438f, + (float16_t)1.79101562f, (float16_t)-3.40429688f, (float16_t)-2.05273438f, (float16_t)3.84960938f, + (float16_t)-2.14453125f, (float16_t)0.240844727f, (float16_t)2.24414062f, (float16_t)0.57421875f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/config_data.h new file mode 100644 index 00000000..5f8c6535 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NCWH_F32_NUM_DIMS 4 +#define TRANSPOSE_NCWH_F32_INPUT_N 5 +#define TRANSPOSE_NCWH_F32_INPUT_H 4 +#define TRANSPOSE_NCWH_F32_INPUT_W 3 +#define TRANSPOSE_NCWH_F32_INPUT_C 9 +#define TRANSPOSE_NCWH_F32_OUTPUT_N 5 +#define TRANSPOSE_NCWH_F32_OUTPUT_H 9 +#define TRANSPOSE_NCWH_F32_OUTPUT_W 3 +#define TRANSPOSE_NCWH_F32_OUTPUT_C 4 +#define TRANSPOSE_NCWH_F32_PERM_0 0 +#define TRANSPOSE_NCWH_F32_PERM_1 3 +#define TRANSPOSE_NCWH_F32_PERM_2 2 +#define TRANSPOSE_NCWH_F32_PERM_3 1 +#define TRANSPOSE_NCWH_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NCWH_F32_SIZE 540 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/input_tensor_data.h new file mode 100644 index 00000000..7a9319d3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/input_tensor_data.h @@ -0,0 +1,80 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_ncwh_f32_input_tensor[540] = { + 2.85200357f, 0.562594891f, -0.643572569f, -3.50023222f, 0.29935196f, 1.4949888f, -0.940311253f, + 0.426963091f, 1.45907736f, -0.21477291f, 1.23403299f, 0.421210974f, -2.90390563f, -1.74694717f, + -1.35283136f, -0.226236358f, -2.41078115f, -0.509195209f, 3.3972156f, 1.1253792f, -2.8170445f, + -3.17534614f, 2.91340733f, 0.277729154f, 2.82738018f, 3.00268888f, -3.60694933f, 0.526361644f, + 0.994987249f, -0.99270767f, 3.84990358f, 2.30874586f, -1.44228339f, -3.32167196f, -0.276103735f, + 3.76682806f, -3.78943253f, 0.285433412f, -2.40561152f, 0.293251932f, 2.83780217f, -3.38034678f, + -0.314714581f, -2.84019852f, -1.9425298f, -3.02438831f, -3.16356802f, 3.97788525f, -3.01570797f, + 0.801448464f, 2.18950939f, -3.86971045f, 0.269789487f, 3.26648188f, -3.69881964f, 0.420179874f, + -2.41547847f, 2.90667772f, -2.67375112f, 3.00524712f, -0.708600163f, 2.88787413f, 3.35724235f, + -0.970267236f, -3.97293639f, 1.91572177f, 0.43625918f, -3.79573011f, -3.00516725f, -1.47137153f, + -1.70840716f, -2.10903358f, -1.50447273f, -1.12328541f, 1.89013267f, -0.463735044f, -2.0259378f, + -3.80329084f, 2.74611449f, 0.910215199f, -1.0921849f, -2.80663395f, 3.92317319f, -0.18390274f, + -2.33989239f, -1.84168434f, 0.0795081258f, -1.47156513f, 2.79806924f, -1.77582955f, 1.62199664f, + 2.96195436f, -1.05659831f, -3.36507607f, -0.110733777f, 3.46627736f, 3.58765912f, 2.53583026f, + 3.4963665f, -0.513730526f, 1.15399301f, 3.22793484f, 3.9036541f, 1.09768164f, -1.89685333f, + 1.39280117f, 3.23789096f, -1.15744412f, -1.92365396f, -3.91761136f, 3.84537578f, -3.29010463f, + 3.18214512f, 1.70327044f, -2.81921291f, -1.00692308f, -1.33278561f, 2.24806213f, 1.3696841f, + -3.83598685f, 0.771509707f, -1.89503515f, 1.73403621f, 0.386085838f, 1.79451871f, -3.96314812f, + 3.0510993f, 0.389507979f, 1.61273217f, -2.51298141f, -0.98675406f, 0.604530931f, 3.30092716f, + -0.0856663287f, -1.98228216f, -0.913957417f, 1.58019435f, -3.12840247f, -0.19509466f, -3.76645279f, + -0.424888313f, 3.95016265f, 1.12945831f, -3.87119889f, 2.33954215f, 1.67955232f, 2.89273643f, + -0.348388374f, 3.53321934f, -2.96588302f, 1.28843987f, 3.84247136f, 1.77822506f, -3.72621679f, + -1.45460534f, -3.59220099f, 2.98943973f, -1.93229878f, 0.205003187f, -1.58329952f, 2.22970676f, + 0.794736028f, -1.1512022f, -3.25540209f, -0.743382335f, -3.37262058f, -2.60568213f, 0.410420209f, + -2.9341197f, 0.206818342f, -3.27366114f, 3.04806113f, 3.64047384f, 0.0480421335f, 0.738891721f, + 3.92733574f, -0.796577513f, -2.59019685f, -1.84079599f, 0.629239142f, -2.25972223f, -0.262720674f, + -0.431860536f, 1.44550991f, 1.54545748f, 0.377838761f, -0.670200229f, 3.36469126f, -3.72781229f, + 2.51224589f, -1.31009007f, 0.627978265f, 3.09183764f, 2.74274993f, 0.850957155f, -2.6038785f, + -1.76759779f, 2.86781311f, -2.90481925f, 0.994843841f, -2.15252066f, -2.23788691f, 2.62313128f, + -2.3519547f, 2.75487304f, 0.502227783f, -0.651702464f, 2.21214986f, -1.26983738f, -1.41759229f, + -3.96611667f, 3.29824805f, 0.385687351f, 3.83670497f, -3.16334462f, -1.62712526f, 3.67493606f, + 1.26539993f, -3.17302871f, -0.52785629f, -0.390052885f, -0.644388497f, 0.873583615f, 3.16408014f, + 0.137400955f, 3.48424363f, -2.17313313f, -3.43368149f, -2.48162317f, 2.1101768f, -0.642356634f, + 0.904429138f, -1.779827f, -3.6471715f, 1.51361251f, 0.980875552f, 2.9151814f, -2.50681639f, + 2.25615621f, -1.75204146f, -3.90801072f, 1.40295517f, -0.797318161f, -0.147931978f, -0.131539464f, + -1.67512727f, 0.164921492f, 3.95130277f, 0.689841092f, -0.497094303f, 2.08735323f, -1.07663226f, + 2.73017979f, 3.68766499f, 3.3444953f, -0.327235073f, -0.206490993f, 2.57354665f, 3.43299556f, + -0.0470865481f, 3.3838284f, -1.5054642f, 1.80126882f, -1.76436865f, -3.81914258f, 1.25196576f, + -1.69981527f, 1.29335785f, 2.41215754f, -1.02181017f, 0.654480219f, -2.02911234f, -2.61473203f, + 3.57660842f, 1.42887485f, 0.989889681f, -2.33916259f, -0.946972609f, -1.60268104f, 1.90807962f, + 3.5191884f, -0.0421517044f, 3.41512442f, 0.505909562f, -0.328449428f, -0.297293186f, -3.89857769f, + -1.70341706f, 3.84482694f, 1.82671583f, -0.758630872f, 2.9115243f, 1.44813883f, 1.91997147f, + 1.05399597f, -0.50671649f, -3.34935236f, -3.71142888f, 0.634905756f, 3.32574844f, -2.64439964f, + -2.0625875f, 3.7621913f, -3.79884768f, 2.73676038f, 2.59010434f, 1.03951657f, -0.339762896f, + -3.78157043f, -3.80927038f, 0.657837033f, 2.5929985f, -0.822044551f, 1.35908127f, 1.55261791f, + 3.40573454f, -3.02127695f, -3.56838059f, -2.6746366f, -0.379871815f, -1.81820965f, 0.747470021f, + -2.17691493f, -3.94937778f, -0.0263714846f, 3.12444615f, -1.57081974f, 0.0488276035f, 1.76406217f, + -0.37155652f, 1.55665338f, -2.95099545f, -3.55287313f, 1.49285734f, -2.11628962f, -1.81856501f, + -3.73908949f, -3.99445748f, 3.90587354f, -3.62113619f, 0.541294992f, -1.65681851f, -2.66165614f, + -2.98671508f, -0.942407012f, 0.869686961f, -3.63422203f, 3.9682169f, -2.17650533f, -2.68889952f, + 0.904331625f, 2.29895067f, -0.610239387f, -0.328680724f, -0.191510096f, 1.20782852f, -0.812342823f, + -2.31928825f, -1.2584765f, -2.90605521f, -0.6601789f, 2.88820839f, -1.87513471f, -2.48462319f, + 2.68945599f, 3.78730893f, 3.84137559f, 1.50516927f, 1.94079447f, 2.92636275f, 3.00596213f, + -2.86739564f, 0.00718027353f, -3.49181747f, 3.07734227f, -2.21385026f, 1.65133786f, 2.61985588f, + -1.18083215f, -1.48947728f, -2.91972995f, -2.78310394f, 1.63295281f, -0.0523061752f, 1.20231223f, + -2.21190476f, 0.929470062f, -1.2201786f, 1.73490644f, 3.38528967f, 3.45549798f, 0.344947189f, + 3.72347999f, -1.7058959f, 0.147528499f, -2.63823771f, -2.4084444f, 0.0043383413f, -1.89084208f, + 2.84639859f, -2.41636181f, -3.83397985f, 1.96554923f, -1.03504193f, 3.20052528f, 2.13630486f, + 2.25297546f, -3.78618479f, -0.262977481f, -0.411438346f, -2.74102902f, -1.78015053f, -2.07175803f, + 1.49526322f, 1.19113994f, -0.55872345f, 3.79223824f, 2.00867319f, 2.49522161f, 3.94437528f, + 1.67195797f, 3.63308573f, 0.644705176f, 3.96112251f, 0.634569407f, 0.682676911f, -1.44025719f, + 1.09287858f, -0.964097083f, 3.89447188f, -0.654254854f, 1.07428324f, 2.86368847f, 0.756112993f, + -1.18636584f, 2.02258635f, -2.37389088f, -2.13429117f, 0.29546538f, -2.69010711f, -3.83100867f, + 0.0123008406f, 2.1560564f, -0.803356647f, 1.8993299f, 2.3771131f, 2.89920878f, -2.34861469f, + -3.90575695f, -1.47236431f, -2.85880589f, -0.722994983f, -2.3927772f, 1.85618746f, 1.66432703f, + -0.450625002f, 3.48099828f, 2.09808016f, -1.58140695f, -1.47944701f, -1.16398501f, 1.88396931f, + 2.29300141f, 1.67595303f, 0.140609279f, -2.92788029f, 1.68374562f, 3.69700336f, 2.88172674f, + 3.11167097f, -2.22597289f, 1.75138199f, -3.63494062f, -0.96775049f, -0.743426025f, -2.37018228f, + 0.137729988f, -2.13218904f, -1.54603386f, -0.62463522f, 3.47029877f, -2.58456182f, -2.13557744f, + 2.79498005f, 3.42037511f, 1.22928798f, 2.92913556f, 1.40778422f, -2.78897929f, 3.30154514f, + -2.00951028f, -3.56536365f, 1.14243662f, -0.842983902f, 0.388233751f, 3.17189765f, -3.06731272f, + 2.31606436f, 3.78064704f, 3.0925231f, 1.4576304f, -1.43554008f, 0.633832872f, -0.190348879f, + -0.106637023f, 2.16207099f, 1.5265044f, -0.941198587f, 1.49308169f, -3.35307741f, -0.455980033f, + 1.91214883f, -3.1236043f, -0.50144738f, 0.42381677f, -1.06038666f, -3.06443787f, -3.63947511f, + 0.626774132f, -2.91808367f, -1.66813159f, 2.8407073f, 1.17352796f, -1.47329652f, 3.51037645f, + 2.09905195f, 3.26039004f, -1.99488652f, -0.792702794f, -2.44163251f, -1.83968377f, -0.403993934f, + 1.52109194f, -3.62043858f, -2.15851283f, -2.25856805f, 2.45252156f, 0.987008274f, 2.78293943f, + -3.72997165f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/output_ref_data.h new file mode 100644 index 00000000..c4269f36 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/output_ref_data.h @@ -0,0 +1,80 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_ncwh_f32_output_ref[540] = { + 2.85200357f, 0.526361644f, -3.69881964f, -2.80663395f, -0.21477291f, -3.78943253f, -0.970267236f, + 1.62199664f, 3.3972156f, -3.02438831f, -1.50447273f, -0.513730526f, 0.562594891f, 0.994987249f, + 0.420179874f, 3.92317319f, 1.23403299f, 0.285433412f, -3.97293639f, 2.96195436f, 1.1253792f, + -3.16356802f, -1.12328541f, 1.15399301f, -0.643572569f, -0.99270767f, -2.41547847f, -0.18390274f, + 0.421210974f, -2.40561152f, 1.91572177f, -1.05659831f, -2.8170445f, 3.97788525f, 1.89013267f, + 3.22793484f, -3.50023222f, 3.84990358f, 2.90667772f, -2.33989239f, -2.90390563f, 0.293251932f, + 0.43625918f, -3.36507607f, -3.17534614f, -3.01570797f, -0.463735044f, 3.9036541f, 0.29935196f, + 2.30874586f, -2.67375112f, -1.84168434f, -1.74694717f, 2.83780217f, -3.79573011f, -0.110733777f, + 2.91340733f, 0.801448464f, -2.0259378f, 1.09768164f, 1.4949888f, -1.44228339f, 3.00524712f, + 0.0795081258f, -1.35283136f, -3.38034678f, -3.00516725f, 3.46627736f, 0.277729154f, 2.18950939f, + -3.80329084f, -1.89685333f, -0.940311253f, -3.32167196f, -0.708600163f, -1.47156513f, -0.226236358f, + -0.314714581f, -1.47137153f, 3.58765912f, 2.82738018f, -3.86971045f, 2.74611449f, 1.39280117f, + 0.426963091f, -0.276103735f, 2.88787413f, 2.79806924f, -2.41078115f, -2.84019852f, -1.70840716f, + 2.53583026f, 3.00268888f, 0.269789487f, 0.910215199f, 3.23789096f, 1.45907736f, 3.76682806f, + 3.35724235f, -1.77582955f, -0.509195209f, -1.9425298f, -2.10903358f, 3.4963665f, -3.60694933f, + 3.26648188f, -1.0921849f, -1.15744412f, -1.92365396f, -0.913957417f, -1.1512022f, 2.51224589f, + 2.24806213f, 2.33954215f, 3.04806113f, -2.90481925f, 3.0510993f, -3.72621679f, -2.25972223f, + 2.21214986f, -3.91761136f, 1.58019435f, -3.25540209f, -1.31009007f, 1.3696841f, 1.67955232f, + 3.64047384f, 0.994843841f, 0.389507979f, -1.45460534f, -0.262720674f, -1.26983738f, 3.84537578f, + -3.12840247f, -0.743382335f, 0.627978265f, -3.83598685f, 2.89273643f, 0.0480421335f, -2.15252066f, + 1.61273217f, -3.59220099f, -0.431860536f, -1.41759229f, -3.29010463f, -0.19509466f, -3.37262058f, + 3.09183764f, 0.771509707f, -0.348388374f, 0.738891721f, -2.23788691f, -2.51298141f, 2.98943973f, + 1.44550991f, -3.96611667f, 3.18214512f, -3.76645279f, -2.60568213f, 2.74274993f, -1.89503515f, + 3.53321934f, 3.92733574f, 2.62313128f, -0.98675406f, -1.93229878f, 1.54545748f, 3.29824805f, + 1.70327044f, -0.424888313f, 0.410420209f, 0.850957155f, 1.73403621f, -2.96588302f, -0.796577513f, + -2.3519547f, 0.604530931f, 0.205003187f, 0.377838761f, 0.385687351f, -2.81921291f, 3.95016265f, + -2.9341197f, -2.6038785f, 0.386085838f, 1.28843987f, -2.59019685f, 2.75487304f, 3.30092716f, + -1.58329952f, -0.670200229f, 3.83670497f, -1.00692308f, 1.12945831f, 0.206818342f, -1.76759779f, + 1.79451871f, 3.84247136f, -1.84079599f, 0.502227783f, -0.0856663287f, 2.22970676f, 3.36469126f, + -3.16334462f, -1.33278561f, -3.87119889f, -3.27366114f, 2.86781311f, -3.96314812f, 1.77822506f, + 0.629239142f, -0.651702464f, -1.98228216f, 0.794736028f, -3.72781229f, -1.62712526f, 3.67493606f, + -0.147931978f, 0.654480219f, -3.71142888f, 3.48424363f, 2.73017979f, 1.90807962f, 1.03951657f, + 1.51361251f, -1.5054642f, 3.84482694f, 3.40573454f, 1.26539993f, -0.131539464f, -2.02911234f, + 0.634905756f, -2.17313313f, 3.68766499f, 3.5191884f, -0.339762896f, 0.980875552f, 1.80126882f, + 1.82671583f, -3.02127695f, -3.17302871f, -1.67512727f, -2.61473203f, 3.32574844f, -3.43368149f, + 3.3444953f, -0.0421517044f, -3.78157043f, 2.9151814f, -1.76436865f, -0.758630872f, -3.56838059f, + -0.52785629f, 0.164921492f, 3.57660842f, -2.64439964f, -2.48162317f, -0.327235073f, 3.41512442f, + -3.80927038f, -2.50681639f, -3.81914258f, 2.9115243f, -2.6746366f, -0.390052885f, 3.95130277f, + 1.42887485f, -2.0625875f, 2.1101768f, -0.206490993f, 0.505909562f, 0.657837033f, 2.25615621f, + 1.25196576f, 1.44813883f, -0.379871815f, -0.644388497f, 0.689841092f, 0.989889681f, 3.7621913f, + -0.642356634f, 2.57354665f, -0.328449428f, 2.5929985f, -1.75204146f, -1.69981527f, 1.91997147f, + -1.81820965f, 0.873583615f, -0.497094303f, -2.33916259f, -3.79884768f, 0.904429138f, 3.43299556f, + -0.297293186f, -0.822044551f, -3.90801072f, 1.29335785f, 1.05399597f, 0.747470021f, 3.16408014f, + 2.08735323f, -0.946972609f, 2.73676038f, -1.779827f, -0.0470865481f, -3.89857769f, 1.35908127f, + 1.40295517f, 2.41215754f, -0.50671649f, -2.17691493f, 0.137400955f, -1.07663226f, -1.60268104f, + 2.59010434f, -3.6471715f, 3.3838284f, -1.70341706f, 1.55261791f, -0.797318161f, -1.02181017f, + -3.34935236f, -3.94937778f, -0.0263714846f, 2.29895067f, -1.18083215f, 2.13630486f, 1.49285734f, + -0.6601789f, -1.2201786f, 1.19113994f, -2.66165614f, 2.92636275f, -2.4084444f, 3.96112251f, + 3.12444615f, -0.610239387f, -1.48947728f, 2.25297546f, -2.11628962f, 2.88820839f, 1.73490644f, + -0.55872345f, -2.98671508f, 3.00596213f, 0.0043383413f, 0.634569407f, -1.57081974f, -0.328680724f, + -2.91972995f, -3.78618479f, -1.81856501f, -1.87513471f, 3.38528967f, 3.79223824f, -0.942407012f, + -2.86739564f, -1.89084208f, 0.682676911f, 0.0488276035f, -0.191510096f, -2.78310394f, -0.262977481f, + -3.73908949f, -2.48462319f, 3.45549798f, 2.00867319f, 0.869686961f, 0.00718027353f, 2.84639859f, + -1.44025719f, 1.76406217f, 1.20782852f, 1.63295281f, -0.411438346f, -3.99445748f, 2.68945599f, + 0.344947189f, 2.49522161f, -3.63422203f, -3.49181747f, -2.41636181f, 1.09287858f, -0.37155652f, + -0.812342823f, -0.0523061752f, -2.74102902f, 3.90587354f, 3.78730893f, 3.72347999f, 3.94437528f, + 3.9682169f, 3.07734227f, -3.83397985f, -0.964097083f, 1.55665338f, -2.31928825f, 1.20231223f, + -1.78015053f, -3.62113619f, 3.84137559f, -1.7058959f, 1.67195797f, -2.17650533f, -2.21385026f, + 1.96554923f, 3.89447188f, -2.95099545f, -1.2584765f, -2.21190476f, -2.07175803f, 0.541294992f, + 1.50516927f, 0.147528499f, 3.63308573f, -2.68889952f, 1.65133786f, -1.03504193f, -0.654254854f, + -3.55287313f, -2.90605521f, 0.929470062f, 1.49526322f, -1.65681851f, 1.94079447f, -2.63823771f, + 0.644705176f, 0.904331625f, 2.61985588f, 3.20052528f, 1.07428324f, 2.86368847f, -1.47944701f, + 2.92913556f, -0.50144738f, 0.0123008406f, 2.88172674f, 3.17189765f, 1.17352796f, -2.85880589f, + -2.13218904f, -0.106637023f, -0.403993934f, 0.756112993f, -1.16398501f, 1.40778422f, 0.42381677f, + 2.1560564f, 3.11167097f, -3.06731272f, -1.47329652f, -0.722994983f, -1.54603386f, 2.16207099f, + 1.52109194f, -1.18636584f, 1.88396931f, -2.78897929f, -1.06038666f, -0.803356647f, -2.22597289f, + 2.31606436f, 3.51037645f, -2.3927772f, -0.62463522f, 1.5265044f, -3.62043858f, 2.02258635f, + 2.29300141f, 3.30154514f, -3.06443787f, 1.8993299f, 1.75138199f, 3.78064704f, 2.09905195f, + 1.85618746f, 3.47029877f, -0.941198587f, -2.15851283f, -2.37389088f, 1.67595303f, -2.00951028f, + -3.63947511f, 2.3771131f, -3.63494062f, 3.0925231f, 3.26039004f, 1.66432703f, -2.58456182f, + 1.49308169f, -2.25856805f, -2.13429117f, 0.140609279f, -3.56536365f, 0.626774132f, 2.89920878f, + -0.96775049f, 1.4576304f, -1.99488652f, -0.450625002f, -2.13557744f, -3.35307741f, 2.45252156f, + 0.29546538f, -2.92788029f, 1.14243662f, -2.91808367f, -2.34861469f, -0.743426025f, -1.43554008f, + -0.792702794f, 3.48099828f, 2.79498005f, -0.455980033f, 0.987008274f, -2.69010711f, 1.68374562f, + -0.842983902f, -1.66813159f, -3.90575695f, -2.37018228f, 0.633832872f, -2.44163251f, 2.09808016f, + 3.42037511f, 1.91214883f, 2.78293943f, -3.83100867f, 3.69700336f, 0.388233751f, 2.8407073f, + -1.47236431f, 0.137729988f, -0.190348879f, -1.83968377f, -1.58140695f, 1.22928798f, -3.1236043f, + -3.72997165f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_ncwh_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/config_data.h new file mode 100644 index 00000000..1417e444 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NHCW_F16_NUM_DIMS 4 +#define TRANSPOSE_NHCW_F16_INPUT_N 1 +#define TRANSPOSE_NHCW_F16_INPUT_H 1 +#define TRANSPOSE_NHCW_F16_INPUT_W 128 +#define TRANSPOSE_NHCW_F16_INPUT_C 128 +#define TRANSPOSE_NHCW_F16_OUTPUT_N 1 +#define TRANSPOSE_NHCW_F16_OUTPUT_H 1 +#define TRANSPOSE_NHCW_F16_OUTPUT_W 128 +#define TRANSPOSE_NHCW_F16_OUTPUT_C 128 +#define TRANSPOSE_NHCW_F16_PERM_0 0 +#define TRANSPOSE_NHCW_F16_PERM_1 1 +#define TRANSPOSE_NHCW_F16_PERM_2 3 +#define TRANSPOSE_NHCW_F16_PERM_3 2 +#define TRANSPOSE_NHCW_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NHCW_F16_SIZE 16384 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/input_tensor_data.h new file mode 100644 index 00000000..96090ec5 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/input_tensor_data.h @@ -0,0 +1,16385 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nhcw_f16_input_tensor[16384] = {(float16_t)-3.49609375f, + (float16_t)-1.48730469f, + (float16_t)-1.81738281f, + (float16_t)3.83398438f, + (float16_t)3.390625f, + (float16_t)-1.4375f, + (float16_t)-0.852050781f, + (float16_t)0.633300781f, + (float16_t)1.86816406f, + (float16_t)-1.671875f, + (float16_t)-0.814453125f, + (float16_t)-1.13769531f, + (float16_t)0.755859375f, + (float16_t)2.09765625f, + (float16_t)3.26171875f, + (float16_t)2.03710938f, + (float16_t)-3.2421875f, + (float16_t)0.553222656f, + (float16_t)-0.197143555f, + (float16_t)-1.03613281f, + (float16_t)3.37890625f, + (float16_t)2.11328125f, + (float16_t)-1.10546875f, + (float16_t)-0.522460938f, + (float16_t)2.3203125f, + (float16_t)-1.63867188f, + (float16_t)3.2890625f, + (float16_t)1.11914062f, + (float16_t)0.284667969f, + (float16_t)-3.22070312f, + (float16_t)-3.72265625f, + (float16_t)2.23046875f, + (float16_t)-2.61523438f, + (float16_t)-2.16601562f, + (float16_t)-3.23046875f, + (float16_t)0.755371094f, + (float16_t)2.9296875f, + (float16_t)-3.90820312f, + (float16_t)3.60546875f, + (float16_t)1.76757812f, + (float16_t)-0.28125f, + (float16_t)3.59570312f, + (float16_t)1.71484375f, + (float16_t)3.21484375f, + (float16_t)3.125f, + (float16_t)-2.85351562f, + (float16_t)0.0975341797f, + (float16_t)1.83105469f, + (float16_t)0.615722656f, + (float16_t)3.05273438f, + (float16_t)0.131835938f, + (float16_t)-1.1171875f, + (float16_t)-0.762207031f, + (float16_t)-3.34960938f, + (float16_t)0.8671875f, + (float16_t)3.89453125f, + (float16_t)-0.640136719f, + (float16_t)0.262451172f, + (float16_t)-2.60742188f, + (float16_t)3.32617188f, + (float16_t)-0.420898438f, + (float16_t)-3.88085938f, + (float16_t)-3.06640625f, + (float16_t)0.046295166f, + (float16_t)2.953125f, + (float16_t)0.864257812f, + (float16_t)2.15039062f, + (float16_t)-2.18945312f, + (float16_t)-3.04882812f, + (float16_t)-0.224365234f, + (float16_t)2.63671875f, + (float16_t)-3.02734375f, + (float16_t)-1.8125f, + (float16_t)-3.33789062f, + (float16_t)-2.66015625f, + (float16_t)1.39257812f, + (float16_t)2.0078125f, + (float16_t)2.47265625f, + (float16_t)-0.512695312f, + (float16_t)-2.83984375f, + (float16_t)2.99609375f, + (float16_t)-0.228393555f, + (float16_t)-0.0427246094f, + (float16_t)-1.5f, + (float16_t)3.09375f, + (float16_t)1.26074219f, 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+ (float16_t)3.56445312f, + (float16_t)3.71679688f, + (float16_t)-3.17773438f, + (float16_t)-0.899902344f, + (float16_t)0.669433594f, + (float16_t)-0.472900391f, + (float16_t)-3.8125f, + (float16_t)2.5234375f, + (float16_t)3.91015625f, + (float16_t)-0.121704102f, + (float16_t)-0.245605469f, + (float16_t)-3.70703125f, + (float16_t)-2.55078125f, + (float16_t)1.56054688f, + (float16_t)-3.00976562f, + (float16_t)0.235961914f, + (float16_t)0.622070312f, + (float16_t)-2.8984375f, + (float16_t)-0.957519531f, + (float16_t)1.96484375f, + (float16_t)-0.857910156f, + (float16_t)-2.22070312f, + (float16_t)-3.8828125f, + (float16_t)-1.02050781f, + (float16_t)-3.18554688f, + (float16_t)0.570800781f, + (float16_t)2.87695312f, + (float16_t)-0.640625f, + (float16_t)-2.546875f, + (float16_t)0.995605469f, + (float16_t)2.98828125f, + (float16_t)-2.74414062f, + (float16_t)-0.365722656f, + (float16_t)-0.570800781f, + (float16_t)3.05664062f, + (float16_t)-1.11523438f, + (float16_t)1.39257812f, + (float16_t)-1.03613281f, + (float16_t)-0.58984375f, + (float16_t)3.31445312f, + (float16_t)-1.90234375f, + (float16_t)-2.3203125f, + (float16_t)-0.242675781f, + (float16_t)-2.95703125f, + (float16_t)-3.28710938f, + (float16_t)-3.48632812f, + (float16_t)3.84375f, + (float16_t)-0.234130859f, + (float16_t)0.946289062f, + (float16_t)1.26367188f, + (float16_t)2.15429688f, + (float16_t)-0.54296875f, + (float16_t)1.5390625f, + (float16_t)1.78320312f, + (float16_t)-2.20507812f, + (float16_t)-3.43359375f, + (float16_t)2.390625f, + (float16_t)-3.09765625f, + (float16_t)3.47265625f, + (float16_t)-2.06054688f, + (float16_t)-0.204101562f, + (float16_t)-1.87402344f, + (float16_t)2.51171875f, + (float16_t)-3.94726562f, + (float16_t)3.02148438f, + (float16_t)-0.992675781f, + (float16_t)-1.48144531f, + (float16_t)2.70117188f, + (float16_t)0.69921875f, + (float16_t)3.78710938f, + (float16_t)0.11932373f, + (float16_t)1.65429688f, + (float16_t)0.380615234f, + (float16_t)0.571777344f, + (float16_t)-3.22460938f, + (float16_t)-2.45898438f, + (float16_t)-0.340087891f, + (float16_t)-2.26171875f, + (float16_t)3.65234375f, + (float16_t)3.28320312f, + (float16_t)-0.196411133f, + (float16_t)-0.909179688f, + (float16_t)1.95800781f, + (float16_t)2.90234375f, + (float16_t)-0.758300781f, + (float16_t)2.07226562f, + (float16_t)-2.81835938f, + (float16_t)-0.944335938f, + (float16_t)2.60546875f, + (float16_t)-2.70898438f, + (float16_t)2.70507812f, + (float16_t)3.94335938f, + (float16_t)0.171875f, + (float16_t)-2.10351562f, + (float16_t)-1.90820312f, + (float16_t)-0.930175781f, + (float16_t)-1.59765625f, + (float16_t)-3.85546875f, + (float16_t)2.21484375f, + (float16_t)3.47460938f, + (float16_t)1.17871094f, + (float16_t)1.03125f, + (float16_t)-2.484375f, + (float16_t)0.607910156f, + (float16_t)-2.19140625f, + (float16_t)0.712402344f, + (float16_t)0.564453125f, + (float16_t)-0.886230469f, + (float16_t)-0.696289062f, + (float16_t)1.92382812f, + (float16_t)3.4609375f, + (float16_t)3.83398438f, + (float16_t)0.172363281f, + (float16_t)2.32617188f, + (float16_t)-1.00488281f, + (float16_t)-3.6640625f, + (float16_t)1.63671875f, + (float16_t)2.27148438f, + (float16_t)2.93164062f, + (float16_t)2.62695312f, + (float16_t)-1.54492188f, + (float16_t)-2.82421875f, + (float16_t)-2.51953125f, + (float16_t)-0.776367188f, + (float16_t)3.90625f, + (float16_t)-1.49609375f, + (float16_t)1.49707031f, + (float16_t)-2.65820312f, + (float16_t)-1.09082031f, + (float16_t)-3.69335938f, + (float16_t)1.17773438f, + (float16_t)1.85546875f, + (float16_t)-0.322998047f, + (float16_t)-3.84375f, + (float16_t)-1.69726562f, + (float16_t)-1.63671875f, + (float16_t)-2.85351562f, + (float16_t)-1.57421875f, + (float16_t)-0.775390625f, + (float16_t)-2.2265625f, + (float16_t)1.34960938f, + (float16_t)1.98242188f, + (float16_t)-2.59179688f, + (float16_t)-0.00173377991f, + (float16_t)0.6953125f, + (float16_t)1.86523438f, + (float16_t)-2.75f, + (float16_t)0.667480469f, + (float16_t)0.438232422f, + (float16_t)1.27148438f, + (float16_t)1.09277344f, + (float16_t)-2.2421875f, + (float16_t)1.26367188f, + (float16_t)-2.1328125f, + (float16_t)0.0192108154f, + (float16_t)3.67578125f, + (float16_t)3.99023438f, + (float16_t)0.48828125f, + (float16_t)-2.40039062f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/output_ref_data.h new file mode 100644 index 00000000..b67903f9 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/output_ref_data.h @@ -0,0 +1,16385 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nhcw_f16_output_ref[16384] = {(float16_t)-3.49609375f, + (float16_t)0.438232422f, + (float16_t)-3.63085938f, + (float16_t)-0.516113281f, + (float16_t)1.50488281f, + (float16_t)-1.45605469f, + (float16_t)-1.08300781f, + (float16_t)-1.43164062f, + (float16_t)-1.69628906f, + (float16_t)-3.0625f, + (float16_t)-2.96875f, + (float16_t)-0.00550460815f, + (float16_t)2.3125f, + (float16_t)0.777832031f, + (float16_t)2.57226562f, + (float16_t)-2.44726562f, + (float16_t)0.645019531f, + (float16_t)-0.817382812f, + (float16_t)-1.75976562f, + (float16_t)0.663085938f, + (float16_t)-1.75488281f, + (float16_t)3.13476562f, + (float16_t)-1.13867188f, + (float16_t)-2.14453125f, + (float16_t)0.244384766f, + (float16_t)-2.1171875f, + (float16_t)-2.875f, + (float16_t)1.78027344f, + (float16_t)1.89453125f, + (float16_t)-0.458496094f, + (float16_t)-1.78613281f, + (float16_t)2.43945312f, + (float16_t)1.3515625f, + (float16_t)3.671875f, + (float16_t)-3.76367188f, + (float16_t)-3.1171875f, + (float16_t)1.68066406f, + (float16_t)1.55761719f, + (float16_t)0.819335938f, + (float16_t)-3.30078125f, + (float16_t)0.627441406f, + (float16_t)2.125f, + (float16_t)2.55859375f, + (float16_t)-0.16027832f, + (float16_t)-2.81640625f, + (float16_t)1.73144531f, + (float16_t)-2.01367188f, + (float16_t)0.6015625f, + (float16_t)1.9140625f, + (float16_t)0.506347656f, + (float16_t)1.73730469f, + (float16_t)2.66015625f, + (float16_t)-1.390625f, + (float16_t)-1.52148438f, + (float16_t)2.10351562f, + (float16_t)0.00889587402f, + (float16_t)0.191894531f, + (float16_t)-1.90625f, + (float16_t)-1.57617188f, + (float16_t)0.271240234f, + (float16_t)0.687988281f, + (float16_t)0.91796875f, + (float16_t)-0.570800781f, + (float16_t)3.484375f, + (float16_t)1.38964844f, + (float16_t)-1.04101562f, + (float16_t)2.8203125f, + (float16_t)-0.818359375f, + (float16_t)1.67675781f, + (float16_t)0.132324219f, + (float16_t)0.424316406f, + (float16_t)-3.69726562f, + (float16_t)-0.466796875f, + (float16_t)-1.34375f, + (float16_t)-0.34765625f, + (float16_t)-2.32226562f, + (float16_t)3.7109375f, + (float16_t)2.82226562f, + (float16_t)1.68457031f, + (float16_t)-1.52832031f, + (float16_t)-0.858398438f, + (float16_t)-1.55859375f, + (float16_t)2.33984375f, + (float16_t)2.52539062f, + (float16_t)-1.90527344f, + (float16_t)2.609375f, + (float16_t)-0.693847656f, + (float16_t)0.869140625f, + (float16_t)-3.2578125f, + (float16_t)2.04101562f, + (float16_t)-2.58398438f, + (float16_t)3.57421875f, + (float16_t)-2.765625f, + (float16_t)-1.65625f, + (float16_t)0.110534668f, + (float16_t)-1.02050781f, + (float16_t)-0.116394043f, + (float16_t)0.984863281f, + (float16_t)-0.307128906f, + (float16_t)1.52050781f, + (float16_t)1.12402344f, + (float16_t)-0.277099609f, + (float16_t)-0.269287109f, + (float16_t)-1.59863281f, + (float16_t)3.7265625f, + (float16_t)0.666992188f, + (float16_t)3.25976562f, + (float16_t)2.171875f, + (float16_t)-3.84960938f, + (float16_t)1.92675781f, + (float16_t)1.90136719f, + (float16_t)1.87890625f, + (float16_t)0.239257812f, + (float16_t)1.94921875f, + (float16_t)3.76757812f, + (float16_t)3.49414062f, + (float16_t)2.94726562f, + (float16_t)2.12109375f, + (float16_t)0.424072266f, + (float16_t)-0.818847656f, + (float16_t)3.49023438f, + (float16_t)-0.788085938f, + (float16_t)-0.473876953f, + (float16_t)2.4921875f, + (float16_t)-2.42773438f, + (float16_t)-1.88964844f, + (float16_t)-0.985839844f, + (float16_t)-2.74414062f, + (float16_t)-1.48730469f, + (float16_t)-2.14648438f, + (float16_t)-1.18164062f, + (float16_t)-0.920898438f, + (float16_t)2.61914062f, + (float16_t)-1.86914062f, + (float16_t)1.06152344f, + (float16_t)3.68945312f, + (float16_t)-1.66796875f, + (float16_t)1.16796875f, + (float16_t)1.1640625f, + (float16_t)-3.51953125f, + (float16_t)3.796875f, + (float16_t)-0.331298828f, + (float16_t)-3.7890625f, + (float16_t)2.59765625f, + (float16_t)-3.2265625f, + (float16_t)-1.80273438f, + (float16_t)-1.68652344f, + (float16_t)-0.450683594f, + (float16_t)-3.66796875f, + (float16_t)0.605957031f, + (float16_t)2.0625f, + (float16_t)0.00124835968f, + (float16_t)1.91113281f, + (float16_t)-2.87304688f, + (float16_t)3.54101562f, + (float16_t)3.77539062f, + (float16_t)1.35253906f, + (float16_t)-3.54101562f, + (float16_t)-0.465332031f, + (float16_t)-1.92382812f, + (float16_t)2.59765625f, + (float16_t)0.869628906f, + (float16_t)-2.24414062f, + (float16_t)2.3125f, + (float16_t)0.739257812f, + (float16_t)-2.28515625f, + (float16_t)-0.0335693359f, + (float16_t)-1.14648438f, + (float16_t)2.80859375f, + (float16_t)3.04101562f, + (float16_t)2.15039062f, + (float16_t)-1.43847656f, + (float16_t)2.25195312f, + (float16_t)2.2890625f, + (float16_t)-1.6875f, + (float16_t)0.544433594f, + (float16_t)3.35351562f, + (float16_t)-0.7265625f, + (float16_t)2.08203125f, + (float16_t)-1.31933594f, + (float16_t)2.02539062f, + (float16_t)-3.96679688f, + (float16_t)-1.37695312f, + (float16_t)-0.971679688f, + (float16_t)-1.74414062f, + (float16_t)1.84667969f, + (float16_t)-0.502441406f, + (float16_t)1.40917969f, + (float16_t)-3.96289062f, + (float16_t)-1.55175781f, + (float16_t)-1.37109375f, + (float16_t)2.65625f, + (float16_t)-1.76953125f, + (float16_t)-3.52929688f, + (float16_t)-0.608398438f, + (float16_t)-3.01171875f, + (float16_t)3.66992188f, + (float16_t)-3.82226562f, + (float16_t)-2.93164062f, + (float16_t)2.671875f, + (float16_t)-2.4296875f, + (float16_t)-2.52734375f, + (float16_t)-2.23242188f, + (float16_t)-1.42773438f, + (float16_t)-1.08105469f, + (float16_t)2.40039062f, + (float16_t)1.36621094f, + (float16_t)3.05664062f, + (float16_t)1.73828125f, + (float16_t)-0.254394531f, + (float16_t)-3.79882812f, + (float16_t)-2.48242188f, + (float16_t)3.56054688f, + (float16_t)0.0208740234f, + (float16_t)2.30078125f, + (float16_t)1.62988281f, + (float16_t)-0.791015625f, + (float16_t)1.18261719f, + (float16_t)2.90039062f, + (float16_t)-1.56347656f, + (float16_t)-0.065246582f, + (float16_t)1.37304688f, + (float16_t)0.926757812f, + (float16_t)1.6875f, + (float16_t)0.877929688f, + (float16_t)-3.56445312f, + (float16_t)1.66699219f, + (float16_t)2.03320312f, + (float16_t)1.57519531f, + (float16_t)-0.662109375f, + (float16_t)-2.40625f, + (float16_t)2.36328125f, + (float16_t)-1.14550781f, + (float16_t)2.71875f, + (float16_t)-1.81152344f, + 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(float16_t)-2.00976562f, + (float16_t)1.6875f, + (float16_t)-0.915039062f, + (float16_t)-1.95117188f, + (float16_t)1.94726562f, + (float16_t)1.92285156f, + (float16_t)-2.50390625f, + (float16_t)1.01757812f, + (float16_t)-2.40234375f, + (float16_t)-1.85546875f, + (float16_t)-3.68164062f, + (float16_t)-1.59375f, + (float16_t)1.12402344f, + (float16_t)2.38476562f, + (float16_t)-2.97265625f, + (float16_t)-0.0422668457f, + (float16_t)-1.99023438f, + (float16_t)-2.99023438f, + (float16_t)-3.02734375f, + (float16_t)-0.0732421875f, + (float16_t)1.58300781f, + (float16_t)-2.91601562f, + (float16_t)2.98828125f, + (float16_t)-2.40039062f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/config_data.h new file mode 100644 index 00000000..367853e6 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NHCW_F32_NUM_DIMS 4 +#define TRANSPOSE_NHCW_F32_INPUT_N 1 +#define TRANSPOSE_NHCW_F32_INPUT_H 1 +#define TRANSPOSE_NHCW_F32_INPUT_W 128 +#define TRANSPOSE_NHCW_F32_INPUT_C 128 +#define TRANSPOSE_NHCW_F32_OUTPUT_N 1 +#define TRANSPOSE_NHCW_F32_OUTPUT_H 1 +#define TRANSPOSE_NHCW_F32_OUTPUT_W 128 +#define TRANSPOSE_NHCW_F32_OUTPUT_C 128 +#define TRANSPOSE_NHCW_F32_PERM_0 0 +#define TRANSPOSE_NHCW_F32_PERM_1 1 +#define TRANSPOSE_NHCW_F32_PERM_2 3 +#define TRANSPOSE_NHCW_F32_PERM_3 2 +#define TRANSPOSE_NHCW_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NHCW_F32_SIZE 16384 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/input_tensor_data.h new file mode 100644 index 00000000..2566c95f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/input_tensor_data.h @@ -0,0 +1,2733 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nhcw_f32_input_tensor[16384] = { + 3.11389589f, -0.98837018f, 0.790179014f, -1.18538094f, -1.08683503f, -2.86315989f, + 2.31340289f, -2.36427522f, 1.96997023f, -2.15258527f, -3.26000214f, 2.19320154f, + -1.94500411f, -1.64923537f, 0.767025232f, -1.89182508f, 2.54318333f, 0.247123197f, + -3.76832557f, -2.89892912f, -2.47637272f, 3.38244891f, -0.216401219f, -3.75298429f, + -0.998969913f, 0.616614282f, -1.87527335f, 1.49443877f, 0.726562142f, -1.69896626f, + 1.45148528f, -0.751815975f, -1.36481249f, 1.23129463f, -3.20536447f, 1.73119402f, + -2.82686639f, 1.6573236f, -2.81981754f, -1.15592873f, 3.55966473f, 2.67671275f, + 0.0596998446f, 2.86820459f, 1.32321632f, 1.40706182f, -0.333768547f, -3.22801876f, + -1.17675781f, 3.04214549f, 3.74418187f, 2.23415923f, 2.83425426f, -2.86778951f, + -3.8139379f, -1.03124022f, -1.17766607f, 3.34847593f, -2.66805053f, -3.99023175f, + -0.934121311f, -3.74104762f, -1.46393871f, -2.33255744f, -2.26466632f, 2.43698478f, + 3.87321687f, 1.23070931f, 0.108278662f, -3.03799534f, 1.03776145f, 2.47174335f, + -1.2557559f, -0.983439505f, 1.84891236f, 3.72957635f, -0.0161110163f, 2.47021079f, + -0.683314323f, 0.741364896f, -3.88390064f, 2.52880931f, 2.01290417f, 3.93833899f, + -0.891426861f, -0.515784979f, 3.79514694f, 1.89140356f, 0.967429936f, -0.273483932f, + 1.43899071f, -3.77437925f, 1.2667954f, -0.480671793f, 2.04120851f, -3.82503247f, + 3.15591812f, 0.664770365f, -3.01879168f, -3.6269989f, -0.944045305f, -1.53793967f, + 2.79453754f, 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3.50226092f, 3.07072258f, 3.05091524f, + 2.03778338f, 3.74940801f, -3.87033415f, -1.55913126f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/output_ref_data.h new file mode 100644 index 00000000..77e47ea3 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/output_ref_data.h @@ -0,0 +1,2733 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nhcw_f32_output_ref[16384] = { + 3.11389589f, 0.591479301f, -3.59616613f, -2.54928112f, -1.72303081f, 2.65448451f, + 1.16001451f, 2.05572367f, 1.42379808f, -0.594864666f, 3.48261833f, 0.236181021f, + 0.843280852f, -3.97525096f, -0.115246914f, 1.26021028f, -3.34682083f, -2.54508662f, + 3.38200474f, -0.344314039f, 0.434586227f, 3.18278813f, 1.84828162f, 0.433607638f, + -0.95845449f, 3.00251889f, 1.27116191f, -1.51577234f, -1.82292056f, 0.472156644f, + -0.774131417f, -3.40633368f, 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1.24882841f, 1.09425366f, -3.41135454f, -2.9545753f, + -2.35027075f, 0.656218112f, 2.43574834f, -1.55913126f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nhcw_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/config_data.h new file mode 100644 index 00000000..78abb0fc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NWHC_F16_NUM_DIMS 4 +#define TRANSPOSE_NWHC_F16_INPUT_N 1 +#define TRANSPOSE_NWHC_F16_INPUT_H 19 +#define TRANSPOSE_NWHC_F16_INPUT_W 3 +#define TRANSPOSE_NWHC_F16_INPUT_C 64 +#define TRANSPOSE_NWHC_F16_OUTPUT_N 1 +#define TRANSPOSE_NWHC_F16_OUTPUT_H 3 +#define TRANSPOSE_NWHC_F16_OUTPUT_W 19 +#define TRANSPOSE_NWHC_F16_OUTPUT_C 64 +#define TRANSPOSE_NWHC_F16_PERM_0 0 +#define TRANSPOSE_NWHC_F16_PERM_1 2 +#define TRANSPOSE_NWHC_F16_PERM_2 1 +#define TRANSPOSE_NWHC_F16_PERM_3 3 +#define TRANSPOSE_NWHC_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NWHC_F16_SIZE 3648 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/input_tensor_data.h new file mode 100644 index 00000000..aea693cb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/input_tensor_data.h @@ -0,0 +1,3649 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nwhc_f16_input_tensor[3648] = {(float16_t)0.859863281f, + (float16_t)-0.37890625f, + (float16_t)3.25195312f, + (float16_t)-3.37109375f, + (float16_t)2.49414062f, + (float16_t)-1.1953125f, + (float16_t)0.305419922f, + (float16_t)-0.248413086f, + (float16_t)-2.10742188f, + (float16_t)-0.188476562f, + (float16_t)-3.11328125f, + (float16_t)-1.52148438f, + (float16_t)-3.55273438f, + (float16_t)-0.295654297f, + (float16_t)-2.64257812f, + (float16_t)1.35253906f, + (float16_t)2.71679688f, + (float16_t)-1.89648438f, + (float16_t)-3.65039062f, + (float16_t)-2.56445312f, + (float16_t)3.50195312f, + (float16_t)-1.48925781f, + (float16_t)-1.81445312f, + (float16_t)-0.845214844f, + (float16_t)3.3828125f, + (float16_t)-0.655273438f, + (float16_t)-1.48144531f, + (float16_t)-3.79882812f, + (float16_t)2.11523438f, + (float16_t)-2.91601562f, + (float16_t)2.57617188f, + (float16_t)-2.86328125f, + (float16_t)1.97167969f, + (float16_t)2.30859375f, + (float16_t)-2.60546875f, + (float16_t)-1.87304688f, + (float16_t)0.651855469f, + (float16_t)-3.6796875f, + (float16_t)-0.117431641f, + (float16_t)-3.55273438f, + (float16_t)3.26367188f, + (float16_t)-1.4453125f, + (float16_t)-3.64453125f, + (float16_t)-2.96875f, + (float16_t)-3.87304688f, + (float16_t)3.27539062f, + (float16_t)-0.343994141f, + (float16_t)-2.03515625f, + (float16_t)1.46875f, + (float16_t)1.07714844f, + (float16_t)-0.20324707f, + (float16_t)2.20703125f, + (float16_t)-2.49023438f, + (float16_t)-2.91796875f, + (float16_t)1.53808594f, + (float16_t)-2.46484375f, + (float16_t)-0.908203125f, + (float16_t)-1.73339844f, + (float16_t)-1.66992188f, + (float16_t)-2.81054688f, + (float16_t)1.20800781f, + (float16_t)-3.30273438f, + (float16_t)-0.90234375f, + (float16_t)-3.03125f, + (float16_t)-1.1640625f, + (float16_t)-3.546875f, + (float16_t)3.8203125f, + (float16_t)-0.256591797f, + (float16_t)-3.56445312f, + (float16_t)-0.6171875f, + (float16_t)-1.29394531f, + (float16_t)-2.08789062f, + (float16_t)-1.99414062f, + (float16_t)3.82421875f, + (float16_t)0.73046875f, + (float16_t)-1.19824219f, + (float16_t)-1.95117188f, + (float16_t)0.281005859f, + (float16_t)-0.639160156f, + (float16_t)-2.47070312f, + (float16_t)-0.0279388428f, + (float16_t)-3.77734375f, + (float16_t)1.1328125f, + (float16_t)-0.978515625f, + (float16_t)2.01953125f, + (float16_t)0.16394043f, + (float16_t)-0.581054688f, + (float16_t)2.203125f, + (float16_t)0.301757812f, + (float16_t)-1.78613281f, + (float16_t)-1.86621094f, + (float16_t)-2.71289062f, + (float16_t)-2.734375f, + (float16_t)0.629394531f, + (float16_t)-1.45703125f, + (float16_t)0.979003906f, + (float16_t)1.02539062f, + (float16_t)-3.75390625f, + (float16_t)-0.425048828f, + (float16_t)3.0f, + (float16_t)2.90429688f, + (float16_t)1.62402344f, + (float16_t)-1.13183594f, + (float16_t)-1.02734375f, + (float16_t)0.450439453f, + (float16_t)-3.66210938f, + (float16_t)-0.443847656f, + (float16_t)-3.77148438f, + (float16_t)1.51171875f, + (float16_t)2.18554688f, + (float16_t)-3.04101562f, + (float16_t)-3.00585938f, + (float16_t)-0.346923828f, + (float16_t)-3.48046875f, + (float16_t)1.34472656f, + (float16_t)-2.59179688f, + (float16_t)2.1328125f, + (float16_t)2.75976562f, + (float16_t)-2.2265625f, + (float16_t)0.706054688f, + (float16_t)-3.265625f, + (float16_t)-1.73046875f, + (float16_t)-3.59375f, + (float16_t)3.54492188f, + (float16_t)3.875f, + (float16_t)1.85058594f, + (float16_t)-3.98632812f, + (float16_t)-1.97363281f, + (float16_t)0.408203125f, + (float16_t)3.25976562f, + (float16_t)1.14746094f, + (float16_t)1.75585938f, + (float16_t)-0.815917969f, + (float16_t)3.40039062f, + (float16_t)-3.41992188f, + (float16_t)-1.59765625f, + (float16_t)-0.401611328f, + (float16_t)3.37890625f, + (float16_t)0.171508789f, + (float16_t)-1.78125f, + (float16_t)1.16699219f, + (float16_t)-3.6171875f, + (float16_t)3.83398438f, + (float16_t)-3.91015625f, + (float16_t)-0.416748047f, + (float16_t)-3.36328125f, + (float16_t)0.297851562f, + (float16_t)0.280029297f, + (float16_t)-3.4140625f, + (float16_t)2.35742188f, + (float16_t)2.47265625f, + (float16_t)2.28515625f, + (float16_t)-1.84765625f, + (float16_t)3.06640625f, + (float16_t)1.75f, + (float16_t)-1.02539062f, + (float16_t)-3.23046875f, + (float16_t)-1.13574219f, + (float16_t)-0.6484375f, + (float16_t)2.58984375f, + (float16_t)2.44140625f, + (float16_t)-1.89648438f, + (float16_t)2.96875f, + (float16_t)1.625f, + (float16_t)-2.98046875f, + (float16_t)-0.878417969f, + (float16_t)2.59960938f, + (float16_t)-3.63867188f, + (float16_t)3.20703125f, + (float16_t)3.4765625f, + (float16_t)-2.23046875f, + (float16_t)-0.0509338379f, + (float16_t)-1.11230469f, + (float16_t)0.134277344f, + (float16_t)-0.642578125f, + (float16_t)3.79882812f, + (float16_t)-1.97949219f, + (float16_t)1.20703125f, + (float16_t)3.8515625f, + (float16_t)-1.58300781f, + (float16_t)2.18359375f, + (float16_t)-1.13476562f, + (float16_t)-2.0625f, + (float16_t)1.00976562f, + (float16_t)1.33300781f, + (float16_t)-0.794921875f, + (float16_t)0.583984375f, + (float16_t)-3.76953125f, + (float16_t)0.505371094f, + (float16_t)1.54492188f, + (float16_t)0.461425781f, + (float16_t)-1.19433594f, + (float16_t)1.66503906f, + (float16_t)-3.35742188f, + (float16_t)2.33984375f, + (float16_t)-1.47265625f, + (float16_t)3.44140625f, + (float16_t)-2.2421875f, + (float16_t)-0.153442383f, + (float16_t)1.78320312f, + (float16_t)-0.739746094f, + (float16_t)0.740722656f, + (float16_t)-3.59570312f, + (float16_t)-3.48632812f, + (float16_t)-0.927246094f, + (float16_t)1.359375f, + (float16_t)0.848632812f, + (float16_t)2.30273438f, + (float16_t)3.68945312f, + (float16_t)-1.36914062f, + (float16_t)2.19335938f, + (float16_t)3.62890625f, + (float16_t)-2.06835938f, + (float16_t)-3.0234375f, + (float16_t)0.907226562f, + (float16_t)0.78515625f, + (float16_t)2.18945312f, + (float16_t)-2.79492188f, + (float16_t)-2.98046875f, + (float16_t)-0.239624023f, + (float16_t)-0.863769531f, + (float16_t)-0.150756836f, + (float16_t)-2.59960938f, + (float16_t)-0.895019531f, + (float16_t)-0.844726562f, + (float16_t)-1.73730469f, + (float16_t)-2.88671875f, + (float16_t)-3.3125f, + (float16_t)-2.36132812f, + (float16_t)0.0520324707f, + (float16_t)-3.6328125f, + (float16_t)-0.0386352539f, + (float16_t)0.982910156f, + (float16_t)-0.828613281f, + (float16_t)3.29882812f, + (float16_t)-1.83398438f, + (float16_t)2.25f, + (float16_t)-3.01757812f, + (float16_t)-0.478515625f, + (float16_t)3.765625f, + (float16_t)3.00976562f, + (float16_t)-3.9765625f, + (float16_t)-2.61132812f, + (float16_t)0.478515625f, + (float16_t)0.268066406f, + (float16_t)-3.3046875f, + (float16_t)-3.19140625f, + (float16_t)0.260986328f, + (float16_t)2.37109375f, + (float16_t)0.0803833008f, + (float16_t)1.70410156f, + (float16_t)2.48046875f, + (float16_t)3.69335938f, + (float16_t)2.30664062f, + (float16_t)-1.27832031f, + (float16_t)-0.3046875f, + (float16_t)-0.545898438f, + (float16_t)-1.59375f, + (float16_t)-3.16992188f, + (float16_t)-3.57226562f, + (float16_t)-3.30078125f, + (float16_t)2.75976562f, + (float16_t)-3.19921875f, + (float16_t)1.00488281f, + (float16_t)-3.3125f, + (float16_t)3.4296875f, + (float16_t)-2.32421875f, + (float16_t)-1.16992188f, + (float16_t)2.5546875f, + (float16_t)3.7578125f, + (float16_t)1.21582031f, + (float16_t)-3.87695312f, + (float16_t)1.40136719f, + (float16_t)3.02929688f, + (float16_t)-1.14355469f, + (float16_t)-2.80078125f, + (float16_t)-1.90917969f, + (float16_t)1.45507812f, + (float16_t)-3.83789062f, + (float16_t)-0.444824219f, + (float16_t)-1.27050781f, + (float16_t)-0.0963745117f, + (float16_t)3.75390625f, + (float16_t)-2.09375f, + (float16_t)3.36328125f, + (float16_t)2.45898438f, + (float16_t)2.11914062f, + (float16_t)-3.54296875f, + (float16_t)3.79492188f, + (float16_t)1.34277344f, + (float16_t)3.48242188f, + (float16_t)1.74902344f, + (float16_t)3.23242188f, + (float16_t)-0.626953125f, + (float16_t)1.39453125f, + (float16_t)1.60351562f, + (float16_t)-3.66601562f, + (float16_t)-1.90039062f, + (float16_t)3.859375f, + (float16_t)1.03515625f, + (float16_t)-2.12109375f, + (float16_t)-1.99707031f, + (float16_t)-2.7421875f, + (float16_t)1.70898438f, + (float16_t)0.87109375f, + (float16_t)-1.07226562f, + (float16_t)-3.62304688f, + (float16_t)2.421875f, + (float16_t)3.37695312f, + (float16_t)2.625f, + (float16_t)3.76367188f, + (float16_t)-0.341064453f, + (float16_t)0.274902344f, + (float16_t)0.0313110352f, + (float16_t)-2.96289062f, + (float16_t)3.1171875f, + (float16_t)2.79101562f, + (float16_t)-0.856933594f, + (float16_t)-0.468017578f, + (float16_t)-1.55078125f, + (float16_t)2.0703125f, + (float16_t)1.75488281f, + (float16_t)3.58984375f, + (float16_t)-1.84277344f, + (float16_t)1.59667969f, + (float16_t)3.84375f, + (float16_t)2.74609375f, + (float16_t)-3.19335938f, + (float16_t)-3.42382812f, + (float16_t)2.953125f, + (float16_t)-3.90625f, + (float16_t)-0.747070312f, + (float16_t)0.227294922f, + (float16_t)2.34765625f, + (float16_t)2.47265625f, + (float16_t)2.30273438f, + (float16_t)1.43359375f, + (float16_t)2.92382812f, + (float16_t)-2.13867188f, + (float16_t)-3.45117188f, + (float16_t)2.86132812f, + (float16_t)-1.27539062f, + (float16_t)3.80078125f, + (float16_t)1.15234375f, + (float16_t)2.1875f, + (float16_t)1.61035156f, + (float16_t)-3.4765625f, + (float16_t)0.483154297f, + (float16_t)-1.30957031f, + (float16_t)-3.88476562f, + (float16_t)-1.1875f, + (float16_t)-2.29882812f, + (float16_t)1.13183594f, + (float16_t)-2.40625f, + (float16_t)3.5546875f, + (float16_t)2.140625f, + (float16_t)0.171386719f, + (float16_t)1.06835938f, + (float16_t)3.15820312f, + (float16_t)-1.14160156f, + (float16_t)2.09570312f, + (float16_t)-1.61132812f, + (float16_t)1.59277344f, + (float16_t)-0.912109375f, + (float16_t)3.8046875f, + (float16_t)-2.87695312f, + (float16_t)-1.11523438f, + (float16_t)3.23828125f, + (float16_t)2.54296875f, + (float16_t)-3.26953125f, + (float16_t)1.44628906f, + (float16_t)-2.60742188f, + (float16_t)1.08105469f, + (float16_t)3.55859375f, + (float16_t)1.10058594f, + 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(float16_t)-0.964355469f, + (float16_t)-3.86914062f, + (float16_t)-0.774902344f, + (float16_t)-0.479492188f, + (float16_t)2.27148438f, + (float16_t)2.94335938f, + (float16_t)0.234985352f, + (float16_t)-3.73632812f, + (float16_t)2.02734375f, + (float16_t)-2.62304688f, + (float16_t)-2.66796875f, + (float16_t)-3.875f, + (float16_t)3.3359375f, + (float16_t)-3.05664062f, + (float16_t)0.650878906f, + (float16_t)-1.375f, + (float16_t)-0.9296875f, + (float16_t)-2.03515625f, + (float16_t)1.34960938f, + (float16_t)-0.0207061768f, + (float16_t)-0.885742188f, + (float16_t)-2.0234375f, + (float16_t)-3.05859375f, + (float16_t)-2.7109375f, + (float16_t)-3.93164062f, + (float16_t)-1.17871094f, + (float16_t)-3.68945312f, + (float16_t)-0.5546875f, + (float16_t)3.625f, + (float16_t)2.90820312f, + (float16_t)0.778808594f, + (float16_t)-0.452636719f, + (float16_t)-2.1640625f, + (float16_t)-2.21875f, + (float16_t)-3.52929688f, + (float16_t)0.487548828f, + (float16_t)-2.6640625f, + (float16_t)3.078125f, + (float16_t)3.17578125f, + (float16_t)-1.75976562f, + (float16_t)-3.45898438f, + (float16_t)0.752929688f, + (float16_t)-1.2578125f, + (float16_t)-3.11914062f, + (float16_t)-0.721679688f, + (float16_t)-2.54296875f, + (float16_t)-2.65234375f, + (float16_t)-0.0830078125f, + (float16_t)-3.67382812f, + (float16_t)0.522949219f, + (float16_t)-1.87109375f, + (float16_t)-0.864746094f, + (float16_t)-1.65820312f, + (float16_t)3.15625f, + (float16_t)0.669433594f, + (float16_t)1.93261719f, + (float16_t)3.7734375f, + (float16_t)-0.0723876953f, + (float16_t)-1.41210938f, + (float16_t)0.265136719f, + (float16_t)-1.98535156f, + (float16_t)-1.68066406f, + (float16_t)-0.717285156f, + (float16_t)-1.68066406f, + (float16_t)-0.160400391f, + (float16_t)2.87304688f, + (float16_t)0.544921875f, + (float16_t)-3.69335938f, + (float16_t)-1.65039062f, + (float16_t)-3.921875f, + (float16_t)0.87109375f, + (float16_t)-1.296875f, + (float16_t)0.987304688f, + (float16_t)2.02734375f, + (float16_t)-0.90234375f, + (float16_t)2.59179688f, + (float16_t)-1.89453125f, + (float16_t)1.89648438f, + (float16_t)-2.8359375f, + (float16_t)-3.23632812f, + (float16_t)-1.60058594f, + (float16_t)3.31445312f, + (float16_t)3.1484375f, + (float16_t)-1.67285156f, + (float16_t)2.04296875f, + (float16_t)-2.22265625f, + (float16_t)1.53125f, + (float16_t)2.99609375f, + (float16_t)2.68554688f, + (float16_t)-2.26171875f, + (float16_t)-3.98046875f, + (float16_t)-0.871582031f, + (float16_t)-2.83398438f, + (float16_t)-1.71679688f, + (float16_t)1.62011719f, + (float16_t)2.57226562f, + (float16_t)1.28808594f, + (float16_t)2.0390625f, + (float16_t)3.00976562f, + (float16_t)1.14160156f, + (float16_t)-0.0353393555f, + (float16_t)3.2109375f, + (float16_t)-2.13867188f, + (float16_t)3.56640625f, + (float16_t)-0.503417969f, + (float16_t)1.85546875f, + (float16_t)0.937988281f, + (float16_t)-0.660644531f, + (float16_t)-3.79101562f, + (float16_t)2.21875f, + (float16_t)-2.13476562f, + (float16_t)-2.29296875f, + (float16_t)-0.169189453f, + (float16_t)-1.00292969f, + (float16_t)3.69921875f, + (float16_t)0.846191406f, + (float16_t)1.70800781f, + (float16_t)0.174316406f, + (float16_t)3.45507812f, + (float16_t)-3.91992188f, + (float16_t)1.81542969f, + (float16_t)1.94628906f, + (float16_t)-0.981445312f, + (float16_t)1.42382812f, + (float16_t)-0.150024414f, + (float16_t)0.618652344f, + (float16_t)-3.28515625f, + (float16_t)-1.88867188f, + (float16_t)-2.8359375f, + (float16_t)-1.68652344f, + (float16_t)-1.57714844f, + (float16_t)2.9140625f, + (float16_t)-3.21679688f, + (float16_t)-3.68554688f, + (float16_t)2.95898438f, + (float16_t)-0.916503906f, + (float16_t)-0.673339844f, + (float16_t)-0.643554688f, + (float16_t)-2.73828125f, + (float16_t)2.36523438f, + (float16_t)-1.43261719f, + (float16_t)-2.39648438f, + (float16_t)-1.46972656f, + (float16_t)0.177978516f, + (float16_t)3.49414062f, + (float16_t)3.05859375f, + (float16_t)3.42578125f, + (float16_t)0.758300781f, + (float16_t)-1.15722656f, + (float16_t)-2.32421875f, + (float16_t)-0.906738281f, + (float16_t)-3.09179688f, + (float16_t)-1.43457031f, + (float16_t)2.96679688f, + (float16_t)3.36328125f, + (float16_t)-2.57226562f, + (float16_t)-3.43554688f, + (float16_t)-2.66992188f, + (float16_t)-3.0078125f, + (float16_t)3.39453125f, + (float16_t)3.42578125f, + (float16_t)2.79101562f, + (float16_t)-3.34765625f, + (float16_t)-1.84472656f, + (float16_t)0.150268555f, + (float16_t)3.22265625f, + (float16_t)1.83789062f, + (float16_t)1.15625f, + (float16_t)-0.0166473389f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/output_ref_data.h new file mode 100644 index 00000000..79686132 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/output_ref_data.h @@ -0,0 +1,3649 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_nwhc_f16_output_ref[3648] = {(float16_t)0.859863281f, + (float16_t)-0.37890625f, + (float16_t)3.25195312f, + (float16_t)-3.37109375f, + (float16_t)2.49414062f, + (float16_t)-1.1953125f, + (float16_t)0.305419922f, + (float16_t)-0.248413086f, + (float16_t)-2.10742188f, + (float16_t)-0.188476562f, + (float16_t)-3.11328125f, + (float16_t)-1.52148438f, + (float16_t)-3.55273438f, + (float16_t)-0.295654297f, + (float16_t)-2.64257812f, + (float16_t)1.35253906f, + (float16_t)2.71679688f, + (float16_t)-1.89648438f, + (float16_t)-3.65039062f, + (float16_t)-2.56445312f, + (float16_t)3.50195312f, + (float16_t)-1.48925781f, + (float16_t)-1.81445312f, + (float16_t)-0.845214844f, + (float16_t)3.3828125f, + (float16_t)-0.655273438f, + (float16_t)-1.48144531f, + (float16_t)-3.79882812f, + (float16_t)2.11523438f, + (float16_t)-2.91601562f, + (float16_t)2.57617188f, + (float16_t)-2.86328125f, + (float16_t)1.97167969f, + (float16_t)2.30859375f, + (float16_t)-2.60546875f, + (float16_t)-1.87304688f, + (float16_t)0.651855469f, + (float16_t)-3.6796875f, + (float16_t)-0.117431641f, + (float16_t)-3.55273438f, + (float16_t)3.26367188f, + (float16_t)-1.4453125f, + (float16_t)-3.64453125f, + (float16_t)-2.96875f, + (float16_t)-3.87304688f, + (float16_t)3.27539062f, + (float16_t)-0.343994141f, + (float16_t)-2.03515625f, + (float16_t)1.46875f, + (float16_t)1.07714844f, + (float16_t)-0.20324707f, + (float16_t)2.20703125f, + (float16_t)-2.49023438f, + (float16_t)-2.91796875f, + (float16_t)1.53808594f, + (float16_t)-2.46484375f, + (float16_t)-0.908203125f, + (float16_t)-1.73339844f, + (float16_t)-1.66992188f, + (float16_t)-2.81054688f, + (float16_t)1.20800781f, + (float16_t)-3.30273438f, + (float16_t)-0.90234375f, + (float16_t)-3.03125f, + (float16_t)1.66503906f, + (float16_t)-3.35742188f, + (float16_t)2.33984375f, + (float16_t)-1.47265625f, + (float16_t)3.44140625f, + (float16_t)-2.2421875f, + (float16_t)-0.153442383f, + (float16_t)1.78320312f, + (float16_t)-0.739746094f, + (float16_t)0.740722656f, + 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(float16_t)1.70800781f, + (float16_t)0.174316406f, + (float16_t)3.45507812f, + (float16_t)-3.91992188f, + (float16_t)1.81542969f, + (float16_t)1.94628906f, + (float16_t)-0.981445312f, + (float16_t)1.42382812f, + (float16_t)-0.150024414f, + (float16_t)0.618652344f, + (float16_t)-3.28515625f, + (float16_t)-1.88867188f, + (float16_t)-2.8359375f, + (float16_t)-1.68652344f, + (float16_t)-1.57714844f, + (float16_t)2.9140625f, + (float16_t)-3.21679688f, + (float16_t)-3.68554688f, + (float16_t)2.95898438f, + (float16_t)-0.916503906f, + (float16_t)-0.673339844f, + (float16_t)-0.643554688f, + (float16_t)-2.73828125f, + (float16_t)2.36523438f, + (float16_t)-1.43261719f, + (float16_t)-2.39648438f, + (float16_t)-1.46972656f, + (float16_t)0.177978516f, + (float16_t)3.49414062f, + (float16_t)3.05859375f, + (float16_t)3.42578125f, + (float16_t)0.758300781f, + (float16_t)-1.15722656f, + (float16_t)-2.32421875f, + (float16_t)-0.906738281f, + (float16_t)-3.09179688f, + (float16_t)-1.43457031f, + (float16_t)2.96679688f, + (float16_t)3.36328125f, + (float16_t)-2.57226562f, + (float16_t)-3.43554688f, + (float16_t)-2.66992188f, + (float16_t)-3.0078125f, + (float16_t)3.39453125f, + (float16_t)3.42578125f, + (float16_t)2.79101562f, + (float16_t)-3.34765625f, + (float16_t)-1.84472656f, + (float16_t)0.150268555f, + (float16_t)3.22265625f, + (float16_t)1.83789062f, + (float16_t)1.15625f, + (float16_t)-0.0166473389f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/config_data.h new file mode 100644 index 00000000..6a8618bc --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_NWHC_F32_NUM_DIMS 4 +#define TRANSPOSE_NWHC_F32_INPUT_N 1 +#define TRANSPOSE_NWHC_F32_INPUT_H 19 +#define TRANSPOSE_NWHC_F32_INPUT_W 3 +#define TRANSPOSE_NWHC_F32_INPUT_C 64 +#define TRANSPOSE_NWHC_F32_OUTPUT_N 1 +#define TRANSPOSE_NWHC_F32_OUTPUT_H 3 +#define TRANSPOSE_NWHC_F32_OUTPUT_W 19 +#define TRANSPOSE_NWHC_F32_OUTPUT_C 64 +#define TRANSPOSE_NWHC_F32_PERM_0 0 +#define TRANSPOSE_NWHC_F32_PERM_1 2 +#define TRANSPOSE_NWHC_F32_PERM_2 1 +#define TRANSPOSE_NWHC_F32_PERM_3 3 +#define TRANSPOSE_NWHC_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_NWHC_F32_SIZE 3648 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/input_tensor_data.h new file mode 100644 index 00000000..e59b424b --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/input_tensor_data.h @@ -0,0 +1,524 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nwhc_f32_input_tensor[3648] = { + -0.0368402526f, 2.64921665f, 3.47808361f, -0.048315946f, -1.28111827f, 0.985652387f, 2.08564067f, + 0.842033744f, -3.53772998f, -1.25980043f, -2.67564654f, 0.938848972f, 2.23404217f, 2.09006405f, + -0.664105058f, 1.52880573f, -3.38979125f, -3.2944963f, -0.540609479f, 0.0618429258f, 2.01595664f, + 2.67220497f, -1.76639044f, -3.95489097f, -0.788839817f, -1.25848699f, 2.0753262f, 2.19376707f, + 2.67533612f, -2.63562703f, 2.46443534f, -1.14551878f, 1.16516876f, -3.51991415f, 3.17401648f, + -2.9992125f, 0.834027708f, -2.42558956f, -1.66922176f, -3.75435376f, 0.320930451f, -3.51890707f, + -3.97358131f, -0.287248343f, -0.871129394f, 0.139525726f, -1.57577682f, 3.3447659f, -2.12490845f, + -1.79819727f, -3.601897f, -3.74564242f, 2.07267237f, -1.57995021f, 2.06137252f, -2.32723546f, + -2.00338721f, 1.2956363f, 0.800319791f, -0.163816378f, -0.513361454f, -2.29176736f, 0.950558901f, + 1.47879314f, 3.73679042f, -2.75576401f, -1.49417198f, 3.75002933f, 2.17370629f, -1.01286793f, + -3.99949408f, -1.12973428f, 0.988459527f, 1.20003986f, -2.29500341f, 1.07580709f, 0.177970648f, + 2.42920613f, 0.974340379f, -3.26087022f, 3.33842301f, 0.208631665f, 2.09556913f, 0.0761497244f, + -2.31682491f, -3.61209464f, -2.90358925f, 2.05950069f, -0.18524164f, -1.02981222f, 1.01437604f, + 1.30802703f, -2.38817239f, 3.09647465f, 3.57606721f, -2.30992889f, -0.557170689f, -0.730799019f, + 1.67517865f, 1.89687812f, 0.722622335f, 1.94760692f, 0.952100158f, -1.61566293f, -2.83121157f, + 3.42232299f, 2.02898145f, -1.37103724f, 0.95807147f, -2.3736465f, 1.06491399f, -2.85764909f, + 2.04929924f, 2.95290065f, 3.58601618f, 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3.5608623f, -3.99105453f, -3.93372011f, -3.90294504f, -1.85368073f, 1.33546197f, -0.647440135f, + 3.98556042f, 2.40382862f, 3.09859133f, -1.25279415f, -2.29991651f, 2.36054778f, -2.19357109f, + 2.33677864f, -3.8760736f, -3.45876503f, -3.90471816f, -1.51097548f, 2.47331548f, -3.60425806f, + -3.20869565f, 1.99847496f, -0.601690412f, -1.51469827f, -0.047418002f, 0.189290494f, -2.20974135f, + 0.456f, 2.99834013f, 2.57641625f, 3.40872502f, 0.826929212f, 2.73810506f, -3.16392422f, + -0.478883833f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/output_ref_data.h new file mode 100644 index 00000000..6ea84104 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/output_ref_data.h @@ -0,0 +1,524 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_nwhc_f32_output_ref[3648] = { + -0.0368402526f, 2.64921665f, 3.47808361f, 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-3.62678099f, 1.7897507f, -3.2252183f, -0.720680714f, 0.539743602f, 0.385432065f, + -0.41962713f, -0.0316259824f, -2.17168522f, 0.10715767f, 1.74576807f, -2.14880109f, 2.85800338f, + -3.56542635f, 1.79294944f, -3.35189748f, -0.917599201f, 0.17227757f, -3.11557293f, -3.40051889f, + 0.810699105f, 2.37223101f, -0.832508504f, 0.665081978f, -2.33161378f, -3.70367742f, 3.86056256f, + -1.27921581f, -2.36495399f, -0.954030395f, -2.41230106f, 1.19342089f, 3.31314373f, 2.64383006f, + 3.98326087f, 1.97318661f, -0.171670496f, -0.298698932f, -2.69725537f, 0.0676822066f, 1.48873127f, + 3.5608623f, -3.99105453f, -3.93372011f, -3.90294504f, -1.85368073f, 1.33546197f, -0.647440135f, + 3.98556042f, 2.40382862f, 3.09859133f, -1.25279415f, -2.29991651f, 2.36054778f, -2.19357109f, + 2.33677864f, -3.8760736f, -3.45876503f, -3.90471816f, -1.51097548f, 2.47331548f, -3.60425806f, + -3.20869565f, 1.99847496f, -0.601690412f, -1.51469827f, -0.047418002f, 0.189290494f, -2.20974135f, + 0.456f, 2.99834013f, 2.57641625f, 3.40872502f, 0.826929212f, 2.73810506f, -3.16392422f, + -0.478883833f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_nwhc_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/config_data.h new file mode 100644 index 00000000..17dd3bb7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_SWAP_LAST2_4D_F16_NUM_DIMS 4 +#define TRANSPOSE_SWAP_LAST2_4D_F16_INPUT_N 2 +#define TRANSPOSE_SWAP_LAST2_4D_F16_INPUT_H 3 +#define TRANSPOSE_SWAP_LAST2_4D_F16_INPUT_W 5 +#define TRANSPOSE_SWAP_LAST2_4D_F16_INPUT_C 7 +#define TRANSPOSE_SWAP_LAST2_4D_F16_OUTPUT_N 2 +#define TRANSPOSE_SWAP_LAST2_4D_F16_OUTPUT_H 3 +#define TRANSPOSE_SWAP_LAST2_4D_F16_OUTPUT_W 7 +#define TRANSPOSE_SWAP_LAST2_4D_F16_OUTPUT_C 5 +#define TRANSPOSE_SWAP_LAST2_4D_F16_PERM_0 0 +#define TRANSPOSE_SWAP_LAST2_4D_F16_PERM_1 1 +#define TRANSPOSE_SWAP_LAST2_4D_F16_PERM_2 3 +#define TRANSPOSE_SWAP_LAST2_4D_F16_PERM_3 2 +#define TRANSPOSE_SWAP_LAST2_4D_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_SWAP_LAST2_4D_F16_SIZE 210 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/input_tensor_data.h new file mode 100644 index 00000000..5bb6723f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/input_tensor_data.h @@ -0,0 +1,55 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_swap_last2_4d_f16_input_tensor[210] = { + (float16_t)3.76367188f, (float16_t)2.26757812f, (float16_t)3.25195312f, (float16_t)-2.3671875f, + (float16_t)0.833496094f, (float16_t)3.16992188f, (float16_t)-0.96875f, (float16_t)-2.99609375f, + (float16_t)-1.60351562f, (float16_t)-3.56835938f, (float16_t)2.921875f, (float16_t)-3.07226562f, + (float16_t)-1.66992188f, (float16_t)3.01757812f, (float16_t)-0.0463256836f, (float16_t)-2.50976562f, + (float16_t)-2.8828125f, (float16_t)-3.81445312f, (float16_t)-2.6796875f, (float16_t)3.703125f, + (float16_t)-0.200927734f, (float16_t)-0.384521484f, (float16_t)-2.83007812f, (float16_t)2.58789062f, + (float16_t)0.0901489258f, (float16_t)1.07421875f, (float16_t)3.87890625f, (float16_t)-0.750488281f, + (float16_t)-3.93554688f, (float16_t)-2.31835938f, (float16_t)0.541503906f, (float16_t)-1.21191406f, + (float16_t)3.68359375f, (float16_t)1.73242188f, (float16_t)1.43554688f, (float16_t)1.046875f, + (float16_t)-3.12109375f, (float16_t)0.88671875f, (float16_t)0.0864868164f, (float16_t)-1.12792969f, + (float16_t)-2.33789062f, (float16_t)1.40429688f, (float16_t)2.37890625f, (float16_t)2.82421875f, + (float16_t)-3.953125f, (float16_t)-2.68164062f, (float16_t)-3.15429688f, (float16_t)-2.34570312f, + (float16_t)0.256103516f, (float16_t)3.30859375f, (float16_t)3.52148438f, (float16_t)-3.57617188f, + (float16_t)1.96972656f, (float16_t)-1.91894531f, (float16_t)0.325195312f, (float16_t)3.59179688f, + (float16_t)-0.904785156f, (float16_t)2.71875f, (float16_t)0.33203125f, (float16_t)-2.58398438f, + (float16_t)0.760742188f, (float16_t)-3.85546875f, (float16_t)-1.79785156f, (float16_t)-3.078125f, + (float16_t)1.30566406f, (float16_t)-1.46386719f, (float16_t)-2.99609375f, (float16_t)-2.11914062f, + (float16_t)-1.89355469f, (float16_t)1.64453125f, (float16_t)-1.30957031f, (float16_t)2.60742188f, + (float16_t)-1.35839844f, (float16_t)0.6484375f, (float16_t)1.5703125f, (float16_t)-0.485351562f, + (float16_t)-2.23632812f, (float16_t)-0.399414062f, (float16_t)3.06835938f, (float16_t)-2.75976562f, + (float16_t)-3.81640625f, (float16_t)-2.33789062f, (float16_t)0.828613281f, (float16_t)-2.09570312f, + (float16_t)-2.20117188f, (float16_t)2.63867188f, (float16_t)-1.44335938f, (float16_t)-2.31835938f, + (float16_t)-1.95996094f, (float16_t)-2.65429688f, (float16_t)3.87695312f, (float16_t)1.04492188f, + (float16_t)1.76074219f, (float16_t)-3.22070312f, (float16_t)-2.89453125f, (float16_t)0.484130859f, + (float16_t)-1.71582031f, (float16_t)-1.57519531f, (float16_t)-2.94726562f, (float16_t)-0.914550781f, + (float16_t)-0.0682983398f, (float16_t)-1.65527344f, (float16_t)2.6640625f, (float16_t)-1.85742188f, + (float16_t)-2.62304688f, (float16_t)0.68359375f, (float16_t)-1.02929688f, (float16_t)-1.30761719f, + (float16_t)-3.15820312f, (float16_t)-3.015625f, (float16_t)3.89453125f, (float16_t)2.7734375f, + (float16_t)-0.216430664f, (float16_t)1.7109375f, (float16_t)-2.3046875f, (float16_t)-0.59375f, + (float16_t)-2.86132812f, (float16_t)0.735839844f, (float16_t)-1.39746094f, (float16_t)-1.48242188f, + (float16_t)-0.472167969f, (float16_t)-1.35742188f, (float16_t)-1.73242188f, (float16_t)-1.25488281f, + (float16_t)-2.12890625f, (float16_t)-3.5546875f, (float16_t)-0.850585938f, (float16_t)-2.57617188f, + (float16_t)-0.836425781f, (float16_t)-0.929199219f, (float16_t)-0.583496094f, (float16_t)3.40625f, + (float16_t)2.11328125f, (float16_t)-3.9609375f, (float16_t)2.42578125f, (float16_t)1.31835938f, + (float16_t)1.14257812f, (float16_t)2.45703125f, (float16_t)0.243286133f, (float16_t)-2.39453125f, + (float16_t)3.890625f, (float16_t)1.73144531f, (float16_t)-2.796875f, (float16_t)0.3984375f, + (float16_t)3.4921875f, (float16_t)0.466552734f, (float16_t)1.25878906f, (float16_t)-1.21582031f, + (float16_t)1.44335938f, (float16_t)-0.146362305f, (float16_t)-1.64941406f, (float16_t)1.61132812f, + (float16_t)0.732421875f, (float16_t)0.928222656f, (float16_t)1.43261719f, (float16_t)-2.42578125f, + (float16_t)-2.29492188f, (float16_t)-1.49414062f, (float16_t)-0.903808594f, (float16_t)-1.31445312f, + (float16_t)2.4453125f, (float16_t)1.51757812f, (float16_t)-1.05566406f, (float16_t)-0.919921875f, + (float16_t)-1.02148438f, (float16_t)1.953125f, (float16_t)1.421875f, (float16_t)0.828613281f, + (float16_t)0.241577148f, (float16_t)-0.310302734f, (float16_t)-1.87988281f, (float16_t)-1.59863281f, + (float16_t)3.953125f, (float16_t)3.3671875f, (float16_t)1.81738281f, (float16_t)-3.71484375f, + (float16_t)0.899902344f, (float16_t)0.359863281f, (float16_t)1.0625f, (float16_t)3.69140625f, + (float16_t)3.28710938f, (float16_t)-3.63085938f, (float16_t)-0.230102539f, (float16_t)0.641113281f, + (float16_t)2.796875f, (float16_t)-0.906738281f, (float16_t)-2.4765625f, (float16_t)-3.5078125f, + (float16_t)3.13085938f, (float16_t)-0.981445312f, (float16_t)0.239379883f, (float16_t)0.420410156f, + (float16_t)-0.208007812f, (float16_t)-2.26953125f, (float16_t)-2.875f, (float16_t)-3.5390625f, + (float16_t)-3.74804688f, (float16_t)1.31835938f, (float16_t)-3.68554688f, (float16_t)-1.94628906f, + (float16_t)-0.467041016f, (float16_t)-2.2421875f, (float16_t)0.390869141f, (float16_t)-2.79101562f, + (float16_t)-0.732910156f, (float16_t)3.81835938f, (float16_t)-2.8671875f, (float16_t)-2.25f, + (float16_t)-0.478515625f, (float16_t)1.27148438f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/output_ref_data.h new file mode 100644 index 00000000..e9a21e4c --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/output_ref_data.h @@ -0,0 +1,55 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_swap_last2_4d_f16_output_ref[210] = { + (float16_t)3.76367188f, (float16_t)-2.99609375f, (float16_t)-0.0463256836f, (float16_t)-0.384521484f, + (float16_t)-3.93554688f, (float16_t)2.26757812f, (float16_t)-1.60351562f, (float16_t)-2.50976562f, + (float16_t)-2.83007812f, (float16_t)-2.31835938f, (float16_t)3.25195312f, (float16_t)-3.56835938f, + (float16_t)-2.8828125f, (float16_t)2.58789062f, (float16_t)0.541503906f, (float16_t)-2.3671875f, + (float16_t)2.921875f, (float16_t)-3.81445312f, (float16_t)0.0901489258f, (float16_t)-1.21191406f, + (float16_t)0.833496094f, (float16_t)-3.07226562f, (float16_t)-2.6796875f, (float16_t)1.07421875f, + (float16_t)3.68359375f, (float16_t)3.16992188f, (float16_t)-1.66992188f, (float16_t)3.703125f, + (float16_t)3.87890625f, (float16_t)1.73242188f, (float16_t)-0.96875f, (float16_t)3.01757812f, + (float16_t)-0.200927734f, (float16_t)-0.750488281f, (float16_t)1.43554688f, (float16_t)1.046875f, + (float16_t)2.37890625f, (float16_t)3.30859375f, (float16_t)-0.904785156f, (float16_t)-3.078125f, + (float16_t)-3.12109375f, (float16_t)2.82421875f, (float16_t)3.52148438f, (float16_t)2.71875f, + (float16_t)1.30566406f, (float16_t)0.88671875f, (float16_t)-3.953125f, (float16_t)-3.57617188f, + (float16_t)0.33203125f, (float16_t)-1.46386719f, (float16_t)0.0864868164f, (float16_t)-2.68164062f, + (float16_t)1.96972656f, (float16_t)-2.58398438f, (float16_t)-2.99609375f, (float16_t)-1.12792969f, + (float16_t)-3.15429688f, (float16_t)-1.91894531f, (float16_t)0.760742188f, (float16_t)-2.11914062f, + (float16_t)-2.33789062f, (float16_t)-2.34570312f, (float16_t)0.325195312f, (float16_t)-3.85546875f, + (float16_t)-1.89355469f, (float16_t)1.40429688f, (float16_t)0.256103516f, (float16_t)3.59179688f, + (float16_t)-1.79785156f, (float16_t)1.64453125f, (float16_t)-1.30957031f, (float16_t)-0.399414062f, + (float16_t)-2.20117188f, (float16_t)1.04492188f, (float16_t)-2.94726562f, (float16_t)2.60742188f, + (float16_t)3.06835938f, (float16_t)2.63867188f, (float16_t)1.76074219f, (float16_t)-0.914550781f, + (float16_t)-1.35839844f, (float16_t)-2.75976562f, (float16_t)-1.44335938f, (float16_t)-3.22070312f, + (float16_t)-0.0682983398f, (float16_t)0.6484375f, (float16_t)-3.81640625f, (float16_t)-2.31835938f, + (float16_t)-2.89453125f, (float16_t)-1.65527344f, (float16_t)1.5703125f, (float16_t)-2.33789062f, + (float16_t)-1.95996094f, (float16_t)0.484130859f, (float16_t)2.6640625f, (float16_t)-0.485351562f, + (float16_t)0.828613281f, (float16_t)-2.65429688f, (float16_t)-1.71582031f, (float16_t)-1.85742188f, + (float16_t)-2.23632812f, (float16_t)-2.09570312f, (float16_t)3.87695312f, (float16_t)-1.57519531f, + (float16_t)-2.62304688f, (float16_t)0.68359375f, (float16_t)-0.216430664f, (float16_t)-1.48242188f, + (float16_t)-0.850585938f, (float16_t)-3.9609375f, (float16_t)-1.02929688f, (float16_t)1.7109375f, + (float16_t)-0.472167969f, (float16_t)-2.57617188f, (float16_t)2.42578125f, (float16_t)-1.30761719f, + (float16_t)-2.3046875f, (float16_t)-1.35742188f, (float16_t)-0.836425781f, (float16_t)1.31835938f, + (float16_t)-3.15820312f, (float16_t)-0.59375f, (float16_t)-1.73242188f, (float16_t)-0.929199219f, + (float16_t)1.14257812f, (float16_t)-3.015625f, (float16_t)-2.86132812f, (float16_t)-1.25488281f, + (float16_t)-0.583496094f, (float16_t)2.45703125f, (float16_t)3.89453125f, (float16_t)0.735839844f, + (float16_t)-2.12890625f, (float16_t)3.40625f, (float16_t)0.243286133f, (float16_t)2.7734375f, + (float16_t)-1.39746094f, (float16_t)-3.5546875f, (float16_t)2.11328125f, (float16_t)-2.39453125f, + (float16_t)3.890625f, (float16_t)-1.21582031f, (float16_t)1.43261719f, (float16_t)1.51757812f, + (float16_t)0.241577148f, (float16_t)1.73144531f, (float16_t)1.44335938f, (float16_t)-2.42578125f, + (float16_t)-1.05566406f, (float16_t)-0.310302734f, (float16_t)-2.796875f, (float16_t)-0.146362305f, + (float16_t)-2.29492188f, (float16_t)-0.919921875f, (float16_t)-1.87988281f, (float16_t)0.3984375f, + (float16_t)-1.64941406f, (float16_t)-1.49414062f, (float16_t)-1.02148438f, (float16_t)-1.59863281f, + (float16_t)3.4921875f, (float16_t)1.61132812f, (float16_t)-0.903808594f, (float16_t)1.953125f, + (float16_t)3.953125f, (float16_t)0.466552734f, (float16_t)0.732421875f, (float16_t)-1.31445312f, + (float16_t)1.421875f, (float16_t)3.3671875f, (float16_t)1.25878906f, (float16_t)0.928222656f, + (float16_t)2.4453125f, (float16_t)0.828613281f, (float16_t)1.81738281f, (float16_t)-3.71484375f, + (float16_t)-0.230102539f, (float16_t)-0.981445312f, (float16_t)-3.74804688f, (float16_t)-2.79101562f, + (float16_t)0.899902344f, (float16_t)0.641113281f, (float16_t)0.239379883f, (float16_t)1.31835938f, + (float16_t)-0.732910156f, (float16_t)0.359863281f, (float16_t)2.796875f, (float16_t)0.420410156f, + (float16_t)-3.68554688f, (float16_t)3.81835938f, (float16_t)1.0625f, (float16_t)-0.906738281f, + (float16_t)-0.208007812f, (float16_t)-1.94628906f, (float16_t)-2.8671875f, (float16_t)3.69140625f, + (float16_t)-2.4765625f, (float16_t)-2.26953125f, (float16_t)-0.467041016f, (float16_t)-2.25f, + (float16_t)3.28710938f, (float16_t)-3.5078125f, (float16_t)-2.875f, (float16_t)-2.2421875f, + (float16_t)-0.478515625f, (float16_t)-3.63085938f, (float16_t)3.13085938f, (float16_t)-3.5390625f, + (float16_t)0.390869141f, (float16_t)1.27148438f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/config_data.h new file mode 100644 index 00000000..3407dfad --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_SWAP_LAST2_4D_F32_NUM_DIMS 4 +#define TRANSPOSE_SWAP_LAST2_4D_F32_INPUT_N 2 +#define TRANSPOSE_SWAP_LAST2_4D_F32_INPUT_H 3 +#define TRANSPOSE_SWAP_LAST2_4D_F32_INPUT_W 5 +#define TRANSPOSE_SWAP_LAST2_4D_F32_INPUT_C 7 +#define TRANSPOSE_SWAP_LAST2_4D_F32_OUTPUT_N 2 +#define TRANSPOSE_SWAP_LAST2_4D_F32_OUTPUT_H 3 +#define TRANSPOSE_SWAP_LAST2_4D_F32_OUTPUT_W 7 +#define TRANSPOSE_SWAP_LAST2_4D_F32_OUTPUT_C 5 +#define TRANSPOSE_SWAP_LAST2_4D_F32_PERM_0 0 +#define TRANSPOSE_SWAP_LAST2_4D_F32_PERM_1 1 +#define TRANSPOSE_SWAP_LAST2_4D_F32_PERM_2 3 +#define TRANSPOSE_SWAP_LAST2_4D_F32_PERM_3 2 +#define TRANSPOSE_SWAP_LAST2_4D_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_SWAP_LAST2_4D_F32_SIZE 210 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/input_tensor_data.h new file mode 100644 index 00000000..6d76e635 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/input_tensor_data.h @@ -0,0 +1,32 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_swap_last2_4d_f32_input_tensor[210] = { + 1.64663672f, 3.87121034f, 2.8697865f, 1.54954183f, -3.10673499f, -2.80519056f, 0.557650983f, + -3.048877f, 3.32953477f, 0.235598892f, -2.51692557f, 3.1121285f, 1.27380407f, 1.14676428f, + 0.710452616f, -3.34175968f, 1.37806177f, -0.72141993f, -0.204989314f, 1.5292238f, -3.12508273f, + -3.96287227f, -0.919861853f, 2.96738219f, -3.31537771f, -2.19656968f, 2.3186512f, -3.48412085f, + -2.23956609f, 2.18017793f, 3.07263541f, -0.949685454f, -2.36813402f, 3.81272602f, -0.734793305f, + 1.75882947f, -1.99429297f, 0.742775619f, -0.836962283f, -0.0898985863f, 3.18285966f, -3.61840367f, + -0.443512678f, 2.0896914f, 3.12245131f, -3.02612495f, -2.16740131f, 1.14793956f, -3.89978027f, + 1.73040891f, -0.341249913f, -3.59225941f, 2.68484259f, 1.57464802f, -1.81671131f, 1.9626416f, + -2.80885696f, -3.50650644f, 1.84024739f, 1.04007125f, -3.27079964f, 0.534041762f, 3.49354815f, + 2.34339428f, 2.44668436f, -3.88623834f, -1.34073877f, 2.16287446f, -0.715326726f, 1.81763685f, + -2.47873306f, -3.94628334f, -2.22603321f, -0.270484388f, 2.45876813f, -0.631937206f, -0.682300031f, + -3.06045437f, -1.34600174f, -0.5156371f, -3.23918509f, -2.59275579f, -2.05660391f, 1.31949079f, + -3.93741512f, -2.32421732f, 1.11477506f, -3.30837297f, 1.19595611f, 0.816208541f, 3.39588404f, + 0.608484805f, -1.38338673f, 1.81324184f, -1.63900936f, -0.106453061f, -2.91092038f, -0.772445679f, + -1.93702674f, 1.9868114f, -2.38265967f, -1.59229016f, -3.64942122f, 0.97695142f, 0.712749481f, + -1.12562764f, 1.97209466f, 1.17072868f, -1.37331617f, 0.370669425f, -1.4682411f, -1.67407906f, + -3.34347439f, 1.10761642f, -3.50783849f, -0.952358603f, 2.31002855f, -2.29318285f, -0.240821123f, + -3.62126184f, 2.36702371f, -1.07114196f, 1.05769539f, -0.225406975f, -1.01790869f, -2.41493654f, + 1.76990139f, -0.121404551f, -1.70073342f, -3.79040456f, 3.0348146f, -1.603863f, -0.582298458f, + 2.34255767f, 3.87250209f, 1.51061547f, -3.69006228f, 2.75977778f, -1.43410957f, 0.903959334f, + -0.0409228653f, -3.81012106f, -2.67392874f, -3.60360289f, -0.444206744f, -3.13778687f, -3.84816003f, + -1.69092619f, -2.82458448f, 3.54363203f, -3.83170891f, -2.30783081f, 1.65180373f, -3.73825622f, + -2.45610046f, 1.3796109f, -2.96004295f, -2.06777573f, -1.53864098f, 2.31769252f, -3.57755446f, + -3.37608933f, -1.77238643f, -2.26827288f, -0.260398656f, 0.0768476352f, 2.58877182f, -0.0670640543f, + -2.55543876f, 0.662976027f, -2.1517911f, -3.2114594f, -0.777870655f, 2.6884141f, 1.71675336f, + 2.88608384f, -2.51604438f, -3.23199105f, -1.51778388f, 0.889393926f, 3.49212956f, -2.22054625f, + 1.4877882f, 1.59705961f, -0.309086382f, 0.115501814f, -0.747568607f, -1.39212954f, 1.15407455f, + 0.665325522f, 2.22580385f, 3.40591383f, 0.599224448f, 1.36673796f, -3.60230803f, 3.16866183f, + 3.89215541f, 1.57598281f, 3.02499294f, -2.74863172f, -2.33335328f, 2.23183012f, 1.82773674f, + -2.50684094f, -2.57926679f, -2.77185369f, 1.18625665f, -2.74600172f, 0.0672280937f, -2.77505326f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/output_ref_data.h new file mode 100644 index 00000000..7942c3c7 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/output_ref_data.h @@ -0,0 +1,32 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_swap_last2_4d_f32_output_ref[210] = { + 1.64663672f, -3.048877f, 0.710452616f, -3.96287227f, -2.23956609f, 3.87121034f, 3.32953477f, + -3.34175968f, -0.919861853f, 2.18017793f, 2.8697865f, 0.235598892f, 1.37806177f, 2.96738219f, + 3.07263541f, 1.54954183f, -2.51692557f, -0.72141993f, -3.31537771f, -0.949685454f, -3.10673499f, + 3.1121285f, -0.204989314f, -2.19656968f, -2.36813402f, -2.80519056f, 1.27380407f, 1.5292238f, + 2.3186512f, 3.81272602f, 0.557650983f, 1.14676428f, -3.12508273f, -3.48412085f, -0.734793305f, + 1.75882947f, -0.443512678f, 1.73040891f, -2.80885696f, 2.34339428f, -1.99429297f, 2.0896914f, + -0.341249913f, -3.50650644f, 2.44668436f, 0.742775619f, 3.12245131f, -3.59225941f, 1.84024739f, + -3.88623834f, -0.836962283f, -3.02612495f, 2.68484259f, 1.04007125f, -1.34073877f, -0.0898985863f, + -2.16740131f, 1.57464802f, -3.27079964f, 2.16287446f, 3.18285966f, 1.14793956f, -1.81671131f, + 0.534041762f, -0.715326726f, -3.61840367f, -3.89978027f, 1.9626416f, 3.49354815f, 1.81763685f, + -2.47873306f, -3.06045437f, -3.93741512f, 0.608484805f, -1.93702674f, -3.94628334f, -1.34600174f, + -2.32421732f, -1.38338673f, 1.9868114f, -2.22603321f, -0.5156371f, 1.11477506f, 1.81324184f, + -2.38265967f, -0.270484388f, -3.23918509f, -3.30837297f, -1.63900936f, -1.59229016f, 2.45876813f, + -2.59275579f, 1.19595611f, -0.106453061f, -3.64942122f, -0.631937206f, -2.05660391f, 0.816208541f, + -2.91092038f, 0.97695142f, -0.682300031f, 1.31949079f, 3.39588404f, -0.772445679f, 0.712749481f, + -1.12562764f, -3.34347439f, -3.62126184f, 1.76990139f, 2.34255767f, 1.97209466f, 1.10761642f, + 2.36702371f, -0.121404551f, 3.87250209f, 1.17072868f, -3.50783849f, -1.07114196f, -1.70073342f, + 1.51061547f, -1.37331617f, -0.952358603f, 1.05769539f, -3.79040456f, -3.69006228f, 0.370669425f, + 2.31002855f, -0.225406975f, 3.0348146f, 2.75977778f, -1.4682411f, -2.29318285f, -1.01790869f, + -1.603863f, -1.43410957f, -1.67407906f, -0.240821123f, -2.41493654f, -0.582298458f, 0.903959334f, + -0.0409228653f, -1.69092619f, -2.45610046f, -3.37608933f, -2.55543876f, -3.81012106f, -2.82458448f, + 1.3796109f, -1.77238643f, 0.662976027f, -2.67392874f, 3.54363203f, -2.96004295f, -2.26827288f, + -2.1517911f, -3.60360289f, -3.83170891f, -2.06777573f, -0.260398656f, -3.2114594f, -0.444206744f, + -2.30783081f, -1.53864098f, 0.0768476352f, -0.777870655f, -3.13778687f, 1.65180373f, 2.31769252f, + 2.58877182f, 2.6884141f, -3.84816003f, -3.73825622f, -3.57755446f, -0.0670640543f, 1.71675336f, + 2.88608384f, 1.4877882f, 0.665325522f, 3.89215541f, -2.50684094f, -2.51604438f, 1.59705961f, + 2.22580385f, 1.57598281f, -2.57926679f, -3.23199105f, -0.309086382f, 3.40591383f, 3.02499294f, + -2.77185369f, -1.51778388f, 0.115501814f, 0.599224448f, -2.74863172f, 1.18625665f, 0.889393926f, + -0.747568607f, 1.36673796f, -2.33335328f, -2.74600172f, 3.49212956f, -1.39212954f, -3.60230803f, + 2.23183012f, 0.0672280937f, -2.22054625f, 1.15407455f, 3.16866183f, 1.82773674f, -2.77505326f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_swap_last2_4d_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/config_data.h new file mode 100644 index 00000000..5dbc6454 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_WCHN_F16_NUM_DIMS 4 +#define TRANSPOSE_WCHN_F16_INPUT_N 4 +#define TRANSPOSE_WCHN_F16_INPUT_H 3 +#define TRANSPOSE_WCHN_F16_INPUT_W 3 +#define TRANSPOSE_WCHN_F16_INPUT_C 21 +#define TRANSPOSE_WCHN_F16_OUTPUT_N 3 +#define TRANSPOSE_WCHN_F16_OUTPUT_H 21 +#define TRANSPOSE_WCHN_F16_OUTPUT_W 3 +#define TRANSPOSE_WCHN_F16_OUTPUT_C 4 +#define TRANSPOSE_WCHN_F16_PERM_0 2 +#define TRANSPOSE_WCHN_F16_PERM_1 3 +#define TRANSPOSE_WCHN_F16_PERM_2 1 +#define TRANSPOSE_WCHN_F16_PERM_3 0 +#define TRANSPOSE_WCHN_F16_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_WCHN_F16_SIZE 756 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/input_tensor_data.h new file mode 100644 index 00000000..96badab0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/input_tensor_data.h @@ -0,0 +1,191 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_wchn_f16_input_tensor[756] = { + (float16_t)1.5703125f, (float16_t)-1.47363281f, (float16_t)-2.70507812f, (float16_t)-2.54296875f, + (float16_t)-1.5390625f, (float16_t)0.963378906f, (float16_t)2.85546875f, (float16_t)-1.96191406f, + (float16_t)-0.046081543f, (float16_t)-0.736328125f, (float16_t)-2.91796875f, (float16_t)-2.91210938f, + (float16_t)1.41699219f, (float16_t)3.53320312f, (float16_t)-2.34375f, (float16_t)1.18359375f, + (float16_t)-3.63085938f, (float16_t)1.38476562f, (float16_t)-2.0859375f, (float16_t)-2.33007812f, + (float16_t)-3.30664062f, (float16_t)2.84960938f, (float16_t)0.137573242f, (float16_t)3.11328125f, + (float16_t)-2.68554688f, (float16_t)1.94140625f, (float16_t)-1.36425781f, (float16_t)1.30566406f, + (float16_t)-2.1796875f, (float16_t)2.4765625f, (float16_t)2.91992188f, (float16_t)-1.66113281f, + (float16_t)2.80859375f, (float16_t)2.72851562f, (float16_t)-2.046875f, (float16_t)1.91113281f, + (float16_t)3.8359375f, (float16_t)-1.47460938f, (float16_t)1.86132812f, (float16_t)-3.59765625f, + (float16_t)-0.945800781f, (float16_t)-2.47460938f, (float16_t)-0.8359375f, (float16_t)-1.08007812f, + (float16_t)-1.73828125f, (float16_t)1.74023438f, (float16_t)-1.99511719f, (float16_t)0.03074646f, + (float16_t)0.087890625f, (float16_t)-0.643066406f, (float16_t)3.9140625f, (float16_t)0.0715942383f, + 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(float16_t)-0.472167969f, + (float16_t)2.52929688f, (float16_t)0.788085938f, (float16_t)-0.138183594f, (float16_t)-3.79492188f, + (float16_t)2.43359375f, (float16_t)0.451660156f, (float16_t)-0.854492188f, (float16_t)3.95898438f, + (float16_t)0.067565918f, (float16_t)-1.50976562f, (float16_t)-0.826660156f, (float16_t)0.118713379f, + (float16_t)-0.317138672f, (float16_t)-3.63867188f, (float16_t)-1.82421875f, (float16_t)1.04589844f, + (float16_t)-2.59375f, (float16_t)-0.61328125f, (float16_t)1.14941406f, (float16_t)3.96289062f, + (float16_t)-1.14160156f, (float16_t)3.05273438f, (float16_t)-1.68066406f, (float16_t)3.50585938f, + (float16_t)2.90234375f, (float16_t)1.76171875f, (float16_t)-2.765625f, (float16_t)1.43164062f, + (float16_t)2.0390625f, (float16_t)3.98632812f, (float16_t)-1.58789062f, (float16_t)1.23242188f, + (float16_t)-0.854980469f, (float16_t)2.12109375f, (float16_t)-3.40429688f, (float16_t)-0.349853516f, + (float16_t)1.01660156f, (float16_t)0.760253906f, (float16_t)2.16796875f, (float16_t)1.21972656f, + (float16_t)-3.55078125f, (float16_t)3.828125f, (float16_t)-0.446533203f, (float16_t)1.05761719f, + (float16_t)0.987304688f, (float16_t)1.42089844f, (float16_t)-0.504882812f, (float16_t)1.40625f, + (float16_t)3.078125f, (float16_t)2.984375f, (float16_t)1.89746094f, (float16_t)0.830566406f, + (float16_t)-3.25585938f, (float16_t)2.22460938f, (float16_t)-2.1015625f, (float16_t)0.660644531f, + (float16_t)2.08398438f, (float16_t)0.131958008f, (float16_t)3.63671875f, (float16_t)-0.756347656f, + (float16_t)3.40625f, (float16_t)-1.12207031f, (float16_t)-3.00195312f, (float16_t)3.16210938f, + (float16_t)3.23632812f, (float16_t)-0.561523438f, (float16_t)-3.96484375f, (float16_t)0.627929688f, + (float16_t)1.89257812f, (float16_t)-2.8203125f, (float16_t)-0.348144531f, (float16_t)3.55078125f, + (float16_t)-3.96875f, (float16_t)3.31640625f, (float16_t)-0.493896484f, (float16_t)3.28515625f, + (float16_t)0.845214844f, (float16_t)0.847167969f, (float16_t)-1.34375f, (float16_t)-3.921875f, + (float16_t)0.259521484f, (float16_t)-0.829589844f, (float16_t)0.153930664f, (float16_t)1.48828125f, + (float16_t)0.658691406f, (float16_t)-3.35351562f, (float16_t)-2.94921875f, (float16_t)2.8515625f, + (float16_t)1.85839844f, (float16_t)2.46679688f, (float16_t)3.71289062f, (float16_t)3.22851562f, + (float16_t)1.82617188f, (float16_t)-1.58984375f, (float16_t)0.534667969f, (float16_t)2.29882812f, + (float16_t)2.04101562f, (float16_t)2.44726562f, (float16_t)3.07421875f, (float16_t)-0.0357055664f, + (float16_t)-0.702636719f, (float16_t)-2.77734375f, (float16_t)0.624511719f, (float16_t)0.893066406f, + (float16_t)3.24609375f, (float16_t)0.812011719f, (float16_t)0.675292969f, (float16_t)-0.642089844f, + (float16_t)0.709960938f, (float16_t)-1.56347656f, (float16_t)0.879394531f, (float16_t)2.71484375f, + (float16_t)-2.85351562f, (float16_t)-0.658691406f, (float16_t)1.515625f, (float16_t)1.26757812f, + (float16_t)-0.0240631104f, (float16_t)0.5625f, (float16_t)-3.97070312f, (float16_t)-2.15820312f, + (float16_t)0.0204162598f, (float16_t)-0.594726562f, (float16_t)3.86914062f, (float16_t)-1.60546875f, + (float16_t)-1.34472656f, (float16_t)-0.73046875f, (float16_t)-3.484375f, (float16_t)2.0078125f, + (float16_t)0.123901367f, (float16_t)2.18554688f, (float16_t)2.42773438f, (float16_t)2.64257812f, + (float16_t)1.85742188f, (float16_t)1.20117188f, (float16_t)3.36328125f, (float16_t)-2.93164062f, + (float16_t)-2.78320312f, (float16_t)-2.25585938f, (float16_t)-3.015625f, (float16_t)3.83984375f, + (float16_t)-0.721191406f, (float16_t)0.272216797f, (float16_t)3.05078125f, (float16_t)-2.84765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/output_ref_data.h new file mode 100644 index 00000000..4882b49f --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/output_ref_data.h @@ -0,0 +1,191 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float16_t transpose_wchn_f16_output_ref[756] = { + (float16_t)1.5703125f, (float16_t)-1.24902344f, (float16_t)0.0246429443f, (float16_t)2.85351562f, + (float16_t)0.0938110352f, (float16_t)0.841308594f, (float16_t)-2.7265625f, (float16_t)1.14941406f, + (float16_t)3.42382812f, (float16_t)1.86621094f, (float16_t)3.01953125f, (float16_t)-0.829589844f, + (float16_t)-1.47363281f, (float16_t)3.32617188f, (float16_t)2.51171875f, (float16_t)-2.56054688f, + (float16_t)-3.25976562f, (float16_t)-1.453125f, (float16_t)0.960449219f, (float16_t)3.96289062f, + (float16_t)3.57226562f, (float16_t)0.962402344f, (float16_t)1.09277344f, (float16_t)0.153930664f, + (float16_t)-2.70507812f, (float16_t)-2.66015625f, (float16_t)0.159423828f, (float16_t)1.13476562f, + (float16_t)-3.890625f, (float16_t)0.998046875f, (float16_t)-1.83105469f, (float16_t)-1.14160156f, + (float16_t)3.70898438f, (float16_t)-3.29296875f, (float16_t)3.1875f, (float16_t)1.48828125f, + (float16_t)-2.54296875f, (float16_t)3.69335938f, (float16_t)-0.357421875f, (float16_t)-0.0827636719f, + (float16_t)1.21386719f, (float16_t)-2.078125f, (float16_t)1.84277344f, (float16_t)3.05273438f, + (float16_t)-0.460205078f, (float16_t)0.0533447266f, (float16_t)-2.77734375f, (float16_t)0.658691406f, + (float16_t)-1.5390625f, (float16_t)-0.349121094f, (float16_t)1.48339844f, (float16_t)-3.91796875f, + (float16_t)2.41992188f, (float16_t)-0.0401000977f, (float16_t)-3.60351562f, (float16_t)-1.68066406f, + (float16_t)-1.93066406f, (float16_t)-1.01855469f, (float16_t)3.625f, (float16_t)-3.35351562f, + (float16_t)0.963378906f, (float16_t)3.55859375f, (float16_t)2.82226562f, (float16_t)1.4921875f, + (float16_t)-0.9609375f, (float16_t)1.72851562f, (float16_t)0.647949219f, (float16_t)3.50585938f, + (float16_t)-0.0127868652f, (float16_t)-1.38378906f, (float16_t)3.27148438f, (float16_t)-2.94921875f, + (float16_t)2.85546875f, (float16_t)-1.625f, (float16_t)-3.74414062f, (float16_t)1.86035156f, + (float16_t)-1.67480469f, (float16_t)-3.89257812f, (float16_t)-1.4453125f, (float16_t)2.90234375f, + (float16_t)-2.45898438f, (float16_t)2.28710938f, (float16_t)-2.02734375f, (float16_t)2.8515625f, + (float16_t)-1.96191406f, (float16_t)1.01855469f, (float16_t)0.672363281f, (float16_t)0.413085938f, + (float16_t)0.70703125f, (float16_t)-2.37109375f, (float16_t)1.02539062f, (float16_t)1.76171875f, + (float16_t)0.778320312f, (float16_t)-3.81835938f, (float16_t)-3.9609375f, (float16_t)1.85839844f, + (float16_t)-0.046081543f, (float16_t)3.84179688f, (float16_t)3.19335938f, (float16_t)-2.12109375f, + (float16_t)1.09472656f, (float16_t)2.14257812f, (float16_t)1.0078125f, (float16_t)-2.765625f, + (float16_t)1.79003906f, (float16_t)0.84765625f, (float16_t)0.013633728f, (float16_t)2.46679688f, + (float16_t)-0.736328125f, (float16_t)-2.12304688f, (float16_t)0.727050781f, (float16_t)1.265625f, + (float16_t)-3.27539062f, (float16_t)-2.73242188f, (float16_t)-3.82421875f, (float16_t)1.43164062f, + (float16_t)-1.87988281f, (float16_t)-3.1015625f, (float16_t)2.4921875f, (float16_t)3.71289062f, + (float16_t)-2.91796875f, (float16_t)1.33398438f, (float16_t)3.5234375f, (float16_t)-2.82421875f, + (float16_t)0.971679688f, (float16_t)0.302490234f, (float16_t)2.49023438f, (float16_t)2.0390625f, + (float16_t)-1.77636719f, (float16_t)-2.35546875f, (float16_t)-1.48242188f, (float16_t)3.22851562f, + (float16_t)-2.91210938f, (float16_t)3.61132812f, (float16_t)3.3203125f, (float16_t)1.14160156f, + (float16_t)-1.04785156f, (float16_t)2.87109375f, (float16_t)-2.61132812f, (float16_t)3.98632812f, + (float16_t)1.14746094f, (float16_t)0.374755859f, (float16_t)-1.11621094f, (float16_t)1.82617188f, + (float16_t)1.41699219f, (float16_t)-1.65527344f, (float16_t)1.44140625f, (float16_t)-3.8125f, + (float16_t)3.26757812f, (float16_t)-2.03320312f, (float16_t)-1.23632812f, (float16_t)-1.58789062f, + (float16_t)-1.88964844f, (float16_t)-3.69335938f, (float16_t)2.02929688f, (float16_t)-1.58984375f, + (float16_t)3.53320312f, (float16_t)3.82226562f, (float16_t)1.78320312f, (float16_t)-2.97851562f, + (float16_t)-2.51953125f, (float16_t)-3.890625f, (float16_t)-1.67871094f, (float16_t)1.23242188f, + (float16_t)-2.3515625f, (float16_t)-3.42578125f, (float16_t)-2.48632812f, (float16_t)0.534667969f, + (float16_t)-2.34375f, (float16_t)-1.79980469f, (float16_t)-0.388427734f, (float16_t)-2.02148438f, + (float16_t)-3.359375f, (float16_t)-0.531738281f, (float16_t)1.92675781f, (float16_t)-0.854980469f, + (float16_t)-0.231567383f, (float16_t)2.609375f, (float16_t)0.625488281f, (float16_t)2.29882812f, + (float16_t)1.18359375f, (float16_t)-2.26953125f, (float16_t)1.15136719f, (float16_t)2.125f, + (float16_t)2.41992188f, (float16_t)0.338623047f, (float16_t)1.43359375f, (float16_t)2.12109375f, + (float16_t)-3.49609375f, (float16_t)1.83691406f, (float16_t)-1.82617188f, (float16_t)2.04101562f, + (float16_t)-3.63085938f, (float16_t)-1.07226562f, (float16_t)-1.296875f, (float16_t)1.48242188f, + (float16_t)2.484375f, (float16_t)-2.93164062f, (float16_t)-1.46777344f, (float16_t)-3.40429688f, + (float16_t)-0.65625f, (float16_t)-2.01953125f, (float16_t)3.67382812f, (float16_t)2.44726562f, + (float16_t)1.38476562f, (float16_t)3.46289062f, (float16_t)3.94140625f, (float16_t)1.62695312f, + (float16_t)2.52734375f, (float16_t)-0.127319336f, (float16_t)0.337158203f, (float16_t)-0.349853516f, + (float16_t)0.501464844f, (float16_t)-2.32617188f, (float16_t)1.45214844f, (float16_t)3.07421875f, + (float16_t)-2.0859375f, (float16_t)-3.26757812f, (float16_t)0.913574219f, (float16_t)2.40039062f, + (float16_t)-2.23046875f, (float16_t)3.44140625f, (float16_t)2.7109375f, (float16_t)1.01660156f, + (float16_t)-3.7421875f, (float16_t)-3.88671875f, (float16_t)1.359375f, (float16_t)-0.0357055664f, + (float16_t)-2.33007812f, (float16_t)-3.99804688f, (float16_t)3.77734375f, (float16_t)-1.43652344f, + (float16_t)-3.796875f, (float16_t)-3.984375f, (float16_t)2.1796875f, (float16_t)0.760253906f, + (float16_t)1.83203125f, (float16_t)-3.22070312f, (float16_t)1.71386719f, (float16_t)-0.702636719f, + (float16_t)-3.30664062f, (float16_t)0.0818481445f, (float16_t)1.375f, (float16_t)0.61328125f, + (float16_t)0.136352539f, (float16_t)-1.94238281f, (float16_t)2.81640625f, (float16_t)2.16796875f, + (float16_t)0.932617188f, (float16_t)-1.52734375f, (float16_t)-2.49609375f, (float16_t)-2.77734375f, + (float16_t)2.84960938f, (float16_t)2.25195312f, (float16_t)-3.8984375f, (float16_t)0.0611572266f, + (float16_t)-1.31542969f, (float16_t)2.125f, (float16_t)3.36132812f, (float16_t)1.21972656f, + (float16_t)-1.99316406f, (float16_t)0.240966797f, (float16_t)2.5f, (float16_t)0.624511719f, + (float16_t)0.137573242f, (float16_t)-3.48046875f, (float16_t)0.807617188f, (float16_t)-2.984375f, + (float16_t)-2.41601562f, (float16_t)-3.6015625f, (float16_t)-0.395019531f, (float16_t)-3.55078125f, + (float16_t)-3.4140625f, (float16_t)-0.586914062f, (float16_t)2.85546875f, (float16_t)0.893066406f, + (float16_t)3.11328125f, (float16_t)-1.83300781f, (float16_t)1.43164062f, (float16_t)0.684570312f, + (float16_t)-2.66992188f, (float16_t)-3.07421875f, (float16_t)-0.907226562f, (float16_t)3.828125f, + (float16_t)3.5234375f, (float16_t)-0.8203125f, (float16_t)1.18554688f, (float16_t)3.24609375f, + (float16_t)-2.68554688f, (float16_t)0.519042969f, (float16_t)1.4921875f, (float16_t)-3.51757812f, + (float16_t)-3.359375f, (float16_t)2.56445312f, (float16_t)-0.432617188f, (float16_t)-0.446533203f, + (float16_t)2.72265625f, (float16_t)0.616699219f, (float16_t)1.43457031f, (float16_t)0.812011719f, + (float16_t)1.94140625f, (float16_t)-3.59570312f, (float16_t)0.923828125f, (float16_t)2.82226562f, + (float16_t)-0.152954102f, (float16_t)3.16015625f, (float16_t)-3.48242188f, (float16_t)1.05761719f, + (float16_t)0.381103516f, (float16_t)-2.63867188f, (float16_t)-2.0625f, (float16_t)0.675292969f, + (float16_t)-1.36425781f, (float16_t)-3.94335938f, (float16_t)0.590820312f, 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(float16_t)3.12109375f, (float16_t)-3.86523438f, (float16_t)0.0176086426f, (float16_t)0.847167969f, + (float16_t)1.5f, (float16_t)1.16894531f, (float16_t)1.19628906f, (float16_t)-0.721191406f, + (float16_t)1.12109375f, (float16_t)-2.54882812f, (float16_t)1.94042969f, (float16_t)1.04589844f, + (float16_t)3.83984375f, (float16_t)-2.7578125f, (float16_t)0.564453125f, (float16_t)-1.34375f, + (float16_t)-0.930175781f, (float16_t)-2.24609375f, (float16_t)-3.79882812f, (float16_t)0.272216797f, + (float16_t)3.51367188f, (float16_t)-3.38867188f, (float16_t)3.73046875f, (float16_t)-2.59375f, + (float16_t)3.01953125f, (float16_t)1.2421875f, (float16_t)1.78027344f, (float16_t)-3.921875f, + (float16_t)3.55078125f, (float16_t)-1.35351562f, (float16_t)-1.00585938f, (float16_t)3.05078125f, + (float16_t)-3.734375f, (float16_t)-3.78515625f, (float16_t)3.71875f, (float16_t)-0.61328125f, + (float16_t)1.88183594f, (float16_t)3.1875f, (float16_t)3.71484375f, (float16_t)0.259521484f, + (float16_t)2.7265625f, (float16_t)2.5390625f, (float16_t)-0.934082031f, (float16_t)-2.84765625f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f16/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/config_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/config_data.h new file mode 100644 index 00000000..70f944ff --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/config_data.h @@ -0,0 +1,16 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#define TRANSPOSE_WCHN_F32_NUM_DIMS 4 +#define TRANSPOSE_WCHN_F32_INPUT_N 4 +#define TRANSPOSE_WCHN_F32_INPUT_H 3 +#define TRANSPOSE_WCHN_F32_INPUT_W 3 +#define TRANSPOSE_WCHN_F32_INPUT_C 21 +#define TRANSPOSE_WCHN_F32_OUTPUT_N 3 +#define TRANSPOSE_WCHN_F32_OUTPUT_H 21 +#define TRANSPOSE_WCHN_F32_OUTPUT_W 3 +#define TRANSPOSE_WCHN_F32_OUTPUT_C 4 +#define TRANSPOSE_WCHN_F32_PERM_0 2 +#define TRANSPOSE_WCHN_F32_PERM_1 3 +#define TRANSPOSE_WCHN_F32_PERM_2 1 +#define TRANSPOSE_WCHN_F32_PERM_3 0 +#define TRANSPOSE_WCHN_F32_LAYOUT ARM_NN_LAYOUT_NHWC +#define TRANSPOSE_WCHN_F32_SIZE 756 diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/input_tensor_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/input_tensor_data.h new file mode 100644 index 00000000..5424bba0 --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/input_tensor_data.h @@ -0,0 +1,110 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_wchn_f32_input_tensor[756] = { + -1.9154309f, 0.269304425f, -0.133462042f, 2.75614285f, 3.3763504f, 3.88787031f, -3.60457301f, + 3.20198679f, 2.66298366f, -3.65973258f, -2.90024948f, 2.63918948f, -2.6540184f, -3.43592572f, + -3.40227652f, 3.17501211f, -0.790973365f, 0.124727085f, -1.76605535f, -1.52236378f, 3.82112455f, + -1.74182725f, 0.234836265f, 2.83322215f, 1.49486411f, 3.91443157f, 1.35132217f, -1.58838451f, + 3.24229121f, -1.33417511f, 1.04061258f, 0.280120999f, 2.31921744f, -1.17235625f, -1.11985826f, + -1.09192324f, -0.639565706f, 2.15303326f, 0.478602022f, -1.99324f, -3.03437614f, -1.26569402f, + 1.69893563f, -0.357825935f, -0.3606022f, 2.4056108f, 2.22296977f, 0.124102473f, -3.84463477f, + -0.272917688f, -1.18760955f, 3.70605659f, 3.12939715f, 3.4701612f, 2.74289036f, 0.5030424f, + -2.88501883f, 1.39201224f, -0.400851846f, 3.30220556f, -0.350747436f, 2.06191206f, -3.27751088f, + -3.38055253f, 2.76444936f, 1.3264035f, 1.78202283f, -1.92842877f, -2.62211847f, 2.70738006f, + -1.16216898f, -2.98613667f, 2.26501322f, 1.8052547f, -3.8321023f, 1.12056565f, 3.46134448f, + 0.603950739f, -3.15185046f, -3.66639948f, 1.53490305f, -1.46972263f, -1.47607529f, -3.79508424f, + -1.13657224f, 2.71500516f, -0.401644707f, 3.0205195f, -0.63682276f, 3.89939117f, 2.78219485f, + -3.65890074f, 1.99004376f, 1.63762331f, -2.06054592f, 3.99060607f, -3.61174893f, 0.679668486f, + -2.66713071f, -1.31908727f, 2.52963567f, 2.90961289f, 1.91540623f, 3.34857249f, -3.05785966f, + 3.72193265f, -0.960686445f, -3.6706841f, 2.84324789f, 3.85955501f, -3.06237102f, -1.89731872f, + 0.671667218f, 1.12635791f, 0.0738736093f, 2.54859781f, -0.623481691f, 0.318857551f, -0.701377511f, + -2.35319638f, 2.53209305f, 0.429763407f, 0.932680011f, 3.59934855f, -3.96427035f, -0.730666757f, + -2.09916806f, -2.95446539f, 2.80597305f, -0.266933501f, 1.66646886f, 0.427473456f, 0.844964623f, + 2.87061548f, -3.32095575f, -0.799126148f, -3.58986807f, 0.402190238f, -2.21039796f, -0.249509752f, + -3.41763067f, -2.29842806f, -3.36442924f, -3.97049236f, 3.14959478f, 1.00373793f, -0.851516843f, + -1.48557484f, 3.02471566f, -3.65038252f, -3.57446098f, -2.206882f, 2.75456071f, 0.236433461f, + 1.84847736f, -1.8878144f, 2.69155669f, 3.60012293f, -1.03354037f, -1.90728259f, -1.06422806f, + -1.90121365f, 0.490665108f, 2.09069419f, -0.441048503f, 0.911757588f, 1.86500549f, -2.60922408f, + -3.55949235f, -3.07535243f, -2.21282625f, -2.69542313f, 2.40442848f, -2.51808882f, -2.11819816f, + 1.37827706f, -2.85977244f, -1.33724439f, 3.41431332f, 2.60075665f, 0.501742125f, -0.212674975f, + -0.826509058f, -1.40256441f, -2.43892002f, 3.34726739f, -0.112739891f, -0.95883745f, 1.08273792f, + -2.307827f, -0.28617844f, 0.839352071f, 2.54348493f, 2.94784379f, -2.03887105f, 0.608555615f, + -1.9870398f, 1.17394423f, 0.291454762f, 1.18028271f, -2.93045449f, -1.68585002f, 0.459935755f, + 1.24810565f, 1.07013106f, 0.784774244f, -1.62079477f, 2.17339945f, 0.128810629f, -0.737915874f, + -2.28288889f, -0.0873620436f, -0.749472797f, -1.63702345f, -0.0808397233f, -2.93362713f, 3.90376472f, + 3.23347831f, -1.22891784f, 0.977974534f, -2.71784186f, 2.83986497f, -2.69924879f, 1.79622877f, + -2.80233717f, 3.72763109f, -1.48645067f, 1.61753905f, -3.46659279f, -2.91586375f, -0.713899016f, + -0.0255081933f, 3.61725688f, 0.311986536f, -1.71894467f, 2.09025216f, 3.60926318f, -3.71830821f, + 2.54437351f, 0.298666298f, 1.06904078f, -1.86409068f, 0.450664401f, -2.1256144f, -0.338533551f, + 3.37507224f, -2.94896913f, -1.63930762f, 0.421742916f, 3.6408155f, 2.34703398f, 1.43122017f, + -1.20403719f, 3.88310432f, -3.69722939f, 2.03819442f, 0.197591722f, 1.35312867f, 3.83053565f, + -0.341582775f, -0.842002273f, -0.354320705f, 3.51305676f, 0.122895338f, 2.5628252f, -3.71636581f, + 0.909990132f, 1.52988195f, 0.16075553f, -2.77241254f, -0.621081769f, 1.21886373f, -0.732399106f, + -1.24473453f, -0.5918926f, -2.75589371f, -2.06348038f, -3.16070127f, -0.231206939f, -3.07918501f, + -1.76040626f, -1.42505467f, 1.33292925f, 3.14124751f, 2.08521771f, 2.06153345f, -1.38326621f, + 0.02887596f, -2.52171993f, -3.26536679f, 1.17713308f, 1.63735056f, -2.52253342f, 3.81552863f, + -3.434762f, 3.64856887f, 1.09924805f, 3.7391727f, 3.42664862f, -0.755659401f, -3.18472338f, + -3.48737144f, 2.10767603f, 1.00018406f, 0.956137657f, 3.76429319f, -1.26028419f, -1.96769476f, + -0.0285594352f, 2.60431671f, -1.672791f, -0.720413983f, 1.28893256f, -3.73747015f, 2.92296052f, + -2.7877152f, 2.24952388f, 3.25920224f, -0.113744885f, -1.05772698f, 1.86993456f, 3.69614387f, + -1.71466327f, 2.25379419f, -1.15732503f, 1.03197646f, 3.03833222f, 1.91416943f, -1.69682884f, + 0.422929317f, 2.56988597f, 0.598933399f, 2.72331095f, -1.96574402f, -2.91238236f, 2.2009573f, + -1.76609051f, -1.19820714f, -2.65887928f, 1.84916425f, -3.16524243f, 3.1826334f, 0.904266596f, + 1.8803339f, 2.93182111f, -2.48109508f, 0.844941556f, -1.41708088f, 0.978583395f, 1.16723752f, + 0.932964683f, -0.496771902f, -2.25009751f, -3.18380237f, 0.219785765f, -3.4954927f, 3.42226768f, + -3.64192176f, -0.903853297f, -0.26460436f, 1.18641162f, 3.46775413f, 1.46660852f, -2.70116448f, + -3.35973406f, -2.87693667f, 1.84796035f, -0.724199057f, 2.838413f, -1.09242094f, 1.29409337f, + 0.285262674f, 3.19915867f, 1.34358954f, -3.34199548f, 3.05481911f, -0.45017159f, -0.0420259647f, + 0.99280268f, -1.97080803f, 3.59253907f, -2.55779672f, -3.80229735f, -0.0104382373f, -1.33409297f, + -2.85556173f, -2.82372952f, -3.27114558f, -3.29236603f, -0.674057484f, 3.30907536f, -1.61519003f, + 2.25173163f, 0.0388748497f, -3.75059509f, -0.191390991f, -3.43041968f, -3.71143341f, -1.39115322f, + 2.44010448f, 2.20924664f, -3.45267057f, -3.10605025f, -0.240666702f, -2.0867269f, -0.944204748f, + 0.677210748f, 0.239480048f, 2.13584661f, 2.29264951f, -0.676971495f, 0.158629075f, 1.76569891f, + 1.76623166f, -3.51686072f, -0.658804297f, -3.74405837f, 0.595068872f, -2.26620221f, -0.461361855f, + -1.81123269f, -0.530540228f, 2.2490561f, -3.38018823f, 0.851357996f, -2.57275915f, 1.58164632f, + -3.35022593f, -3.658602f, -0.0387321226f, 0.338758439f, 2.89772964f, -1.13635206f, -1.14879572f, + 3.19555521f, -2.21290588f, 2.19607115f, -1.06818902f, 0.794228017f, 3.27384639f, -1.74888837f, + -2.14327097f, -1.47008049f, -3.39373207f, 1.09224129f, 3.05868626f, -2.53714108f, 1.70266259f, + -3.49518442f, -0.569448829f, 1.69816458f, -2.7183485f, 2.03839946f, 2.02283597f, 2.60167694f, + -3.44348216f, -3.00504875f, 0.405901492f, 3.23925567f, 0.561673403f, -3.16076136f, -2.55698943f, + 3.58535123f, 1.02124584f, 0.802136958f, 0.0755514428f, 3.36798358f, -3.69638371f, 3.43296766f, + 3.09997272f, -0.464022607f, -3.20134568f, -3.81429863f, -3.56269836f, -3.18564248f, -3.10585666f, + -2.8441236f, -3.72074866f, 1.24839652f, 0.474095941f, 3.11972594f, 3.41124606f, 3.38297319f, + -3.51133657f, 0.437336713f, -2.06666803f, -0.597672582f, -3.11204886f, 2.76443601f, -3.57088542f, + -3.66928792f, -2.8985405f, 3.00189614f, 1.24474001f, -2.84115291f, -0.100014366f, -0.556467652f, + -1.69745016f, -0.0613391884f, 2.35876107f, -2.32594848f, 2.39251804f, -0.371293336f, -3.50906253f, + -2.7323432f, 2.88985753f, 0.000782876916f, -2.74309063f, 1.32416189f, -1.85180604f, 0.461813241f, + 1.22637105f, -3.21709371f, 2.0075624f, -3.07965112f, -2.51653218f, 1.4432404f, -2.95230794f, + -2.58335185f, 3.421911f, -2.13927746f, 1.45973849f, 0.0913186818f, -0.765070498f, -0.663478315f, + -2.6574893f, -2.96946406f, 2.82649708f, -0.340848088f, -1.55133963f, 0.614462197f, 2.07116914f, + 0.346923977f, -2.10509968f, 2.29954147f, -2.84095979f, 2.40494609f, 1.17117226f, 1.71456838f, + 1.75467598f, -0.224287346f, 2.77435827f, 3.8513751f, 1.08740091f, 2.25462222f, -1.09881604f, + -1.89361823f, 0.379915059f, -1.4632436f, -1.9572624f, -3.64453959f, -1.95698845f, 2.97818637f, + 1.62688279f, 3.66300917f, -1.02562606f, -3.9370203f, 2.53671622f, -0.441065043f, -2.20865726f, + 1.53606737f, -2.91179514f, -1.0018208f, 3.74498558f, 2.57483244f, 2.49337053f, 3.1677196f, + 1.0581851f, -0.784518957f, 2.80271983f, 2.23078918f, 3.75258923f, 3.7259872f, 3.53228283f, + 0.753906131f, -1.82842898f, 3.79296732f, 1.48216248f, 2.44902873f, -3.07631564f, -3.19608665f, + -0.132026508f, 0.726027429f, 1.5185231f, 0.868023634f, 0.0206223354f, -2.61916256f, 0.601593435f, + 1.93794477f, -1.75084853f, -1.16340184f, -1.77323937f, 3.2521522f, 0.047319673f, -2.32790089f, + -3.44593453f, 3.29475665f, -3.93497419f, -2.49513364f, -2.70395708f, 0.843062043f, -0.307176858f, + -3.0250001f, 0.54807651f, -2.33278489f, -3.71461439f, 2.52217317f, 1.60996389f, 0.0343313701f, + 0.050209865f, -1.4282124f, -2.67056775f, -3.03758597f, -3.36535311f, -3.45757484f, -1.26731348f, + 1.91300499f, 1.81868124f, -0.10384959f, 3.50089931f, -0.748250067f, 3.07943988f, 3.75273085f, + 3.37243748f, -3.39059639f, -1.64098465f, 1.12644553f, -2.2709415f, -1.12617826f, -3.28701425f, + 3.28688192f, -0.445365399f, -3.25525284f, 1.75311506f, 3.18045735f, -2.16754532f, -1.12812841f, + 0.556985795f, -3.04243541f, 0.571631968f, -3.48431396f, 0.177874699f, 1.31774902f, -3.703017f, + 3.64103174f, 0.704647958f, 1.64597881f, -0.946469247f, 1.28192472f, 2.07956028f, -3.91348934f, + -1.94204211f, 2.08177447f, 3.07931089f, -0.126777336f, -2.52285218f, 3.96944356f, 2.93906665f, + 2.41227365f, -2.80787969f, 0.972552001f, 2.60150719f, 0.237361774f, -3.50661421f, 1.389256f, + -0.444547236f, 0.486261636f, 3.64740801f, -3.07964349f, 2.6008513f, -3.11309361f, -3.09636569f, + -3.84007692f, -3.09942579f, 1.71639192f, 3.85595155f, 2.13018775f, 0.715868711f, 2.52136707f, + -3.33790731f, 0.4567568f, -3.07512426f, -1.59974563f, 1.11795473f, -0.651138723f, -1.62172818f, + -1.87461901f, -0.263314813f, -1.19527662f, -2.00618291f, -0.754839182f, 1.0383085f, -2.1588335f, + -1.72691202f, 1.86805952f, -3.37753558f, -3.90888071f, -1.92737198f, -0.838362515f, -2.77338171f, + 2.90530419f, 1.63959992f, -0.527776301f, -3.48337579f, -3.91685462f, -1.64816642f, -1.22948503f, + -0.65534091f, -1.19738173f, -1.30448616f, -2.36915708f, -3.82156086f, 3.57208705f, 0.661693811f, + -3.24805069f, -2.47141194f, -2.70870471f, 0.708105922f, -0.47720173f, -3.71234369f, -1.98177826f, + 2.40979338f, 1.69113123f, 2.09740233f, 0.67496556f, -2.12864232f, 3.40165067f, 0.420542091f, + 3.2625277f, 2.86835933f, -1.35186791f, 1.41839993f, -2.49581385f, 0.646074235f, 2.21320963f, + 1.28301489f, -0.509675324f, 3.72481894f, 0.859227717f, -3.26496959f, 0.527379692f, -1.82368064f, + 3.31233144f, -3.83984065f, 3.18722606f, -3.2697432f, -3.7743578f, -0.909754634f, 3.96235633f, + 3.30257702f, -2.59309506f, -1.90761268f, 0.16042456f, -2.2832365f, -3.58373618f, 3.06421614f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/output_ref_data.h new file mode 100644 index 00000000..406945eb --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/output_ref_data.h @@ -0,0 +1,110 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +const float32_t transpose_wchn_f32_output_ref[756] = { + -1.9154309f, -2.307827f, 0.99280268f, 1.0581851f, -3.38055253f, -1.20403719f, -2.14327097f, + 3.28688192f, -2.09916806f, -2.7877152f, -2.7323432f, -1.72691202f, 0.269304425f, -0.28617844f, + -1.97080803f, -0.784518957f, 2.76444936f, 3.88310432f, -1.47008049f, -0.445365399f, -2.95446539f, + 2.24952388f, 2.88985753f, 1.86805952f, -0.133462042f, 0.839352071f, 3.59253907f, 2.80271983f, + 1.3264035f, -3.69722939f, -3.39373207f, -3.25525284f, 2.80597305f, 3.25920224f, 0.000782876916f, + -3.37753558f, 2.75614285f, 2.54348493f, -2.55779672f, 2.23078918f, 1.78202283f, 2.03819442f, + 1.09224129f, 1.75311506f, -0.266933501f, -0.113744885f, -2.74309063f, -3.90888071f, 3.3763504f, + 2.94784379f, -3.80229735f, 3.75258923f, -1.92842877f, 0.197591722f, 3.05868626f, 3.18045735f, + 1.66646886f, -1.05772698f, 1.32416189f, -1.92737198f, 3.88787031f, -2.03887105f, -0.0104382373f, + 3.7259872f, -2.62211847f, 1.35312867f, -2.53714108f, -2.16754532f, 0.427473456f, 1.86993456f, + -1.85180604f, -0.838362515f, -3.60457301f, 0.608555615f, -1.33409297f, 3.53228283f, 2.70738006f, + 3.83053565f, 1.70266259f, -1.12812841f, 0.844964623f, 3.69614387f, 0.461813241f, -2.77338171f, + 3.20198679f, -1.9870398f, -2.85556173f, 0.753906131f, -1.16216898f, -0.341582775f, -3.49518442f, + 0.556985795f, 2.87061548f, -1.71466327f, 1.22637105f, 2.90530419f, 2.66298366f, 1.17394423f, + -2.82372952f, -1.82842898f, -2.98613667f, -0.842002273f, -0.569448829f, -3.04243541f, -3.32095575f, + 2.25379419f, -3.21709371f, 1.63959992f, -3.65973258f, 0.291454762f, -3.27114558f, 3.79296732f, + 2.26501322f, -0.354320705f, 1.69816458f, 0.571631968f, -0.799126148f, -1.15732503f, 2.0075624f, + -0.527776301f, -2.90024948f, 1.18028271f, -3.29236603f, 1.48216248f, 1.8052547f, 3.51305676f, + -2.7183485f, -3.48431396f, -3.58986807f, 1.03197646f, -3.07965112f, -3.48337579f, 2.63918948f, + -2.93045449f, -0.674057484f, 2.44902873f, -3.8321023f, 0.122895338f, 2.03839946f, 0.177874699f, + 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-1.06818902f, 1.12644553f, 0.932680011f, -0.720413983f, + -2.32594848f, -2.00618291f, 3.34726739f, -3.34199548f, 3.74498558f, 0.16042456f, -0.350747436f, + 3.6408155f, 0.794228017f, -2.2709415f, 3.59934855f, 1.28893256f, 2.39251804f, -0.754839182f, + -0.112739891f, 3.05481911f, 2.57483244f, -2.2832365f, 2.06191206f, 2.34703398f, 3.27384639f, + -1.12617826f, -3.96427035f, -3.73747015f, -0.371293336f, 1.0383085f, -0.95883745f, -0.45017159f, + 2.49337053f, -3.58373618f, -3.27751088f, 1.43122017f, -1.74888837f, -3.28701425f, -0.730666757f, + 2.92296052f, -3.50906253f, -2.1588335f, 1.08273792f, -0.0420259647f, 3.1677196f, 3.06421614f}; diff --git a/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/test_data.h b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/test_data.h new file mode 100644 index 00000000..a924d03d --- /dev/null +++ b/Tests/UnitTest/TestCases/TestData/transpose_wchn_f32/test_data.h @@ -0,0 +1,4 @@ +// Generated by transpose_settings_flt.py using PyTorch version 2.10.0+cu128. +#include "config_data.h" +#include "input_tensor_data.h" +#include "output_ref_data.h" diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_activation_f16/CMakeLists.txt new file mode 100644 index 00000000..fb52035a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_activation_f16) + +target_sources(test_arm_activation_f16 PRIVATE + Unity/unity_test_arm_activation_f16.c + Unity/TestRunner/unity_test_arm_activation_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f16/Unity/unity_test_arm_activation_f16.c b/Tests/UnitTest/TestCases/test_arm_activation_f16/Unity/unity_test_arm_activation_f16.c new file mode 100644 index 00000000..39469146 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f16/Unity/unity_test_arm_activation_f16.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_activation_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_activation_f16_arm_nn_activation_f16_sigmoid(void) { activation_f16_arm_nn_activation_f16_sigmoid(); } +void test_activation_f16_arm_nn_activation_f16_tanh(void) { activation_f16_arm_nn_activation_f16_tanh(); } +void test_activation_f16_arm_nn_activation_f16_hardswish(void) { activation_f16_arm_nn_activation_f16_hardswish(); } +void test_activation_f16_arm_nn_activation_f16_leaky_relu(void) { activation_f16_arm_nn_activation_f16_leaky_relu(); } diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f16/test_arm_activation_f16.c b/Tests/UnitTest/TestCases/test_arm_activation_f16/test_arm_activation_f16.c new file mode 100644 index 00000000..52b29987 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f16/test_arm_activation_f16.c @@ -0,0 +1,71 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/activation_f16/test_data.h" + +void activation_f16_arm_nn_activation_f16_sigmoid(void) +{ + float16_t output[ACTIVATION_F16_SIZE] = {0}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f16( + activation_f16_input, output, ACTIVATION_F16_SIZE, ARM_NN_FLT_ACT_SIGMOID, (float16_t)0.0f)); + + for (int i = 0; i < ACTIVATION_F16_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-3f, (float)activation_f16_output_ref_sigmoid[i], (float)output[i]); + } +} + +void activation_f16_arm_nn_activation_f16_tanh(void) +{ + float16_t output[ACTIVATION_F16_SIZE] = {0}; + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f16(activation_f16_input, output, ACTIVATION_F16_SIZE, ARM_NN_FLT_ACT_TANH, (float16_t)0.0f)); + + for (int i = 0; i < ACTIVATION_F16_SIZE; ++i) + { + /* The float16 scalar path uses a compact rational tanh approximation. */ + TEST_ASSERT_FLOAT_WITHIN(2.5e-2f, (float)activation_f16_output_ref_tanh[i], (float)output[i]); + } +} + +void activation_f16_arm_nn_activation_f16_hardswish(void) +{ + float16_t output[ACTIVATION_F16_SIZE] = {0}; + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f16( + activation_f16_input, output, ACTIVATION_F16_SIZE, ARM_NN_FLT_ACT_HARDSWISH, (float16_t)0.0f)); + + for (int i = 0; i < ACTIVATION_F16_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(6.0e-3f, (float)activation_f16_output_ref_hardswish[i], (float)output[i]); + } +} + +void activation_f16_arm_nn_activation_f16_leaky_relu(void) +{ + float16_t output[ACTIVATION_F16_SIZE] = {0}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f16(activation_f16_input, + output, + ACTIVATION_F16_SIZE, + ARM_NN_FLT_ACT_LEAKY_RELU, + ACTIVATION_F16_LEAKY_RELU_PARAM)); + + for (int i = 0; i < ACTIVATION_F16_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-3f, (float)activation_f16_output_ref_leaky_relu[i], (float)output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_activation_f32/CMakeLists.txt new file mode 100644 index 00000000..5b4452e4 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_activation_f32) + +target_sources(test_arm_activation_f32 PRIVATE + Unity/unity_test_arm_activation_f32.c + Unity/TestRunner/unity_test_arm_activation_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f32/Unity/unity_test_arm_activation_f32.c b/Tests/UnitTest/TestCases/test_arm_activation_f32/Unity/unity_test_arm_activation_f32.c new file mode 100644 index 00000000..a316d055 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f32/Unity/unity_test_arm_activation_f32.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_activation_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_activation_f32_arm_nn_activation_f32_sigmoid(void) { activation_f32_arm_nn_activation_f32_sigmoid(); } +void test_activation_f32_arm_nn_activation_f32_tanh(void) { activation_f32_arm_nn_activation_f32_tanh(); } +void test_activation_f32_arm_nn_activation_f32_hardswish(void) { activation_f32_arm_nn_activation_f32_hardswish(); } +void test_activation_f32_arm_nn_activation_f32_leaky_relu(void) { activation_f32_arm_nn_activation_f32_leaky_relu(); } diff --git a/Tests/UnitTest/TestCases/test_arm_activation_f32/test_arm_activation_f32.c b/Tests/UnitTest/TestCases/test_arm_activation_f32/test_arm_activation_f32.c new file mode 100644 index 00000000..cabf2b80 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_activation_f32/test_arm_activation_f32.c @@ -0,0 +1,69 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/activation_f32/test_data.h" + +void activation_f32_arm_nn_activation_f32_sigmoid(void) +{ + float32_t output[ACTIVATION_F32_SIZE] = {0}; + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f32(activation_f32_input, output, ACTIVATION_F32_SIZE, ARM_NN_FLT_ACT_SIGMOID, 0.0f)); + + for (int i = 0; i < ACTIVATION_F32_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, activation_f32_output_ref_sigmoid[i], output[i]); + } +} + +void activation_f32_arm_nn_activation_f32_tanh(void) +{ + float32_t output[ACTIVATION_F32_SIZE] = {0}; + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f32(activation_f32_input, output, ACTIVATION_F32_SIZE, ARM_NN_FLT_ACT_TANH, 0.0f)); + + for (int i = 0; i < ACTIVATION_F32_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(3.0e-4f, activation_f32_output_ref_tanh[i], output[i]); + } +} + +void activation_f32_arm_nn_activation_f32_hardswish(void) +{ + float32_t output[ACTIVATION_F32_SIZE] = {0}; + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f32(activation_f32_input, output, ACTIVATION_F32_SIZE, ARM_NN_FLT_ACT_HARDSWISH, 0.0f)); + + for (int i = 0; i < ACTIVATION_F32_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, activation_f32_output_ref_hardswish[i], output[i]); + } +} + +void activation_f32_arm_nn_activation_f32_leaky_relu(void) +{ + float32_t output[ACTIVATION_F32_SIZE] = {0}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_nn_activation_f32(activation_f32_input, + output, + ACTIVATION_F32_SIZE, + ARM_NN_FLT_ACT_LEAKY_RELU, + ACTIVATION_F32_LEAKY_RELU_PARAM)); + + for (int i = 0; i < ACTIVATION_F32_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, activation_f32_output_ref_leaky_relu[i], output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/CMakeLists.txt new file mode 100644 index 00000000..05e8073b --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_avg_pool_f16) + +target_sources(test_arm_avg_pool_f16 PRIVATE + Unity/unity_test_arm_avg_pool_f16.c + Unity/TestRunner/unity_test_arm_avg_pool_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/Unity/unity_test_arm_avg_pool_f16.c b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/Unity/unity_test_arm_avg_pool_f16.c new file mode 100644 index 00000000..7a1d09ae --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/Unity/unity_test_arm_avg_pool_f16.c @@ -0,0 +1,34 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_avg_pool_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_avgpooling_f16_arm_avg_pool_f16(void) { avgpooling_f16_arm_avg_pool_f16(); } +void test_avgpooling_f16_1_arm_avg_pool_f16(void) { avgpooling_f16_1_arm_avg_pool_f16(); } +void test_avgpooling_f16_2_arm_avg_pool_f16(void) { avgpooling_f16_2_arm_avg_pool_f16(); } +void test_avgpooling_f16_global_arm_avg_pool_f16(void) { avgpooling_f16_global_arm_avg_pool_f16(); } +void test_avgpooling_match_2_f16_arm_avg_pool_f16(void) { avgpooling_match_2_f16_arm_avg_pool_f16(); } +void test_avgpooling_match_3_f16_arm_avg_pool_f16(void) { avgpooling_match_3_f16_arm_avg_pool_f16(); } +void test_avgpooling_match_4_f16_arm_avg_pool_f16(void) { avgpooling_match_4_f16_arm_avg_pool_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/test_arm_avg_pool_f16.c b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/test_arm_avg_pool_f16.c new file mode 100644 index 00000000..2cbc094f --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f16/test_arm_avg_pool_f16.c @@ -0,0 +1,55 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/avgpooling_f16/test_data.h" +#include "../TestData/avgpooling_f16_1/test_data.h" +#include "../TestData/avgpooling_f16_2/test_data.h" +#include "../TestData/avgpooling_f16_global/test_data.h" +#include "../TestData/avgpooling_match_2_f16/test_data.h" +#include "../TestData/avgpooling_match_3_f16/test_data.h" +#include "../TestData/avgpooling_match_4_f16/test_data.h" + +#define RUN_AVG_POOL_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_avg_pool_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_pool_params_f16 pool_params = { \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims filter_dims = { \ + .n = 1, .w = CASE_PREFIX##_FILTER_W, .h = CASE_PREFIX##_FILTER_H, .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_BATCH_SIZE, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_avg_pool_f16( \ + &ctx, &pool_params, &input_dims, case_name##_input_tensor, &filter_dims, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_AVG_POOL_F16_CASE(AVGPOOLING_F16, avgpooling_f16, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_F16_1, avgpooling_f16_1, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_F16_2, avgpooling_f16_2, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_F16_GLOBAL, avgpooling_f16_global, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_MATCH_2_F16, avgpooling_match_2_f16, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_MATCH_3_F16, avgpooling_match_3_f16, 8.0e-3f) +RUN_AVG_POOL_F16_CASE(AVGPOOLING_MATCH_4_F16, avgpooling_match_4_f16, 8.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/CMakeLists.txt new file mode 100644 index 00000000..ca0d8af8 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_avg_pool_f32) + +target_sources(test_arm_avg_pool_f32 PRIVATE + Unity/unity_test_arm_avg_pool_f32.c + Unity/TestRunner/unity_test_arm_avg_pool_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/Unity/unity_test_arm_avg_pool_f32.c b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/Unity/unity_test_arm_avg_pool_f32.c new file mode 100644 index 00000000..e314e3fd --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/Unity/unity_test_arm_avg_pool_f32.c @@ -0,0 +1,34 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_avg_pool_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_avgpooling_f32_arm_avg_pool_f32(void) { avgpooling_f32_arm_avg_pool_f32(); } +void test_avgpooling_f32_1_arm_avg_pool_f32(void) { avgpooling_f32_1_arm_avg_pool_f32(); } +void test_avgpooling_f32_2_arm_avg_pool_f32(void) { avgpooling_f32_2_arm_avg_pool_f32(); } +void test_avgpooling_f32_global_arm_avg_pool_f32(void) { avgpooling_f32_global_arm_avg_pool_f32(); } +void test_avgpooling_match_2_f32_arm_avg_pool_f32(void) { avgpooling_match_2_f32_arm_avg_pool_f32(); } +void test_avgpooling_match_3_f32_arm_avg_pool_f32(void) { avgpooling_match_3_f32_arm_avg_pool_f32(); } +void test_avgpooling_match_4_f32_arm_avg_pool_f32(void) { avgpooling_match_4_f32_arm_avg_pool_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/test_arm_avg_pool_f32.c b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/test_arm_avg_pool_f32.c new file mode 100644 index 00000000..9906f370 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_avg_pool_f32/test_arm_avg_pool_f32.c @@ -0,0 +1,55 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/avgpooling_f32/test_data.h" +#include "../TestData/avgpooling_f32_1/test_data.h" +#include "../TestData/avgpooling_f32_2/test_data.h" +#include "../TestData/avgpooling_f32_global/test_data.h" +#include "../TestData/avgpooling_match_2_f32/test_data.h" +#include "../TestData/avgpooling_match_3_f32/test_data.h" +#include "../TestData/avgpooling_match_4_f32/test_data.h" + +#define RUN_AVG_POOL_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_avg_pool_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_pool_params_f32 pool_params = { \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims filter_dims = { \ + .n = 1, .w = CASE_PREFIX##_FILTER_W, .h = CASE_PREFIX##_FILTER_H, .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_BATCH_SIZE, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_avg_pool_f32( \ + &ctx, &pool_params, &input_dims, case_name##_input_tensor, &filter_dims, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_AVG_POOL_F32_CASE(AVGPOOLING_F32, avgpooling_f32, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_F32_1, avgpooling_f32_1, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_F32_2, avgpooling_f32_2, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_F32_GLOBAL, avgpooling_f32_global, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_MATCH_2_F32, avgpooling_match_2_f32, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_MATCH_3_F32, avgpooling_match_3_f32, 2.0e-4f) +RUN_AVG_POOL_F32_CASE(AVGPOOLING_MATCH_4_F32, avgpooling_match_4_f32, 2.0e-4f) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/CMakeLists.txt new file mode 100644 index 00000000..43fae6f9 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_batch_matmul_f16) + +target_sources(test_arm_batch_matmul_f16 PRIVATE + Unity/unity_test_arm_batch_matmul_f16.c + Unity/TestRunner/unity_test_arm_batch_matmul_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/Unity/unity_test_arm_batch_matmul_f16.c b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/Unity/unity_test_arm_batch_matmul_f16.c new file mode 100644 index 00000000..56e152c3 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/Unity/unity_test_arm_batch_matmul_f16.c @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_batch_matmul_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_batch_matmul_1_f16_arm_batch_matmul_f16(void) { batch_matmul_1_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_2_f16_arm_batch_matmul_f16(void) { batch_matmul_2_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_3_f16_arm_batch_matmul_f16(void) { batch_matmul_3_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_4_f16_arm_batch_matmul_f16(void) { batch_matmul_4_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_5_f16_arm_batch_matmul_f16(void) { batch_matmul_5_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_6_f16_arm_batch_matmul_f16(void) { batch_matmul_6_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_et_small_f16_arm_batch_matmul_f16(void) { batch_matmul_et_small_f16_arm_batch_matmul_f16(); } +void test_batch_matmul_et_small_f16_arm_batch_matmul_f16_packed(void) +{ + batch_matmul_et_small_f16_arm_batch_matmul_f16_packed(); +} +void test_batch_matmul_2_f16_arm_batch_matmul_f16_packed_invalid(void) +{ + batch_matmul_2_f16_arm_batch_matmul_f16_packed_invalid(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/test_arm_batch_matmul_f16.c b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/test_arm_batch_matmul_f16.c new file mode 100644 index 00000000..272fe57e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f16/test_arm_batch_matmul_f16.c @@ -0,0 +1,146 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/batch_matmul_1_f16/test_data.h" +#include "../TestData/batch_matmul_2_f16/test_data.h" +#include "../TestData/batch_matmul_3_f16/test_data.h" +#include "../TestData/batch_matmul_4_f16/test_data.h" +#include "../TestData/batch_matmul_5_f16/test_data.h" +#include "../TestData/batch_matmul_6_f16/test_data.h" +#include "../TestData/batch_matmul_et_small_f16/test_data.h" + +#define RUN_BATCH_MATMUL_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_batch_matmul_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_bmm_params_f16 bmm_params = { \ + .adj_x = CASE_PREFIX##_ADJ_X, \ + .adj_y = CASE_PREFIX##_ADJ_Y, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims lhs_dims = {.n = CASE_PREFIX##_LHS_BATCH, \ + .h = CASE_PREFIX##_LHS_HEIGHT, \ + .c = CASE_PREFIX##_ADJ_X ? CASE_PREFIX##_LHS_COLS : CASE_PREFIX##_LHS_ROWS, \ + .w = CASE_PREFIX##_ADJ_X ? CASE_PREFIX##_LHS_ROWS : CASE_PREFIX##_LHS_COLS}; \ + const cmsis_nn_dims rhs_dims = {.n = CASE_PREFIX##_RHS_BATCH, \ + .h = CASE_PREFIX##_RHS_HEIGHT, \ + .c = CASE_PREFIX##_ADJ_Y ? CASE_PREFIX##_RHS_ROWS : CASE_PREFIX##_RHS_COLS, \ + .w = CASE_PREFIX##_ADJ_Y ? CASE_PREFIX##_RHS_COLS : CASE_PREFIX##_RHS_ROWS}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_BATCH, \ + .h = CASE_PREFIX##_OUTPUT_HEIGHT, \ + .c = CASE_PREFIX##_OUTPUT_ROWS, \ + .w = CASE_PREFIX##_OUTPUT_COLS}; \ + const float16_t *lhs_input = \ + CASE_PREFIX##_ADJ_X ? case_name##_lhs_transposed_tensor : case_name##_lhs_input_tensor; \ + const float16_t *rhs_input = \ + CASE_PREFIX##_ADJ_Y ? case_name##_rhs_input_tensor : case_name##_rhs_transposed_tensor; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_batch_matmul_f16( \ + &ctx, &bmm_params, &lhs_dims, lhs_input, &rhs_dims, rhs_input, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output[i], (float)output[i]); \ + } \ + } + +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_1_F16, batch_matmul_1_f16, 8.0e-3f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_2_F16, batch_matmul_2_f16, 8.0e-3f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_3_F16, batch_matmul_3_f16, 8.0e-3f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_4_F16, batch_matmul_4_f16, 8.0e-3f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_5_F16, batch_matmul_5_f16, 8.0e-3f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_6_F16, batch_matmul_6_f16, 2.0e-2f) +RUN_BATCH_MATMUL_F16_CASE(BATCH_MATMUL_ET_SMALL_F16, batch_matmul_et_small_f16, 8.0e-3f) + +/* + * The packed RHS coverage allocates a temporary repacked tensor and frees it, + * so it needs an explicit test body instead of the standard + * RUN_BATCH_MATMUL_F16_CASE macro. + */ +void batch_matmul_et_small_f16_arm_batch_matmul_f16_packed(void) +{ + float16_t output[BATCH_MATMUL_ET_SMALL_F16_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_bmm_params_f16 bmm_params = {.adj_x = BATCH_MATMUL_ET_SMALL_F16_ADJ_X, + .adj_y = BATCH_MATMUL_ET_SMALL_F16_ADJ_Y, + .activation = {.min = BATCH_MATMUL_ET_SMALL_F16_ACTIVATION_MIN, + .max = BATCH_MATMUL_ET_SMALL_F16_ACTIVATION_MAX}, + .rhs_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims lhs_dims = {.n = BATCH_MATMUL_ET_SMALL_F16_LHS_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F16_LHS_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F16_LHS_ROWS, + .w = BATCH_MATMUL_ET_SMALL_F16_LHS_COLS}; + const cmsis_nn_dims rhs_dims = {.n = BATCH_MATMUL_ET_SMALL_F16_RHS_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F16_RHS_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F16_RHS_COLS, + .w = BATCH_MATMUL_ET_SMALL_F16_RHS_ROWS}; + const cmsis_nn_dims output_dims = {.n = BATCH_MATMUL_ET_SMALL_F16_OUTPUT_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F16_OUTPUT_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F16_OUTPUT_ROWS, + .w = BATCH_MATMUL_ET_SMALL_F16_OUTPUT_COLS}; + float16_t *packed_rhs = pack_rhs_nt_n_from_nt_t_f16(batch_matmul_et_small_f16_rhs_transposed_tensor, + BATCH_MATMUL_ET_SMALL_F16_RHS_COLS, + BATCH_MATMUL_ET_SMALL_F16_RHS_ROWS); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_batch_matmul_f16(&ctx, + &bmm_params, + &lhs_dims, + batch_matmul_et_small_f16_lhs_input_tensor, + &rhs_dims, + packed_rhs, + &output_dims, + output)); + + for (int i = 0; i < BATCH_MATMUL_ET_SMALL_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(8.0e-3f, (float)batch_matmul_et_small_f16_output[i], (float)output[i]); + } + + free(packed_rhs); +} + +void batch_matmul_2_f16_arm_batch_matmul_f16_packed_invalid(void) +{ + float16_t output[BATCH_MATMUL_2_F16_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_bmm_params_f16 bmm_params = { + .adj_x = BATCH_MATMUL_2_F16_ADJ_X, + .adj_y = BATCH_MATMUL_2_F16_ADJ_Y, + .activation = {.min = BATCH_MATMUL_2_F16_ACTIVATION_MIN, .max = BATCH_MATMUL_2_F16_ACTIVATION_MAX}, + .rhs_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims lhs_dims = { + .n = BATCH_MATMUL_2_F16_LHS_BATCH, + .h = BATCH_MATMUL_2_F16_LHS_HEIGHT, + .c = BATCH_MATMUL_2_F16_ADJ_X ? BATCH_MATMUL_2_F16_LHS_COLS : BATCH_MATMUL_2_F16_LHS_ROWS, + .w = BATCH_MATMUL_2_F16_ADJ_X ? BATCH_MATMUL_2_F16_LHS_ROWS : BATCH_MATMUL_2_F16_LHS_COLS}; + const cmsis_nn_dims rhs_dims = { + .n = BATCH_MATMUL_2_F16_RHS_BATCH, + .h = BATCH_MATMUL_2_F16_RHS_HEIGHT, + .c = BATCH_MATMUL_2_F16_ADJ_Y ? BATCH_MATMUL_2_F16_RHS_ROWS : BATCH_MATMUL_2_F16_RHS_COLS, + .w = BATCH_MATMUL_2_F16_ADJ_Y ? BATCH_MATMUL_2_F16_RHS_COLS : BATCH_MATMUL_2_F16_RHS_ROWS}; + const cmsis_nn_dims output_dims = {.n = BATCH_MATMUL_2_F16_OUTPUT_BATCH, + .h = BATCH_MATMUL_2_F16_OUTPUT_HEIGHT, + .c = BATCH_MATMUL_2_F16_OUTPUT_ROWS, + .w = BATCH_MATMUL_2_F16_OUTPUT_COLS}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, + arm_batch_matmul_f16(&ctx, + &bmm_params, + &lhs_dims, + batch_matmul_2_f16_lhs_input_tensor, + &rhs_dims, + batch_matmul_2_f16_rhs_input_tensor, + &output_dims, + output)); +} diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/CMakeLists.txt new file mode 100644 index 00000000..0cf7aa88 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_batch_matmul_f32) + +target_sources(test_arm_batch_matmul_f32 PRIVATE + Unity/unity_test_arm_batch_matmul_f32.c + Unity/TestRunner/unity_test_arm_batch_matmul_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/Unity/unity_test_arm_batch_matmul_f32.c b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/Unity/unity_test_arm_batch_matmul_f32.c new file mode 100644 index 00000000..b4cc5081 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/Unity/unity_test_arm_batch_matmul_f32.c @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_batch_matmul_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_batch_matmul_1_f32_arm_batch_matmul_f32(void) { batch_matmul_1_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_2_f32_arm_batch_matmul_f32(void) { batch_matmul_2_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_3_f32_arm_batch_matmul_f32(void) { batch_matmul_3_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_4_f32_arm_batch_matmul_f32(void) { batch_matmul_4_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_5_f32_arm_batch_matmul_f32(void) { batch_matmul_5_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_6_f32_arm_batch_matmul_f32(void) { batch_matmul_6_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_et_small_f32_arm_batch_matmul_f32(void) { batch_matmul_et_small_f32_arm_batch_matmul_f32(); } +void test_batch_matmul_et_small_f32_arm_batch_matmul_f32_packed(void) +{ + batch_matmul_et_small_f32_arm_batch_matmul_f32_packed(); +} +void test_batch_matmul_2_f32_arm_batch_matmul_f32_packed_invalid(void) +{ + batch_matmul_2_f32_arm_batch_matmul_f32_packed_invalid(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/test_arm_batch_matmul_f32.c b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/test_arm_batch_matmul_f32.c new file mode 100644 index 00000000..52446713 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_matmul_f32/test_arm_batch_matmul_f32.c @@ -0,0 +1,146 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/batch_matmul_1_f32/test_data.h" +#include "../TestData/batch_matmul_2_f32/test_data.h" +#include "../TestData/batch_matmul_3_f32/test_data.h" +#include "../TestData/batch_matmul_4_f32/test_data.h" +#include "../TestData/batch_matmul_5_f32/test_data.h" +#include "../TestData/batch_matmul_6_f32/test_data.h" +#include "../TestData/batch_matmul_et_small_f32/test_data.h" + +#define RUN_BATCH_MATMUL_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_batch_matmul_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_bmm_params_f32 bmm_params = { \ + .adj_x = CASE_PREFIX##_ADJ_X, \ + .adj_y = CASE_PREFIX##_ADJ_Y, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims lhs_dims = {.n = CASE_PREFIX##_LHS_BATCH, \ + .h = CASE_PREFIX##_LHS_HEIGHT, \ + .c = CASE_PREFIX##_ADJ_X ? CASE_PREFIX##_LHS_COLS : CASE_PREFIX##_LHS_ROWS, \ + .w = CASE_PREFIX##_ADJ_X ? CASE_PREFIX##_LHS_ROWS : CASE_PREFIX##_LHS_COLS}; \ + const cmsis_nn_dims rhs_dims = {.n = CASE_PREFIX##_RHS_BATCH, \ + .h = CASE_PREFIX##_RHS_HEIGHT, \ + .c = CASE_PREFIX##_ADJ_Y ? CASE_PREFIX##_RHS_ROWS : CASE_PREFIX##_RHS_COLS, \ + .w = CASE_PREFIX##_ADJ_Y ? CASE_PREFIX##_RHS_COLS : CASE_PREFIX##_RHS_ROWS}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_BATCH, \ + .h = CASE_PREFIX##_OUTPUT_HEIGHT, \ + .c = CASE_PREFIX##_OUTPUT_ROWS, \ + .w = CASE_PREFIX##_OUTPUT_COLS}; \ + const float32_t *lhs_input = \ + CASE_PREFIX##_ADJ_X ? case_name##_lhs_transposed_tensor : case_name##_lhs_input_tensor; \ + const float32_t *rhs_input = \ + CASE_PREFIX##_ADJ_Y ? case_name##_rhs_input_tensor : case_name##_rhs_transposed_tensor; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_batch_matmul_f32( \ + &ctx, &bmm_params, &lhs_dims, lhs_input, &rhs_dims, rhs_input, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output[i], output[i]); \ + } \ + } + +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_1_F32, batch_matmul_1_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_2_F32, batch_matmul_2_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_3_F32, batch_matmul_3_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_4_F32, batch_matmul_4_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_5_F32, batch_matmul_5_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_6_F32, batch_matmul_6_f32, 2.0e-4f) +RUN_BATCH_MATMUL_F32_CASE(BATCH_MATMUL_ET_SMALL_F32, batch_matmul_et_small_f32, 2.0e-4f) + +/* + * The packed RHS coverage allocates a temporary repacked tensor and frees it, + * so it needs an explicit test body instead of the standard + * RUN_BATCH_MATMUL_F32_CASE macro. + */ +void batch_matmul_et_small_f32_arm_batch_matmul_f32_packed(void) +{ + float32_t output[BATCH_MATMUL_ET_SMALL_F32_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_bmm_params_f32 bmm_params = {.adj_x = false, + .adj_y = false, + .activation = {.min = BATCH_MATMUL_ET_SMALL_F32_ACTIVATION_MIN, + .max = BATCH_MATMUL_ET_SMALL_F32_ACTIVATION_MAX}, + .rhs_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims lhs_dims = {.n = BATCH_MATMUL_ET_SMALL_F32_LHS_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F32_LHS_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F32_LHS_ROWS, + .w = BATCH_MATMUL_ET_SMALL_F32_LHS_COLS}; + const cmsis_nn_dims rhs_dims = {.n = BATCH_MATMUL_ET_SMALL_F32_RHS_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F32_RHS_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F32_RHS_COLS, + .w = BATCH_MATMUL_ET_SMALL_F32_RHS_ROWS}; + const cmsis_nn_dims output_dims = {.n = BATCH_MATMUL_ET_SMALL_F32_OUTPUT_BATCH, + .h = BATCH_MATMUL_ET_SMALL_F32_OUTPUT_HEIGHT, + .c = BATCH_MATMUL_ET_SMALL_F32_OUTPUT_ROWS, + .w = BATCH_MATMUL_ET_SMALL_F32_OUTPUT_COLS}; + float32_t *packed_rhs = pack_rhs_nt_n_from_nt_t_f32(batch_matmul_et_small_f32_rhs_transposed_tensor, + BATCH_MATMUL_ET_SMALL_F32_OUTPUT_COLS, + BATCH_MATMUL_ET_SMALL_F32_LHS_COLS); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_batch_matmul_f32(&ctx, + &bmm_params, + &lhs_dims, + batch_matmul_et_small_f32_lhs_input_tensor, + &rhs_dims, + packed_rhs, + &output_dims, + output)); + + for (int i = 0; i < BATCH_MATMUL_ET_SMALL_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, batch_matmul_et_small_f32_output[i], output[i]); + } + + free(packed_rhs); +} + +void batch_matmul_2_f32_arm_batch_matmul_f32_packed_invalid(void) +{ + float32_t output[BATCH_MATMUL_2_F32_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_bmm_params_f32 bmm_params = { + .adj_x = BATCH_MATMUL_2_F32_ADJ_X, + .adj_y = BATCH_MATMUL_2_F32_ADJ_Y, + .activation = {.min = BATCH_MATMUL_2_F32_ACTIVATION_MIN, .max = BATCH_MATMUL_2_F32_ACTIVATION_MAX}, + .rhs_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims lhs_dims = { + .n = BATCH_MATMUL_2_F32_LHS_BATCH, + .h = BATCH_MATMUL_2_F32_LHS_HEIGHT, + .c = BATCH_MATMUL_2_F32_ADJ_X ? BATCH_MATMUL_2_F32_LHS_COLS : BATCH_MATMUL_2_F32_LHS_ROWS, + .w = BATCH_MATMUL_2_F32_ADJ_X ? BATCH_MATMUL_2_F32_LHS_ROWS : BATCH_MATMUL_2_F32_LHS_COLS}; + const cmsis_nn_dims rhs_dims = { + .n = BATCH_MATMUL_2_F32_RHS_BATCH, + .h = BATCH_MATMUL_2_F32_RHS_HEIGHT, + .c = BATCH_MATMUL_2_F32_ADJ_Y ? BATCH_MATMUL_2_F32_RHS_ROWS : BATCH_MATMUL_2_F32_RHS_COLS, + .w = BATCH_MATMUL_2_F32_ADJ_Y ? BATCH_MATMUL_2_F32_RHS_COLS : BATCH_MATMUL_2_F32_RHS_ROWS}; + const cmsis_nn_dims output_dims = {.n = BATCH_MATMUL_2_F32_OUTPUT_BATCH, + .h = BATCH_MATMUL_2_F32_OUTPUT_HEIGHT, + .c = BATCH_MATMUL_2_F32_OUTPUT_ROWS, + .w = BATCH_MATMUL_2_F32_OUTPUT_COLS}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, + arm_batch_matmul_f32(&ctx, + &bmm_params, + &lhs_dims, + batch_matmul_2_f32_lhs_transposed_tensor, + &rhs_dims, + batch_matmul_2_f32_rhs_input_tensor, + &output_dims, + output)); +} diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/CMakeLists.txt new file mode 100644 index 00000000..47bf2935 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_batch_norm_f16) + +target_sources(test_arm_batch_norm_f16 PRIVATE + Unity/unity_test_arm_batch_norm_f16.c + Unity/TestRunner/unity_test_arm_batch_norm_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/Unity/unity_test_arm_batch_norm_f16.c b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/Unity/unity_test_arm_batch_norm_f16.c new file mode 100644 index 00000000..86e25111 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/Unity/unity_test_arm_batch_norm_f16.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_batch_norm_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_bn_basic_f16_arm_batch_norm_f16(void) { bn_basic_f16_arm_batch_norm_f16(); } +void test_bn_basic_nhwc_f16_arm_batch_norm_f16(void) { bn_basic_nhwc_f16_arm_batch_norm_f16(); } +void test_bn_op_f16_arm_batch_norm_f16(void) { bn_op_f16_arm_batch_norm_f16(); } +void test_bn_op_nhwc_f16_arm_batch_norm_f16(void) { bn_op_nhwc_f16_arm_batch_norm_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/test_arm_batch_norm_f16.c b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/test_arm_batch_norm_f16.c new file mode 100644 index 00000000..d4ff8f90 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f16/test_arm_batch_norm_f16.c @@ -0,0 +1,38 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/bn_basic_f16/test_data.h" +#include "../TestData/bn_basic_nhwc_f16/test_data.h" +#include "../TestData/bn_op_f16/test_data.h" +#include "../TestData/bn_op_nhwc_f16/test_data.h" + +#define RUN_BATCH_NORM_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_batch_norm_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_SIZE] = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_batch_norm_f16( \ + case_name##_input, output, case_name##_scale, case_name##_bias, &input_dims, CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_BATCH_NORM_F16_CASE(BN_BASIC_F16, bn_basic_f16, 5.0e-3f) +RUN_BATCH_NORM_F16_CASE(BN_BASIC_NHWC_F16, bn_basic_nhwc_f16, 5.0e-3f) +RUN_BATCH_NORM_F16_CASE(BN_OP_F16, bn_op_f16, 5.0e-3f) +RUN_BATCH_NORM_F16_CASE(BN_OP_NHWC_F16, bn_op_nhwc_f16, 5.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/CMakeLists.txt new file mode 100644 index 00000000..f86d20f8 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_batch_norm_f32) + +target_sources(test_arm_batch_norm_f32 PRIVATE + Unity/unity_test_arm_batch_norm_f32.c + Unity/TestRunner/unity_test_arm_batch_norm_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/Unity/unity_test_arm_batch_norm_f32.c b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/Unity/unity_test_arm_batch_norm_f32.c new file mode 100644 index 00000000..eece1c6e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/Unity/unity_test_arm_batch_norm_f32.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_batch_norm_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_bn_basic_f32_arm_batch_norm_f32(void) { bn_basic_f32_arm_batch_norm_f32(); } +void test_bn_basic_nhwc_f32_arm_batch_norm_f32(void) { bn_basic_nhwc_f32_arm_batch_norm_f32(); } +void test_bn_op_f32_arm_batch_norm_f32(void) { bn_op_f32_arm_batch_norm_f32(); } +void test_bn_op_nhwc_f32_arm_batch_norm_f32(void) { bn_op_nhwc_f32_arm_batch_norm_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/test_arm_batch_norm_f32.c b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/test_arm_batch_norm_f32.c new file mode 100644 index 00000000..0cd56b00 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_batch_norm_f32/test_arm_batch_norm_f32.c @@ -0,0 +1,38 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/bn_basic_f32/test_data.h" +#include "../TestData/bn_basic_nhwc_f32/test_data.h" +#include "../TestData/bn_op_f32/test_data.h" +#include "../TestData/bn_op_nhwc_f32/test_data.h" + +#define RUN_BATCH_NORM_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_batch_norm_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_SIZE] = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_batch_norm_f32( \ + case_name##_input, output, case_name##_scale, case_name##_bias, &input_dims, CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_BATCH_NORM_F32_CASE(BN_BASIC_F32, bn_basic_f32, 1.0e-5f) +RUN_BATCH_NORM_F32_CASE(BN_BASIC_NHWC_F32, bn_basic_nhwc_f32, 1.0e-5f) +RUN_BATCH_NORM_F32_CASE(BN_OP_F32, bn_op_f32, 1.0e-5f) +RUN_BATCH_NORM_F32_CASE(BN_OP_NHWC_F32, bn_op_nhwc_f32, 1.0e-5f) diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/CMakeLists.txt new file mode 100644 index 00000000..90487ed1 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_concatenation_f16) + +target_sources(test_arm_concatenation_f16 PRIVATE + Unity/unity_test_arm_concatenation_f16.c + Unity/TestRunner/unity_test_arm_concatenation_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f16/Unity/unity_test_arm_concatenation_f16.c b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/Unity/unity_test_arm_concatenation_f16.c new file mode 100644 index 00000000..6c3f8bea --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/Unity/unity_test_arm_concatenation_f16.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_concatenation_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_concat_c_f16_arm_concatenation_f16(void) { concat_c_f16_arm_concatenation_f16(); } +void test_concat_c_nhwc_f16_arm_concatenation_f16(void) { concat_c_nhwc_f16_arm_concatenation_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f16/test_arm_concatenation_f16.c b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/test_arm_concatenation_f16.c new file mode 100644 index 00000000..f74a7230 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f16/test_arm_concatenation_f16.c @@ -0,0 +1,41 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/concat_c_f16/test_data.h" +#include "../TestData/concat_c_nhwc_f16/test_data.h" + +#define RUN_CONCATENATION_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_concatenation_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_SIZE] = {0}; \ + arm_concatenation_f16_x(case_name##_lhs_input, \ + CASE_PREFIX##_LHS_C, \ + CASE_PREFIX##_INPUT_W, \ + CASE_PREFIX##_INPUT_H, \ + CASE_PREFIX##_INPUT_N, \ + output, \ + CASE_PREFIX##_OUTPUT_C, \ + 0); \ + arm_concatenation_f16_x(case_name##_rhs_input, \ + CASE_PREFIX##_RHS_C, \ + CASE_PREFIX##_INPUT_W, \ + CASE_PREFIX##_INPUT_H, \ + CASE_PREFIX##_INPUT_N, \ + output, \ + CASE_PREFIX##_OUTPUT_C, \ + CASE_PREFIX##_LHS_C); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_CONCATENATION_F16_CASE(CONCAT_C_F16, concat_c_f16, 1.0e-3f) +RUN_CONCATENATION_F16_CASE(CONCAT_C_NHWC_F16, concat_c_nhwc_f16, 1.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/CMakeLists.txt new file mode 100644 index 00000000..b8c3a608 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_concatenation_f32) + +target_sources(test_arm_concatenation_f32 PRIVATE + Unity/unity_test_arm_concatenation_f32.c + Unity/TestRunner/unity_test_arm_concatenation_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f32/Unity/unity_test_arm_concatenation_f32.c b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/Unity/unity_test_arm_concatenation_f32.c new file mode 100644 index 00000000..a954cdf8 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/Unity/unity_test_arm_concatenation_f32.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_concatenation_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_concat_c_f32_arm_concatenation_f32(void) { concat_c_f32_arm_concatenation_f32(); } +void test_concat_c_nhwc_f32_arm_concatenation_f32(void) { concat_c_nhwc_f32_arm_concatenation_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_concatenation_f32/test_arm_concatenation_f32.c b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/test_arm_concatenation_f32.c new file mode 100644 index 00000000..f16d476a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_concatenation_f32/test_arm_concatenation_f32.c @@ -0,0 +1,41 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/concat_c_f32/test_data.h" +#include "../TestData/concat_c_nhwc_f32/test_data.h" + +#define RUN_CONCATENATION_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_concatenation_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_SIZE] = {0}; \ + arm_concatenation_f32_x(case_name##_lhs_input, \ + CASE_PREFIX##_LHS_C, \ + CASE_PREFIX##_INPUT_W, \ + CASE_PREFIX##_INPUT_H, \ + CASE_PREFIX##_INPUT_N, \ + output, \ + CASE_PREFIX##_OUTPUT_C, \ + 0); \ + arm_concatenation_f32_x(case_name##_rhs_input, \ + CASE_PREFIX##_RHS_C, \ + CASE_PREFIX##_INPUT_W, \ + CASE_PREFIX##_INPUT_H, \ + CASE_PREFIX##_INPUT_N, \ + output, \ + CASE_PREFIX##_OUTPUT_C, \ + CASE_PREFIX##_LHS_C); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_CONCATENATION_F32_CASE(CONCAT_C_F32, concat_c_f32, 1.0e-6f) +RUN_CONCATENATION_F32_CASE(CONCAT_C_NHWC_F32, concat_c_nhwc_f32, 1.0e-6f) diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_convolve_f16/CMakeLists.txt new file mode 100644 index 00000000..9865cc7e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_convolve_f16) + +target_sources(test_arm_convolve_f16 PRIVATE + Unity/unity_test_arm_convolve_f16.c + Unity/TestRunner/unity_test_arm_convolve_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f16/Unity/unity_test_arm_convolve_f16.c b/Tests/UnitTest/TestCases/test_arm_convolve_f16/Unity/unity_test_arm_convolve_f16.c new file mode 100755 index 00000000..90555d5a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f16/Unity/unity_test_arm_convolve_f16.c @@ -0,0 +1,72 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_convolve_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_conv_basic_f16_arm_convolve_f16(void) { conv_basic_f16_arm_convolve_f16(); } +void test_conv_basic_nhwc_f16_arm_convolve_f16(void) { conv_basic_nhwc_f16_arm_convolve_f16(); } +void test_conv_1x1_stride2_nhwc_f16_arm_convolve_f16(void) { conv_1x1_stride2_nhwc_f16_arm_convolve_f16(); } +void test_conv_k3_opt_f16_arm_convolve_f16(void) { conv_k3_opt_f16_arm_convolve_f16(); } +void test_conv_k5_opt_f16_arm_convolve_f16(void) { conv_k5_opt_f16_arm_convolve_f16(); } +void test_conv_k3_opt_nhwc_tuned_f16_arm_convolve_f16(void) { conv_k3_opt_nhwc_tuned_f16_arm_convolve_f16(); } +void test_conv_k5_opt_nhwc_tuned_f16_arm_convolve_f16(void) { conv_k5_opt_nhwc_tuned_f16_arm_convolve_f16(); } +void test_conv_kernel_2x2_f16_arm_convolve_f16(void) { conv_kernel_2x2_f16_arm_convolve_f16(); } +void test_conv_kernel_3x3_pad1_f16_arm_convolve_f16(void) { conv_kernel_3x3_pad1_f16_arm_convolve_f16(); } +void test_conv_match_basic_f16_arm_convolve_f16(void) { conv_match_basic_f16_arm_convolve_f16(); } +void test_conv_match_stride2pad1_f16_arm_convolve_f16(void) { conv_match_stride2pad1_f16_arm_convolve_f16(); } +void test_conv_match_conv_2_f16_arm_convolve_f16(void) { conv_match_conv_2_f16_arm_convolve_f16(); } +void test_conv_match_conv_3_f16_arm_convolve_f16(void) { conv_match_conv_3_f16_arm_convolve_f16(); } +void test_conv_match_conv_4_f16_arm_convolve_f16(void) { conv_match_conv_4_f16_arm_convolve_f16(); } +void test_conv_match_conv_5_f16_arm_convolve_f16(void) { conv_match_conv_5_f16_arm_convolve_f16(); } +void test_conv_match_out_activation_f16_arm_convolve_f16(void) { conv_match_out_activation_f16_arm_convolve_f16(); } +void test_conv_match_dilation_golden_f16_arm_convolve_f16(void) { conv_match_dilation_golden_f16_arm_convolve_f16(); } +void test_conv_match_2x2_dilation_f16_arm_convolve_f16(void) { conv_match_2x2_dilation_f16_arm_convolve_f16(); } +void test_conv_match_2x3_dilation_f16_arm_convolve_f16(void) { conv_match_2x3_dilation_f16_arm_convolve_f16(); } +void test_conv_match_1x1_basic_f16_arm_convolve_f16(void) { conv_match_1x1_basic_f16_arm_convolve_f16(); } +void test_conv_match_1x1_stride_x_f16_arm_convolve_f16(void) { conv_match_1x1_stride_x_f16_arm_convolve_f16(); } +void test_conv_match_1x1_stride_x_y_f16_arm_convolve_f16(void) { conv_match_1x1_stride_x_y_f16_arm_convolve_f16(); } +void test_conv_match_1x1_stride_x_y_1_f16_arm_convolve_f16(void) { conv_match_1x1_stride_x_y_1_f16_arm_convolve_f16(); } +void test_conv_match_1x1_stride_x_y_2_f16_arm_convolve_f16(void) { conv_match_1x1_stride_x_y_2_f16_arm_convolve_f16(); } +void test_conv_match_1xn_1_f16_arm_convolve_f16(void) { conv_match_1xn_1_f16_arm_convolve_f16(); } +void test_conv_match_1xn_2_f16_arm_convolve_f16(void) { conv_match_1xn_2_f16_arm_convolve_f16(); } +void test_conv_match_1xn_3_f16_arm_convolve_f16(void) { conv_match_1xn_3_f16_arm_convolve_f16(); } +void test_conv_match_1xn_4_f16_arm_convolve_f16(void) { conv_match_1xn_4_f16_arm_convolve_f16(); } +void test_conv_match_1xn_5_f16_arm_convolve_f16(void) { conv_match_1xn_5_f16_arm_convolve_f16(); } +void test_conv_match_1xn_6_generic_f16_arm_convolve_f16(void) { conv_match_1xn_6_generic_f16_arm_convolve_f16(); } +void test_conv_match_1xn_7_f16_arm_convolve_f16(void) { conv_match_1xn_7_f16_arm_convolve_f16(); } +void test_conv_match_1xn_8_f16_arm_convolve_f16(void) { conv_match_1xn_8_f16_arm_convolve_f16(); } +void test_conv_match_3x2_dilation_f16_arm_convolve_f16(void) { conv_match_3x2_dilation_f16_arm_convolve_f16(); } +void test_conv_match_3x3_dilation_5x5_input_f16_arm_convolve_f16(void) +{ + conv_match_3x3_dilation_5x5_input_f16_arm_convolve_f16(); +} +void test_conv_match_2x2_dilation_5x5_input_f16_arm_convolve_f16(void) +{ + conv_match_2x2_dilation_5x5_input_f16_arm_convolve_f16(); +} +void test_conv_match_1x1_basic_f16_arm_convolve_f16_packed(void) { conv_match_1x1_basic_f16_arm_convolve_f16_packed(); } +void test_conv_basic_f16_arm_convolve_f16_packed(void) { conv_basic_f16_arm_convolve_f16_packed(); } +void test_conv_k3_opt_f16_arm_convolve_f16_packed(void) { conv_k3_opt_f16_arm_convolve_f16_packed(); } +void test_conv_k5_opt_f16_arm_convolve_f16_packed(void) { conv_k5_opt_f16_arm_convolve_f16_packed(); } diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f16/test_arm_convolve_f16.c b/Tests/UnitTest/TestCases/test_arm_convolve_f16/test_arm_convolve_f16.c new file mode 100755 index 00000000..44704e9b --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f16/test_arm_convolve_f16.c @@ -0,0 +1,393 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/conv_1x1_stride2_nhwc_f16/test_data.h" +#include "../TestData/conv_basic_f16/test_data.h" +#include "../TestData/conv_basic_nhwc_f16/test_data.h" +#include "../TestData/conv_k3_opt_f16/test_data.h" +#include "../TestData/conv_k3_opt_nhwc_tuned_f16/test_data.h" +#include "../TestData/conv_k5_opt_f16/test_data.h" +#include "../TestData/conv_k5_opt_nhwc_tuned_f16/test_data.h" +#include "../TestData/conv_kernel_2x2_f16/test_data.h" +#include "../TestData/conv_kernel_3x3_pad1_f16/test_data.h" +#include "../TestData/conv_match_1x1_basic_f16/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_f16/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_1_f16/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_2_f16/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_f16/test_data.h" +#include "../TestData/conv_match_1xn_1_f16/test_data.h" +#include "../TestData/conv_match_1xn_2_f16/test_data.h" +#include "../TestData/conv_match_1xn_3_f16/test_data.h" +#include "../TestData/conv_match_1xn_4_f16/test_data.h" +#include "../TestData/conv_match_1xn_5_f16/test_data.h" +#include "../TestData/conv_match_1xn_6_generic_f16/test_data.h" +#include "../TestData/conv_match_1xn_7_f16/test_data.h" +#include "../TestData/conv_match_1xn_8_f16/test_data.h" +#include "../TestData/conv_match_2x2_dilation_5x5_input_f16/test_data.h" +#include "../TestData/conv_match_2x2_dilation_f16/test_data.h" +#include "../TestData/conv_match_2x3_dilation_f16/test_data.h" +#include "../TestData/conv_match_3x2_dilation_f16/test_data.h" +#include "../TestData/conv_match_3x3_dilation_5x5_input_f16/test_data.h" +#include "../TestData/conv_match_basic_f16/test_data.h" +#include "../TestData/conv_match_conv_2_f16/test_data.h" +#include "../TestData/conv_match_conv_3_f16/test_data.h" +#include "../TestData/conv_match_conv_4_f16/test_data.h" +#include "../TestData/conv_match_conv_5_f16/test_data.h" +#include "../TestData/conv_match_dilation_golden_f16/test_data.h" +#include "../TestData/conv_match_out_activation_f16/test_data.h" +#include "../TestData/conv_match_stride2pad1_f16/test_data.h" + +#define RUN_CONV_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_convolve_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + const cmsis_nn_conv_params_f16 conv_params = { \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .dilation = {.w = CASE_PREFIX##_DILATION_W, .h = CASE_PREFIX##_DILATION_H}, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_OUT_CH, \ + .w = CASE_PREFIX##_FILTER_W, \ + .h = CASE_PREFIX##_FILTER_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const int32_t buf_size = arm_convolve_f16_get_buffer_size( \ + &conv_params, &input_dims, &filter_dims, &output_dims, CASE_PREFIX##_LAYOUT); \ + \ + if (buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)buf_size); \ + ctx.size = buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_convolve_f16(&ctx, \ + &conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + case_name##_biases_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref_data[i], (float)output[i]); \ + output[i] = (float16_t)0.0f; \ + } \ + \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)buf_size); \ + free(ctx.buf); \ + ctx.buf = NULL; \ + ctx.size = 0; \ + } \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + const int32_t wrapper_buf_size = \ + arm_convolve_wrapper_f16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims); \ + if (wrapper_buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)wrapper_buf_size); \ + ctx.size = wrapper_buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_convolve_wrapper_f16(&ctx, \ + &conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + case_name##_biases_data, \ + &output_dims, \ + output)); \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref_data[i], (float)output[i]); \ + } \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)wrapper_buf_size); \ + free(ctx.buf); \ + } \ + } \ + } + +RUN_CONV_F16_CASE(CONV_BASIC_F16, conv_basic_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_BASIC_NHWC_F16, conv_basic_nhwc_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_1X1_STRIDE2_NHWC_F16, conv_1x1_stride2_nhwc_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_KERNEL_2X2_F16, conv_kernel_2x2_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_KERNEL_3X3_PAD1_F16, conv_kernel_3x3_pad1_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_K3_OPT_F16, conv_k3_opt_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_K5_OPT_F16, conv_k5_opt_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_K3_OPT_NHWC_TUNED_F16, conv_k3_opt_nhwc_tuned_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_K5_OPT_NHWC_TUNED_F16, conv_k5_opt_nhwc_tuned_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_BASIC_F16, conv_match_basic_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_STRIDE2PAD1_F16, conv_match_stride2pad1_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_CONV_2_F16, conv_match_conv_2_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_CONV_3_F16, conv_match_conv_3_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_CONV_4_F16, conv_match_conv_4_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_CONV_5_F16, conv_match_conv_5_f16, 3.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_OUT_ACTIVATION_F16, conv_match_out_activation_f16, 4.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_DILATION_GOLDEN_F16, conv_match_dilation_golden_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_2X2_DILATION_F16, conv_match_2x2_dilation_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_2X3_DILATION_F16, conv_match_2x3_dilation_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1X1_BASIC_F16, conv_match_1x1_basic_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1X1_STRIDE_X_F16, conv_match_1x1_stride_x_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1X1_STRIDE_X_Y_F16, conv_match_1x1_stride_x_y_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1X1_STRIDE_X_Y_1_F16, conv_match_1x1_stride_x_y_1_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1X1_STRIDE_X_Y_2_F16, conv_match_1x1_stride_x_y_2_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_1_F16, conv_match_1xn_1_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_2_F16, conv_match_1xn_2_f16, 3.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_3_F16, conv_match_1xn_3_f16, 3.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_4_F16, conv_match_1xn_4_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_5_F16, conv_match_1xn_5_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_6_GENERIC_F16, conv_match_1xn_6_generic_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_7_F16, conv_match_1xn_7_f16, 2.0e-1f) +RUN_CONV_F16_CASE(CONV_MATCH_1XN_8_F16, conv_match_1xn_8_f16, 2.0e-1f) +RUN_CONV_F16_CASE(CONV_MATCH_3X2_DILATION_F16, conv_match_3x2_dilation_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_3X3_DILATION_5X5_INPUT_F16, conv_match_3x3_dilation_5x5_input_f16, 2.0e-2f) +RUN_CONV_F16_CASE(CONV_MATCH_2X2_DILATION_5X5_INPUT_F16, conv_match_2x2_dilation_5x5_input_f16, 2.0e-2f) + +/* + * The packed-convolution coverage allocates a temporary repacked filter and, + * for generic conv, may also allocate scratch, so it needs explicit test + * bodies instead of the standard RUN_CONV_F16_CASE macro. + */ +void conv_match_1x1_basic_f16_arm_convolve_f16_packed(void) +{ + float16_t output[CONV_MATCH_1X1_BASIC_F16_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = CONV_MATCH_1X1_BASIC_F16_PADDING_W, .h = CONV_MATCH_1X1_BASIC_F16_PADDING_H}, + .stride = {.w = CONV_MATCH_1X1_BASIC_F16_STRIDE_W, .h = CONV_MATCH_1X1_BASIC_F16_STRIDE_H}, + .dilation = {.w = CONV_MATCH_1X1_BASIC_F16_DILATION_W, .h = CONV_MATCH_1X1_BASIC_F16_DILATION_H}, + .activation = {.min = CONV_MATCH_1X1_BASIC_F16_OUT_ACTIVATION_MIN, + .max = CONV_MATCH_1X1_BASIC_F16_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_MATCH_1X1_BASIC_F16_INPUT_BATCHES, + .w = CONV_MATCH_1X1_BASIC_F16_INPUT_W, + .h = CONV_MATCH_1X1_BASIC_F16_INPUT_H, + .c = CONV_MATCH_1X1_BASIC_F16_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_MATCH_1X1_BASIC_F16_OUT_CH, + .w = CONV_MATCH_1X1_BASIC_F16_FILTER_W, + .h = CONV_MATCH_1X1_BASIC_F16_FILTER_H, + .c = CONV_MATCH_1X1_BASIC_F16_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_MATCH_1X1_BASIC_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_MATCH_1X1_BASIC_F16_INPUT_BATCHES, + .w = CONV_MATCH_1X1_BASIC_F16_OUTPUT_W, + .h = CONV_MATCH_1X1_BASIC_F16_OUTPUT_H, + .c = CONV_MATCH_1X1_BASIC_F16_OUTPUT_C}; + float16_t *packed_weights = pack_rhs_nt_n_from_nt_t_f16( + conv_match_1x1_basic_f16_weights_data, + CONV_MATCH_1X1_BASIC_F16_OUT_CH, + CONV_MATCH_1X1_BASIC_F16_FILTER_H * CONV_MATCH_1X1_BASIC_F16_FILTER_W * CONV_MATCH_1X1_BASIC_F16_IN_CH); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f16(&ctx, + &conv_params, + &input_dims, + conv_match_1x1_basic_f16_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_match_1x1_basic_f16_biases_data, + &output_dims, + output, + CONV_MATCH_1X1_BASIC_F16_LAYOUT)); + + for (int i = 0; i < CONV_MATCH_1X1_BASIC_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)conv_match_1x1_basic_f16_output_ref_data[i], (float)output[i]); + } + + free(packed_weights); +} + +void conv_basic_f16_arm_convolve_f16_packed(void) +{ + float16_t output[CONV_BASIC_F16_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = CONV_BASIC_F16_PADDING_W, .h = CONV_BASIC_F16_PADDING_H}, + .stride = {.w = CONV_BASIC_F16_STRIDE_W, .h = CONV_BASIC_F16_STRIDE_H}, + .dilation = {.w = CONV_BASIC_F16_DILATION_W, .h = CONV_BASIC_F16_DILATION_H}, + .activation = {.min = CONV_BASIC_F16_OUT_ACTIVATION_MIN, .max = CONV_BASIC_F16_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_BASIC_F16_INPUT_BATCHES, + .w = CONV_BASIC_F16_INPUT_W, + .h = CONV_BASIC_F16_INPUT_H, + .c = CONV_BASIC_F16_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_BASIC_F16_OUT_CH, + .w = CONV_BASIC_F16_FILTER_W, + .h = CONV_BASIC_F16_FILTER_H, + .c = CONV_BASIC_F16_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_BASIC_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_BASIC_F16_INPUT_BATCHES, + .w = CONV_BASIC_F16_OUTPUT_W, + .h = CONV_BASIC_F16_OUTPUT_H, + .c = CONV_BASIC_F16_OUTPUT_C}; + const int32_t buf_size = + arm_convolve_f16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims, CONV_BASIC_F16_LAYOUT); + float16_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f16(conv_basic_f16_weights_data, + CONV_BASIC_F16_OUT_CH, + CONV_BASIC_F16_FILTER_H * CONV_BASIC_F16_FILTER_W * CONV_BASIC_F16_IN_CH); + + if (buf_size > 0) + { + ctx.buf = malloc((size_t)buf_size); + ctx.size = buf_size; + TEST_ASSERT_NOT_NULL(ctx.buf); + } + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f16(&ctx, + &conv_params, + &input_dims, + conv_basic_f16_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_basic_f16_biases_data, + &output_dims, + output, + CONV_BASIC_F16_LAYOUT)); + + for (int i = 0; i < CONV_BASIC_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)conv_basic_f16_output_ref_data[i], (float)output[i]); + } + + if (ctx.buf != NULL) + { + memset(ctx.buf, 0, (size_t)buf_size); + free(ctx.buf); + } + free(packed_weights); +} + +void conv_k3_opt_f16_arm_convolve_f16_packed(void) +{ + float16_t output[CONV_K3_OPT_F16_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = CONV_K3_OPT_F16_PADDING_W, .h = CONV_K3_OPT_F16_PADDING_H}, + .stride = {.w = CONV_K3_OPT_F16_STRIDE_W, .h = CONV_K3_OPT_F16_STRIDE_H}, + .dilation = {.w = CONV_K3_OPT_F16_DILATION_W, .h = CONV_K3_OPT_F16_DILATION_H}, + .activation = {.min = CONV_K3_OPT_F16_OUT_ACTIVATION_MIN, .max = CONV_K3_OPT_F16_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_K3_OPT_F16_INPUT_BATCHES, + .w = CONV_K3_OPT_F16_INPUT_W, + .h = CONV_K3_OPT_F16_INPUT_H, + .c = CONV_K3_OPT_F16_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_K3_OPT_F16_OUT_CH, + .w = CONV_K3_OPT_F16_FILTER_W, + .h = CONV_K3_OPT_F16_FILTER_H, + .c = CONV_K3_OPT_F16_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_K3_OPT_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_K3_OPT_F16_INPUT_BATCHES, + .w = CONV_K3_OPT_F16_OUTPUT_W, + .h = CONV_K3_OPT_F16_OUTPUT_H, + .c = CONV_K3_OPT_F16_OUTPUT_C}; + float16_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f16(conv_k3_opt_f16_weights_data, + CONV_K3_OPT_F16_OUT_CH, + CONV_K3_OPT_F16_FILTER_H * CONV_K3_OPT_F16_FILTER_W * CONV_K3_OPT_F16_IN_CH); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f16(&ctx, + &conv_params, + &input_dims, + conv_k3_opt_f16_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_k3_opt_f16_biases_data, + &output_dims, + output, + CONV_K3_OPT_F16_LAYOUT)); + + for (int i = 0; i < CONV_K3_OPT_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)conv_k3_opt_f16_output_ref_data[i], (float)output[i]); + } + + free(packed_weights); +} + +void conv_k5_opt_f16_arm_convolve_f16_packed(void) +{ + float16_t output[CONV_K5_OPT_F16_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = CONV_K5_OPT_F16_PADDING_W, .h = CONV_K5_OPT_F16_PADDING_H}, + .stride = {.w = CONV_K5_OPT_F16_STRIDE_W, .h = CONV_K5_OPT_F16_STRIDE_H}, + .dilation = {.w = CONV_K5_OPT_F16_DILATION_W, .h = CONV_K5_OPT_F16_DILATION_H}, + .activation = {.min = CONV_K5_OPT_F16_OUT_ACTIVATION_MIN, .max = CONV_K5_OPT_F16_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_K5_OPT_F16_INPUT_BATCHES, + .w = CONV_K5_OPT_F16_INPUT_W, + .h = CONV_K5_OPT_F16_INPUT_H, + .c = CONV_K5_OPT_F16_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_K5_OPT_F16_OUT_CH, + .w = CONV_K5_OPT_F16_FILTER_W, + .h = CONV_K5_OPT_F16_FILTER_H, + .c = CONV_K5_OPT_F16_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_K5_OPT_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_K5_OPT_F16_INPUT_BATCHES, + .w = CONV_K5_OPT_F16_OUTPUT_W, + .h = CONV_K5_OPT_F16_OUTPUT_H, + .c = CONV_K5_OPT_F16_OUTPUT_C}; + float16_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f16(conv_k5_opt_f16_weights_data, + CONV_K5_OPT_F16_OUT_CH, + CONV_K5_OPT_F16_FILTER_H * CONV_K5_OPT_F16_FILTER_W * CONV_K5_OPT_F16_IN_CH); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f16(&ctx, + &conv_params, + &input_dims, + conv_k5_opt_f16_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_k5_opt_f16_biases_data, + &output_dims, + output, + CONV_K5_OPT_F16_LAYOUT)); + + for (int i = 0; i < CONV_K5_OPT_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)conv_k5_opt_f16_output_ref_data[i], (float)output[i]); + } + + free(packed_weights); +} diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_convolve_f32/CMakeLists.txt new file mode 100644 index 00000000..b3b595c4 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_convolve_f32) + +target_sources(test_arm_convolve_f32 PRIVATE + Unity/unity_test_arm_convolve_f32.c + Unity/TestRunner/unity_test_arm_convolve_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f32/Unity/unity_test_arm_convolve_f32.c b/Tests/UnitTest/TestCases/test_arm_convolve_f32/Unity/unity_test_arm_convolve_f32.c new file mode 100755 index 00000000..ebd55321 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f32/Unity/unity_test_arm_convolve_f32.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_convolve_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_conv_basic_f32_arm_convolve_f32(void) { conv_basic_f32_arm_convolve_f32(); } +void test_conv_basic_nhwc_f32_arm_convolve_f32(void) { conv_basic_nhwc_f32_arm_convolve_f32(); } +void test_conv_1x1_stride2_nhwc_f32_arm_convolve_f32(void) { conv_1x1_stride2_nhwc_f32_arm_convolve_f32(); } +void test_conv_k3_opt_f32_arm_convolve_f32(void) { conv_k3_opt_f32_arm_convolve_f32(); } +void test_conv_k5_opt_f32_arm_convolve_f32(void) { conv_k5_opt_f32_arm_convolve_f32(); } +void test_conv_k3_opt_nhwc_tuned_f32_arm_convolve_f32(void) { conv_k3_opt_nhwc_tuned_f32_arm_convolve_f32(); } +void test_conv_k5_opt_nhwc_tuned_f32_arm_convolve_f32(void) { conv_k5_opt_nhwc_tuned_f32_arm_convolve_f32(); } +void test_conv_kernel_2x2_f32_arm_convolve_f32(void) { conv_kernel_2x2_f32_arm_convolve_f32(); } +void test_conv_kernel_3x3_pad1_f32_arm_convolve_f32(void) { conv_kernel_3x3_pad1_f32_arm_convolve_f32(); } +void test_conv_match_basic_f32_arm_convolve_f32(void) { conv_match_basic_f32_arm_convolve_f32(); } +void test_conv_match_stride2pad1_f32_arm_convolve_f32(void) { conv_match_stride2pad1_f32_arm_convolve_f32(); } +void test_conv_match_conv_2_f32_arm_convolve_f32(void) { conv_match_conv_2_f32_arm_convolve_f32(); } +void test_conv_match_conv_3_f32_arm_convolve_f32(void) { conv_match_conv_3_f32_arm_convolve_f32(); } +void test_conv_match_conv_4_f32_arm_convolve_f32(void) { conv_match_conv_4_f32_arm_convolve_f32(); } +void test_conv_match_conv_5_f32_arm_convolve_f32(void) +{ +#if defined(USING_FVP_CORSTONE_300) + TEST_IGNORE_MESSAGE("Host-only due to FVP image size"); +#else + conv_match_conv_5_f32_arm_convolve_f32(); +#endif +} +void test_conv_match_out_activation_f32_arm_convolve_f32(void) { conv_match_out_activation_f32_arm_convolve_f32(); } +void test_conv_match_dilation_golden_f32_arm_convolve_f32(void) { conv_match_dilation_golden_f32_arm_convolve_f32(); } +void test_conv_match_2x2_dilation_f32_arm_convolve_f32(void) { conv_match_2x2_dilation_f32_arm_convolve_f32(); } +void test_conv_match_2x3_dilation_f32_arm_convolve_f32(void) { conv_match_2x3_dilation_f32_arm_convolve_f32(); } +void test_conv_match_1x1_basic_f32_arm_convolve_f32(void) { conv_match_1x1_basic_f32_arm_convolve_f32(); } +void test_conv_match_1x1_stride_x_f32_arm_convolve_f32(void) { conv_match_1x1_stride_x_f32_arm_convolve_f32(); } +void test_conv_match_1x1_stride_x_y_f32_arm_convolve_f32(void) { conv_match_1x1_stride_x_y_f32_arm_convolve_f32(); } +void test_conv_match_1x1_stride_x_y_1_f32_arm_convolve_f32(void) { conv_match_1x1_stride_x_y_1_f32_arm_convolve_f32(); } +void test_conv_match_1x1_stride_x_y_2_f32_arm_convolve_f32(void) { conv_match_1x1_stride_x_y_2_f32_arm_convolve_f32(); } +void test_conv_match_1xn_1_f32_arm_convolve_f32(void) { conv_match_1xn_1_f32_arm_convolve_f32(); } +void test_conv_match_1xn_2_f32_arm_convolve_f32(void) { conv_match_1xn_2_f32_arm_convolve_f32(); } +void test_conv_match_1xn_3_f32_arm_convolve_f32(void) { conv_match_1xn_3_f32_arm_convolve_f32(); } +void test_conv_match_1xn_4_f32_arm_convolve_f32(void) { conv_match_1xn_4_f32_arm_convolve_f32(); } +void test_conv_match_1xn_5_f32_arm_convolve_f32(void) { conv_match_1xn_5_f32_arm_convolve_f32(); } +void test_conv_match_1xn_6_generic_f32_arm_convolve_f32(void) { conv_match_1xn_6_generic_f32_arm_convolve_f32(); } +void test_conv_match_1xn_7_f32_arm_convolve_f32(void) { conv_match_1xn_7_f32_arm_convolve_f32(); } +void test_conv_match_1xn_8_f32_arm_convolve_f32(void) { conv_match_1xn_8_f32_arm_convolve_f32(); } +void test_conv_match_3x2_dilation_f32_arm_convolve_f32(void) { conv_match_3x2_dilation_f32_arm_convolve_f32(); } +void test_conv_match_3x3_dilation_5x5_input_f32_arm_convolve_f32(void) +{ + conv_match_3x3_dilation_5x5_input_f32_arm_convolve_f32(); +} +void test_conv_match_2x2_dilation_5x5_input_f32_arm_convolve_f32(void) +{ + conv_match_2x2_dilation_5x5_input_f32_arm_convolve_f32(); +} +void test_conv_match_1x1_basic_f32_arm_convolve_f32_packed(void) { conv_match_1x1_basic_f32_arm_convolve_f32_packed(); } +void test_conv_basic_f32_arm_convolve_f32_packed(void) { conv_basic_f32_arm_convolve_f32_packed(); } +void test_conv_k3_opt_f32_arm_convolve_f32_packed(void) { conv_k3_opt_f32_arm_convolve_f32_packed(); } +void test_conv_k5_opt_f32_arm_convolve_f32_packed(void) { conv_k5_opt_f32_arm_convolve_f32_packed(); } diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_f32/test_arm_convolve_f32.c b/Tests/UnitTest/TestCases/test_arm_convolve_f32/test_arm_convolve_f32.c new file mode 100755 index 00000000..5acf3add --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_convolve_f32/test_arm_convolve_f32.c @@ -0,0 +1,411 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/conv_1x1_stride2_nhwc_f32/test_data.h" +#include "../TestData/conv_basic_f32/test_data.h" +#include "../TestData/conv_basic_nhwc_f32/test_data.h" +#include "../TestData/conv_k3_opt_f32/test_data.h" +#include "../TestData/conv_k3_opt_nhwc_tuned_f32/test_data.h" +#include "../TestData/conv_k5_opt_f32/test_data.h" +#include "../TestData/conv_k5_opt_nhwc_tuned_f32/test_data.h" +#include "../TestData/conv_kernel_2x2_f32/test_data.h" +#include "../TestData/conv_kernel_3x3_pad1_f32/test_data.h" +#include "../TestData/conv_match_1x1_basic_f32/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_f32/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_1_f32/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_2_f32/test_data.h" +#include "../TestData/conv_match_1x1_stride_x_y_f32/test_data.h" +#include "../TestData/conv_match_1xn_1_f32/test_data.h" +#include "../TestData/conv_match_1xn_2_f32/test_data.h" +#include "../TestData/conv_match_1xn_3_f32/test_data.h" +#include "../TestData/conv_match_1xn_4_f32/test_data.h" +#include "../TestData/conv_match_1xn_5_f32/test_data.h" +#include "../TestData/conv_match_1xn_6_generic_f32/test_data.h" +#include "../TestData/conv_match_1xn_7_f32/test_data.h" +#include "../TestData/conv_match_1xn_8_f32/test_data.h" +#include "../TestData/conv_match_2x2_dilation_5x5_input_f32/test_data.h" +#include "../TestData/conv_match_2x2_dilation_f32/test_data.h" +#include "../TestData/conv_match_2x3_dilation_f32/test_data.h" +#include "../TestData/conv_match_3x2_dilation_f32/test_data.h" +#include "../TestData/conv_match_3x3_dilation_5x5_input_f32/test_data.h" +#include "../TestData/conv_match_basic_f32/test_data.h" +#include "../TestData/conv_match_conv_2_f32/test_data.h" +#include "../TestData/conv_match_conv_3_f32/test_data.h" +#include "../TestData/conv_match_conv_4_f32/test_data.h" +#if !defined(USING_FVP_CORSTONE_300) + #include "../TestData/conv_match_conv_5_f32/test_data.h" +#endif +#include "../TestData/conv_match_dilation_golden_f32/test_data.h" +#include "../TestData/conv_match_out_activation_f32/test_data.h" +#include "../TestData/conv_match_stride2pad1_f32/test_data.h" + +#define RUN_CONV_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_convolve_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + const cmsis_nn_conv_params_f32 conv_params = { \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .dilation = {.w = CASE_PREFIX##_DILATION_W, .h = CASE_PREFIX##_DILATION_H}, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_OUT_CH, \ + .w = CASE_PREFIX##_FILTER_W, \ + .h = CASE_PREFIX##_FILTER_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const int32_t buf_size = arm_convolve_f32_get_buffer_size( \ + &conv_params, &input_dims, &filter_dims, &output_dims, CASE_PREFIX##_LAYOUT); \ + \ + if (buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)buf_size); \ + ctx.size = buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_convolve_f32(&ctx, \ + &conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + case_name##_biases_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref_data[i], output[i]); \ + output[i] = 0.0f; \ + } \ + \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)buf_size); \ + free(ctx.buf); \ + ctx.buf = NULL; \ + ctx.size = 0; \ + } \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + const int32_t wrapper_buf_size = \ + arm_convolve_wrapper_f32_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims); \ + if (wrapper_buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)wrapper_buf_size); \ + ctx.size = wrapper_buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_convolve_wrapper_f32(&ctx, \ + &conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + case_name##_biases_data, \ + &output_dims, \ + output)); \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref_data[i], output[i]); \ + } \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)wrapper_buf_size); \ + free(ctx.buf); \ + } \ + } \ + } + +RUN_CONV_F32_CASE(CONV_BASIC_F32, conv_basic_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_BASIC_NHWC_F32, conv_basic_nhwc_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_1X1_STRIDE2_NHWC_F32, conv_1x1_stride2_nhwc_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_KERNEL_2X2_F32, conv_kernel_2x2_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_KERNEL_3X3_PAD1_F32, conv_kernel_3x3_pad1_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_K3_OPT_F32, conv_k3_opt_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_K5_OPT_F32, conv_k5_opt_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_K3_OPT_NHWC_TUNED_F32, conv_k3_opt_nhwc_tuned_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_K5_OPT_NHWC_TUNED_F32, conv_k5_opt_nhwc_tuned_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_BASIC_F32, conv_match_basic_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_STRIDE2PAD1_F32, conv_match_stride2pad1_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_CONV_2_F32, conv_match_conv_2_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_CONV_3_F32, conv_match_conv_3_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_CONV_4_F32, conv_match_conv_4_f32, 5.0e-4f) +#if !defined(USING_FVP_CORSTONE_300) +RUN_CONV_F32_CASE(CONV_MATCH_CONV_5_F32, conv_match_conv_5_f32, 5.0e-4f) +#endif +RUN_CONV_F32_CASE(CONV_MATCH_OUT_ACTIVATION_F32, conv_match_out_activation_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_DILATION_GOLDEN_F32, conv_match_dilation_golden_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_2X2_DILATION_F32, conv_match_2x2_dilation_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_2X3_DILATION_F32, conv_match_2x3_dilation_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1X1_BASIC_F32, conv_match_1x1_basic_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1X1_STRIDE_X_F32, conv_match_1x1_stride_x_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1X1_STRIDE_X_Y_F32, conv_match_1x1_stride_x_y_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1X1_STRIDE_X_Y_1_F32, conv_match_1x1_stride_x_y_1_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1X1_STRIDE_X_Y_2_F32, conv_match_1x1_stride_x_y_2_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_1_F32, conv_match_1xn_1_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_2_F32, conv_match_1xn_2_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_3_F32, conv_match_1xn_3_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_4_F32, conv_match_1xn_4_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_5_F32, conv_match_1xn_5_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_6_GENERIC_F32, conv_match_1xn_6_generic_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_7_F32, conv_match_1xn_7_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_1XN_8_F32, conv_match_1xn_8_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_3X2_DILATION_F32, conv_match_3x2_dilation_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_3X3_DILATION_5X5_INPUT_F32, conv_match_3x3_dilation_5x5_input_f32, 5.0e-4f) +RUN_CONV_F32_CASE(CONV_MATCH_2X2_DILATION_5X5_INPUT_F32, conv_match_2x2_dilation_5x5_input_f32, 5.0e-4f) + +/* + * The packed-convolution coverage allocates a temporary repacked filter and, + * for generic conv, may also allocate scratch, so it needs explicit test + * bodies instead of the standard RUN_CONV_F32_CASE macro. + */ +void conv_match_1x1_basic_f32_arm_convolve_f32_packed(void) +{ + float32_t output[CONV_MATCH_1X1_BASIC_F32_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = CONV_MATCH_1X1_BASIC_F32_PADDING_W, .h = CONV_MATCH_1X1_BASIC_F32_PADDING_H}, + .stride = {.w = CONV_MATCH_1X1_BASIC_F32_STRIDE_W, .h = CONV_MATCH_1X1_BASIC_F32_STRIDE_H}, + .dilation = {.w = CONV_MATCH_1X1_BASIC_F32_DILATION_W, .h = CONV_MATCH_1X1_BASIC_F32_DILATION_H}, + .activation = {.min = CONV_MATCH_1X1_BASIC_F32_OUT_ACTIVATION_MIN, + .max = CONV_MATCH_1X1_BASIC_F32_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_MATCH_1X1_BASIC_F32_INPUT_BATCHES, + .w = CONV_MATCH_1X1_BASIC_F32_INPUT_W, + .h = CONV_MATCH_1X1_BASIC_F32_INPUT_H, + .c = CONV_MATCH_1X1_BASIC_F32_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_MATCH_1X1_BASIC_F32_OUT_CH, + .w = CONV_MATCH_1X1_BASIC_F32_FILTER_W, + .h = CONV_MATCH_1X1_BASIC_F32_FILTER_H, + .c = CONV_MATCH_1X1_BASIC_F32_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_MATCH_1X1_BASIC_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_MATCH_1X1_BASIC_F32_INPUT_BATCHES, + .w = CONV_MATCH_1X1_BASIC_F32_OUTPUT_W, + .h = CONV_MATCH_1X1_BASIC_F32_OUTPUT_H, + .c = CONV_MATCH_1X1_BASIC_F32_OUTPUT_C}; + const int32_t buf_size = arm_convolve_f32_get_buffer_size( + &conv_params, &input_dims, &filter_dims, &output_dims, CONV_MATCH_1X1_BASIC_F32_LAYOUT); + float32_t *packed_weights = pack_rhs_nt_n_from_nt_t_f32( + conv_match_1x1_basic_f32_weights_data, + CONV_MATCH_1X1_BASIC_F32_OUT_CH, + CONV_MATCH_1X1_BASIC_F32_FILTER_W * CONV_MATCH_1X1_BASIC_F32_FILTER_H * CONV_MATCH_1X1_BASIC_F32_IN_CH); + + if (buf_size > 0) + { + ctx.buf = malloc((size_t)buf_size); + ctx.size = buf_size; + TEST_ASSERT_NOT_NULL(ctx.buf); + } + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f32(&ctx, + &conv_params, + &input_dims, + conv_match_1x1_basic_f32_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_match_1x1_basic_f32_biases_data, + &output_dims, + output, + CONV_MATCH_1X1_BASIC_F32_LAYOUT)); + + for (int i = 0; i < CONV_MATCH_1X1_BASIC_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-4f, conv_match_1x1_basic_f32_output_ref_data[i], output[i]); + } + + free(packed_weights); + if (ctx.buf != NULL) + { + memset(ctx.buf, 0, (size_t)buf_size); + free(ctx.buf); + } +} + +void conv_basic_f32_arm_convolve_f32_packed(void) +{ + float32_t output[CONV_BASIC_F32_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = CONV_BASIC_F32_PADDING_W, .h = CONV_BASIC_F32_PADDING_H}, + .stride = {.w = CONV_BASIC_F32_STRIDE_W, .h = CONV_BASIC_F32_STRIDE_H}, + .dilation = {.w = CONV_BASIC_F32_DILATION_W, .h = CONV_BASIC_F32_DILATION_H}, + .activation = {.min = CONV_BASIC_F32_OUT_ACTIVATION_MIN, .max = CONV_BASIC_F32_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_BASIC_F32_INPUT_BATCHES, + .w = CONV_BASIC_F32_INPUT_W, + .h = CONV_BASIC_F32_INPUT_H, + .c = CONV_BASIC_F32_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_BASIC_F32_OUT_CH, + .w = CONV_BASIC_F32_FILTER_W, + .h = CONV_BASIC_F32_FILTER_H, + .c = CONV_BASIC_F32_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_BASIC_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_BASIC_F32_INPUT_BATCHES, + .w = CONV_BASIC_F32_OUTPUT_W, + .h = CONV_BASIC_F32_OUTPUT_H, + .c = CONV_BASIC_F32_OUTPUT_C}; + const int32_t buf_size = + arm_convolve_f32_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims, CONV_BASIC_F32_LAYOUT); + float32_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f32(conv_basic_f32_weights_data, + CONV_BASIC_F32_OUT_CH, + CONV_BASIC_F32_FILTER_W * CONV_BASIC_F32_FILTER_H * CONV_BASIC_F32_IN_CH); + + if (buf_size > 0) + { + ctx.buf = malloc((size_t)buf_size); + ctx.size = buf_size; + TEST_ASSERT_NOT_NULL(ctx.buf); + } + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f32(&ctx, + &conv_params, + &input_dims, + conv_basic_f32_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_basic_f32_biases_data, + &output_dims, + output, + CONV_BASIC_F32_LAYOUT)); + + for (int i = 0; i < CONV_BASIC_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-4f, conv_basic_f32_output_ref_data[i], output[i]); + } + + free(packed_weights); + if (ctx.buf != NULL) + { + memset(ctx.buf, 0, (size_t)buf_size); + free(ctx.buf); + } +} + +void conv_k3_opt_f32_arm_convolve_f32_packed(void) +{ + float32_t output[CONV_K3_OPT_F32_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = CONV_K3_OPT_F32_PADDING_W, .h = CONV_K3_OPT_F32_PADDING_H}, + .stride = {.w = CONV_K3_OPT_F32_STRIDE_W, .h = CONV_K3_OPT_F32_STRIDE_H}, + .dilation = {.w = CONV_K3_OPT_F32_DILATION_W, .h = CONV_K3_OPT_F32_DILATION_H}, + .activation = {.min = CONV_K3_OPT_F32_OUT_ACTIVATION_MIN, .max = CONV_K3_OPT_F32_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_K3_OPT_F32_INPUT_BATCHES, + .w = CONV_K3_OPT_F32_INPUT_W, + .h = CONV_K3_OPT_F32_INPUT_H, + .c = CONV_K3_OPT_F32_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_K3_OPT_F32_OUT_CH, + .w = CONV_K3_OPT_F32_FILTER_W, + .h = CONV_K3_OPT_F32_FILTER_H, + .c = CONV_K3_OPT_F32_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_K3_OPT_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_K3_OPT_F32_INPUT_BATCHES, + .w = CONV_K3_OPT_F32_OUTPUT_W, + .h = CONV_K3_OPT_F32_OUTPUT_H, + .c = CONV_K3_OPT_F32_OUTPUT_C}; + float32_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f32(conv_k3_opt_f32_weights_data, + CONV_K3_OPT_F32_OUT_CH, + CONV_K3_OPT_F32_FILTER_H * CONV_K3_OPT_F32_FILTER_W * CONV_K3_OPT_F32_IN_CH); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f32(&ctx, + &conv_params, + &input_dims, + conv_k3_opt_f32_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_k3_opt_f32_biases_data, + &output_dims, + output, + CONV_K3_OPT_F32_LAYOUT)); + + for (int i = 0; i < CONV_K3_OPT_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-4f, conv_k3_opt_f32_output_ref_data[i], output[i]); + } + + free(packed_weights); +} + +void conv_k5_opt_f32_arm_convolve_f32_packed(void) +{ + float32_t output[CONV_K5_OPT_F32_DST_SIZE] = {0}; + cmsis_nn_context ctx = {0}; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = CONV_K5_OPT_F32_PADDING_W, .h = CONV_K5_OPT_F32_PADDING_H}, + .stride = {.w = CONV_K5_OPT_F32_STRIDE_W, .h = CONV_K5_OPT_F32_STRIDE_H}, + .dilation = {.w = CONV_K5_OPT_F32_DILATION_W, .h = CONV_K5_OPT_F32_DILATION_H}, + .activation = {.min = CONV_K5_OPT_F32_OUT_ACTIVATION_MIN, .max = CONV_K5_OPT_F32_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = CONV_K5_OPT_F32_INPUT_BATCHES, + .w = CONV_K5_OPT_F32_INPUT_W, + .h = CONV_K5_OPT_F32_INPUT_H, + .c = CONV_K5_OPT_F32_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = CONV_K5_OPT_F32_OUT_CH, + .w = CONV_K5_OPT_F32_FILTER_W, + .h = CONV_K5_OPT_F32_FILTER_H, + .c = CONV_K5_OPT_F32_IN_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CONV_K5_OPT_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = CONV_K5_OPT_F32_INPUT_BATCHES, + .w = CONV_K5_OPT_F32_OUTPUT_W, + .h = CONV_K5_OPT_F32_OUTPUT_H, + .c = CONV_K5_OPT_F32_OUTPUT_C}; + float32_t *packed_weights = + pack_rhs_nt_n_from_nt_t_f32(conv_k5_opt_f32_weights_data, + CONV_K5_OPT_F32_OUT_CH, + CONV_K5_OPT_F32_FILTER_H * CONV_K5_OPT_F32_FILTER_W * CONV_K5_OPT_F32_IN_CH); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_f32(&ctx, + &conv_params, + &input_dims, + conv_k5_opt_f32_input_data, + &filter_dims, + packed_weights, + &bias_dims, + conv_k5_opt_f32_biases_data, + &output_dims, + output, + CONV_K5_OPT_F32_LAYOUT)); + + for (int i = 0; i < CONV_K5_OPT_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-4f, conv_k5_opt_f32_output_ref_data[i], output[i]); + } + + free(packed_weights); +} diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/CMakeLists.txt new file mode 100644 index 00000000..d869a723 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_depthwise_conv_f16) + +target_sources(test_arm_depthwise_conv_f16 PRIVATE + Unity/unity_test_arm_depthwise_conv_f16.c + Unity/TestRunner/unity_test_arm_depthwise_conv_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/Unity/unity_test_arm_depthwise_conv_f16.c b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/Unity/unity_test_arm_depthwise_conv_f16.c new file mode 100644 index 00000000..c9ba85c5 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/Unity/unity_test_arm_depthwise_conv_f16.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_depthwise_conv_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_depthwise_basic_f16_arm_depthwise_conv_f16(void) { depthwise_basic_f16_arm_depthwise_conv_f16(); } +void test_depthwise_basic_smallc_nhwc_f16_arm_depthwise_conv_f16(void) +{ + depthwise_basic_smallc_nhwc_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_ic1_to_conv_nhwc_f16_arm_depthwise_conv_f16(void) +{ + depthwise_ic1_to_conv_nhwc_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_k3_1d_opt_batch2_f16_arm_depthwise_conv_f16(void) +{ + depthwise_k3_1d_opt_batch2_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_k3_1d_opt_nhwc_f16_arm_depthwise_conv_f16(void) +{ + depthwise_k3_1d_opt_nhwc_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_2x5_opt_batch2_f16_arm_depthwise_conv_f16(void) +{ + depthwise_2x5_opt_batch2_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_2x5_opt_nhwc_chmult16_f16_arm_depthwise_conv_f16(void) +{ + depthwise_2x5_opt_nhwc_chmult16_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_kernel_2x2_f16_arm_depthwise_conv_f16(void) { depthwise_kernel_2x2_f16_arm_depthwise_conv_f16(); } +void test_depthwise_kernel_3x3_f16_arm_depthwise_conv_f16(void) { depthwise_kernel_3x3_f16_arm_depthwise_conv_f16(); } +void test_depthwise_kernel_3x3_null_bias_f16_arm_depthwise_conv_f16(void) +{ + depthwise_kernel_3x3_null_bias_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_match_basic_f16_arm_depthwise_conv_f16(void) { depthwise_match_basic_f16_arm_depthwise_conv_f16(); } +void test_depthwise_match_sub_block_f16_arm_depthwise_conv_f16(void) +{ + depthwise_match_sub_block_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_match_dilation_f16_arm_depthwise_conv_f16(void) +{ + depthwise_match_dilation_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_match_out_activation_f16_arm_depthwise_conv_f16(void) +{ + depthwise_match_out_activation_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_match_stride2pad1_f16_arm_depthwise_conv_f16(void) +{ + depthwise_match_stride2pad1_f16_arm_depthwise_conv_f16(); +} +void test_depthwise_ic1_to_conv_nhwc_f16_arm_depthwise_conv_f16_no_ctx(void) +{ + depthwise_ic1_to_conv_nhwc_f16_arm_depthwise_conv_f16_no_ctx(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/test_arm_depthwise_conv_f16.c b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/test_arm_depthwise_conv_f16.c new file mode 100644 index 00000000..11f2f5a5 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f16/test_arm_depthwise_conv_f16.c @@ -0,0 +1,182 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include +#include + +#include "../TestData/depthwise_2x5_opt_batch2_f16/test_data.h" +#include "../TestData/depthwise_2x5_opt_nhwc_chmult16_f16/test_data.h" +#include "../TestData/depthwise_basic_f16/test_data.h" +#include "../TestData/depthwise_basic_smallc_nhwc_f16/test_data.h" +#include "../TestData/depthwise_ic1_to_conv_nhwc_f16/test_data.h" +#include "../TestData/depthwise_k3_1d_opt_batch2_f16/test_data.h" +#include "../TestData/depthwise_k3_1d_opt_nhwc_f16/test_data.h" +#include "../TestData/depthwise_kernel_2x2_f16/test_data.h" +#include "../TestData/depthwise_kernel_3x3_f16/test_data.h" +#include "../TestData/depthwise_kernel_3x3_null_bias_f16/test_data.h" +#include "../TestData/depthwise_match_basic_f16/test_data.h" +#include "../TestData/depthwise_match_dilation_f16/test_data.h" +#include "../TestData/depthwise_match_out_activation_f16/test_data.h" +#include "../TestData/depthwise_match_stride2pad1_f16/test_data.h" +#include "../TestData/depthwise_match_sub_block_f16/test_data.h" + +#define RUN_DEPTHWISE_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_depthwise_conv_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dw_conv_params_f16 dw_conv_params = { \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .dilation = {.w = CASE_PREFIX##_DILATION_W, .h = CASE_PREFIX##_DILATION_H}, \ + .ch_mult = CASE_PREFIX##_CH_MULT, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_IN_CH, \ + .w = CASE_PREFIX##_FILTER_W, \ + .h = CASE_PREFIX##_FILTER_H, \ + .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const int32_t buf_size = arm_depthwise_conv_f16_get_buffer_size( \ + &dw_conv_params, &input_dims, &filter_dims, &output_dims, CASE_PREFIX##_LAYOUT); \ + \ + if (buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)buf_size); \ + ctx.size = buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_depthwise_conv_f16(&ctx, \ + &dw_conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + CASE_PREFIX##_USE_NULL_BIAS ? NULL : case_name##_biases_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref_data[i], (float)output[i]); \ + output[i] = (float16_t)0.0f; \ + } \ + \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)buf_size); \ + free(ctx.buf); \ + ctx.buf = NULL; \ + ctx.size = 0; \ + } \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + const int32_t wrapper_buf_size = arm_depthwise_conv_wrapper_f16_get_buffer_size( \ + &dw_conv_params, &input_dims, &filter_dims, &output_dims); \ + if (wrapper_buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)wrapper_buf_size); \ + ctx.size = wrapper_buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_depthwise_conv_wrapper_f16(&ctx, \ + &dw_conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + CASE_PREFIX##_USE_NULL_BIAS ? NULL : case_name##_biases_data, \ + &output_dims, \ + output)); \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref_data[i], (float)output[i]); \ + } \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)wrapper_buf_size); \ + free(ctx.buf); \ + } \ + } \ + } + +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_BASIC_F16, depthwise_basic_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_BASIC_SMALLC_NHWC_F16, depthwise_basic_smallc_nhwc_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_IC1_TO_CONV_NHWC_F16, depthwise_ic1_to_conv_nhwc_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_KERNEL_2X2_F16, depthwise_kernel_2x2_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_KERNEL_3X3_F16, depthwise_kernel_3x3_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_KERNEL_3X3_NULL_BIAS_F16, depthwise_kernel_3x3_null_bias_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_K3_1D_OPT_BATCH2_F16, depthwise_k3_1d_opt_batch2_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_K3_1D_OPT_NHWC_F16, depthwise_k3_1d_opt_nhwc_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_2X5_OPT_BATCH2_F16, depthwise_2x5_opt_batch2_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F16, depthwise_2x5_opt_nhwc_chmult16_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_MATCH_BASIC_F16, depthwise_match_basic_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_MATCH_SUB_BLOCK_F16, depthwise_match_sub_block_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_MATCH_DILATION_F16, depthwise_match_dilation_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_MATCH_OUT_ACTIVATION_F16, depthwise_match_out_activation_f16, 2.0e-2f) +RUN_DEPTHWISE_F16_CASE(DEPTHWISE_MATCH_STRIDE2PAD1_F16, depthwise_match_stride2pad1_f16, 2.0e-2f) + +void depthwise_ic1_to_conv_nhwc_f16_arm_depthwise_conv_f16_no_ctx(void) +{ + float16_t output[DEPTHWISE_IC1_TO_CONV_NHWC_F16_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_dw_conv_params_f16 dw_conv_params = { + .padding = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_PADDING_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_PADDING_H}, + .stride = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_STRIDE_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_STRIDE_H}, + .dilation = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_DILATION_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_DILATION_H}, + .ch_mult = DEPTHWISE_IC1_TO_CONV_NHWC_F16_CH_MULT, + .activation = {.min = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_ACTIVATION_MIN, + .max = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_ACTIVATION_MAX}}; + const cmsis_nn_dims input_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_BATCHES, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F16_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F16_IN_CH, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_FILTER_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_FILTER_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F16_INPUT_BATCHES, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F16_OUTPUT_C}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_depthwise_conv_f16(&ctx, + &dw_conv_params, + &input_dims, + depthwise_ic1_to_conv_nhwc_f16_input_data, + &filter_dims, + depthwise_ic1_to_conv_nhwc_f16_weights_data, + &bias_dims, + depthwise_ic1_to_conv_nhwc_f16_biases_data, + &output_dims, + output, + DEPTHWISE_IC1_TO_CONV_NHWC_F16_LAYOUT)); + + for (int i = 0; i < DEPTHWISE_IC1_TO_CONV_NHWC_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)depthwise_ic1_to_conv_nhwc_f16_output_ref_data[i], (float)output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/CMakeLists.txt new file mode 100644 index 00000000..50efb324 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_depthwise_conv_f32) + +target_sources(test_arm_depthwise_conv_f32 PRIVATE + Unity/unity_test_arm_depthwise_conv_f32.c + Unity/TestRunner/unity_test_arm_depthwise_conv_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/Unity/unity_test_arm_depthwise_conv_f32.c b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/Unity/unity_test_arm_depthwise_conv_f32.c new file mode 100644 index 00000000..a983ac64 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/Unity/unity_test_arm_depthwise_conv_f32.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_depthwise_conv_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_depthwise_basic_f32_arm_depthwise_conv_f32(void) { depthwise_basic_f32_arm_depthwise_conv_f32(); } +void test_depthwise_basic_smallc_nhwc_f32_arm_depthwise_conv_f32(void) +{ + depthwise_basic_smallc_nhwc_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_ic1_to_conv_nhwc_f32_arm_depthwise_conv_f32(void) +{ + depthwise_ic1_to_conv_nhwc_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_k3_1d_opt_batch2_f32_arm_depthwise_conv_f32(void) +{ + depthwise_k3_1d_opt_batch2_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_k3_1d_opt_nhwc_f32_arm_depthwise_conv_f32(void) +{ + depthwise_k3_1d_opt_nhwc_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_2x5_opt_batch2_f32_arm_depthwise_conv_f32(void) +{ + depthwise_2x5_opt_batch2_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_2x5_opt_nhwc_chmult16_f32_arm_depthwise_conv_f32(void) +{ + depthwise_2x5_opt_nhwc_chmult16_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_kernel_2x2_f32_arm_depthwise_conv_f32(void) { depthwise_kernel_2x2_f32_arm_depthwise_conv_f32(); } +void test_depthwise_kernel_3x3_f32_arm_depthwise_conv_f32(void) { depthwise_kernel_3x3_f32_arm_depthwise_conv_f32(); } +void test_depthwise_kernel_3x3_null_bias_f32_arm_depthwise_conv_f32(void) +{ + depthwise_kernel_3x3_null_bias_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_match_basic_f32_arm_depthwise_conv_f32(void) { depthwise_match_basic_f32_arm_depthwise_conv_f32(); } +void test_depthwise_match_sub_block_f32_arm_depthwise_conv_f32(void) +{ + depthwise_match_sub_block_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_match_dilation_f32_arm_depthwise_conv_f32(void) +{ + depthwise_match_dilation_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_match_out_activation_f32_arm_depthwise_conv_f32(void) +{ + depthwise_match_out_activation_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_match_stride2pad1_f32_arm_depthwise_conv_f32(void) +{ + depthwise_match_stride2pad1_f32_arm_depthwise_conv_f32(); +} +void test_depthwise_ic1_to_conv_nhwc_f32_arm_depthwise_conv_f32_no_ctx(void) +{ + depthwise_ic1_to_conv_nhwc_f32_arm_depthwise_conv_f32_no_ctx(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/test_arm_depthwise_conv_f32.c b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/test_arm_depthwise_conv_f32.c new file mode 100644 index 00000000..b289a312 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_depthwise_conv_f32/test_arm_depthwise_conv_f32.c @@ -0,0 +1,182 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include +#include + +#include "../TestData/depthwise_2x5_opt_batch2_f32/test_data.h" +#include "../TestData/depthwise_2x5_opt_nhwc_chmult16_f32/test_data.h" +#include "../TestData/depthwise_basic_f32/test_data.h" +#include "../TestData/depthwise_basic_smallc_nhwc_f32/test_data.h" +#include "../TestData/depthwise_ic1_to_conv_nhwc_f32/test_data.h" +#include "../TestData/depthwise_k3_1d_opt_batch2_f32/test_data.h" +#include "../TestData/depthwise_k3_1d_opt_nhwc_f32/test_data.h" +#include "../TestData/depthwise_kernel_2x2_f32/test_data.h" +#include "../TestData/depthwise_kernel_3x3_f32/test_data.h" +#include "../TestData/depthwise_kernel_3x3_null_bias_f32/test_data.h" +#include "../TestData/depthwise_match_basic_f32/test_data.h" +#include "../TestData/depthwise_match_dilation_f32/test_data.h" +#include "../TestData/depthwise_match_out_activation_f32/test_data.h" +#include "../TestData/depthwise_match_stride2pad1_f32/test_data.h" +#include "../TestData/depthwise_match_sub_block_f32/test_data.h" + +#define RUN_DEPTHWISE_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_depthwise_conv_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dw_conv_params_f32 dw_conv_params = { \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .dilation = {.w = CASE_PREFIX##_DILATION_W, .h = CASE_PREFIX##_DILATION_H}, \ + .ch_mult = CASE_PREFIX##_CH_MULT, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_IN_CH, \ + .w = CASE_PREFIX##_FILTER_W, \ + .h = CASE_PREFIX##_FILTER_H, \ + .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const int32_t buf_size = arm_depthwise_conv_f32_get_buffer_size( \ + &dw_conv_params, &input_dims, &filter_dims, &output_dims, CASE_PREFIX##_LAYOUT); \ + \ + if (buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)buf_size); \ + ctx.size = buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_depthwise_conv_f32(&ctx, \ + &dw_conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + CASE_PREFIX##_USE_NULL_BIAS ? NULL : case_name##_biases_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref_data[i], output[i]); \ + output[i] = 0.0f; \ + } \ + \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)buf_size); \ + free(ctx.buf); \ + ctx.buf = NULL; \ + ctx.size = 0; \ + } \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + const int32_t wrapper_buf_size = arm_depthwise_conv_wrapper_f32_get_buffer_size( \ + &dw_conv_params, &input_dims, &filter_dims, &output_dims); \ + if (wrapper_buf_size > 0) \ + { \ + ctx.buf = malloc((size_t)wrapper_buf_size); \ + ctx.size = wrapper_buf_size; \ + TEST_ASSERT_NOT_NULL(ctx.buf); \ + } \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_depthwise_conv_wrapper_f32(&ctx, \ + &dw_conv_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + CASE_PREFIX##_USE_NULL_BIAS ? NULL : case_name##_biases_data, \ + &output_dims, \ + output)); \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref_data[i], output[i]); \ + } \ + if (ctx.buf != NULL) \ + { \ + memset(ctx.buf, 0, (size_t)wrapper_buf_size); \ + free(ctx.buf); \ + } \ + } \ + } + +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_BASIC_F32, depthwise_basic_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_BASIC_SMALLC_NHWC_F32, depthwise_basic_smallc_nhwc_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_IC1_TO_CONV_NHWC_F32, depthwise_ic1_to_conv_nhwc_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_KERNEL_2X2_F32, depthwise_kernel_2x2_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_KERNEL_3X3_F32, depthwise_kernel_3x3_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_KERNEL_3X3_NULL_BIAS_F32, depthwise_kernel_3x3_null_bias_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_K3_1D_OPT_BATCH2_F32, depthwise_k3_1d_opt_batch2_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_K3_1D_OPT_NHWC_F32, depthwise_k3_1d_opt_nhwc_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_2X5_OPT_BATCH2_F32, depthwise_2x5_opt_batch2_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_2X5_OPT_NHWC_CHMULT16_F32, depthwise_2x5_opt_nhwc_chmult16_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_MATCH_BASIC_F32, depthwise_match_basic_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_MATCH_SUB_BLOCK_F32, depthwise_match_sub_block_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_MATCH_DILATION_F32, depthwise_match_dilation_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_MATCH_OUT_ACTIVATION_F32, depthwise_match_out_activation_f32, 5.0e-4f) +RUN_DEPTHWISE_F32_CASE(DEPTHWISE_MATCH_STRIDE2PAD1_F32, depthwise_match_stride2pad1_f32, 5.0e-4f) + +void depthwise_ic1_to_conv_nhwc_f32_arm_depthwise_conv_f32_no_ctx(void) +{ + float32_t output[DEPTHWISE_IC1_TO_CONV_NHWC_F32_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_dw_conv_params_f32 dw_conv_params = { + .padding = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_PADDING_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_PADDING_H}, + .stride = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_STRIDE_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_STRIDE_H}, + .dilation = {.w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_DILATION_W, .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_DILATION_H}, + .ch_mult = DEPTHWISE_IC1_TO_CONV_NHWC_F32_CH_MULT, + .activation = {.min = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_ACTIVATION_MIN, + .max = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_ACTIVATION_MAX}}; + const cmsis_nn_dims input_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_BATCHES, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F32_IN_CH}; + const cmsis_nn_dims filter_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F32_IN_CH, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_FILTER_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_FILTER_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = DEPTHWISE_IC1_TO_CONV_NHWC_F32_INPUT_BATCHES, + .w = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_W, + .h = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_H, + .c = DEPTHWISE_IC1_TO_CONV_NHWC_F32_OUTPUT_C}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_depthwise_conv_f32(&ctx, + &dw_conv_params, + &input_dims, + depthwise_ic1_to_conv_nhwc_f32_input_data, + &filter_dims, + depthwise_ic1_to_conv_nhwc_f32_weights_data, + &bias_dims, + depthwise_ic1_to_conv_nhwc_f32_biases_data, + &output_dims, + output, + DEPTHWISE_IC1_TO_CONV_NHWC_F32_LAYOUT)); + + for (int i = 0; i < DEPTHWISE_IC1_TO_CONV_NHWC_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-4f, depthwise_ic1_to_conv_nhwc_f32_output_ref_data[i], output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/CMakeLists.txt new file mode 100644 index 00000000..276cbf89 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_ds_cnn_s_body_f16) + +target_sources(test_arm_ds_cnn_s_body_f16 PRIVATE + Unity/unity_test_arm_ds_cnn_s_body_f16.c + Unity/TestRunner/unity_test_arm_ds_cnn_s_body_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/Unity/unity_test_arm_ds_cnn_s_body_f16.c b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/Unity/unity_test_arm_ds_cnn_s_body_f16.c new file mode 100644 index 00000000..8487f056 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/Unity/unity_test_arm_ds_cnn_s_body_f16.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_ds_cnn_s_body_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_ds_cnn_s_body_f16_arm_ds_cnn_s_body_f16(void) { ds_cnn_s_body_f16_arm_ds_cnn_s_body_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/test_arm_ds_cnn_s_body_f16.c b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/test_arm_ds_cnn_s_body_f16.c new file mode 100644 index 00000000..69202a43 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f16/test_arm_ds_cnn_s_body_f16.c @@ -0,0 +1,251 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/ds_cnn_s_body_f16/test_data.h" + +static int32_t ds_cnn_s_body_f16_get_max_buffer_size(void) +{ + const cmsis_nn_dw_conv_params_f16 dw_conv_params = { + .padding = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_H}, + .ch_mult = DS_CNN_S_BODY_F16_DEPTHWISE_CH_MULT, + .activation = {.min = DS_CNN_S_BODY_F16_RELU_MIN, .max = DS_CNN_S_BODY_F16_RELU_MAX}, + }; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = DS_CNN_S_BODY_F16_POINTWISE_PADDING_W, .h = DS_CNN_S_BODY_F16_POINTWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F16_POINTWISE_STRIDE_W, .h = DS_CNN_S_BODY_F16_POINTWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F16_POINTWISE_DILATION_W, .h = DS_CNN_S_BODY_F16_POINTWISE_DILATION_H}, + .activation = {.min = DS_CNN_S_BODY_F16_RELU_MIN, .max = DS_CNN_S_BODY_F16_RELU_MAX}, + }; + const cmsis_nn_fc_params_f16 fc_params = { + .activation = {.min = DS_CNN_S_BODY_F16_IDENTITY_MIN, .max = DS_CNN_S_BODY_F16_IDENTITY_MAX}, + }; + const cmsis_nn_dims feature_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F16_INPUT_W, + .h = DS_CNN_S_BODY_F16_INPUT_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims dw_filter_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_C, + .w = DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_W, + .h = DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims pw_filter_dims = { + .n = DS_CNN_S_BODY_F16_CHANNELS, + .w = DS_CNN_S_BODY_F16_POINTWISE_FILTER_W, + .h = DS_CNN_S_BODY_F16_POINTWISE_FILTER_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims pooled_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_W, + .h = DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims fc_filter_dims = { + .n = DS_CNN_S_BODY_F16_POOLED_SIZE, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F16_NUM_CLASSES, + }; + const cmsis_nn_dims fc_output_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F16_NUM_CLASSES, + }; + + int32_t max_buffer_size = + arm_depthwise_conv_wrapper_f16_get_buffer_size(&dw_conv_params, &feature_dims, &dw_filter_dims, &feature_dims); + int32_t size = + arm_convolve_wrapper_f16_get_buffer_size(&conv_params, &feature_dims, &pw_filter_dims, &feature_dims); + if (size > max_buffer_size) + { + max_buffer_size = size; + } + size = arm_fully_connected_f16_get_buffer_size( + &fc_params, &pooled_dims, &fc_filter_dims, &fc_output_dims, ARM_NN_LAYOUT_NHWC); + if (size > max_buffer_size) + { + max_buffer_size = size; + } + return max_buffer_size; +} + +void ds_cnn_s_body_f16_arm_ds_cnn_s_body_f16(void) +{ + static float16_t feature_buffer_0[DS_CNN_S_BODY_F16_FEATURE_MAP_SIZE]; + static float16_t feature_buffer_1[DS_CNN_S_BODY_F16_FEATURE_MAP_SIZE]; + static float16_t pooled_output[DS_CNN_S_BODY_F16_POOLED_SIZE]; + static float16_t fc_output[DS_CNN_S_BODY_F16_DST_SIZE]; + static float16_t softmax_output[DS_CNN_S_BODY_F16_DST_SIZE]; + static uint8_t scratch_storage[DS_CNN_S_BODY_F16_FEATURE_MAP_SIZE * sizeof(float16_t)]; + const int32_t scratch_size = ds_cnn_s_body_f16_get_max_buffer_size(); + void *scratch_buf = scratch_size > 0 ? scratch_storage : NULL; + const cmsis_nn_context scratch_ctx = {.buf = scratch_buf, .size = scratch_size}; + const cmsis_nn_context no_ctx = {0}; + const cmsis_nn_dw_conv_params_f16 dw_conv_params = { + .padding = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_W, .h = DS_CNN_S_BODY_F16_DEPTHWISE_DILATION_H}, + .ch_mult = DS_CNN_S_BODY_F16_DEPTHWISE_CH_MULT, + .activation = {.min = DS_CNN_S_BODY_F16_RELU_MIN, .max = DS_CNN_S_BODY_F16_RELU_MAX}, + }; + const cmsis_nn_conv_params_f16 conv_params = { + .padding = {.w = DS_CNN_S_BODY_F16_POINTWISE_PADDING_W, .h = DS_CNN_S_BODY_F16_POINTWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F16_POINTWISE_STRIDE_W, .h = DS_CNN_S_BODY_F16_POINTWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F16_POINTWISE_DILATION_W, .h = DS_CNN_S_BODY_F16_POINTWISE_DILATION_H}, + .activation = {.min = DS_CNN_S_BODY_F16_RELU_MIN, .max = DS_CNN_S_BODY_F16_RELU_MAX}, + }; + const cmsis_nn_pool_params_f16 pool_params = { + .stride = {.w = 1, .h = 1}, + .padding = {.w = 0, .h = 0}, + .activation = {.min = DS_CNN_S_BODY_F16_IDENTITY_MIN, .max = DS_CNN_S_BODY_F16_IDENTITY_MAX}, + }; + const cmsis_nn_fc_params_f16 fc_params = { + .activation = {.min = DS_CNN_S_BODY_F16_IDENTITY_MIN, .max = DS_CNN_S_BODY_F16_IDENTITY_MAX}, + }; + const cmsis_nn_dims feature_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F16_INPUT_W, + .h = DS_CNN_S_BODY_F16_INPUT_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims dw_filter_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_C, + .w = DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_W, + .h = DS_CNN_S_BODY_F16_DEPTHWISE_FILTER_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims pw_filter_dims = { + .n = DS_CNN_S_BODY_F16_CHANNELS, + .w = DS_CNN_S_BODY_F16_POINTWISE_FILTER_W, + .h = DS_CNN_S_BODY_F16_POINTWISE_FILTER_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims block_bias_dims = {.n = 1, .w = 1, .h = 1, .c = DS_CNN_S_BODY_F16_CHANNELS}; + const cmsis_nn_dims avg_filter_dims = { + .n = 1, + .w = DS_CNN_S_BODY_F16_AVGPOOL_FILTER_W, + .h = DS_CNN_S_BODY_F16_AVGPOOL_FILTER_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims pooled_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_W, + .h = DS_CNN_S_BODY_F16_AVGPOOL_OUTPUT_H, + .c = DS_CNN_S_BODY_F16_CHANNELS, + }; + const cmsis_nn_dims fc_filter_dims = { + .n = DS_CNN_S_BODY_F16_POOLED_SIZE, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F16_NUM_CLASSES, + }; + const cmsis_nn_dims fc_bias_dims = {.n = 1, .w = 1, .h = 1, .c = DS_CNN_S_BODY_F16_NUM_CLASSES}; + const cmsis_nn_dims fc_output_dims = { + .n = DS_CNN_S_BODY_F16_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F16_NUM_CLASSES, + }; + const float16_t *dw_weights[DS_CNN_S_BODY_F16_NUM_BLOCKS] = { + ds_cnn_s_body_f16_dwconv0_weights_data, + ds_cnn_s_body_f16_dwconv1_weights_data, + ds_cnn_s_body_f16_dwconv2_weights_data, + ds_cnn_s_body_f16_dwconv3_weights_data, + }; + const float16_t *dw_biases[DS_CNN_S_BODY_F16_NUM_BLOCKS] = { + ds_cnn_s_body_f16_dwconv0_biases_data, + ds_cnn_s_body_f16_dwconv1_biases_data, + ds_cnn_s_body_f16_dwconv2_biases_data, + ds_cnn_s_body_f16_dwconv3_biases_data, + }; + const float16_t *pw_weights[DS_CNN_S_BODY_F16_NUM_BLOCKS] = { + ds_cnn_s_body_f16_pwconv0_weights_data, + ds_cnn_s_body_f16_pwconv1_weights_data, + ds_cnn_s_body_f16_pwconv2_weights_data, + ds_cnn_s_body_f16_pwconv3_weights_data, + }; + const float16_t *pw_biases[DS_CNN_S_BODY_F16_NUM_BLOCKS] = { + ds_cnn_s_body_f16_pwconv0_biases_data, + ds_cnn_s_body_f16_pwconv1_biases_data, + ds_cnn_s_body_f16_pwconv2_biases_data, + ds_cnn_s_body_f16_pwconv3_biases_data, + }; + const float16_t *current = ds_cnn_s_body_f16_input_data; + + TEST_ASSERT_NOT_NULL(feature_buffer_0); + TEST_ASSERT_NOT_NULL(feature_buffer_1); + TEST_ASSERT_NOT_NULL(pooled_output); + TEST_ASSERT_NOT_NULL(fc_output); + TEST_ASSERT_NOT_NULL(softmax_output); + if (scratch_size > 0) + { + TEST_ASSERT_NOT_NULL(scratch_buf); + TEST_ASSERT_TRUE(scratch_size <= (int32_t)sizeof(scratch_storage)); + } + + for (int32_t block_idx = 0; block_idx < DS_CNN_S_BODY_F16_NUM_BLOCKS; ++block_idx) + { + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_depthwise_conv_wrapper_f16(&scratch_ctx, + &dw_conv_params, + &feature_dims, + current, + &dw_filter_dims, + dw_weights[block_idx], + &block_bias_dims, + dw_biases[block_idx], + &feature_dims, + feature_buffer_0)); + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_wrapper_f16(&scratch_ctx, + &conv_params, + &feature_dims, + feature_buffer_0, + &pw_filter_dims, + pw_weights[block_idx], + &block_bias_dims, + pw_biases[block_idx], + &feature_dims, + feature_buffer_1)); + current = feature_buffer_1; + } + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_avg_pool_f16(&no_ctx, &pool_params, &feature_dims, current, &avg_filter_dims, &pooled_dims, pooled_output)); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_fully_connected_f16(&scratch_ctx, + &fc_params, + &pooled_dims, + pooled_output, + &fc_filter_dims, + ds_cnn_s_body_f16_fc_weights_data, + &fc_bias_dims, + ds_cnn_s_body_f16_fc_biases_data, + &fc_output_dims, + fc_output, + ARM_NN_LAYOUT_NHWC)); + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_softmax_f16(fc_output, DS_CNN_S_BODY_F16_SOFTMAX_ROWS, DS_CNN_S_BODY_F16_SOFTMAX_COLS, softmax_output)); + + for (int32_t i = 0; i < DS_CNN_S_BODY_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN( + 2.5e-2f, (float32_t)ds_cnn_s_body_f16_output_ref_data[i], (float32_t)softmax_output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/CMakeLists.txt new file mode 100644 index 00000000..1e6db5e2 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_ds_cnn_s_body_f32) + +target_sources(test_arm_ds_cnn_s_body_f32 PRIVATE + Unity/unity_test_arm_ds_cnn_s_body_f32.c + Unity/TestRunner/unity_test_arm_ds_cnn_s_body_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/Unity/unity_test_arm_ds_cnn_s_body_f32.c b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/Unity/unity_test_arm_ds_cnn_s_body_f32.c new file mode 100644 index 00000000..fc59aee0 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/Unity/unity_test_arm_ds_cnn_s_body_f32.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_ds_cnn_s_body_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_ds_cnn_s_body_f32_arm_ds_cnn_s_body_f32(void) { ds_cnn_s_body_f32_arm_ds_cnn_s_body_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/test_arm_ds_cnn_s_body_f32.c b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/test_arm_ds_cnn_s_body_f32.c new file mode 100644 index 00000000..8bfe8498 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_ds_cnn_s_body_f32/test_arm_ds_cnn_s_body_f32.c @@ -0,0 +1,251 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/ds_cnn_s_body_f32/test_data.h" + +static int32_t ds_cnn_s_body_f32_get_max_buffer_size(void) +{ + const cmsis_nn_dw_conv_params_f32 dw_conv_params = { + .padding = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_H}, + .ch_mult = DS_CNN_S_BODY_F32_DEPTHWISE_CH_MULT, + .activation = {.min = DS_CNN_S_BODY_F32_RELU_MIN, .max = DS_CNN_S_BODY_F32_RELU_MAX}, + }; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = DS_CNN_S_BODY_F32_POINTWISE_PADDING_W, .h = DS_CNN_S_BODY_F32_POINTWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F32_POINTWISE_STRIDE_W, .h = DS_CNN_S_BODY_F32_POINTWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F32_POINTWISE_DILATION_W, .h = DS_CNN_S_BODY_F32_POINTWISE_DILATION_H}, + .activation = {.min = DS_CNN_S_BODY_F32_RELU_MIN, .max = DS_CNN_S_BODY_F32_RELU_MAX}, + }; + const cmsis_nn_fc_params_f32 fc_params = { + .activation = {.min = DS_CNN_S_BODY_F32_IDENTITY_MIN, .max = DS_CNN_S_BODY_F32_IDENTITY_MAX}, + }; + const cmsis_nn_dims feature_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F32_INPUT_W, + .h = DS_CNN_S_BODY_F32_INPUT_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims dw_filter_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_C, + .w = DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_W, + .h = DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims pw_filter_dims = { + .n = DS_CNN_S_BODY_F32_CHANNELS, + .w = DS_CNN_S_BODY_F32_POINTWISE_FILTER_W, + .h = DS_CNN_S_BODY_F32_POINTWISE_FILTER_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims pooled_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_W, + .h = DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims fc_filter_dims = { + .n = DS_CNN_S_BODY_F32_POOLED_SIZE, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F32_NUM_CLASSES, + }; + const cmsis_nn_dims fc_output_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F32_NUM_CLASSES, + }; + + int32_t max_buffer_size = + arm_depthwise_conv_wrapper_f32_get_buffer_size(&dw_conv_params, &feature_dims, &dw_filter_dims, &feature_dims); + int32_t size = + arm_convolve_wrapper_f32_get_buffer_size(&conv_params, &feature_dims, &pw_filter_dims, &feature_dims); + if (size > max_buffer_size) + { + max_buffer_size = size; + } + size = arm_fully_connected_f32_get_buffer_size( + &fc_params, &pooled_dims, &fc_filter_dims, &fc_output_dims, ARM_NN_LAYOUT_NHWC); + if (size > max_buffer_size) + { + max_buffer_size = size; + } + return max_buffer_size; +} + +void ds_cnn_s_body_f32_arm_ds_cnn_s_body_f32(void) +{ + static float32_t feature_buffer_0[DS_CNN_S_BODY_F32_FEATURE_MAP_SIZE]; + static float32_t feature_buffer_1[DS_CNN_S_BODY_F32_FEATURE_MAP_SIZE]; + static float32_t pooled_output[DS_CNN_S_BODY_F32_POOLED_SIZE]; + static float32_t fc_output[DS_CNN_S_BODY_F32_DST_SIZE]; + static float32_t softmax_output[DS_CNN_S_BODY_F32_DST_SIZE]; + /* Reserve extra headroom for the packed conv/depthwise scratch paths on target. */ + static uint8_t scratch_storage[DS_CNN_S_BODY_F32_FEATURE_MAP_SIZE * 4 * sizeof(float32_t)]; + const int32_t scratch_size = ds_cnn_s_body_f32_get_max_buffer_size(); + void *scratch_buf = scratch_size > 0 ? scratch_storage : NULL; + const cmsis_nn_context scratch_ctx = {.buf = scratch_buf, .size = scratch_size}; + const cmsis_nn_context no_ctx = {0}; + const cmsis_nn_dw_conv_params_f32 dw_conv_params = { + .padding = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_W, .h = DS_CNN_S_BODY_F32_DEPTHWISE_DILATION_H}, + .ch_mult = DS_CNN_S_BODY_F32_DEPTHWISE_CH_MULT, + .activation = {.min = DS_CNN_S_BODY_F32_RELU_MIN, .max = DS_CNN_S_BODY_F32_RELU_MAX}, + }; + const cmsis_nn_conv_params_f32 conv_params = { + .padding = {.w = DS_CNN_S_BODY_F32_POINTWISE_PADDING_W, .h = DS_CNN_S_BODY_F32_POINTWISE_PADDING_H}, + .stride = {.w = DS_CNN_S_BODY_F32_POINTWISE_STRIDE_W, .h = DS_CNN_S_BODY_F32_POINTWISE_STRIDE_H}, + .dilation = {.w = DS_CNN_S_BODY_F32_POINTWISE_DILATION_W, .h = DS_CNN_S_BODY_F32_POINTWISE_DILATION_H}, + .activation = {.min = DS_CNN_S_BODY_F32_RELU_MIN, .max = DS_CNN_S_BODY_F32_RELU_MAX}, + }; + const cmsis_nn_pool_params_f32 pool_params = { + .stride = {.w = 1, .h = 1}, + .padding = {.w = 0, .h = 0}, + .activation = {.min = DS_CNN_S_BODY_F32_IDENTITY_MIN, .max = DS_CNN_S_BODY_F32_IDENTITY_MAX}, + }; + const cmsis_nn_fc_params_f32 fc_params = { + .activation = {.min = DS_CNN_S_BODY_F32_IDENTITY_MIN, .max = DS_CNN_S_BODY_F32_IDENTITY_MAX}, + }; + const cmsis_nn_dims feature_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F32_INPUT_W, + .h = DS_CNN_S_BODY_F32_INPUT_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims dw_filter_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_C, + .w = DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_W, + .h = DS_CNN_S_BODY_F32_DEPTHWISE_FILTER_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims pw_filter_dims = { + .n = DS_CNN_S_BODY_F32_CHANNELS, + .w = DS_CNN_S_BODY_F32_POINTWISE_FILTER_W, + .h = DS_CNN_S_BODY_F32_POINTWISE_FILTER_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims block_bias_dims = {.n = 1, .w = 1, .h = 1, .c = DS_CNN_S_BODY_F32_CHANNELS}; + const cmsis_nn_dims avg_filter_dims = { + .n = 1, + .w = DS_CNN_S_BODY_F32_AVGPOOL_FILTER_W, + .h = DS_CNN_S_BODY_F32_AVGPOOL_FILTER_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims pooled_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_W, + .h = DS_CNN_S_BODY_F32_AVGPOOL_OUTPUT_H, + .c = DS_CNN_S_BODY_F32_CHANNELS, + }; + const cmsis_nn_dims fc_filter_dims = { + .n = DS_CNN_S_BODY_F32_POOLED_SIZE, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F32_NUM_CLASSES, + }; + const cmsis_nn_dims fc_bias_dims = {.n = 1, .w = 1, .h = 1, .c = DS_CNN_S_BODY_F32_NUM_CLASSES}; + const cmsis_nn_dims fc_output_dims = { + .n = DS_CNN_S_BODY_F32_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = DS_CNN_S_BODY_F32_NUM_CLASSES, + }; + const float32_t *dw_weights[DS_CNN_S_BODY_F32_NUM_BLOCKS] = { + ds_cnn_s_body_f32_dwconv0_weights_data, + ds_cnn_s_body_f32_dwconv1_weights_data, + ds_cnn_s_body_f32_dwconv2_weights_data, + ds_cnn_s_body_f32_dwconv3_weights_data, + }; + const float32_t *dw_biases[DS_CNN_S_BODY_F32_NUM_BLOCKS] = { + ds_cnn_s_body_f32_dwconv0_biases_data, + ds_cnn_s_body_f32_dwconv1_biases_data, + ds_cnn_s_body_f32_dwconv2_biases_data, + ds_cnn_s_body_f32_dwconv3_biases_data, + }; + const float32_t *pw_weights[DS_CNN_S_BODY_F32_NUM_BLOCKS] = { + ds_cnn_s_body_f32_pwconv0_weights_data, + ds_cnn_s_body_f32_pwconv1_weights_data, + ds_cnn_s_body_f32_pwconv2_weights_data, + ds_cnn_s_body_f32_pwconv3_weights_data, + }; + const float32_t *pw_biases[DS_CNN_S_BODY_F32_NUM_BLOCKS] = { + ds_cnn_s_body_f32_pwconv0_biases_data, + ds_cnn_s_body_f32_pwconv1_biases_data, + ds_cnn_s_body_f32_pwconv2_biases_data, + ds_cnn_s_body_f32_pwconv3_biases_data, + }; + const float32_t *current = ds_cnn_s_body_f32_input_data; + + TEST_ASSERT_NOT_NULL(feature_buffer_0); + TEST_ASSERT_NOT_NULL(feature_buffer_1); + TEST_ASSERT_NOT_NULL(pooled_output); + TEST_ASSERT_NOT_NULL(fc_output); + TEST_ASSERT_NOT_NULL(softmax_output); + if (scratch_size > 0) + { + TEST_ASSERT_NOT_NULL(scratch_buf); + TEST_ASSERT_TRUE(scratch_size <= (int32_t)sizeof(scratch_storage)); + } + + for (int32_t block_idx = 0; block_idx < DS_CNN_S_BODY_F32_NUM_BLOCKS; ++block_idx) + { + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_depthwise_conv_wrapper_f32(&scratch_ctx, + &dw_conv_params, + &feature_dims, + current, + &dw_filter_dims, + dw_weights[block_idx], + &block_bias_dims, + dw_biases[block_idx], + &feature_dims, + feature_buffer_0)); + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_convolve_wrapper_f32(&scratch_ctx, + &conv_params, + &feature_dims, + feature_buffer_0, + &pw_filter_dims, + pw_weights[block_idx], + &block_bias_dims, + pw_biases[block_idx], + &feature_dims, + feature_buffer_1)); + current = feature_buffer_1; + } + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_avg_pool_f32(&no_ctx, &pool_params, &feature_dims, current, &avg_filter_dims, &pooled_dims, pooled_output)); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_fully_connected_f32(&scratch_ctx, + &fc_params, + &pooled_dims, + pooled_output, + &fc_filter_dims, + ds_cnn_s_body_f32_fc_weights_data, + &fc_bias_dims, + ds_cnn_s_body_f32_fc_biases_data, + &fc_output_dims, + fc_output, + ARM_NN_LAYOUT_NHWC)); + + TEST_ASSERT_EQUAL( + ARM_CMSIS_NN_SUCCESS, + arm_softmax_f32(fc_output, DS_CNN_S_BODY_F32_SOFTMAX_ROWS, DS_CNN_S_BODY_F32_SOFTMAX_COLS, softmax_output)); + + for (int32_t i = 0; i < DS_CNN_S_BODY_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(1.0e-4f, ds_cnn_s_body_f32_output_ref_data[i], softmax_output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/CMakeLists.txt new file mode 100644 index 00000000..6a276672 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_elementwise_add_f16) + +target_sources(test_arm_elementwise_add_f16 PRIVATE + Unity/unity_test_arm_elementwise_add_f16.c + Unity/TestRunner/unity_test_arm_elementwise_add_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/Unity/unity_test_arm_elementwise_add_f16.c b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/Unity/unity_test_arm_elementwise_add_f16.c new file mode 100644 index 00000000..3d98923e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/Unity/unity_test_arm_elementwise_add_f16.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_elementwise_add_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_add_f16_arm_elementwise_add_f16(void) { add_f16_arm_elementwise_add_f16(); } +void test_add_f16_spill_arm_elementwise_add_f16(void) { add_f16_spill_arm_elementwise_add_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/test_arm_elementwise_add_f16.c b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/test_arm_elementwise_add_f16.c new file mode 100644 index 00000000..b0c08372 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f16/test_arm_elementwise_add_f16.c @@ -0,0 +1,33 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/add_f16/test_data.h" +#include "../TestData/add_f16_spill/test_data.h" + +#define RUN_ADD_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_elementwise_add_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_elementwise_add_f16(case_name##_input1, \ + case_name##_input2, \ + output, \ + CASE_PREFIX##_OUT_ACTIVATION_MIN, \ + CASE_PREFIX##_OUT_ACTIVATION_MAX, \ + CASE_PREFIX##_DST_SIZE)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_ADD_F16_CASE(ADD_F16, add_f16, 6.0e-3f) +RUN_ADD_F16_CASE(ADD_F16_SPILL, add_f16_spill, 6.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/CMakeLists.txt new file mode 100644 index 00000000..30e01592 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_elementwise_add_f32) + +target_sources(test_arm_elementwise_add_f32 PRIVATE + Unity/unity_test_arm_elementwise_add_f32.c + Unity/TestRunner/unity_test_arm_elementwise_add_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/Unity/unity_test_arm_elementwise_add_f32.c b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/Unity/unity_test_arm_elementwise_add_f32.c new file mode 100644 index 00000000..ed42fd8a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/Unity/unity_test_arm_elementwise_add_f32.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_elementwise_add_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_add_f32_arm_elementwise_add_f32(void) { add_f32_arm_elementwise_add_f32(); } +void test_add_f32_spill_arm_elementwise_add_f32(void) { add_f32_spill_arm_elementwise_add_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/test_arm_elementwise_add_f32.c b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/test_arm_elementwise_add_f32.c new file mode 100644 index 00000000..51ac74ea --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_add_f32/test_arm_elementwise_add_f32.c @@ -0,0 +1,33 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/add_f32/test_data.h" +#include "../TestData/add_f32_spill/test_data.h" + +#define RUN_ADD_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_elementwise_add_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_elementwise_add_f32(case_name##_input1, \ + case_name##_input2, \ + output, \ + CASE_PREFIX##_OUT_ACTIVATION_MIN, \ + CASE_PREFIX##_OUT_ACTIVATION_MAX, \ + CASE_PREFIX##_DST_SIZE)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_ADD_F32_CASE(ADD_F32, add_f32, 2.0e-4f) +RUN_ADD_F32_CASE(ADD_F32_SPILL, add_f32_spill, 2.0e-4f) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/CMakeLists.txt new file mode 100644 index 00000000..ca0dccaf --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_elementwise_mul_f16) + +target_sources(test_arm_elementwise_mul_f16 PRIVATE + Unity/unity_test_arm_elementwise_mul_f16.c + Unity/TestRunner/unity_test_arm_elementwise_mul_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/Unity/unity_test_arm_elementwise_mul_f16.c b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/Unity/unity_test_arm_elementwise_mul_f16.c new file mode 100644 index 00000000..156c9e67 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/Unity/unity_test_arm_elementwise_mul_f16.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_elementwise_mul_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_mul_f16_arm_elementwise_mul_f16(void) { mul_f16_arm_elementwise_mul_f16(); } +void test_mul_f16_spill_arm_elementwise_mul_f16(void) { mul_f16_spill_arm_elementwise_mul_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/test_arm_elementwise_mul_f16.c b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/test_arm_elementwise_mul_f16.c new file mode 100644 index 00000000..09a77a25 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f16/test_arm_elementwise_mul_f16.c @@ -0,0 +1,33 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/mul_f16/test_data.h" +#include "../TestData/mul_f16_spill/test_data.h" + +#define RUN_MUL_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_elementwise_mul_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_elementwise_mul_f16(case_name##_input1, \ + case_name##_input2, \ + output, \ + CASE_PREFIX##_OUT_ACTIVATION_MIN, \ + CASE_PREFIX##_OUT_ACTIVATION_MAX, \ + CASE_PREFIX##_DST_SIZE)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_MUL_F16_CASE(MUL_F16, mul_f16, 8.0e-3f) +RUN_MUL_F16_CASE(MUL_F16_SPILL, mul_f16_spill, 8.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/CMakeLists.txt new file mode 100644 index 00000000..6f92e38f --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_elementwise_mul_f32) + +target_sources(test_arm_elementwise_mul_f32 PRIVATE + Unity/unity_test_arm_elementwise_mul_f32.c + Unity/TestRunner/unity_test_arm_elementwise_mul_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/Unity/unity_test_arm_elementwise_mul_f32.c b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/Unity/unity_test_arm_elementwise_mul_f32.c new file mode 100644 index 00000000..dd4b9c2c --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/Unity/unity_test_arm_elementwise_mul_f32.c @@ -0,0 +1,29 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_elementwise_mul_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_mul_f32_arm_elementwise_mul_f32(void) { mul_f32_arm_elementwise_mul_f32(); } +void test_mul_f32_spill_arm_elementwise_mul_f32(void) { mul_f32_spill_arm_elementwise_mul_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/test_arm_elementwise_mul_f32.c b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/test_arm_elementwise_mul_f32.c new file mode 100644 index 00000000..7910476a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_elementwise_mul_f32/test_arm_elementwise_mul_f32.c @@ -0,0 +1,33 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/mul_f32/test_data.h" +#include "../TestData/mul_f32_spill/test_data.h" + +#define RUN_MUL_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_elementwise_mul_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_elementwise_mul_f32(case_name##_input1, \ + case_name##_input2, \ + output, \ + CASE_PREFIX##_OUT_ACTIVATION_MIN, \ + CASE_PREFIX##_OUT_ACTIVATION_MAX, \ + CASE_PREFIX##_DST_SIZE)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_MUL_F32_CASE(MUL_F32, mul_f32, 2.0e-4f) +RUN_MUL_F32_CASE(MUL_F32_SPILL, mul_f32_spill, 2.0e-4f) diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/CMakeLists.txt new file mode 100644 index 00000000..60fcf1f6 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_fully_connected_f16) + +target_sources(test_arm_fully_connected_f16 PRIVATE + Unity/unity_test_arm_fully_connected_f16.c + Unity/TestRunner/unity_test_arm_fully_connected_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/Unity/unity_test_arm_fully_connected_f16.c b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/Unity/unity_test_arm_fully_connected_f16.c new file mode 100644 index 00000000..45b2529e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/Unity/unity_test_arm_fully_connected_f16.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_fully_connected_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_fully_connected_small_f16_arm_fully_connected_f16(void) +{ + fully_connected_small_f16_arm_fully_connected_f16(); +} +void test_fully_connected_medium_f16_arm_fully_connected_f16(void) +{ + fully_connected_medium_f16_arm_fully_connected_f16(); +} +void test_fully_connected_large_f16_arm_fully_connected_f16(void) +{ + fully_connected_large_f16_arm_fully_connected_f16(); +} +void test_fully_connected_2out_batch2_f16_arm_fully_connected_f16(void) +{ + fully_connected_2out_batch2_f16_arm_fully_connected_f16(); +} +void test_fully_connected_2out_tail17_f16_arm_fully_connected_f16(void) +{ + fully_connected_2out_tail17_f16_arm_fully_connected_f16(); +} +void test_fully_connected_2out_tail21_f16_arm_fully_connected_f16(void) +{ + fully_connected_2out_tail21_f16_arm_fully_connected_f16(); +} +void test_fully_connected_null_bias_f16_arm_fully_connected_f16(void) +{ + fully_connected_null_bias_f16_arm_fully_connected_f16(); +} +void test_fully_connected_out_activation_f16_arm_fully_connected_f16(void) +{ + fully_connected_out_activation_f16_arm_fully_connected_f16(); +} +void test_fully_connected_match_basic_f16_arm_fully_connected_f16(void) +{ + fully_connected_match_basic_f16_arm_fully_connected_f16(); +} +void test_fully_connected_match_mve_0_f16_arm_fully_connected_f16(void) +{ + fully_connected_match_mve_0_f16_arm_fully_connected_f16(); +} +void test_fully_connected_match_mve_1_f16_arm_fully_connected_f16(void) +{ + fully_connected_match_mve_1_f16_arm_fully_connected_f16(); +} +void test_fully_connected_match_fc_per_ch_f16_arm_fully_connected_f16(void) +{ + fully_connected_match_fc_per_ch_f16_arm_fully_connected_f16(); +} +void test_fully_connected_match_mve_1_f16_arm_fully_connected_f16_packed(void) +{ + fully_connected_match_mve_1_f16_arm_fully_connected_f16_packed(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/test_arm_fully_connected_f16.c b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/test_arm_fully_connected_f16.c new file mode 100644 index 00000000..9611d780 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f16/test_arm_fully_connected_f16.c @@ -0,0 +1,126 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/fully_connected_2out_batch2_f16/test_data.h" +#include "../TestData/fully_connected_2out_tail17_f16/test_data.h" +#include "../TestData/fully_connected_2out_tail21_f16/test_data.h" +#include "../TestData/fully_connected_large_f16/test_data.h" +#include "../TestData/fully_connected_match_basic_f16/test_data.h" +#include "../TestData/fully_connected_match_fc_per_ch_f16/test_data.h" +#include "../TestData/fully_connected_match_mve_0_f16/test_data.h" +#include "../TestData/fully_connected_match_mve_1_f16/test_data.h" +#include "../TestData/fully_connected_medium_f16/test_data.h" +#include "../TestData/fully_connected_null_bias_f16/test_data.h" +#include "../TestData/fully_connected_out_activation_f16/test_data.h" +#include "../TestData/fully_connected_small_f16/test_data.h" + +#define RUN_FULLY_CONNECTED_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_fully_connected_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_fc_params_f16 fc_params = { \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_INPUT_SIZE, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const float16_t *bias_data = CASE_PREFIX##_HAS_BIAS ? case_name##_biases_data : NULL; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_fully_connected_f16(&ctx, \ + &fc_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + bias_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref_data[i], (float)output[i]); \ + } \ + } + +/* + * The float16 path accumulates directly in float16, so larger fully connected + * test shapes need a wider tolerance than the float32 reference generation. + */ +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_SMALL_F16, fully_connected_small_f16, 4.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_MEDIUM_F16, fully_connected_medium_f16, 7.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_LARGE_F16, fully_connected_large_f16, 1.0e-1f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_2OUT_BATCH2_F16, fully_connected_2out_batch2_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_2OUT_TAIL17_F16, fully_connected_2out_tail17_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_2OUT_TAIL21_F16, fully_connected_2out_tail21_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_NULL_BIAS_F16, fully_connected_null_bias_f16, 1.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_OUT_ACTIVATION_F16, fully_connected_out_activation_f16, 5.0e-3f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_MATCH_BASIC_F16, fully_connected_match_basic_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_MATCH_MVE_0_F16, fully_connected_match_mve_0_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_MATCH_MVE_1_F16, fully_connected_match_mve_1_f16, 2.0e-2f) +RUN_FULLY_CONNECTED_F16_CASE(FULLY_CONNECTED_MATCH_FC_PER_CH_F16, fully_connected_match_fc_per_ch_f16, 5.0e-2f) + +/* + * The packed-weight coverage needs explicit setup/teardown: + * pack_rhs_nt_n_from_nt_t_f16() allocates a temporary packed buffer and this + * test frees it, so it cannot use the standard RUN_FULLY_CONNECTED_F16_CASE + * macro that assumes the static reference weights are passed directly. + */ +void fully_connected_match_mve_1_f16_arm_fully_connected_f16_packed(void) +{ + float16_t output[FULLY_CONNECTED_MATCH_MVE_1_F16_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_fc_params_f16 fc_params = {.activation = {.min = FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_ACTIVATION_MIN, + .max = FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_BATCHES, + .w = FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_W, + .h = FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_H, + .c = FULLY_CONNECTED_MATCH_MVE_1_F16_IN_CH}; + const cmsis_nn_dims filter_dims = { + .n = FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_SIZE, .w = 1, .h = 1, .c = FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_CH}; + float16_t *packed_weights = pack_rhs_nt_n_from_nt_t_f16(fully_connected_match_mve_1_f16_weights_data, + FULLY_CONNECTED_MATCH_MVE_1_F16_OUT_CH, + FULLY_CONNECTED_MATCH_MVE_1_F16_INPUT_SIZE); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_fully_connected_f16(&ctx, + &fc_params, + &input_dims, + fully_connected_match_mve_1_f16_input_data, + &filter_dims, + packed_weights, + &bias_dims, + fully_connected_match_mve_1_f16_biases_data, + &output_dims, + output, + FULLY_CONNECTED_MATCH_MVE_1_F16_LAYOUT)); + + for (int i = 0; i < FULLY_CONNECTED_MATCH_MVE_1_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-2f, (float)fully_connected_match_mve_1_f16_output_ref_data[i], (float)output[i]); + } + + free(packed_weights); +} diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/CMakeLists.txt new file mode 100644 index 00000000..152c283b --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_fully_connected_f32) + +target_sources(test_arm_fully_connected_f32 PRIVATE + Unity/unity_test_arm_fully_connected_f32.c + Unity/TestRunner/unity_test_arm_fully_connected_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/Unity/unity_test_arm_fully_connected_f32.c b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/Unity/unity_test_arm_fully_connected_f32.c new file mode 100644 index 00000000..8c59d75c --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/Unity/unity_test_arm_fully_connected_f32.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_fully_connected_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_fully_connected_small_f32_arm_fully_connected_f32(void) +{ + fully_connected_small_f32_arm_fully_connected_f32(); +} +void test_fully_connected_medium_f32_arm_fully_connected_f32(void) +{ + fully_connected_medium_f32_arm_fully_connected_f32(); +} +void test_fully_connected_large_f32_arm_fully_connected_f32(void) +{ + fully_connected_large_f32_arm_fully_connected_f32(); +} +void test_fully_connected_2out_batch2_f32_arm_fully_connected_f32(void) +{ + fully_connected_2out_batch2_f32_arm_fully_connected_f32(); +} +void test_fully_connected_2out_tail17_f32_arm_fully_connected_f32(void) +{ + fully_connected_2out_tail17_f32_arm_fully_connected_f32(); +} +void test_fully_connected_2out_tail21_f32_arm_fully_connected_f32(void) +{ + fully_connected_2out_tail21_f32_arm_fully_connected_f32(); +} +void test_fully_connected_null_bias_f32_arm_fully_connected_f32(void) +{ + fully_connected_null_bias_f32_arm_fully_connected_f32(); +} +void test_fully_connected_out_activation_f32_arm_fully_connected_f32(void) +{ + fully_connected_out_activation_f32_arm_fully_connected_f32(); +} +void test_fully_connected_match_basic_f32_arm_fully_connected_f32(void) +{ + fully_connected_match_basic_f32_arm_fully_connected_f32(); +} +void test_fully_connected_match_mve_0_f32_arm_fully_connected_f32(void) +{ + fully_connected_match_mve_0_f32_arm_fully_connected_f32(); +} +void test_fully_connected_match_mve_1_f32_arm_fully_connected_f32(void) +{ + fully_connected_match_mve_1_f32_arm_fully_connected_f32(); +} +void test_fully_connected_match_fc_per_ch_f32_arm_fully_connected_f32(void) +{ + fully_connected_match_fc_per_ch_f32_arm_fully_connected_f32(); +} +void test_fully_connected_match_mve_1_f32_arm_fully_connected_f32_packed(void) +{ + fully_connected_match_mve_1_f32_arm_fully_connected_f32_packed(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/test_arm_fully_connected_f32.c b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/test_arm_fully_connected_f32.c new file mode 100644 index 00000000..f45692d9 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_fully_connected_f32/test_arm_fully_connected_f32.c @@ -0,0 +1,122 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include + +#include +#include + +#include "../Common/float_packed_test_utils.h" +#include "../TestData/fully_connected_2out_batch2_f32/test_data.h" +#include "../TestData/fully_connected_2out_tail17_f32/test_data.h" +#include "../TestData/fully_connected_2out_tail21_f32/test_data.h" +#include "../TestData/fully_connected_large_f32/test_data.h" +#include "../TestData/fully_connected_match_basic_f32/test_data.h" +#include "../TestData/fully_connected_match_fc_per_ch_f32/test_data.h" +#include "../TestData/fully_connected_match_mve_0_f32/test_data.h" +#include "../TestData/fully_connected_match_mve_1_f32/test_data.h" +#include "../TestData/fully_connected_medium_f32/test_data.h" +#include "../TestData/fully_connected_null_bias_f32/test_data.h" +#include "../TestData/fully_connected_out_activation_f32/test_data.h" +#include "../TestData/fully_connected_small_f32/test_data.h" + +#define RUN_FULLY_CONNECTED_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_fully_connected_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_fc_params_f32 fc_params = { \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_INPUT_SIZE, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .w = 1, .h = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const float32_t *bias_data = CASE_PREFIX##_HAS_BIAS ? case_name##_biases_data : NULL; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_fully_connected_f32(&ctx, \ + &fc_params, \ + &input_dims, \ + case_name##_input_data, \ + &filter_dims, \ + case_name##_weights_data, \ + &bias_dims, \ + bias_data, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref_data[i], output[i]); \ + } \ + } + +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_SMALL_F32, fully_connected_small_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_MEDIUM_F32, fully_connected_medium_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_LARGE_F32, fully_connected_large_f32, 3.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_2OUT_BATCH2_F32, fully_connected_2out_batch2_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_2OUT_TAIL17_F32, fully_connected_2out_tail17_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_2OUT_TAIL21_F32, fully_connected_2out_tail21_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_NULL_BIAS_F32, fully_connected_null_bias_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_OUT_ACTIVATION_F32, fully_connected_out_activation_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_MATCH_BASIC_F32, fully_connected_match_basic_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_MATCH_MVE_0_F32, fully_connected_match_mve_0_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_MATCH_MVE_1_F32, fully_connected_match_mve_1_f32, 2.0e-4f) +RUN_FULLY_CONNECTED_F32_CASE(FULLY_CONNECTED_MATCH_FC_PER_CH_F32, fully_connected_match_fc_per_ch_f32, 3.0e-4f) + +/* + * The packed-weight coverage needs explicit setup/teardown: + * pack_rhs_nt_n_from_nt_t_f32() allocates a temporary packed buffer and this + * test frees it, so it cannot use the standard RUN_FULLY_CONNECTED_F32_CASE + * macro that assumes the static reference weights are passed directly. + */ +void fully_connected_match_mve_1_f32_arm_fully_connected_f32_packed(void) +{ + float32_t output[FULLY_CONNECTED_MATCH_MVE_1_F32_DST_SIZE] = {0}; + const cmsis_nn_context ctx = {0}; + const cmsis_nn_fc_params_f32 fc_params = {.activation = {.min = FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_ACTIVATION_MIN, + .max = FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_ACTIVATION_MAX}, + .weight_format = ARM_NN_WEIGHT_FORMAT_NT_N_PACKED}; + const cmsis_nn_dims input_dims = {.n = FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_BATCHES, + .w = FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_W, + .h = FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_H, + .c = FULLY_CONNECTED_MATCH_MVE_1_F32_IN_CH}; + const cmsis_nn_dims filter_dims = { + .n = FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_SIZE, .w = 1, .h = 1, .c = FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_CH}; + const cmsis_nn_dims bias_dims = {.n = 1, .w = 1, .h = 1, .c = FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_CH}; + const cmsis_nn_dims output_dims = {.n = FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_BATCHES, + .w = 1, + .h = 1, + .c = FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_CH}; + float32_t *packed_weights = pack_rhs_nt_n_from_nt_t_f32(fully_connected_match_mve_1_f32_weights_data, + FULLY_CONNECTED_MATCH_MVE_1_F32_OUT_CH, + FULLY_CONNECTED_MATCH_MVE_1_F32_INPUT_SIZE); + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_fully_connected_f32(&ctx, + &fc_params, + &input_dims, + fully_connected_match_mve_1_f32_input_data, + &filter_dims, + packed_weights, + &bias_dims, + fully_connected_match_mve_1_f32_biases_data, + &output_dims, + output, + FULLY_CONNECTED_MATCH_MVE_1_F32_LAYOUT)); + + for (int i = 0; i < FULLY_CONNECTED_MATCH_MVE_1_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, fully_connected_match_mve_1_f32_output_ref_data[i], output[i]); + } + + free(packed_weights); +} diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/CMakeLists.txt new file mode 100644 index 00000000..5d8d9c8c --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_lstm_unidirectional_f16) + +target_sources(test_arm_lstm_unidirectional_f16 PRIVATE + Unity/unity_test_arm_lstm_unidirectional_f16.c + Unity/TestRunner/unity_test_arm_lstm_unidirectional_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/Unity/unity_test_arm_lstm_unidirectional_f16.c b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/Unity/unity_test_arm_lstm_unidirectional_f16.c new file mode 100644 index 00000000..c44bcb63 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/Unity/unity_test_arm_lstm_unidirectional_f16.c @@ -0,0 +1,36 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_lstm_unidirectional_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_lstm_small_f16_arm_lstm_unidirectional_f16(void) { lstm_small_f16_arm_lstm_unidirectional_f16(); } +void test_lstm_medium_f16_arm_lstm_unidirectional_f16(void) { lstm_medium_f16_arm_lstm_unidirectional_f16(); } +void test_lstm_large_f16_arm_lstm_unidirectional_f16(void) { lstm_large_f16_arm_lstm_unidirectional_f16(); } +void test_lstm_match_1_f16_arm_lstm_unidirectional_f16(void) { lstm_match_1_f16_arm_lstm_unidirectional_f16(); } +void test_lstm_match_2_f16_arm_lstm_unidirectional_f16(void) { lstm_match_2_f16_arm_lstm_unidirectional_f16(); } +void test_lstm_match_one_time_step_f16_arm_lstm_unidirectional_f16(void) +{ + lstm_match_one_time_step_f16_arm_lstm_unidirectional_f16(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/test_arm_lstm_unidirectional_f16.c b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/test_arm_lstm_unidirectional_f16.c new file mode 100644 index 00000000..aef0d292 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f16/test_arm_lstm_unidirectional_f16.c @@ -0,0 +1,62 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/lstm_large_f16/test_data.h" +#include "../TestData/lstm_match_1_f16/test_data.h" +#include "../TestData/lstm_match_2_f16/test_data.h" +#include "../TestData/lstm_match_one_time_step_f16/test_data.h" +#include "../TestData/lstm_medium_f16/test_data.h" +#include "../TestData/lstm_small_f16/test_data.h" + +#define RUN_LSTM_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_lstm_unidirectional_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + float16_t temp1[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + float16_t temp2[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + float16_t cell_state[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + const cmsis_nn_lstm_params_f16 params = {.time_major = CASE_PREFIX##_TIME_MAJOR, \ + .batch_size = CASE_PREFIX##_BATCH_SIZE, \ + .time_steps = CASE_PREFIX##_TIME_STEPS, \ + .input_size = CASE_PREFIX##_INPUT_SIZE, \ + .hidden_size = CASE_PREFIX##_HIDDEN_SIZE, \ + .cell_clip = (float16_t)CASE_PREFIX##_CELL_CLIP, \ + .forget_gate = {.input_weights = case_name##_forget_input_weights, \ + .hidden_weights = case_name##_forget_hidden_weights, \ + .bias = case_name##_forget_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}, \ + .input_gate = {.input_weights = case_name##_input_input_weights, \ + .hidden_weights = case_name##_input_hidden_weights, \ + .bias = case_name##_input_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}, \ + .cell_gate = {.input_weights = case_name##_cell_input_weights, \ + .hidden_weights = case_name##_cell_hidden_weights, \ + .bias = case_name##_cell_bias, \ + .activation_type = ARM_NN_FLT_ACT_TANH}, \ + .output_gate = {.input_weights = case_name##_output_input_weights, \ + .hidden_weights = case_name##_output_hidden_weights, \ + .bias = case_name##_output_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}}; \ + cmsis_nn_lstm_context_f16 buffers = {.temp1 = temp1, .temp2 = temp2, .cell_state = cell_state}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_lstm_unidirectional_f16(case_name##_input, output, ¶ms, &buffers)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_LSTM_F16_CASE(LSTM_SMALL_F16, lstm_small_f16, 8.0e-2f) +RUN_LSTM_F16_CASE(LSTM_MEDIUM_F16, lstm_medium_f16, 1.0e-1f) +RUN_LSTM_F16_CASE(LSTM_LARGE_F16, lstm_large_f16, 1.2e-1f) +RUN_LSTM_F16_CASE(LSTM_MATCH_1_F16, lstm_match_1_f16, 1.2e-1f) +RUN_LSTM_F16_CASE(LSTM_MATCH_2_F16, lstm_match_2_f16, 1.2e-1f) +RUN_LSTM_F16_CASE(LSTM_MATCH_ONE_TIME_STEP_F16, lstm_match_one_time_step_f16, 1.2e-1f) diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/CMakeLists.txt new file mode 100644 index 00000000..fba25174 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_lstm_unidirectional_f32) + +target_sources(test_arm_lstm_unidirectional_f32 PRIVATE + Unity/unity_test_arm_lstm_unidirectional_f32.c + Unity/TestRunner/unity_test_arm_lstm_unidirectional_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/Unity/unity_test_arm_lstm_unidirectional_f32.c b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/Unity/unity_test_arm_lstm_unidirectional_f32.c new file mode 100644 index 00000000..aa6c7368 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/Unity/unity_test_arm_lstm_unidirectional_f32.c @@ -0,0 +1,36 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_lstm_unidirectional_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_lstm_small_f32_arm_lstm_unidirectional_f32(void) { lstm_small_f32_arm_lstm_unidirectional_f32(); } +void test_lstm_medium_f32_arm_lstm_unidirectional_f32(void) { lstm_medium_f32_arm_lstm_unidirectional_f32(); } +void test_lstm_large_f32_arm_lstm_unidirectional_f32(void) { lstm_large_f32_arm_lstm_unidirectional_f32(); } +void test_lstm_match_1_f32_arm_lstm_unidirectional_f32(void) { lstm_match_1_f32_arm_lstm_unidirectional_f32(); } +void test_lstm_match_2_f32_arm_lstm_unidirectional_f32(void) { lstm_match_2_f32_arm_lstm_unidirectional_f32(); } +void test_lstm_match_one_time_step_f32_arm_lstm_unidirectional_f32(void) +{ + lstm_match_one_time_step_f32_arm_lstm_unidirectional_f32(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/test_arm_lstm_unidirectional_f32.c b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/test_arm_lstm_unidirectional_f32.c new file mode 100644 index 00000000..787c3244 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_lstm_unidirectional_f32/test_arm_lstm_unidirectional_f32.c @@ -0,0 +1,62 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/lstm_large_f32/test_data.h" +#include "../TestData/lstm_match_1_f32/test_data.h" +#include "../TestData/lstm_match_2_f32/test_data.h" +#include "../TestData/lstm_match_one_time_step_f32/test_data.h" +#include "../TestData/lstm_medium_f32/test_data.h" +#include "../TestData/lstm_small_f32/test_data.h" + +#define RUN_LSTM_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_lstm_unidirectional_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + float32_t temp1[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + float32_t temp2[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + float32_t cell_state[CASE_PREFIX##_CELL_STATE_SIZE] = {0}; \ + const cmsis_nn_lstm_params_f32 params = {.time_major = CASE_PREFIX##_TIME_MAJOR, \ + .batch_size = CASE_PREFIX##_BATCH_SIZE, \ + .time_steps = CASE_PREFIX##_TIME_STEPS, \ + .input_size = CASE_PREFIX##_INPUT_SIZE, \ + .hidden_size = CASE_PREFIX##_HIDDEN_SIZE, \ + .cell_clip = CASE_PREFIX##_CELL_CLIP, \ + .forget_gate = {.input_weights = case_name##_forget_input_weights, \ + .hidden_weights = case_name##_forget_hidden_weights, \ + .bias = case_name##_forget_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}, \ + .input_gate = {.input_weights = case_name##_input_input_weights, \ + .hidden_weights = case_name##_input_hidden_weights, \ + .bias = case_name##_input_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}, \ + .cell_gate = {.input_weights = case_name##_cell_input_weights, \ + .hidden_weights = case_name##_cell_hidden_weights, \ + .bias = case_name##_cell_bias, \ + .activation_type = ARM_NN_FLT_ACT_TANH}, \ + .output_gate = {.input_weights = case_name##_output_input_weights, \ + .hidden_weights = case_name##_output_hidden_weights, \ + .bias = case_name##_output_bias, \ + .activation_type = ARM_NN_FLT_ACT_SIGMOID}}; \ + cmsis_nn_lstm_context_f32 buffers = {.temp1 = temp1, .temp2 = temp2, .cell_state = cell_state}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_lstm_unidirectional_f32(case_name##_input, output, ¶ms, &buffers)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_LSTM_F32_CASE(LSTM_SMALL_F32, lstm_small_f32, 2.0e-5f) +RUN_LSTM_F32_CASE(LSTM_MEDIUM_F32, lstm_medium_f32, 3.0e-5f) +RUN_LSTM_F32_CASE(LSTM_LARGE_F32, lstm_large_f32, 5.0e-5f) +RUN_LSTM_F32_CASE(LSTM_MATCH_1_F32, lstm_match_1_f32, 5.0e-5f) +RUN_LSTM_F32_CASE(LSTM_MATCH_2_F32, lstm_match_2_f32, 5.0e-5f) +RUN_LSTM_F32_CASE(LSTM_MATCH_ONE_TIME_STEP_F32, lstm_match_one_time_step_f32, 5.0e-5f) diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/CMakeLists.txt new file mode 100644 index 00000000..a33cb0f5 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_max_pool_f16) + +target_sources(test_arm_max_pool_f16 PRIVATE + Unity/unity_test_arm_max_pool_f16.c + Unity/TestRunner/unity_test_arm_max_pool_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f16/Unity/unity_test_arm_max_pool_f16.c b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/Unity/unity_test_arm_max_pool_f16.c new file mode 100755 index 00000000..58398773 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/Unity/unity_test_arm_max_pool_f16.c @@ -0,0 +1,38 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_max_pool_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_maxpooling_f16_arm_max_pool_f16(void) { maxpooling_f16_arm_max_pool_f16(); } +void test_maxpooling_f16_1_arm_max_pool_f16(void) { maxpooling_f16_1_arm_max_pool_f16(); } +void test_maxpooling_f16_2_arm_max_pool_f16(void) { maxpooling_f16_2_arm_max_pool_f16(); } +void test_maxpooling_f16_3_arm_max_pool_f16(void) { maxpooling_f16_3_arm_max_pool_f16(); } +void test_maxpooling_match_2_f16_arm_max_pool_f16(void) { maxpooling_match_2_f16_arm_max_pool_f16(); } +void test_maxpooling_match_3_f16_arm_max_pool_f16(void) { maxpooling_match_3_f16_arm_max_pool_f16(); } +void test_maxpooling_match_4_f16_arm_max_pool_f16(void) { maxpooling_match_4_f16_arm_max_pool_f16(); } +void test_maxpooling_match_5_f16_arm_max_pool_f16(void) { maxpooling_match_5_f16_arm_max_pool_f16(); } +void test_maxpooling_1d_k2s2_noclip_f16_arm_max_pool_f16(void) { maxpooling_1d_k2s2_noclip_f16_arm_max_pool_f16(); } +void test_maxpooling_1d_k2s2_clip_f16_arm_max_pool_f16(void) { maxpooling_1d_k2s2_clip_f16_arm_max_pool_f16(); } +void test_maxpooling_1d_k3s3_noclip_f16_arm_max_pool_f16(void) { maxpooling_1d_k3s3_noclip_f16_arm_max_pool_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f16/test_arm_max_pool_f16.c b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/test_arm_max_pool_f16.c new file mode 100755 index 00000000..af99a253 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f16/test_arm_max_pool_f16.c @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/maxpooling_1d_k2s2_clip_f16/test_data.h" +#include "../TestData/maxpooling_1d_k2s2_noclip_f16/test_data.h" +#include "../TestData/maxpooling_1d_k3s3_noclip_f16/test_data.h" +#include "../TestData/maxpooling_f16/test_data.h" +#include "../TestData/maxpooling_f16_1/test_data.h" +#include "../TestData/maxpooling_f16_2/test_data.h" +#include "../TestData/maxpooling_f16_3/test_data.h" +#include "../TestData/maxpooling_match_2_f16/test_data.h" +#include "../TestData/maxpooling_match_3_f16/test_data.h" +#include "../TestData/maxpooling_match_4_f16/test_data.h" +#include "../TestData/maxpooling_match_5_f16/test_data.h" + +#define RUN_MAX_POOL_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_max_pool_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_pool_params_f16 pool_params = { \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims filter_dims = { \ + .n = 1, .w = CASE_PREFIX##_FILTER_W, .h = CASE_PREFIX##_FILTER_H, .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_BATCH_SIZE, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_max_pool_f16( \ + &ctx, &pool_params, &input_dims, case_name##_input_tensor, &filter_dims, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_MAX_POOL_F16_CASE(MAXPOOLING_F16, maxpooling_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_F16_1, maxpooling_f16_1, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_F16_2, maxpooling_f16_2, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_F16_3, maxpooling_f16_3, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_MATCH_2_F16, maxpooling_match_2_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_MATCH_3_F16, maxpooling_match_3_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_MATCH_4_F16, maxpooling_match_4_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_MATCH_5_F16, maxpooling_match_5_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_1D_K2S2_NOCLIP_F16, maxpooling_1d_k2s2_noclip_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_1D_K2S2_CLIP_F16, maxpooling_1d_k2s2_clip_f16, 1.0e-3f) +RUN_MAX_POOL_F16_CASE(MAXPOOLING_1D_K3S3_NOCLIP_F16, maxpooling_1d_k3s3_noclip_f16, 1.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/CMakeLists.txt new file mode 100644 index 00000000..c0638a1f --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_max_pool_f32) + +target_sources(test_arm_max_pool_f32 PRIVATE + Unity/unity_test_arm_max_pool_f32.c + Unity/TestRunner/unity_test_arm_max_pool_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f32/Unity/unity_test_arm_max_pool_f32.c b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/Unity/unity_test_arm_max_pool_f32.c new file mode 100755 index 00000000..1ac76657 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/Unity/unity_test_arm_max_pool_f32.c @@ -0,0 +1,38 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_max_pool_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_maxpooling_f32_arm_max_pool_f32(void) { maxpooling_f32_arm_max_pool_f32(); } +void test_maxpooling_f32_1_arm_max_pool_f32(void) { maxpooling_f32_1_arm_max_pool_f32(); } +void test_maxpooling_f32_2_arm_max_pool_f32(void) { maxpooling_f32_2_arm_max_pool_f32(); } +void test_maxpooling_f32_3_arm_max_pool_f32(void) { maxpooling_f32_3_arm_max_pool_f32(); } +void test_maxpooling_match_2_f32_arm_max_pool_f32(void) { maxpooling_match_2_f32_arm_max_pool_f32(); } +void test_maxpooling_match_3_f32_arm_max_pool_f32(void) { maxpooling_match_3_f32_arm_max_pool_f32(); } +void test_maxpooling_match_4_f32_arm_max_pool_f32(void) { maxpooling_match_4_f32_arm_max_pool_f32(); } +void test_maxpooling_match_5_f32_arm_max_pool_f32(void) { maxpooling_match_5_f32_arm_max_pool_f32(); } +void test_maxpooling_1d_k2s2_noclip_f32_arm_max_pool_f32(void) { maxpooling_1d_k2s2_noclip_f32_arm_max_pool_f32(); } +void test_maxpooling_1d_k2s2_clip_f32_arm_max_pool_f32(void) { maxpooling_1d_k2s2_clip_f32_arm_max_pool_f32(); } +void test_maxpooling_1d_k3s3_noclip_f32_arm_max_pool_f32(void) { maxpooling_1d_k3s3_noclip_f32_arm_max_pool_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_max_pool_f32/test_arm_max_pool_f32.c b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/test_arm_max_pool_f32.c new file mode 100755 index 00000000..c106d53b --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_max_pool_f32/test_arm_max_pool_f32.c @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/maxpooling_1d_k2s2_clip_f32/test_data.h" +#include "../TestData/maxpooling_1d_k2s2_noclip_f32/test_data.h" +#include "../TestData/maxpooling_1d_k3s3_noclip_f32/test_data.h" +#include "../TestData/maxpooling_f32/test_data.h" +#include "../TestData/maxpooling_f32_1/test_data.h" +#include "../TestData/maxpooling_f32_2/test_data.h" +#include "../TestData/maxpooling_f32_3/test_data.h" +#include "../TestData/maxpooling_match_2_f32/test_data.h" +#include "../TestData/maxpooling_match_3_f32/test_data.h" +#include "../TestData/maxpooling_match_4_f32/test_data.h" +#include "../TestData/maxpooling_match_5_f32/test_data.h" + +#define RUN_MAX_POOL_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_max_pool_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_pool_params_f32 pool_params = { \ + .stride = {.w = CASE_PREFIX##_STRIDE_W, .h = CASE_PREFIX##_STRIDE_H}, \ + .padding = {.w = CASE_PREFIX##_PADDING_W, .h = CASE_PREFIX##_PADDING_H}, \ + .activation = {.min = CASE_PREFIX##_ACTIVATION_MIN, .max = CASE_PREFIX##_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .w = CASE_PREFIX##_INPUT_W, \ + .h = CASE_PREFIX##_INPUT_H, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims filter_dims = { \ + .n = 1, .w = CASE_PREFIX##_FILTER_W, .h = CASE_PREFIX##_FILTER_H, .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_BATCH_SIZE, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_max_pool_f32( \ + &ctx, &pool_params, &input_dims, case_name##_input_tensor, &filter_dims, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_MAX_POOL_F32_CASE(MAXPOOLING_F32, maxpooling_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_F32_1, maxpooling_f32_1, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_F32_2, maxpooling_f32_2, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_F32_3, maxpooling_f32_3, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_MATCH_2_F32, maxpooling_match_2_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_MATCH_3_F32, maxpooling_match_3_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_MATCH_4_F32, maxpooling_match_4_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_MATCH_5_F32, maxpooling_match_5_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_1D_K2S2_NOCLIP_F32, maxpooling_1d_k2s2_noclip_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_1D_K2S2_CLIP_F32, maxpooling_1d_k2s2_clip_f32, 1.0e-6f) +RUN_MAX_POOL_F32_CASE(MAXPOOLING_1D_K3S3_NOCLIP_F32, maxpooling_1d_k3s3_noclip_f32, 1.0e-6f) diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/CMakeLists.txt new file mode 100644 index 00000000..1d80ca91 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_maximum_minimum_f16) + +target_sources(test_arm_maximum_minimum_f16 PRIVATE + Unity/unity_test_arm_maximum_minimum_f16.c + Unity/TestRunner/unity_test_arm_maximum_minimum_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/Unity/unity_test_arm_maximum_minimum_f16.c b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/Unity/unity_test_arm_maximum_minimum_f16.c new file mode 100644 index 00000000..7652072d --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/Unity/unity_test_arm_maximum_minimum_f16.c @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_maximum_minimum_f16.c" + +void test_maximum_scalar_1_f16_arm_maximum_f16(void) { maximum_scalar_1_f16_arm_maximum_f16(); } +void test_maximum_scalar_2_f16_arm_maximum_f16(void) { maximum_scalar_2_f16_arm_maximum_f16(); } +void test_maximum_no_broadcast_f16_arm_maximum_f16(void) { maximum_no_broadcast_f16_arm_maximum_f16(); } +void test_maximum_broadcast_batch_f16_arm_maximum_f16(void) { maximum_broadcast_batch_f16_arm_maximum_f16(); } +void test_maximum_broadcast_height_f16_arm_maximum_f16(void) { maximum_broadcast_height_f16_arm_maximum_f16(); } +void test_maximum_broadcast_width_f16_arm_maximum_f16(void) { maximum_broadcast_width_f16_arm_maximum_f16(); } +void test_maximum_broadcast_ch_f16_arm_maximum_f16(void) { maximum_broadcast_ch_f16_arm_maximum_f16(); } +void test_minimum_scalar_1_f16_arm_minimum_f16(void) { minimum_scalar_1_f16_arm_minimum_f16(); } +void test_minimum_scalar_2_f16_arm_minimum_f16(void) { minimum_scalar_2_f16_arm_minimum_f16(); } +void test_minimum_no_broadcast_f16_arm_minimum_f16(void) { minimum_no_broadcast_f16_arm_minimum_f16(); } +void test_minimum_broadcast_batch_f16_arm_minimum_f16(void) { minimum_broadcast_batch_f16_arm_minimum_f16(); } +void test_minimum_broadcast_height_f16_arm_minimum_f16(void) { minimum_broadcast_height_f16_arm_minimum_f16(); } +void test_minimum_broadcast_width_f16_arm_minimum_f16(void) { minimum_broadcast_width_f16_arm_minimum_f16(); } +void test_minimum_broadcast_ch_f16_arm_minimum_f16(void) { minimum_broadcast_ch_f16_arm_minimum_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/test_arm_maximum_minimum_f16.c b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/test_arm_maximum_minimum_f16.c new file mode 100644 index 00000000..3761dc50 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f16/test_arm_maximum_minimum_f16.c @@ -0,0 +1,72 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/maximum_broadcast_batch_f16/test_data.h" +#include "../TestData/maximum_broadcast_ch_f16/test_data.h" +#include "../TestData/maximum_broadcast_height_f16/test_data.h" +#include "../TestData/maximum_broadcast_width_f16/test_data.h" +#include "../TestData/maximum_no_broadcast_f16/test_data.h" +#include "../TestData/maximum_scalar_1_f16/test_data.h" +#include "../TestData/maximum_scalar_2_f16/test_data.h" +#include "../TestData/minimum_broadcast_batch_f16/test_data.h" +#include "../TestData/minimum_broadcast_ch_f16/test_data.h" +#include "../TestData/minimum_broadcast_height_f16/test_data.h" +#include "../TestData/minimum_broadcast_width_f16/test_data.h" +#include "../TestData/minimum_no_broadcast_f16/test_data.h" +#include "../TestData/minimum_scalar_1_f16/test_data.h" +#include "../TestData/minimum_scalar_2_f16/test_data.h" + +#define RUN_MINMAX_F16_CASE(CASE_PREFIX, case_name, api_fn, tolerance) \ + void case_name##_##api_fn(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dims input_1_dims = {.n = CASE_PREFIX##_BATCH_1, \ + .h = CASE_PREFIX##_HEIGHT_1, \ + .w = CASE_PREFIX##_WIDTH_1, \ + .c = CASE_PREFIX##_CHANNEL_1}; \ + const cmsis_nn_dims input_2_dims = {.n = CASE_PREFIX##_BATCH_2, \ + .h = CASE_PREFIX##_HEIGHT_2, \ + .w = CASE_PREFIX##_WIDTH_2, \ + .c = CASE_PREFIX##_CHANNEL_2}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_BATCH, \ + .h = CASE_PREFIX##_OUTPUT_HEIGHT, \ + .w = CASE_PREFIX##_OUTPUT_WIDTH, \ + .c = CASE_PREFIX##_OUTPUT_CHANNEL}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + api_fn(&ctx, \ + case_name##_input_tensor_1, \ + &input_1_dims, \ + case_name##_input_tensor_2, \ + &input_2_dims, \ + output, \ + &output_dims)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_MINMAX_F16_CASE(MAXIMUM_SCALAR_1_F16, maximum_scalar_1_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_SCALAR_2_F16, maximum_scalar_2_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_NO_BROADCAST_F16, maximum_no_broadcast_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_BROADCAST_BATCH_F16, maximum_broadcast_batch_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_BROADCAST_HEIGHT_F16, maximum_broadcast_height_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_BROADCAST_WIDTH_F16, maximum_broadcast_width_f16, arm_maximum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MAXIMUM_BROADCAST_CH_F16, maximum_broadcast_ch_f16, arm_maximum_f16, 1.0e-3f) + +RUN_MINMAX_F16_CASE(MINIMUM_SCALAR_1_F16, minimum_scalar_1_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_SCALAR_2_F16, minimum_scalar_2_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_NO_BROADCAST_F16, minimum_no_broadcast_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_BROADCAST_BATCH_F16, minimum_broadcast_batch_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_BROADCAST_HEIGHT_F16, minimum_broadcast_height_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_BROADCAST_WIDTH_F16, minimum_broadcast_width_f16, arm_minimum_f16, 1.0e-3f) +RUN_MINMAX_F16_CASE(MINIMUM_BROADCAST_CH_F16, minimum_broadcast_ch_f16, arm_minimum_f16, 1.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/CMakeLists.txt new file mode 100644 index 00000000..19df18cd --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_maximum_minimum_f32) + +target_sources(test_arm_maximum_minimum_f32 PRIVATE + Unity/unity_test_arm_maximum_minimum_f32.c + Unity/TestRunner/unity_test_arm_maximum_minimum_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/Unity/unity_test_arm_maximum_minimum_f32.c b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/Unity/unity_test_arm_maximum_minimum_f32.c new file mode 100644 index 00000000..c86ad0c7 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/Unity/unity_test_arm_maximum_minimum_f32.c @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_maximum_minimum_f32.c" + +void test_maximum_scalar_1_f32_arm_maximum_f32(void) { maximum_scalar_1_f32_arm_maximum_f32(); } +void test_maximum_scalar_2_f32_arm_maximum_f32(void) { maximum_scalar_2_f32_arm_maximum_f32(); } +void test_maximum_no_broadcast_f32_arm_maximum_f32(void) { maximum_no_broadcast_f32_arm_maximum_f32(); } +void test_maximum_broadcast_batch_f32_arm_maximum_f32(void) { maximum_broadcast_batch_f32_arm_maximum_f32(); } +void test_maximum_broadcast_height_f32_arm_maximum_f32(void) { maximum_broadcast_height_f32_arm_maximum_f32(); } +void test_maximum_broadcast_width_f32_arm_maximum_f32(void) { maximum_broadcast_width_f32_arm_maximum_f32(); } +void test_maximum_broadcast_ch_f32_arm_maximum_f32(void) { maximum_broadcast_ch_f32_arm_maximum_f32(); } +void test_minimum_scalar_1_f32_arm_minimum_f32(void) { minimum_scalar_1_f32_arm_minimum_f32(); } +void test_minimum_scalar_2_f32_arm_minimum_f32(void) { minimum_scalar_2_f32_arm_minimum_f32(); } +void test_minimum_no_broadcast_f32_arm_minimum_f32(void) { minimum_no_broadcast_f32_arm_minimum_f32(); } +void test_minimum_broadcast_batch_f32_arm_minimum_f32(void) { minimum_broadcast_batch_f32_arm_minimum_f32(); } +void test_minimum_broadcast_height_f32_arm_minimum_f32(void) { minimum_broadcast_height_f32_arm_minimum_f32(); } +void test_minimum_broadcast_width_f32_arm_minimum_f32(void) { minimum_broadcast_width_f32_arm_minimum_f32(); } +void test_minimum_broadcast_ch_f32_arm_minimum_f32(void) { minimum_broadcast_ch_f32_arm_minimum_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/test_arm_maximum_minimum_f32.c b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/test_arm_maximum_minimum_f32.c new file mode 100644 index 00000000..85439fc5 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_maximum_minimum_f32/test_arm_maximum_minimum_f32.c @@ -0,0 +1,72 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/maximum_broadcast_batch_f32/test_data.h" +#include "../TestData/maximum_broadcast_ch_f32/test_data.h" +#include "../TestData/maximum_broadcast_height_f32/test_data.h" +#include "../TestData/maximum_broadcast_width_f32/test_data.h" +#include "../TestData/maximum_no_broadcast_f32/test_data.h" +#include "../TestData/maximum_scalar_1_f32/test_data.h" +#include "../TestData/maximum_scalar_2_f32/test_data.h" +#include "../TestData/minimum_broadcast_batch_f32/test_data.h" +#include "../TestData/minimum_broadcast_ch_f32/test_data.h" +#include "../TestData/minimum_broadcast_height_f32/test_data.h" +#include "../TestData/minimum_broadcast_width_f32/test_data.h" +#include "../TestData/minimum_no_broadcast_f32/test_data.h" +#include "../TestData/minimum_scalar_1_f32/test_data.h" +#include "../TestData/minimum_scalar_2_f32/test_data.h" + +#define RUN_MINMAX_F32_CASE(CASE_PREFIX, case_name, api_fn, tolerance) \ + void case_name##_##api_fn(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dims input_1_dims = {.n = CASE_PREFIX##_BATCH_1, \ + .h = CASE_PREFIX##_HEIGHT_1, \ + .w = CASE_PREFIX##_WIDTH_1, \ + .c = CASE_PREFIX##_CHANNEL_1}; \ + const cmsis_nn_dims input_2_dims = {.n = CASE_PREFIX##_BATCH_2, \ + .h = CASE_PREFIX##_HEIGHT_2, \ + .w = CASE_PREFIX##_WIDTH_2, \ + .c = CASE_PREFIX##_CHANNEL_2}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_BATCH, \ + .h = CASE_PREFIX##_OUTPUT_HEIGHT, \ + .w = CASE_PREFIX##_OUTPUT_WIDTH, \ + .c = CASE_PREFIX##_OUTPUT_CHANNEL}; \ + \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + api_fn(&ctx, \ + case_name##_input_tensor_1, \ + &input_1_dims, \ + case_name##_input_tensor_2, \ + &input_2_dims, \ + output, \ + &output_dims)); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_MINMAX_F32_CASE(MAXIMUM_SCALAR_1_F32, maximum_scalar_1_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_SCALAR_2_F32, maximum_scalar_2_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_NO_BROADCAST_F32, maximum_no_broadcast_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_BROADCAST_BATCH_F32, maximum_broadcast_batch_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_BROADCAST_HEIGHT_F32, maximum_broadcast_height_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_BROADCAST_WIDTH_F32, maximum_broadcast_width_f32, arm_maximum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MAXIMUM_BROADCAST_CH_F32, maximum_broadcast_ch_f32, arm_maximum_f32, 1.0e-7f) + +RUN_MINMAX_F32_CASE(MINIMUM_SCALAR_1_F32, minimum_scalar_1_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_SCALAR_2_F32, minimum_scalar_2_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_NO_BROADCAST_F32, minimum_no_broadcast_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_BROADCAST_BATCH_F32, minimum_broadcast_batch_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_BROADCAST_HEIGHT_F32, minimum_broadcast_height_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_BROADCAST_WIDTH_F32, minimum_broadcast_width_f32, arm_minimum_f32, 1.0e-7f) +RUN_MINMAX_F32_CASE(MINIMUM_BROADCAST_CH_F32, minimum_broadcast_ch_f32, arm_minimum_f32, 1.0e-7f) diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_pad_f16/CMakeLists.txt new file mode 100644 index 00000000..a3b0613d --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_pad_f16) + +target_sources(test_arm_pad_f16 PRIVATE + Unity/unity_test_arm_pad_f16.c + Unity/TestRunner/unity_test_arm_pad_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f16/Unity/unity_test_arm_pad_f16.c b/Tests/UnitTest/TestCases/test_arm_pad_f16/Unity/unity_test_arm_pad_f16.c new file mode 100644 index 00000000..4a548fae --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f16/Unity/unity_test_arm_pad_f16.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_pad_f16.c" + +void test_pad_int8_1_f16_arm_pad_f16(void) { pad_int8_1_f16_arm_pad_f16(); } +void test_pad_int8_2_f16_arm_pad_f16(void) { pad_int8_2_f16_arm_pad_f16(); } +void test_pad_basic_f16_arm_pad_f16(void) { pad_basic_f16_arm_pad_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f16/test_arm_pad_f16.c b/Tests/UnitTest/TestCases/test_arm_pad_f16/test_arm_pad_f16.c new file mode 100644 index 00000000..b9928749 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f16/test_arm_pad_f16.c @@ -0,0 +1,43 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/pad_basic_f16/test_data.h" +#include "../TestData/pad_int8_1_f16/test_data.h" +#include "../TestData/pad_int8_2_f16/test_data.h" + +#define RUN_PAD_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_pad_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_OUTPUT_SIZE] = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims pre_pad = {.n = CASE_PREFIX##_PRE_PAD_N, \ + .h = CASE_PREFIX##_PRE_PAD_H, \ + .w = CASE_PREFIX##_PRE_PAD_W, \ + .c = CASE_PREFIX##_PRE_PAD_C}; \ + const cmsis_nn_dims post_pad = {.n = CASE_PREFIX##_POST_PAD_N, \ + .h = CASE_PREFIX##_POST_PAD_H, \ + .w = CASE_PREFIX##_POST_PAD_W, \ + .c = CASE_PREFIX##_POST_PAD_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_pad_f16(case_name##_input_tensor, output, CASE_PREFIX##_PAD_VALUE, &input_dims, &pre_pad, &post_pad)); \ + \ + for (int i = 0; i < CASE_PREFIX##_OUTPUT_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_PAD_F16_CASE(PAD_INT8_1_F16, pad_int8_1_f16, 1.0e-3f) +RUN_PAD_F16_CASE(PAD_INT8_2_F16, pad_int8_2_f16, 1.0e-3f) +RUN_PAD_F16_CASE(PAD_BASIC_F16, pad_basic_f16, 1.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_pad_f32/CMakeLists.txt new file mode 100644 index 00000000..5d2a5c2e --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_pad_f32) + +target_sources(test_arm_pad_f32 PRIVATE + Unity/unity_test_arm_pad_f32.c + Unity/TestRunner/unity_test_arm_pad_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f32/Unity/unity_test_arm_pad_f32.c b/Tests/UnitTest/TestCases/test_arm_pad_f32/Unity/unity_test_arm_pad_f32.c new file mode 100644 index 00000000..ec4f0ba7 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f32/Unity/unity_test_arm_pad_f32.c @@ -0,0 +1,31 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_pad_f32.c" + +void test_pad_int8_1_f32_arm_pad_f32(void) { pad_int8_1_f32_arm_pad_f32(); } +void test_pad_int8_2_f32_arm_pad_f32(void) { pad_int8_2_f32_arm_pad_f32(); } +void test_pad_basic_f32_arm_pad_f32(void) { pad_basic_f32_arm_pad_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_pad_f32/test_arm_pad_f32.c b/Tests/UnitTest/TestCases/test_arm_pad_f32/test_arm_pad_f32.c new file mode 100644 index 00000000..8ddf0e73 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_pad_f32/test_arm_pad_f32.c @@ -0,0 +1,43 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/pad_basic_f32/test_data.h" +#include "../TestData/pad_int8_1_f32/test_data.h" +#include "../TestData/pad_int8_2_f32/test_data.h" + +#define RUN_PAD_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_pad_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_OUTPUT_SIZE] = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims pre_pad = {.n = CASE_PREFIX##_PRE_PAD_N, \ + .h = CASE_PREFIX##_PRE_PAD_H, \ + .w = CASE_PREFIX##_PRE_PAD_W, \ + .c = CASE_PREFIX##_PRE_PAD_C}; \ + const cmsis_nn_dims post_pad = {.n = CASE_PREFIX##_POST_PAD_N, \ + .h = CASE_PREFIX##_POST_PAD_H, \ + .w = CASE_PREFIX##_POST_PAD_W, \ + .c = CASE_PREFIX##_POST_PAD_C}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_pad_f32(case_name##_input_tensor, output, CASE_PREFIX##_PAD_VALUE, &input_dims, &pre_pad, &post_pad)); \ + \ + for (int i = 0; i < CASE_PREFIX##_OUTPUT_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_PAD_F32_CASE(PAD_INT8_1_F32, pad_int8_1_f32, 1.0e-7f) +RUN_PAD_F32_CASE(PAD_INT8_2_F32, pad_int8_2_f32, 1.0e-7f) +RUN_PAD_F32_CASE(PAD_BASIC_F32, pad_basic_f32, 1.0e-7f) diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_reshape_f16/CMakeLists.txt new file mode 100644 index 00000000..72640ecd --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_reshape_f16) + +target_sources(test_arm_reshape_f16 PRIVATE + Unity/unity_test_arm_reshape_f16.c + Unity/TestRunner/unity_test_arm_reshape_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f16/Unity/unity_test_arm_reshape_f16.c b/Tests/UnitTest/TestCases/test_arm_reshape_f16/Unity/unity_test_arm_reshape_f16.c new file mode 100644 index 00000000..c1b8574c --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f16/Unity/unity_test_arm_reshape_f16.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_reshape_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_reshape_f16_arm_reshape_f16(void) { reshape_f16_arm_reshape_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f16/test_arm_reshape_f16.c b/Tests/UnitTest/TestCases/test_arm_reshape_f16/test_arm_reshape_f16.c new file mode 100644 index 00000000..406ee321 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f16/test_arm_reshape_f16.c @@ -0,0 +1,22 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +void reshape_f16_arm_reshape_f16(void) +{ + const float16_t input[6] = { + (float16_t)0.0f, (float16_t)-1.25f, (float16_t)3.5f, (float16_t)8.0f, (float16_t)-0.125f, (float16_t)0.25f}; + float16_t output[6] = {0}; + + arm_reshape_f16(input, output, 6); + + for (int i = 0; i < 6; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(1.0e-3f, (float)input[i], (float)output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_reshape_f32/CMakeLists.txt new file mode 100644 index 00000000..98f897d8 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_reshape_f32) + +target_sources(test_arm_reshape_f32 PRIVATE + Unity/unity_test_arm_reshape_f32.c + Unity/TestRunner/unity_test_arm_reshape_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f32/Unity/unity_test_arm_reshape_f32.c b/Tests/UnitTest/TestCases/test_arm_reshape_f32/Unity/unity_test_arm_reshape_f32.c new file mode 100644 index 00000000..61b55881 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f32/Unity/unity_test_arm_reshape_f32.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_reshape_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_reshape_f32_arm_reshape_f32(void) { reshape_f32_arm_reshape_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_reshape_f32/test_arm_reshape_f32.c b/Tests/UnitTest/TestCases/test_arm_reshape_f32/test_arm_reshape_f32.c new file mode 100644 index 00000000..1468be12 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_reshape_f32/test_arm_reshape_f32.c @@ -0,0 +1,21 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +void reshape_f32_arm_reshape_f32(void) +{ + const float32_t input[6] = {0.0f, -1.25f, 3.5f, 8.0f, -0.125f, 0.25f}; + float32_t output[6] = {0}; + + arm_reshape_f32(input, output, 6); + + for (int i = 0; i < 6; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(1.0e-7f, input[i], output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_softmax_f16/CMakeLists.txt new file mode 100644 index 00000000..e3181534 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_softmax_f16) + +target_sources(test_arm_softmax_f16 PRIVATE + Unity/unity_test_arm_softmax_f16.c + Unity/TestRunner/unity_test_arm_softmax_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f16/Unity/unity_test_arm_softmax_f16.c b/Tests/UnitTest/TestCases/test_arm_softmax_f16/Unity/unity_test_arm_softmax_f16.c new file mode 100644 index 00000000..44b428d6 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f16/Unity/unity_test_arm_softmax_f16.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_softmax_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_softmax_f16_arm_softmax_f16(void) { softmax_f16_arm_softmax_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f16/test_arm_softmax_f16.c b/Tests/UnitTest/TestCases/test_arm_softmax_f16/test_arm_softmax_f16.c new file mode 100644 index 00000000..14395dbd --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f16/test_arm_softmax_f16.c @@ -0,0 +1,23 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/softmax_f16/test_data.h" + +void softmax_f16_arm_softmax_f16(void) +{ + float16_t output[SOFTMAX_F16_DST_SIZE] = {0}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_softmax_f16(softmax_f16_input, SOFTMAX_F16_NUM_ROWS, SOFTMAX_F16_ROW_SIZE, output)); + + for (int i = 0; i < SOFTMAX_F16_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(5.0e-3f, (float)softmax_f16_output_ref[i], (float)output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_softmax_f32/CMakeLists.txt new file mode 100644 index 00000000..e2268c69 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_softmax_f32) + +target_sources(test_arm_softmax_f32 PRIVATE + Unity/unity_test_arm_softmax_f32.c + Unity/TestRunner/unity_test_arm_softmax_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f32/Unity/unity_test_arm_softmax_f32.c b/Tests/UnitTest/TestCases/test_arm_softmax_f32/Unity/unity_test_arm_softmax_f32.c new file mode 100644 index 00000000..7414f452 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f32/Unity/unity_test_arm_softmax_f32.c @@ -0,0 +1,28 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_softmax_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_softmax_f32_arm_softmax_f32(void) { softmax_f32_arm_softmax_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_softmax_f32/test_arm_softmax_f32.c b/Tests/UnitTest/TestCases/test_arm_softmax_f32/test_arm_softmax_f32.c new file mode 100644 index 00000000..0ee0ea70 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_softmax_f32/test_arm_softmax_f32.c @@ -0,0 +1,23 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/softmax_f32/test_data.h" + +void softmax_f32_arm_softmax_f32(void) +{ + float32_t output[SOFTMAX_F32_DST_SIZE] = {0}; + + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, + arm_softmax_f32(softmax_f32_input, SOFTMAX_F32_NUM_ROWS, SOFTMAX_F32_ROW_SIZE, output)); + + for (int i = 0; i < SOFTMAX_F32_DST_SIZE; ++i) + { + TEST_ASSERT_FLOAT_WITHIN(2.0e-4f, softmax_f32_output_ref[i], output[i]); + } +} diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_svdf_f16/CMakeLists.txt new file mode 100644 index 00000000..4f0ce90c --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_svdf_f16) + +target_sources(test_arm_svdf_f16 PRIVATE + Unity/unity_test_arm_svdf_f16.c + Unity/TestRunner/unity_test_arm_svdf_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f16/Unity/unity_test_arm_svdf_f16.c b/Tests/UnitTest/TestCases/test_arm_svdf_f16/Unity/unity_test_arm_svdf_f16.c new file mode 100644 index 00000000..54464d46 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f16/Unity/unity_test_arm_svdf_f16.c @@ -0,0 +1,32 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_svdf_f16.c" + +void test_svdf_small_f16_arm_svdf_f16(void) { svdf_small_f16_arm_svdf_f16(); } +void test_svdf_batch2_f16_arm_svdf_f16(void) { svdf_batch2_f16_arm_svdf_f16(); } +void test_svdf_match_1_f16_arm_svdf_f16(void) { svdf_match_1_f16_arm_svdf_f16(); } +void test_svdf_match_2_f16_arm_svdf_f16(void) { svdf_match_2_f16_arm_svdf_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f16/test_arm_svdf_f16.c b/Tests/UnitTest/TestCases/test_arm_svdf_f16/test_arm_svdf_f16.c new file mode 100644 index 00000000..d5111cdf --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f16/test_arm_svdf_f16.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/svdf_batch2_f16/test_data.h" +#include "../TestData/svdf_match_1_f16/test_data.h" +#include "../TestData/svdf_match_2_f16/test_data.h" +#include "../TestData/svdf_small_f16/test_data.h" + +#define RUN_SVDF_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_svdf_f16(void) \ + { \ + float16_t state[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES * CASE_PREFIX##_TIME_BATCHES]; \ + float16_t scratch_input[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES]; \ + float16_t scratch_output[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_UNIT_COUNT]; \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + cmsis_nn_context input_ctx = {.buf = scratch_input, .size = sizeof(scratch_input)}; \ + cmsis_nn_context output_ctx = {.buf = scratch_output, .size = sizeof(scratch_output)}; \ + const cmsis_nn_svdf_params_f16 params = {.rank = CASE_PREFIX##_RANK, \ + .input_activation = {.min = CASE_PREFIX##_INPUT_ACTIVATION_MIN, \ + .max = CASE_PREFIX##_INPUT_ACTIVATION_MAX}, \ + .output_activation = {.min = CASE_PREFIX##_OUTPUT_ACTIVATION_MIN, \ + .max = CASE_PREFIX##_OUTPUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .h = CASE_PREFIX##_INPUT_SIZE, .w = 1, .c = 1}; \ + const cmsis_nn_dims state_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_FEATURE_BATCHES, \ + .w = CASE_PREFIX##_TIME_BATCHES, \ + .c = 1}; \ + const cmsis_nn_dims weights_feature_dims = { \ + .n = CASE_PREFIX##_FEATURE_BATCHES, .h = CASE_PREFIX##_INPUT_SIZE, .w = 1, .c = 1}; \ + const cmsis_nn_dims weights_time_dims = { \ + .n = CASE_PREFIX##_FEATURE_BATCHES, .h = CASE_PREFIX##_TIME_BATCHES, .w = 1, .c = 1}; \ + const cmsis_nn_dims bias_dims = {.n = CASE_PREFIX##_UNIT_COUNT, .h = 1, .w = 1, .c = 1}; \ + const cmsis_nn_dims output_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .h = CASE_PREFIX##_UNIT_COUNT, .w = 1, .c = 1}; \ + \ + for (int i = 0; i < CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES * CASE_PREFIX##_TIME_BATCHES; \ + ++i) \ + { \ + state[i] = case_name##_initial_state[i]; \ + } \ + for (int step = 0; step < CASE_PREFIX##_SEQUENCE_STEPS; ++step) \ + { \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_svdf_f16(&ctx, \ + &input_ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input_sequence + \ + (step * CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_INPUT_SIZE), \ + &state_dims, \ + state, \ + &weights_feature_dims, \ + case_name##_weights_feature, \ + &weights_time_dims, \ + case_name##_weights_time, \ + &bias_dims, \ + case_name##_bias, \ + &output_dims, \ + output)); \ + } \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_SVDF_F16_CASE(SVDF_SMALL_F16, svdf_small_f16, 8.0e-3f) +RUN_SVDF_F16_CASE(SVDF_BATCH2_F16, svdf_batch2_f16, 8.0e-3f) +RUN_SVDF_F16_CASE(SVDF_MATCH_1_F16, svdf_match_1_f16, 8.0e-3f) +RUN_SVDF_F16_CASE(SVDF_MATCH_2_F16, svdf_match_2_f16, 8.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_svdf_f32/CMakeLists.txt new file mode 100644 index 00000000..bfc63b28 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_svdf_f32) + +target_sources(test_arm_svdf_f32 PRIVATE + Unity/unity_test_arm_svdf_f32.c + Unity/TestRunner/unity_test_arm_svdf_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f32/Unity/unity_test_arm_svdf_f32.c b/Tests/UnitTest/TestCases/test_arm_svdf_f32/Unity/unity_test_arm_svdf_f32.c new file mode 100644 index 00000000..2ef74926 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f32/Unity/unity_test_arm_svdf_f32.c @@ -0,0 +1,32 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_svdf_f32.c" + +void test_svdf_small_f32_arm_svdf_f32(void) { svdf_small_f32_arm_svdf_f32(); } +void test_svdf_batch2_f32_arm_svdf_f32(void) { svdf_batch2_f32_arm_svdf_f32(); } +void test_svdf_match_1_f32_arm_svdf_f32(void) { svdf_match_1_f32_arm_svdf_f32(); } +void test_svdf_match_2_f32_arm_svdf_f32(void) { svdf_match_2_f32_arm_svdf_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_svdf_f32/test_arm_svdf_f32.c b/Tests/UnitTest/TestCases/test_arm_svdf_f32/test_arm_svdf_f32.c new file mode 100644 index 00000000..dc88caf0 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_svdf_f32/test_arm_svdf_f32.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/svdf_batch2_f32/test_data.h" +#include "../TestData/svdf_match_1_f32/test_data.h" +#include "../TestData/svdf_match_2_f32/test_data.h" +#include "../TestData/svdf_small_f32/test_data.h" + +#define RUN_SVDF_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_svdf_f32(void) \ + { \ + float32_t state[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES * CASE_PREFIX##_TIME_BATCHES]; \ + float32_t scratch_input[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES]; \ + float32_t scratch_output[CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_UNIT_COUNT]; \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + cmsis_nn_context ctx = {0}; \ + cmsis_nn_context input_ctx = {.buf = scratch_input, .size = sizeof(scratch_input)}; \ + cmsis_nn_context output_ctx = {.buf = scratch_output, .size = sizeof(scratch_output)}; \ + const cmsis_nn_svdf_params_f32 params = {.rank = CASE_PREFIX##_RANK, \ + .input_activation = {.min = CASE_PREFIX##_INPUT_ACTIVATION_MIN, \ + .max = CASE_PREFIX##_INPUT_ACTIVATION_MAX}, \ + .output_activation = {.min = CASE_PREFIX##_OUTPUT_ACTIVATION_MIN, \ + .max = CASE_PREFIX##_OUTPUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .h = CASE_PREFIX##_INPUT_SIZE, .w = 1, .c = 1}; \ + const cmsis_nn_dims state_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_FEATURE_BATCHES, \ + .w = CASE_PREFIX##_TIME_BATCHES, \ + .c = 1}; \ + const cmsis_nn_dims weights_feature_dims = { \ + .n = CASE_PREFIX##_FEATURE_BATCHES, .h = CASE_PREFIX##_INPUT_SIZE, .w = 1, .c = 1}; \ + const cmsis_nn_dims weights_time_dims = { \ + .n = CASE_PREFIX##_FEATURE_BATCHES, .h = CASE_PREFIX##_TIME_BATCHES, .w = 1, .c = 1}; \ + const cmsis_nn_dims bias_dims = {.n = CASE_PREFIX##_UNIT_COUNT, .h = 1, .w = 1, .c = 1}; \ + const cmsis_nn_dims output_dims = { \ + .n = CASE_PREFIX##_INPUT_BATCHES, .h = CASE_PREFIX##_UNIT_COUNT, .w = 1, .c = 1}; \ + \ + for (int i = 0; i < CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_FEATURE_BATCHES * CASE_PREFIX##_TIME_BATCHES; \ + ++i) \ + { \ + state[i] = case_name##_initial_state[i]; \ + } \ + for (int step = 0; step < CASE_PREFIX##_SEQUENCE_STEPS; ++step) \ + { \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, \ + arm_svdf_f32(&ctx, \ + &input_ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input_sequence + \ + (step * CASE_PREFIX##_INPUT_BATCHES * CASE_PREFIX##_INPUT_SIZE), \ + &state_dims, \ + state, \ + &weights_feature_dims, \ + case_name##_weights_feature, \ + &weights_time_dims, \ + case_name##_weights_time, \ + &bias_dims, \ + case_name##_bias, \ + &output_dims, \ + output)); \ + } \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_SVDF_F32_CASE(SVDF_SMALL_F32, svdf_small_f32, 2.0e-4f) +RUN_SVDF_F32_CASE(SVDF_BATCH2_F32, svdf_batch2_f32, 2.0e-4f) +RUN_SVDF_F32_CASE(SVDF_MATCH_1_F32, svdf_match_1_f32, 2.0e-4f) +RUN_SVDF_F32_CASE(SVDF_MATCH_2_F32, svdf_match_2_f32, 2.0e-4f) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/CMakeLists.txt new file mode 100644 index 00000000..b9b0b215 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_transpose_conv_f16) + +target_sources(test_arm_transpose_conv_f16 PRIVATE + Unity/unity_test_arm_transpose_conv_f16.c + Unity/TestRunner/unity_test_arm_transpose_conv_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/Unity/unity_test_arm_transpose_conv_f16.c b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/Unity/unity_test_arm_transpose_conv_f16.c new file mode 100644 index 00000000..12645322 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/Unity/unity_test_arm_transpose_conv_f16.c @@ -0,0 +1,32 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_transpose_conv_f16.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_transpose_conv_basic_f16_arm_transpose_conv_f16(void) { transpose_conv_basic_f16_arm_transpose_conv_f16(); } +void test_transpose_conv_basic_nhwc_f16_arm_transpose_conv_f16(void) +{ + transpose_conv_basic_nhwc_f16_arm_transpose_conv_f16(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/test_arm_transpose_conv_f16.c b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/test_arm_transpose_conv_f16.c new file mode 100644 index 00000000..7f222923 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f16/test_arm_transpose_conv_f16.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/transpose_conv_basic_f16/test_data.h" +#include "../TestData/transpose_conv_basic_nhwc_f16/test_data.h" + +#define RUN_TRANSPOSE_CONV_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_transpose_conv_f16(void) \ + { \ + float16_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_context output_ctx = {0}; \ + const cmsis_nn_transpose_conv_params_f16 params = { \ + .stride = {.h = CASE_PREFIX##_STRIDE_H, .w = CASE_PREFIX##_STRIDE_W}, \ + .padding = {.h = CASE_PREFIX##_PADDING_H, .w = CASE_PREFIX##_PADDING_W}, \ + .padding_offsets = {.h = CASE_PREFIX##_PADDING_OFFSET_H, .w = CASE_PREFIX##_PADDING_OFFSET_W}, \ + .dilation = {.h = CASE_PREFIX##_DILATION_H, .w = CASE_PREFIX##_DILATION_W}, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_OUT_CH, \ + .h = CASE_PREFIX##_FILTER_H, \ + .w = CASE_PREFIX##_FILTER_W, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .h = 1, .w = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + arm_cmsis_nn_status status; \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + status = arm_transpose_conv_wrapper_f16(&ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input, \ + &filter_dims, \ + case_name##_weights, \ + &bias_dims, \ + case_name##_biases, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT); \ + } \ + else \ + { \ + status = arm_transpose_conv_f16(&ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input, \ + &filter_dims, \ + case_name##_weights, \ + &bias_dims, \ + case_name##_biases, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT); \ + } \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, status); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_TRANSPOSE_CONV_F16_CASE(TRANSPOSE_CONV_BASIC_F16, transpose_conv_basic_f16, 5.0e-2f) +RUN_TRANSPOSE_CONV_F16_CASE(TRANSPOSE_CONV_BASIC_NHWC_F16, transpose_conv_basic_nhwc_f16, 5.0e-2f) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/CMakeLists.txt new file mode 100644 index 00000000..d4b0f45a --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_transpose_conv_f32) + +target_sources(test_arm_transpose_conv_f32 PRIVATE + Unity/unity_test_arm_transpose_conv_f32.c + Unity/TestRunner/unity_test_arm_transpose_conv_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/Unity/unity_test_arm_transpose_conv_f32.c b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/Unity/unity_test_arm_transpose_conv_f32.c new file mode 100644 index 00000000..02ae4429 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/Unity/unity_test_arm_transpose_conv_f32.c @@ -0,0 +1,32 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "../test_arm_transpose_conv_f32.c" +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +void test_transpose_conv_basic_f32_arm_transpose_conv_f32(void) { transpose_conv_basic_f32_arm_transpose_conv_f32(); } +void test_transpose_conv_basic_nhwc_f32_arm_transpose_conv_f32(void) +{ + transpose_conv_basic_nhwc_f32_arm_transpose_conv_f32(); +} diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/test_arm_transpose_conv_f32.c b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/test_arm_transpose_conv_f32.c new file mode 100644 index 00000000..1593c3f0 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_conv_f32/test_arm_transpose_conv_f32.c @@ -0,0 +1,79 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include + +#include "../TestData/transpose_conv_basic_f32/test_data.h" +#include "../TestData/transpose_conv_basic_nhwc_f32/test_data.h" + +#define RUN_TRANSPOSE_CONV_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_transpose_conv_f32(void) \ + { \ + float32_t output[CASE_PREFIX##_DST_SIZE] = {0}; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_context output_ctx = {0}; \ + const cmsis_nn_transpose_conv_params_f32 params = { \ + .stride = {.h = CASE_PREFIX##_STRIDE_H, .w = CASE_PREFIX##_STRIDE_W}, \ + .padding = {.h = CASE_PREFIX##_PADDING_H, .w = CASE_PREFIX##_PADDING_W}, \ + .padding_offsets = {.h = CASE_PREFIX##_PADDING_OFFSET_H, .w = CASE_PREFIX##_PADDING_OFFSET_W}, \ + .dilation = {.h = CASE_PREFIX##_DILATION_H, .w = CASE_PREFIX##_DILATION_W}, \ + .activation = {.min = CASE_PREFIX##_OUT_ACTIVATION_MIN, .max = CASE_PREFIX##_OUT_ACTIVATION_MAX}}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims filter_dims = {.n = CASE_PREFIX##_OUT_CH, \ + .h = CASE_PREFIX##_FILTER_H, \ + .w = CASE_PREFIX##_FILTER_W, \ + .c = CASE_PREFIX##_IN_CH}; \ + const cmsis_nn_dims bias_dims = {.n = 1, .h = 1, .w = 1, .c = CASE_PREFIX##_OUT_CH}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_INPUT_BATCHES, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + arm_cmsis_nn_status status; \ + \ + if (CASE_PREFIX##_USE_WRAPPER) \ + { \ + status = arm_transpose_conv_wrapper_f32(&ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input, \ + &filter_dims, \ + case_name##_weights, \ + &bias_dims, \ + case_name##_biases, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT); \ + } \ + else \ + { \ + status = arm_transpose_conv_f32(&ctx, \ + &output_ctx, \ + ¶ms, \ + &input_dims, \ + case_name##_input, \ + &filter_dims, \ + case_name##_weights, \ + &bias_dims, \ + case_name##_biases, \ + &output_dims, \ + output, \ + CASE_PREFIX##_LAYOUT); \ + } \ + TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, status); \ + \ + for (int i = 0; i < CASE_PREFIX##_DST_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_TRANSPOSE_CONV_F32_CASE(TRANSPOSE_CONV_BASIC_F32, transpose_conv_basic_f32, 2.0e-5f) +RUN_TRANSPOSE_CONV_F32_CASE(TRANSPOSE_CONV_BASIC_NHWC_F32, transpose_conv_basic_nhwc_f32, 2.0e-5f) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_transpose_f16/CMakeLists.txt new file mode 100644 index 00000000..40d200ca --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f16/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_transpose_f16) + +target_sources(test_arm_transpose_f16 PRIVATE + Unity/unity_test_arm_transpose_f16.c + Unity/TestRunner/unity_test_arm_transpose_f16_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f16/Unity/unity_test_arm_transpose_f16.c b/Tests/UnitTest/TestCases/test_arm_transpose_f16/Unity/unity_test_arm_transpose_f16.c new file mode 100644 index 00000000..1fc52280 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f16/Unity/unity_test_arm_transpose_f16.c @@ -0,0 +1,40 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_transpose_f16.c" + +void test_transpose_matrix_f16_arm_transpose_f16(void) { transpose_matrix_f16_arm_transpose_f16(); } +void test_transpose_matrix_small_f16_arm_transpose_f16(void) { transpose_matrix_small_f16_arm_transpose_f16(); } +void test_transpose_3dim_f16_arm_transpose_f16(void) { transpose_3dim_f16_arm_transpose_f16(); } +void test_transpose_3dim2_f16_arm_transpose_f16(void) { transpose_3dim2_f16_arm_transpose_f16(); } +void test_transpose_chwn_f16_arm_transpose_f16(void) { transpose_chwn_f16_arm_transpose_f16(); } +void test_transpose_default_f16_arm_transpose_f16(void) { transpose_default_f16_arm_transpose_f16(); } +void test_transpose_nhcw_f16_arm_transpose_f16(void) { transpose_nhcw_f16_arm_transpose_f16(); } +void test_transpose_nchw_f16_arm_transpose_f16(void) { transpose_nchw_f16_arm_transpose_f16(); } +void test_transpose_nwhc_f16_arm_transpose_f16(void) { transpose_nwhc_f16_arm_transpose_f16(); } +void test_transpose_ncwh_f16_arm_transpose_f16(void) { transpose_ncwh_f16_arm_transpose_f16(); } +void test_transpose_swap_last2_4d_f16_arm_transpose_f16(void) { transpose_swap_last2_4d_f16_arm_transpose_f16(); } +void test_transpose_wchn_f16_arm_transpose_f16(void) { transpose_wchn_f16_arm_transpose_f16(); } diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f16/test_arm_transpose_f16.c b/Tests/UnitTest/TestCases/test_arm_transpose_f16/test_arm_transpose_f16.c new file mode 100644 index 00000000..e8a8f2dc --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f16/test_arm_transpose_f16.c @@ -0,0 +1,68 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include + +#include "../TestData/transpose_3dim2_f16/test_data.h" +#include "../TestData/transpose_3dim_f16/test_data.h" +#include "../TestData/transpose_chwn_f16/test_data.h" +#include "../TestData/transpose_default_f16/test_data.h" +#include "../TestData/transpose_matrix_f16/test_data.h" +#include "../TestData/transpose_matrix_small_f16/test_data.h" +#include "../TestData/transpose_nchw_f16/test_data.h" +#include "../TestData/transpose_ncwh_f16/test_data.h" +#include "../TestData/transpose_nhcw_f16/test_data.h" +#include "../TestData/transpose_nwhc_f16/test_data.h" +#include "../TestData/transpose_swap_last2_4d_f16/test_data.h" +#include "../TestData/transpose_wchn_f16/test_data.h" + +#define TRANSPOSE_F16_OUTPUT_MAX_SIZE TRANSPOSE_NHCW_F16_SIZE + +static float16_t transpose_f16_output_buf[TRANSPOSE_F16_OUTPUT_MAX_SIZE]; + +#define RUN_TRANSPOSE_F16_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_transpose_f16(void) \ + { \ + memset(transpose_f16_output_buf, 0, sizeof(transpose_f16_output_buf)); \ + float16_t *output = transpose_f16_output_buf; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_N, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const cmsis_nn_transpose_params_f16 params = { \ + .num_dims = CASE_PREFIX##_NUM_DIMS, \ + .perm = {CASE_PREFIX##_PERM_0, CASE_PREFIX##_PERM_1, CASE_PREFIX##_PERM_2, CASE_PREFIX##_PERM_3}, \ + .layout = CASE_PREFIX##_LAYOUT}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_transpose_f16(&ctx, ¶ms, &input_dims, case_name##_input_tensor, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), (float)case_name##_output_ref[i], (float)output[i]); \ + } \ + } + +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_MATRIX_F16, transpose_matrix_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_MATRIX_SMALL_F16, transpose_matrix_small_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_3DIM_F16, transpose_3dim_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_3DIM2_F16, transpose_3dim2_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_CHWN_F16, transpose_chwn_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_DEFAULT_F16, transpose_default_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_NHCW_F16, transpose_nhcw_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_NCHW_F16, transpose_nchw_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_NWHC_F16, transpose_nwhc_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_NCWH_F16, transpose_ncwh_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_SWAP_LAST2_4D_F16, transpose_swap_last2_4d_f16, 1.0e-3f) +RUN_TRANSPOSE_F16_CASE(TRANSPOSE_WCHN_F16, transpose_wchn_f16, 1.0e-3f) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f32/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_transpose_f32/CMakeLists.txt new file mode 100644 index 00000000..144bb958 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f32/CMakeLists.txt @@ -0,0 +1,23 @@ +# +# Copyright (C) 2026 Arm Limited or its affiliates. +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +add_cmsis_nn_unit_test_executable(test_arm_transpose_f32) + +target_sources(test_arm_transpose_f32 PRIVATE + Unity/unity_test_arm_transpose_f32.c + Unity/TestRunner/unity_test_arm_transpose_f32_runner.c) diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f32/Unity/unity_test_arm_transpose_f32.c b/Tests/UnitTest/TestCases/test_arm_transpose_f32/Unity/unity_test_arm_transpose_f32.c new file mode 100644 index 00000000..feb71eb6 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f32/Unity/unity_test_arm_transpose_f32.c @@ -0,0 +1,39 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include +#include + +#include "unity.h" + +#ifdef USING_FVP_CORSTONE_300 +extern void uart_init(void); +#endif + +void setUp(void) +{ +#ifdef USING_FVP_CORSTONE_300 + uart_init(); +#endif +} + +void tearDown(void) {} +#include "../test_arm_transpose_f32.c" + +void test_transpose_matrix_f32_arm_transpose_f32(void) { transpose_matrix_f32_arm_transpose_f32(); } +void test_transpose_3dim_f32_arm_transpose_f32(void) { transpose_3dim_f32_arm_transpose_f32(); } +void test_transpose_3dim2_f32_arm_transpose_f32(void) { transpose_3dim2_f32_arm_transpose_f32(); } +void test_transpose_chwn_f32_arm_transpose_f32(void) { transpose_chwn_f32_arm_transpose_f32(); } +void test_transpose_default_f32_arm_transpose_f32(void) { transpose_default_f32_arm_transpose_f32(); } +void test_transpose_nhcw_f32_arm_transpose_f32(void) { transpose_nhcw_f32_arm_transpose_f32(); } +void test_transpose_nchw_f32_arm_transpose_f32(void) { transpose_nchw_f32_arm_transpose_f32(); } +void test_transpose_nwhc_f32_arm_transpose_f32(void) { transpose_nwhc_f32_arm_transpose_f32(); } +void test_transpose_ncwh_f32_arm_transpose_f32(void) { transpose_ncwh_f32_arm_transpose_f32(); } +void test_transpose_swap_last2_4d_f32_arm_transpose_f32(void) { transpose_swap_last2_4d_f32_arm_transpose_f32(); } +void test_transpose_wchn_f32_arm_transpose_f32(void) { transpose_wchn_f32_arm_transpose_f32(); } diff --git a/Tests/UnitTest/TestCases/test_arm_transpose_f32/test_arm_transpose_f32.c b/Tests/UnitTest/TestCases/test_arm_transpose_f32/test_arm_transpose_f32.c new file mode 100644 index 00000000..6544c106 --- /dev/null +++ b/Tests/UnitTest/TestCases/test_arm_transpose_f32/test_arm_transpose_f32.c @@ -0,0 +1,66 @@ +/* + * SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include + +#include "../TestData/transpose_3dim2_f32/test_data.h" +#include "../TestData/transpose_3dim_f32/test_data.h" +#include "../TestData/transpose_chwn_f32/test_data.h" +#include "../TestData/transpose_default_f32/test_data.h" +#include "../TestData/transpose_matrix_f32/test_data.h" +#include "../TestData/transpose_nchw_f32/test_data.h" +#include "../TestData/transpose_ncwh_f32/test_data.h" +#include "../TestData/transpose_nhcw_f32/test_data.h" +#include "../TestData/transpose_nwhc_f32/test_data.h" +#include "../TestData/transpose_swap_last2_4d_f32/test_data.h" +#include "../TestData/transpose_wchn_f32/test_data.h" + +#define TRANSPOSE_F32_OUTPUT_MAX_SIZE TRANSPOSE_NHCW_F32_SIZE + +static float32_t transpose_f32_output_buf[TRANSPOSE_F32_OUTPUT_MAX_SIZE]; + +#define RUN_TRANSPOSE_F32_CASE(CASE_PREFIX, case_name, tolerance) \ + void case_name##_arm_transpose_f32(void) \ + { \ + memset(transpose_f32_output_buf, 0, sizeof(transpose_f32_output_buf)); \ + float32_t *output = transpose_f32_output_buf; \ + const cmsis_nn_context ctx = {0}; \ + const cmsis_nn_dims input_dims = {.n = CASE_PREFIX##_INPUT_N, \ + .h = CASE_PREFIX##_INPUT_H, \ + .w = CASE_PREFIX##_INPUT_W, \ + .c = CASE_PREFIX##_INPUT_C}; \ + const cmsis_nn_dims output_dims = {.n = CASE_PREFIX##_OUTPUT_N, \ + .h = CASE_PREFIX##_OUTPUT_H, \ + .w = CASE_PREFIX##_OUTPUT_W, \ + .c = CASE_PREFIX##_OUTPUT_C}; \ + const cmsis_nn_transpose_params_f32 params = { \ + .num_dims = CASE_PREFIX##_NUM_DIMS, \ + .perm = {CASE_PREFIX##_PERM_0, CASE_PREFIX##_PERM_1, CASE_PREFIX##_PERM_2, CASE_PREFIX##_PERM_3}, \ + .layout = CASE_PREFIX##_LAYOUT}; \ + \ + TEST_ASSERT_EQUAL( \ + ARM_CMSIS_NN_SUCCESS, \ + arm_transpose_f32(&ctx, ¶ms, &input_dims, case_name##_input_tensor, &output_dims, output)); \ + \ + for (int i = 0; i < CASE_PREFIX##_SIZE; ++i) \ + { \ + TEST_ASSERT_FLOAT_WITHIN((tolerance), case_name##_output_ref[i], output[i]); \ + } \ + } + +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_MATRIX_F32, transpose_matrix_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_3DIM_F32, transpose_3dim_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_3DIM2_F32, transpose_3dim2_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_CHWN_F32, transpose_chwn_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_DEFAULT_F32, transpose_default_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_NHCW_F32, transpose_nhcw_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_NCHW_F32, transpose_nchw_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_NWHC_F32, transpose_nwhc_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_NCWH_F32, transpose_ncwh_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_SWAP_LAST2_4D_F32, transpose_swap_last2_4d_f32, 1.0e-7f) +RUN_TRANSPOSE_F32_CASE(TRANSPOSE_WCHN_F32, transpose_wchn_f32, 1.0e-7f) diff --git a/Tests/UnitTest/activation_settings_flt.py b/Tests/UnitTest/activation_settings_flt.py new file mode 100644 index 00000000..902349e9 --- /dev/null +++ b/Tests/UnitTest/activation_settings_flt.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass +class ActivationSettingsFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + size: int = 9 + leaky_relu_param: float = 0.125 + + outdir: Path = Path("TestCases/TestData") + pregenerated_dir: Path = Path("PregeneratedData") + + def __post_init__(self) -> None: + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_input(self, array: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, array) + + def _load_input(self) -> np.ndarray: + return load_pregenerated_array(self.input_table_file, (self.size,)) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + input_data = np.array([-5.0, -3.0, -1.25, -0.1, 0.0, 0.5, 1.5, 3.0, 6.0], dtype=np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_input(input_data) + return input_data + + def _write_config_header(self) -> None: + prefix = self.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_SIZE {self.size}\n") + if self.dtype_name == "f32": + fh.write(f"#define {prefix}_LEAKY_RELU_PARAM {format_float32(self.leaky_relu_param)}\n") + else: + fh.write(f"#define {prefix}_LEAKY_RELU_PARAM {format_float16(self.leaky_relu_param)}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.dataset}_{name}", values, self.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + input_tensor = torch.tensor(input_data, dtype=torch.float32) + + outputs = { + "input": input_data.astype(np.float32 if self.dtype_name == "f32" else np.float16), + "output_ref_sigmoid": torch.sigmoid(input_tensor).to(dtype=self.torch_dtype).cpu().numpy(), + "output_ref_tanh": torch.tanh(input_tensor).to(dtype=self.torch_dtype).cpu().numpy(), + "output_ref_hardswish": F.hardswish(input_tensor).to(dtype=self.torch_dtype).cpu().numpy(), + "output_ref_leaky_relu": F.leaky_relu(input_tensor, negative_slope=self.leaky_relu_param) + .to(dtype=self.torch_dtype) + .cpu() + .numpy(), + } + + if self.dtype_name == "f16": + outputs = {name: value.astype(np.float16) for name, value in outputs.items()} + + self.generated_header_files = [] + self._write_config_header() + for name, values in outputs.items(): + self._write_array_header(name, values) + self._write_wrapper_header() + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float activation unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + choices=["activation_f32", "activation_f16", "all"], + default="all", + help="Dataset to generate.", + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate the pregenerated input sample instead of reusing input.txt.", + ) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + + jobs: list[ActivationSettingsFlt] = [] + if args.dataset in ("activation_f32", "all"): + jobs.append(ActivationSettingsFlt(dataset="activation_f32", dtype_name="f32", torch_dtype=torch.float32)) + if args.dataset in ("activation_f16", "all"): + jobs.append(ActivationSettingsFlt(dataset="activation_f16", dtype_name="f16", torch_dtype=torch.float16)) + + for job in jobs: + print(f"Generating {job.dataset}...") + job.generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/add_mul_settings_flt.py b/Tests/UnitTest/add_mul_settings_flt.py new file mode 100644 index 00000000..d248b870 --- /dev/null +++ b/Tests/UnitTest/add_mul_settings_flt.py @@ -0,0 +1,301 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class AddMulCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + op_name: str + block_size: int + activation_min: float + activation_max: float + seed: int + input_min: float + input_max: float + + +class AddMulSettingsFlt: + def __init__(self, case: AddMulCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input1_table_file = self.pregenerated_data_dir / "input1.txt" + self.input2_table_file = self.pregenerated_data_dir / "input2.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_inputs(self, input1: np.ndarray, input2: np.ndarray) -> None: + save_pregenerated_array(self.input1_table_file, input1) + save_pregenerated_array(self.input2_table_file, input2) + + def _load_inputs(self) -> tuple[np.ndarray, np.ndarray]: + input1 = load_pregenerated_array(self.input1_table_file, (self.case.block_size,)) + input2 = load_pregenerated_array(self.input2_table_file, (self.case.block_size,)) + return input1, input2 + + def get_input_data(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray]: + if self.input1_table_file.exists() and self.input2_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input1_table_file} and {self.input2_table_file}") + return self._load_inputs() + + rng = np.random.default_rng(self.case.seed) + input1 = rng.uniform(self.case.input_min, self.case.input_max, size=self.case.block_size).astype(np.float32) + input2 = rng.uniform(self.case.input_min, self.case.input_max, size=self.case.block_size).astype(np.float32) + print(f"Saving data to {self.input1_table_file} and {self.input2_table_file}") + self._save_inputs(input1, input2) + return input1, input2 + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_DST_SIZE {self.case.block_size}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + fh.write(f"#define {prefix}_INPUT1_OFFSET 0\n") + fh.write(f"#define {prefix}_INPUT2_OFFSET 0\n") + fh.write(f"#define {prefix}_OUTPUT_MULT 0\n") + fh.write(f"#define {prefix}_OUTPUT_SHIFT 0\n") + fh.write(f"#define {prefix}_OUTPUT_OFFSET 0\n") + if self.case.op_name == "add": + fh.write(f"#define {prefix}_LEFT_SHIFT 0\n") + fh.write(f"#define {prefix}_INPUT1_SHIFT 0\n") + fh.write(f"#define {prefix}_INPUT2_SHIFT 0\n") + fh.write(f"#define {prefix}_INPUT1_MULT 0\n") + fh.write(f"#define {prefix}_INPUT2_MULT 0\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input1, input2 = self.get_input_data(regenerate_input) + input1_tensor = torch.tensor(input1, dtype=torch.float32) + input2_tensor = torch.tensor(input2, dtype=torch.float32) + if self.case.op_name == "add": + output_tensor = input1_tensor + input2_tensor + elif self.case.op_name == "mul": + output_tensor = input1_tensor * input2_tensor + else: + raise RuntimeError("Unsupported elementwise op") + + output_tensor = torch.clamp(output_tensor, min=self.case.activation_min, max=self.case.activation_max) + input1_array = input1_tensor.to(self.case.torch_dtype).cpu().numpy() + input2_array = input2_tensor.to(self.case.torch_dtype).cpu().numpy() + output_array = output_tensor.to(self.case.torch_dtype).cpu().numpy() + if self.case.dtype_name == "f16": + input1_array = input1_array.astype(np.float16) + input2_array = input2_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header() + self._write_array_header("input1", input1_array) + self._write_array_header("input2", input2_array) + self._write_array_header("output_ref", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[AddMulCaseFlt]: + return [ + AddMulCaseFlt( + dataset="add_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="add", + block_size=128, + activation_min=-10.0, + activation_max=10.0, + seed=2001, + input_min=-4.0, + input_max=4.0, + ), + AddMulCaseFlt( + dataset="add_f32_spill", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="add", + block_size=105, + activation_min=-2.0, + activation_max=3.0, + seed=2002, + input_min=-4.0, + input_max=4.0, + ), + AddMulCaseFlt( + dataset="add_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="add", + block_size=128, + activation_min=-10.0, + activation_max=10.0, + seed=2101, + input_min=-4.0, + input_max=4.0, + ), + AddMulCaseFlt( + dataset="add_f16_spill", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="add", + block_size=105, + activation_min=-2.0, + activation_max=3.0, + seed=2102, + input_min=-4.0, + input_max=4.0, + ), + AddMulCaseFlt( + dataset="mul_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="mul", + block_size=160, + activation_min=-10.0, + activation_max=10.0, + seed=2201, + input_min=-2.5, + input_max=2.5, + ), + AddMulCaseFlt( + dataset="mul_f32_spill", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="mul", + block_size=245, + activation_min=-1.5, + activation_max=1.0, + seed=2202, + input_min=-2.5, + input_max=2.5, + ), + AddMulCaseFlt( + dataset="mul_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="mul", + block_size=160, + activation_min=-10.0, + activation_max=10.0, + seed=2301, + input_min=-2.5, + input_max=2.5, + ), + AddMulCaseFlt( + dataset="mul_f16_spill", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="mul", + block_size=245, + activation_min=-1.5, + activation_max=1.0, + seed=2302, + input_min=-2.5, + input_max=2.5, + ), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float add/mul unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, add_f32_family, add_f16_family, " + "mul_f32_family, mul_f16_family, or a specific dataset name such as add_f32_spill." + ), + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate pregenerated input samples instead of reusing input files.", + ) + return parser.parse_args() + + +def _matches_selector(case: AddMulCaseFlt, selector: str) -> bool: + if selector == "all": + return True + if selector == case.dataset: + return True + if selector == "add_f32_family": + return case.dataset.startswith("add_f32") + if selector == "add_f16_family": + return case.dataset.startswith("add_f16") + if selector == "mul_f32_family": + return case.dataset.startswith("mul_f32") + if selector == "mul_f16_family": + return case.dataset.startswith("mul_f16") + return False + + +def main() -> None: + args = parse_args() + cases = [case for case in _build_cases() if _matches_selector(case, args.dataset)] + if not cases: + raise SystemExit(f"Unknown dataset selector: {args.dataset}") + + for case in cases: + print(f"Generating {case.dataset}...") + AddMulSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/batch_matmul_settings_flt.py b/Tests/UnitTest/batch_matmul_settings_flt.py new file mode 100644 index 00000000..92ca00ab --- /dev/null +++ b/Tests/UnitTest/batch_matmul_settings_flt.py @@ -0,0 +1,343 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class BatchMatmulCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + lhs_batch: int + lhs_height: int + lhs_rows: int + lhs_cols: int + rhs_batch: int + rhs_height: int + rhs_rows: int + rhs_cols: int + adj_x: int + adj_y: int + activation_min: float + activation_max: float + seed: int + input_min: float = -2.0 + input_max: float = 2.0 + + +class BatchMatmulSettingsFlt: + def __init__(self, case: BatchMatmulCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.lhs_input_table_file = self.pregenerated_data_dir / "lhs_input.txt" + self.rhs_input_table_file = self.pregenerated_data_dir / "rhs_input.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, path: Path, values: np.ndarray) -> None: + save_pregenerated_array(path, values) + + def _load_array(self, path: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(path, shape) + + def get_lhs_input(self, regenerate_input: bool) -> np.ndarray: + shape = (self.case.lhs_batch, self.case.lhs_height, self.case.lhs_rows, self.case.lhs_cols) + if self.lhs_input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.lhs_input_table_file}") + return self._load_array(self.lhs_input_table_file, shape) + + rng = np.random.default_rng(self.case.seed) + lhs = rng.uniform(self.case.input_min, self.case.input_max, size=shape).astype(np.float32) + print(f"Saving data to {self.lhs_input_table_file}") + self._save_array(self.lhs_input_table_file, lhs) + return lhs + + def get_rhs_input(self, regenerate_input: bool) -> np.ndarray: + shape = (self.case.rhs_batch, self.case.rhs_height, self.case.rhs_rows, self.case.rhs_cols) + if self.rhs_input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.rhs_input_table_file}") + return self._load_array(self.rhs_input_table_file, shape) + + rng = np.random.default_rng(self.case.seed + 101) + rhs = rng.uniform(self.case.input_min, self.case.input_max, size=shape).astype(np.float32) + print(f"Saving data to {self.rhs_input_table_file}") + self._save_array(self.rhs_input_table_file, rhs) + return rhs + + def _write_config_header(self, output_shape: tuple[int, int, int, int]) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + output_batch, output_height, output_rows, output_cols = output_shape + dst_size = output_batch * output_height * output_rows * output_cols + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_LHS_BATCH {self.case.lhs_batch}\n") + fh.write(f"#define {prefix}_LHS_HEIGHT {self.case.lhs_height}\n") + fh.write(f"#define {prefix}_LHS_ROWS {self.case.lhs_rows}\n") + fh.write(f"#define {prefix}_LHS_COLS {self.case.lhs_cols}\n") + fh.write(f"#define {prefix}_RHS_BATCH {self.case.rhs_batch}\n") + fh.write(f"#define {prefix}_RHS_HEIGHT {self.case.rhs_height}\n") + fh.write(f"#define {prefix}_RHS_ROWS {self.case.rhs_rows}\n") + fh.write(f"#define {prefix}_RHS_COLS {self.case.rhs_cols}\n") + fh.write(f"#define {prefix}_ADJ_X {self.case.adj_x}\n") + fh.write(f"#define {prefix}_ADJ_Y {self.case.adj_y}\n") + fh.write(f"#define {prefix}_DST_SIZE {dst_size}\n") + fh.write(f"#define {prefix}_OUTPUT_BATCH {output_batch}\n") + fh.write(f"#define {prefix}_OUTPUT_HEIGHT {output_height}\n") + fh.write(f"#define {prefix}_OUTPUT_ROWS {output_rows}\n") + fh.write(f"#define {prefix}_OUTPUT_COLS {output_cols}\n") + fh.write(f"#define {prefix}_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + format_generated_file(filepath) + + def _write_named_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def _emulate_batch_matmul_output(self, + lhs_input: np.ndarray, + lhs_transposed: np.ndarray, + rhs_input: np.ndarray, + rhs_transposed: np.ndarray) -> np.ndarray: + lhs_tensor = lhs_transposed if self.case.adj_x else lhs_input + rhs_tensor = rhs_input if self.case.adj_y else rhs_transposed + + lhs_rows = self.case.lhs_cols if self.case.adj_x else self.case.lhs_rows + rhs_rows = self.case.rhs_rows if self.case.adj_y else self.case.rhs_cols + rhs_cols = self.case.rhs_cols if self.case.adj_y else self.case.rhs_rows + + output_batch = max(self.case.lhs_batch, self.case.rhs_batch) + output_height = max(self.case.lhs_height, self.case.rhs_height) + output = np.zeros((output_batch, output_height, lhs_rows, rhs_rows), dtype=np.float32) + + input_lhs = lhs_tensor.reshape(-1) + input_rhs = rhs_tensor.reshape(-1) + + inner_lhs_diff = 0 if self.case.lhs_height >= self.case.rhs_height else lhs_rows * rhs_cols + inner_rhs_diff = rhs_rows * rhs_cols if self.case.rhs_height >= self.case.lhs_height else 0 + outer_lhs_diff = ( + inner_lhs_diff if self.case.lhs_batch >= self.case.rhs_batch + else -((lhs_rows * rhs_cols) - inner_lhs_diff) * self.case.lhs_height + ) + outer_rhs_diff = ( + (rhs_rows * rhs_cols) - inner_rhs_diff if self.case.rhs_batch >= self.case.lhs_batch + else -inner_rhs_diff * self.case.rhs_height + ) + + lhs_offset = 0 + rhs_offset = 0 + for i_out_batch in range(output_batch): + for i_out_height in range(output_height): + lhs_mat = input_lhs[lhs_offset:] + rhs_mat = input_rhs[rhs_offset:] + for i_lhs_rows in range(lhs_rows): + for i_rhs_rows in range(rhs_rows): + acc = 0.0 + for k in range(rhs_cols): + lhs_v = ( + lhs_mat[k * lhs_rows + i_lhs_rows] + if self.case.adj_x + else lhs_mat[i_lhs_rows * rhs_cols + k] + ) + rhs_v = ( + rhs_mat[k * rhs_rows + i_rhs_rows] + if self.case.adj_y + else rhs_mat[i_rhs_rows * rhs_cols + k] + ) + acc += lhs_v * rhs_v + output[i_out_batch, i_out_height, i_lhs_rows, i_rhs_rows] = np.clip( + acc, self.case.activation_min, self.case.activation_max + ) + lhs_offset += lhs_rows * rhs_cols + lhs_offset -= inner_lhs_diff + rhs_offset += inner_rhs_diff + lhs_offset += outer_lhs_diff + rhs_offset += outer_rhs_diff + + return output + + def generate_data(self, regenerate_input: bool = False) -> None: + lhs_input = self.get_lhs_input(regenerate_input) + rhs_input = self.get_rhs_input(regenerate_input) + + lhs_tensor = torch.tensor(lhs_input, dtype=torch.float32) + rhs_tensor = torch.tensor(rhs_input, dtype=torch.float32) + lhs_transposed = lhs_tensor.transpose(-1, -2).contiguous() + rhs_transposed = rhs_tensor.transpose(-1, -2).contiguous() + + output = self._emulate_batch_matmul_output( + lhs_tensor.cpu().numpy(), + lhs_transposed.cpu().numpy(), + rhs_tensor.cpu().numpy(), + rhs_transposed.cpu().numpy(), + ) + + output_shape = tuple(int(dim) for dim in output.shape) + + lhs_input_array = lhs_tensor.to(self.case.torch_dtype).cpu().numpy() + lhs_transposed_array = lhs_transposed.to(self.case.torch_dtype).cpu().numpy() + rhs_input_array = rhs_tensor.to(self.case.torch_dtype).cpu().numpy() + rhs_transposed_array = rhs_transposed.to(self.case.torch_dtype).cpu().numpy() + output_array = output.astype(np.float32, copy=False) + if self.case.dtype_name == "f16": + lhs_input_array = lhs_input_array.astype(np.float16) + lhs_transposed_array = lhs_transposed_array.astype(np.float16) + rhs_input_array = rhs_input_array.astype(np.float16) + rhs_transposed_array = rhs_transposed_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_shape) + self._write_named_array_header("lhs_input_tensor", lhs_input_array) + self._write_named_array_header("lhs_transposed_tensor", lhs_transposed_array) + self._write_named_array_header("rhs_input_tensor", rhs_input_array) + self._write_named_array_header("rhs_transposed_tensor", rhs_transposed_array) + self._write_named_array_header("output", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[BatchMatmulCaseFlt]: + base_cases = [ + ("batch_matmul_1", 2, 2, 8, 5, 2, 2, 5, 7, 0, 0, -10.0, 10.0, 3101), + ("batch_matmul_2", 2, 2, 8, 5, 1, 1, 7, 5, 0, 1, -10.0, 10.0, 3102), + ("batch_matmul_3", 1, 1, 5, 8, 2, 2, 5, 7, 1, 0, -10.0, 10.0, 3103), + ("batch_matmul_4", 2, 1, 5, 8, 1, 2, 7, 5, 1, 1, -10.0, 10.0, 3104), + ("batch_matmul_5", 1, 2, 8, 5, 2, 1, 7, 5, 0, 1, -1.0, 1.0, 3105), + # Match the geometry used by the first s8 batch matmul regression case. + ("batch_matmul_6", 3, 3, 32, 16, 3, 3, 16, 24, 0, 0, -10.0, 10.0, 3106), + # Match the smallest ET float batch-matmul case: + # lhs [1, 2, 3], rhs [1, 3, 4], rhs provided transposed to CMSIS. + ("batch_matmul_et_small", 1, 1, 2, 3, 1, 1, 3, 4, 0, 0, -10.0, 10.0, 3191), + ] + cases: list[BatchMatmulCaseFlt] = [] + for base_name, lb, lh, lr, lc, rb, rh, rr, rc, adj_x, adj_y, act_min, act_max, seed in base_cases: + cases.append( + BatchMatmulCaseFlt( + dataset=f"{base_name}_f32", + dtype_name="f32", + torch_dtype=torch.float32, + lhs_batch=lb, + lhs_height=lh, + lhs_rows=lr, + lhs_cols=lc, + rhs_batch=rb, + rhs_height=rh, + rhs_rows=rr, + rhs_cols=rc, + adj_x=adj_x, + adj_y=adj_y, + activation_min=act_min, + activation_max=act_max, + seed=seed, + ) + ) + cases.append( + BatchMatmulCaseFlt( + dataset=f"{base_name}_f16", + dtype_name="f16", + torch_dtype=torch.float16, + lhs_batch=lb, + lhs_height=lh, + lhs_rows=lr, + lhs_cols=lc, + rhs_batch=rb, + rhs_height=rh, + rhs_rows=rr, + rhs_cols=rc, + adj_x=adj_x, + adj_y=adj_y, + activation_min=act_min, + activation_max=act_max, + seed=seed + 100, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float batch matmul unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, batch_matmul_f32_family, " + "batch_matmul_f16_family, or a specific dataset name such as batch_matmul_3_f16." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def _select_cases(all_cases: list[BatchMatmulCaseFlt], selector: str) -> list[BatchMatmulCaseFlt]: + if selector == "all": + return all_cases + if selector == "batch_matmul_f32_family": + return [case for case in all_cases if case.dtype_name == "f32"] + if selector == "batch_matmul_f16_family": + return [case for case in all_cases if case.dtype_name == "f16"] + return [case for case in all_cases if case.dataset == selector] + + +def main() -> None: + args = parse_args() + cases = _select_cases(_build_cases(), args.dataset) + if not cases: + raise RuntimeError(f"Unknown dataset selector: {args.dataset}") + + for case in cases: + BatchMatmulSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/batch_norm_settings_flt.py b/Tests/UnitTest/batch_norm_settings_flt.py new file mode 100644 index 00000000..7be4cbbf --- /dev/null +++ b/Tests/UnitTest/batch_norm_settings_flt.py @@ -0,0 +1,218 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +LAYOUT_VALUE = {"nhwc": "ARM_NN_LAYOUT_NHWC"} + + +@dataclass(frozen=True) +class BatchNormCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + layout: str + channels: int + input_shape_nchw: tuple[int, int, int, int] + seed: int + input_min: float = -4.0 + input_max: float = 4.0 + + +class BatchNormModel(nn.Module): + def __init__(self, channels: int): + super().__init__() + self.bn = nn.BatchNorm2d(channels, affine=True, track_running_stats=True) + with torch.no_grad(): + self.bn.weight.copy_(torch.linspace(0.8, 1.2, channels)) + self.bn.bias.copy_(torch.linspace(-0.15, 0.15, channels)) + self.bn.running_mean.copy_(torch.linspace(-0.2, 0.2, channels)) + self.bn.running_var.copy_(torch.linspace(0.5, 1.5, channels)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.bn(x) + + +class BatchNormSettingsFlt: + def __init__(self, case: BatchNormCaseFlt): + self.case = case + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs(case.dataset) + self.input_table_file = self.pregenerated_data_dir / "input_tensor.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_input(self, values: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, values) + + def _load_input(self) -> np.ndarray: + return load_pregenerated_array(self.input_table_file, self.case.input_shape_nchw) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + rng = np.random.default_rng(self.case.seed) + values = rng.uniform( + self.case.input_min, + self.case.input_max, + size=self.case.input_shape_nchw, + ).astype(np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_input(values) + return values + + def _reorder(self, values_nchw: np.ndarray) -> np.ndarray: + return np.transpose(values_nchw, (0, 2, 3, 1)) + + def _fold_affine(self, model: BatchNormModel) -> tuple[np.ndarray, np.ndarray]: + bn = model.bn + weight = bn.weight.detach().cpu().numpy().astype(np.float32) + bias = bn.bias.detach().cpu().numpy().astype(np.float32) + mean = bn.running_mean.detach().cpu().numpy().astype(np.float32) + var = bn.running_var.detach().cpu().numpy().astype(np.float32) + scale = weight / np.sqrt(var + float(bn.eps)) + folded_bias = bias - mean * scale + return scale, folded_bias + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + n, c, h, w = self.case.input_shape_nchw + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_N {n}\n") + fh.write(f"#define {prefix}_INPUT_H {h}\n") + fh.write(f"#define {prefix}_INPUT_W {w}\n") + fh.write(f"#define {prefix}_INPUT_C {c}\n") + fh.write(f"#define {prefix}_SIZE {n * c * h * w}\n") + fh.write(f"#define {prefix}_LAYOUT {LAYOUT_VALUE[self.case.layout]}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = f"{suffix}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{suffix}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + write_wrapper_header(self.headers_dir / "test_data.h", self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_nchw = self.get_input_data(regenerate_input) + model = BatchNormModel(self.case.channels).eval() + scale, bias = self._fold_affine(model) + output_nchw = model(torch.tensor(input_nchw, dtype=torch.float32)).detach().cpu().numpy().astype(np.float32) + + input_export = self._reorder(input_nchw) + output_export = self._reorder(output_nchw) + if self.case.dtype_name == "f16": + input_export = input_export.astype(np.float16) + scale = scale.astype(np.float16) + bias = bias.astype(np.float16) + output_export = output_export.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header() + self._write_array_header("input", input_export) + self._write_array_header("scale", scale) + self._write_array_header("bias", bias) + self._write_array_header("output_ref", output_export) + self._write_wrapper_header() + + +def build_cases() -> list[BatchNormCaseFlt]: + base_cases = [ + ("bn_basic", 6, (1, 6, 8, 8), "nhwc", 8101), + ("bn_basic_nhwc", 6, (1, 6, 8, 8), "nhwc", 8102), + ("bn_op", 7, (1, 7, 5, 6), "nhwc", 8103), + ("bn_op_nhwc", 7, (1, 7, 5, 6), "nhwc", 8104), + ] + cases: list[BatchNormCaseFlt] = [] + for dtype_name, torch_dtype, seed_offset in (("f32", torch.float32, 0), ("f16", torch.float16, 100)): + for name, channels, shape, layout, seed in base_cases: + cases.append( + BatchNormCaseFlt( + dataset=f"{name}_{dtype_name}", + dtype_name=dtype_name, + torch_dtype=torch_dtype, + layout=layout, + channels=channels, + input_shape_nchw=shape, + seed=seed + seed_offset, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float batch-norm unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, batch_norm_f32_family, " + "batch_norm_f16_family, or a specific dataset name." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def matches(dataset_filter: str, case: BatchNormCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "batch_norm_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "batch_norm_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + selected = [case for case in build_cases() if matches(args.dataset, case)] + if not selected: + raise RuntimeError(f"No batch_norm dataset matched '{args.dataset}'") + for case in selected: + print(f"Generating {case.dataset}...") + BatchNormSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/build_lib_variants.py b/Tests/UnitTest/build_lib_variants.py new file mode 100644 index 00000000..29bb2e76 --- /dev/null +++ b/Tests/UnitTest/build_lib_variants.py @@ -0,0 +1,563 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# +# SPDX-License-Identifier: Apache-2.0 +# + +import argparse +import dataclasses +import os +import re +import shutil +import subprocess +import sys +from pathlib import Path +from typing import Iterable + + +REPO_ROOT = Path(__file__).resolve().parents[2] +DEFAULT_BUILD_ROOT = Path("/tmp/cmsis-nn-lib-variants") + + +@dataclasses.dataclass(frozen=True) +class Variant: + key: str + label: str + enable_f16: bool + enable_f32: bool + + +@dataclasses.dataclass(frozen=True) +class Toolchain: + requested: str + family: str + cc: str + cxx: str + c_flags: str + cxx_flags: str + ar: str | None = None + ranlib: str | None = None + size_mode: str = "section" + size_tool: str | None = None + + +@dataclasses.dataclass +class VariantResult: + variant: Variant + status: str + archive_bytes: int = 0 + code_bytes: int = 0 + rodata_bytes: int = 0 + data_bytes: int = 0 + bss_bytes: int = 0 + other_bytes: int = 0 + detail: str = "" + + +VARIANTS: list[Variant] = [ + Variant("baseline", "baseline (integer)", False, False), + Variant("f16", "baseline + float16", True, False), + Variant("f32", "baseline + float32", False, True), + Variant("f16_f32", "baseline + float16 + float32", True, True), +] + + +SECTION_LINE_RE = re.compile(r"^(\S+)\s+(\d+)\s+\d+$") +FROMELF_LINE_RE = re.compile(r"^\s*(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+.+$") + + +def run_command(cmd: list[str], log_path: Path) -> subprocess.CompletedProcess[str]: + log_path.parent.mkdir(parents=True, exist_ok=True) + completed = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) + log_path.write_text(completed.stdout, encoding="utf-8") + return completed + + +def format_bytes(value: int) -> str: + if value >= 1024 * 1024: + return f"{value / (1024 * 1024):.2f} MiB" + if value >= 1024: + return f"{value / 1024:.1f} KiB" + return str(value) + + +def env_name_for_toolchain(toolchain: str) -> str | None: + match = re.match(r"^([A-Za-z0-9]+)@([0-9][A-Za-z0-9_.-]*)$", toolchain) + if not match: + return None + family = match.group(1).upper() + version = re.sub(r"[^A-Za-z0-9]", "_", match.group(2)) + return f"{family}_TOOLCHAIN_{version}" + + +def resolve_tool(bin_dir: Path | None, name: str) -> str | None: + if bin_dir is not None: + candidate = bin_dir / name + if candidate.exists(): + return str(candidate) + return shutil.which(name) + + +def resolve_size_tool(family: str, gcc_bin_dir: Path | None, ac6_bin_dir: Path | None) -> tuple[str, str]: + if family == "GCC": + size_tool = resolve_tool(gcc_bin_dir, "arm-none-eabi-size") or shutil.which("llvm-size") + if size_tool: + return ("section", size_tool) + raise RuntimeError("Unable to find arm-none-eabi-size or llvm-size for GCC builds.") + + if family == "AC6": + size_tool = shutil.which("llvm-size") + if size_tool: + return ("section", size_tool) + size_tool = resolve_tool(ac6_bin_dir, "fromelf") + if size_tool: + return ("fromelf", size_tool) + size_tool = resolve_tool(gcc_bin_dir, "arm-none-eabi-size") + if size_tool: + return ("section", size_tool) + raise RuntimeError("Unable to find llvm-size, fromelf, or arm-none-eabi-size for AC6 builds.") + + if family == "CLANG": + size_tool = shutil.which("llvm-size") or resolve_tool(gcc_bin_dir, "arm-none-eabi-size") + if size_tool: + return ("section", size_tool) + raise RuntimeError("Unable to find llvm-size or arm-none-eabi-size for CLANG builds.") + + raise RuntimeError(f"Unsupported toolchain family: {family}") + + +def clang_runtime_root(bin_dir: Path | None) -> Path | None: + if bin_dir is None: + return None + + candidate = bin_dir.parent / "lib" / "clang-runtimes" / "arm-none-eabi" + if candidate.exists(): + return candidate + + lib_dir = bin_dir.parent / "lib" + if lib_dir.exists(): + matches = sorted(lib_dir.glob("**/clang-runtimes/arm-none-eabi")) + if matches: + return matches[0] + + return None + + +def parse_version(value: str) -> tuple[int | str, ...]: + parts: list[int | str] = [] + for part in re.split(r"[.-]", value): + if part.isdigit(): + parts.append(int(part)) + else: + parts.append(part) + return tuple(parts) + + +def resolve_cmsis_path(cmsis_path: str | None, cmsis_pack_root: str | None, cmsis_version: str) -> Path: + if cmsis_path: + path = Path(cmsis_path).resolve() + if (path / "CMSIS" / "Core" / "Include").exists(): + return path + raise RuntimeError(f"CMSIS_PATH does not contain CMSIS/Core/Include: {path}") + + if cmsis_pack_root: + pack_root = Path(cmsis_pack_root).resolve() + pack_base = pack_root / "ARM" / "CMSIS" + if not pack_base.exists(): + raise RuntimeError(f"CMSIS pack root does not contain ARM/CMSIS: {pack_base}") + + preferred = pack_base / cmsis_version + if preferred.exists(): + return preferred + + versions = sorted((path for path in pack_base.iterdir() if path.is_dir()), key=lambda path: parse_version(path.name)) + if versions: + return versions[-1] + raise RuntimeError(f"No ARM::CMSIS pack versions found under {pack_base}") + + raise RuntimeError("Provide either --cmsis-path or --cmsis-pack-root/CMSIS_PACK_ROOT for library builds.") + + +def resolve_toolchain(requested: str, cpu: str, toolchain_bin: str | None) -> Toolchain: + family = requested.split("@", 1)[0].upper() + exact_env = env_name_for_toolchain(requested) + + explicit_bin_dir = Path(toolchain_bin).resolve() if toolchain_bin else None + gcc_env_dir = Path(os.environ[exact_env]).resolve() if exact_env and exact_env in os.environ else None + generic_env_dir = None + if family == "GCC" and "GCC_TOOLCHAIN" in os.environ: + generic_env_dir = Path(os.environ["GCC_TOOLCHAIN"]).resolve() + elif family == "AC6" and "AC6_TOOLCHAIN" in os.environ: + generic_env_dir = Path(os.environ["AC6_TOOLCHAIN"]).resolve() + elif family == "CLANG" and "CLANG_TOOLCHAIN" in os.environ: + generic_env_dir = Path(os.environ["CLANG_TOOLCHAIN"]).resolve() + + bin_dir = explicit_bin_dir or gcc_env_dir or generic_env_dir + + gcc_bin_dir = None + if "GCC_TOOLCHAIN_15_2_1" in os.environ: + gcc_bin_dir = Path(os.environ["GCC_TOOLCHAIN_15_2_1"]).resolve() + elif "GCC_TOOLCHAIN" in os.environ: + gcc_bin_dir = Path(os.environ["GCC_TOOLCHAIN"]).resolve() + + ac6_bin_dir = None + if "AC6_TOOLCHAIN_6_24_0" in os.environ: + ac6_bin_dir = Path(os.environ["AC6_TOOLCHAIN_6_24_0"]).resolve() + elif "AC6_TOOLCHAIN" in os.environ: + ac6_bin_dir = Path(os.environ["AC6_TOOLCHAIN"]).resolve() + + size_mode, size_tool = resolve_size_tool(family, gcc_bin_dir, ac6_bin_dir) + + if family == "GCC": + cc = resolve_tool(bin_dir, "arm-none-eabi-gcc") + cxx = resolve_tool(bin_dir, "arm-none-eabi-g++") + ar = resolve_tool(bin_dir, "arm-none-eabi-ar") + ranlib = resolve_tool(bin_dir, "arm-none-eabi-ranlib") + if not cc or not cxx: + raise RuntimeError("Unable to resolve arm-none-eabi-gcc/g++ for GCC toolchain.") + flags = ( + f"-mcpu={cpu} -mthumb -mfloat-abi=hard " + "-fdisable-rtl-ce1 -fdisable-rtl-ce2" + ) + return Toolchain(requested, family, cc, cxx, flags, flags, ar=ar, ranlib=ranlib, size_mode=size_mode, size_tool=size_tool) + + if family == "AC6": + cc = resolve_tool(bin_dir, "armclang") + ar = resolve_tool(bin_dir, "armar") + if not cc: + raise RuntimeError("Unable to resolve armclang for AC6 toolchain.") + flags = f"--target=arm-arm-none-eabi -mcpu={cpu} -mfloat-abi=hard" + return Toolchain(requested, family, cc, cc, flags, flags, ar=ar, size_mode=size_mode, size_tool=size_tool) + + if family == "CLANG": + cc = resolve_tool(bin_dir, "clang") + cxx = resolve_tool(bin_dir, "clang++") + ar = resolve_tool(bin_dir, "llvm-ar") + ranlib = resolve_tool(bin_dir, "llvm-ranlib") + if not cc or not cxx: + raise RuntimeError("Unable to resolve clang/clang++ for CLANG toolchain.") + flags = f"--target=arm-arm-none-eabi -mcpu={cpu} -mthumb -mfloat-abi=hard" + runtime_root = clang_runtime_root(bin_dir) + if runtime_root: + flags = f"{flags} --sysroot {runtime_root}" + return Toolchain(requested, family, cc, cxx, flags, flags, ar=ar, ranlib=ranlib, size_mode=size_mode, size_tool=size_tool) + + raise RuntimeError(f"Unsupported toolchain family '{family}'. Supported families: GCC, AC6, CLANG.") + + +def should_count_as_code(section: str) -> bool: + return section.startswith((".text", ".init", ".fini", ".ARM.exidx", ".ARM.extab")) + + +def should_count_as_rodata(section: str) -> bool: + return section.startswith((".rodata", ".rdata", ".constdata", ".srodata")) + + +def should_count_as_data(section: str) -> bool: + return section.startswith((".data", ".sdata", ".tdata")) + + +def should_count_as_bss(section: str) -> bool: + return section.startswith((".bss", ".sbss", ".tbss")) + + +def should_ignore_section(section: str) -> bool: + return section.startswith( + ( + ".debug", + ".comment", + ".note", + ".ARM.attributes", + ".eh_frame", + ".symtab", + ".strtab", + ".shstrtab", + ".rela", + ".rel", + ".llvm", + ) + ) + + +def parse_section_size_output(output: str) -> tuple[int, int, int, int, int]: + code_bytes = 0 + rodata_bytes = 0 + data_bytes = 0 + bss_bytes = 0 + other_bytes = 0 + + for raw_line in output.splitlines(): + line = raw_line.strip() + match = SECTION_LINE_RE.match(line) + if not match: + continue + section = match.group(1) + size = int(match.group(2)) + + if should_ignore_section(section): + continue + if should_count_as_code(section): + code_bytes += size + elif should_count_as_rodata(section): + rodata_bytes += size + elif should_count_as_data(section): + data_bytes += size + elif should_count_as_bss(section): + bss_bytes += size + else: + other_bytes += size + + return code_bytes, rodata_bytes, data_bytes, bss_bytes, other_bytes + + +def parse_fromelf_size_output(output: str) -> tuple[int, int, int, int, int]: + code_bytes = 0 + rodata_bytes = 0 + data_bytes = 0 + bss_bytes = 0 + other_bytes = 0 + + for raw_line in output.splitlines(): + match = FROMELF_LINE_RE.match(raw_line) + if not match: + continue + code_bytes += int(match.group(1)) + rodata_bytes += int(match.group(2)) + data_bytes += int(match.group(3)) + bss_bytes += int(match.group(4)) + + return code_bytes, rodata_bytes, data_bytes, bss_bytes, other_bytes + + +def summarize_archive(archive_path: Path, toolchain: Toolchain) -> tuple[int, int, int, int, int]: + if not toolchain.size_tool: + raise RuntimeError("No size tool is configured.") + + if toolchain.size_mode == "fromelf": + cmd = [toolchain.size_tool, "-z", str(archive_path)] + else: + cmd = [toolchain.size_tool, "-A", "-t", str(archive_path)] + + completed = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) + if completed.returncode != 0: + raise RuntimeError(f"Size tool failed for {archive_path}:\n{completed.stdout}") + + if toolchain.size_mode == "fromelf": + return parse_fromelf_size_output(completed.stdout) + + return parse_section_size_output(completed.stdout) + + +def variant_total_bytes(result: VariantResult) -> int: + return result.code_bytes + result.rodata_bytes + result.data_bytes + result.bss_bytes + + +def cmake_generator_args() -> list[str]: + if shutil.which("ninja"): + return ["-G", "Ninja"] + return [] + + +def build_variant( + repo_root: Path, + build_root: Path, + cmsis_path: Path, + toolchain: Toolchain, + variant: Variant, + cpu: str, + optimization: str, + jobs: int, +) -> VariantResult: + build_dir = build_root / toolchain.family.lower() / variant.key + shutil.rmtree(build_dir, ignore_errors=True) + build_dir.mkdir(parents=True, exist_ok=True) + + configure_log = build_dir / "configure.log" + build_log = build_dir / "build.log" + + cmake_cmd = [ + "cmake", + "-S", + str(repo_root), + "-B", + str(build_dir), + *cmake_generator_args(), + "-DCMAKE_BUILD_TYPE=Release", + f"-DCMSIS_PATH={cmsis_path}", + "-DCMAKE_SYSTEM_NAME=Generic", + f"-DCMAKE_SYSTEM_PROCESSOR={cpu}", + "-DCMAKE_TRY_COMPILE_TARGET_TYPE=STATIC_LIBRARY", + f"-DCMAKE_C_COMPILER={toolchain.cc}", + f"-DCMAKE_CXX_COMPILER={toolchain.cxx}", + f"-DCMAKE_C_FLAGS={toolchain.c_flags}", + f"-DCMAKE_CXX_FLAGS={toolchain.cxx_flags}", + f"-DCMSIS_OPTIMIZATION_LEVEL={optimization}", + f"-DARM_NN_ENABLE_F32={'ON' if variant.enable_f32 else 'OFF'}", + f"-DARM_NN_ENABLE_F16={'ON' if variant.enable_f16 else 'OFF'}", + ] + if toolchain.ar: + cmake_cmd.append(f"-DCMAKE_AR={toolchain.ar}") + if toolchain.ranlib: + cmake_cmd.append(f"-DCMAKE_RANLIB={toolchain.ranlib}") + + configured = run_command(cmake_cmd, configure_log) + if configured.returncode != 0: + tail = "\n".join(configured.stdout.splitlines()[-40:]) + return VariantResult(variant=variant, status="FAIL", detail=f"configure failed\n{tail}") + + build_cmd = ["cmake", "--build", str(build_dir), "--target", "cmsis-nn", "-j", str(jobs)] + built = run_command(build_cmd, build_log) + if built.returncode != 0: + tail = "\n".join(built.stdout.splitlines()[-40:]) + return VariantResult(variant=variant, status="FAIL", detail=f"build failed\n{tail}") + + archive_path = build_dir / "libcmsis-nn.a" + if not archive_path.exists(): + return VariantResult(variant=variant, status="FAIL", detail="libcmsis-nn.a was not produced") + + code_bytes, rodata_bytes, data_bytes, bss_bytes, other_bytes = summarize_archive(archive_path, toolchain) + return VariantResult( + variant=variant, + status="PASS", + archive_bytes=archive_path.stat().st_size, + code_bytes=code_bytes, + rodata_bytes=rodata_bytes, + data_bytes=data_bytes, + bss_bytes=bss_bytes, + other_bytes=other_bytes, + detail=str(build_dir), + ) + + +def print_summary(toolchain: Toolchain, results: Iterable[VariantResult]) -> None: + rows = [] + for result in results: + rows.append( + [ + result.variant.key, + result.status, + format_bytes(result.archive_bytes), + format_bytes(result.code_bytes), + format_bytes(result.rodata_bytes), + format_bytes(result.data_bytes), + format_bytes(result.bss_bytes), + format_bytes(variant_total_bytes(result)), + format_bytes(result.other_bytes), + ] + ) + + headers = ["Variant", "Status", "Archive", "Code", "RO", "RW", "ZI", "Total", "Other"] + widths = [len(h) for h in headers] + for row in rows: + for index, value in enumerate(row): + widths[index] = max(widths[index], len(value)) + + print("") + print(f"CMSIS-NN library variant summary for {toolchain.requested}") + header_line = " | ".join(header.ljust(widths[index]) for index, header in enumerate(headers)) + separator_line = "-+-".join("-" * width for width in widths) + print(header_line) + print(separator_line) + for row in rows: + print(" | ".join(value.ljust(widths[index]) for index, value in enumerate(row))) + + failures = [result for result in results if result.status != "PASS"] + if failures: + print("") + print("Failures") + print("--------") + for result in failures: + print(f"{result.variant.key}: {result.detail}") + + +def parse_variants(selected: str) -> list[Variant]: + if selected.strip().lower() == "all": + return list(VARIANTS) + wanted = {token.strip() for token in selected.split(",") if token.strip()} + chosen = [variant for variant in VARIANTS if variant.key in wanted] + missing = sorted(wanted - {variant.key for variant in chosen}) + if missing: + raise RuntimeError(f"Unknown variant keys: {', '.join(missing)}") + return chosen + + +def main() -> int: + parser = argparse.ArgumentParser(description="Build and summarize CMSIS-NN library configuration variants.") + parser.add_argument( + "--toolchain", + required=True, + help="Toolchain selector, for example GCC@15.2.1 or AC6@6.24.0.", + ) + parser.add_argument( + "--toolchain-bin", + help="Optional toolchain bin directory. If omitted, the script falls back to toolchain-specific environment variables or PATH.", + ) + parser.add_argument( + "--cmsis-path", + default=os.environ.get("CMSIS_PATH", ""), + help="Optional CMSIS root containing CMSIS/Core/Include. Overrides CMSIS pack resolution.", + ) + parser.add_argument( + "--cmsis-pack-root", + default=os.environ.get("CMSIS_PACK_ROOT", ""), + help="CMSIS pack root used to resolve ARM::CMSIS when --cmsis-path is not provided.", + ) + parser.add_argument( + "--cmsis-version", + default="6.3.0", + help="Preferred ARM::CMSIS pack version.", + ) + parser.add_argument( + "--variants", + default="all", + help="Comma-separated variant keys to build. Defaults to 'all'.", + ) + parser.add_argument( + "--cpu", + default="cortex-m55", + help="Target CPU passed to the toolchain. Defaults to cortex-m55.", + ) + parser.add_argument( + "--optimization", + default="-Ofast", + help="Value forwarded to CMSIS_OPTIMIZATION_LEVEL. Defaults to -Ofast.", + ) + parser.add_argument( + "--jobs", + type=int, + default=max(1, os.cpu_count() or 1), + help="Parallel build jobs. Defaults to the local CPU count.", + ) + parser.add_argument( + "--build-root", + default=str(DEFAULT_BUILD_ROOT), + help=f"Root directory for per-variant build trees. Defaults to {DEFAULT_BUILD_ROOT}.", + ) + args = parser.parse_args() + + toolchain = resolve_toolchain(args.toolchain, args.cpu, args.toolchain_bin) + build_root = Path(args.build_root).resolve() + cmsis_path = resolve_cmsis_path(args.cmsis_path or None, args.cmsis_pack_root or None, args.cmsis_version) + variants = parse_variants(args.variants) + + results: list[VariantResult] = [] + failed = False + + for variant in variants: + print(f"==> Building {variant.label} [{variant.key}] with {toolchain.requested}") + result = build_variant(REPO_ROOT, build_root, cmsis_path, toolchain, variant, args.cpu, args.optimization, args.jobs) + results.append(result) + if result.status != "PASS": + failed = True + print(f" FAIL: {variant.key}") + else: + print(f" PASS: {variant.key}") + + print_summary(toolchain, results) + return 1 if failed else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/Tests/UnitTest/cmsis/README.md b/Tests/UnitTest/cmsis/README.md new file mode 100644 index 00000000..3ce6c5ae --- /dev/null +++ b/Tests/UnitTest/cmsis/README.md @@ -0,0 +1,396 @@ +# CMSIS-Toolbox Float Unit Tests + +This folder contains a small CMSIS-Toolbox setup for running selected float +unit tests from a local CMSIS-NN checkout directly on the Corstone-300 FVP. + +The float test inputs and references are generated offline by the Python +generators under `Tests/UnitTest/`: + +- raw reproducible samples are kept in `Tests/UnitTest/PregeneratedData/` +- generated C headers used by the tests are written to + `Tests/UnitTest/TestCases/TestData/` + +Regenerate those datasets before building if you update a float generator or +want fresh sampled inputs. + +Current shared projects: + +- `test_arm_activation_flt` +- `test_arm_avg_pool_flt` +- `test_arm_batch_matmul_flt` +- `test_arm_batch_norm_flt` +- `test_arm_concatenation_flt` +- `test_arm_convolve_flt` +- `test_arm_depthwise_conv_flt` +- `test_arm_ds_cnn_s_body_flt` +- `test_arm_elementwise_add_flt` +- `test_arm_elementwise_mul_flt` +- `test_arm_fully_connected_flt` +- `test_arm_lstm_unidirectional_flt` +- `test_arm_maximum_minimum_flt` +- `test_arm_max_pool_flt` +- `test_arm_pad_flt` +- `test_arm_reshape_flt` +- `test_arm_softmax_flt` +- `test_arm_svdf_flt` +- `test_arm_transpose_conv_flt` +- `test_arm_transpose_flt` + +## Unified Runner + +For day-to-day float bring-up, you can use the shared runner script instead of +calling the generators, host `cmake`, `cbuild`, and FVP manually: + +```bash +cd /Tests/UnitTest + +# Default flow: generate + host build + host run +python3 run_float_unit_tests.py --tests softmax,activation --dtypes f16 + +# CMSIS build with a specific toolchain +python3 run_float_unit_tests.py \ + --tests softmax \ + --dtypes f16 \ + --build-cmsis \ + --toolchains GCC@15.2.1 + +# Full CMSIS + FVP flow +python3 run_float_unit_tests.py \ + --tests svdf \ + --dtypes f16 \ + --build-cmsis \ + --run-fvp \ + --toolchains GCC@15.2.1 \ + --fvp-bin +``` + +Useful options: + +- `--tests`: comma-separated family list such as `softmax,avg_pool,svdf` + or `ds_cnn_s_body` +- `--dtypes`: `f32`, `f16`, or `f32,f16` +- `--toolchains`: comma-separated `cbuild` toolchains such as `GCC@15.2.1,AC6@6.24.0` +- `--regenerate-input`: refresh pregenerated float samples before rebuilding + +Run the full float matrix: + +```bash +cd /Tests/UnitTest + +# Host-only: all float families, both f32 and f16 +python3 run_float_unit_tests.py --tests all --dtypes f32,f16 + +# Host + CMSIS build +python3 run_float_unit_tests.py \ + --tests all \ + --dtypes f32,f16 \ + --build-cmsis \ + --toolchains GCC@15.2.1 + +# Host + CMSIS + FVP +python3 run_float_unit_tests.py \ + --tests all \ + --dtypes f32,f16 \ + --build-cmsis \ + --run-fvp \ + --toolchains GCC@15.2.1 \ + --fvp-bin +``` + +Each project exposes two contexts: + +- `.F32+Corstone-300-FVP` +- `.F16+Corstone-300-FVP` + +## Prerequisites + +1. Register the local CMSIS-NN pack: + + ```bash + cd + cpackget add ARM.CMSIS-NN.pdsc \ + -R \ + -F + ``` + +2. Make sure the usual CMSIS packs used by the solution are already installed in + the same pack root. + +## Build + +Example with GCC: + +```bash +export PATH=:$PATH +export GCC_TOOLCHAIN_15_2_1= + +cd /Tests/UnitTest/cmsis + +cbuild --update-rte \ + --context test_arm_softmax_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j4 \ + --toolchain GCC@15.2.1 + +cbuild --context test_arm_softmax_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j4 \ + --toolchain GCC@15.2.1 +``` + +If you cleaned generated artifacts and removed `Tests/UnitTest/Unity`, restore the +local Unity checkout and regenerate the Unity runners before calling `cbuild`: + +```bash +cd /Tests/UnitTest +python3 unittest_targets.py --download-and-generate-test-runners +``` + +You can replace `test_arm_softmax_flt` with any of: + +- `test_arm_activation_flt` +- `test_arm_avg_pool_flt` +- `test_arm_batch_matmul_flt` +- `test_arm_batch_norm_flt` +- `test_arm_concatenation_flt` +- `test_arm_convolve_flt` +- `test_arm_depthwise_conv_flt` +- `test_arm_ds_cnn_s_body_flt` +- `test_arm_elementwise_add_flt` +- `test_arm_elementwise_mul_flt` +- `test_arm_fully_connected_flt` +- `test_arm_lstm_unidirectional_flt` +- `test_arm_maximum_minimum_flt` +- `test_arm_max_pool_flt` +- `test_arm_pad_flt` +- `test_arm_reshape_flt` +- `test_arm_softmax_flt` +- `test_arm_svdf_flt` +- `test_arm_transpose_conv_flt` +- `test_arm_transpose_flt` + +and replace `F32` with `F16`. + +Examples for the newly added families: + +```bash +# avg pool f16 +cbuild --update-rte \ + --context test_arm_avg_pool_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_avg_pool_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# max pool f32 +cbuild --update-rte \ + --context test_arm_max_pool_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_max_pool_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# elementwise add f16 +cbuild --update-rte \ + --context test_arm_elementwise_add_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_elementwise_add_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# elementwise mul f32 +cbuild --update-rte \ + --context test_arm_elementwise_mul_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_elementwise_mul_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# fully connected f16 +cbuild --update-rte \ + --context test_arm_fully_connected_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_fully_connected_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# lstm unidirectional f16 +cbuild --update-rte \ + --context test_arm_lstm_unidirectional_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_lstm_unidirectional_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# batch matmul f32 +cbuild --update-rte \ + --context test_arm_batch_matmul_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_batch_matmul_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# batch norm f16 +cbuild --update-rte \ + --context test_arm_batch_norm_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_batch_norm_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# concatenation f32 +cbuild --update-rte \ + --context test_arm_concatenation_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_concatenation_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# convolve f16 +cbuild --update-rte \ + --context test_arm_convolve_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_convolve_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# depthwise conv f32 +cbuild --update-rte \ + --context test_arm_depthwise_conv_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_depthwise_conv_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# ds_cnn_s body f16 +cbuild --update-rte \ + --context test_arm_ds_cnn_s_body_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_ds_cnn_s_body_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# maximum/minimum f16 +cbuild --update-rte \ + --context test_arm_maximum_minimum_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_maximum_minimum_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# pad f32 +cbuild --update-rte \ + --context test_arm_pad_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_pad_flt.F32+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# transpose f16 +cbuild --update-rte \ + --context test_arm_transpose_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_transpose_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# transpose conv f16 +cbuild --update-rte \ + --context test_arm_transpose_conv_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_transpose_conv_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 + +# svdf f16 +cbuild --update-rte \ + --context test_arm_svdf_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +cbuild \ + --context test_arm_svdf_flt.F16+Corstone-300-FVP \ + cmsis_nn_unit_tests_flt.csolution.yml \ + -j1 --toolchain GCC@15.2.1 +``` + +## Run on FVP + +Example for the `f32` softmax test built with GCC: + +```bash + \ + test_arm_softmax_flt.F32+Corstone-300-FVP-GCC/outdir/test_arm_softmax_f32.elf \ + -C mps3_board.visualisation.disable-visualisation=1 \ + -C mps3_board.telnetterminal0.start_telnet=0 \ + -C mps3_board.uart0.out_file=- \ + -C mps3_board.uart0.unbuffered_output=1 \ + -C mps3_board.uart0.shutdown_on_eot=1 +``` + +Example for the validated `f16` depthwise-conv test built with GCC: + +```bash + \ + test_arm_depthwise_conv_flt.F16+Corstone-300-FVP-GCC/outdir/test_arm_depthwise_conv_f16.elf \ + -C mps3_board.visualisation.disable-visualisation=1 \ + -C mps3_board.telnetterminal0.start_telnet=0 \ + -C mps3_board.uart0.out_file=- \ + -C mps3_board.uart0.unbuffered_output=1 \ + -C mps3_board.uart0.shutdown_on_eot=1 +``` + +Example for the validated `f16` DS-CNN-S body integration test built with GCC: + +```bash + \ + test_arm_ds_cnn_s_body_flt.F16+Corstone-300-FVP-GCC/outdir/test_arm_ds_cnn_s_body_f16.elf \ + -C mps3_board.visualisation.disable-visualisation=1 \ + -C mps3_board.telnetterminal0.start_telnet=0 \ + -C mps3_board.uart0.out_file=- \ + -C mps3_board.uart0.unbuffered_output=1 \ + -C mps3_board.uart0.shutdown_on_eot=1 +``` + +Example for the validated `f16` SVDF test built with GCC: + +```bash + \ + test_arm_svdf_flt.F16+Corstone-300-FVP-GCC/outdir/test_arm_svdf_f16.elf \ + -C mps3_board.visualisation.disable-visualisation=1 \ + -C mps3_board.telnetterminal0.start_telnet=0 \ + -C mps3_board.uart0.out_file=- \ + -C mps3_board.uart0.unbuffered_output=1 \ + -C mps3_board.uart0.shutdown_on_eot=1 +``` diff --git a/Tests/UnitTest/cmsis/cdefault.yml b/Tests/UnitTest/cmsis/cdefault.yml new file mode 100644 index 00000000..3572edeb --- /dev/null +++ b/Tests/UnitTest/cmsis/cdefault.yml @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +default: + misc: + - for-compiler: GCC + C: + # Temporary workaround for an Arm GNU 14.3.1 ICE in the float16 + # min/max path on Cortex-M55 until the compiler is fixed upstream. + - -fdisable-rtl-ce1 + - -fdisable-rtl-ce2 + - for-compiler: CLANG + C: + - -std=gnu11 + Link: + - -Wl,-Map=$elf()$.map diff --git a/Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml b/Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml new file mode 100644 index 00000000..360e3124 --- /dev/null +++ b/Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml @@ -0,0 +1,57 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +solution: + created-for: CMSIS-Toolbox@2.0.0 + + cdefault: + + packs: + - pack: ARM::CMSIS-NN@0.0.0 + - pack: ARM::CMSIS@6.3.0 + - pack: ARM::Cortex_DFP@1.1.0 + - pack: ARM::V2M_MPS3_SSE_300_BSP@1.5.0 + - pack: ARM::CMSIS-Compiler + + target-types: + - type: Corstone-300-FVP + device: ARM::SSE-300-MPS3 + define: + - FVP + - USING_FVP_CORSTONE_300 + + build-types: + - type: F32 + optimize: speed + debug: on + + - type: F16 + optimize: speed + debug: on + + projects: + - project: ./test_arm_activation_flt/test_arm_activation_flt.cproject.yml + - project: ./test_arm_avg_pool_flt/test_arm_avg_pool_flt.cproject.yml + - project: ./test_arm_batch_matmul_flt/test_arm_batch_matmul_flt.cproject.yml + - project: ./test_arm_batch_norm_flt/test_arm_batch_norm_flt.cproject.yml + - project: ./test_arm_concatenation_flt/test_arm_concatenation_flt.cproject.yml + - project: ./test_arm_convolve_flt/test_arm_convolve_flt.cproject.yml + - project: ./test_arm_depthwise_conv_flt/test_arm_depthwise_conv_flt.cproject.yml + - project: ./test_arm_ds_cnn_s_body_flt/test_arm_ds_cnn_s_body_flt.cproject.yml + - project: ./test_arm_elementwise_add_flt/test_arm_elementwise_add_flt.cproject.yml + - project: ./test_arm_elementwise_mul_flt/test_arm_elementwise_mul_flt.cproject.yml + - project: ./test_arm_fully_connected_flt/test_arm_fully_connected_flt.cproject.yml + - project: ./test_arm_lstm_unidirectional_flt/test_arm_lstm_unidirectional_flt.cproject.yml + - project: ./test_arm_max_pool_flt/test_arm_max_pool_flt.cproject.yml + - project: ./test_arm_maximum_minimum_flt/test_arm_maximum_minimum_flt.cproject.yml + - project: ./test_arm_pad_flt/test_arm_pad_flt.cproject.yml + - project: ./test_arm_reshape_flt/test_arm_reshape_flt.cproject.yml + - project: ./test_arm_softmax_flt/test_arm_softmax_flt.cproject.yml + - project: ./test_arm_svdf_flt/test_arm_svdf_flt.cproject.yml + - project: ./test_arm_transpose_flt/test_arm_transpose_flt.cproject.yml + - project: ./test_arm_transpose_conv_flt/test_arm_transpose_conv_flt.cproject.yml + + output-dirs: + cprjdir: ./$Project$.$BuildType$+$TargetType$-$Compiler$ + outdir: ./$Project$.$BuildType$+$TargetType$-$Compiler$/outdir diff --git a/Tests/UnitTest/cmsis/corstone300_unittest.clayer.yml b/Tests/UnitTest/cmsis/corstone300_unittest.clayer.yml new file mode 100644 index 00000000..fa864b41 --- /dev/null +++ b/Tests/UnitTest/cmsis/corstone300_unittest.clayer.yml @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +layer: + description: Corstone-300 FVP support for CMSIS-NN float unit tests + + groups: + - group: Corstone-300 + files: + - file: ../Corstone-300/retarget.c + - file: ../Corstone-300/stdout_uart.c + - file: ../Corstone-300/uart.c + - file: ../Corstone-300/wait.c + + components: + - component: ARM::CMSIS:CORE + define: + - ARM_MATH_LOOPUNROLL + - component: ARM::CMSIS-Compiler:CORE + - component: ARM::CMSIS-Compiler:STDOUT:Custom + - component: ARM::CMSIS-Compiler:STDERR:Custom + - component: ARM::Device:Definition + - component: ARM::Device:Startup&C Startup + - component: ARM::Device:Native Driver:Timeout + - component: ARM::Device:Native Driver:SysCounter + - component: ARM::Device:Native Driver:SysTimer + + linker: + - script: ../Corstone-300/linker.scatter + for-compiler: AC6 + - script: ../Corstone-300/linker_clang.sct + for-compiler: CLANG + - script: ../Corstone-300/linker.ld + for-compiler: GCC + - regions: ../Corstone-300/region_defs.h + for-compiler: CLANG diff --git a/Tests/UnitTest/cmsis/test_arm_activation_flt/test_arm_activation_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_activation_flt/test_arm_activation_flt.cproject.yml new file mode 100644 index 00000000..990f08f2 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_activation_flt/test_arm_activation_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: activation-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_activation_f32 + + - setup: activation-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_activation_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Activation F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_activation_f32/Unity/unity_test_arm_activation_f32.c + - file: ../../TestCases/test_arm_activation_f32/Unity/TestRunner/unity_test_arm_activation_f32_runner.c + + - group: Activation F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_activation_f16/Unity/unity_test_arm_activation_f16.c + - file: ../../TestCases/test_arm_activation_f16/Unity/TestRunner/unity_test_arm_activation_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_avg_pool_flt/test_arm_avg_pool_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_avg_pool_flt/test_arm_avg_pool_flt.cproject.yml new file mode 100644 index 00000000..4b118887 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_avg_pool_flt/test_arm_avg_pool_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: avg-pool-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_avg_pool_f32 + + - setup: avg-pool-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_avg_pool_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Avg Pool F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_avg_pool_f32/Unity/unity_test_arm_avg_pool_f32.c + - file: ../../TestCases/test_arm_avg_pool_f32/Unity/TestRunner/unity_test_arm_avg_pool_f32_runner.c + + - group: Avg Pool F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_avg_pool_f16/Unity/unity_test_arm_avg_pool_f16.c + - file: ../../TestCases/test_arm_avg_pool_f16/Unity/TestRunner/unity_test_arm_avg_pool_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_batch_matmul_flt/test_arm_batch_matmul_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_batch_matmul_flt/test_arm_batch_matmul_flt.cproject.yml new file mode 100644 index 00000000..bb6f3b31 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_batch_matmul_flt/test_arm_batch_matmul_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: batch-matmul-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_batch_matmul_f32 + + - setup: batch-matmul-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_batch_matmul_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Batch Matmul F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_batch_matmul_f32/Unity/unity_test_arm_batch_matmul_f32.c + - file: ../../TestCases/test_arm_batch_matmul_f32/Unity/TestRunner/unity_test_arm_batch_matmul_f32_runner.c + + - group: Batch Matmul F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_batch_matmul_f16/Unity/unity_test_arm_batch_matmul_f16.c + - file: ../../TestCases/test_arm_batch_matmul_f16/Unity/TestRunner/unity_test_arm_batch_matmul_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_batch_norm_flt/test_arm_batch_norm_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_batch_norm_flt/test_arm_batch_norm_flt.cproject.yml new file mode 100644 index 00000000..6edb26f2 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_batch_norm_flt/test_arm_batch_norm_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: batch-norm-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_batch_norm_f32 + + - setup: batch-norm-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_batch_norm_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Batch Norm F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_batch_norm_f32/Unity/unity_test_arm_batch_norm_f32.c + - file: ../../TestCases/test_arm_batch_norm_f32/Unity/TestRunner/unity_test_arm_batch_norm_f32_runner.c + + - group: Batch Norm F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_batch_norm_f16/Unity/unity_test_arm_batch_norm_f16.c + - file: ../../TestCases/test_arm_batch_norm_f16/Unity/TestRunner/unity_test_arm_batch_norm_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_concatenation_flt/test_arm_concatenation_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_concatenation_flt/test_arm_concatenation_flt.cproject.yml new file mode 100644 index 00000000..f40c199b --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_concatenation_flt/test_arm_concatenation_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: concatenation-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_concatenation_f32 + + - setup: concatenation-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_concatenation_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Concatenation F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_concatenation_f32/Unity/unity_test_arm_concatenation_f32.c + - file: ../../TestCases/test_arm_concatenation_f32/Unity/TestRunner/unity_test_arm_concatenation_f32_runner.c + + - group: Concatenation F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_concatenation_f16/Unity/unity_test_arm_concatenation_f16.c + - file: ../../TestCases/test_arm_concatenation_f16/Unity/TestRunner/unity_test_arm_concatenation_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_convolve_flt/test_arm_convolve_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_convolve_flt/test_arm_convolve_flt.cproject.yml new file mode 100644 index 00000000..c1bd5f59 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_convolve_flt/test_arm_convolve_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: convolve-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_convolve_f32 + + - setup: convolve-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_convolve_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Convolve F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_convolve_f32/Unity/unity_test_arm_convolve_f32.c + - file: ../../TestCases/test_arm_convolve_f32/Unity/TestRunner/unity_test_arm_convolve_f32_runner.c + + - group: Convolve F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_convolve_f16/Unity/unity_test_arm_convolve_f16.c + - file: ../../TestCases/test_arm_convolve_f16/Unity/TestRunner/unity_test_arm_convolve_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_depthwise_conv_flt/test_arm_depthwise_conv_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_depthwise_conv_flt/test_arm_depthwise_conv_flt.cproject.yml new file mode 100644 index 00000000..83a154ec --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_depthwise_conv_flt/test_arm_depthwise_conv_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: depthwise-conv-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_depthwise_conv_f32 + + - setup: depthwise-conv-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_depthwise_conv_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Depthwise Conv F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_depthwise_conv_f32/Unity/unity_test_arm_depthwise_conv_f32.c + - file: ../../TestCases/test_arm_depthwise_conv_f32/Unity/TestRunner/unity_test_arm_depthwise_conv_f32_runner.c + + - group: Depthwise Conv F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_depthwise_conv_f16/Unity/unity_test_arm_depthwise_conv_f16.c + - file: ../../TestCases/test_arm_depthwise_conv_f16/Unity/TestRunner/unity_test_arm_depthwise_conv_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_ds_cnn_s_body_flt/test_arm_ds_cnn_s_body_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_ds_cnn_s_body_flt/test_arm_ds_cnn_s_body_flt.cproject.yml new file mode 100644 index 00000000..0666094a --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_ds_cnn_s_body_flt/test_arm_ds_cnn_s_body_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: ds-cnn-s-body-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_ds_cnn_s_body_f32 + + - setup: ds-cnn-s-body-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_ds_cnn_s_body_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: DS-CNN-S Body F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_ds_cnn_s_body_f32/Unity/unity_test_arm_ds_cnn_s_body_f32.c + - file: ../../TestCases/test_arm_ds_cnn_s_body_f32/Unity/TestRunner/unity_test_arm_ds_cnn_s_body_f32_runner.c + + - group: DS-CNN-S Body F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_ds_cnn_s_body_f16/Unity/unity_test_arm_ds_cnn_s_body_f16.c + - file: ../../TestCases/test_arm_ds_cnn_s_body_f16/Unity/TestRunner/unity_test_arm_ds_cnn_s_body_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_elementwise_add_flt/test_arm_elementwise_add_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_elementwise_add_flt/test_arm_elementwise_add_flt.cproject.yml new file mode 100644 index 00000000..47b51ce6 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_elementwise_add_flt/test_arm_elementwise_add_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: elementwise-add-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_elementwise_add_f32 + + - setup: elementwise-add-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_elementwise_add_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Elementwise Add F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_elementwise_add_f32/Unity/unity_test_arm_elementwise_add_f32.c + - file: ../../TestCases/test_arm_elementwise_add_f32/Unity/TestRunner/unity_test_arm_elementwise_add_f32_runner.c + + - group: Elementwise Add F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_elementwise_add_f16/Unity/unity_test_arm_elementwise_add_f16.c + - file: ../../TestCases/test_arm_elementwise_add_f16/Unity/TestRunner/unity_test_arm_elementwise_add_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_elementwise_mul_flt/test_arm_elementwise_mul_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_elementwise_mul_flt/test_arm_elementwise_mul_flt.cproject.yml new file mode 100644 index 00000000..078e3373 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_elementwise_mul_flt/test_arm_elementwise_mul_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: elementwise-mul-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_elementwise_mul_f32 + + - setup: elementwise-mul-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_elementwise_mul_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Elementwise Mul F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_elementwise_mul_f32/Unity/unity_test_arm_elementwise_mul_f32.c + - file: ../../TestCases/test_arm_elementwise_mul_f32/Unity/TestRunner/unity_test_arm_elementwise_mul_f32_runner.c + + - group: Elementwise Mul F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_elementwise_mul_f16/Unity/unity_test_arm_elementwise_mul_f16.c + - file: ../../TestCases/test_arm_elementwise_mul_f16/Unity/TestRunner/unity_test_arm_elementwise_mul_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_fully_connected_flt/test_arm_fully_connected_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_fully_connected_flt/test_arm_fully_connected_flt.cproject.yml new file mode 100644 index 00000000..e26d1f46 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_fully_connected_flt/test_arm_fully_connected_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: fully-connected-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_fully_connected_f32 + + - setup: fully-connected-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_fully_connected_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Fully Connected F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_fully_connected_f32/Unity/unity_test_arm_fully_connected_f32.c + - file: ../../TestCases/test_arm_fully_connected_f32/Unity/TestRunner/unity_test_arm_fully_connected_f32_runner.c + + - group: Fully Connected F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_fully_connected_f16/Unity/unity_test_arm_fully_connected_f16.c + - file: ../../TestCases/test_arm_fully_connected_f16/Unity/TestRunner/unity_test_arm_fully_connected_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_lstm_unidirectional_flt/test_arm_lstm_unidirectional_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_lstm_unidirectional_flt/test_arm_lstm_unidirectional_flt.cproject.yml new file mode 100644 index 00000000..c68a54f0 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_lstm_unidirectional_flt/test_arm_lstm_unidirectional_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: lstm-unidirectional-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_lstm_unidirectional_f32 + + - setup: lstm-unidirectional-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_lstm_unidirectional_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: LSTM F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_lstm_unidirectional_f32/Unity/unity_test_arm_lstm_unidirectional_f32.c + - file: ../../TestCases/test_arm_lstm_unidirectional_f32/Unity/TestRunner/unity_test_arm_lstm_unidirectional_f32_runner.c + + - group: LSTM F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_lstm_unidirectional_f16/Unity/unity_test_arm_lstm_unidirectional_f16.c + - file: ../../TestCases/test_arm_lstm_unidirectional_f16/Unity/TestRunner/unity_test_arm_lstm_unidirectional_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_max_pool_flt/test_arm_max_pool_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_max_pool_flt/test_arm_max_pool_flt.cproject.yml new file mode 100644 index 00000000..faf882d2 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_max_pool_flt/test_arm_max_pool_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: max-pool-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_max_pool_f32 + + - setup: max-pool-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_max_pool_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Max Pool F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_max_pool_f32/Unity/unity_test_arm_max_pool_f32.c + - file: ../../TestCases/test_arm_max_pool_f32/Unity/TestRunner/unity_test_arm_max_pool_f32_runner.c + + - group: Max Pool F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_max_pool_f16/Unity/unity_test_arm_max_pool_f16.c + - file: ../../TestCases/test_arm_max_pool_f16/Unity/TestRunner/unity_test_arm_max_pool_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_maximum_minimum_flt/test_arm_maximum_minimum_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_maximum_minimum_flt/test_arm_maximum_minimum_flt.cproject.yml new file mode 100644 index 00000000..6b34d8b6 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_maximum_minimum_flt/test_arm_maximum_minimum_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: maximum-minimum-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_maximum_minimum_f32 + + - setup: maximum-minimum-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_maximum_minimum_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Maximum Minimum F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_maximum_minimum_f32/Unity/unity_test_arm_maximum_minimum_f32.c + - file: ../../TestCases/test_arm_maximum_minimum_f32/Unity/TestRunner/unity_test_arm_maximum_minimum_f32_runner.c + + - group: Maximum Minimum F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_maximum_minimum_f16/Unity/unity_test_arm_maximum_minimum_f16.c + - file: ../../TestCases/test_arm_maximum_minimum_f16/Unity/TestRunner/unity_test_arm_maximum_minimum_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_pad_flt/test_arm_pad_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_pad_flt/test_arm_pad_flt.cproject.yml new file mode 100644 index 00000000..c231da78 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_pad_flt/test_arm_pad_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: pad-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_pad_f32 + + - setup: pad-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_pad_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Pad F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_pad_f32/Unity/unity_test_arm_pad_f32.c + - file: ../../TestCases/test_arm_pad_f32/Unity/TestRunner/unity_test_arm_pad_f32_runner.c + + - group: Pad F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_pad_f16/Unity/unity_test_arm_pad_f16.c + - file: ../../TestCases/test_arm_pad_f16/Unity/TestRunner/unity_test_arm_pad_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_reshape_flt/test_arm_reshape_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_reshape_flt/test_arm_reshape_flt.cproject.yml new file mode 100644 index 00000000..8a2bf37a --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_reshape_flt/test_arm_reshape_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: reshape-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_reshape_f32 + + - setup: reshape-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_reshape_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Reshape F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_reshape_f32/Unity/unity_test_arm_reshape_f32.c + - file: ../../TestCases/test_arm_reshape_f32/Unity/TestRunner/unity_test_arm_reshape_f32_runner.c + + - group: Reshape F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_reshape_f16/Unity/unity_test_arm_reshape_f16.c + - file: ../../TestCases/test_arm_reshape_f16/Unity/TestRunner/unity_test_arm_reshape_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_softmax_flt/test_arm_softmax_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_softmax_flt/test_arm_softmax_flt.cproject.yml new file mode 100644 index 00000000..9e41c9ec --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_softmax_flt/test_arm_softmax_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: softmax-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_softmax_f32 + + - setup: softmax-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_softmax_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Softmax F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_softmax_f32/Unity/unity_test_arm_softmax_f32.c + - file: ../../TestCases/test_arm_softmax_f32/Unity/TestRunner/unity_test_arm_softmax_f32_runner.c + + - group: Softmax F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_softmax_f16/Unity/unity_test_arm_softmax_f16.c + - file: ../../TestCases/test_arm_softmax_f16/Unity/TestRunner/unity_test_arm_softmax_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_svdf_flt/test_arm_svdf_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_svdf_flt/test_arm_svdf_flt.cproject.yml new file mode 100644 index 00000000..dc954354 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_svdf_flt/test_arm_svdf_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: svdf-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_svdf_f32 + + - setup: svdf-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_svdf_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: SVDF F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_svdf_f32/Unity/unity_test_arm_svdf_f32.c + - file: ../../TestCases/test_arm_svdf_f32/Unity/TestRunner/unity_test_arm_svdf_f32_runner.c + + - group: SVDF F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_svdf_f16/Unity/unity_test_arm_svdf_f16.c + - file: ../../TestCases/test_arm_svdf_f16/Unity/TestRunner/unity_test_arm_svdf_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_transpose_conv_flt/test_arm_transpose_conv_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_transpose_conv_flt/test_arm_transpose_conv_flt.cproject.yml new file mode 100644 index 00000000..2709d926 --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_transpose_conv_flt/test_arm_transpose_conv_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: transpose-conv-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_transpose_conv_f32 + + - setup: transpose-conv-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_transpose_conv_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Transpose Conv F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_transpose_conv_f32/Unity/unity_test_arm_transpose_conv_f32.c + - file: ../../TestCases/test_arm_transpose_conv_f32/Unity/TestRunner/unity_test_arm_transpose_conv_f32_runner.c + + - group: Transpose Conv F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_transpose_conv_f16/Unity/unity_test_arm_transpose_conv_f16.c + - file: ../../TestCases/test_arm_transpose_conv_f16/Unity/TestRunner/unity_test_arm_transpose_conv_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/cmsis/test_arm_transpose_flt/test_arm_transpose_flt.cproject.yml b/Tests/UnitTest/cmsis/test_arm_transpose_flt/test_arm_transpose_flt.cproject.yml new file mode 100644 index 00000000..0d60842e --- /dev/null +++ b/Tests/UnitTest/cmsis/test_arm_transpose_flt/test_arm_transpose_flt.cproject.yml @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 + +project: + define: + - ARM_NN_ENABLE_F32 + - ARM_NN_ENABLE_F16 + - ARM_NN_USE_EXP_LUT + + setups: + - setup: transpose-f32 + for-context: .F32+Corstone-300-FVP + output: + base-name: test_arm_transpose_f32 + + - setup: transpose-f16 + for-context: .F16+Corstone-300-FVP + output: + base-name: test_arm_transpose_f16 + + groups: + - group: Unity + files: + - file: ../../Unity/src/unity.c + + - group: Transpose F32 Test + for-context: .F32+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_transpose_f32/Unity/unity_test_arm_transpose_f32.c + - file: ../../TestCases/test_arm_transpose_f32/Unity/TestRunner/unity_test_arm_transpose_f32_runner.c + + - group: Transpose F16 Test + for-context: .F16+Corstone-300-FVP + files: + - file: ../../TestCases/test_arm_transpose_f16/Unity/unity_test_arm_transpose_f16.c + - file: ../../TestCases/test_arm_transpose_f16/Unity/TestRunner/unity_test_arm_transpose_f16_runner.c + + add-path: + - ../../Unity/src + - ../../Corstone-300 + + components: + - component: ARM::CMSIS:NN Lib + + layers: + - layer: ../corstone300_unittest.clayer.yml diff --git a/Tests/UnitTest/concatenation_settings_flt.py b/Tests/UnitTest/concatenation_settings_flt.py new file mode 100644 index 00000000..799f9b19 --- /dev/null +++ b/Tests/UnitTest/concatenation_settings_flt.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +LAYOUT_VALUE = {"nhwc": "ARM_NN_LAYOUT_NHWC"} + + +@dataclass(frozen=True) +class ConcatenationCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + layout: str + lhs_shape_nchw: tuple[int, int, int, int] + rhs_channels: int + seed: int + input_min: float = -2.0 + input_max: float = 2.0 + + +class ConcatenationSettingsFlt: + def __init__(self, case: ConcatenationCaseFlt): + self.case = case + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs(case.dataset) + self.lhs_table_file = self.pregenerated_data_dir / "lhs_input.txt" + self.rhs_table_file = self.pregenerated_data_dir / "rhs_input.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, filepath: Path, values: np.ndarray) -> None: + save_pregenerated_array(filepath, values) + + def _load_array(self, filepath: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(filepath, shape) + + def get_inputs(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray]: + lhs_shape = self.case.lhs_shape_nchw + rhs_shape = (lhs_shape[0], self.case.rhs_channels, lhs_shape[2], lhs_shape[3]) + if self.lhs_table_file.exists() and self.rhs_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.pregenerated_data_dir}") + return ( + self._load_array(self.lhs_table_file, lhs_shape), + self._load_array(self.rhs_table_file, rhs_shape), + ) + + rng = np.random.default_rng(self.case.seed) + lhs = rng.uniform(self.case.input_min, self.case.input_max, size=lhs_shape).astype(np.float32) + rhs = rng.uniform(self.case.input_min, self.case.input_max, size=rhs_shape).astype(np.float32) + print(f"Saving data to {self.pregenerated_data_dir}") + self._save_array(self.lhs_table_file, lhs) + self._save_array(self.rhs_table_file, rhs) + return lhs, rhs + + def _reorder(self, values_nchw: np.ndarray) -> np.ndarray: + return np.transpose(values_nchw, (0, 2, 3, 1)) + + def _write_config_header(self, lhs_shape: tuple[int, int, int, int]) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + n, lhs_c, h, w = lhs_shape + out_c = lhs_c + self.case.rhs_channels + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_N {n}\n") + fh.write(f"#define {prefix}_INPUT_H {h}\n") + fh.write(f"#define {prefix}_INPUT_W {w}\n") + fh.write(f"#define {prefix}_LHS_C {lhs_c}\n") + fh.write(f"#define {prefix}_RHS_C {self.case.rhs_channels}\n") + fh.write(f"#define {prefix}_OUTPUT_C {out_c}\n") + fh.write(f"#define {prefix}_SIZE {n * h * w * out_c}\n") + fh.write(f"#define {prefix}_LAYOUT {LAYOUT_VALUE[self.case.layout]}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = f"{suffix}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{suffix}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + write_wrapper_header(self.headers_dir / "test_data.h", self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + lhs_nchw, rhs_nchw = self.get_inputs(regenerate_input) + output_nchw = torch.cat( + [torch.tensor(lhs_nchw, dtype=torch.float32), torch.tensor(rhs_nchw, dtype=torch.float32)], + dim=1, + ).cpu().numpy().astype(np.float32) + + lhs_export = self._reorder(lhs_nchw) + rhs_export = self._reorder(rhs_nchw) + output_export = self._reorder(output_nchw) + if self.case.dtype_name == "f16": + lhs_export = lhs_export.astype(np.float16) + rhs_export = rhs_export.astype(np.float16) + output_export = output_export.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(lhs_nchw.shape) + self._write_array_header("lhs_input", lhs_export) + self._write_array_header("rhs_input", rhs_export) + self._write_array_header("output_ref", output_export) + self._write_wrapper_header() + + +def build_cases() -> list[ConcatenationCaseFlt]: + base_cases = [ + ("concat_c", "nhwc", (1, 5, 8, 8), 3, 8201), + ("concat_c_nhwc", "nhwc", (1, 5, 8, 8), 3, 8202), + ] + cases: list[ConcatenationCaseFlt] = [] + for dtype_name, torch_dtype, seed_offset in (("f32", torch.float32, 0), ("f16", torch.float16, 100)): + for name, layout, lhs_shape, rhs_c, seed in base_cases: + cases.append( + ConcatenationCaseFlt( + dataset=f"{name}_{dtype_name}", + dtype_name=dtype_name, + torch_dtype=torch_dtype, + layout=layout, + lhs_shape_nchw=lhs_shape, + rhs_channels=rhs_c, + seed=seed + seed_offset, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float concatenation unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, concatenation_f32_family, " + "concatenation_f16_family, or a specific dataset name." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def matches(dataset_filter: str, case: ConcatenationCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "concatenation_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "concatenation_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + selected = [case for case in build_cases() if matches(args.dataset, case)] + if not selected: + raise RuntimeError(f"No concatenation dataset matched '{args.dataset}'") + for case in selected: + print(f"Generating {case.dataset}...") + ConcatenationSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/conv_settings_flt.py b/Tests/UnitTest/conv_settings_flt.py new file mode 100644 index 00000000..d15d9d7a --- /dev/null +++ b/Tests/UnitTest/conv_settings_flt.py @@ -0,0 +1,1050 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class ConvCaseFlt: + dataset: str + dtype_name: str + layout: str + use_wrapper: bool + batches: int + input_h: int + input_w: int + input_channels: int + output_channels: int + kernel_h: int + kernel_w: int + stride_h: int + stride_w: int + padding_h: int + padding_w: int + dilation_h: int + dilation_w: int + activation_min: float + activation_max: float + seed: int + input_min: float = -1.0 + input_max: float = 1.0 + weight_min: float = -0.75 + weight_max: float = 0.75 + bias_min: float = -0.5 + bias_max: float = 0.5 + + +class ConvSettingsFlt: + def __init__(self, case: ConvCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.weights_table_file = self.pregenerated_data_dir / "weights.txt" + self.bias_table_file = self.pregenerated_data_dir / "bias.txt" + + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, filepath: Path, array: np.ndarray) -> None: + save_pregenerated_array(filepath, array) + + def _load_array(self, filepath: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(filepath, shape) + + def _get_raw_tensors(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + input_shape = (self.case.batches, self.case.input_channels, self.case.input_h, self.case.input_w) + weight_shape = (self.case.output_channels, self.case.input_channels, self.case.kernel_h, self.case.kernel_w) + bias_shape = (self.case.output_channels,) + + if ( + self.input_table_file.exists() + and self.weights_table_file.exists() + and self.bias_table_file.exists() + and not regenerate_input + ): + print(f"Loading data from {self.pregenerated_data_dir}") + return ( + self._load_array(self.input_table_file, input_shape), + self._load_array(self.weights_table_file, weight_shape), + self._load_array(self.bias_table_file, bias_shape), + ) + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=input_shape).astype(np.float32) + weights = rng.uniform(self.case.weight_min, self.case.weight_max, size=weight_shape).astype(np.float32) + bias = rng.uniform(self.case.bias_min, self.case.bias_max, size=bias_shape).astype(np.float32) + + print(f"Saving data to {self.pregenerated_data_dir}") + self._save_array(self.input_table_file, input_data) + self._save_array(self.weights_table_file, weights) + self._save_array(self.bias_table_file, bias) + return input_data, weights, bias + + def _reorder_input(self, input_nchw: np.ndarray) -> np.ndarray: + return np.transpose(input_nchw, (0, 2, 3, 1)) + + def _reorder_output(self, output_nchw: np.ndarray) -> np.ndarray: + return np.transpose(output_nchw, (0, 2, 3, 1)) + + def _reorder_weights(self, weights_oihw: np.ndarray) -> np.ndarray: + return np.transpose(weights_oihw, (0, 2, 3, 1)) + + def _compute_reference(self, input_nchw: np.ndarray, weights_oihw: np.ndarray, bias: np.ndarray) -> np.ndarray: + input_tensor = torch.tensor(input_nchw, dtype=torch.float32) + weight_tensor = torch.tensor(weights_oihw, dtype=torch.float32) + bias_tensor = torch.tensor(bias, dtype=torch.float32) + output = F.conv2d( + input_tensor, + weight_tensor, + bias_tensor, + stride=(self.case.stride_h, self.case.stride_w), + padding=(self.case.padding_h, self.case.padding_w), + dilation=(self.case.dilation_h, self.case.dilation_w), + ) + output = torch.clamp(output, min=self.case.activation_min, max=self.case.activation_max) + return output.cpu().numpy().astype(np.float32) + + def _write_config_header(self, output_h: int, output_w: int) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + layout_macro = "ARM_NN_LAYOUT_NHWC" + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.batches}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_IN_CH {self.case.input_channels}\n") + fh.write(f"#define {prefix}_FILTER_H {self.case.kernel_h}\n") + fh.write(f"#define {prefix}_FILTER_W {self.case.kernel_w}\n") + fh.write(f"#define {prefix}_OUT_CH {self.case.output_channels}\n") + fh.write(f"#define {prefix}_STRIDE_H {self.case.stride_h}\n") + fh.write(f"#define {prefix}_STRIDE_W {self.case.stride_w}\n") + fh.write(f"#define {prefix}_PADDING_H {self.case.padding_h}\n") + fh.write(f"#define {prefix}_PADDING_W {self.case.padding_w}\n") + fh.write(f"#define {prefix}_DILATION_H {self.case.dilation_h}\n") + fh.write(f"#define {prefix}_DILATION_W {self.case.dilation_w}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_h}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_w}\n") + fh.write(f"#define {prefix}_OUTPUT_C {self.case.output_channels}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.batches * output_h * output_w * self.case.output_channels}\n") + fh.write(f"#define {prefix}_LAYOUT {layout_macro}\n") + fh.write(f"#define {prefix}_USE_WRAPPER {1 if self.case.use_wrapper else 0}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = suffix + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + symbol_suffix = Path(suffix).stem + write_float_array_header( + filepath, + self.script_name, + f"{self.case.dataset}_{symbol_suffix}", + values, + self.case.dtype_name, + ) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_nchw, weights_oihw, bias = self._get_raw_tensors(regenerate_input) + output_nchw = self._compute_reference(input_nchw, weights_oihw, bias) + output_h = output_nchw.shape[2] + output_w = output_nchw.shape[3] + + input_export = self._reorder_input(input_nchw) + weights_export = self._reorder_weights(weights_oihw) + output_export = self._reorder_output(output_nchw) + + if self.case.dtype_name == "f16": + input_export = input_export.astype(np.float16) + weights_export = weights_export.astype(np.float16) + bias = bias.astype(np.float16) + output_export = output_export.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_h, output_w) + self._write_array_header("input_data.h", input_export) + self._write_array_header("weights_data.h", weights_export) + self._write_array_header("biases_data.h", bias) + self._write_array_header("output_ref_data.h", output_export) + self._write_wrapper_header() + + +def _build_cases() -> list[ConvCaseFlt]: + base_cases = [ + ConvCaseFlt( + dataset="conv_basic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=9, + input_w=11, + input_channels=6, + output_channels=10, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2001, + ), + ConvCaseFlt( + dataset="conv_basic_nhwc_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=9, + input_w=11, + input_channels=6, + output_channels=10, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2001, + ), + ConvCaseFlt( + dataset="conv_k3_opt_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=21, + input_channels=8, + output_channels=12, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2002, + ), + ConvCaseFlt( + dataset="conv_k5_opt_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=21, + input_channels=8, + output_channels=12, + kernel_h=1, + kernel_w=5, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2003, + ), + ConvCaseFlt( + dataset="conv_k3_opt_nhwc_tuned_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=1, + input_w=21, + input_channels=16, + output_channels=16, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2004, + ), + ConvCaseFlt( + dataset="conv_k5_opt_nhwc_tuned_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=1, + input_w=21, + input_channels=16, + output_channels=16, + kernel_h=1, + kernel_w=5, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2005, + ), + ConvCaseFlt( + dataset="conv_1x1_stride2_nhwc_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=3, + input_w=17, + input_channels=11, + output_channels=13, + kernel_h=1, + kernel_w=1, + stride_h=1, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-0.75, + activation_max=0.75, + seed=2008, + ), + ConvCaseFlt( + dataset="conv_kernel_2x2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=6, + input_w=7, + input_channels=4, + output_channels=5, + kernel_h=2, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2006, + ), + ConvCaseFlt( + dataset="conv_kernel_3x3_pad1_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=3, + input_w=6, + input_channels=2, + output_channels=4, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2007, + ), + ConvCaseFlt( + dataset="conv_match_basic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=8, + input_w=5, + input_channels=1, + output_channels=1, + kernel_h=4, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-128.0, + activation_max=127.0, + seed=2101, + ), + ConvCaseFlt( + dataset="conv_match_stride2pad1_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=7, + input_w=7, + input_channels=1, + output_channels=1, + kernel_h=3, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-128.0, + activation_max=127.0, + seed=2102, + ), + ConvCaseFlt( + dataset="conv_match_conv_2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=3, + input_w=6, + input_channels=2, + output_channels=4, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-101.0, + activation_max=127.0, + seed=2103, + ), + ConvCaseFlt( + dataset="conv_match_conv_3_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=49, + input_w=10, + input_channels=3, + output_channels=1, + kernel_h=10, + kernel_w=4, + stride_h=2, + stride_w=1, + padding_h=4, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-127.0, + activation_max=127.0, + seed=2104, + ), + ConvCaseFlt( + dataset="conv_match_conv_4_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=3, + input_h=5, + input_w=5, + input_channels=3, + output_channels=3, + kernel_h=3, + kernel_w=2, + stride_h=2, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-109.0, + activation_max=127.0, + seed=2105, + ), + ConvCaseFlt( + dataset="conv_match_conv_5_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=1, + input_w=128, + input_channels=128, + output_channels=1, + kernel_h=3, + kernel_w=3, + stride_h=4, + stride_w=4, + padding_h=1, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-88.0, + activation_max=127.0, + seed=2106, + ), + ConvCaseFlt( + dataset="conv_match_out_activation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=3, + input_w=3, + input_channels=4, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-61.0, + activation_max=107.0, + seed=2107, + input_min=-8.0, + input_max=8.0, + weight_min=-4.0, + weight_max=4.0, + bias_min=-20.0, + bias_max=20.0, + ), + ConvCaseFlt( + dataset="conv_match_dilation_golden_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=2, + input_h=4, + input_w=6, + input_channels=1, + output_channels=3, + kernel_h=2, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=1, + padding_w=1, + dilation_h=2, + dilation_w=3, + activation_min=-128.0, + activation_max=127.0, + seed=2108, + ), + ConvCaseFlt( + dataset="conv_match_2x2_dilation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=10, + input_w=10, + input_channels=2, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=2, + dilation_w=2, + activation_min=-61.0, + activation_max=107.0, + seed=2109, + ), + ConvCaseFlt( + dataset="conv_match_2x3_dilation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=3, + input_w=3, + input_channels=2, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=2, + padding_w=2, + dilation_h=2, + dilation_w=2, + activation_min=-61.0, + activation_max=107.0, + seed=2110, + ), + ConvCaseFlt( + dataset="conv_match_3x2_dilation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=3, + input_w=3, + input_channels=2, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=2, + padding_w=3, + dilation_h=2, + dilation_w=3, + activation_min=-61.0, + activation_max=107.0, + seed=2111, + ), + ConvCaseFlt( + dataset="conv_match_3x3_dilation_5x5_input_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=11, + input_w=9, + input_channels=2, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=2, + padding_w=2, + dilation_h=2, + dilation_w=2, + activation_min=-61.0, + activation_max=107.0, + seed=2112, + ), + ConvCaseFlt( + dataset="conv_match_2x2_dilation_5x5_input_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=5, + input_w=5, + input_channels=2, + output_channels=2, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=2, + padding_w=2, + dilation_h=2, + dilation_w=2, + activation_min=-61.0, + activation_max=107.0, + seed=2113, + ), + ConvCaseFlt( + dataset="conv_match_1x1_basic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=5, + input_w=7, + input_channels=19, + output_channels=7, + kernel_h=1, + kernel_w=1, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-126.0, + activation_max=127.0, + seed=2114, + ), + ConvCaseFlt( + dataset="conv_match_1x1_stride_x_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=4, + input_w=7, + input_channels=9, + output_channels=5, + kernel_h=1, + kernel_w=1, + stride_h=1, + stride_w=3, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-126.0, + activation_max=127.0, + seed=2115, + ), + ConvCaseFlt( + dataset="conv_match_1x1_stride_x_y_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=3, + input_h=6, + input_w=7, + input_channels=23, + output_channels=15, + kernel_h=1, + kernel_w=1, + stride_h=2, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-6.0, + activation_max=127.0, + seed=2116, + ), + ConvCaseFlt( + dataset="conv_match_1x1_stride_x_y_1_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=4, + input_w=4, + input_channels=5, + output_channels=5, + kernel_h=1, + kernel_w=1, + stride_h=2, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-126.0, + activation_max=127.0, + seed=2117, + ), + ConvCaseFlt( + dataset="conv_match_1x1_stride_x_y_2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=4, + input_w=4, + input_channels=5, + output_channels=5, + kernel_h=1, + kernel_w=1, + stride_h=3, + stride_w=3, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-126.0, + activation_max=127.0, + seed=2118, + ), + ConvCaseFlt( + dataset="conv_match_1xn_1_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=1, + input_w=2, + input_channels=4, + output_channels=3, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-127.0, + activation_max=127.0, + seed=2119, + ), + ConvCaseFlt( + dataset="conv_match_1xn_2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=296, + input_channels=4, + output_channels=3, + kernel_h=1, + kernel_w=48, + stride_h=1, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-111.0, + activation_max=127.0, + seed=2120, + ), + ConvCaseFlt( + dataset="conv_match_1xn_3_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=296, + input_channels=4, + output_channels=1, + kernel_h=1, + kernel_w=48, + stride_h=1, + stride_w=2, + padding_h=0, + padding_w=23, + dilation_h=1, + dilation_w=1, + activation_min=-111.0, + activation_max=127.0, + seed=2121, + ), + ConvCaseFlt( + dataset="conv_match_1xn_4_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=16, + input_channels=4, + output_channels=4, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-88.0, + activation_max=127.0, + seed=2122, + ), + ConvCaseFlt( + dataset="conv_match_1xn_5_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=17, + input_channels=4, + output_channels=1, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=3, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-88.0, + activation_max=127.0, + seed=2123, + ), + ConvCaseFlt( + dataset="conv_match_1xn_6_generic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=1, + input_w=4, + input_channels=1, + output_channels=16, + kernel_h=1, + kernel_w=8, + stride_h=1, + stride_w=4, + padding_h=0, + padding_w=2, + dilation_h=1, + dilation_w=1, + activation_min=-125.0, + activation_max=126.0, + seed=2124, + ), + ConvCaseFlt( + dataset="conv_match_1xn_7_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=1, + input_w=148, + input_channels=20, + output_channels=15, + kernel_h=1, + kernel_w=32, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=15, + dilation_h=1, + dilation_w=1, + activation_min=-127.0, + activation_max=127.0, + seed=2125, + ), + ConvCaseFlt( + dataset="conv_match_1xn_8_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=1, + input_w=148, + input_channels=12, + output_channels=10, + kernel_h=1, + kernel_w=48, + stride_h=1, + stride_w=2, + padding_h=0, + padding_w=23, + dilation_h=1, + dilation_w=1, + activation_min=-127.0, + activation_max=127.0, + seed=2126, + ), + ] + + f16_cases = [ + ConvCaseFlt(**{**case.__dict__, "dataset": case.dataset.replace("_f32", "_f16"), "dtype_name": "f16"}) + for case in base_cases + ] + return base_cases + f16_cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float convolution unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, conv_f32_family, " + "conv_f16_family, or a specific dataset name such as conv_basic_nhwc_f16." + ), + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate the pregenerated raw input/weight/bias tensors instead of reusing them.", + ) + return parser.parse_args() + + +def _select_cases(all_cases: list[ConvCaseFlt], selector: str) -> list[ConvCaseFlt]: + if selector == "all": + return all_cases + if selector == "conv_f32_family": + return [case for case in all_cases if case.dtype_name == "f32"] + if selector == "conv_f16_family": + return [case for case in all_cases if case.dtype_name == "f16"] + return [case for case in all_cases if case.dataset == selector] + + +def main() -> None: + args = parse_args() + selected = _select_cases(_build_cases(), args.dataset) + if not selected: + raise SystemExit(f"Unknown dataset selector: {args.dataset}") + + for case in selected: + print(f"Generating {case.dataset}...") + ConvSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/depthwise_conv_settings_flt.py b/Tests/UnitTest/depthwise_conv_settings_flt.py new file mode 100644 index 00000000..a05b840e --- /dev/null +++ b/Tests/UnitTest/depthwise_conv_settings_flt.py @@ -0,0 +1,617 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. You may obtain a copy +# of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class DepthwiseConvCaseFlt: + dataset: str + dtype_name: str + layout: str + use_wrapper: bool + batches: int + input_h: int + input_w: int + input_channels: int + channel_multiplier: int + kernel_h: int + kernel_w: int + stride_h: int + stride_w: int + padding_h: int + padding_w: int + dilation_h: int + dilation_w: int + activation_min: float + activation_max: float + seed: int + input_min: float = -1.0 + input_max: float = 1.0 + weight_min: float = -0.75 + weight_max: float = 0.75 + bias_min: float = -0.5 + bias_max: float = 0.5 + use_null_bias: bool = False + + @property + def output_channels(self) -> int: + return self.input_channels * self.channel_multiplier + + +class DepthwiseConvSettingsFlt: + def __init__(self, case: DepthwiseConvCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.weights_table_file = self.pregenerated_data_dir / "weights.txt" + self.bias_table_file = self.pregenerated_data_dir / "bias.txt" + + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, filepath: Path, array: np.ndarray) -> None: + save_pregenerated_array(filepath, array) + + def _load_array(self, filepath: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(filepath, shape) + + def _get_raw_tensors(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + input_shape = (self.case.batches, self.case.input_channels, self.case.input_h, self.case.input_w) + weight_shape = (self.case.output_channels, 1, self.case.kernel_h, self.case.kernel_w) + bias_shape = (self.case.output_channels,) + + if ( + self.input_table_file.exists() + and self.weights_table_file.exists() + and self.bias_table_file.exists() + and not regenerate_input + ): + print(f"Loading data from {self.pregenerated_data_dir}") + return ( + self._load_array(self.input_table_file, input_shape), + self._load_array(self.weights_table_file, weight_shape), + self._load_array(self.bias_table_file, bias_shape), + ) + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=input_shape).astype(np.float32) + weights = rng.uniform(self.case.weight_min, self.case.weight_max, size=weight_shape).astype(np.float32) + bias = rng.uniform(self.case.bias_min, self.case.bias_max, size=bias_shape).astype(np.float32) + if self.case.use_null_bias: + bias.fill(0.0) + + print(f"Saving data to {self.pregenerated_data_dir}") + self._save_array(self.input_table_file, input_data) + self._save_array(self.weights_table_file, weights) + self._save_array(self.bias_table_file, bias) + return input_data, weights, bias + + def _reorder_input(self, input_nchw: np.ndarray) -> np.ndarray: + return np.transpose(input_nchw, (0, 2, 3, 1)) + + def _reorder_output(self, output_nchw: np.ndarray) -> np.ndarray: + return np.transpose(output_nchw, (0, 2, 3, 1)) + + def _reorder_weights(self, weights_oc1hw: np.ndarray) -> np.ndarray: + weights_ock = np.squeeze(weights_oc1hw, axis=1) + return np.transpose(weights_ock, (1, 2, 0)) + + def _compute_reference(self, input_nchw: np.ndarray, weights_oc1hw: np.ndarray, bias: np.ndarray) -> np.ndarray: + input_tensor = torch.tensor(input_nchw, dtype=torch.float32) + weight_tensor = torch.tensor(weights_oc1hw, dtype=torch.float32) + bias_tensor = torch.tensor(bias, dtype=torch.float32) + output = F.conv2d( + input_tensor, + weight_tensor, + bias_tensor, + stride=(self.case.stride_h, self.case.stride_w), + padding=(self.case.padding_h, self.case.padding_w), + dilation=(self.case.dilation_h, self.case.dilation_w), + groups=self.case.input_channels, + ) + output = torch.clamp(output, min=self.case.activation_min, max=self.case.activation_max) + return output.cpu().numpy().astype(np.float32) + + def _write_config_header(self, output_h: int, output_w: int) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + layout_macro = "ARM_NN_LAYOUT_NHWC" + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.batches}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_IN_CH {self.case.input_channels}\n") + fh.write(f"#define {prefix}_CH_MULT {self.case.channel_multiplier}\n") + fh.write(f"#define {prefix}_FILTER_H {self.case.kernel_h}\n") + fh.write(f"#define {prefix}_FILTER_W {self.case.kernel_w}\n") + fh.write(f"#define {prefix}_OUT_CH {self.case.output_channels}\n") + fh.write(f"#define {prefix}_STRIDE_H {self.case.stride_h}\n") + fh.write(f"#define {prefix}_STRIDE_W {self.case.stride_w}\n") + fh.write(f"#define {prefix}_PADDING_H {self.case.padding_h}\n") + fh.write(f"#define {prefix}_PADDING_W {self.case.padding_w}\n") + fh.write(f"#define {prefix}_DILATION_H {self.case.dilation_h}\n") + fh.write(f"#define {prefix}_DILATION_W {self.case.dilation_w}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_h}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_w}\n") + fh.write(f"#define {prefix}_OUTPUT_C {self.case.output_channels}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.batches * output_h * output_w * self.case.output_channels}\n") + fh.write(f"#define {prefix}_LAYOUT {layout_macro}\n") + fh.write(f"#define {prefix}_USE_WRAPPER {1 if self.case.use_wrapper else 0}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + fh.write(f"#define {prefix}_USE_NULL_BIAS {1 if self.case.use_null_bias else 0}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = suffix + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + symbol_suffix = Path(suffix).stem + write_float_array_header( + filepath, + self.script_name, + f"{self.case.dataset}_{symbol_suffix}", + values, + self.case.dtype_name, + ) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_nchw, weights_oc1hw, bias = self._get_raw_tensors(regenerate_input) + output_nchw = self._compute_reference(input_nchw, weights_oc1hw, bias) + output_h = int(output_nchw.shape[2]) + output_w = int(output_nchw.shape[3]) + + input_export = self._reorder_input(input_nchw) + weights_export = self._reorder_weights(weights_oc1hw) + output_export = self._reorder_output(output_nchw) + + if self.case.dtype_name == "f16": + input_export = input_export.astype(np.float16) + weights_export = weights_export.astype(np.float16) + bias = bias.astype(np.float16) + output_export = output_export.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_h, output_w) + self._write_array_header("input_data.h", input_export) + self._write_array_header("weights_data.h", weights_export) + self._write_array_header("biases_data.h", bias) + self._write_array_header("output_ref_data.h", output_export) + self._write_wrapper_header() + + +def _build_cases() -> list[DepthwiseConvCaseFlt]: + base_cases = [ + DepthwiseConvCaseFlt( + dataset="depthwise_basic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=9, + input_w=11, + input_channels=8, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2401, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_basic_smallc_nhwc_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=9, + input_w=11, + input_channels=6, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2402, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_k3_1d_opt_batch2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=1, + input_w=21, + input_channels=8, + channel_multiplier=1, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2403, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_k3_1d_opt_nhwc_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=1, + input_w=21, + input_channels=8, + channel_multiplier=1, + kernel_h=1, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2404, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_2x5_opt_batch2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=2, + input_h=2, + input_w=21, + input_channels=8, + channel_multiplier=1, + kernel_h=2, + kernel_w=5, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2405, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_2x5_opt_nhwc_chmult16_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=2, + input_w=21, + input_channels=1, + channel_multiplier=16, + kernel_h=2, + kernel_w=5, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2406, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_ic1_to_conv_nhwc_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=9, + input_w=11, + input_channels=1, + channel_multiplier=16, + kernel_h=4, + kernel_w=4, + stride_h=2, + stride_w=1, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2410, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_kernel_2x2_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=6, + input_w=5, + input_channels=4, + channel_multiplier=1, + kernel_h=2, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2407, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_kernel_3x3_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=5, + input_w=4, + input_channels=5, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=1, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2408, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_kernel_3x3_null_bias_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=False, + batches=1, + input_h=5, + input_w=4, + input_channels=5, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=1, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=2409, + use_null_bias=True, + ), + # Match representative legacy s8 depthwise regression shapes for direct cycle comparisons. + DepthwiseConvCaseFlt( + dataset="depthwise_match_basic_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=8, + input_w=5, + input_channels=1, + channel_multiplier=1, + kernel_h=4, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-128.0, + activation_max=127.0, + seed=2411, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_match_sub_block_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=5, + input_w=7, + input_channels=9, + channel_multiplier=1, + kernel_h=2, + kernel_w=2, + stride_h=1, + stride_w=1, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-128.0, + activation_max=127.0, + seed=2412, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_match_dilation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=7, + input_w=7, + input_channels=1, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=1, + stride_w=1, + padding_h=2, + padding_w=2, + dilation_h=2, + dilation_w=2, + activation_min=-70.0, + activation_max=127.0, + seed=2413, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_match_out_activation_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=5, + input_w=6, + input_channels=3, + channel_multiplier=1, + kernel_h=4, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=0, + padding_w=0, + dilation_h=1, + dilation_w=1, + activation_min=-45.0, + activation_max=103.0, + seed=2414, + ), + DepthwiseConvCaseFlt( + dataset="depthwise_match_stride2pad1_f32", + dtype_name="f32", + layout="nhwc", + use_wrapper=True, + batches=1, + input_h=7, + input_w=7, + input_channels=1, + channel_multiplier=1, + kernel_h=3, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=1, + padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-128.0, + activation_max=127.0, + seed=2415, + ), + ] + + f16_cases = [ + DepthwiseConvCaseFlt(**{**case.__dict__, "dataset": case.dataset.replace("_f32", "_f16"), "dtype_name": "f16"}) + for case in base_cases + ] + return base_cases + f16_cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float depthwise-convolution unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, depthwise_conv_f32_family, " + "depthwise_conv_f16_family, or a specific dataset name such as depthwise_basic_smallc_nhwc_f16." + ), + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate the pregenerated raw input/weight/bias tensors instead of reusing them.", + ) + return parser.parse_args() + + +def _select_cases(all_cases: list[DepthwiseConvCaseFlt], selector: str) -> list[DepthwiseConvCaseFlt]: + if selector == "all": + return all_cases + if selector == "depthwise_conv_f32_family": + return [case for case in all_cases if case.dtype_name == "f32"] + if selector == "depthwise_conv_f16_family": + return [case for case in all_cases if case.dtype_name == "f16"] + return [case for case in all_cases if case.dataset == selector] + + +def main() -> None: + args = parse_args() + selected = _select_cases(_build_cases(), args.dataset) + if not selected: + raise SystemExit(f"Unknown dataset selector: {args.dataset}") + + for case in selected: + print(f"Generating {case.dataset}...") + DepthwiseConvSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/ds_cnn_s_body_settings_flt.py b/Tests/UnitTest/ds_cnn_s_body_settings_flt.py new file mode 100644 index 00000000..2adf6b82 --- /dev/null +++ b/Tests/UnitTest/ds_cnn_s_body_settings_flt.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class DsCnnSBodyCaseFlt: + dataset: str + dtype_name: str + seed: int + activation_max: float + input_batches: int = 1 + input_h: int = 25 + input_w: int = 5 + channels: int = 64 + num_blocks: int = 4 + num_classes: int = 12 + input_min: float = -1.0 + input_max: float = 1.0 + depthwise_weight_min: float = -0.25 + depthwise_weight_max: float = 0.25 + pointwise_weight_min: float = -0.12 + pointwise_weight_max: float = 0.12 + fc_weight_min: float = -0.2 + fc_weight_max: float = 0.2 + bias_min: float = -0.05 + bias_max: float = 0.05 + + @property + def input_shape_nchw(self) -> tuple[int, int, int, int]: + return (self.input_batches, self.channels, self.input_h, self.input_w) + + @property + def feature_map_size(self) -> int: + return self.input_batches * self.input_h * self.input_w * self.channels + + +class DsCnnSBodySettingsFlt: + def __init__(self, case: DsCnnSBodyCaseFlt): + self.case = case + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, + Path("TestCases/TestData"), + Path("PregeneratedData"), + ) + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _pregenerated_path(self, stem: str) -> Path: + return self.pregenerated_data_dir / f"{stem}.txt" + + def _load_or_create_array( + self, + stem: str, + shape: tuple[int, ...], + low: float, + high: float, + rng: np.random.Generator, + regenerate_input: bool, + ) -> np.ndarray: + filepath = self._pregenerated_path(stem) + if filepath.exists() and not regenerate_input: + return load_pregenerated_array(filepath, shape) + + values = rng.uniform(low, high, size=shape).astype(np.float32) + save_pregenerated_array(filepath, values) + return values + + def _load_or_create_tensors(self, regenerate_input: bool) -> dict[str, np.ndarray]: + rng = np.random.default_rng(self.case.seed) + arrays: dict[str, np.ndarray] = {} + + arrays["input"] = self._load_or_create_array( + "input", + self.case.input_shape_nchw, + self.case.input_min, + self.case.input_max, + rng, + regenerate_input, + ) + + for block_idx in range(self.case.num_blocks): + arrays[f"dwconv{block_idx}_weights"] = self._load_or_create_array( + f"dwconv{block_idx}_weights", + (self.case.channels, 1, 3, 3), + self.case.depthwise_weight_min, + self.case.depthwise_weight_max, + rng, + regenerate_input, + ) + arrays[f"dwconv{block_idx}_biases"] = self._load_or_create_array( + f"dwconv{block_idx}_biases", + (self.case.channels,), + self.case.bias_min, + self.case.bias_max, + rng, + regenerate_input, + ) + arrays[f"pwconv{block_idx}_weights"] = self._load_or_create_array( + f"pwconv{block_idx}_weights", + (self.case.channels, self.case.channels, 1, 1), + self.case.pointwise_weight_min, + self.case.pointwise_weight_max, + rng, + regenerate_input, + ) + arrays[f"pwconv{block_idx}_biases"] = self._load_or_create_array( + f"pwconv{block_idx}_biases", + (self.case.channels,), + self.case.bias_min, + self.case.bias_max, + rng, + regenerate_input, + ) + + arrays["fc_weights"] = self._load_or_create_array( + "fc_weights", + (self.case.num_classes, self.case.channels), + self.case.fc_weight_min, + self.case.fc_weight_max, + rng, + regenerate_input, + ) + arrays["fc_biases"] = self._load_or_create_array( + "fc_biases", + (self.case.num_classes,), + self.case.bias_min, + self.case.bias_max, + rng, + regenerate_input, + ) + return arrays + + def _run_reference(self, arrays: dict[str, np.ndarray]) -> np.ndarray: + # Mirror the ds_cnn_s body topology used by the float manifest tests: + # 4x [depthwise 3x3 + relu, pointwise 1x1 + relu], avgpool, fc, softmax. + x = torch.tensor(arrays["input"], dtype=torch.float32) + for block_idx in range(self.case.num_blocks): + x = F.conv2d( + x, + torch.tensor(arrays[f"dwconv{block_idx}_weights"], dtype=torch.float32), + torch.tensor(arrays[f"dwconv{block_idx}_biases"], dtype=torch.float32), + stride=(1, 1), + padding=(1, 1), + dilation=(1, 1), + groups=self.case.channels, + ) + x = F.relu(x) + x = F.conv2d( + x, + torch.tensor(arrays[f"pwconv{block_idx}_weights"], dtype=torch.float32), + torch.tensor(arrays[f"pwconv{block_idx}_biases"], dtype=torch.float32), + stride=(1, 1), + padding=(0, 0), + dilation=(1, 1), + ) + x = F.relu(x) + + x = F.avg_pool2d( + x, + kernel_size=(self.case.input_h, self.case.input_w), + stride=(1, 1), + padding=(0, 0), + count_include_pad=False, + ) + x = x.reshape(self.case.input_batches, self.case.channels) + x = F.linear( + x, + torch.tensor(arrays["fc_weights"], dtype=torch.float32), + torch.tensor(arrays["fc_biases"], dtype=torch.float32), + ) + x = torch.softmax(x, dim=-1) + return x.cpu().numpy().astype(np.float32).reshape(-1) + + def _export_input(self, input_nchw: np.ndarray) -> np.ndarray: + return np.transpose(input_nchw, (0, 2, 3, 1)) + + def _export_depthwise_weights(self, weights_oc1hw: np.ndarray) -> np.ndarray: + return np.transpose(np.squeeze(weights_oc1hw, axis=1), (1, 2, 0)) + + def _export_pointwise_weights(self, weights_oihw: np.ndarray) -> np.ndarray: + return np.transpose(weights_oihw, (0, 2, 3, 1)) + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + identity_min = -self.case.activation_max + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.input_batches}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_INPUT_C {self.case.channels}\n") + fh.write(f"#define {prefix}_CHANNELS {self.case.channels}\n") + fh.write(f"#define {prefix}_NUM_BLOCKS {self.case.num_blocks}\n") + fh.write(f"#define {prefix}_NUM_CLASSES {self.case.num_classes}\n") + fh.write(f"#define {prefix}_FEATURE_MAP_SIZE {self.case.feature_map_size}\n") + fh.write(f"#define {prefix}_POOLED_SIZE {self.case.channels}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.num_classes}\n") + fh.write(f"#define {prefix}_SOFTMAX_ROWS 1\n") + fh.write(f"#define {prefix}_SOFTMAX_COLS {self.case.num_classes}\n") + fh.write(f"#define {prefix}_LAYOUT ARM_NN_LAYOUT_NHWC\n") + fh.write(f"#define {prefix}_DEPTHWISE_CH_MULT 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_FILTER_H 3\n") + fh.write(f"#define {prefix}_DEPTHWISE_FILTER_W 3\n") + fh.write(f"#define {prefix}_DEPTHWISE_STRIDE_H 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_STRIDE_W 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_PADDING_H 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_PADDING_W 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_DILATION_H 1\n") + fh.write(f"#define {prefix}_DEPTHWISE_DILATION_W 1\n") + fh.write(f"#define {prefix}_POINTWISE_FILTER_H 1\n") + fh.write(f"#define {prefix}_POINTWISE_FILTER_W 1\n") + fh.write(f"#define {prefix}_POINTWISE_STRIDE_H 1\n") + fh.write(f"#define {prefix}_POINTWISE_STRIDE_W 1\n") + fh.write(f"#define {prefix}_POINTWISE_PADDING_H 0\n") + fh.write(f"#define {prefix}_POINTWISE_PADDING_W 0\n") + fh.write(f"#define {prefix}_POINTWISE_DILATION_H 1\n") + fh.write(f"#define {prefix}_POINTWISE_DILATION_W 1\n") + fh.write(f"#define {prefix}_AVGPOOL_FILTER_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_AVGPOOL_FILTER_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_AVGPOOL_OUTPUT_H 1\n") + fh.write(f"#define {prefix}_AVGPOOL_OUTPUT_W 1\n") + fh.write(f"#define {prefix}_RELU_MIN {formatter(0.0)}\n") + fh.write(f"#define {prefix}_RELU_MAX {formatter(self.case.activation_max)}\n") + fh.write(f"#define {prefix}_IDENTITY_MIN {formatter(identity_min)}\n") + fh.write(f"#define {prefix}_IDENTITY_MAX {formatter(self.case.activation_max)}\n") + format_generated_file(filepath) + + def _write_array_header(self, filename: str, symbol_name: str, values: np.ndarray) -> None: + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, symbol_name, values, self.case.dtype_name) + + def _write_headers(self, arrays: dict[str, np.ndarray], output_ref: np.ndarray) -> None: + exported_input = self._export_input(arrays["input"]) + if self.case.dtype_name == "f16": + exported_input = exported_input.astype(np.float16) + output_ref = output_ref.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header() + self._write_array_header("input_data.h", f"{self.case.dataset}_input_data", exported_input) + + for block_idx in range(self.case.num_blocks): + dw_weights = self._export_depthwise_weights(arrays[f"dwconv{block_idx}_weights"]) + pw_weights = self._export_pointwise_weights(arrays[f"pwconv{block_idx}_weights"]) + dw_biases = arrays[f"dwconv{block_idx}_biases"] + pw_biases = arrays[f"pwconv{block_idx}_biases"] + if self.case.dtype_name == "f16": + dw_weights = dw_weights.astype(np.float16) + pw_weights = pw_weights.astype(np.float16) + dw_biases = dw_biases.astype(np.float16) + pw_biases = pw_biases.astype(np.float16) + + self._write_array_header( + f"dwconv{block_idx}_weights_data.h", + f"{self.case.dataset}_dwconv{block_idx}_weights_data", + dw_weights, + ) + self._write_array_header( + f"dwconv{block_idx}_biases_data.h", + f"{self.case.dataset}_dwconv{block_idx}_biases_data", + dw_biases, + ) + self._write_array_header( + f"pwconv{block_idx}_weights_data.h", + f"{self.case.dataset}_pwconv{block_idx}_weights_data", + pw_weights, + ) + self._write_array_header( + f"pwconv{block_idx}_biases_data.h", + f"{self.case.dataset}_pwconv{block_idx}_biases_data", + pw_biases, + ) + + fc_weights = arrays["fc_weights"] + fc_biases = arrays["fc_biases"] + if self.case.dtype_name == "f16": + fc_weights = fc_weights.astype(np.float16) + fc_biases = fc_biases.astype(np.float16) + + self._write_array_header("fc_weights_data.h", f"{self.case.dataset}_fc_weights_data", fc_weights) + self._write_array_header("fc_biases_data.h", f"{self.case.dataset}_fc_biases_data", fc_biases) + self._write_array_header("output_ref_data.h", f"{self.case.dataset}_output_ref_data", output_ref) + write_wrapper_header(self.headers_dir / "test_data.h", self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + arrays = self._load_or_create_tensors(regenerate_input) + output_ref = self._run_reference(arrays) + self._write_headers(arrays, output_ref) + + +def _build_cases() -> list[DsCnnSBodyCaseFlt]: + return [ + DsCnnSBodyCaseFlt( + dataset="ds_cnn_s_body_f32", + dtype_name="f32", + seed=9101, + activation_max=3.4028234663852886e38, + ), + DsCnnSBodyCaseFlt( + dataset="ds_cnn_s_body_f16", + dtype_name="f16", + seed=9101, + activation_max=65504.0, + ), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate DS-CNN-S body float references using PyTorch.") + parser.add_argument( + "--dataset", + choices=[ + "ds_cnn_s_body_f32", + "ds_cnn_s_body_f16", + "ds_cnn_s_body_f32_family", + "ds_cnn_s_body_f16_family", + "all", + ], + default="all", + help="Dataset or family selector to generate.", + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate the pregenerated DS-CNN-S body tensors instead of reusing them.", + ) + return parser.parse_args() + + +def _matches_selector(case: DsCnnSBodyCaseFlt, selector: str) -> bool: + if selector == "all": + return True + if selector == case.dataset: + return True + if selector == "ds_cnn_s_body_f32_family": + return case.dtype_name == "f32" + if selector == "ds_cnn_s_body_f16_family": + return case.dtype_name == "f16" + return False + + +def main() -> None: + args = parse_args() + for case in _build_cases(): + if not _matches_selector(case, args.dataset): + continue + print(f"Generating {case.dataset}...") + DsCnnSBodySettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/float_test_gen_utils.py b/Tests/UnitTest/float_test_gen_utils.py new file mode 100644 index 00000000..f9fe7c19 --- /dev/null +++ b/Tests/UnitTest/float_test_gen_utils.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import os +import shlex +import shutil +import subprocess +import sys +from pathlib import Path + +import numpy as np +import torch + +TEST_DATA_ROOT = Path("TestCases/TestData") +PREGENERATED_DATA_ROOT = Path("PregeneratedData") + + +def format_generated_file(filepath: Path) -> None: + formatter_cmd = os.getenv("CLANG_FORMAT") + if formatter_cmd: + command = shlex.split(formatter_cmd) + else: +#Prefer the formatter shipped with the active Python environment so +#generation and CI checks see the same clang - format version. + formatter = None + python_bin_dir = Path(sys.executable).parent + sibling_formatter = python_bin_dir / "clang-format" + if sibling_formatter.is_file(): + formatter = str(sibling_formatter) + else: + virtual_env = os.getenv("VIRTUAL_ENV") + if virtual_env: + venv_formatter = Path(virtual_env) / "bin" / "clang-format" + if venv_formatter.is_file(): + formatter = str(venv_formatter) + if formatter is None: + formatter = shutil.which("clang-format") + if formatter is None: + raise RuntimeError( + "clang-format not found. Set CLANG_FORMAT or add clang-format to PATH " + "before generating unit-test headers." + ) + command = [formatter] + + if "-i" not in command: + command.append("-i") + + result = subprocess.run([*command, str(filepath)], check=False) + if result.returncode != 0: + raise RuntimeError(f"Failed to format generated file: {filepath}") + + +def as_c_float_literal(value: float) -> str: + literal = f"{value:.9g}" + if "." not in literal and "e" not in literal and "E" not in literal: + literal += ".0" + return literal + + +def format_float32(value: float) -> str: + return f"{as_c_float_literal(float(np.float32(value)))}f" + + +def format_float16(value: float) -> str: + return f"(float16_t){as_c_float_literal(float(np.float16(value).astype(np.float32)))}f" + + +def get_generator_comment(script_name: str) -> str: + return f"// Generated by {script_name} using PyTorch version {torch.__version__}.\n" + + +def prepare_dataset_dirs( + dataset: str, + outdir: Path = TEST_DATA_ROOT, + pregenerated_dir: Path = PREGENERATED_DATA_ROOT, +) -> tuple[Path, Path]: + headers_dir = Path(outdir) / dataset + pregenerated_data_dir = Path(pregenerated_dir) / dataset + headers_dir.mkdir(parents=True, exist_ok=True) + pregenerated_data_dir.mkdir(parents=True, exist_ok=True) + return headers_dir, pregenerated_data_dir + + +def save_pregenerated_array(filepath: Path, values: np.ndarray) -> None: + np.savetxt(filepath, np.asarray(values).reshape(-1), delimiter=",") + + +def load_pregenerated_array( + filepath: Path, + shape: tuple[int, ...], + dtype: np.dtype = np.float32, +) -> np.ndarray: + return np.loadtxt(filepath, delimiter=",", dtype=dtype).reshape(shape) + + +def write_common_header(fh, script_name: str) -> None: + fh.write(get_generator_comment(script_name)) + + +def write_wrapper_header(filepath: Path, script_name: str, filenames: list[str]) -> None: + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, script_name) + for filename in filenames: + fh.write(f'#include "{filename}"\n') + format_generated_file(filepath) + + +def write_float_array_header( + filepath: Path, + script_name: str, + symbol_name: str, + values: np.ndarray, + dtype_name: str, +) -> None: + flat_values = values.reshape(-1) + if dtype_name == "f32": + datatype = "float32_t" + formatter = format_float32 + else: + datatype = "float16_t" + formatter = format_float16 + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, script_name) + fh.write(f"const {datatype} {symbol_name}[{flat_values.size}] = {{\n") + for index, value in enumerate(flat_values): + suffix = "," if index + 1 < flat_values.size else "" + fh.write(f" {formatter(float(value))}{suffix}\n") + fh.write("};\n") + format_generated_file(filepath) diff --git a/Tests/UnitTest/float_unit_test_coverage.md b/Tests/UnitTest/float_unit_test_coverage.md new file mode 100644 index 00000000..b63ed7b8 --- /dev/null +++ b/Tests/UnitTest/float_unit_test_coverage.md @@ -0,0 +1,329 @@ +# Float Unit Test Coverage + +This file summarizes the float unit-test families currently covered by the +fork-native test flow. + +Source of truth: +- `Tests/UnitTest/run_float_unit_tests.py` +- `Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml` +- `Tests/UnitTest/float_unit_test_coverage.yml` + +Covered dtypes: `f32`, `f16` + +## Family Summary + +| Family | Generator | Host Targets | CMSIS Project | +| --- | --- | --- | --- | +| `activation` | `activation_settings_flt.py` | `test_arm_activation_f32`, `test_arm_activation_f16` | `test_arm_activation_flt` | +| `avg_pool` | `pooling_settings_flt.py` | `test_arm_avg_pool_f32`, `test_arm_avg_pool_f16` | `test_arm_avg_pool_flt` | +| `batch_matmul` | `batch_matmul_settings_flt.py` | `test_arm_batch_matmul_f32`, `test_arm_batch_matmul_f16` | `test_arm_batch_matmul_flt` | +| `batch_norm` | `batch_norm_settings_flt.py` | `test_arm_batch_norm_f32`, `test_arm_batch_norm_f16` | `test_arm_batch_norm_flt` | +| `concatenation` | `concatenation_settings_flt.py` | `test_arm_concatenation_f32`, `test_arm_concatenation_f16` | `test_arm_concatenation_flt` | +| `convolve` | `conv_settings_flt.py` | `test_arm_convolve_f32`, `test_arm_convolve_f16` | `test_arm_convolve_flt` | +| `depthwise_conv` | `depthwise_conv_settings_flt.py` | `test_arm_depthwise_conv_f32`, `test_arm_depthwise_conv_f16` | `test_arm_depthwise_conv_flt` | +| `ds_cnn_s_body` | `ds_cnn_s_body_settings_flt.py` | `test_arm_ds_cnn_s_body_f32`, `test_arm_ds_cnn_s_body_f16` | `test_arm_ds_cnn_s_body_flt` | +| `elementwise_add` | `add_mul_settings_flt.py` | `test_arm_elementwise_add_f32`, `test_arm_elementwise_add_f16` | `test_arm_elementwise_add_flt` | +| `elementwise_mul` | `add_mul_settings_flt.py` | `test_arm_elementwise_mul_f32`, `test_arm_elementwise_mul_f16` | `test_arm_elementwise_mul_flt` | +| `fully_connected` | `fully_connected_settings_flt.py` | `test_arm_fully_connected_f32`, `test_arm_fully_connected_f16` | `test_arm_fully_connected_flt` | +| `lstm` | `lstm_settings_flt.py` | `test_arm_lstm_unidirectional_f32`, `test_arm_lstm_unidirectional_f16` | `test_arm_lstm_unidirectional_flt` | +| `max_pool` | `pooling_settings_flt.py` | `test_arm_max_pool_f32`, `test_arm_max_pool_f16` | `test_arm_max_pool_flt` | +| `maximum_minimum` | `minmax_settings_flt.py` | `test_arm_maximum_minimum_f32`, `test_arm_maximum_minimum_f16` | `test_arm_maximum_minimum_flt` | +| `pad` | `pad_settings_flt.py` | `test_arm_pad_f32`, `test_arm_pad_f16` | `test_arm_pad_flt` | +| `reshape` | `none` | `test_arm_reshape_f32`, `test_arm_reshape_f16` | `test_arm_reshape_flt` | +| `softmax` | `softmax_settings_flt.py` | `test_arm_softmax_f32`, `test_arm_softmax_f16` | `test_arm_softmax_flt` | +| `svdf` | `svdf_settings_flt.py` | `test_arm_svdf_f32`, `test_arm_svdf_f16` | `test_arm_svdf_flt` | +| `transpose` | `transpose_settings_flt.py` | `test_arm_transpose_f32`, `test_arm_transpose_f16` | `test_arm_transpose_flt` | +| `transpose_conv` | `transpose_conv_settings_flt.py` | `test_arm_transpose_conv_f32`, `test_arm_transpose_conv_f16` | `test_arm_transpose_conv_flt` | + +## Coverage Details + +### `activation` + +1D activation sanity coverage for the float public activation API. + +- Generator: `activation_settings_flt.py` +- Host targets: `test_arm_activation_f32`, `test_arm_activation_f16` +- CMSIS project: `test_arm_activation_flt` +- CMSIS contexts: `test_arm_activation_flt.F32+Corstone-300-FVP`, `test_arm_activation_flt.F16+Corstone-300-FVP` +- Covered cases: + - Vector length 9 with mixed negative, zero, and positive values. + - Covers sigmoid, tanh, hardswish, and leaky_relu with slope 0.125. + +### `avg_pool` + +NHWC average-pooling coverage across padding modes and activation clamp ranges. + +- Generator: `pooling_settings_flt.py` +- Host targets: `test_arm_avg_pool_f32`, `test_arm_avg_pool_f16` +- CMSIS project: `test_arm_avg_pool_flt` +- CMSIS contexts: `test_arm_avg_pool_flt.F32+Corstone-300-FVP`, `test_arm_avg_pool_flt.F16+Corstone-300-FVP` +- Covered cases: + - 1x12x22x20, filter 5x6, stride 5x9, SAME padding. + - 1x5x9x3, filter 5x9, stride 2x1, VALID padding. + - 1x3x3x1, filter 3x1, stride 1x1, SAME padding, activation clamp [0, 6]. + +### `batch_matmul` + +Batched matrix multiplication coverage with broadcasted batch/height dimensions and transpose flags. + +- Generator: `batch_matmul_settings_flt.py` +- Host targets: `test_arm_batch_matmul_f32`, `test_arm_batch_matmul_f16` +- CMSIS project: `test_arm_batch_matmul_flt` +- CMSIS contexts: `test_arm_batch_matmul_flt.F32+Corstone-300-FVP`, `test_arm_batch_matmul_flt.F16+Corstone-300-FVP` +- Covered cases: + - lhs(2,2,8,5) x rhs(2,2,5,7), adj_x=0 adj_y=0. + - lhs(2,2,8,5) x rhs(1,1,7,5), adj_x=0 adj_y=1. + - lhs(1,1,5,8) x rhs(2,2,5,7), adj_x=1 adj_y=0. + - lhs(2,1,5,8) x rhs(1,2,7,5), adj_x=1 adj_y=1. + - lhs(1,2,8,5) x rhs(2,1,7,5), adj_x=0 adj_y=1 with tighter activation clamp [-1, 1]. + +### `batch_norm` + +Float batch-normalization coverage in NHWC layout. + +- Generator: `batch_norm_settings_flt.py` +- Host targets: `test_arm_batch_norm_f32`, `test_arm_batch_norm_f16` +- CMSIS project: `test_arm_batch_norm_flt` +- CMSIS contexts: `test_arm_batch_norm_flt.F32+Corstone-300-FVP`, `test_arm_batch_norm_flt.F16+Corstone-300-FVP` +- Covered cases: + - NHWC case for shape 1x6x8x8. + - NHWC case for shape 1x7x5x6. + +### `concatenation` + +Channel concatenation coverage in NHWC layout. + +- Generator: `concatenation_settings_flt.py` +- Host targets: `test_arm_concatenation_f32`, `test_arm_concatenation_f16` +- CMSIS project: `test_arm_concatenation_flt` +- CMSIS contexts: `test_arm_concatenation_flt.F32+Corstone-300-FVP`, `test_arm_concatenation_flt.F16+Corstone-300-FVP` +- Covered cases: + - lhs 1x5x8x8 with rhs channel count 3 exported as NHWC. + +### `convolve` + +Regular convolution coverage spanning generic 2D shapes and float-specific optimized 1D cases. + +- Generator: `conv_settings_flt.py` +- Host targets: `test_arm_convolve_f32`, `test_arm_convolve_f16` +- CMSIS project: `test_arm_convolve_flt` +- CMSIS contexts: `test_arm_convolve_flt.F32+Corstone-300-FVP`, `test_arm_convolve_flt.F16+Corstone-300-FVP` +- Covered cases: + - Generic 3x3 case: input 1x6x9x11, out_ch=10, stride 1, no padding. + - Generic 3x3 wrapper case with the same logical dimensions. + - 1x1 NHWC stride case: input 1x3x17x11, out_ch=13, stride 1x2, activation clamp [-0.75, 0.75]. + - Optimized 1x3 case: input 1x8x1x21, out_ch=12. + - Optimized 1x5 case: input 1x8x1x21, out_ch=12. + - Tuned NHWC 1x3 case: input 1x1x21x16, out_ch=16. + - Tuned NHWC 1x5 case: input 1x1x21x16, out_ch=16. + - Common 2x2 case: input 1x4x6x7, out_ch=5. + - Common 3x3 pad1 case: input 1x2x3x6, out_ch=4. + +### `depthwise_conv` + +Depthwise coverage for generic kernels plus the specialized 1D and 2x5 float paths. + +- Generator: `depthwise_conv_settings_flt.py` +- Host targets: `test_arm_depthwise_conv_f32`, `test_arm_depthwise_conv_f16` +- CMSIS project: `test_arm_depthwise_conv_flt` +- CMSIS contexts: `test_arm_depthwise_conv_flt.F32+Corstone-300-FVP`, `test_arm_depthwise_conv_flt.F16+Corstone-300-FVP` +- Covered cases: + - Generic 3x3 case: input 1x8x9x11, ch_mult=1. + - Generic NHWC small-channel 3x3 case: input 1x9x11x6, ch_mult=1. + - Optimized 1x3 batch2 case: input 2x8x1x21. + - Optimized NHWC 1x3 case: input 1x1x21x8. + - Optimized 2x5 batch2 case: input 2x8x2x21. + - Optimized NHWC 2x5 case with ch_mult=16: input 1x2x21x1. + - Common 2x2 case: input 1x4x6x5. + - Common 3x3 case: input 1x5x5x4, stride 2x2, pad_h=1 pad_w=0. + - 3x3 null-bias variant with the same shape as the previous case. + +### `ds_cnn_s_body` + +Multi-operator body-network integration test inspired by the ds_cnn_s topology. + +- Generator: `ds_cnn_s_body_settings_flt.py` +- Host targets: `test_arm_ds_cnn_s_body_f32`, `test_arm_ds_cnn_s_body_f16` +- CMSIS project: `test_arm_ds_cnn_s_body_flt` +- CMSIS contexts: `test_arm_ds_cnn_s_body_flt.F32+Corstone-300-FVP`, `test_arm_ds_cnn_s_body_flt.F16+Corstone-300-FVP` +- Covered cases: + - Input NHWC shape 1x25x5x64. + - Four blocks of depthwise 3x3 + ReLU followed by pointwise 1x1 + ReLU. + - Global average pool over 25x5 to 1x1x64. + - Fully connected 64 -> 12 followed by softmax. + +### `elementwise_add` + +Elementwise add coverage with full-vector and tail/spill lengths plus activation clamp. + +- Generator: `add_mul_settings_flt.py` +- Host targets: `test_arm_elementwise_add_f32`, `test_arm_elementwise_add_f16` +- CMSIS project: `test_arm_elementwise_add_flt` +- CMSIS contexts: `test_arm_elementwise_add_flt.F32+Corstone-300-FVP`, `test_arm_elementwise_add_flt.F16+Corstone-300-FVP` +- Covered cases: + - Block size 128 with activation clamp [-10, 10]. + - Spill case block size 105 with activation clamp [-2, 3]. + +### `elementwise_mul` + +Elementwise multiply coverage with full-vector and tail/spill lengths plus activation clamp. + +- Generator: `add_mul_settings_flt.py` +- Host targets: `test_arm_elementwise_mul_f32`, `test_arm_elementwise_mul_f16` +- CMSIS project: `test_arm_elementwise_mul_flt` +- CMSIS contexts: `test_arm_elementwise_mul_flt.F32+Corstone-300-FVP`, `test_arm_elementwise_mul_flt.F16+Corstone-300-FVP` +- Covered cases: + - Block size 160 with activation clamp [-10, 10]. + - Spill case block size 245 with activation clamp [-1.5, 1.0]. + +### `fully_connected` + +Fully connected coverage across small, medium, and larger flattened inputs, plus specialized 2-output cases with tail predication testing. + +- Generator: `fully_connected_settings_flt.py` +- Host targets: `test_arm_fully_connected_f32`, `test_arm_fully_connected_f16` +- CMSIS project: `test_arm_fully_connected_flt` +- CMSIS contexts: `test_arm_fully_connected_flt.F32+Corstone-300-FVP`, `test_arm_fully_connected_flt.F16+Corstone-300-FVP` +- Covered cases: + - Small: batches=1, input=8x8x1, output_c=16. + - Medium: batches=1, input=16x16x1, output_c=32. + - Large: batches=2, input=16x32x1, output_c=64. + - 2-output aligned: batches=2, input=8x8x1, output_c=2 (tests FC 2-output optimization path). + - 2-output tail17: batches=2, input=1x1x17, output_c=2 (tests tail predication with 1 remainder). + - 2-output tail21: batches=2, input=1x1x21, output_c=2 (tests tail predication with 1/5 remainder for f32/f16). + - Null bias: batches=2, input=1x1x33, output_c=5, no bias. + - Activation clamp: batches=1, input=1x1x10, output_c=4, clamp [-0.7, 1.0]. + +### `lstm` + +Unidirectional float LSTM coverage with increasing sequence and hidden sizes. + +- Generator: `lstm_settings_flt.py` +- Host targets: `test_arm_lstm_unidirectional_f32`, `test_arm_lstm_unidirectional_f16` +- CMSIS project: `test_arm_lstm_unidirectional_flt` +- CMSIS contexts: `test_arm_lstm_unidirectional_flt.F32+Corstone-300-FVP`, `test_arm_lstm_unidirectional_flt.F16+Corstone-300-FVP` +- Covered cases: + - Small: time_steps=5, batch=2, input_size=8, hidden_size=8. + - Medium: time_steps=8, batch=3, input_size=12, hidden_size=16. + - Large: time_steps=12, batch=4, input_size=16, hidden_size=24. + +### `max_pool` + +NHWC max-pooling coverage including tail-channel and clamp-heavy cases. + +- Generator: `pooling_settings_flt.py` +- Host targets: `test_arm_max_pool_f32`, `test_arm_max_pool_f16` +- CMSIS project: `test_arm_max_pool_flt` +- CMSIS contexts: `test_arm_max_pool_flt.F32+Corstone-300-FVP`, `test_arm_max_pool_flt.F16+Corstone-300-FVP` +- Covered cases: + - 2x12x22x8, filter 5x6, stride 5x9, SAME padding. + - 1x5x9x3, filter 5x9, stride 2x1, VALID padding. + - 1x5x1x17, filter 4x3, stride 3x1, SAME padding. + - 1x2x4x1, filter 2x2, stride 2x2, VALID padding, activation clamp [0, 6]. + +### `maximum_minimum` + +Broadcasting coverage for float maximum/minimum over common tensor alignment patterns. + +- Generator: `minmax_settings_flt.py` +- Host targets: `test_arm_maximum_minimum_f32`, `test_arm_maximum_minimum_f16` +- CMSIS project: `test_arm_maximum_minimum_flt` +- CMSIS contexts: `test_arm_maximum_minimum_flt.F32+Corstone-300-FVP`, `test_arm_maximum_minimum_flt.F16+Corstone-300-FVP` +- Covered cases: + - Scalar-to-tensor broadcast in both operand orders. + - No-broadcast equal-shape case: 2x2x3x18. + - Broadcast batch case: 2x1x6x21 with 1x1x6x21. + - Broadcast height/width/channel cases including shapes with trailing singleton dimensions. + +### `pad` + +NHWC pad coverage with asymmetric pre/post padding and constant fill values. + +- Generator: `pad_settings_flt.py` +- Host targets: `test_arm_pad_f32`, `test_arm_pad_f16` +- CMSIS project: `test_arm_pad_flt` +- CMSIS contexts: `test_arm_pad_flt.F32+Corstone-300-FVP`, `test_arm_pad_flt.F16+Corstone-300-FVP` +- Covered cases: + - 1x2x2x2 with pre=(0,0,1,1) and post=(0,0,2,2), pad_value=-3.0. + - 1x2x2x2 with pre=(0,2,2,0) and post=(0,1,1,0), pad_value=-2.5. + - 1x7x9x5 with pre=(0,2,1,0) and post=(0,1,2,0), pad_value=0.125. + +### `reshape` + +Simple flat reshape/copy sanity coverage. + +- Generator: `none` +- Host targets: `test_arm_reshape_f32`, `test_arm_reshape_f16` +- CMSIS project: `test_arm_reshape_flt` +- CMSIS contexts: `test_arm_reshape_flt.F32+Corstone-300-FVP`, `test_arm_reshape_flt.F16+Corstone-300-FVP` +- Covered cases: + - Single contiguous buffer of length 6 copied input -> output. + +### `softmax` + +Small fixed-logit softmax sanity coverage. + +- Generator: `softmax_settings_flt.py` +- Host targets: `test_arm_softmax_f32`, `test_arm_softmax_f16` +- CMSIS project: `test_arm_softmax_flt` +- CMSIS contexts: `test_arm_softmax_flt.F32+Corstone-300-FVP`, `test_arm_softmax_flt.F16+Corstone-300-FVP` +- Covered cases: + - Two rows and five columns with hand-picked mixed-sign logits. + +### `svdf` + +Float SVDF coverage for single-batch and batch-2 sequence processing. + +- Generator: `svdf_settings_flt.py` +- Host targets: `test_arm_svdf_f32`, `test_arm_svdf_f16` +- CMSIS project: `test_arm_svdf_flt` +- CMSIS contexts: `test_arm_svdf_flt.F32+Corstone-300-FVP`, `test_arm_svdf_flt.F16+Corstone-300-FVP` +- Covered cases: + - Small: batch=1, input_size=16, unit_count=6, rank=2, memory=4, steps=3. + - Batch2: batch=2, input_size=20, unit_count=8, rank=2, memory=3, steps=4. + +### `transpose` + +Transpose coverage from matrix-like inputs to general float tensor reorders. + +- Generator: `transpose_settings_flt.py` +- Host targets: `test_arm_transpose_f32`, `test_arm_transpose_f16` +- CMSIS project: `test_arm_transpose_flt` +- CMSIS contexts: `test_arm_transpose_flt.F32+Corstone-300-FVP`, `test_arm_transpose_flt.F16+Corstone-300-FVP` +- Covered cases: + - 2D matrix case: input dims (5,20) with perm (1,0). + - 3D case: dims (5,4,20) with perm (0,2,1). + - 4D default reverse case: dims 4x3x3x22 with perm (3,2,1,0). + - 4D swap-last-two case for dims 2x3x5x7. + +### `transpose_conv` + +Transpose-convolution coverage in NHWC direct and wrapper paths. + +- Generator: `transpose_conv_settings_flt.py` +- Host targets: `test_arm_transpose_conv_f32`, `test_arm_transpose_conv_f16` +- CMSIS project: `test_arm_transpose_conv_flt` +- CMSIS contexts: `test_arm_transpose_conv_flt.F32+Corstone-300-FVP`, `test_arm_transpose_conv_flt.F16+Corstone-300-FVP` +- Covered cases: + - Input 1x4x5x6, output_ch=6, kernel 3x3, stride 2x2, padding 1x1, output_padding 1x1. + - Matching wrapper case with the same logical dimensions. + +## Aliases + +| Alias | Canonical Family | +| --- | --- | +| `batchnorm` | `batch_norm` | +| `bmm` | `batch_matmul` | +| `bn` | `batch_norm` | +| `concat` | `concatenation` | +| `conv` | `convolve` | +| `depthwise` | `depthwise_conv` | +| `ds_cnn_s` | `ds_cnn_s_body` | +| `dwconv` | `depthwise_conv` | +| `fc` | `fully_connected` | +| `lstm_unidirectional` | `lstm` | +| `maximum` | `maximum_minimum` | +| `minimum` | `maximum_minimum` | +| `minmax` | `maximum_minimum` | +| `tconv` | `transpose_conv` | +| `transposeconv` | `transpose_conv` | diff --git a/Tests/UnitTest/float_unit_test_coverage.yml b/Tests/UnitTest/float_unit_test_coverage.yml new file mode 100644 index 00000000..7adccf58 --- /dev/null +++ b/Tests/UnitTest/float_unit_test_coverage.yml @@ -0,0 +1,463 @@ +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Generated from Tests/UnitTest/run_float_unit_tests.py and +# Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml +schema_version: 1 +scope: float_unit_tests +source_of_truth: + runner: Tests/UnitTest/run_float_unit_tests.py + cmsis_solution: Tests/UnitTest/cmsis/cmsis_nn_unit_tests_flt.csolution.yml +dtypes: [f32, f16] +target_types: + host: ctest/cmake + cmsis: Corstone-300-FVP +aliases: + batchnorm: batch_norm + bmm: batch_matmul + bn: batch_norm + concat: concatenation + conv: convolve + depthwise: depthwise_conv + ds_cnn_s: ds_cnn_s_body + dwconv: depthwise_conv + fc: fully_connected + lstm_unidirectional: lstm + maximum: maximum_minimum + minimum: maximum_minimum + minmax: maximum_minimum + tconv: transpose_conv + transposeconv: transpose_conv +families: + - name: activation + generator_script: activation_settings_flt.py + cmsis_project: test_arm_activation_flt + coverage_summary: 1D activation sanity coverage for the float public activation API. + coverage_cases: + - Vector length 9 with mixed negative, zero, and positive values. + - Covers sigmoid, tanh, hardswish, and leaky_relu with slope 0.125. + dtypes: + f32: + host_target: test_arm_activation_f32 + cmsis_context: test_arm_activation_flt.F32+Corstone-300-FVP + selectors: + - activation_f32 + notes: generated offline by activation_settings_flt.py + f16: + host_target: test_arm_activation_f16 + cmsis_context: test_arm_activation_flt.F16+Corstone-300-FVP + selectors: + - activation_f16 + notes: generated offline by activation_settings_flt.py + - name: avg_pool + generator_script: pooling_settings_flt.py + cmsis_project: test_arm_avg_pool_flt + coverage_summary: NHWC average-pooling coverage across padding modes and activation clamp ranges. + coverage_cases: + - 1x12x22x20, filter 5x6, stride 5x9, SAME padding. + - 1x5x9x3, filter 5x9, stride 2x1, VALID padding. + - 1x3x3x1, filter 3x1, stride 1x1, SAME padding, activation clamp [0, 6]. + dtypes: + f32: + host_target: test_arm_avg_pool_f32 + cmsis_context: test_arm_avg_pool_flt.F32+Corstone-300-FVP + selectors: + - avgpool_f32 + notes: generated offline by pooling_settings_flt.py + f16: + host_target: test_arm_avg_pool_f16 + cmsis_context: test_arm_avg_pool_flt.F16+Corstone-300-FVP + selectors: + - avgpool_f16 + notes: generated offline by pooling_settings_flt.py + - name: batch_matmul + generator_script: batch_matmul_settings_flt.py + cmsis_project: test_arm_batch_matmul_flt + coverage_summary: Batched matrix multiplication coverage with broadcasted batch/height dimensions and transpose flags. + coverage_cases: + - lhs(2,2,8,5) x rhs(2,2,5,7), adj_x=0 adj_y=0. + - lhs(2,2,8,5) x rhs(1,1,7,5), adj_x=0 adj_y=1. + - lhs(1,1,5,8) x rhs(2,2,5,7), adj_x=1 adj_y=0. + - lhs(2,1,5,8) x rhs(1,2,7,5), adj_x=1 adj_y=1. + - lhs(1,2,8,5) x rhs(2,1,7,5), adj_x=0 adj_y=1 with tighter activation clamp [-1, 1]. + dtypes: + f32: + host_target: test_arm_batch_matmul_f32 + cmsis_context: test_arm_batch_matmul_flt.F32+Corstone-300-FVP + selectors: + - batch_matmul_f32_family + notes: generated offline by batch_matmul_settings_flt.py + f16: + host_target: test_arm_batch_matmul_f16 + cmsis_context: test_arm_batch_matmul_flt.F16+Corstone-300-FVP + selectors: + - batch_matmul_f16_family + notes: generated offline by batch_matmul_settings_flt.py + - name: batch_norm + generator_script: batch_norm_settings_flt.py + cmsis_project: test_arm_batch_norm_flt + coverage_summary: Float batch-normalization coverage in NHWC layout. + coverage_cases: + - NHWC case for shape 1x6x8x8. + - NHWC case for shape 1x7x5x6. + dtypes: + f32: + host_target: test_arm_batch_norm_f32 + cmsis_context: test_arm_batch_norm_flt.F32+Corstone-300-FVP + selectors: + - batch_norm_f32_family + notes: generated offline by batch_norm_settings_flt.py + f16: + host_target: test_arm_batch_norm_f16 + cmsis_context: test_arm_batch_norm_flt.F16+Corstone-300-FVP + selectors: + - batch_norm_f16_family + notes: generated offline by batch_norm_settings_flt.py + - name: concatenation + generator_script: concatenation_settings_flt.py + cmsis_project: test_arm_concatenation_flt + coverage_summary: Channel concatenation coverage in NHWC layout. + coverage_cases: + - lhs 1x5x8x8 with rhs channel count 3 exported as NHWC. + dtypes: + f32: + host_target: test_arm_concatenation_f32 + cmsis_context: test_arm_concatenation_flt.F32+Corstone-300-FVP + selectors: + - concatenation_f32_family + notes: generated offline by concatenation_settings_flt.py + f16: + host_target: test_arm_concatenation_f16 + cmsis_context: test_arm_concatenation_flt.F16+Corstone-300-FVP + selectors: + - concatenation_f16_family + notes: generated offline by concatenation_settings_flt.py + - name: convolve + generator_script: conv_settings_flt.py + cmsis_project: test_arm_convolve_flt + coverage_summary: Regular convolution coverage spanning generic 2D shapes and float-specific optimized 1D cases. + coverage_cases: + - Generic 3x3: input 1x6x9x11, out_ch=10, stride 1, no padding. + - Generic 3x3 wrapper case with the same logical dimensions. + - 1x1 NHWC stride case: input 1x3x17x11, out_ch=13, stride 1x2, activation clamp [-0.75, 0.75]. + - Optimized 1x3 case: input 1x8x1x21, out_ch=12. + - Optimized 1x5 case: input 1x8x1x21, out_ch=12. + - Tuned NHWC 1x3 case: input 1x1x21x16, out_ch=16. + - Tuned NHWC 1x5 case: input 1x1x21x16, out_ch=16. + - Common 2x2 case: input 1x4x6x7, out_ch=5. + - Common 3x3 pad1 case: input 1x2x3x6, out_ch=4. + dtypes: + f32: + host_target: test_arm_convolve_f32 + cmsis_context: test_arm_convolve_flt.F32+Corstone-300-FVP + selectors: + - conv_f32_family + notes: generated offline by conv_settings_flt.py + f16: + host_target: test_arm_convolve_f16 + cmsis_context: test_arm_convolve_flt.F16+Corstone-300-FVP + selectors: + - conv_f16_family + notes: generated offline by conv_settings_flt.py + - name: depthwise_conv + generator_script: depthwise_conv_settings_flt.py + cmsis_project: test_arm_depthwise_conv_flt + coverage_summary: Depthwise coverage for generic kernels plus the specialized 1D and 2x5 float paths. + coverage_cases: + - Generic 3x3 case: input 1x8x9x11, ch_mult=1. + - Generic NHWC small-channel 3x3 case: input 1x9x11x6, ch_mult=1. + - Optimized 1x3 batch2 case: input 2x8x1x21. + - Optimized NHWC 1x3 case: input 1x1x21x8. + - Optimized 2x5 batch2 case: input 2x8x2x21. + - Optimized NHWC 2x5 case with ch_mult=16: input 1x2x21x1. + - Common 2x2 case: input 1x4x6x5. + - Common 3x3 case: input 1x5x5x4, stride 2x2, pad_h=1 pad_w=0. + - 3x3 null-bias variant with the same shape as the previous case. + dtypes: + f32: + host_target: test_arm_depthwise_conv_f32 + cmsis_context: test_arm_depthwise_conv_flt.F32+Corstone-300-FVP + selectors: + - depthwise_conv_f32_family + notes: generated offline by depthwise_conv_settings_flt.py + f16: + host_target: test_arm_depthwise_conv_f16 + cmsis_context: test_arm_depthwise_conv_flt.F16+Corstone-300-FVP + selectors: + - depthwise_conv_f16_family + notes: generated offline by depthwise_conv_settings_flt.py + - name: ds_cnn_s_body + generator_script: ds_cnn_s_body_settings_flt.py + cmsis_project: test_arm_ds_cnn_s_body_flt + coverage_summary: Multi-operator body-network integration test inspired by the ds_cnn_s topology. + coverage_cases: + - Input NHWC shape 1x25x5x64. + - Four blocks of depthwise 3x3 + ReLU followed by pointwise 1x1 + ReLU. + - Global average pool over 25x5 to 1x1x64. + - Fully connected 64 -> 12 followed by softmax. + dtypes: + f32: + host_target: test_arm_ds_cnn_s_body_f32 + cmsis_context: test_arm_ds_cnn_s_body_flt.F32+Corstone-300-FVP + selectors: + - ds_cnn_s_body_f32_family + notes: generated offline by ds_cnn_s_body_settings_flt.py + f16: + host_target: test_arm_ds_cnn_s_body_f16 + cmsis_context: test_arm_ds_cnn_s_body_flt.F16+Corstone-300-FVP + selectors: + - ds_cnn_s_body_f16_family + notes: generated offline by ds_cnn_s_body_settings_flt.py + - name: elementwise_add + generator_script: add_mul_settings_flt.py + cmsis_project: test_arm_elementwise_add_flt + coverage_summary: Elementwise add coverage with full-vector and tail/spill lengths plus activation clamp. + coverage_cases: + - Block size 128 with activation clamp [-10, 10]. + - Spill case block size 105 with activation clamp [-2, 3]. + dtypes: + f32: + host_target: test_arm_elementwise_add_f32 + cmsis_context: test_arm_elementwise_add_flt.F32+Corstone-300-FVP + selectors: + - add_f32_family + notes: generated offline by add_mul_settings_flt.py + f16: + host_target: test_arm_elementwise_add_f16 + cmsis_context: test_arm_elementwise_add_flt.F16+Corstone-300-FVP + selectors: + - add_f16_family + notes: generated offline by add_mul_settings_flt.py + - name: elementwise_mul + generator_script: add_mul_settings_flt.py + cmsis_project: test_arm_elementwise_mul_flt + coverage_summary: Elementwise multiply coverage with full-vector and tail/spill lengths plus activation clamp. + coverage_cases: + - Block size 160 with activation clamp [-10, 10]. + - Spill case block size 245 with activation clamp [-1.5, 1.0]. + dtypes: + f32: + host_target: test_arm_elementwise_mul_f32 + cmsis_context: test_arm_elementwise_mul_flt.F32+Corstone-300-FVP + selectors: + - mul_f32_family + notes: generated offline by add_mul_settings_flt.py + f16: + host_target: test_arm_elementwise_mul_f16 + cmsis_context: test_arm_elementwise_mul_flt.F16+Corstone-300-FVP + selectors: + - mul_f16_family + notes: generated offline by add_mul_settings_flt.py + - name: fully_connected + generator_script: fully_connected_settings_flt.py + cmsis_project: test_arm_fully_connected_flt + coverage_summary: Fully connected coverage across small, medium, and larger flattened inputs, plus specialized 2-output cases with tail predication testing. + coverage_cases: + - Small: batches=1, input=8x8x1, output_c=16. + - Medium: batches=1, input=16x16x1, output_c=32. + - Large: batches=2, input=16x32x1, output_c=64. + - 2-output aligned: batches=2, input=8x8x1, output_c=2 (tests FC 2-output optimization path). + - 2-output tail17: batches=2, input=1x1x17, output_c=2 (tests tail predication with 1 remainder). + - 2-output tail21: batches=2, input=1x1x21, output_c=2 (tests tail predication with 1/5 remainder for f32/f16). + - Null bias: batches=2, input=1x1x33, output_c=5, no bias. + - Activation clamp: batches=1, input=1x1x10, output_c=4, clamp [-0.7, 1.0]. + dtypes: + f32: + host_target: test_arm_fully_connected_f32 + cmsis_context: test_arm_fully_connected_flt.F32+Corstone-300-FVP + selectors: + - fully_connected_f32_family + notes: generated offline by fully_connected_settings_flt.py + f16: + host_target: test_arm_fully_connected_f16 + cmsis_context: test_arm_fully_connected_flt.F16+Corstone-300-FVP + selectors: + - fully_connected_f16_family + notes: generated offline by fully_connected_settings_flt.py + - name: lstm + generator_script: lstm_settings_flt.py + cmsis_project: test_arm_lstm_unidirectional_flt + coverage_summary: Unidirectional float LSTM coverage with increasing sequence and hidden sizes. + coverage_cases: + - Small: time_steps=5, batch=2, input_size=8, hidden_size=8. + - Medium: time_steps=8, batch=3, input_size=12, hidden_size=16. + - Large: time_steps=12, batch=4, input_size=16, hidden_size=24. + dtypes: + f32: + host_target: test_arm_lstm_unidirectional_f32 + cmsis_context: test_arm_lstm_unidirectional_flt.F32+Corstone-300-FVP + selectors: + - lstm_f32_family + notes: generated offline by lstm_settings_flt.py + f16: + host_target: test_arm_lstm_unidirectional_f16 + cmsis_context: test_arm_lstm_unidirectional_flt.F16+Corstone-300-FVP + selectors: + - lstm_f16_family + notes: generated offline by lstm_settings_flt.py + - name: max_pool + generator_script: pooling_settings_flt.py + cmsis_project: test_arm_max_pool_flt + coverage_summary: NHWC max-pooling coverage including tail-channel and clamp-heavy cases. + coverage_cases: + - 2x12x22x8, filter 5x6, stride 5x9, SAME padding. + - 1x5x9x3, filter 5x9, stride 2x1, VALID padding. + - 1x5x1x17, filter 4x3, stride 3x1, SAME padding. + - 1x2x4x1, filter 2x2, stride 2x2, VALID padding, activation clamp [0, 6]. + dtypes: + f32: + host_target: test_arm_max_pool_f32 + cmsis_context: test_arm_max_pool_flt.F32+Corstone-300-FVP + selectors: + - maxpool_f32 + notes: generated offline by pooling_settings_flt.py + f16: + host_target: test_arm_max_pool_f16 + cmsis_context: test_arm_max_pool_flt.F16+Corstone-300-FVP + selectors: + - maxpool_f16 + notes: generated offline by pooling_settings_flt.py + - name: maximum_minimum + generator_script: minmax_settings_flt.py + cmsis_project: test_arm_maximum_minimum_flt + coverage_summary: Broadcasting coverage for float maximum/minimum over common tensor alignment patterns. + coverage_cases: + - Scalar-to-tensor broadcast in both operand orders. + - No-broadcast equal-shape case: 2x2x3x18. + - Broadcast batch case: 2x1x6x21 with 1x1x6x21. + - Broadcast height/width/channel cases including shapes with trailing singleton dimensions. + dtypes: + f32: + host_target: test_arm_maximum_minimum_f32 + cmsis_context: test_arm_maximum_minimum_flt.F32+Corstone-300-FVP + selectors: + - maximum_f32_family + - minimum_f32_family + notes: generated offline by minmax_settings_flt.py + f16: + host_target: test_arm_maximum_minimum_f16 + cmsis_context: test_arm_maximum_minimum_flt.F16+Corstone-300-FVP + selectors: + - maximum_f16_family + - minimum_f16_family + notes: generated offline by minmax_settings_flt.py + - name: pad + generator_script: pad_settings_flt.py + cmsis_project: test_arm_pad_flt + coverage_summary: NHWC pad coverage with asymmetric pre/post padding and constant fill values. + coverage_cases: + - 1x2x2x2 with pre=(0,0,1,1) and post=(0,0,2,2), pad_value=-3.0. + - 1x2x2x2 with pre=(0,2,2,0) and post=(0,1,1,0), pad_value=-2.5. + - 1x7x9x5 with pre=(0,2,1,0) and post=(0,1,2,0), pad_value=0.125. + dtypes: + f32: + host_target: test_arm_pad_f32 + cmsis_context: test_arm_pad_flt.F32+Corstone-300-FVP + selectors: + - pad_f32_family + notes: generated offline by pad_settings_flt.py + f16: + host_target: test_arm_pad_f16 + cmsis_context: test_arm_pad_flt.F16+Corstone-300-FVP + selectors: + - pad_f16_family + notes: generated offline by pad_settings_flt.py + - name: reshape + generator_script: null + cmsis_project: test_arm_reshape_flt + coverage_summary: Simple flat reshape/copy sanity coverage. + coverage_cases: + - Single contiguous buffer of length 6 copied input -> output. + dtypes: + f32: + host_target: test_arm_reshape_f32 + cmsis_context: test_arm_reshape_flt.F32+Corstone-300-FVP + selectors: + notes: no offline generator required + f16: + host_target: test_arm_reshape_f16 + cmsis_context: test_arm_reshape_flt.F16+Corstone-300-FVP + selectors: + notes: no offline generator required + - name: softmax + generator_script: softmax_settings_flt.py + cmsis_project: test_arm_softmax_flt + coverage_summary: Small fixed-logit softmax sanity coverage. + coverage_cases: + - Two rows and five columns with hand-picked mixed-sign logits. + dtypes: + f32: + host_target: test_arm_softmax_f32 + cmsis_context: test_arm_softmax_flt.F32+Corstone-300-FVP + selectors: + - softmax_f32 + notes: generated offline by softmax_settings_flt.py + f16: + host_target: test_arm_softmax_f16 + cmsis_context: test_arm_softmax_flt.F16+Corstone-300-FVP + selectors: + - softmax_f16 + notes: generated offline by softmax_settings_flt.py + - name: svdf + generator_script: svdf_settings_flt.py + cmsis_project: test_arm_svdf_flt + coverage_summary: Float SVDF coverage for single-batch and batch-2 sequence processing. + coverage_cases: + - Small: batch=1, input_size=16, unit_count=6, rank=2, memory=4, steps=3. + - Batch2: batch=2, input_size=20, unit_count=8, rank=2, memory=3, steps=4. + dtypes: + f32: + host_target: test_arm_svdf_f32 + cmsis_context: test_arm_svdf_flt.F32+Corstone-300-FVP + selectors: + - svdf_f32_family + notes: generated offline by svdf_settings_flt.py + f16: + host_target: test_arm_svdf_f16 + cmsis_context: test_arm_svdf_flt.F16+Corstone-300-FVP + selectors: + - svdf_f16_family + notes: generated offline by svdf_settings_flt.py + - name: transpose + generator_script: transpose_settings_flt.py + cmsis_project: test_arm_transpose_flt + coverage_summary: Transpose coverage from matrix-like inputs to general float tensor reorders. + coverage_cases: + - 2D matrix case: input dims (5,20) with perm (1,0). + - 3D case: dims (5,4,20) with perm (0,2,1). + - 4D default reverse case: dims 4x3x3x22 with perm (3,2,1,0). + - 4D swap-last-two case for dims 2x3x5x7. + dtypes: + f32: + host_target: test_arm_transpose_f32 + cmsis_context: test_arm_transpose_flt.F32+Corstone-300-FVP + selectors: + - transpose_f32_family + notes: generated offline by transpose_settings_flt.py + f16: + host_target: test_arm_transpose_f16 + cmsis_context: test_arm_transpose_flt.F16+Corstone-300-FVP + selectors: + - transpose_f16_family + notes: generated offline by transpose_settings_flt.py + - name: transpose_conv + generator_script: transpose_conv_settings_flt.py + cmsis_project: test_arm_transpose_conv_flt + coverage_summary: Transpose-convolution coverage in NHWC direct and wrapper paths. + coverage_cases: + - Input 1x4x5x6, output_ch=6, kernel 3x3, stride 2x2, padding 1x1, output_padding 1x1. + - Matching wrapper case with the same logical dimensions. + dtypes: + f32: + host_target: test_arm_transpose_conv_f32 + cmsis_context: test_arm_transpose_conv_flt.F32+Corstone-300-FVP + selectors: + - transpose_conv_f32_family + notes: generated offline by transpose_conv_settings_flt.py + f16: + host_target: test_arm_transpose_conv_f16 + cmsis_context: test_arm_transpose_conv_flt.F16+Corstone-300-FVP + selectors: + - transpose_conv_f16_family + notes: generated offline by transpose_conv_settings_flt.py diff --git a/Tests/UnitTest/fully_connected_settings_flt.py b/Tests/UnitTest/fully_connected_settings_flt.py new file mode 100644 index 00000000..1ce9d266 --- /dev/null +++ b/Tests/UnitTest/fully_connected_settings_flt.py @@ -0,0 +1,732 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class FullyConnectedCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + batches: int + input_h: int + input_w: int + input_c: int + output_c: int + layout: str + activation_min: float + activation_max: float + seed: int + input_min: float + input_max: float + weight_min: float + weight_max: float + bias_min: float + bias_max: float + use_bias: bool = True + + +class FullyConnectedSettingsFlt: + def __init__(self, case: FullyConnectedCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.weights_table_file = self.pregenerated_data_dir / "weights.txt" + self.biases_table_file = self.pregenerated_data_dir / "biases.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _input_shape(self) -> tuple[int, ...]: + return (self.case.batches, self.case.input_h, self.case.input_w, self.case.input_c) + + def _input_size(self) -> int: + return self.case.input_h * self.case.input_w * self.case.input_c + + def _save_array(self, path: Path, values: np.ndarray) -> None: + save_pregenerated_array(path, values) + + def _load_array(self, path: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(path, shape) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + shape = self._input_shape() + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_array(self.input_table_file, shape) + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=shape).astype(np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_array(self.input_table_file, input_data) + return input_data + + def get_weights_data(self, regenerate_weights: bool) -> np.ndarray: + shape = (self.case.output_c, self._input_size()) + if self.weights_table_file.exists() and not regenerate_weights: + print(f"Loading data from {self.weights_table_file}") + return self._load_array(self.weights_table_file, shape) + + rng = np.random.default_rng(self.case.seed + 101) + weights = rng.uniform(self.case.weight_min, self.case.weight_max, size=shape).astype(np.float32) + print(f"Saving data to {self.weights_table_file}") + self._save_array(self.weights_table_file, weights) + return weights + + def get_bias_data(self, regenerate_biases: bool) -> np.ndarray: + shape = (self.case.output_c,) + if self.biases_table_file.exists() and not regenerate_biases: + print(f"Loading data from {self.biases_table_file}") + return self._load_array(self.biases_table_file, shape) + + if self.case.use_bias: + rng = np.random.default_rng(self.case.seed + 202) + biases = rng.uniform(self.case.bias_min, self.case.bias_max, size=shape).astype(np.float32) + else: + biases = np.zeros(shape, dtype=np.float32) + print(f"Saving data to {self.biases_table_file}") + self._save_array(self.biases_table_file, biases) + return biases + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + layout_macro = "ARM_NN_LAYOUT_NHWC" + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_OUT_CH {self.case.output_c}\n") + fh.write(f"#define {prefix}_IN_CH {self.case.input_c}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.batches * self.case.output_c}\n") + fh.write(f"#define {prefix}_INPUT_SIZE {self._input_size()}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.batches}\n") + fh.write(f"#define {prefix}_LAYOUT {layout_macro}\n") + fh.write(f"#define {prefix}_HAS_BIAS {1 if self.case.use_bias else 0}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name[:-2] if name.endswith('_h') else name}", values, self.case.dtype_name) + + def _write_named_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name[:-2] if name.endswith('_h') else name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False, regenerate_weights: bool = False, regenerate_biases: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + weights = self.get_weights_data(regenerate_weights) + biases = self.get_bias_data(regenerate_biases) + + input_tensor = torch.tensor(input_data, dtype=torch.float32) + weights_tensor = torch.tensor(weights, dtype=torch.float32) + biases_tensor = torch.tensor(biases, dtype=torch.float32) + + flattened = input_tensor.reshape(self.case.batches, -1) + output = torch.matmul(flattened, weights_tensor.transpose(0, 1)) + if self.case.use_bias: + output = output + biases_tensor + output = torch.clamp(output, min=self.case.activation_min, max=self.case.activation_max) + + input_array = input_tensor.to(self.case.torch_dtype).cpu().numpy() + weights_array = weights_tensor.to(self.case.torch_dtype).cpu().numpy() + biases_array = biases_tensor.to(self.case.torch_dtype).cpu().numpy() + output_array = output.to(self.case.torch_dtype).cpu().numpy() + if self.case.dtype_name == "f16": + input_array = input_array.astype(np.float16) + weights_array = weights_array.astype(np.float16) + biases_array = biases_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header() + self._write_named_array_header("input_data", input_array) + self._write_named_array_header("weights_data", weights_array) + self._write_named_array_header("biases_data", biases_array) + self._write_named_array_header("output_ref_data", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[FullyConnectedCaseFlt]: + return [ + FullyConnectedCaseFlt( + dataset="fully_connected_small_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=8, + input_w=8, + input_c=1, + output_c=16, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3001, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_small_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=8, + input_w=8, + input_c=1, + output_c=16, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3002, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_medium_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=16, + input_w=16, + input_c=1, + output_c=32, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3003, + input_min=-1.5, + input_max=1.5, + weight_min=-1.0, + weight_max=1.0, + bias_min=-0.5, + bias_max=0.5, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_medium_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=16, + input_w=16, + input_c=1, + output_c=32, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3004, + input_min=-1.5, + input_max=1.5, + weight_min=-1.0, + weight_max=1.0, + bias_min=-0.5, + bias_max=0.5, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_large_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=2, + input_h=16, + input_w=32, + input_c=1, + output_c=64, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3005, + input_min=-1.25, + input_max=1.25, + weight_min=-0.75, + weight_max=0.75, + bias_min=-0.4, + bias_max=0.4, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_large_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=2, + input_h=16, + input_w=32, + input_c=1, + output_c=64, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3006, + input_min=-1.25, + input_max=1.25, + weight_min=-0.75, + weight_max=0.75, + bias_min=-0.4, + bias_max=0.4, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_batch2_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=2, + input_h=8, + input_w=8, + input_c=1, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3007, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_batch2_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=2, + input_h=8, + input_w=8, + input_c=1, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3008, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_tail17_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=2, + input_h=1, + input_w=1, + input_c=17, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3009, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_tail17_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=2, + input_h=1, + input_w=1, + input_c=17, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3010, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_tail21_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=2, + input_h=1, + input_w=1, + input_c=21, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3011, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_2out_tail21_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=2, + input_h=1, + input_w=1, + input_c=21, + output_c=2, + layout="nhwc", + activation_min=-10.0, + activation_max=10.0, + seed=3012, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_null_bias_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=2, + input_h=1, + input_w=1, + input_c=33, + output_c=5, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3013, + input_min=-2.0, + input_max=2.0, + weight_min=-1.0, + weight_max=1.0, + bias_min=0.0, + bias_max=0.0, + use_bias=False, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_null_bias_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=2, + input_h=1, + input_w=1, + input_c=33, + output_c=5, + layout="nhwc", + activation_min=-100.0, + activation_max=100.0, + seed=3014, + input_min=-2.0, + input_max=2.0, + weight_min=-1.0, + weight_max=1.0, + bias_min=0.0, + bias_max=0.0, + use_bias=False, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_out_activation_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=1, + input_w=1, + input_c=10, + output_c=4, + layout="nhwc", + activation_min=-0.7, + activation_max=1.0, + seed=3015, + input_min=-3.0, + input_max=3.0, + weight_min=-2.0, + weight_max=2.0, + bias_min=-1.0, + bias_max=1.0, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_out_activation_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=1, + input_w=1, + input_c=10, + output_c=4, + layout="nhwc", + activation_min=-0.7, + activation_max=1.0, + seed=3016, + input_min=-3.0, + input_max=3.0, + weight_min=-2.0, + weight_max=2.0, + bias_min=-1.0, + bias_max=1.0, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_basic_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=3, + input_h=1, + input_w=2, + input_c=10, + output_c=6, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3017, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_basic_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=3, + input_h=1, + input_w=2, + input_c=10, + output_c=6, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3018, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_mve_0_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=1, + input_w=1, + input_c=16, + output_c=9, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3019, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_mve_0_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=1, + input_w=1, + input_c=16, + output_c=9, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3020, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_mve_1_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=1, + input_w=1, + input_c=20, + output_c=4, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3021, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_mve_1_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=1, + input_w=1, + input_c=20, + output_c=4, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3022, + input_min=-2.0, + input_max=2.0, + weight_min=-1.5, + weight_max=1.5, + bias_min=-0.75, + bias_max=0.75, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_fc_per_ch_f32", + dtype_name="f32", + torch_dtype=torch.float32, + batches=1, + input_h=1, + input_w=1, + input_c=89, + output_c=22, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3023, + input_min=-2.0, + input_max=2.0, + weight_min=-1.0, + weight_max=1.0, + bias_min=-0.5, + bias_max=0.5, + ), + FullyConnectedCaseFlt( + dataset="fully_connected_match_fc_per_ch_f16", + dtype_name="f16", + torch_dtype=torch.float16, + batches=1, + input_h=1, + input_w=1, + input_c=89, + output_c=22, + layout="nhwc", + activation_min=-128.0, + activation_max=127.0, + seed=3024, + input_min=-2.0, + input_max=2.0, + weight_min=-1.0, + weight_max=1.0, + bias_min=-0.5, + bias_max=0.5, + ), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float fully connected unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, fully_connected_f32_family, " + "fully_connected_f16_family, or a specific dataset name such as fully_connected_2out_batch2_f16." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + parser.add_argument("--regenerate-weights", action="store_true", help="Regenerate pregenerated weights.") + parser.add_argument("--regenerate-biases", action="store_true", help="Regenerate pregenerated biases.") + return parser.parse_args() + + +def _select_cases(all_cases: list[FullyConnectedCaseFlt], selector: str) -> list[FullyConnectedCaseFlt]: + if selector == "all": + return all_cases + if selector == "fully_connected_f32_family": + return [case for case in all_cases if case.dtype_name == "f32"] + if selector == "fully_connected_f16_family": + return [case for case in all_cases if case.dtype_name == "f16"] + return [case for case in all_cases if case.dataset == selector] + + +def main() -> None: + args = parse_args() + cases = _select_cases(_build_cases(), args.dataset) + if not cases: + raise RuntimeError(f"Unknown dataset selector: {args.dataset}") + + for case in cases: + FullyConnectedSettingsFlt(case).generate_data( + regenerate_input=args.regenerate_input, + regenerate_weights=args.regenerate_weights, + regenerate_biases=args.regenerate_biases, + ) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/lstm_settings_flt.py b/Tests/UnitTest/lstm_settings_flt.py new file mode 100644 index 00000000..1fbe9689 --- /dev/null +++ b/Tests/UnitTest/lstm_settings_flt.py @@ -0,0 +1,262 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +GATE_ORDER = { + "input": 0, + "forget": 1, + "cell": 2, + "output": 3, +} + + +@dataclass(frozen=True) +class LstmCaseFlt: + dataset: str + dtype_name: str + time_major: int + time_steps: int + batch_size: int + input_size: int + hidden_size: int + seed: int + input_min: float = -1.0 + input_max: float = 1.0 + weight_min: float = -0.35 + weight_max: float = 0.35 + bias_min: float = -0.25 + bias_max: float = 0.25 + + +class LstmSettingsFlt: + def __init__(self, case: LstmCaseFlt): + self.case = case + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs(case.dataset) + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.weight_ih_table_file = self.pregenerated_data_dir / "weight_ih.txt" + self.weight_hh_table_file = self.pregenerated_data_dir / "weight_hh.txt" + self.bias_ih_table_file = self.pregenerated_data_dir / "bias_ih.txt" + self.bias_hh_table_file = self.pregenerated_data_dir / "bias_hh.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _input_shape(self) -> tuple[int, int, int]: + if self.case.time_major: + return (self.case.time_steps, self.case.batch_size, self.case.input_size) + return (self.case.batch_size, self.case.time_steps, self.case.input_size) + + def _save_array(self, filepath: Path, values: np.ndarray) -> None: + save_pregenerated_array(filepath, values) + + def _load_array(self, filepath: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(filepath, shape) + + def get_raw_tensors(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + input_shape = self._input_shape() + weight_ih_shape = (4 * self.case.hidden_size, self.case.input_size) + weight_hh_shape = (4 * self.case.hidden_size, self.case.hidden_size) + bias_shape = (4 * self.case.hidden_size,) + if ( + self.input_table_file.exists() + and self.weight_ih_table_file.exists() + and self.weight_hh_table_file.exists() + and self.bias_ih_table_file.exists() + and self.bias_hh_table_file.exists() + and not regenerate_input + ): + print(f"Loading data from {self.pregenerated_data_dir}") + return ( + self._load_array(self.input_table_file, input_shape), + self._load_array(self.weight_ih_table_file, weight_ih_shape), + self._load_array(self.weight_hh_table_file, weight_hh_shape), + self._load_array(self.bias_ih_table_file, bias_shape), + self._load_array(self.bias_hh_table_file, bias_shape), + ) + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=input_shape).astype(np.float32) + weight_ih = rng.uniform(self.case.weight_min, self.case.weight_max, size=weight_ih_shape).astype(np.float32) + weight_hh = rng.uniform(self.case.weight_min, self.case.weight_max, size=weight_hh_shape).astype(np.float32) + bias_ih = rng.uniform(self.case.bias_min, self.case.bias_max, size=bias_shape).astype(np.float32) + bias_hh = rng.uniform(self.case.bias_min, self.case.bias_max, size=bias_shape).astype(np.float32) + print(f"Saving data to {self.pregenerated_data_dir}") + self._save_array(self.input_table_file, input_data) + self._save_array(self.weight_ih_table_file, weight_ih) + self._save_array(self.weight_hh_table_file, weight_hh) + self._save_array(self.bias_ih_table_file, bias_ih) + self._save_array(self.bias_hh_table_file, bias_hh) + return input_data, weight_ih, weight_hh, bias_ih, bias_hh + + def _run_reference( + self, + input_data: np.ndarray, + weight_ih: np.ndarray, + weight_hh: np.ndarray, + bias_ih: np.ndarray, + bias_hh: np.ndarray, + ) -> np.ndarray: + batch_first = not bool(self.case.time_major) + lstm = nn.LSTM( + input_size=self.case.input_size, + hidden_size=self.case.hidden_size, + num_layers=1, + bias=True, + batch_first=batch_first, + bidirectional=False, + ).eval() + with torch.no_grad(): + lstm.weight_ih_l0.copy_(torch.tensor(weight_ih)) + lstm.weight_hh_l0.copy_(torch.tensor(weight_hh)) + lstm.bias_ih_l0.copy_(torch.tensor(bias_ih)) + lstm.bias_hh_l0.copy_(torch.tensor(bias_hh)) + output, _ = lstm(torch.tensor(input_data, dtype=torch.float32)) + return output.detach().cpu().numpy().astype(np.float32) + + def _gate_slice(self, values: np.ndarray, gate_name: str) -> np.ndarray: + gate_idx = GATE_ORDER[gate_name] + hidden_size = self.case.hidden_size + return values[gate_idx * hidden_size:(gate_idx + 1) * hidden_size].astype(np.float32, copy=False) + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_TIME_MAJOR {self.case.time_major}\n") + fh.write(f"#define {prefix}_TIME_STEPS {self.case.time_steps}\n") + fh.write(f"#define {prefix}_BATCH_SIZE {self.case.batch_size}\n") + fh.write(f"#define {prefix}_INPUT_SIZE {self.case.input_size}\n") + fh.write(f"#define {prefix}_HIDDEN_SIZE {self.case.hidden_size}\n") + fh.write(f"#define {prefix}_CELL_CLIP 0.0f\n") + fh.write(f"#define {prefix}_CELL_STATE_SIZE {self.case.batch_size * self.case.hidden_size}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.time_steps * self.case.batch_size * self.case.hidden_size}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = f"{suffix}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{suffix}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + write_wrapper_header(self.headers_dir / "test_data.h", self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data, weight_ih, weight_hh, bias_ih, bias_hh = self.get_raw_tensors(regenerate_input) + output_ref = self._run_reference(input_data, weight_ih, weight_hh, bias_ih, bias_hh) + dtype = np.float16 if self.case.dtype_name == "f16" else np.float32 + + self.generated_header_files = [] + self._write_config_header() + self._write_array_header("input", input_data.astype(dtype)) + self._write_array_header("output_ref", output_ref.astype(dtype)) + for gate_name in ("forget", "input", "cell", "output"): + self._write_array_header(f"{gate_name}_input_weights", self._gate_slice(weight_ih, gate_name).astype(dtype)) + self._write_array_header(f"{gate_name}_hidden_weights", self._gate_slice(weight_hh, gate_name).astype(dtype)) + combined_bias = self._gate_slice(bias_ih, gate_name) + self._gate_slice(bias_hh, gate_name) + self._write_array_header(f"{gate_name}_bias", combined_bias.astype(dtype)) + self._write_wrapper_header() + + +def build_cases() -> list[LstmCaseFlt]: + base_cases = [ + ("lstm_small", 1, 5, 2, 8, 8, 8401), + ("lstm_medium", 1, 8, 3, 12, 16, 8402), + ("lstm_large", 1, 12, 4, 16, 24, 8403), + # Match legacy s8 lstm_1 geometry. + ("lstm_match_1", 1, 10, 1, 22, 11, 8404), + # Match legacy s8 lstm_2 geometry. + ("lstm_match_2", 0, 9, 1, 6, 7, 8405), + # Match legacy s8 lstm_one_time_step geometry. + ("lstm_match_one_time_step", 0, 1, 3, 22, 3, 8406), + ] + cases: list[LstmCaseFlt] = [] + for dtype_name, seed_offset in (("f32", 0), ("f16", 100)): + for name, time_major, time_steps, batch_size, input_size, hidden_size, seed in base_cases: + cases.append( + LstmCaseFlt( + dataset=f"{name}_{dtype_name}", + dtype_name=dtype_name, + time_major=time_major, + time_steps=time_steps, + batch_size=batch_size, + input_size=input_size, + hidden_size=hidden_size, + seed=seed + seed_offset, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float LSTM unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, lstm_f32_family, " + "lstm_f16_family, or a specific dataset name." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def matches(dataset_filter: str, case: LstmCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "lstm_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "lstm_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + selected = [case for case in build_cases() if matches(args.dataset, case)] + if not selected: + raise RuntimeError(f"No lstm dataset matched '{args.dataset}'") + for case in selected: + print(f"Generating {case.dataset}...") + LstmSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/minmax_settings_flt.py b/Tests/UnitTest/minmax_settings_flt.py new file mode 100644 index 00000000..22196ee9 --- /dev/null +++ b/Tests/UnitTest/minmax_settings_flt.py @@ -0,0 +1,226 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class MinMaxCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + op_name: str + input_1_shape: tuple[int, int, int, int] + input_2_shape: tuple[int, int, int, int] + seed: int + input_min: float = -6.0 + input_max: float = 6.0 + + +class MinMaxSettingsFlt: + def __init__(self, case: MinMaxCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_1_table_file = self.pregenerated_data_dir / "input_tensor_1.txt" + self.input_2_table_file = self.pregenerated_data_dir / "input_tensor_2.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, path: Path, values: np.ndarray) -> None: + save_pregenerated_array(path, values) + + def _load_array(self, path: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(path, shape) + + def _make_input(self, shape: tuple[int, int, int, int], seed_offset: int) -> np.ndarray: + rng = np.random.default_rng(self.case.seed + seed_offset) + return rng.uniform(self.case.input_min, self.case.input_max, size=shape).astype(np.float32) + + def get_input_data(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray]: + if self.input_1_table_file.exists() and self.input_2_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_1_table_file} and {self.input_2_table_file}") + return ( + self._load_array(self.input_1_table_file, self.case.input_1_shape), + self._load_array(self.input_2_table_file, self.case.input_2_shape), + ) + + input_1 = self._make_input(self.case.input_1_shape, 101) + input_2 = self._make_input(self.case.input_2_shape, 202) + print(f"Saving data to {self.input_1_table_file} and {self.input_2_table_file}") + self._save_array(self.input_1_table_file, input_1) + self._save_array(self.input_2_table_file, input_2) + return input_1, input_2 + + def _write_config_header(self, output_shape: tuple[int, int, int, int]) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_BATCH_1 {self.case.input_1_shape[0]}\n") + fh.write(f"#define {prefix}_HEIGHT_1 {self.case.input_1_shape[1]}\n") + fh.write(f"#define {prefix}_WIDTH_1 {self.case.input_1_shape[2]}\n") + fh.write(f"#define {prefix}_CHANNEL_1 {self.case.input_1_shape[3]}\n") + fh.write(f"#define {prefix}_BATCH_2 {self.case.input_2_shape[0]}\n") + fh.write(f"#define {prefix}_HEIGHT_2 {self.case.input_2_shape[1]}\n") + fh.write(f"#define {prefix}_WIDTH_2 {self.case.input_2_shape[2]}\n") + fh.write(f"#define {prefix}_CHANNEL_2 {self.case.input_2_shape[3]}\n") + fh.write(f"#define {prefix}_OUTPUT_BATCH {output_shape[0]}\n") + fh.write(f"#define {prefix}_OUTPUT_HEIGHT {output_shape[1]}\n") + fh.write(f"#define {prefix}_OUTPUT_WIDTH {output_shape[2]}\n") + fh.write(f"#define {prefix}_OUTPUT_CHANNEL {output_shape[3]}\n") + fh.write(f"#define {prefix}_DST_SIZE {int(np.prod(output_shape))}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_1, input_2 = self.get_input_data(regenerate_input) + input_1_tensor = torch.tensor(input_1, dtype=torch.float32) + input_2_tensor = torch.tensor(input_2, dtype=torch.float32) + + if self.case.op_name == "maximum": + output_tensor = torch.maximum(input_1_tensor, input_2_tensor) + elif self.case.op_name == "minimum": + output_tensor = torch.minimum(input_1_tensor, input_2_tensor) + else: + raise RuntimeError("Unsupported min/max op") + + input_1_array = input_1_tensor.to(self.case.torch_dtype).cpu().numpy() + input_2_array = input_2_tensor.to(self.case.torch_dtype).cpu().numpy() + output_array = output_tensor.to(self.case.torch_dtype).cpu().numpy() + if self.case.dtype_name == "f16": + input_1_array = input_1_array.astype(np.float16) + input_2_array = input_2_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(tuple(output_tensor.shape)) + self._write_array_header("input_tensor_1", input_1_array) + self._write_array_header("input_tensor_2", input_2_array) + self._write_array_header("output_ref", output_array) + self._write_wrapper_header() + + +def _make_case(op_name: str, dtype_name: str, torch_dtype: torch.dtype, name: str, shape_1, shape_2, seed: int) -> MinMaxCaseFlt: + return MinMaxCaseFlt( + dataset=f"{op_name}_{name}_{dtype_name}", + dtype_name=dtype_name, + torch_dtype=torch_dtype, + op_name=op_name, + input_1_shape=shape_1, + input_2_shape=shape_2, + seed=seed, + ) + + +def _build_cases() -> list[MinMaxCaseFlt]: + shape_cases = [ + ("scalar_1", (1, 1, 1, 1), (1, 2, 4, 19), 4001), + ("scalar_2", (1, 2, 4, 19), (1, 1, 1, 1), 4002), + ("no_broadcast", (2, 2, 3, 18), (2, 2, 3, 18), 4003), + ("broadcast_batch", (2, 1, 6, 21), (1, 1, 6, 21), 4004), + ("broadcast_height", (2, 1, 2, 17), (2, 4, 2, 17), 4005), + ("broadcast_width", (2, 1, 4, 19), (2, 1, 1, 19), 4006), + ("broadcast_ch", (2, 2, 4, 1), (2, 2, 4, 24), 4007), + ] + cases: list[MinMaxCaseFlt] = [] + for dtype_name, torch_dtype, seed_base in (("f32", torch.float32, 0), ("f16", torch.float16, 100)): + for op_name in ("maximum", "minimum"): + for name, shape_1, shape_2, seed in shape_cases: + cases.append(_make_case(op_name, dtype_name, torch_dtype, name, shape_1, shape_2, seed + seed_base)) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float minimum/maximum unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, maximum_f32_family, maximum_f16_family, " + "minimum_f32_family, minimum_f16_family, or a specific dataset name such as maximum_scalar_1_f32." + ), + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate pregenerated input samples instead of reusing input files.", + ) + return parser.parse_args() + + +def _matches(dataset_filter: str, case: MinMaxCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "maximum_f32_family": + return case.op_name == "maximum" and case.dtype_name == "f32" + if dataset_filter == "maximum_f16_family": + return case.op_name == "maximum" and case.dtype_name == "f16" + if dataset_filter == "minimum_f32_family": + return case.op_name == "minimum" and case.dtype_name == "f32" + if dataset_filter == "minimum_f16_family": + return case.op_name == "minimum" and case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + matches = [case for case in _build_cases() if _matches(args.dataset, case)] + if not matches: + raise RuntimeError(f"No min/max dataset matched '{args.dataset}'") + + for case in matches: + print(f"Generating {case.dataset}...") + MinMaxSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/pad_settings_flt.py b/Tests/UnitTest/pad_settings_flt.py new file mode 100644 index 00000000..c327126b --- /dev/null +++ b/Tests/UnitTest/pad_settings_flt.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class PadCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + input_shape: tuple[int, int, int, int] + pre_pad: tuple[int, int, int, int] + post_pad: tuple[int, int, int, int] + pad_value: float + seed: int + input_min: float = -4.0 + input_max: float = 4.0 + + +class PadSettingsFlt: + def __init__(self, case: PadCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input_tensor.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_input(self, array: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, array) + + def _load_input(self) -> np.ndarray: + return load_pregenerated_array(self.input_table_file, self.case.input_shape) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=self.case.input_shape).astype(np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_input(input_data) + return input_data + + def _pad_reference(self, input_array: np.ndarray) -> np.ndarray: + out_shape = tuple(self.case.pre_pad[i] + self.case.input_shape[i] + self.case.post_pad[i] for i in range(4)) + output = np.full(out_shape, self.case.pad_value, dtype=np.float32) + output[ + self.case.pre_pad[0]:self.case.pre_pad[0] + self.case.input_shape[0], + self.case.pre_pad[1]:self.case.pre_pad[1] + self.case.input_shape[1], + self.case.pre_pad[2]:self.case.pre_pad[2] + self.case.input_shape[2], + self.case.pre_pad[3]:self.case.pre_pad[3] + self.case.input_shape[3], + ] = input_array + return output + + def _write_config_header(self, output_shape: tuple[int, int, int, int]) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_N {self.case.input_shape[0]}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_shape[1]}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_shape[2]}\n") + fh.write(f"#define {prefix}_INPUT_C {self.case.input_shape[3]}\n") + fh.write(f"#define {prefix}_PRE_PAD_N {self.case.pre_pad[0]}\n") + fh.write(f"#define {prefix}_PRE_PAD_H {self.case.pre_pad[1]}\n") + fh.write(f"#define {prefix}_PRE_PAD_W {self.case.pre_pad[2]}\n") + fh.write(f"#define {prefix}_PRE_PAD_C {self.case.pre_pad[3]}\n") + fh.write(f"#define {prefix}_POST_PAD_N {self.case.post_pad[0]}\n") + fh.write(f"#define {prefix}_POST_PAD_H {self.case.post_pad[1]}\n") + fh.write(f"#define {prefix}_POST_PAD_W {self.case.post_pad[2]}\n") + fh.write(f"#define {prefix}_POST_PAD_C {self.case.post_pad[3]}\n") + fh.write(f"#define {prefix}_OUTPUT_N {output_shape[0]}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_shape[1]}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_shape[2]}\n") + fh.write(f"#define {prefix}_OUTPUT_C {output_shape[3]}\n") + fh.write(f"#define {prefix}_OUTPUT_SIZE {int(np.prod(output_shape))}\n") + fh.write(f"#define {prefix}_PAD_VALUE {formatter(self.case.pad_value)}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + output_data = self._pad_reference(input_data) + + input_tensor = torch.tensor(input_data, dtype=self.case.torch_dtype) + output_tensor = torch.tensor(output_data, dtype=self.case.torch_dtype) + input_array = input_tensor.cpu().numpy() + output_array = output_tensor.cpu().numpy() + if self.case.dtype_name == "f16": + input_array = input_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_data.shape) + self._write_array_header("input_tensor", input_array) + self._write_array_header("output_ref", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[PadCaseFlt]: + return [ + PadCaseFlt("pad_int8_1_f32", "f32", torch.float32, (1, 2, 2, 2), (0, 0, 1, 1), (0, 0, 2, 2), -3.0, 5001), + PadCaseFlt("pad_int8_2_f32", "f32", torch.float32, (1, 2, 2, 2), (0, 2, 2, 0), (0, 1, 1, 0), -2.5, 5002), + PadCaseFlt("pad_basic_f32", "f32", torch.float32, (1, 7, 9, 5), (0, 2, 1, 0), (0, 1, 2, 0), 0.125, 5003), + PadCaseFlt("pad_int8_1_f16", "f16", torch.float16, (1, 2, 2, 2), (0, 0, 1, 1), (0, 0, 2, 2), -3.0, 5101), + PadCaseFlt("pad_int8_2_f16", "f16", torch.float16, (1, 2, 2, 2), (0, 2, 2, 0), (0, 1, 1, 0), -2.5, 5102), + PadCaseFlt("pad_basic_f16", "f16", torch.float16, (1, 7, 9, 5), (0, 2, 1, 0), (0, 1, 2, 0), 0.125, 5103), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float pad unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help="Dataset or family to generate. Supported values: all, pad_f32_family, pad_f16_family, or a specific dataset name.", + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def _matches(dataset_filter: str, case: PadCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "pad_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "pad_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + matches = [case for case in _build_cases() if _matches(args.dataset, case)] + if not matches: + raise RuntimeError(f"No pad dataset matched '{args.dataset}'") + + for case in matches: + print(f"Generating {case.dataset}...") + PadSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/pooling_settings_flt.py b/Tests/UnitTest/pooling_settings_flt.py new file mode 100644 index 00000000..a907a6e4 --- /dev/null +++ b/Tests/UnitTest/pooling_settings_flt.py @@ -0,0 +1,813 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class PoolingCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + op_name: str + batches: int + input_h: int + input_w: int + channels: int + filter_h: int + filter_w: int + stride_h: int + stride_w: int + padding: str + activation_min: float + activation_max: float + seed: int + input_min: float = -6.0 + input_max: float = 6.0 + + +class PoolingSettingsFlt: + def __init__(self, case: PoolingCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_input(self, array: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, array) + + def _load_input(self) -> np.ndarray: + shape = (self.case.batches, self.case.input_h, self.case.input_w, self.case.channels) + return load_pregenerated_array(self.input_table_file, shape) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform( + self.case.input_min, + self.case.input_max, + size=(self.case.batches, self.case.input_h, self.case.input_w, self.case.channels), + ).astype(np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_input(input_data) + return input_data + + def _compute_output_shape_and_padding(self) -> tuple[int, int, int, int]: + if self.case.padding.lower() == "same": + output_h = (self.case.input_h + self.case.stride_h - 1) // self.case.stride_h + output_w = (self.case.input_w + self.case.stride_w - 1) // self.case.stride_w + pad_along_h = max((output_h - 1) * self.case.stride_h + self.case.filter_h - self.case.input_h, 0) + pad_along_w = max((output_w - 1) * self.case.stride_w + self.case.filter_w - self.case.input_w, 0) + pad_h = pad_along_h // 2 + pad_w = pad_along_w // 2 + else: + output_h = max((self.case.input_h - self.case.filter_h + self.case.stride_h) // self.case.stride_h, 0) + output_w = max((self.case.input_w - self.case.filter_w + self.case.stride_w) // self.case.stride_w, 0) + pad_h = 0 + pad_w = 0 + return output_h, output_w, pad_h, pad_w + + def _pool_reference( + self, + input_tensor: torch.Tensor, + output_h: int, + output_w: int, + pad_h: int, + pad_w: int, + ) -> torch.Tensor: + output = torch.empty( + (self.case.batches, output_h, output_w, self.case.channels), + dtype=torch.float32, + ) + + for batch in range(self.case.batches): + for out_y in range(output_h): + in_y_origin = out_y * self.case.stride_h - pad_h + y_start = max(in_y_origin, 0) + y_end = min(in_y_origin + self.case.filter_h, self.case.input_h) + + for out_x in range(output_w): + in_x_origin = out_x * self.case.stride_w - pad_w + x_start = max(in_x_origin, 0) + x_end = min(in_x_origin + self.case.filter_w, self.case.input_w) + + window = input_tensor[batch, y_start:y_end, x_start:x_end, :] + if self.case.op_name == "avgpool": + pooled = window.mean(dim=(0, 1)) + elif self.case.op_name == "maxpool": + pooled = window.amax(dim=(0, 1)) + else: + raise RuntimeError("Unsupported pooling op") + output[batch, out_y, out_x, :] = pooled + + return torch.clamp(output, min=self.case.activation_min, max=self.case.activation_max) + + def _write_config_header(self, output_h: int, output_w: int, pad_h: int, pad_w: int) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + dst_size = self.case.batches * output_h * output_w * self.case.channels + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_BATCH_SIZE {self.case.batches}\n") + fh.write(f"#define {prefix}_INPUT_N {self.case.batches}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_INPUT_C {self.case.channels}\n") + fh.write(f"#define {prefix}_FILTER_W {self.case.filter_w}\n") + fh.write(f"#define {prefix}_FILTER_H {self.case.filter_h}\n") + fh.write(f"#define {prefix}_STRIDE_W {self.case.stride_w}\n") + fh.write(f"#define {prefix}_STRIDE_H {self.case.stride_h}\n") + fh.write(f"#define {prefix}_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + fh.write(f"#define {prefix}_OUTPUT_C {self.case.channels}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_w}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_h}\n") + fh.write(f"#define {prefix}_PADDING_H {pad_h}\n") + fh.write(f"#define {prefix}_PADDING_W {pad_w}\n") + fh.write(f"#define {prefix}_DST_SIZE {dst_size}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + input_tensor = torch.tensor(input_data, dtype=torch.float32) + output_h, output_w, pad_h, pad_w = self._compute_output_shape_and_padding() + output_tensor = self._pool_reference(input_tensor, output_h, output_w, pad_h, pad_w).to(self.case.torch_dtype) + + input_array = input_tensor.to(self.case.torch_dtype).cpu().numpy() + output_array = output_tensor.cpu().numpy() + if self.case.dtype_name == "f16": + input_array = input_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_h, output_w, pad_h, pad_w) + self._write_array_header("input_tensor", input_array) + self._write_array_header("output_ref", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[PoolingCaseFlt]: + return [ + PoolingCaseFlt( + dataset="avgpooling_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=12, + input_w=22, + channels=20, + filter_h=5, + filter_w=6, + stride_h=5, + stride_w=9, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1001, + ), + PoolingCaseFlt( + dataset="avgpooling_f32_1", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=5, + input_w=9, + channels=3, + filter_h=5, + filter_w=9, + stride_h=2, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1002, + ), + PoolingCaseFlt( + dataset="avgpooling_f32_2", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=3, + input_w=3, + channels=1, + filter_h=3, + filter_w=1, + stride_h=1, + stride_w=1, + padding="same", + activation_min=0.0, + activation_max=6.0, + seed=1003, + input_min=-3.0, + input_max=9.0, + ), + PoolingCaseFlt( + dataset="avgpooling_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=12, + input_w=22, + channels=20, + filter_h=5, + filter_w=6, + stride_h=5, + stride_w=9, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1101, + ), + PoolingCaseFlt( + dataset="avgpooling_f16_1", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=5, + input_w=9, + channels=3, + filter_h=5, + filter_w=9, + stride_h=2, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1102, + ), + PoolingCaseFlt( + dataset="avgpooling_f16_2", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=3, + input_w=3, + channels=1, + filter_h=3, + filter_w=1, + stride_h=1, + stride_w=1, + padding="same", + activation_min=0.0, + activation_max=6.0, + seed=1103, + input_min=-3.0, + input_max=9.0, + ), + # Match legacy s8 avgpooling_2 geometry. + PoolingCaseFlt( + dataset="avgpooling_match_2_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=1, + input_w=12, + channels=5, + filter_h=1, + filter_w=3, + stride_h=2, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1005, + ), + PoolingCaseFlt( + dataset="avgpooling_match_2_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=1, + input_w=12, + channels=5, + filter_h=1, + filter_w=3, + stride_h=2, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1105, + ), + # Match legacy s8 avgpooling_3 geometry. + PoolingCaseFlt( + dataset="avgpooling_match_3_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=1, + input_w=9, + channels=2, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=2, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1006, + ), + PoolingCaseFlt( + dataset="avgpooling_match_3_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=1, + input_w=9, + channels=2, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=2, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1106, + ), + # Match legacy s8 avgpooling_4 geometry. + PoolingCaseFlt( + dataset="avgpooling_match_4_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=3, + input_h=20, + input_w=1, + channels=2, + filter_h=3, + filter_w=1, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1007, + ), + PoolingCaseFlt( + dataset="avgpooling_match_4_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=3, + input_h=20, + input_w=1, + channels=2, + filter_h=3, + filter_w=1, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1107, + ), + PoolingCaseFlt( + dataset="avgpooling_f32_global", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="avgpool", + batches=1, + input_h=25, + input_w=5, + channels=64, + filter_h=25, + filter_w=5, + stride_h=1, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1004, + ), + PoolingCaseFlt( + dataset="avgpooling_f16_global", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="avgpool", + batches=1, + input_h=25, + input_w=5, + channels=64, + filter_h=25, + filter_w=5, + stride_h=1, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1104, + ), + PoolingCaseFlt( + dataset="maxpooling_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=2, + input_h=12, + input_w=22, + channels=8, + filter_h=5, + filter_w=6, + stride_h=5, + stride_w=9, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1201, + ), + PoolingCaseFlt( + dataset="maxpooling_f32_1", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=5, + input_w=9, + channels=3, + filter_h=5, + filter_w=9, + stride_h=2, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1202, + ), + PoolingCaseFlt( + dataset="maxpooling_f32_2", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=5, + input_w=1, + channels=17, + filter_h=4, + filter_w=3, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1203, + ), + PoolingCaseFlt( + dataset="maxpooling_f32_3", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=2, + input_w=4, + channels=1, + filter_h=2, + filter_w=2, + stride_h=2, + stride_w=2, + padding="valid", + activation_min=0.0, + activation_max=6.0, + seed=1204, + input_min=-4.0, + input_max=10.0, + ), + PoolingCaseFlt( + dataset="maxpooling_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=2, + input_h=12, + input_w=22, + channels=8, + filter_h=5, + filter_w=6, + stride_h=5, + stride_w=9, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1301, + ), + PoolingCaseFlt( + dataset="maxpooling_f16_1", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=5, + input_w=9, + channels=3, + filter_h=5, + filter_w=9, + stride_h=2, + stride_w=1, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1302, + ), + PoolingCaseFlt( + dataset="maxpooling_f16_2", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=5, + input_w=1, + channels=17, + filter_h=4, + filter_w=3, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1303, + ), + PoolingCaseFlt( + dataset="maxpooling_f16_3", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=2, + input_w=4, + channels=1, + filter_h=2, + filter_w=2, + stride_h=2, + stride_w=2, + padding="valid", + activation_min=0.0, + activation_max=6.0, + seed=1304, + input_min=-4.0, + input_max=10.0, + ), + # Match legacy s8 maxpooling_2 geometry. + PoolingCaseFlt( + dataset="maxpooling_match_2_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=1, + input_w=12, + channels=5, + filter_h=1, + filter_w=3, + stride_h=2, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1205, + ), + PoolingCaseFlt( + dataset="maxpooling_match_2_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=1, + input_w=12, + channels=5, + filter_h=1, + filter_w=3, + stride_h=2, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1305, + ), + # Match legacy s8 maxpooling_3 geometry. + PoolingCaseFlt( + dataset="maxpooling_match_3_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=1, + input_w=9, + channels=2, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=2, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1206, + ), + PoolingCaseFlt( + dataset="maxpooling_match_3_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=1, + input_w=9, + channels=2, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=2, + padding="valid", + activation_min=-100.0, + activation_max=100.0, + seed=1306, + ), + # Match legacy s8 maxpooling_4 geometry. + PoolingCaseFlt( + dataset="maxpooling_match_4_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=20, + input_w=1, + channels=2, + filter_h=3, + filter_w=1, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1207, + ), + PoolingCaseFlt( + dataset="maxpooling_match_4_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=20, + input_w=1, + channels=2, + filter_h=3, + filter_w=1, + stride_h=3, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1307, + ), + # Match legacy s8 maxpooling_5 geometry. + PoolingCaseFlt( + dataset="maxpooling_match_5_f32", + dtype_name="f32", + torch_dtype=torch.float32, + op_name="maxpool", + batches=1, + input_h=3, + input_w=3, + channels=20, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1208, + ), + PoolingCaseFlt( + dataset="maxpooling_match_5_f16", + dtype_name="f16", + torch_dtype=torch.float16, + op_name="maxpool", + batches=1, + input_h=3, + input_w=3, + channels=20, + filter_h=1, + filter_w=1, + stride_h=1, + stride_w=1, + padding="same", + activation_min=-100.0, + activation_max=100.0, + seed=1308, + ), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float pooling unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, avgpool_f32, avgpool_f16, " + "maxpool_f32, maxpool_f16, or a specific dataset name such as avgpooling_f32_1." + ), + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate pregenerated input samples instead of reusing input.txt.", + ) + return parser.parse_args() + + +def _matches_selector(case: PoolingCaseFlt, selector: str) -> bool: + if selector == "all": + return True + if selector == case.dataset: + return True + if selector == "avgpool_f32": + return case.op_name == "avgpool" and case.dtype_name == "f32" + if selector == "avgpool_f16": + return case.op_name == "avgpool" and case.dtype_name == "f16" + if selector == "maxpool_f32": + return case.op_name == "maxpool" and case.dtype_name == "f32" + if selector == "maxpool_f16": + return case.op_name == "maxpool" and case.dtype_name == "f16" + return False + + +def main() -> None: + args = parse_args() + cases = [case for case in _build_cases() if _matches_selector(case, args.dataset)] + if not cases: + raise SystemExit(f"Unknown dataset selector: {args.dataset}") + + for case in cases: + print(f"Generating {case.dataset}...") + PoolingSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/run_float_unit_tests.py b/Tests/UnitTest/run_float_unit_tests.py new file mode 100644 index 00000000..642f7cc9 --- /dev/null +++ b/Tests/UnitTest/run_float_unit_tests.py @@ -0,0 +1,734 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +import importlib +import os +import re +import shutil +import subprocess +import sys +from dataclasses import dataclass +from pathlib import Path + + +@dataclass(frozen=True) +class FloatTestFamily: + name: str + generator_script: str | None + selector_by_dtype: dict[str, list[str]] + cmsis_project: str + + @property + def host_target_prefix(self) -> str: + return self.cmsis_project.removesuffix("_flt") + + def host_target(self, dtype_name: str) -> str: + return f"{self.host_target_prefix}_{dtype_name}" + + def cmsis_context(self, dtype_name: str) -> str: + return f"{self.cmsis_project}.{dtype_name.upper()}+Corstone-300-FVP" + + +@dataclass(frozen=True) +class StepResult: + stage: str + family: str + dtype: str + toolchain: str + status: str + detail: str = "" + + +REPO_ROOT = Path(__file__).resolve().parents[2] +UNIT_TEST_ROOT = Path(__file__).resolve().parent +CMSIS_UNIT_TEST_ROOT = UNIT_TEST_ROOT / "cmsis" +CMSIS_SOLUTION = CMSIS_UNIT_TEST_ROOT / "cmsis_nn_unit_tests_flt.csolution.yml" +CMSIS_OUTPUT_ROOT = os.environ.get("CMSIS_NN_CBUILD_OUTPUT_ROOT", "").strip() + +FAMILY_CONFIGS: dict[str, FloatTestFamily] = { + "activation": FloatTestFamily( + name="activation", + generator_script="activation_settings_flt.py", + selector_by_dtype={"f32": ["activation_f32"], "f16": ["activation_f16"]}, + cmsis_project="test_arm_activation_flt", + ), + "avg_pool": FloatTestFamily( + name="avg_pool", + generator_script="pooling_settings_flt.py", + selector_by_dtype={"f32": ["avgpool_f32"], "f16": ["avgpool_f16"]}, + cmsis_project="test_arm_avg_pool_flt", + ), + "batch_matmul": FloatTestFamily( + name="batch_matmul", + generator_script="batch_matmul_settings_flt.py", + selector_by_dtype={"f32": ["batch_matmul_f32_family"], "f16": ["batch_matmul_f16_family"]}, + cmsis_project="test_arm_batch_matmul_flt", + ), + "batch_norm": FloatTestFamily( + name="batch_norm", + generator_script="batch_norm_settings_flt.py", + selector_by_dtype={"f32": ["batch_norm_f32_family"], "f16": ["batch_norm_f16_family"]}, + cmsis_project="test_arm_batch_norm_flt", + ), + "concatenation": FloatTestFamily( + name="concatenation", + generator_script="concatenation_settings_flt.py", + selector_by_dtype={"f32": ["concatenation_f32_family"], "f16": ["concatenation_f16_family"]}, + cmsis_project="test_arm_concatenation_flt", + ), + "convolve": FloatTestFamily( + name="convolve", + generator_script="conv_settings_flt.py", + selector_by_dtype={"f32": ["conv_f32_family"], "f16": ["conv_f16_family"]}, + cmsis_project="test_arm_convolve_flt", + ), + "depthwise_conv": FloatTestFamily( + name="depthwise_conv", + generator_script="depthwise_conv_settings_flt.py", + selector_by_dtype={"f32": ["depthwise_conv_f32_family"], "f16": ["depthwise_conv_f16_family"]}, + cmsis_project="test_arm_depthwise_conv_flt", + ), + "ds_cnn_s_body": FloatTestFamily( + name="ds_cnn_s_body", + generator_script="ds_cnn_s_body_settings_flt.py", + selector_by_dtype={"f32": ["ds_cnn_s_body_f32_family"], "f16": ["ds_cnn_s_body_f16_family"]}, + cmsis_project="test_arm_ds_cnn_s_body_flt", + ), + "elementwise_add": FloatTestFamily( + name="elementwise_add", + generator_script="add_mul_settings_flt.py", + selector_by_dtype={"f32": ["add_f32_family"], "f16": ["add_f16_family"]}, + cmsis_project="test_arm_elementwise_add_flt", + ), + "elementwise_mul": FloatTestFamily( + name="elementwise_mul", + generator_script="add_mul_settings_flt.py", + selector_by_dtype={"f32": ["mul_f32_family"], "f16": ["mul_f16_family"]}, + cmsis_project="test_arm_elementwise_mul_flt", + ), + "fully_connected": FloatTestFamily( + name="fully_connected", + generator_script="fully_connected_settings_flt.py", + selector_by_dtype={"f32": ["fully_connected_f32_family"], "f16": ["fully_connected_f16_family"]}, + cmsis_project="test_arm_fully_connected_flt", + ), + "lstm": FloatTestFamily( + name="lstm", + generator_script="lstm_settings_flt.py", + selector_by_dtype={"f32": ["lstm_f32_family"], "f16": ["lstm_f16_family"]}, + cmsis_project="test_arm_lstm_unidirectional_flt", + ), + "max_pool": FloatTestFamily( + name="max_pool", + generator_script="pooling_settings_flt.py", + selector_by_dtype={"f32": ["maxpool_f32"], "f16": ["maxpool_f16"]}, + cmsis_project="test_arm_max_pool_flt", + ), + "maximum_minimum": FloatTestFamily( + name="maximum_minimum", + generator_script="minmax_settings_flt.py", + selector_by_dtype={ + "f32": ["maximum_f32_family", "minimum_f32_family"], + "f16": ["maximum_f16_family", "minimum_f16_family"], + }, + cmsis_project="test_arm_maximum_minimum_flt", + ), + "pad": FloatTestFamily( + name="pad", + generator_script="pad_settings_flt.py", + selector_by_dtype={"f32": ["pad_f32_family"], "f16": ["pad_f16_family"]}, + cmsis_project="test_arm_pad_flt", + ), + "reshape": FloatTestFamily( + name="reshape", + generator_script=None, + selector_by_dtype={"f32": [], "f16": []}, + cmsis_project="test_arm_reshape_flt", + ), + "softmax": FloatTestFamily( + name="softmax", + generator_script="softmax_settings_flt.py", + selector_by_dtype={"f32": ["softmax_f32"], "f16": ["softmax_f16"]}, + cmsis_project="test_arm_softmax_flt", + ), + "svdf": FloatTestFamily( + name="svdf", + generator_script="svdf_settings_flt.py", + selector_by_dtype={"f32": ["svdf_f32_family"], "f16": ["svdf_f16_family"]}, + cmsis_project="test_arm_svdf_flt", + ), + "transpose": FloatTestFamily( + name="transpose", + generator_script="transpose_settings_flt.py", + selector_by_dtype={"f32": ["transpose_f32_family"], "f16": ["transpose_f16_family"]}, + cmsis_project="test_arm_transpose_flt", + ), + "transpose_conv": FloatTestFamily( + name="transpose_conv", + generator_script="transpose_conv_settings_flt.py", + selector_by_dtype={"f32": ["transpose_conv_f32_family"], "f16": ["transpose_conv_f16_family"]}, + cmsis_project="test_arm_transpose_conv_flt", + ), +} + +FAMILY_ALIASES = { + "bn": "batch_norm", + "batchnorm": "batch_norm", + "concat": "concatenation", + "conv": "convolve", + "depthwise": "depthwise_conv", + "ds_cnn_s": "ds_cnn_s_body", + "dwconv": "depthwise_conv", + "fc": "fully_connected", + "bmm": "batch_matmul", + "lstm_unidirectional": "lstm", + "minmax": "maximum_minimum", + "maximum": "maximum_minimum", + "minimum": "maximum_minimum", + "transposeconv": "transpose_conv", + "tconv": "transpose_conv", +} + + +def parse_csv(value: str) -> list[str]: + return [item.strip() for item in value.split(",") if item.strip()] + + +def parse_args() -> argparse.Namespace: + default_cmsis_path = os.environ.get("CMSIS_PATH", "") + parser = argparse.ArgumentParser(description="Run CMSIS-NN float unit-test generation/build flows.") + parser.add_argument( + "--tests", + default="all", + help=( + "Comma-separated float test families to run. Supported values include " + "softmax, activation, reshape, avg_pool, batch_norm, batch_matmul, " + "concatenation, convolve, depthwise_conv, ds_cnn_s_body, elementwise_add, " + "elementwise_mul, fully_connected, lstm, max_pool, maximum_minimum, " + "pad, svdf, transpose, transpose_conv, or all." + ), + ) + parser.add_argument( + "--dtypes", + default="f32,f16", + help="Comma-separated dtype list. Supported values: f32, f16.", + ) + parser.add_argument("--list", action="store_true", help="List supported float test families and exit.") + parser.add_argument("--generate", action="store_true", help="Generate float reference data.") + parser.add_argument("--build-host", action="store_true", help="Configure and build host unit tests.") + parser.add_argument("--run-host", action="store_true", help="Run host unit-test executables.") + parser.add_argument("--build-cmsis", action="store_true", help="Build CMSIS-Toolbox Corstone-300 test contexts.") + parser.add_argument("--run-fvp", action="store_true", help="Run CMSIS-Toolbox test images on FVP.") + parser.add_argument( + "--cbuild-packs", + action="store_true", + help="Pass --packs to cbuild so missing packs are installed automatically.", + ) + parser.add_argument( + "--toolchains", + default="GCC@15.2.1", + help="Comma-separated CMSIS-Toolbox toolchains, for example GCC@15.2.1 or AC6@6.24.0.", + ) + parser.add_argument( + "--jobs", + type=int, + default=4, + help="Parallel jobs for cmake/cbuild. FVP test images are still run sequentially.", + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated float samples.") + parser.add_argument( + "--cmsis-path", + default=default_cmsis_path, + help="CMSIS path passed to the host CMake build. Defaults to the CMSIS_PATH environment variable.", + ) + parser.add_argument( + "--host-build-dir", + default="/tmp/cmsis-nn-float-unit-runner", + help="Host unit-test build directory.", + ) + parser.add_argument("--clean-host", action="store_true", help="Delete the host build directory before configuring.") + parser.add_argument( + "--fvp-bin", + help="Path to the Corstone-300 FVP binary. Required when --run-fvp is used.", + ) + parser.add_argument( + "--fvp-image-arg", + default="", + help="Optional FVP flag used before the image path, for example -a. Defaults to positional image argument.", + ) + parser.add_argument( + "--fvp-timeout", + type=int, + default=30, + help="Timeout in seconds for each FVP run before the runner is terminated.", + ) + return parser.parse_args() + + +def resolve_families(selector: str) -> list[FloatTestFamily]: + if selector == "all": + return [FAMILY_CONFIGS[name] for name in sorted(FAMILY_CONFIGS)] + + resolved: list[FloatTestFamily] = [] + seen: set[str] = set() + for item in parse_csv(selector): + normalized = FAMILY_ALIASES.get(item, item) + if normalized not in FAMILY_CONFIGS: + raise SystemExit(f"Unknown float test family: {item}") + if normalized not in seen: + seen.add(normalized) + resolved.append(FAMILY_CONFIGS[normalized]) + return resolved + + +def resolve_dtypes(selector: str) -> list[str]: + dtypes = parse_csv(selector) + if not dtypes: + raise SystemExit("At least one dtype must be selected.") + invalid = [dtype_name for dtype_name in dtypes if dtype_name not in {"f32", "f16"}] + if invalid: + raise SystemExit(f"Unsupported dtype selector(s): {', '.join(invalid)}") + return dtypes + + +def run_command(args: list[str], cwd: Path) -> None: + print(f"$ (cd {cwd} && {' '.join(args)})", flush=True) + subprocess.run(args, cwd=str(cwd), check=True) + + +def try_command(args: list[str], cwd: Path) -> tuple[bool, str]: + print(f"$ (cd {cwd} && {' '.join(args)})", flush=True) + completed = subprocess.run(args, cwd=str(cwd), check=False) + if completed.returncode == 0: + return True, "" + return False, f"exit {completed.returncode}" + + +def _decode_timeout_output(stream: bytes | str | None) -> str: + if stream is None: + return "" + if isinstance(stream, bytes): + return stream.decode(errors="replace") + return stream + + +def summarize_unity_ticks(output: str) -> str: + tick_matches = re.findall(r":PASS\s+\((\d+)\s+ticks\)", output) + if not tick_matches: + return "" + + tick_values = [int(value) for value in tick_matches] + total_ticks = sum(tick_values) + test_count = len(tick_values) + ticks_label = "tick" if total_ticks == 1 else "ticks" + tests_label = "test" if test_count == 1 else "tests" + return f"sum {total_ticks} {ticks_label} ({test_count} {tests_label})" + + +def try_fvp_command(args: list[str], cwd: Path, timeout_seconds: int) -> tuple[bool, str]: + print(f"$ (cd {cwd} && {' '.join(args)})", flush=True) + try: + completed = subprocess.run( + args, + cwd=str(cwd), + check=False, + capture_output=True, + text=True, + timeout=timeout_seconds, + ) + except subprocess.TimeoutExpired as exc: + stdout_text = _decode_timeout_output(exc.stdout) + stderr_text = _decode_timeout_output(exc.stderr) + if stdout_text: + print(stdout_text, end="", flush=True) + if stderr_text: + print(stderr_text, end="", file=sys.stderr, flush=True) + + timed_out_after_success = ( + "0 Failures" in stdout_text + and "\nOK" in stdout_text + and "FAIL" not in stdout_text + ) + if timed_out_after_success: + tick_summary = summarize_unity_ticks(stdout_text) + detail = f"timeout {timeout_seconds}s after PASS output" + if tick_summary: + detail = f"{tick_summary}; {detail}" + return True, detail + return False, f"timeout {timeout_seconds}s" + + if completed.stdout: + print(completed.stdout, end="", flush=True) + if completed.stderr: + print(completed.stderr, end="", file=sys.stderr, flush=True) + + output = completed.stdout or "" + passed = completed.returncode == 0 and "0 Failures" in output and "\nOK" in output and ":FAIL:" not in output + if passed: + return True, summarize_unity_ticks(output) + + fail_line = next((line.strip() for line in output.splitlines() if ":FAIL:" in line), "") + if fail_line: + return False, fail_line + + if completed.returncode == 0: + return False, "fvp run failed" + return False, f"exit {completed.returncode}" + + +def append_result( + results: list[StepResult], + stage: str, + family: str, + dtype_name: str = "-", + toolchain: str = "-", + status: str = "PASS", + detail: str = "", +) -> None: + results.append( + StepResult( + stage=stage, + family=family, + dtype=dtype_name, + toolchain=toolchain, + status=status, + detail=detail, + ) + ) + + +def print_summary(results: list[StepResult]) -> None: + if not results: + return + + headers = ("Stage", "Family", "DType", "Toolchain", "Status", "Detail") + rows = [(r.stage, r.family, r.dtype, r.toolchain, r.status, r.detail) for r in results] + widths = [len(header) for header in headers] + for row in rows: + for idx, value in enumerate(row): + widths[idx] = max(widths[idx], len(value)) + + def format_row(row: tuple[str, str, str, str, str, str]) -> str: + return " | ".join(value.ljust(widths[idx]) for idx, value in enumerate(row)) + + separator = "-+-".join("-" * width for width in widths) + passed = sum(1 for result in results if result.status == "PASS") + failed = sum(1 for result in results if result.status == "FAIL") + skipped = sum(1 for result in results if result.status == "SKIP") + + print("\nFloat Unit-Test Summary") + print(format_row(headers)) + print(separator) + for row in rows: + print(format_row(row)) + print(separator) + print(f"PASS={passed} FAIL={failed} SKIP={skipped}") + + +def ensure_python_modules(requirements: list[tuple[str, str]], reason: str) -> None: + missing: list[str] = [] + for module_name, package_name in requirements: + try: + importlib.import_module(module_name) + except ImportError: + missing.append(package_name) + + if missing: + raise SystemExit( + f"Missing Python module(s) required to {reason}: " + + ", ".join(sorted(missing)) + + ". Install them in the active environment and rerun." + ) + + +def ensure_host_python_modules() -> None: + ensure_python_modules( + [("serial", "pyserial"), ("termcolor", "termcolor")], + "run the existing host unit-test harness", + ) + + +def generate_float_test_data( + families: list[FloatTestFamily], dtypes: list[str], regenerate_input: bool, results: list[StepResult] +) -> bool: + all_ok = True + for family in families: + if family.generator_script is None: + for dtype_name in dtypes: + append_result(results, "generate", family.name, dtype_name, status="SKIP", detail="no generator") + continue + script_path = UNIT_TEST_ROOT / family.generator_script + for dtype_name in dtypes: + family_ok = True + for selector in family.selector_by_dtype[dtype_name]: + cmd = [sys.executable, str(script_path), "--dataset", selector] + if regenerate_input: + cmd.append("--regenerate-input") + if family.name == "fully_connected" and regenerate_input: + cmd.extend(["--regenerate-weights", "--regenerate-biases"]) + ok, detail = try_command(cmd, UNIT_TEST_ROOT) + if not ok: + family_ok = False + all_ok = False + append_result(results, "generate", family.name, dtype_name, status="FAIL", detail=f"{selector} {detail}") + break + if family_ok: + append_result(results, "generate", family.name, dtype_name) + return all_ok + + +def configure_and_build_host( + families: list[FloatTestFamily], + dtypes: list[str], + jobs: int, + cmsis_path: Path, + build_dir: Path, + clean_host: bool, + results: list[StepResult], +) -> bool: + ensure_host_python_modules() + if clean_host and build_dir.exists(): + shutil.rmtree(build_dir) + if not str(cmsis_path): + raise SystemExit("--cmsis-path or the CMSIS_PATH environment variable is required for host builds.") + + cmake_args = [ + "cmake", + "-S", + str(UNIT_TEST_ROOT), + "-B", + str(build_dir), + "-DBUILD_CMSIS_NN_UNIT=ON", + f"-DCMSIS_PATH={cmsis_path}", + f"-DARM_NN_ENABLE_F32={'ON' if 'f32' in dtypes else 'OFF'}", + f"-DARM_NN_ENABLE_F16={'ON' if 'f16' in dtypes else 'OFF'}", + ] + run_command(cmake_args, REPO_ROOT) + + all_ok = True + for family in families: + for dtype_name in dtypes: + target = family.host_target(dtype_name) + ok, detail = try_command(["cmake", "--build", str(build_dir), "-j", str(jobs), "--target", target], REPO_ROOT) + if not ok: + all_ok = False + append_result(results, "build-host", family.name, dtype_name, status="FAIL", detail=detail) + else: + append_result(results, "build-host", family.name, dtype_name) + return all_ok + + +def executable_path(build_dir: Path, target_name: str) -> Path: + target_dir = build_dir / "TestCases" / target_name + direct_path = target_dir / target_name + if direct_path.exists(): + return direct_path + + matches = sorted(target_dir.glob(f"{target_name}*")) + if not matches: + raise SystemExit(f"Unable to find host executable for target {target_name} in {target_dir}") + return matches[0] + + +def run_host_tests(families: list[FloatTestFamily], dtypes: list[str], build_dir: Path, results: list[StepResult]) -> bool: + all_ok = True + for family in families: + for dtype_name in dtypes: + target_name = family.host_target(dtype_name) + ok, detail = try_command([str(executable_path(build_dir, target_name))], REPO_ROOT) + if not ok: + all_ok = False + append_result(results, "run-host", family.name, dtype_name, status="FAIL", detail=detail) + else: + append_result(results, "run-host", family.name, dtype_name) + return all_ok + + +def toolchain_family(toolchain: str) -> str: + return toolchain.split("@", 1)[0] + + +def cmsis_image_suffix(toolchain: str) -> str: + return "axf" if toolchain_family(toolchain) == "AC6" else "elf" + + +def build_cmsis_tests( + families: list[FloatTestFamily], + dtypes: list[str], + toolchains: list[str], + jobs: int, + results: list[StepResult], + cbuild_packs: bool, +) -> bool: + all_ok = True + for toolchain in toolchains: + for family in families: + for dtype_name in dtypes: + context = family.cmsis_context(dtype_name) + update_cmd = [ + "cbuild", + "--update-rte", + "--context", + context, + str(CMSIS_SOLUTION), + "-j", + str(jobs), + "--toolchain", + toolchain, + ] + if CMSIS_OUTPUT_ROOT: + update_cmd.extend(["--output", CMSIS_OUTPUT_ROOT]) + if cbuild_packs: + update_cmd.append("--packs") + ok, detail = try_command(update_cmd, CMSIS_UNIT_TEST_ROOT) + if not ok: + all_ok = False + append_result(results, "build-cmsis", family.name, dtype_name, toolchain, "FAIL", f"update-rte {detail}") + continue + build_cmd = [ + "cbuild", + "--context", + context, + str(CMSIS_SOLUTION), + "-j", + str(jobs), + "--toolchain", + toolchain, + ] + if CMSIS_OUTPUT_ROOT: + build_cmd.extend(["--output", CMSIS_OUTPUT_ROOT]) + if cbuild_packs: + build_cmd.append("--packs") + ok, detail = try_command(build_cmd, CMSIS_UNIT_TEST_ROOT) + if not ok: + all_ok = False + append_result(results, "build-cmsis", family.name, dtype_name, toolchain, "FAIL", detail) + else: + append_result(results, "build-cmsis", family.name, dtype_name, toolchain) + return all_ok + + +def cmsis_image_path(family: FloatTestFamily, dtype_name: str, toolchain: str) -> Path: + family_name = toolchain_family(toolchain) + binary_name = family.host_target(dtype_name) + output_root = Path(CMSIS_OUTPUT_ROOT) if CMSIS_OUTPUT_ROOT else CMSIS_UNIT_TEST_ROOT + return output_root / f"{family.cmsis_context(dtype_name)}-{family_name}" / "outdir" / f"{binary_name}.{cmsis_image_suffix(toolchain)}" + + +def run_fvp_tests( + families: list[FloatTestFamily], + dtypes: list[str], + toolchains: list[str], + fvp_bin: Path, + fvp_image_arg: str, + fvp_timeout: int, + results: list[StepResult], +) -> bool: + if not fvp_bin: + raise SystemExit("--fvp-bin is required when --run-fvp is used.") + + fvp_common_args = [ + "-C", + "mps3_board.visualisation.disable-visualisation=1", + "-C", + "mps3_board.telnetterminal0.start_telnet=0", + "-C", + "mps3_board.uart0.out_file=-", + "-C", + "mps3_board.uart0.unbuffered_output=1", + "-C", + "mps3_board.uart0.shutdown_on_eot=1", + ] + + all_ok = True + for toolchain in toolchains: + for family in families: + for dtype_name in dtypes: + image_path = cmsis_image_path(family, dtype_name, toolchain) + fvp_cmd = [str(fvp_bin)] + if fvp_image_arg: + fvp_cmd.extend([fvp_image_arg, str(image_path)]) + else: + fvp_cmd.append(str(image_path)) + fvp_cmd.extend(fvp_common_args) + ok, detail = try_fvp_command(fvp_cmd, CMSIS_UNIT_TEST_ROOT, fvp_timeout) + if not ok: + all_ok = False + append_result(results, "run-fvp", family.name, dtype_name, toolchain, "FAIL", detail) + else: + append_result(results, "run-fvp", family.name, dtype_name, toolchain, detail=detail) + return all_ok + + +def main() -> None: + args = parse_args() + if args.list: + for family_name in sorted(FAMILY_CONFIGS): + print(family_name) + return + + if not any((args.generate, args.build_host, args.run_host, args.build_cmsis, args.run_fvp)): + args.generate = True + args.build_host = True + args.run_host = True + + families = resolve_families(args.tests) + dtypes = resolve_dtypes(args.dtypes) + toolchains = parse_csv(args.toolchains) + results: list[StepResult] = [] + failed = False + + if args.generate: + ensure_python_modules([("numpy", "numpy"), ("torch", "torch")], "generate float reference data") + failed |= not generate_float_test_data(families, dtypes, args.regenerate_input, results) + if args.build_host: + failed |= not configure_and_build_host( + families, + dtypes, + args.jobs, + Path(args.cmsis_path), + Path(args.host_build_dir), + args.clean_host, + results, + ) + if args.run_host: + failed |= not run_host_tests(families, dtypes, Path(args.host_build_dir), results) + if args.build_cmsis: + failed |= not build_cmsis_tests(families, dtypes, toolchains, args.jobs, results, args.cbuild_packs) + if args.run_fvp: + failed |= not run_fvp_tests( + families, + dtypes, + toolchains, + Path(args.fvp_bin) if args.fvp_bin else None, + args.fvp_image_arg, + args.fvp_timeout, + results, + ) + + print_summary(results) + if failed: + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/run_integer_unit_tests.py b/Tests/UnitTest/run_integer_unit_tests.py new file mode 100644 index 00000000..3d2111bc --- /dev/null +++ b/Tests/UnitTest/run_integer_unit_tests.py @@ -0,0 +1,528 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +import os +import re +import shutil +import subprocess +import sys +from dataclasses import dataclass +from pathlib import Path + + +UNIT_TEST_ROOT = Path(__file__).resolve().parent +REPO_ROOT = UNIT_TEST_ROOT.parents[1] + +INTEGER_TEST_DIR = UNIT_TEST_ROOT / "TestCases" + + +@dataclass(frozen=True) +class Toolchain: + requested: str + family: str + cc: str + cxx: str + c_flags: str + cxx_flags: str + + +@dataclass(frozen=True) +class StepResult: + stage: str + test: str + toolchain: str + status: str + detail: str = "" + + +@dataclass(frozen=True) +class IntegerTestSpec: + name: str + cmake_target: str + + +def parse_csv(value: str) -> list[str]: + return [item.strip() for item in value.split(",") if item.strip()] + + +def discover_integer_tests() -> list[IntegerTestSpec]: + specs: list[IntegerTestSpec] = [] + pattern = re.compile(r"add_cmsis_nn_unit_test_executable\(([^)]+)\)") + + for test_dir in sorted(INTEGER_TEST_DIR.iterdir()): + if not test_dir.is_dir(): + continue + if not test_dir.name.startswith("test_arm_"): + continue + if "f16" in test_dir.name or "f32" in test_dir.name: + continue + + cmake_lists = test_dir / "CMakeLists.txt" + if not cmake_lists.exists(): + continue + + match = pattern.search(cmake_lists.read_text(encoding="utf-8")) + if not match: + raise RuntimeError(f"Unable to find test target in {cmake_lists}") + + specs.append(IntegerTestSpec(name=test_dir.name, cmake_target=match.group(1).strip())) + + if not specs: + raise RuntimeError(f"No integer unit tests found under {INTEGER_TEST_DIR}") + + return specs + + +INTEGER_TEST_SPECS = discover_integer_tests() +INTEGER_TEST_NAMES = [spec.name for spec in INTEGER_TEST_SPECS] +INTEGER_TEST_MAP = {spec.name: spec for spec in INTEGER_TEST_SPECS} + + +def parse_version(value: str) -> tuple[int | str, ...]: + parts: list[int | str] = [] + for part in re.split(r"[.-]", value): + if part.isdigit(): + parts.append(int(part)) + else: + parts.append(part) + return tuple(parts) + + +def env_name_for_toolchain(toolchain: str) -> str | None: + match = re.match(r"^([A-Za-z0-9]+)@([0-9][A-Za-z0-9_.-]*)$", toolchain) + if not match: + return None + family = match.group(1).upper() + version = re.sub(r"[^A-Za-z0-9]", "_", match.group(2)) + return f"{family}_TOOLCHAIN_{version}" + + +def resolve_tool(bin_dir: Path | None, name: str) -> str | None: + if bin_dir is not None: + candidate = bin_dir / name + if candidate.exists(): + return str(candidate) + return shutil.which(name) + + +def toolchain_dirs_from_env(prefix: str) -> list[Path]: + matches: list[Path] = [] + generic_name = f"{prefix}_TOOLCHAIN" + if generic_name in os.environ: + matches.append(Path(os.environ[generic_name]).resolve()) + + versioned = sorted( + (name for name in os.environ if name.startswith(f"{prefix}_TOOLCHAIN_")), + key=lambda name: parse_version(name.split(f"{prefix}_TOOLCHAIN_", 1)[1]), + reverse=True, + ) + for name in versioned: + matches.append(Path(os.environ[name]).resolve()) + + deduped: list[Path] = [] + seen: set[Path] = set() + for path in matches: + if path not in seen: + deduped.append(path) + seen.add(path) + return deduped + + +def resolve_toolchain(requested: str) -> Toolchain: + family = requested.split("@", 1)[0].upper() + exact_env = env_name_for_toolchain(requested) + search_dirs: list[Path | None] = [] + if exact_env and exact_env in os.environ: + search_dirs.append(Path(os.environ[exact_env]).resolve()) + if family == "GCC": + search_dirs.extend(toolchain_dirs_from_env("GCC")) + elif family == "AC6": + search_dirs.extend(toolchain_dirs_from_env("AC6")) + search_dirs.append(None) + + if family == "GCC": + cc = None + cxx = None + for search_dir in search_dirs: + cc = resolve_tool(search_dir, "arm-none-eabi-gcc") + cxx = resolve_tool(search_dir, "arm-none-eabi-g++") + if cc and cxx: + break + if not cc or not cxx: + raise RuntimeError( + "Unable to resolve arm-none-eabi-gcc/g++. Export GCC_TOOLCHAIN " + "(or a versioned GCC_TOOLCHAIN_) or add the compiler to PATH." + ) + flags = "-mcpu=cortex-m55 -mthumb -mfloat-abi=hard" + return Toolchain(requested=requested, family=family, cc=cc, cxx=cxx, c_flags=flags, cxx_flags=flags) + + if family == "AC6": + cc = None + for search_dir in search_dirs: + cc = resolve_tool(search_dir, "armclang") + if cc: + break + if not cc: + raise RuntimeError( + "Unable to resolve armclang. Export AC6_TOOLCHAIN " + "(or a versioned AC6_TOOLCHAIN_) or add armclang to PATH." + ) + flags = "--target=arm-arm-none-eabi -mcpu=Cortex-M55" + return Toolchain(requested=requested, family=family, cc=cc, cxx=cc, c_flags=flags, cxx_flags=flags) + + raise RuntimeError(f"Unsupported toolchain family '{family}'. Supported families: GCC, AC6.") + + +def resolve_pack_dir(pack_root: Path, vendor: str, name: str, preferred_version: str) -> Path: + base = pack_root / vendor / name + if not base.exists(): + raise RuntimeError(f"Required pack directory not found: {base}") + + preferred = base / preferred_version + if preferred.exists(): + return preferred + + versions = sorted((path for path in base.iterdir() if path.is_dir()), key=lambda path: parse_version(path.name)) + if not versions: + raise RuntimeError(f"No installed versions found for pack {vendor}::{name} under {base}") + return versions[-1] + + +def create_cmsis_overlay(overlay_root: Path, pack_root: Path, cmsis_version: str, cortex_dfp_version: str) -> Path: + cmsis_dir = resolve_pack_dir(pack_root, "ARM", "CMSIS", cmsis_version) + cortex_dfp_dir = resolve_pack_dir(pack_root, "ARM", "Cortex_DFP", cortex_dfp_version) + + if overlay_root.exists(): + shutil.rmtree(overlay_root) + + (overlay_root / "Device" / "ARM").mkdir(parents=True, exist_ok=True) + + os.symlink(cmsis_dir / "CMSIS", overlay_root / "CMSIS", target_is_directory=True) + os.symlink(cortex_dfp_dir / "Device" / "ARMCM55", overlay_root / "Device" / "ARM" / "ARMCM55", target_is_directory=True) + + return overlay_root + + +def run_command(cmd: list[str], log_path: Path, timeout: int | None = None) -> subprocess.CompletedProcess[str]: + log_path.parent.mkdir(parents=True, exist_ok=True) + try: + completed = subprocess.run( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + timeout=timeout, + ) + except subprocess.TimeoutExpired as exc: + output = exc.stdout or "" + if exc.stderr: + output += exc.stderr + log_path.write_text(output, encoding="utf-8") + raise + + log_path.write_text(completed.stdout, encoding="utf-8") + return completed + + +def summarize_unity_ticks(output: str) -> str: + tick_matches = [int(match) for match in re.findall(r":PASS\s+\((\d+)\s+ticks\)", output)] + if not tick_matches: + return "" + return f"sum {sum(tick_matches)} ticks ({len(tick_matches)} tests)" + + +def _decode_timeout_output(stream: bytes | str | None) -> str: + if stream is None: + return "" + if isinstance(stream, bytes): + return stream.decode(errors="replace") + return stream + + +def configure_build( + build_dir: Path, + overlay_root: Path, + toolchain: Toolchain, + optimization_level: str, + log_dir: Path, +) -> subprocess.CompletedProcess[str]: + cmd = [ + "cmake", + "-S", + str(UNIT_TEST_ROOT), + "-B", + str(build_dir), + f"-DCMSIS_PATH={overlay_root}", + "-DCMAKE_SYSTEM_NAME=Generic", + "-DCMAKE_SYSTEM_PROCESSOR=cortex-m55", + "-DCMAKE_TRY_COMPILE_TARGET_TYPE=STATIC_LIBRARY", + f"-DCMAKE_C_COMPILER={toolchain.cc}", + f"-DCMAKE_CXX_COMPILER={toolchain.cxx}", + f"-DCMAKE_C_FLAGS={toolchain.c_flags}", + f"-DCMAKE_CXX_FLAGS={toolchain.cxx_flags}", + f"-DCMAKE_ASM_FLAGS={toolchain.c_flags}", + f"-DCMSIS_OPTIMIZATION_LEVEL={optimization_level}", + "-Wno-dev", + ] + return run_command(cmd, log_dir / "configure.log") + + +def build_targets( + build_dir: Path, + toolchain: Toolchain, + targets: list[str], + jobs: int, + log_dir: Path, +) -> subprocess.CompletedProcess[str]: + cmd = ["cmake", "--build", str(build_dir), f"-j{jobs}", "--target", *targets] + return run_command(cmd, log_dir / "build.log") + + +def target_image(build_dir: Path, test_name: str, cmake_target: str) -> Path: + direct = build_dir / "TestCases" / test_name / f"{cmake_target}.elf" + if direct.exists(): + return direct + + test_dir = build_dir / "TestCases" / test_name + if test_dir.exists(): + matches = sorted(test_dir.glob("*.elf")) + if len(matches) == 1: + return matches[0] + + raise FileNotFoundError(f"Expected test image not found for {test_name} (target {cmake_target})") + + +def run_fvp_target( + build_dir: Path, + test_name: str, + cmake_target: str, + fvp_bin: str, + fvp_image_arg: str, + timeout: int, + log_dir: Path, +) -> subprocess.CompletedProcess[str]: + image = target_image(build_dir, test_name, cmake_target) + + cmd = [fvp_bin] + if fvp_image_arg: + cmd.append(fvp_image_arg) + cmd.append(str(image)) + cmd.extend( + [ + "-C", + "mps3_board.visualisation.disable-visualisation=1", + "-C", + "mps3_board.telnetterminal0.start_telnet=0", + "-C", + "mps3_board.uart0.out_file=-", + "-C", + "mps3_board.uart0.unbuffered_output=1", + "-C", + "mps3_board.uart0.shutdown_on_eot=1", + ] + ) + return run_command(cmd, log_dir / f"{test_name}.log", timeout=timeout) + + +def print_summary(results: list[StepResult]) -> None: + headers = ["Stage", "Test", "Toolchain", "Status", "Detail"] + rows = [[result.stage, result.test, result.toolchain, result.status, result.detail] for result in results] + widths = [len(header) for header in headers] + for row in rows: + for index, value in enumerate(row): + widths[index] = max(widths[index], len(value)) + + def fmt(row: list[str]) -> str: + return " | ".join(value.ljust(widths[index]) for index, value in enumerate(row)) + + separator = "-+-".join("-" * width for width in widths) + + print("\nInteger Unit-Test Summary") + print(fmt(headers)) + print(separator) + for row in rows: + print(fmt(row)) + + counts = {"PASS": 0, "FAIL": 0, "SKIP": 0} + for result in results: + counts[result.status] = counts.get(result.status, 0) + 1 + print(separator) + print(f"PASS={counts['PASS']} FAIL={counts['FAIL']} SKIP={counts['SKIP']}") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Build and run legacy CMSIS-NN integer unit tests on Corstone-300 FVP.") + parser.add_argument("--tests", default="all", help="Comma-separated integer test targets to run, or all.") + parser.add_argument("--toolchains", default="GCC,AC6", help="Comma-separated toolchains to use. Supported: GCC, AC6.") + parser.add_argument("--list", action="store_true", help="List supported integer test targets and exit.") + parser.add_argument("--build-fvp", action="store_true", help="Configure and build integer FVP tests.") + parser.add_argument("--run-fvp", action="store_true", help="Run built integer FVP tests.") + parser.add_argument("--jobs", type=int, default=4, help="Parallel jobs for CMake builds.") + parser.add_argument("--clean", action="store_true", help="Delete existing build directories before configuring.") + parser.add_argument("--build-root", default="/tmp/cmsis-nn-integer-fvp", help="Root build directory.") + parser.add_argument("--cmsis-pack-root", default=os.environ.get("CMSIS_PACK_ROOT", "/home/runner/.cache/arm/packs"), help="CMSIS pack root.") + parser.add_argument("--cmsis-version", default="6.3.0", help="Preferred ARM::CMSIS pack version.") + parser.add_argument("--cortex-dfp-version", default="1.1.0", help="Preferred ARM::Cortex_DFP pack version.") + parser.add_argument("--optimization-level", default="-O3", help="CMSIS optimization level for the integer FVP build.") + parser.add_argument("--fvp-bin", default="FVP_Corstone_SSE-300", help="FVP executable.") + parser.add_argument("--fvp-image-arg", default="", help="Optional image argument flag for the FVP binary, for example -a.") + parser.add_argument("--fvp-timeout", type=int, default=90, help="FVP timeout in seconds per test image.") + return parser.parse_args() + + +def resolve_tests(selected: str) -> list[str]: + requested = parse_csv(selected) + if not requested or requested == ["all"]: + return list(INTEGER_TEST_NAMES) + + supported = set(INTEGER_TEST_NAMES) + unknown = [item for item in requested if item not in supported] + if unknown: + raise RuntimeError(f"Unsupported integer test target(s): {', '.join(unknown)}") + + return requested + + +def main() -> int: + args = parse_args() + + if args.list: + for target in INTEGER_TEST_NAMES: + print(target) + return 0 + + selected_tests = resolve_tests(args.tests) + requested_toolchains = parse_csv(args.toolchains) + if not requested_toolchains: + raise RuntimeError("No toolchains selected.") + + if not args.build_fvp and not args.run_fvp: + args.build_fvp = True + args.run_fvp = True + + build_root = Path(args.build_root).resolve() + pack_root = Path(args.cmsis_pack_root).resolve() + overlay_root = build_root / "cmsis-overlay" + overlay_root = create_cmsis_overlay(overlay_root, pack_root, args.cmsis_version, args.cortex_dfp_version) + + results: list[StepResult] = [] + failed = False + + for requested_toolchain in requested_toolchains: + toolchain = resolve_toolchain(requested_toolchain) + build_dir = build_root / toolchain.family.lower() + log_dir = build_dir / "logs" + + if args.clean and build_dir.exists(): + shutil.rmtree(build_dir) + + if args.build_fvp: + configure = configure_build( + build_dir, + overlay_root, + toolchain, + args.optimization_level, + log_dir, + ) + if configure.returncode != 0: + for target in selected_tests: + results.append(StepResult("build-fvp", target, toolchain.requested, "FAIL", "cmake configure failed")) + failed = True + continue + + cmake_targets = [INTEGER_TEST_MAP[test_name].cmake_target for test_name in selected_tests] + build = build_targets(build_dir, toolchain, cmake_targets, args.jobs, log_dir) + build_status = "PASS" if build.returncode == 0 else "FAIL" + build_detail = "" if build.returncode == 0 else "cmake build failed" + for test_name in selected_tests: + results.append(StepResult("build-fvp", test_name, toolchain.requested, build_status, build_detail)) + if build.returncode != 0: + failed = True + continue + + if args.run_fvp: + for test_name in selected_tests: + spec = INTEGER_TEST_MAP[test_name] + try: + completed = run_fvp_target( + build_dir, + test_name, + spec.cmake_target, + args.fvp_bin, + args.fvp_image_arg, + args.fvp_timeout, + log_dir / "fvp", + ) + output = completed.stdout + passed = completed.returncode == 0 and "0 Failures" in output and "OK" in output + if passed: + results.append( + StepResult( + "run-fvp", + test_name, + toolchain.requested, + "PASS", + summarize_unity_ticks(output), + ) + ) + else: + detail = "fvp run failed" + fail_line = next((line.strip() for line in output.splitlines() if ":FAIL:" in line), "") + if fail_line: + detail = fail_line + results.append(StepResult("run-fvp", test_name, toolchain.requested, "FAIL", detail)) + failed = True + except subprocess.TimeoutExpired: + timeout_log = (log_dir / "fvp" / f"{test_name}.log") + output = timeout_log.read_text(encoding="utf-8") if timeout_log.exists() else "" + timed_out_after_success = ( + "0 Failures" in output + and "\nOK" in output + and "FAIL" not in output + ) + if timed_out_after_success: + tick_summary = summarize_unity_ticks(output) + timeout_detail = f"timeout {args.fvp_timeout}s after PASS output" + if tick_summary: + timeout_detail = f"{tick_summary}; {timeout_detail}" + results.append( + StepResult( + "run-fvp", + test_name, + toolchain.requested, + "PASS", + timeout_detail, + ) + ) + else: + results.append(StepResult("run-fvp", test_name, toolchain.requested, "FAIL", "fvp timeout")) + failed = True + except FileNotFoundError as exc: + results.append(StepResult("run-fvp", test_name, toolchain.requested, "FAIL", str(exc))) + failed = True + + print_summary(results) + return 1 if failed else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/Tests/UnitTest/softmax_settings_flt.py b/Tests/UnitTest/softmax_settings_flt.py new file mode 100644 index 00000000..508ac5c0 --- /dev/null +++ b/Tests/UnitTest/softmax_settings_flt.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass +class SoftmaxSettingsFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + num_rows: int = 2 + row_size: int = 5 + + outdir: Path = Path("TestCases/TestData") + pregenerated_dir: Path = Path("PregeneratedData") + + def __post_init__(self) -> None: + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_input(self, array: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, array) + + def _load_input(self) -> np.ndarray: + return load_pregenerated_array(self.input_table_file, (self.num_rows, self.row_size)) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + input_data = np.array( + [ + [1.0, 0.25, -0.5, 2.0, -1.25], + [-2.0, -0.125, 0.75, 3.0, 1.5], + ], + dtype=np.float32, + ) + print(f"Saving data to {self.input_table_file}") + self._save_input(input_data) + return input_data + + def _write_config_header(self) -> None: + prefix = self.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_NUM_ROWS {self.num_rows}\n") + fh.write(f"#define {prefix}_ROW_SIZE {self.row_size}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.num_rows * self.row_size}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.dataset}_{name}", values, self.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + input_tensor = torch.tensor(input_data, dtype=torch.float32) + output_tensor = torch.softmax(input_tensor, dim=1).to(dtype=self.torch_dtype) + + output_data = output_tensor.cpu().numpy().astype(np.float32) + if self.dtype_name == "f16": + output_data = output_data.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header() + self._write_array_header("input", input_data.astype(np.float32 if self.dtype_name == "f32" else np.float16)) + self._write_array_header("output_ref", output_data) + self._write_wrapper_header() + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float softmax unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + choices=["softmax_f32", "softmax_f16", "all"], + default="all", + help="Dataset to generate.", + ) + parser.add_argument( + "--regenerate-input", + action="store_true", + help="Regenerate the pregenerated input sample instead of reusing input.txt.", + ) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + + jobs: list[SoftmaxSettingsFlt] = [] + if args.dataset in ("softmax_f32", "all"): + jobs.append(SoftmaxSettingsFlt(dataset="softmax_f32", dtype_name="f32", torch_dtype=torch.float32)) + if args.dataset in ("softmax_f16", "all"): + jobs.append(SoftmaxSettingsFlt(dataset="softmax_f16", dtype_name="f16", torch_dtype=torch.float16)) + + for job in jobs: + print(f"Generating {job.dataset}...") + job.generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/svdf_settings_flt.py b/Tests/UnitTest/svdf_settings_flt.py new file mode 100644 index 00000000..59de42e6 --- /dev/null +++ b/Tests/UnitTest/svdf_settings_flt.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +@dataclass(frozen=True) +class SvdfCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + input_batches: int + input_size: int + unit_count: int + rank: int + memory_size: int + sequence_steps: int + seed: int + input_act_min: float + input_act_max: float + output_act_min: float + output_act_max: float + input_min: float = -1.5 + input_max: float = 1.5 + weight_min: float = -0.5 + weight_max: float = 0.5 + bias_min: float = -0.25 + bias_max: float = 0.25 + + +class SvdfSettingsFlt: + def __init__(self, case: SvdfCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_sequence_file = self.pregenerated_data_dir / "input_sequence.txt" + self.weights_feature_file = self.pregenerated_data_dir / "weights_feature.txt" + self.weights_time_file = self.pregenerated_data_dir / "weights_time.txt" + self.bias_file = self.pregenerated_data_dir / "bias.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + @property + def feature_batches(self) -> int: + return self.case.unit_count * self.case.rank + + def _save_array(self, path: Path, values: np.ndarray) -> None: + save_pregenerated_array(path, values) + + def _load_array(self, path: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(path, shape) + + def get_input_sequence(self, regenerate_input: bool) -> np.ndarray: + shape = (self.case.sequence_steps, self.case.input_batches, self.case.input_size) + if self.input_sequence_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_sequence_file}") + return self._load_array(self.input_sequence_file, shape) + + rng = np.random.default_rng(self.case.seed + 1) + values = rng.uniform(self.case.input_min, self.case.input_max, size=shape).astype(np.float32) + print(f"Saving data to {self.input_sequence_file}") + self._save_array(self.input_sequence_file, values) + return values + + def get_weights_feature(self, regenerate_input: bool) -> np.ndarray: + shape = (self.feature_batches, self.case.input_size) + if self.weights_feature_file.exists() and not regenerate_input: + print(f"Loading data from {self.weights_feature_file}") + return self._load_array(self.weights_feature_file, shape) + + rng = np.random.default_rng(self.case.seed + 2) + values = rng.uniform(self.case.weight_min, self.case.weight_max, size=shape).astype(np.float32) + print(f"Saving data to {self.weights_feature_file}") + self._save_array(self.weights_feature_file, values) + return values + + def get_weights_time(self, regenerate_input: bool) -> np.ndarray: + shape = (self.feature_batches, self.case.memory_size) + if self.weights_time_file.exists() and not regenerate_input: + print(f"Loading data from {self.weights_time_file}") + return self._load_array(self.weights_time_file, shape) + + rng = np.random.default_rng(self.case.seed + 3) + values = rng.uniform(self.case.weight_min, self.case.weight_max, size=shape).astype(np.float32) + print(f"Saving data to {self.weights_time_file}") + self._save_array(self.weights_time_file, values) + return values + + def get_bias(self, regenerate_input: bool) -> np.ndarray: + shape = (self.case.unit_count,) + if self.bias_file.exists() and not regenerate_input: + print(f"Loading data from {self.bias_file}") + return self._load_array(self.bias_file, shape) + + rng = np.random.default_rng(self.case.seed + 4) + values = rng.uniform(self.case.bias_min, self.case.bias_max, size=shape).astype(np.float32) + print(f"Saving data to {self.bias_file}") + self._save_array(self.bias_file, values) + return values + + def _svdf_reference(self, input_sequence: np.ndarray, weights_feature: np.ndarray, weights_time: np.ndarray, bias: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + state = np.zeros((self.case.input_batches, self.feature_batches, self.case.memory_size), dtype=np.float32) + output = np.zeros((self.case.input_batches, self.case.unit_count), dtype=np.float32) + + for step in range(self.case.sequence_steps): + state[:, :, :-1] = state[:, :, 1:] + projected = input_sequence[step] @ weights_feature.T + projected = np.clip(projected, self.case.input_act_min, self.case.input_act_max) + state[:, :, -1] = projected + + out_a = np.sum(state * weights_time[None, :, :], axis=2) + out_b = out_a.reshape(self.case.input_batches, self.case.unit_count, self.case.rank).sum(axis=2) + bias[None, :] + output = np.clip(out_b, self.case.output_act_min, self.case.output_act_max) + + return state, output + + def _write_config_header(self) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.input_batches}\n") + fh.write(f"#define {prefix}_INPUT_SIZE {self.case.input_size}\n") + fh.write(f"#define {prefix}_UNIT_COUNT {self.case.unit_count}\n") + fh.write(f"#define {prefix}_RANK {self.case.rank}\n") + fh.write(f"#define {prefix}_FEATURE_BATCHES {self.feature_batches}\n") + fh.write(f"#define {prefix}_TIME_BATCHES {self.case.memory_size}\n") + fh.write(f"#define {prefix}_SEQUENCE_STEPS {self.case.sequence_steps}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.input_batches * self.case.unit_count}\n") + fh.write(f"#define {prefix}_INPUT_ACTIVATION_MIN {formatter(self.case.input_act_min)}\n") + fh.write(f"#define {prefix}_INPUT_ACTIVATION_MAX {formatter(self.case.input_act_max)}\n") + fh.write(f"#define {prefix}_OUTPUT_ACTIVATION_MIN {formatter(self.case.output_act_min)}\n") + fh.write(f"#define {prefix}_OUTPUT_ACTIVATION_MAX {formatter(self.case.output_act_max)}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_sequence = self.get_input_sequence(regenerate_input) + weights_feature = self.get_weights_feature(regenerate_input) + weights_time = self.get_weights_time(regenerate_input) + bias = self.get_bias(regenerate_input) + state_ref, output_ref = self._svdf_reference(input_sequence, weights_feature, weights_time, bias) + + arrays = { + "input_sequence": torch.tensor(input_sequence, dtype=self.case.torch_dtype).cpu().numpy(), + "weights_feature": torch.tensor(weights_feature, dtype=self.case.torch_dtype).cpu().numpy(), + "weights_time": torch.tensor(weights_time, dtype=self.case.torch_dtype).cpu().numpy(), + "bias": torch.tensor(bias, dtype=self.case.torch_dtype).cpu().numpy(), + "initial_state": np.zeros_like(state_ref, dtype=np.float16 if self.case.dtype_name == "f16" else np.float32), + "output_ref": torch.tensor(output_ref, dtype=self.case.torch_dtype).cpu().numpy(), + } + if self.case.dtype_name == "f16": + arrays = {name: value.astype(np.float16) for name, value in arrays.items()} + + self.generated_header_files = [] + self._write_config_header() + for name, values in arrays.items(): + self._write_array_header(name, values) + self._write_wrapper_header() + + +def _build_cases() -> list[SvdfCaseFlt]: + return [ + SvdfCaseFlt("svdf_small_f32", "f32", torch.float32, 1, 16, 6, 2, 4, 3, 7001, -1.0, 1.0, -100.0, 100.0), + SvdfCaseFlt("svdf_batch2_f32", "f32", torch.float32, 2, 20, 8, 2, 3, 4, 7002, -1.0, 1.0, -100.0, 100.0), + # Match legacy s8 svdf_int8 geometry. + SvdfCaseFlt("svdf_match_1_f32", "f32", torch.float32, 1, 20, 12, 1, 2, 3, 7003, -1.0, 1.0, -100.0, 100.0), + # Match legacy s8 svdf_int8_2 geometry. + SvdfCaseFlt("svdf_match_2_f32", "f32", torch.float32, 2, 40, 13, 2, 3, 4, 7004, -1.0, 1.0, -100.0, 100.0), + SvdfCaseFlt("svdf_small_f16", "f16", torch.float16, 1, 16, 6, 2, 4, 3, 7101, -1.0, 1.0, -100.0, 100.0), + SvdfCaseFlt("svdf_batch2_f16", "f16", torch.float16, 2, 20, 8, 2, 3, 4, 7102, -1.0, 1.0, -100.0, 100.0), + SvdfCaseFlt("svdf_match_1_f16", "f16", torch.float16, 1, 20, 12, 1, 2, 3, 7103, -1.0, 1.0, -100.0, 100.0), + SvdfCaseFlt("svdf_match_2_f16", "f16", torch.float16, 2, 40, 13, 2, 3, 4, 7104, -1.0, 1.0, -100.0, 100.0), + ] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float SVDF unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help="Dataset or family to generate. Supported values: all, svdf_f32_family, svdf_f16_family, or a specific dataset name.", + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def _matches(dataset_filter: str, case: SvdfCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "svdf_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "svdf_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + matches = [case for case in _build_cases() if _matches(args.dataset, case)] + if not matches: + raise RuntimeError(f"No SVDF dataset matched '{args.dataset}'") + + for case in matches: + print(f"Generating {case.dataset}...") + SvdfSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/transpose_conv_settings_flt.py b/Tests/UnitTest/transpose_conv_settings_flt.py new file mode 100644 index 00000000..a6e118a2 --- /dev/null +++ b/Tests/UnitTest/transpose_conv_settings_flt.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from float_test_gen_utils import ( + format_generated_file, + format_float16, + format_float32, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +LAYOUT_VALUE = {"nhwc": "ARM_NN_LAYOUT_NHWC"} + + +@dataclass(frozen=True) +class TransposeConvCaseFlt: + dataset: str + dtype_name: str + layout: str + use_wrapper: bool + batches: int + input_h: int + input_w: int + input_channels: int + output_channels: int + kernel_h: int + kernel_w: int + stride_h: int + stride_w: int + padding_h: int + padding_w: int + output_padding_h: int + output_padding_w: int + dilation_h: int + dilation_w: int + activation_min: float + activation_max: float + seed: int + input_min: float = -1.0 + input_max: float = 1.0 + weight_min: float = -0.75 + weight_max: float = 0.75 + bias_min: float = -0.5 + bias_max: float = 0.5 + + +class TransposeConvSettingsFlt: + def __init__(self, case: TransposeConvCaseFlt): + self.case = case + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs(case.dataset) + self.input_table_file = self.pregenerated_data_dir / "input.txt" + self.weights_table_file = self.pregenerated_data_dir / "weights.txt" + self.bias_table_file = self.pregenerated_data_dir / "bias.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _save_array(self, filepath: Path, values: np.ndarray) -> None: + save_pregenerated_array(filepath, values) + + def _load_array(self, filepath: Path, shape: tuple[int, ...]) -> np.ndarray: + return load_pregenerated_array(filepath, shape) + + def _get_raw_tensors(self, regenerate_input: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + input_shape = (self.case.batches, self.case.input_channels, self.case.input_h, self.case.input_w) + weight_shape = (self.case.input_channels, self.case.output_channels, self.case.kernel_h, self.case.kernel_w) + bias_shape = (self.case.output_channels,) + if ( + self.input_table_file.exists() + and self.weights_table_file.exists() + and self.bias_table_file.exists() + and not regenerate_input + ): + print(f"Loading data from {self.pregenerated_data_dir}") + return ( + self._load_array(self.input_table_file, input_shape), + self._load_array(self.weights_table_file, weight_shape), + self._load_array(self.bias_table_file, bias_shape), + ) + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=input_shape).astype(np.float32) + weights = rng.uniform(self.case.weight_min, self.case.weight_max, size=weight_shape).astype(np.float32) + bias = rng.uniform(self.case.bias_min, self.case.bias_max, size=bias_shape).astype(np.float32) + print(f"Saving data to {self.pregenerated_data_dir}") + self._save_array(self.input_table_file, input_data) + self._save_array(self.weights_table_file, weights) + self._save_array(self.bias_table_file, bias) + return input_data, weights, bias + + def _reorder_input(self, input_nchw: np.ndarray) -> np.ndarray: + return np.transpose(input_nchw, (0, 2, 3, 1)) + + def _reorder_output(self, output_nchw: np.ndarray) -> np.ndarray: + return np.transpose(output_nchw, (0, 2, 3, 1)) + + def _reorder_weights(self, weights_iohw: np.ndarray) -> np.ndarray: + return np.transpose(weights_iohw, (1, 2, 3, 0)) + + def _compute_reference(self, input_nchw: np.ndarray, weights_iohw: np.ndarray, bias: np.ndarray) -> np.ndarray: + output = F.conv_transpose2d( + torch.tensor(input_nchw, dtype=torch.float32), + torch.tensor(weights_iohw, dtype=torch.float32), + torch.tensor(bias, dtype=torch.float32), + stride=(self.case.stride_h, self.case.stride_w), + padding=(self.case.padding_h, self.case.padding_w), + output_padding=(self.case.output_padding_h, self.case.output_padding_w), + dilation=(self.case.dilation_h, self.case.dilation_w), + ) + return torch.clamp(output, min=self.case.activation_min, max=self.case.activation_max).cpu().numpy().astype(np.float32) + + def _write_config_header(self, output_h: int, output_w: int) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + formatter = format_float32 if self.case.dtype_name == "f32" else format_float16 + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_INPUT_BATCHES {self.case.batches}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_h}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_w}\n") + fh.write(f"#define {prefix}_IN_CH {self.case.input_channels}\n") + fh.write(f"#define {prefix}_FILTER_H {self.case.kernel_h}\n") + fh.write(f"#define {prefix}_FILTER_W {self.case.kernel_w}\n") + fh.write(f"#define {prefix}_OUT_CH {self.case.output_channels}\n") + fh.write(f"#define {prefix}_STRIDE_H {self.case.stride_h}\n") + fh.write(f"#define {prefix}_STRIDE_W {self.case.stride_w}\n") + fh.write(f"#define {prefix}_PADDING_H {self.case.padding_h}\n") + fh.write(f"#define {prefix}_PADDING_W {self.case.padding_w}\n") + # Keep transpose-convolution padding offsets at zero to match the + # existing manifest/export convention. PyTorch output_padding is + # already reflected in the exported output shape and reference. + fh.write(f"#define {prefix}_PADDING_OFFSET_H 0\n") + fh.write(f"#define {prefix}_PADDING_OFFSET_W 0\n") + fh.write(f"#define {prefix}_DILATION_H {self.case.dilation_h}\n") + fh.write(f"#define {prefix}_DILATION_W {self.case.dilation_w}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_h}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_w}\n") + fh.write(f"#define {prefix}_OUTPUT_C {self.case.output_channels}\n") + fh.write(f"#define {prefix}_DST_SIZE {self.case.batches * output_h * output_w * self.case.output_channels}\n") + fh.write(f"#define {prefix}_LAYOUT {LAYOUT_VALUE[self.case.layout]}\n") + fh.write(f"#define {prefix}_USE_WRAPPER {1 if self.case.use_wrapper else 0}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MIN {formatter(self.case.activation_min)}\n") + fh.write(f"#define {prefix}_OUT_ACTIVATION_MAX {formatter(self.case.activation_max)}\n") + format_generated_file(filepath) + + def _write_array_header(self, suffix: str, values: np.ndarray) -> None: + filename = f"{suffix}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{suffix}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + write_wrapper_header(self.headers_dir / "test_data.h", self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_nchw, weights_iohw, bias = self._get_raw_tensors(regenerate_input) + output_nchw = self._compute_reference(input_nchw, weights_iohw, bias) + output_h = output_nchw.shape[2] + output_w = output_nchw.shape[3] + + input_export = self._reorder_input(input_nchw) + weights_export = self._reorder_weights(weights_iohw) + output_export = self._reorder_output(output_nchw) + bias_export = bias + if self.case.dtype_name == "f16": + input_export = input_export.astype(np.float16) + weights_export = weights_export.astype(np.float16) + bias_export = bias_export.astype(np.float16) + output_export = output_export.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_h, output_w) + self._write_array_header("input", input_export) + self._write_array_header("weights", weights_export) + self._write_array_header("biases", bias_export) + self._write_array_header("output_ref", output_export) + self._write_wrapper_header() + + +def build_cases() -> list[TransposeConvCaseFlt]: + base_cases = [ + ("transpose_conv_basic", "nhwc", False, 8301), + ("transpose_conv_basic_nhwc", "nhwc", True, 8302), + ] + cases: list[TransposeConvCaseFlt] = [] + for dtype_name, seed_offset in (("f32", 0), ("f16", 100)): + for name, layout, use_wrapper, seed in base_cases: + cases.append( + TransposeConvCaseFlt( + dataset=f"{name}_{dtype_name}", + dtype_name=dtype_name, + layout=layout, + use_wrapper=use_wrapper, + batches=1, + input_h=5, + input_w=6, + input_channels=4, + output_channels=6, + kernel_h=3, + kernel_w=3, + stride_h=2, + stride_w=2, + padding_h=1, + padding_w=1, + output_padding_h=1, + output_padding_w=1, + dilation_h=1, + dilation_w=1, + activation_min=-1000.0, + activation_max=1000.0, + seed=seed + seed_offset, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float transpose-convolution unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help=( + "Dataset or family to generate. Supported values: all, transpose_conv_f32_family, " + "transpose_conv_f16_family, or a specific dataset name." + ), + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def matches(dataset_filter: str, case: TransposeConvCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "transpose_conv_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "transpose_conv_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + selected = [case for case in build_cases() if matches(args.dataset, case)] + if not selected: + raise RuntimeError(f"No transpose_conv dataset matched '{args.dataset}'") + for case in selected: + print(f"Generating {case.dataset}...") + TransposeConvSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/transpose_settings_flt.py b/Tests/UnitTest/transpose_settings_flt.py new file mode 100644 index 00000000..d5c53489 --- /dev/null +++ b/Tests/UnitTest/transpose_settings_flt.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +# +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates +# +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the License); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an AS IS BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import annotations + +import argparse +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from float_test_gen_utils import ( + format_generated_file, + load_pregenerated_array, + prepare_dataset_dirs, + save_pregenerated_array, + write_common_header, + write_float_array_header, + write_wrapper_header, +) + + +LAYOUT_VALUE = {"nhwc": "ARM_NN_LAYOUT_NHWC"} + + +@dataclass(frozen=True) +class TransposeCaseFlt: + dataset: str + dtype_name: str + torch_dtype: torch.dtype + layout: str + num_dims: int + input_dims: tuple[int, int, int, int] + perm: tuple[int, ...] + seed: int + input_min: float = -4.0 + input_max: float = 4.0 + + +class TransposeSettingsFlt: + def __init__(self, case: TransposeCaseFlt): + self.case = case + self.outdir = Path("TestCases/TestData") + self.pregenerated_dir = Path("PregeneratedData") + + self.headers_dir, self.pregenerated_data_dir = prepare_dataset_dirs( + self.case.dataset, self.outdir, self.pregenerated_dir + ) + + self.input_table_file = self.pregenerated_data_dir / "input_tensor.txt" + self.generated_header_files: list[str] = [] + self.script_name = Path(__file__).name + + def _logical_shape(self) -> tuple[int, ...]: + active = self.case.input_dims[:self.case.num_dims] + if self.case.num_dims < 4: + return active + return self.case.input_dims + + def _output_dims(self) -> tuple[int, int, int, int]: + active_in = self.case.input_dims[:self.case.num_dims] + active_out = [active_in[i] for i in self.case.perm] + padded = list(active_out) + [1] * (4 - self.case.num_dims) + return tuple(int(x) for x in padded) + + def _save_input(self, array: np.ndarray) -> None: + save_pregenerated_array(self.input_table_file, array) + + def _load_input(self) -> np.ndarray: + return load_pregenerated_array(self.input_table_file, self._logical_shape()) + + def get_input_data(self, regenerate_input: bool) -> np.ndarray: + if self.input_table_file.exists() and not regenerate_input: + print(f"Loading data from {self.input_table_file}") + return self._load_input() + + rng = np.random.default_rng(self.case.seed) + input_data = rng.uniform(self.case.input_min, self.case.input_max, size=self._logical_shape()).astype(np.float32) + print(f"Saving data to {self.input_table_file}") + self._save_input(input_data) + return input_data + + def _transpose_reference(self, input_array: np.ndarray) -> np.ndarray: + return np.transpose(input_array, self.case.perm).copy() + + def _write_config_header(self, output_array: np.ndarray) -> None: + prefix = self.case.dataset.upper() + filepath = self.headers_dir / "config_data.h" + self.generated_header_files.append("config_data.h") + output_dims = self._output_dims() + + with filepath.open("w", encoding="utf-8") as fh: + write_common_header(fh, self.script_name) + fh.write(f"#define {prefix}_NUM_DIMS {self.case.num_dims}\n") + fh.write(f"#define {prefix}_INPUT_N {self.case.input_dims[0]}\n") + fh.write(f"#define {prefix}_INPUT_H {self.case.input_dims[1]}\n") + fh.write(f"#define {prefix}_INPUT_W {self.case.input_dims[2]}\n") + fh.write(f"#define {prefix}_INPUT_C {self.case.input_dims[3]}\n") + fh.write(f"#define {prefix}_OUTPUT_N {output_dims[0]}\n") + fh.write(f"#define {prefix}_OUTPUT_H {output_dims[1]}\n") + fh.write(f"#define {prefix}_OUTPUT_W {output_dims[2]}\n") + fh.write(f"#define {prefix}_OUTPUT_C {output_dims[3]}\n") + fh.write(f"#define {prefix}_PERM_0 {self.case.perm[0]}\n") + fh.write(f"#define {prefix}_PERM_1 {self.case.perm[1]}\n") + fh.write(f"#define {prefix}_PERM_2 {self.case.perm[2] if self.case.num_dims > 2 else 2}\n") + fh.write(f"#define {prefix}_PERM_3 {self.case.perm[3] if self.case.num_dims > 3 else 3}\n") + fh.write(f"#define {prefix}_LAYOUT {LAYOUT_VALUE[self.case.layout]}\n") + fh.write(f"#define {prefix}_SIZE {output_array.size}\n") + format_generated_file(filepath) + + def _write_array_header(self, name: str, values: np.ndarray) -> None: + filename = f"{name}_data.h" + filepath = self.headers_dir / filename + self.generated_header_files.append(filename) + write_float_array_header(filepath, self.script_name, f"{self.case.dataset}_{name}", values, self.case.dtype_name) + + def _write_wrapper_header(self) -> None: + filepath = self.headers_dir / "test_data.h" + write_wrapper_header(filepath, self.script_name, self.generated_header_files) + + def generate_data(self, regenerate_input: bool = False) -> None: + input_data = self.get_input_data(regenerate_input) + output_data = self._transpose_reference(input_data) + + input_tensor = torch.tensor(input_data, dtype=self.case.torch_dtype) + output_tensor = torch.tensor(output_data, dtype=self.case.torch_dtype) + input_array = input_tensor.cpu().numpy() + output_array = output_tensor.cpu().numpy() + if self.case.dtype_name == "f16": + input_array = input_array.astype(np.float16) + output_array = output_array.astype(np.float16) + + self.generated_header_files = [] + self._write_config_header(output_array) + self._write_array_header("input_tensor", input_array) + self._write_array_header("output_ref", output_array) + self._write_wrapper_header() + + +def _build_cases() -> list[TransposeCaseFlt]: + cases: list[TransposeCaseFlt] = [] + base_cases = [ + ("transpose_matrix_small", "nhwc", 2, (2, 4, 1, 1), (1, 0), 6000), + ("transpose_matrix", "nhwc", 2, (5, 20, 1, 1), (1, 0), 6001), + ("transpose_3dim", "nhwc", 3, (5, 4, 20, 1), (0, 2, 1), 6002), + ("transpose_default", "nhwc", 4, (4, 3, 3, 22), (3, 2, 1, 0), 6003), + # Match legacy s8 transpose_3dim2 geometry. + ("transpose_3dim2", "nhwc", 3, (5, 4, 20, 1), (1, 2, 0), 6004), + # Match legacy s8 transpose_chwn geometry. + ("transpose_chwn", "nhwc", 4, (5, 4, 12, 17), (3, 1, 2, 0), 6005), + ("transpose_swap_last2_4d", "nhwc", 4, (2, 3, 5, 7), (0, 1, 3, 2), 6006), + # Match legacy s8 transpose_nhcw geometry. + ("transpose_nhcw", "nhwc", 4, (1, 1, 128, 128), (0, 1, 3, 2), 6007), + # Match legacy s8 transpose_nchw geometry. + ("transpose_nchw", "nhwc", 4, (4, 9, 3, 19), (0, 3, 1, 2), 6008), + # Match legacy s8 transpose_wchn geometry. + ("transpose_wchn", "nhwc", 4, (4, 3, 3, 21), (2, 3, 1, 0), 6009), + # Match legacy s8 transpose_nwhc geometry. + ("transpose_nwhc", "nhwc", 4, (1, 19, 3, 64), (0, 2, 1, 3), 6010), + # Match legacy s8 transpose_ncwh geometry. + ("transpose_ncwh", "nhwc", 4, (5, 4, 3, 9), (0, 3, 2, 1), 6011), + ] + for dtype_name, torch_dtype, seed_base in (("f32", torch.float32, 0), ("f16", torch.float16, 100)): + for name, layout, num_dims, input_dims, perm, seed in base_cases: + cases.append( + TransposeCaseFlt( + dataset=f"{name}_{dtype_name}", + dtype_name=dtype_name, + torch_dtype=torch_dtype, + layout=layout, + num_dims=num_dims, + input_dims=input_dims, + perm=perm, + seed=seed + seed_base, + ) + ) + return cases + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Generate float transpose unit-test references using PyTorch.") + parser.add_argument( + "--dataset", + default="all", + help="Dataset or family to generate. Supported values: all, transpose_f32_family, transpose_f16_family, or a specific dataset name.", + ) + parser.add_argument("--regenerate-input", action="store_true", help="Regenerate pregenerated input samples.") + return parser.parse_args() + + +def _matches(dataset_filter: str, case: TransposeCaseFlt) -> bool: + if dataset_filter == "all": + return True + if dataset_filter == "transpose_f32_family": + return case.dtype_name == "f32" + if dataset_filter == "transpose_f16_family": + return case.dtype_name == "f16" + return case.dataset == dataset_filter + + +def main() -> None: + args = parse_args() + matches = [case for case in _build_cases() if _matches(args.dataset, case)] + if not matches: + raise RuntimeError(f"No transpose dataset matched '{args.dataset}'") + + for case in matches: + print(f"Generating {case.dataset}...") + TransposeSettingsFlt(case).generate_data(regenerate_input=args.regenerate_input) + + +if __name__ == "__main__": + main() diff --git a/Tests/UnitTest/unittest_targets.py b/Tests/UnitTest/unittest_targets.py index 97e768db..691f1297 100755 --- a/Tests/UnitTest/unittest_targets.py +++ b/Tests/UnitTest/unittest_targets.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 # -# SPDX-FileCopyrightText: Copyright 2010-2025 Arm Limited and/or its affiliates +# SPDX-FileCopyrightText: Copyright 2010-2026 Arm Limited and/or its affiliates # # SPDX-License-Identifier: Apache-2.0 # @@ -224,15 +224,18 @@ def test_target_with_unity(target, args, main_test): str_line = inputQueue.get() print(str_line) test = None + test_status = None try: - test = str_line.split(':')[2] - test_result = ':'.join(str_line.split(':')[2:4]) + match = re.match(r"^[^:]+:[^:]+:([^:]+):(PASS|FAIL|IGNORE)\b", str_line) + if match: + test = match.group(1) + test_status = match.group(2) except IndexError: pass if test in tests: tests.remove(test) target[test]["tested"] = True - if test_result == test + ':PASS': + if test_status == 'PASS': target[test]["pass"] = True if len(tests) == 0: break @@ -405,6 +408,10 @@ def is_func_to_parse(func): with open(test_runner, "w") as f: for line in lines: sline = line.strip('\n') + if sline.strip() == 'return UNITY_END();': + indent = line[:len(line) - len(line.lstrip())] + f.write(f"{indent}exit(UNITY_END());\n") + continue if not re.search(r"\(void\);", sline): f.write(line) else: diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json new file mode 100644 index 00000000..242ead4b --- /dev/null +++ b/vcpkg-configuration.json @@ -0,0 +1,18 @@ +{ + "registries": [ + { + "name": "arm", + "kind": "artifact", + "location": "https://artifacts.tools.arm.com/vcpkg-registry" + } + ], + "requires": { + "arm:tools/open-cmsis-pack/cmsis-toolbox": "^2.13.0", + "arm:tools/kitware/cmake": "^3.31.5", + "arm:tools/ninja-build/ninja": "^1.13.2", + "arm:compilers/arm/armclang": "^6.24.0", + "arm:compilers/arm/arm-none-eabi-gcc": "^14.3.1", + "arm:compilers/arm/llvm-embedded": "^20.1.0", + "arm:models/arm/avh-fvp": "^11.31.28" + } +} From b8c76710821e09ed066c20d3e0f5a36492ab3eb0 Mon Sep 17 00:00:00 2001 From: Fabien Klein Date: Mon, 15 Jun 2026 16:43:36 +0200 Subject: [PATCH 2/2] Fix CI pack install prompts and formatting Pass --agree-embedded-license to cpackget installs used by the CI workflows so fresh runners do not stop at an embedded license prompt when the pack cache is empty. Also apply clang-format spacing fixes reported by the formatter check. Signed-off-by: Fabien Klein --- .github/workflows/float-fvp.yml | 2 +- .github/workflows/integer-fvp.yml | 4 ++-- .github/workflows/lib-variants.yml | 2 +- Include/arm_nn_math_types.h | 2 +- Include/arm_nn_math_types_flt.h | 2 +- Include/arm_nnsupportfunctions.h | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/float-fvp.yml b/.github/workflows/float-fvp.yml index c4352bc9..93cba041 100644 --- a/.github/workflows/float-fvp.yml +++ b/.github/workflows/float-fvp.yml @@ -92,7 +92,7 @@ jobs: - name: Register local CMSIS-NN pack run: | rm -rf "${CMSIS_PACK_ROOT}/.Local" - cpackget add ARM.CMSIS-NN.pdsc -R "${CMSIS_PACK_ROOT}" -F + cpackget add ARM.CMSIS-NN.pdsc -R "${CMSIS_PACK_ROOT}" -F --agree-embedded-license - name: Bootstrap Unity test runners working-directory: Tests/UnitTest diff --git a/.github/workflows/integer-fvp.yml b/.github/workflows/integer-fvp.yml index 90f84c7e..9c61c3b4 100644 --- a/.github/workflows/integer-fvp.yml +++ b/.github/workflows/integer-fvp.yml @@ -111,8 +111,8 @@ jobs: - name: Install required CMSIS packs if: steps.select.outputs.enabled == 'true' run: | - cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" - cpackget add "ARM::Cortex_DFP@1.1.0" -R "${CMSIS_PACK_ROOT}" + cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" --agree-embedded-license + cpackget add "ARM::Cortex_DFP@1.1.0" -R "${CMSIS_PACK_ROOT}" --agree-embedded-license - name: Export generic toolchain aliases if: steps.select.outputs.enabled == 'true' diff --git a/.github/workflows/lib-variants.yml b/.github/workflows/lib-variants.yml index 4bdc7481..199cda3f 100644 --- a/.github/workflows/lib-variants.yml +++ b/.github/workflows/lib-variants.yml @@ -111,7 +111,7 @@ jobs: - name: Install required CMSIS packs if: steps.select.outputs.enabled == 'true' run: | - cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" + cpackget add "ARM::CMSIS@6.3.0" -R "${CMSIS_PACK_ROOT}" --agree-embedded-license - name: Activate Arm tool license if: steps.select.outputs.enabled == 'true' && matrix.toolchain == 'AC6' diff --git a/Include/arm_nn_math_types.h b/Include/arm_nn_math_types.h index 227a11a3..ee982d86 100644 --- a/Include/arm_nn_math_types.h +++ b/Include/arm_nn_math_types.h @@ -102,7 +102,7 @@ extern "C" { * stride must fit within UINT16_MAX / (lanes - 1). */ #define ARM_NN_MVE_F16_GATHER_OFFSET_MAX ((size_t)UINT16_MAX) - #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE(_lanes) (ARM_NN_MVE_F16_GATHER_OFFSET_MAX / ((size_t)(_lanes)-1U)) + #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE(_lanes) (ARM_NN_MVE_F16_GATHER_OFFSET_MAX / ((size_t)(_lanes) - 1U)) #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_4 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(4U) #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_8 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(8U) #define ARM_NN_MVE_F16_MAX_GATHER_STRIDE_16 ARM_NN_MVE_F16_MAX_GATHER_STRIDE(16U) diff --git a/Include/arm_nn_math_types_flt.h b/Include/arm_nn_math_types_flt.h index 53223e2b..4906dc0c 100644 --- a/Include/arm_nn_math_types_flt.h +++ b/Include/arm_nn_math_types_flt.h @@ -98,7 +98,7 @@ typedef _Float16 float16_t; /** * @brief Lowest finite float16 value representable by the toolchain. */ - #define ARM_NN_F16_FINITE_LOWEST ((float16_t)-ARM_NN_F16_FINITE_MAX) + #define ARM_NN_F16_FINITE_LOWEST ((float16_t) - ARM_NN_F16_FINITE_MAX) #endif diff --git a/Include/arm_nnsupportfunctions.h b/Include/arm_nnsupportfunctions.h index 0b9a0e8d..df27c0de 100644 --- a/Include/arm_nnsupportfunctions.h +++ b/Include/arm_nnsupportfunctions.h @@ -53,7 +53,7 @@ extern "C" { #define MAX(A, B) ((A) > (B) ? (A) : (B)) #define MIN(A, B) ((A) < (B) ? (A) : (B)) #define CLAMP(x, h, l) MAX(MIN((x), (h)), (l)) -#define ARM_NN_ROUND_UP(x, multiple) ((((x) + (multiple)-1) / (multiple)) * (multiple)) +#define ARM_NN_ROUND_UP(x, multiple) ((((x) + (multiple) - 1) / (multiple)) * (multiple)) #define REDUCE_MULTIPLIER(_mult) ((_mult < 0x7FFF0000) ? ((_mult + (1 << 15)) >> 16) : 0x7FFF) // Number of channels processed in a block for DW Conv with Int8 weights(MVE)